From d583d36daa65df4d78037ee616118260182a4d33 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 26 Aug 2025 19:52:05 -0400 Subject: [PATCH 001/103] Including examples for 2025+ --- PUQ/designmethods/gen_funcs/acquisition.py | 762 +++++++++++++++++++ PUQ/designmethods/gen_funcs/allocate_reps.py | 190 +++++ PUQ/designmethods/sequential.py | 183 +++++ PUQ/designmethods/support.py | 37 + examples/2025+/1_example_3d_solo.py | 93 +++ examples/2025+/Figure1_2.py | 101 +++ examples/2025+/paper_figs.py | 731 ++++++++++++++++++ examples/2025+/test_functions.py | 249 ++++++ examples/2025+/utils.py | 344 +++++++++ examples/2025+/utils_sample.py | 143 ++++ 10 files changed, 2833 insertions(+) create mode 100644 PUQ/designmethods/gen_funcs/acquisition.py create mode 100644 PUQ/designmethods/gen_funcs/allocate_reps.py create mode 100644 PUQ/designmethods/sequential.py create mode 100644 PUQ/designmethods/support.py create mode 100644 examples/2025+/1_example_3d_solo.py create mode 100644 examples/2025+/Figure1_2.py create mode 100644 examples/2025+/paper_figs.py create mode 100644 examples/2025+/test_functions.py create mode 100644 examples/2025+/utils.py create mode 100644 examples/2025+/utils_sample.py diff --git a/PUQ/designmethods/gen_funcs/acquisition.py b/PUQ/designmethods/gen_funcs/acquisition.py new file mode 100644 index 0000000..2220a8a --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition.py @@ -0,0 +1,762 @@ +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from hetgpy.covariance_functions import cov_gen +from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult +import emcee +from PUQ.designmethods.gen_funcs.allocate_reps import allocate + + +def generate_neighborhood(acq): + + rand = np.random.default_rng(acq.seed) + neigh_type = acq.args.get("neighbor", None) + + if neigh_type == "LHS": + N = int(acq.nL) + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L = sampling(N) + else: + N = int(acq.nL * 0.5) + + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L_explore = sampling(N) + + sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) + Lt = sampling(N) + + num_options = len(acq.x) + + # Distribute the rows as evenly as possible + counts = np.full(num_options, N // num_options) # Base count for each row type + counts[ + rand.choice(num_options, N % num_options, replace=False) + ] += 1 # Assign extra rows randomly + + # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) + Lx = np.vstack( + [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] + ) + + # Shuffle the rows randomly + rand.shuffle(Lx) + + L_exploit = np.concatenate((Lx, Lt), axis=1) + + L = np.concatenate((L_explore, L_exploit)) + + return L + + +class acquisition_function: + def __init__(self, model, cls_func, args): + self.model = model.copy() + self.cls_func = cls_func + #self.persis_info = persis_info + self.args = args + self.x = self.cls_func.x + self.d = self.cls_func.d + self.p = self.cls_func.p + self.dx = self.cls_func.dx + self.dt = self.cls_func.dt + self.zlim = cls_func.zlim + self.xlim = cls_func.zlim[0 : cls_func.dx, :] + self.tlim = cls_func.zlim[cls_func.dx : cls_func.p, :] + self.y = self.cls_func.real_data + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) + self.twopiddet = ( + (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + ) + + def acquire_new(self): + + if isinstance(self, lookahead): + return eval("self.evaluate")() + + else: + return eval("self.evaluate")( + self.args.get("new", True), return_pseudo=False + ) + + def gen_pred(self, model, x, t, return_id=False, return_flat=False): + nm, d = t.shape[0], x.shape[0] + ntot = nm * d + + # print(t.shape) + # (ntot, d) + x_tiled = np.tile(x, (t.shape[0], 1)) + # (ntot, p-d) + t_repeated = np.repeat(t, x.shape[0], axis=0) + # (ntot, p) + z = np.hstack([x_tiled, t_repeated]) + + # to construct S matrix + id_row = np.arange(0, ntot) + id_col = np.arange(0, ntot).reshape(nm, d) + id_col = np.repeat(id_col, repeats=d, axis=0) + + # predict at mesh + meshPr = model.predict(x=z, xprime=z) + + # ntot, ntot x ntot, ntot + mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] + + muT = mu.reshape(nm, d) + S = Sn[id_row[:, None], id_col].reshape(nm, d, d) + + if return_id: + return muT, S, z, id_row[:, None], id_col, mu[:, None] + else: + if return_flat: + return muT, S, z, mu[:, None] + else: + return muT, S, z + + def crit_pvar(self, model, x, t): + + # posterior variance + mu, S, z = self.gen_pred(model, x, t) + + M = S + 0.5 * self.Sigma3d + N = S + self.Sigma3d + + f = multiple_pdfs(self.y, mu, M) + g = multiple_pdfs(self.y, mu, N) + + vals = (1 / self.twopiddet) * f - g**2 + + return vals + + def total_var(self, model, x, t): + vals = self.crit_pvar(model=model, x=x, t=t) + return np.mean(self.weights * vals) + + +class var(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", True) + self.nL = args.get("nL", 100) + + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + if new: + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + + def evaluate_exploit(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + + +class imse(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + if new: + sampling = LHS(xlimits=self.zlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + def evaluate_exploit(self, L): + IMSPE_grid = np.array( + [crit_IMSPE(id=[x_id], model=self.model) for x_id, x in enumerate(L)] + ) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + +class ivar(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + + # import pandas as pd + # import seaborn as sns + # import matplotlib.pyplot as plt + # df = pd.DataFrame(self.t_ref) + # sns.pairplot(df, diag_kind="kde") # Use KDE for diagonal histograms + # plt.show() + # print(self.t_ref.shape) + # import matplotlib.pyplot as plt + # if self.t_ref.shape[1] > 1: + # plt.scatter(self.t_ref[:, 0], self.t_ref[:, 1]) + # plt.show() + # else: + # plt.hist(self.t_ref) + # plt.show() + + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + if new: + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + + + nm = self.t_ref.shape[0] + + vals = np.zeros(self.nL) + + mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) + + V1 = S + self.Sigma3d + + # for i in np.arange(S.shape[0]): + # print(np.diag(S[i, :, :])) + + # predict at candidates + candPr = self.model.predict(x=L, xprime=z) + + # nL, nL, nL x ntot + candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] + candtotvat = candvar + candnugs + + for k in range(0, self.nL): + # (mn x d x 1) + covc = candcov[k, :].reshape(nm, self.d, 1) + + # (mn x 1 x d) + covcT = np.transpose(covc, (0, 2, 1)) + + # (nm, d, d) + phic = (covc * covcT) / candtotvat[k] + + # C1: nm x d x d, C2: nm x d x d + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(self.y, mu, C1) + dets = multiple_determinants(C2) + + part2 = (1 / self.twopid) * (rpdf / np.sqrt(dets)) + + vals[k] = np.sum(self.weights * part2) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + + return new_input + + def evaluate_exploit(self, L): + + nL = L.shape[0] + + nm = self.t_ref.shape[0] + vals = np.zeros(nL) + nrep = 1 + + mu, S, z, idr, idc, muf = self.gen_pred( + self.model, self.x, self.t_ref, return_id=True + ) + + V1 = S + self.Sigma3d + V2 = S + 0.5 * self.Sigma3d + + Ki, mult = self.model.Ki, self.model.mult + + candPr = self.model.predict(x=self.model["X0"]) + candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] + smean = self.model["Z0"] + + # compute B + if self.model.get("Lambda") is None: + tmp = self.model.g + tmp = np.repeat(tmp, nL) + else: + tmp = self.model.Lambda + + denom = (mult * (mult + nrep)) / (nrep * tmp) - np.diag(Ki) + Ki3D = Ki[:, :, None] * Ki[:, None, :] + B = Ki3D / denom[:, None, None] + KB = self.model.Ki + B + + # ntot x ntr + kx = cov_gen( + X1=z, X2=self.model.X0, theta=self.model.theta, type=self.model.covtype + ) + + # coefficients to be used + ap1 = mult + 1 + mean_coefs = (candmean - smean) / ap1 + var_coefs = (candvar + candnugs) / (ap1**2) + smean = smean[:, None] + + KBe_all = KB[np.arange(nL), :, np.arange(nL)] + kKBe_all = KBe_all @ kx.T + + B_kx = np.einsum("ijk, bk -> ijb", B, kx) + phi_all = self.model.nu_hat * np.einsum("aj, ijb -> aib", kx, B_kx) + + Bs = B @ smean + c1, c2 = (1 / self.twopiddet), (1 / self.twopid) + for k in range(0, nL): + + phi_k = phi_all[idr, k, idc].reshape(nm, self.d, self.d) + + KBe_k = KB[k, :, k : k + 1] + mu_k = muf + kx @ (mean_coefs[k] * KBe_k + Bs[k, :, :]) + mu_kT = mu_k.reshape(nm, self.d) + + kKBe = kKBe_all[k, :] + covc = kKBe.reshape(nm, self.d, 1) + covcT = np.transpose(covc, (0, 2, 1)) + + # nm x d x d + gamma_k = var_coefs[k] * (covc * covcT) + + M = V2 - phi_k + gamma_k + part1 = c1 * multiple_pdfs(self.y, mu_kT, M) + + C1 = V1 - phi_k + N = 0.5 * C1 + gamma_k + part2 = multiple_pdfs(self.y, mu_kT, N) + + dets1 = multiple_determinants(C1) + part2 = c2 * (1 / np.sqrt(dets1)) * part2 + + vals[k] = np.sum(self.weights * (part1 - part2)) + + # print(np.argmin(vals)) + new_input = L[np.argmin(vals), :].reshape(1, self.p) + + # import matplotlib.pyplot as plt + # plt.scatter(L[:, 1], L[:, 2], c=vals) + # plt.scatter(L[np.argmin(vals), :][1], L[np.argmin(vals), :][2], marker="*") + # plt.show() + + return new_input + + +class lookahead(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.method = args.get("method", "ivar") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + + def determine_h(self): + horizon = self.args.get("horizon", None) + # print(horizon) + if horizon.get("method") == "target": + if horizon.get("previous_ratio") is None: + horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + self.h = horizon.get("h0") + else: + target = horizon.get("target_ratio") + previous_ratio = horizon.get("previous_ratio") + current_horizon = horizon.get("h0") + + ratio = len(self.model.Z0) / len(self.model.Z) + + # Ratio increased while too small + if ratio < target and ratio < previous_ratio: + self.h = max(-1, current_horizon - 1) + + # Ratio decreased while too high + elif ratio > target and ratio > previous_ratio: + self.h = current_horizon + 1 + else: + self.h = current_horizon + + horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + horizon["h0"] = self.h + + elif horizon.get("method") == "adaptive": + budget = np.sum(self.model.mult) + + if self.method == "imse": + mult_star = allocate_mult(model=self.model, N=budget).astype(int) + + + + else: + alloc_obj = allocate(budget, + self.model, + self.cls_func, + {"theta":self.t_ref, "weight":self.weights}) + + alloc_obj.allocatereps() + mult_star = alloc_obj.reps + + tab_input = mult_star - self.model.mult + tab_input[tab_input < 0] = 0 + + # import matplotlib.pyplot as plt + # for label, x_count, y_count in zip(tab_input, self.model.X0[:, 1], self.model.X0[:, 2]): + # col = "blue" + # plt.annotate( + # label, + # xy=(x_count, y_count), + # xytext=(0, 0), + # textcoords="offset points", + # fontsize=12, + # color=col + # ) + # plt.show() + + rand = np.random.default_rng(self.seed) + u, counts = np.unique(tab_input, return_counts=True) + self.h = rand.choice(u, p=counts / counts.sum()) + + elif horizon.get("h0") == -1: + self.h = -1 + else: + self.h = horizon.get("h0") + + def crit_eval(self, model): + if self.method == "imse": + value = IMSPE(model) + else: + value = self.total_var(model, self.x, self.t_ref) + return value + + def evaluate(self): + + if self.args.get("long"): + self.evaluate_vl() + else: + self.evaluate_vq() + + def evaluate_vl(self): + + acq_obj = eval(self.method)( + model=self.model, + cls_func=self.cls_func, + args=self.args, + ) + + self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( + acq_obj, "weights", None + ) + + # define horizon + self.determine_h() + + if self.h == -1: + acq_obj.evaluate(new=True, return_pseudo=True) + self.znew, self.new = acq_obj.znew, True + elif self.h == 0: + acq_obj.evaluate(new=True, return_pseudo=True) + z_A = acq_obj.znew + acq_obj.model.update(Xnew=z_A, Znew=acq_obj.fnew, maxit=0) + model_A = acq_obj.model.copy() + IVAR_A1 = self.crit_eval(model_A) + + acq_obj.model = self.model.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + z_B = acq_obj.znew + acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) + + model_B = acq_obj.model.copy() + IVAR_B1 = self.crit_eval(model_B) + + if IVAR_A1 < IVAR_B1: + self.znew, self.new = z_A, True + return + else: + self.znew, self.new = z_B, False + return + else: + depth = self.h + 1 + paths = np.eye(depth, dtype=int).tolist() + inputs, values = [], [] + if self.args.get("parallel", None) is None: + for pid, path in enumerate(paths): + znew, vals = self.eval_branch(acq_obj, path) + inputs.append(znew) + values.append(vals) + # print(np.round(vals*1000000)) + + vals_id = np.argmin(values) + new_input = inputs[vals_id][0] + a = paths[vals_id][0] + self.znew = new_input + self.new = True if a == 1 else False + return self + else: + from joblib import Parallel, delayed + + results = Parallel(n_jobs=len(paths))( + delayed(self.eval_branch)(acq_obj, path) + for pid, path in enumerate(paths) + ) + inputs, vals = zip(*results) + vals_id = np.argmin(vals) + return inputs[vals_id][0] + + def eval_branch(self, acq_obj, action): + + acq_obj.model = self.model.copy() + + # Initialize actions + input_action = [] + + for a in action: + + new = True if a == 1 else False + + acq_obj.evaluate(new=new, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + input_action.append(acq_obj.znew) + + value = self.crit_eval(acq_obj.model) + + return input_action, value + + def evaluate_vq(self): + designs = [] + + acq_obj = eval(self.method)( + model=self.model, + cls_func=self.cls_func, + args=self.args, + ) + + self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( + acq_obj, "weights", None + ) + + # define horizon + self.determine_h() + + acq_obj.evaluate(new=True, return_pseudo=True) + z_A, f_A = acq_obj.znew, acq_obj.fnew + path_A = [{"par": z_A, "new": True}] + + if self.h == -1: + self.znew, self.new = z_A, True + return + else: + acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) + model_A = acq_obj.model.copy() + IVAR_A1 = self.crit_eval(model_A) + + if self.h > 0: + + acq_obj.model = model_A.copy() + for i in range(0, self.h): + acq_obj.evaluate(new=False, return_pseudo=True) + path_A.append({"par": acq_obj.znew, "new": False}) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + + IVAR_A = self.crit_eval(acq_obj.model) + + designs.append({"input": z_A, "path": path_A, "value": IVAR_A}) + + if self.h == 0: + acq_obj.model = self.model.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + z_B = acq_obj.znew + acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) + + model_B = acq_obj.model.copy() + IVAR_B1 = self.crit_eval(model_B) + + if IVAR_A1 < IVAR_B1: + self.znew, self.new = z_A, True + return + else: + self.znew, self.new = z_B, False + return + + else: + newmodelB = self.model.copy() + for i in range(self.h): + # Choose a new replicate + acq_obj.model = newmodelB.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + + if i == 0: + z0 = 1 * acq_obj.znew + path_B = [] + + path_B.append({"par": acq_obj.znew, "new": False}) + newmodelB = acq_obj.model.copy() + + # Choose a new design + acq_obj.evaluate(new=True, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + path_C = [{"par": acq_obj.znew, "new": True}] + + for j in range(i, self.h - 1): + # Remaining replicates + acq_obj.evaluate(new=False, return_pseudo=True) + acq_obj.model.update( + Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0 + ) + path_C.append({"par": acq_obj.znew, "new": False}) + + IVAR_C = self.crit_eval(acq_obj.model) + designs.append( + {"input": z0, "path": path_B + path_C, "value": IVAR_C} + ) + + if IVAR_C < IVAR_A: + self.znew, self.new = z0, False + return + + self.znew, self.new = z_A, True + return diff --git a/PUQ/designmethods/gen_funcs/allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps.py new file mode 100644 index 0000000..f16eda1 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/allocate_reps.py @@ -0,0 +1,190 @@ +import numpy as np +import scipy +from numpy.linalg import cholesky, inv, det +from hetgpy.covariance_functions import cov_gen +from PUQ.designmethods.support import multiple_pdfs + +class allocate: + def __init__(self, + budget, + model, + func_cls, + mesh, + trace=1, + alloc_settings={}, + rand_stream=None): + + self.budget = budget + self.model = model + self.x = func_cls.x + self.theta_mesh = mesh["theta"] + self.w_mesh = mesh["weight"] + self.obs = func_cls.real_data + self.obsvar = func_cls.obsvar + self.use_Ki = alloc_settings.get('use_Ki', True) + self.eps = np.sqrt(np.finfo(float).eps) + self.trace = trace + self.func_cls = func_cls + self.rand_stream = rand_stream + self.z = self.model['X0'] + self.a0 = self.model['mult'] + + + + def allocatereps(self): + + Cinfo = self.compute_ivar_weights() + C = Cinfo.flatten() + + idneg = C < 0 + weight = np.sqrt(np.abs(C[idneg])) + a_frac = np.zeros(len(C)) + a_frac_ub = np.zeros(len(C)) + total_a = np.sum(self.a0) + self.budget + + # Find an upper bound + a_frac_ub[idneg] = total_a*weight/sum(weight) + a_ub = self.make_int(total_a, a_frac_ub) + a_ub = np.maximum(0, a_ub - self.a0) + + a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) + a_final = self.make_int(self.budget, a_frac) + a_final = np.array([int(a) for a in a_final]) + if np.sum(a_final) != self.budget: + raise ValueError("Budget constraint is not satisfied.") + + self.reps = a_final + return + + + def compute_ivar_weights(self): + + d = len(self.x) + n_integ = self.theta_mesh.shape[0] + n = self.z.shape[0] + + #pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + #pred_mesh = self.model.predict(x=self.theta_mesh) + + # obsvar3d = self.obsvar.reshape(1, d, d) + # # ntest x d + # mu = pred_mesh._info['mean'].T + # S = pred_mesh._info['S'] + # St = np.transpose(S, (2, 0, 1)) + + obsvar3d = self.obsvar.reshape(1, d, d) + + x = self.x + ntot = n_integ*d + t = self.theta_mesh + x_tiled = np.tile(x, (t.shape[0], 1)) + t_repeated = np.repeat(t, x.shape[0], axis=0) + z = np.hstack([x_tiled, t_repeated]) + + # to construct S matrix + id_row = np.arange(0, ntot) + id_col = np.arange(0, ntot).reshape(n_integ, d) + id_col = np.repeat(id_col, repeats=d, axis=0) + + # predict at mesh + meshPr = self.model.predict(x=z, xprime=z) + + # ntot, ntot x ntot, ntot + mu, Sn = meshPr["mean"], meshPr["cov"] + + muT = mu.reshape(n_integ, d) + S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) + + # ntest x d x d + Nb = S + 0.5*obsvar3d + N = S + obsvar3d + + f = multiple_pdfs(self.obs, muT, Nb) + g = multiple_pdfs(self.obs, muT, N) + + # ntest x 1 x d + h = (self.obs - muT).reshape(n_integ, 1, d) + + # ntest x d x d + Nbinv = inv(Nb) + + # ntest x d x d + Ninv = inv(N) + + # ntest x 1 x d + hNb = np.matmul(h, Nbinv) + hN = np.matmul(h, Ninv) + + coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) + + Mb = np.zeros((n, n_integ, d, d), dtype=np.float64) + + pred_nugs = self.model.predict(x=self.z, nugs_only=True) + + # n x n + if self.use_Ki: + Ki = self.model['Ki'] + else: + K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) + Ki = scipy.linalg.pinv(K, rcond=self.eps) + + ids = np.arange(0, len(z), d) + for i in range(0, n): + J = np.zeros((n, n)) + J[i, i] = 1 + + # n x n + A = Ki@J@Ki + + for j in range(0, d): + # n_integ x 1 + zo = z[ids + j] + + # n x n_integ + kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) + + for jp in range(0, d): + # n_integ x 1 + zop = z[ids + jp] + + # n x n_integ + kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) + + Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + + + dlogfdai = np.zeros((n, n_integ)) + dloggdai = np.zeros((n, n_integ)) + C = np.zeros((n, 1)) + for i in range(0, n): + + M = Mb[i, :, :, :] + + part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) + part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) + part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) + part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) + + dlogfdai = part1f + part2f.flatten() + dloggdai = part1g + part2g.flatten() + subC = self.w_mesh*(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) + + C[i] = np.sum(subC) + + return C + + + def make_int(self, bdg, n_frac): + # Make integer + n_floor = np.floor(n_frac) + remain = n_frac - n_floor + tot_remain = bdg - np.sum(n_floor) + sorted_indices = np.array(np.argsort(remain)[::-1]) + if tot_remain > 0: + idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] + n_floor[idx] = n_floor[idx] + 1 + + return n_floor + + + \ No newline at end of file diff --git a/PUQ/designmethods/sequential.py b/PUQ/designmethods/sequential.py new file mode 100644 index 0000000..862be45 --- /dev/null +++ b/PUQ/designmethods/sequential.py @@ -0,0 +1,183 @@ +from hetgpy import hetGP +import numpy as np +import matplotlib.pyplot as plt +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from PUQ.designmethods.gen_funcs.acquisition import var, ivar, imse, lookahead +import time + + +class sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): + + if test is not None: + self.test_data_gen(test) + + H = [] + timel = [] + metric_sum = {} + for t in range(0, T): + # print(f"t: {t}") if self.trace else None + print(t) + tic = time.time() + + # hetGP + model = hetGP() + model.mle(X=z0, + Z=f0, + covtype="Gaussian", + known={"beta0":np.mean(f0)}) + + toc = time.time() + timel.append(toc - tic) + print(toc - tic) + args["seed"] += 1 + + if test is not None: + metric_sum = self.eval_perf(model, args.get("extra_metric", True)) + + acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) + + acq_func.acquire_new() + znew = acq_func.znew + fnew = self.cls_func.sim_f(znew.flatten(), persis_info=persis_info).reshape( + 1, 1 + ) + + f0 = np.concatenate((f0, fnew), axis=0) + z0 = np.concatenate((z0, znew), axis=0) + + H.append( + { + "t": t, + "new": acq_func.new, + "h": getattr(acq_func, "h", None), + "f": fnew, + "z": znew, + "MSE": metric_sum.get("MSE", None), + "MAD": metric_sum.get("MAD", None), + "VAR": metric_sum.get("VAR", None), + "MSEy": metric_sum.get("MSEy", None), + "MADy": metric_sum.get("MADy", None), + "MSEn": metric_sum.get("MSEn", None), + "MADn": metric_sum.get("MADn", None), + "summary_metric": metric_sum.get("sum_metric", None), + } + ) + + unique_rows, counts = np.unique(z0, axis=0, return_counts=True) + + self.xt = unique_rows + self.reps = counts + self.fs = f0 + self.zs = z0 + self.time = timel + self.H = H + + return self + + def test_data_gen(self, test): + + # Gen test data + self.ttest, self.ptest, self.wtest, self.ntest, self.ftest = ( + test["theta"], + test["p"], + test["w"], + test["noise"], + test["f"], + ) + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ftest) + # plt.show() + + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ntest) + # plt.show() + + x_tiled = np.tile(self.cls_func.x, (self.ttest.shape[0], 1)) + t_repeated = np.repeat(self.ttest, self.cls_func.x.shape[0], axis=0) + self.ztest = np.hstack([x_tiled, t_repeated]) + + # To be used for performance evaluations + ntot, nm, d = self.ztest.shape[0], self.ttest.shape[0], self.cls_func.d + + # to construct S matrix + self.idr = np.arange(0, ntot)[:, None] + idc = np.arange(0, ntot).reshape(nm, d) + self.idc = np.repeat(idc, repeats=d, axis=0) + + def eval_perf(self, model, extra_metric=False): + + nm, d = self.ttest.shape[0], self.cls_func.d + # sc = plt.scatter(self.ztest[:, 1], self.ztest[:, 2], c=self.wtest, cmap="viridis") # Use any colormap + # plt.colorbar(sc, label="wtest values") # Add a colorbar + # plt.show() + + # predict at mesh + pr = model.predict(x=self.ztest, xprime=self.ztest) + + # ntot, ntot x ntot, ntot + mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] + + muT = mu.reshape(nm, d) + S = Sn[self.idr, self.idc].reshape(nm, d, d) + + N = S + self.Sigma3d + g = multiple_pdfs(self.y, muT, N) + + MSE = np.mean(((g.flatten() - self.ptest.flatten()) ** 2) * self.wtest) + MAD = np.mean(np.abs(g.flatten() - self.ptest.flatten()) * self.wtest) + + M = S + 0.5 * self.Sigma3d + f = multiple_pdfs(self.y, muT, M) + + twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) + + if extra_metric: + diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) + diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) + + + MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) + MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) + + MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) + MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) + + # Weighted absolute differences + f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) + n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) + + # Unweighted absolute differences + f_wh = np.mean(np.abs(diff_y), axis=0) + n_wh = np.mean(np.abs(diff_n), axis=0) + summary = {} + for di in range(d): + summary[f"f{di+1}w"] = f_w[di] + summary[f"f{di+1}"] = f_wh[di] + summary[f"n{di+1}w"] = n_w[di] + summary[f"n{di+1}"] = n_wh[di] + else: + MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 + + metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, + "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, + "summary": summary} + return metric_sum diff --git a/PUQ/designmethods/support.py b/PUQ/designmethods/support.py new file mode 100644 index 0000000..d128472 --- /dev/null +++ b/PUQ/designmethods/support.py @@ -0,0 +1,37 @@ +import numpy as np +from numpy.linalg import inv, det + +def multiple_pdfs(x, means, covs): + # Cite: http://gregorygundersen.com/blog/2020/12/12/group-multivariate-normal-pdf/ + + # NumPy broadcasts `eigh`. + vals, vecs = np.linalg.eigh(covs) + + # Compute the log determinants across the second axis. + logdets = np.sum(np.log(vals), axis=1) + + # Invert the eigenvalues. + valsinvs = 1.0 / vals + + # Add a dimension to `valsinvs` so that NumPy broadcasts appropriately. + Us = vecs * np.sqrt(valsinvs)[:, None] + devs = x - means + + # Use `einsum` for matrix-vector multiplications across the first dimension. + devUs = np.einsum("ni,nij->nj", devs, Us) + + # Compute the Mahalanobis distance by squaring each term and summing. + mahas = np.sum(np.square(devUs), axis=1) + + # Compute and broadcast scalar normalizers. + dim = len(vals[0]) + log2pi = np.log(2 * np.pi) + return np.exp(-0.5 * (dim * log2pi + mahas + logdets)) + + +def multiple_determinants(covs): + vals, vecs = np.linalg.eigh(covs) + # Compute the log determinants across the second axis. + # dets = np.prod(vals, axis=1) + logdets = np.sum(np.log(vals), axis=1) + return np.exp(logdets) # dets#np.exp(logdets) \ No newline at end of file diff --git a/examples/2025+/1_example_3d_solo.py b/examples/2025+/1_example_3d_solo.py new file mode 100644 index 0000000..698f6e8 --- /dev/null +++ b/examples/2025+/1_example_3d_solo.py @@ -0,0 +1,93 @@ +import numpy as np +import matplotlib.pyplot as plt +from test_functions import unimodalx, bimodalx, braninx +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential import sequential_design +from utils import fig6, create_entry, save_output +from paper_figs import lineplot, boxplot +from utils_sample import test_data_gen +import pandas as pd + + +explore = True +maxiter = 50 +seedmin, seedmax = 0, 1 + +dfl = [] + +example = "braninx" + +if __name__ == "__main__": + + cex = eval(example)() + cex.realdata(x=np.array([0.5])[:, None], seed=None) + + tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl = test_data_gen(cex, sample=True) + + for s in np.arange(seedmin, seedmax): + + # Set random stream for initial design + persis_info = {'rand_stream': np.random.default_rng(s)} + + if explore: + # Initial sample + n0, rep0 = 30, 5 + sampling = LHS(xlimits=cex.zlim, random_state=int(s)) + z0u = sampling(n0) + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cex.sim_f(z0[i, :], persis_info=persis_info) for i in range(n0 * rep0)]) + else: + # Initial sample + grid_size, rep0 = 10, 2 + z1 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], grid_size) + z2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], grid_size) + zm1, zm2 = np.meshgrid(z1, z2) + z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T + z0 = np.repeat(z0u, rep0, axis=0) + x_tiled = np.tile(np.array([[0.40], [0.50]]), (z0.shape[0], 1)) + t_repeated = np.repeat(z0, np.array([[0.40], [0.50]]).shape[0], axis=0) + z0 = np.hstack([x_tiled, t_repeated]) + f0 = np.array([cex.sim_f(z, persis_info=persis_info) for z in z0]) + + test = {'f': f_s, + 'theta': t_s, + 'xt': None, + 'p': p_s, + 'noise': n_s, + "w": w_s, + "p_prior": 1} + + + # Methods to iterate over + methods = ["ivar", "imse", "var"] + + labs = ["ivar", "imse", "var"] + for mid, method in enumerate(methods): + + des_obj = sequential_design(cex) + des_obj.build_design(z0=z0, + f0=f0, + T=maxiter, + persis_info=persis_info, + test=test, + af=method, + args={"new":explore, + "nL":200, + "seed":s, + "t_grid":None, + "integral":"importance"}) + + dfl.extend(create_entry(des_obj, cex.data_name, method, s, labs[mid])) + + fig, ax = plt.subplots(1, 1, figsize=(4, 3), constrained_layout=True) + fig6(des_obj, z0[:, 1:3], rep0, Xpl, Ypl, pg, ng, ax, {}, fig, exp=explore) + plt.show() + + df = pd.DataFrame(dfl) + df2 = df.groupby(["s", "h", "example", "method"])["new"].mean().mul(100).reset_index() + df2.rename(columns={"new": "percent"}, inplace=True) + + fig, ax = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True) + lineplot(df, [example], metric="MAD", hue="method", ax=ax[0]) + boxplot(df=df2, ax=ax[1], x="h", hue="method") + plt.show() \ No newline at end of file diff --git a/examples/2025+/Figure1_2.py b/examples/2025+/Figure1_2.py new file mode 100644 index 0000000..79a63a2 --- /dev/null +++ b/examples/2025+/Figure1_2.py @@ -0,0 +1,101 @@ +import numpy as np +from test_functions import sinfunc +import scipy.stats as sps +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential import sequential_design +from utils import toy_example, visual_xt + +maxiter = 50 +new = True +seedmin, seedmax = 2, 3 +#seedmin, seedmax = 1, 2 +dfl = [] + +if __name__ == "__main__": + + for s in np.arange(seedmin, seedmax): + + cls_data = sinfunc() + dt = len(cls_data.theta_true) + cls_data.realdata(x=np.array([0.22222222, 0.88888889])[:, None], seed=None) + + # test data + nmesh, nt = 50, 100 + xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + zg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + tg = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nt)[:, None] + feval = np.array( + [[cls_data.function(x, t) for x in cls_data.x] for t in tg] + ).squeeze() + pg = np.array( + [ + sps.multivariate_normal(mean=f, cov=cls_data.obsvar).pdf( + cls_data.real_data + ) + for f in feval + ] + )[:, None] + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Visualize + toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) + + if new: + # Initial sample + n0, rep0 = 20, 5 + sampling = LHS(xlimits=cls_data.zlim, random_state=int(s)) + z0u = sampling(n0) + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + else: + # Initial sample + grid_size = 10 + rep0 = 5 + z1 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + z2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + zm1, zm2 = np.meshgrid(z1, z2) + z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + + + test = { + "f": fg, + "theta": tg, + "xt": zg, + "p": pg, + "noise": ng, + "p_prior": 1, + "w": 1 + } + + # Methods to iterate over + methods = ["ivar", "imse"] + for mid, method in enumerate(methods): + des_obj = sequential_design(cls_data) + des_obj.build_design( + z0=z0, + f0=f0, + T=maxiter, + persis_info=persis_info, + test=test, + af=method, + args={ + "new": new, + "nL": 200, + "seed": s, + "method": method, + "t_grid": tg, + "neighbor": "LHS", + "extra_metric": False, + }, + ) + + visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new) diff --git a/examples/2025+/paper_figs.py b/examples/2025+/paper_figs.py new file mode 100644 index 0000000..3d527a7 --- /dev/null +++ b/examples/2025+/paper_figs.py @@ -0,0 +1,731 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue May 20 14:45:08 2025 + +@author: surero +""" +import seaborn as sns +import matplotlib.pyplot as plt +from utils import read_data, read_output, read_summary_metric +import matplotlib as mpl +import pandas as pd +import numpy as np +from matplotlib.colors import ListedColormap + +# Custom yellow-orange colormap +yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), + (1, 0.9, 0), + (1, 0.8, 0), + (1, 0.6, 0), + (1, 0.4, 0), + (1, 0.2, 0), +] +yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + +custom_palette = {"ivar": "red", "imse": "blue", "var": "green", "varx": "magenta"} +hue_order = ["ivar", "imse", "var"] + +hatch_color_map = { + (0.875, 0.125, 0.125, 1): "//", + (0.125, 0.125, 0.875, 1): "\\", + (0.06274509803921569, 0.4392156862745098, 0.06274509803921569, 1): "xx", +} + + + + +def lineplot( + df, + examples, + metric="TV", + ci=None, + label=None, + custom_labels=None, + ax=None, + hue=None, + figset={}, + scaled=True, +): + ifLeg = figset.get("ifLeg", True) + sety = figset.get("sety", True) + ft = figset.get("ft", 12) + lw = figset.get("lw", 3) + + for i, example in enumerate(examples): + df1 = df.loc[df["example"] == example] + + if scaled: + group_cols = ["t", hue, "h"] + mean_df = df1.groupby(group_cols)[metric].mean().reset_index() + max_val = mean_df[metric].max() + mean_df[metric] = mean_df[metric] / max_val + data = mean_df.copy() + else: + data = df1.copy() + + sns.lineplot( + data=data, + x="t", + y=metric, + hue=hue, + style="h", + palette=custom_palette, + errorbar=ci, + linewidth=lw, + ax=ax, + ) + + lgd = ax.legend( + loc="upper center", + bbox_to_anchor=(1.3, 1), + fancybox=True, + shadow=True, + ncol=1, + fontsize=ft, + ) + + ax.set_yscale("log") + ax.set_xlabel("t", fontsize=ft) + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel(metric, fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + if ifLeg == False: + ax.legend().remove() + + +def boxplot(df, ax, x, hue, figset={}): + ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) + ft = figset.get("ft", 12) + + sns.boxplot( + x=x, + y="percent", + hue="method", + data=df, + ax=ax, + showfliers=False, + palette=custom_palette, + hue_order=hue_order, + ) + + # select the correct patches + patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch] + + # iterate through the patches for each subplot + for patch in patches: + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + # # # Fix legend to match hatches and colors + handles, labels = ax.get_legend_handles_labels() + legend = ax.legend( + handles, + labels, + title="method", + loc="center left", + bbox_to_anchor=(1.02, 0.5), + borderaxespad=0.0, + ) + + for patch in legend.get_patches(): + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel("Percent of exploration", fontsize=ft) + + plt.xticks(fontsize=ft) + + if ifLeg == False: + ax.legend().remove() + + +def plot_experiment_figure( + examples, + path, + ms, + ls, + lg, + ly, + reps=(1, 31), + metric="MAD", + layout="2xN", + figsize=(12, 6), + ft=10, + r0=5, + n0=150, + title=None, +): + + single_example = len(examples) == 1 + if layout == "1x2" and single_example: + fig, ax = plt.subplots(1, 2, figsize=figsize) + elif layout == "2xN": + fig, ax = plt.subplots(2, len(examples), figsize=figsize) + else: + raise ValueError("Unsupported layout or mismatched number of examples.") + + for i, ex in enumerate(examples): + print(ex) + figset = {"ifLeg": lg[i], "sety": ly[i], "ft": ft, "r0": r0, "n0": n0} + df1 = read_data( + rep0=reps[0], + repf=reps[1], + methods=ms, + examples=[ex], + folderpath=path, + label=ls, + ) + print(df1["s"]) + df2 = ( + df1.groupby(["s", "h", "example", "method"])["new"] + .mean() + .mul(100) + .reset_index() + ) + df2.rename(columns={"new": "percent"}, inplace=True) + + if layout == "1x2": + lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0], figset=figset) + boxplot_alternative(path, ms, ls, ex, ax=ax[1], figset=figset) + #boxplot(df=df2, ax=ax[1], x="h", hue="method", figset=figset) + else: # layout == "2xN" + lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0, i], figset=figset) + boxplot_alternative(path, ms, ls, ex, ax=ax[1, i], figset=figset) + #boxplot(df=df2, ax=ax[1, i], x="h", hue="method", figset=figset) + + plt.tight_layout(pad=0.2) + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + + + +def plot_SIR_experiment_figure( + path, ms, ls, reps=(1, 31), metric="MAD", figsize=(12, 6), ft=10, figset={}, title=None, +): + usual_boxplot = figset.get("usual_boxplot", True) + shift, scaled = figset.get("shift_x", 0), figset.get("scaled", True) + + fig, ax = plt.subplots( + 1, 4, figsize=figsize, gridspec_kw={"wspace": 0.25} + ) + #, "width_ratios": [1.25, 1.25, 1, 1] + lw = 3 + + df1 = read_data(rep0=reps[0], repf=reps[1], methods=ms, examples=["SIR"], folderpath=path, label=ls) + print(df1["s"]) + df3 = df1.loc[df1["t"] == 199] + + + if scaled: + group_cols = ["t", "method", "h"] + mean_df = df1.groupby(group_cols)[metric].mean().reset_index() + max_val = mean_df[metric].max() + mean_df[metric] = mean_df[metric] / max_val + data = mean_df.copy() + else: + data = df1.copy() + + # Lineplot + sns.lineplot( + data=data, + x="t", + y=metric, + hue="method", + style="h", + palette=custom_palette, + errorbar=None, + linewidth=lw, + ax=ax[0], + ) + ax[0].legend( + loc="upper center", + bbox_to_anchor=(0.5, -0.2), + fancybox=True, + shadow=True, + ncol=2, + fontsize=ft, + ) + ax[0].set_yscale("log") + + if usual_boxplot: + df2 = ( + df1.groupby(["s", "h", "example", "method"])["new"] + .mean() + .mul(100) + .reset_index() + ) + df2.rename(columns={"new": "percent"}, inplace=True) + box_data = [ + (df2, "percent", ax[1], "percent"), + (df3, "MADy", ax[2], r"$MAD^y$"), + (df3, "MADn", ax[3], r"$MAD^n$"), + ] + else: + figset["legend_right"] = False + boxplot_alternative(path, ms, ls, "SIR", ax=ax[1], figset=figset)#{"legend_right":False}) + box_data = [ + (df3, "MADy", ax[2], r"$MAD^y$"), + (df3, "MADn", ax[3], r"$MAD^n$"), + ] + + for boxid, box in enumerate(box_data): + sns.boxplot( + x=box[1], + y="h", + hue="method", + data=box[0], + showfliers=False, + palette=custom_palette, + hue_order=hue_order, + ax=box[2], + ) + + # select the correct patches + patches = [ + patch for patch in box[2].patches if type(patch) == mpl.patches.PathPatch + ] + + # iterate through the patches for each subplot + for patch in patches: + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + box[2].legend_.remove() + + # # # Fix legend to match hatches and colors + if boxid == 1: + handles, labels = box[2].get_legend_handles_labels() + legend = box[2].legend( + handles, + labels, + title="method", + loc="lower center", + bbox_to_anchor=(0.5+shift, -0.5), + ncol=3, + ) + box[2].set_yticks([]) + + for patch in legend.get_patches(): + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + for label in box[2].get_yticklabels(): + label.set_rotation(90) + + box[2].set_ylabel("") + box[2].set_xlabel(box[3], fontsize=ft) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + + + +def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1,31)): + + df = read_summary_metric(path, examples, methods, ls, rep) + + for metric in metrics: + fig, ax = plt.subplots(1, 1, figsize=(6, 4), constrained_layout=True) + sns.lineplot( + data=df, + x="t", + y=metric, + hue="method", + style="h", + ax=ax, + ) + ax.set_yscale("log") + plt.show() + + + +def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, repid=(1, 2), title=None): + + # Prepare data + from utils_SIR import test_gen_SIR + from SIR_funcs import SIRx + + cls_func = SIRx() + cls_func.realdata(x=np.array([[0.25, 0.25], [0.75, 0.75]]), seed=None) + p_grid, t_grid, noise_grid, Xpl, Ypl = test_gen_SIR(cls_func, return_XY=False) + + + for r in np.arange(repid[0], repid[1]): + # Plotting + fig, ax = plt.subplots(1, len(methods), figsize=(11, 3), constrained_layout=True) + + for mid, method in enumerate(methods): + desobj = read_output(path, "SIR", method, r, label=label) + + # Contour plots + ax[mid].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm", zorder=2) + cs = ax[mid].contourf( + Xpl, + Ypl, + np.sum(noise_grid, axis=1).reshape(50, 50), + cmap=yellow_cmap, + alpha=0.75, + zorder=1, + ) + + # Add colorbar only to the last axis + if mid == len(methods) - 1: + fig.colorbar(cs, ax=ax[mid], pad=0.1) + + # Annotate sample locations and frequency + unique_rows, counts = np.unique( + desobj.zs[200:, 2:4], axis=0, return_counts=True + ) + print(counts) + for lbl, x, y in zip(counts, unique_rows[:, 0], unique_rows[:, 1]): + ax[mid].annotate( + lbl, + xy=(x, y), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="blue", + zorder=3, + ) + + # Axis formatting + ax[mid].set_xticks([0, 0.5, 1]) + ax[mid].set_xlabel(r"$\theta_1$", fontsize=ft + 2) + ax[mid].tick_params(axis="both", labelsize=ft + 2) + if mid == 0: + ax[mid].set_yticks([0, 0.5, 1]) + ax[mid].set_ylabel(r"$\theta_2$", fontsize=ft + 2) + else: + ax[mid].set_yticks([]) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + +def plot_pritam(path, repid=1, label="target", title=None): + from test_functions import pritam + from utils_sample import test_data_gen_pri + + # Plot settings + ft = 12 + ms = ["ivar", "imse", "var"] + n0 = 150 + + + # Define test points + x = np.array([0.2, 0.8]) + y = np.array([0.2, 0.8]) + xr = np.array([[xx, yy] for xx in x for yy in y]) + + # Instantiate test function object and generate real data + cex = pritam() + cex.realdata(x=xr, seed=None) + + # Generate test grid + nmesh = 50 + x1 = np.linspace(cex.zlim[0][0], cex.zlim[0][1], nmesh) + x2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], nmesh) + X1, X2 = np.meshgrid(x1, x2) + Xg = np.vstack([X1.ravel(), X2.ravel()]).T + + # Evaluate noise and function values on the grid + ng = np.array([cex.noise(x[0], x[1], cex.theta_true[0]) for x in Xg]) + fg = np.array([cex.function(x[0], x[1], cex.theta_true[0]) for x in Xg]) + + # Create subplots + fig, ax = plt.subplots(2, 3, figsize=(9, 5), constrained_layout=True) + + for mid, m in enumerate(ms): + desobj = read_output(path, "pritam", m, repid, label="target") + + # Top row: histogram of theta samples + ax[0, mid].hist( + desobj.zs[n0:, 2], + bins=30, + edgecolor="black", + alpha=0.75, + color="blue" + ) + ax[0, mid].set_xlim(0, 1) + ax[0, mid].set_xlabel(r"$\vartheta$", fontsize=ft) + if mid == 0: + ax[0, mid].set_ylabel("Frequency", fontsize=ft) + + # Bottom row: scatter plot of x-samples with contour background + ax[1, mid].scatter( + desobj.zs[n0:, 0], + desobj.zs[n0:, 1], + marker="o", + facecolors="none", + edgecolors="blue", + zorder=2 + ) + + # Overlay test design points + for xpt in xr: + ax[1, mid].scatter(xpt[0], xpt[1], marker="x", color="black", s=100, zorder=3) + + # Contour of noise + cs = ax[1, mid].contourf( + X1, X2, ng.reshape(nmesh, nmesh), + cmap=yellow_cmap, + alpha=0.75, + zorder=1 + ) + + ax[1, mid].set_xlabel(r"$x_1$", fontsize=ft) + ax[1, mid].set_ylabel(r"$x_2$", fontsize=ft) + ax[1, mid].set_xticks([0, 0.5, 1]) + ax[1, mid].set_yticks([0, 0.5, 1]) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + +def plot_horizon_progress( + path, + example="SIR", + method="ivar", + single_seed=1, + labels=("target", "adapt"), + reps=(1, 31), + figsize=(8, 2), + colors=("gray", "orange"), + title=None, +): + + fig, ax = plt.subplots(1, 2, figsize=figsize) + + for mid, label in enumerate(labels): + # Read and filter data + df = read_data( + rep0=reps[0], + repf=reps[1], + methods=[method], + examples=[example], + folderpath=path, + label=[label], + ) + + df_label = df.loc[df["h"] == label].copy() + df_label["new_cumsum"] = df_label.groupby("s")["new"].cumsum() + + # Plot for a single seed + df_seed1 = df_label[df_label["s"] == single_seed] + ax[0].plot(df_seed1["t"], df_seed1["horizon"], color=colors[mid], linestyle=':') + + # Median horizon across reps + df_median = df_label.groupby("t", as_index=False)["horizon"].mean() + ax[0].plot(df_median["t"], df_median["horizon"], color=colors[mid], label=label, linewidth=3) + + # Mean cumulative new per t + df_cumsum_avg = df_label.groupby("t", as_index=False)["new_cumsum"].mean() + ax[1].plot( + df_cumsum_avg["t"], + df_cumsum_avg["new_cumsum"] / np.arange(1, len(df_cumsum_avg) + 1), + color=colors[mid], + linewidth=3, + label=label, + ) + + # Axis labels and legends + ax[0].set_xlabel("t") + ax[0].set_ylabel("Horizon") + ax[0].legend() + + ax[1].set_xlabel("t") + ax[1].set_ylabel(r"$n_t / \sum_{i=1}^{n_t} a_i$") + ax[1].legend() + plt.tight_layout() + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + + plt.show() + + +def plot_pairwise_from_output(path, example, ms, n0=90, rep0=5, d=2, label="h=-1"): + + for m in ms: + desobj = read_output(path, example, m, 1, label=label) + df = pd.DataFrame(desobj.zs[(n0 * rep0):, d:]) + sns.pairplot(df, diag_kind="kde") + plt.suptitle(f"m = {m}", y=1.02) + plt.tight_layout() + plt.show() + + +def boxplot_alternative(path, methods, horizons, example, ax, figset={}): + ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) + legend_right = figset.get("legend_right", True) + ft = figset.get("ft", 10) + r0, n0 = figset.get("r0", 5), figset.get("n0", 50) + + lst = [] + for m in methods: + for hor in horizons: + for i in np.arange(1, 31): + desobj = read_output(path, example, m, i, label=hor) + + # Eliminate the initial sample + initial_des = desobj.zs[0:n0, :] + unique_init, counts_init = np.unique(initial_des, axis=0, return_counts=True) + + # Convert unique_init to a set of tuple rows for fast lookup + unique_init_set = set(map(tuple, unique_init)) + + count_reps = [] + for xtid, xt in enumerate(desobj.xt): + xt_tuple = tuple(xt) + if (xt_tuple in unique_init_set) and (desobj.reps[xtid] == r0): + continue # skip this one + count_reps.append(desobj.reps[xtid]) + + count_reps = np.array(count_reps) + total = len(count_reps) + # Count percentages + percent_1 = np.sum(count_reps == 1) / total * 100 + percent_2 = np.sum(count_reps == 2) / total * 100 + percent_3 = np.sum(count_reps == 3) / total * 100 + percent_4 = np.sum(count_reps == 4) / total * 100 + percent_5 = np.sum(count_reps >= 5) / total * 100 + + lst.append({ + "1": percent_1, + "2": percent_2, + "3": percent_3, + "4": percent_4, + "5": percent_5, + "r": i, + "method": m, + "h": hor + }) + + df = pd.DataFrame(lst) + + # Average over r before plotting + components = ['1', '2', '3', '4', '5'] + + df_avg = df.groupby(['h', 'method'])[components].mean().reset_index() + + # Prepare for plotting + h_values = sorted(df_avg['h'].unique()) + methods_u = df_avg['method'].unique() + num_methods = len(methods_u) + + bar_width = 0.1 + group_spacing = 0.1 + hatches = ['/', '\\', 'x', 'o', '.'] + colors = ['blue', 'green', 'orange', 'red', 'purple'] + + # Create x positions + x_positions = [] + current_x = 0 + grouped = df_avg.sort_values(['h', 'method']).reset_index(drop=True) + + for i, h in enumerate(h_values): + methods_in_group = grouped[grouped['h'] == h] + for _ in range(len(methods_in_group)): + x_positions.append(current_x) + current_x += bar_width + current_x += group_spacing + + # Plot + #fig, ax = plt.subplots(figsize=(6, 6)) + bottoms = np.zeros(len(grouped)) + + # First pass: fill with white so we can hatch over it + for i, comp in enumerate(components): + ax.bar( + x_positions, + grouped[comp], + bottom=bottoms, + width=bar_width, + color='white', + edgecolor='black' + ) + bottoms += grouped[comp].values + + # Second pass: hatch overlay with colored edges + bottoms = np.zeros(len(grouped)) + for i, comp in enumerate(components): + ax.bar( + x_positions, + grouped[comp], + bottom=bottoms, + width=bar_width, + fill=False, + hatch=hatches[i % len(hatches)], + edgecolor=colors[i % len(colors)], + linewidth=1.5, + label=f'{comp}' + ) + bottoms += grouped[comp].values + + # Method labels on x-axis + ax.set_xticks(x_positions) + ax.set_xticklabels(grouped['method'].values, rotation=45, ha='right') + + # h group labels below the x-axis + for i, h in enumerate(h_values): + group_start = i * (num_methods * bar_width + group_spacing) + group_mid = group_start + (num_methods - 1) * bar_width / 2 + ax.text( + group_mid, -0.002 * ax.get_ylim()[1], + f'h = {h}', + ha='center', + va='top', + fontsize=ft, + transform=ax.get_xaxis_transform() + ) + + if legend_right: + ax.legend( + loc="upper center", + bbox_to_anchor=(1.3, 1), + fancybox=True, + shadow=True, + ncol=1, + fontsize=ft) + else: + ax.legend( + loc="lower center", + bbox_to_anchor=(0.5, -0.6), + fancybox=True, + shadow=True, + ncol=3, + fontsize=ft) + + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel("Percentage (%)", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + if ifLeg == False: + ax.legend().remove() diff --git a/examples/2025+/test_functions.py b/examples/2025+/test_functions.py new file mode 100644 index 0000000..1566078 --- /dev/null +++ b/examples/2025+/test_functions.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Feb 12 13:13:23 2025 + +@author: surero +""" +import numpy as np +import scipy + +class sinfunc: + def __init__(self): + self.data_name = "sinfunc" + self.zlim = np.array([[0, 1], [0, 1]]) + self.theta_true = np.array([0.5]) # np.array([np.pi / 5]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.dx = 1 + self.dt = 1 + self.x = None + self.sigma2 = 0.1 + + def function(self, x, theta): + f = np.sin(10 * x - 5 * theta) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + var_noise = self.noise(thetas[0], thetas[1]) + noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise), 1) + f += noise + return f + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + + M = np.array([self.function(x, self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + def noise(self, x, theta): + alpha = 10 + min_value = 0.01#0.05 + max_value = 0.1#0.3 + + weight = 1 / (1 + np.exp(-alpha * (x - 0.5))) # Sigmoid transition + value = min_value + (max_value - min_value) * weight # Scale between 0.1 and 10 + return value + + +class unimodalx: + def __init__(self): + self.data_name = "unimodalx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + self.sigma2 = 0.1 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + t1 = -10 + t1 * 20 + t2 = -10 + t2 * 20 + f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 + (2 * x - 1) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + V = 0.01 + 1.2 * (t1**2 + t2**2) * 2 + V = V + return V + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + +class bimodalx: + def __init__(self): + self.data_name = "bimodalx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.theta_true = np.array([0.35, 0.35]) + self.real_data = None + self.out = [("f", float)] + self.p = 3 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.sigma2 = 0.05 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + mu1 = (0.35, 0.35) + mu2 = (0.65, 0.65) + sigma = 0.15 + term1 = np.exp(-((t1 - mu1[0]) ** 2 + (t2 - mu1[1]) ** 2) / sigma**2) + term2 = np.exp(-((t1 - mu2[0]) ** 2 + (t2 - mu2[1]) ** 2) / sigma**2) + f = (term1 + term2) + (2 * x - 1) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + cov = np.array([[0.05, 0], [0, 0.05]]) + var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) + return 0.1*var.pdf(np.array([t1, t2])) + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + +class braninx: + def __init__(self): + self.data_name = "braninx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) + self.sigma2 = 5 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + t1 = -5 + 15 * t1 + t2 = 15 * t2 + f = ( + (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 + + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) + + 10 + + (2 * x - 1) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + alpha = 10 + min_value = 0.1 + max_value = 15 + + weight = 1 / (1 + np.exp(-alpha * (t2 - 0.5))) + value = min_value + (max_value - min_value) * weight + + return value + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + +class pritam: + def __init__(self): + self.data_name = "pritam" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.theta_true = np.array([0.5]) + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.real_data = None + self.dx = 2 + self.sigma2 = 10 #0.5**2 + self.dx = 2 + self.dt = 1 + + def function(self, x1, x2, theta1): + f = (30 + 5 * x1 * np.sin(5 * x1)) * (6 * theta1 + 1 + np.exp(-5 * x2)) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x[0], x[1], self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + def noise(self, x1, x2, t1): + # return 5 + + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return (15*t1)*var.pdf(np.array([x1, x2])) + diff --git a/examples/2025+/utils.py b/examples/2025+/utils.py new file mode 100644 index 0000000..a296d44 --- /dev/null +++ b/examples/2025+/utils.py @@ -0,0 +1,344 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import ListedColormap +import pandas as pd +import matplotlib as mpl +import matplotlib.patches as mpatches + +yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + (1, 0, 0), # very dark orange +] +yellow_cmap = ListedColormap(yellow_colors, name="yellow") + +def heatmap(Xpl, Ypl, noise_grid, f_grid, p_grid, t_sample=None): + + + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + cs = ax[0].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax[0].contour(Xpl, Ypl, f_grid.reshape(50, 50), colors="black") + ax[0].clabel(CS, inline=True, fontsize=10) + ax[0].set_xlabel(r"$\theta_1$", fontsize=16) + ax[0].set_ylabel(r"$\theta_2$", fontsize=16) + ax[0].tick_params(axis="both", labelsize=16) + + cs = ax[1].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax[1].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm") + if t_sample is not None: + ax[1].scatter(t_sample[:, 0], t_sample[:, 1]) + ax[1].clabel(CS, inline=True, fontsize=10) + ax[1].set_xlabel(r"$\theta_1$", fontsize=16) + ax[1].set_ylabel(r"$\theta_2$", fontsize=16) + ax[1].tick_params(axis="both", labelsize=16) + plt.show() + +def heatmap_pritam(cls_data): + # test data + nmesh = 50 + x1 = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + x2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + X1, X2 = np.meshgrid(x1, x2) + Xg = np.vstack([X1.ravel(), X2.ravel()]).T + #cls_data.theta_true[0] + ng = np.array([cls_data.noise(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) + fg = np.array([cls_data.function(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) + + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + cs = ax[0].contourf(X1, X2, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + #ax[1].clabel(CS, inline=True, fontsize=10) + for x in cls_data.x: + ax[0].scatter(x[0], x[1], marker="*", color="black") + ax[0].set_xlabel(r"$x_1$", fontsize=16) + ax[0].set_ylabel(r"$x_2$", fontsize=16) + ax[0].tick_params(axis="both", labelsize=16) + + cs = ax[1].contourf(X1, X2, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + #ax[0].clabel(CS, inline=True, fontsize=10) + for x in cls_data.x: + ax[1].scatter(x[0], x[1], marker="*", color="black") + ax[1].set_xlabel(r"$x_1$", fontsize=16) + ax[1].set_ylabel(r"$x_2$", fontsize=16) + ax[1].tick_params(axis="both", labelsize=16) + plt.show() + + + + + + + +def toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info): + + from mpl_toolkits.axes_grid1 import make_axes_locatable + + ft = 12 + fig, axs = plt.subplots(1, 3, figsize=(14, 3.5), constrained_layout=False) # Disable constrained_layout + fig.subplots_adjust(wspace=0.4, left=0.1, right=0.9) # Adjust space between subplots + + # Contour plot for axs[1] + divider = make_axes_locatable(axs[0]) + cax1 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cs1 = axs[0].contourf(Xpl, Ypl, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + fig.colorbar(cs1, cax=cax1) + + xt_joint = np.column_stack((cls_data.x, np.repeat(cls_data.theta_true, len(cls_data.x)))) + axs[0].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") + axs[0].set_xlabel(r"$x$", fontsize=ft) + axs[0].set_ylabel(r"$\vartheta$", fontsize=ft) + axs[0].tick_params(axis="both", labelsize=ft) + + # Contour plot for axs[2] + divider = make_axes_locatable(axs[1]) + cax2 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cs2 = axs[1].contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + fig.colorbar(cs2, cax=cax2) + + axs[1].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") + axs[1].set_xlabel(r"$x$", fontsize=ft) + axs[1].set_ylabel(r"$\vartheta$", fontsize=ft) + axs[1].tick_params(axis="both", labelsize=ft) + + # Scatter plot for axs[0] + xs = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], 100) + for k in range(10): + axs[2].scatter(xs, + np.array([cls_data.sim_f(np.array([x, cls_data.theta_true[0]]), persis_info) for x in xs]), + facecolors="none", edgecolors="green", s=80, linewidth=2) + axs[2].plot(xs, + np.array([cls_data.function(x, cls_data.theta_true[0]) for x in xs]), + linestyle="dotted", linewidth=3, color="black") + axs[2].set_xlabel(r"$x$", fontsize=ft) + #axs[2].set_ylabel(r"$\theta$", fontsize=ft) + axs[2].tick_params(axis="both", labelsize=ft) + + plt.show() + + + +def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): + + z, reps = des_obj.xt, des_obj.reps + + ft = 14 + fig, ax = plt.subplots(figsize=(5, 4)) + + for xitem in cls_data.x: + ax.vlines( + xitem, 0, 1, linestyles="dotted", colors="green", linewidth=3, zorder=5 + ) + for label, x_count, y_count in zip(reps, z[:, 0], z[:, 1]): + if new: + if np.any(np.all(z0u == np.array([x_count, y_count]), axis=1)): + col = "cyan" + else: + col = "blue" + else: + if label > rep0: + col = "blue" + else: + col = "cyan" + + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft-2, + color=col, + weight="bold", + zorder=6, + ) + + ax.contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.set_xlabel(r"$x$", fontsize=ft) + ax.set_ylabel(r"$\theta$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + ax2 = ax.twiny() + ax2.plot(pg, tg, color='black', label="Y-axis scatter") + ax2.set_xlabel(r"$p(y|\theta)$", fontsize=ft) + ax2.tick_params(axis="both", labelsize=ft) + plt.show() + + +def fig6(desobj, theta0, rep0, Xpl, Ypl, pg, ng, ax, figset={}, fig={}, exp=True): + + th, reps = desobj.xt[:, 1:3], desobj.reps + + xtick, ytick, cbarf = figset.get("xtick", True), figset.get("ytick", True), figset.get("cbar", True) + + ft = 12 + nm = Xpl.shape[0] + cs = ax.contourf(Xpl, Ypl, ng.reshape(nm, nm), cmap=yellow_cmap, alpha=0.75) + cp = ax.contour(Xpl, Ypl, pg.reshape(nm, nm), cmap="coolwarm") + + + if cbarf: + cbar = fig.colorbar(cs, ax=ax, pad=0.1) + + for label, x_count, y_count in zip(reps, th[:, 0], th[:, 1]): + + if exp: + if np.any(np.all(theta0 == np.array([x_count, y_count]), axis=1)): + col = "cyan" + else: + col = "blue" + else: + if label > rep0: + col = "blue" + else: + col = "cyan" + + ax.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color=col + ) + + if xtick: + ax.set_xticks([0, 0.5, 1]) + ax.set_xlabel(r"$\vartheta_1$", fontsize=ft) + else: + ax.set_xticks([]) + + if ytick: + ax.set_yticks([0, 0.5, 1]) + ax.set_ylabel(r"$\vartheta_2$", fontsize=ft) + else: + ax.set_yticks([]) + + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.tick_params(axis="both", labelsize=ft) + + + +def visual_pritam(des_obj, z0u): + ft = 16 + xtu, repu = des_obj.xt, des_obj.reps + for xtid, xt in enumerate(xtu): + # print(xt) + if xt in z0u: + col = "cyan" + else: + col = "blue" + + plt.annotate( + repu[xtid], + xy=(xt[0], xt[1]), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color=col, + weight="bold", + ) + + plt.xlabel(r"$x_1$") + plt.ylabel(r"$x_2$") + plt.show() + + ut = [] + for xtid, xt in enumerate(xtu): + if xt not in z0u: + ut.append(xt[2]) + plt.hist(ut) + plt.show() + +import os +import dill as pickle +def save_output(desing_obj, name, al_func, seedno, path=None, label=None): + + if path is None: + if not os.path.isdir('output'): + os.mkdir('output') + + design_path = 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + else: + design_path = path + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + + with open(design_path, 'wb') as file: + pickle.dump(desing_obj, file) + +def read_output(path1, name, al_func, seedno, label=None): + + design_path = path1 + 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + with open(design_path, 'rb') as file: + design_obj = pickle.load(file) + + return design_obj + + + +def create_entry(desobj, fname, method, s, h): + return [ + {"MSE": he["MSE"], + "MAD": he["MAD"], + "VAR": he["VAR"], + "MSEy": he["MSEy"], + "MADy": he["MADy"], + "MSEn": he["MSEn"], + "MADn": he["MADn"], + "t": he["t"], + "new": he["new"], + "method": method, + "example": fname, + "s": s, + "h": h, + "horizon": he["h"]} + for he in desobj["H"] + ] + +def read_data(rep0=0, + repf=10, + methods=["ivar", "imse", "unif"], + examples=["unimodal", "banana", "bimodal"], + folderpath=None, + label=None, + ): + + datalist = [] + for eid, example in enumerate(examples): + for mid, m in enumerate(methods): + for r in range(rep0, repf): + + desobj = read_output(folderpath, example, m, r, label=label[mid]) + entry = create_entry(desobj, example, m, r, label[mid]) + datalist.extend(entry) + + df = pd.DataFrame(datalist) + return df + +def read_summary_metric(folderpath, examples, methods, ls, rep=(1,31)): + dfl = [] + for eid, example in enumerate(examples): + for mid, m in enumerate(methods): + for r in range(rep[0], rep[1]): + + desobj = read_output(folderpath, example, m, r, label=ls[mid]) + for heid, he in enumerate(desobj.H): + sm = he["summary_metric"] + sm["r"] = r + sm["method"] = m + sm["h"] = ls[mid] + sm["t"] = heid + dfl.append(sm) + + df = pd.DataFrame(dfl) + return df diff --git a/examples/2025+/utils_sample.py b/examples/2025+/utils_sample.py new file mode 100644 index 0000000..35ae0b4 --- /dev/null +++ b/examples/2025+/utils_sample.py @@ -0,0 +1,143 @@ +import numpy as np +import scipy.stats as sps +from utils import heatmap, heatmap_pritam +import matplotlib.pyplot as plt +import sys + +def sample_from_posterior(cls_data, seed): + import emcee + import scipy.stats as sps + from smt.sampling_methods import LHS + + discard = 250 + nsteps = 1000 + thin = 30 + nwalkers = 40 + seed = int(seed) + + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + if cls_data.dx == 1: + feval = np.array([cls_data.function(x, *ctheta) for x in cls_data.x]).squeeze() + elif cls_data.dx == 2: + feval = np.array([cls_data.function(x[0], x[1], *ctheta) for x in cls_data.x]).squeeze() + + + rnd = sps.multivariate_normal(mean=feval, cov=cls_data.obsvar) + pvar = rnd.pdf(cls_data.real_data) + sys.float_info.epsilon + return np.log(pvar) + + def sample(ndim, nwalkers, seed): + np.random.seed(seed) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + sampling = LHS( + xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed + ) + loc0 = sampling(nwalkers) + sampler.run_mcmc(initial_state=loc0, nsteps=nsteps, progress=False) + samples = sampler.get_chain(discard=discard, thin=thin, flat=True) + return samples + + return sample(cls_data.dt, nwalkers, seed) + +def test_data_gen(cls_data, sample=False): + + # test data + nmesh = 50 + xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + tg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + pg = np.array( + [ + sps.multivariate_normal( + mean=np.array([cls_data.function(x, *t) for x in cls_data.x]), + cov=cls_data.obsvar, + ).pdf(cls_data.real_data) + for t in tg + ] + )[:, None] + + zg = np.column_stack((np.tile(cls_data.x, (tg.shape[0], 1)), tg)) + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + if sample: + t_s = sample_from_posterior(cls_data, 1234) + p_s = np.zeros((t_s.shape[0], 1)) + f_s = np.zeros((t_s.shape[0], 1)) + n_s = np.zeros((t_s.shape[0], 1)) + for t_id, t in enumerate(t_s): + f_s[t_id, 0] = np.array([cls_data.function(x, t[0], t[1]) for x in cls_data.x]) + n_s[t_id, 0] = np.array([cls_data.noise(x, t[0], t[1]) for x in cls_data.x]) + rnd = sps.multivariate_normal(mean=f_s[t_id, 0], cov=cls_data.obsvar) + p_s[t_id, 0] = rnd.pdf(cls_data.real_data) + + p_se = p_s + sys.float_info.epsilon + w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() + + + heatmap(Xpl, Ypl, ng, fg, pg, t_s) + + return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl + else: + return tg, fg, pg, zg, ng, Xpl, Ypl + + +def test_data_gen_pri(cls_data, sample=False): + + nt = 100 + tg = np.linspace(cls_data.zlim[2][0], cls_data.zlim[2][1], nt)[:, None] + pg = np.array( + [ + sps.multivariate_normal( + mean=np.array( + [cls_data.function(x[0], x[1], t) for x in cls_data.x] + ).squeeze(), + cov=cls_data.obsvar, + ).pdf(cls_data.real_data) + for t in tg + ] + ) + + + + heatmap_pritam(cls_data) + + # (ntot, d) + x_tiled = np.tile(cls_data.x, (tg.shape[0], 1)) + # (ntot, p-d) + t_repeated = np.repeat(tg, cls_data.x.shape[0], axis=0) + # (ntot, p) + zg = np.hstack([x_tiled, t_repeated]) + + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + if sample: + t_s = sample_from_posterior(cls_data, 1234) + #t_s = np.linspace(0, 1, 900)[:, None] + p_s = np.zeros((t_s.shape[0], 1)) + f_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) + n_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) + + for t_id, t in enumerate(t_s): + feval = np.array([cls_data.function(x[0], x[1], t) for x in cls_data.x]) + f_s[t_id*4 : (t_id + 1)*4, 0] = feval.flatten() + n_s[t_id*4 : (t_id + 1)*4, 0] = np.array([cls_data.noise(x[0], x[1], t) for x in cls_data.x]).flatten() + + rnd = sps.multivariate_normal(mean=feval.flatten(), cov=cls_data.obsvar) + p_s[t_id, 0] = rnd.pdf(cls_data.real_data) + + p_se = p_s + sys.float_info.epsilon + w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() + + plt.scatter(tg, pg, alpha=0.5, marker="+", color="blue", s=50) + plt.scatter(t_s, p_s, alpha=0.1, marker="*", color="red", s=10) + plt.show() + + return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s + else: + return tg, fg, pg, zg, ng From ba13626e3c9683e28b7434a75bf15769c088fd45 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 26 Aug 2025 23:48:05 -0400 Subject: [PATCH 002/103] include batch sequential one --- PUQ/design.py | 9 +- PUQ/designmethods/batch_support.py | 71 ++ PUQ/designmethods/gen_funcs/acquire_new.py | 315 +++++ .../batch_acquisition_funcs_support.py | 122 ++ .../gen_funcs/batch_allocate_reps.py | 279 +++++ PUQ/designmethods/p_sto_bseq.py | 413 +++++++ PUQ/prior.py | 119 +- PUQ/surrogate.py | 18 +- PUQ/surrogatemethods/covariances.py | 57 + PUQ/surrogatemethods/distance.py | 141 +++ PUQ/surrogatemethods/helpers.py | 108 ++ PUQ/surrogatemethods/hetGP.py | 1081 +++++++++++++++++ PUQ/surrogatemethods/homGP.py | 369 ++++++ PUQ/surrogatemethods/pcHetGP.py | 222 ++++ examples/Technometrics2025/Figure1_2.py | 125 ++ examples/Technometrics2025/Figure3.py | 201 +++ examples/Technometrics2025/bseq2d_exploit.py | 163 +++ examples/Technometrics2025/bseq2d_explore.py | 182 +++ examples/Technometrics2025/test_funcs.py | 562 +++++++++ examples/Technometrics2025/utilities.py | 818 +++++++++++++ 20 files changed, 5313 insertions(+), 62 deletions(-) create mode 100644 PUQ/designmethods/batch_support.py create mode 100644 PUQ/designmethods/gen_funcs/acquire_new.py create mode 100644 PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py create mode 100644 PUQ/designmethods/gen_funcs/batch_allocate_reps.py create mode 100644 PUQ/designmethods/p_sto_bseq.py create mode 100644 PUQ/surrogatemethods/covariances.py create mode 100644 PUQ/surrogatemethods/distance.py create mode 100644 PUQ/surrogatemethods/helpers.py create mode 100644 PUQ/surrogatemethods/hetGP.py create mode 100644 PUQ/surrogatemethods/homGP.py create mode 100644 PUQ/surrogatemethods/pcHetGP.py create mode 100644 examples/Technometrics2025/Figure1_2.py create mode 100644 examples/Technometrics2025/Figure3.py create mode 100644 examples/Technometrics2025/bseq2d_exploit.py create mode 100644 examples/Technometrics2025/bseq2d_explore.py create mode 100644 examples/Technometrics2025/test_funcs.py create mode 100644 examples/Technometrics2025/utilities.py diff --git a/PUQ/design.py b/PUQ/design.py index 1d0e8f2..ce42661 100644 --- a/PUQ/design.py +++ b/PUQ/design.py @@ -4,13 +4,12 @@ import importlib - class designer(object): - def __init__(self, data_cls=None, method="SEQCAL", args={}): + def __init__(self, data_cls=None, acquisition=None, method="SEQCAL", args={}): self.method = importlib.import_module("PUQ.designmethods." + method) self._info = {"method": method} - self.fit(data_cls, args) + self.fit(data_cls, acquisition, **args) - def fit(self, data_cls, args): - self.method.fit(self._info, data_cls, args) + def fit(self, data_cls, acquisition, **args): + self.method.fit(self._info, data_cls, acquisition, **args) diff --git a/PUQ/designmethods/batch_support.py b/PUQ/designmethods/batch_support.py new file mode 100644 index 0000000..3194f18 --- /dev/null +++ b/PUQ/designmethods/batch_support.py @@ -0,0 +1,71 @@ +import numpy as np + + +def rebuild_condition(complete, prev_complete, n_theta=2): + + if (np.sum(complete) - np.sum(prev_complete) < n_theta): + nflag = False + else: + nflag = True + return nflag + +def create_arrays(n_x, n_thetas): + """Create 2D (point * rows) arrays fevals, pending and complete""" + + fevals = np.full((n_x, n_thetas), np.nan) + pending = np.full((1, n_thetas), True) + prev_pending = pending.copy() + complete = np.full((1, n_thetas), False) + prev_complete = np.full((1, n_thetas), False) + + return fevals, pending, prev_pending, complete, prev_complete + +def pad_arrays(n_x, thetanew, theta, fevals, pending, prev_pending, complete, prev_complete): + """Extend arrays to appropriate sizes.""" + n_thetanew = len(thetanew) + theta = np.vstack((theta, thetanew)) + fevals = np.hstack((fevals, np.full((n_x, n_thetanew), np.nan))) + pending = np.hstack((pending, np.full((1, n_thetanew), True))) + prev_pending = np.hstack((prev_pending, np.full((1, n_thetanew), True))) + complete = np.hstack((complete, np.full((1, n_thetanew), False))) + prev_complete = np.hstack((prev_complete, np.full((1, n_thetanew), False))) + + return theta, fevals, pending, prev_pending, complete, prev_complete + + +def update_arrays(n_x, fevals, pending, complete, calc_in, obs_offset, theta_offset): + """Unpack from calc_in into 2D (point * rows) fevals""" + + sim_id = calc_in['sim_id'] + r = np.repeat(0, len(sim_id - obs_offset)) + c = sim_id - obs_offset + + if n_x < 2: + fevals[r, c+theta_offset] = calc_in['f'] + else: + rc = [i for j in range(len(sim_id - obs_offset)) for i in range(n_x)] + cc = np.repeat(c, n_x) + fevals[rc, cc] = calc_in['f'].flatten() + + pending[r, c+theta_offset] = False + complete[r, c+theta_offset] = True + return + + +def load_H(H, thetas, mse, offset=0): + """Fill inputs into H0. + There will be num_points x num_thetas entries + """ + n_thetas = len(thetas) + start = offset*n_thetas + + if thetas.shape[1] < 2: + print(H['thetas'].shape) + print(thetas.flatten().shape) + H['thetas'][start:start+n_thetas] = thetas#.flatten() + else: + H['thetas'][start:start+n_thetas] = thetas + + H['TV'][start:start+n_thetas] = np.repeat(mse, n_thetas) + + return H \ No newline at end of file diff --git a/PUQ/designmethods/gen_funcs/acquire_new.py b/PUQ/designmethods/gen_funcs/acquire_new.py new file mode 100644 index 0000000..ed77294 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquire_new.py @@ -0,0 +1,315 @@ +"""Contains acquisition functions.""" + +import numpy as np +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + build_emulator, + compute_ivar, + impute, + impute_CL, + multiple_determinants, + multiple_pdfs, +) +from PUQ.surrogatemethods.pcHetGP import update + + +def get_pred(cL, emu, x, ttest, reps): + + cP = emu.predict(x=x, theta=cL) + var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps + + testP = emu.predict(x=x, theta=ttest, thetaprime=cL) + mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] + + mut = mu.T + St = np.transpose(S, (2, 0, 1)) + + return mut, St, cov, var_cand + + +def last_iteration(x, obs, obsvar, tE, fE, des_obj): + tmesh, skip, pc_settings = des_obj.tmesh, des_obj.skip, des_obj.pc_settings + if skip: + ivar = 0 + else: + emu = build_emulator(x=x, theta=tE, f=fE, pcset=pc_settings) + ivar = compute_ivar(emu=emu, ttest=tmesh, x=x, obs=obs, obsvar=obsvar) + return ivar + + +def impute_strategy(ct, x, fE, tE, emu, liar, des_obj): + + rep = des_obj.rep + impute_str = des_obj.impute_str + rand_stream = des_obj.rand_stream + pc_settings = des_obj.pc_settings + + if impute_str == "update": + update(emu._info, x=x, X0new=ct, mult=rep) + fE, tE = impute( + ct=ct, x=x, fE=fE, tE=tE, reps=rep, emu=emu, rnd_str=rand_stream + ) + else: + if impute_str == "KB": + fE, tE = impute( + ct=ct, x=x, fE=fE, tE=tE, reps=rep, emu=emu, rnd_str=rand_stream + ) + elif impute_str == "CL": + fE, tE = impute_CL(ct=ct, x=x, fE=fE, tE=tE, reps=rep, liar=liar) + + emu = build_emulator(x=x, theta=tE, f=fE, pcset=pc_settings) + + return fE, tE, emu + + +class acquire: + def __init__( + self, + bnew, + rep, + emu, + func_cls, + theta_mesh, + prior, + method="simse", + nL=200, + pc_settings={}, + rand_stream=None, + impute_str="KB", + skip=False, + ): + + self.bnew = bnew + self.rep = rep + self.emu = emu + self.x = func_cls.x + self.tmesh = theta_mesh + self.obs = func_cls.real_data + self.obsvar = func_cls.obsvar + self.method = method + self.nL = nL + self.prior = prior + self.pc_settings = pc_settings + self.func_cls = func_cls + self.rand_stream = rand_stream + self.impute_str = "KB" if impute_str is None else impute_str + self.skip = False if skip is None else skip + + # print("Explore rule: ", self.method, " with ", self.impute_str) + + def acquire_new(self): + if self.method == "simse": + self.simse() + elif self.method == "seivar": + self.seivar() + elif self.method == "var": + self.var() + + return + + def seivar(self): + + x = self.x + obs = self.obs + obsvar = self.obsvar + n_x = x.shape[0] + # 1 x d x d + obsvar3d = obsvar.reshape(1, n_x, n_x) + + nm = self.tmesh.shape[0] + p = self.tmesh.shape[1] + + # Create a candidate list + nL = self.nL + cL = self.prior.rnd(self.nL, self.rand_stream) + + # Get emulator + emu = self.emu + # cov : q x nm x nL + # cvar : q x nL + # mu : nm x d + # S : nm x d x d + mu, S, cov, cvar = get_pred( + cL=cL, emu=emu, x=x, ttest=self.tmesh, reps=self.rep + ) + V1 = S + obsvar3d + + fE = emu._info["f"] + tE = emu._info["theta"] + + liar = np.mean(fE, axis=1) + + tnew = [] + for i in range(self.bnew): + G = emu._info["G"] + B = emu._info["B"] + GB = G @ B + BTG = B.T @ G + + d = G.shape[0] + q = B.shape[1] + + tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) + + for j in range(0, q): + tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + for k in range(0, nL): + phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + vals = [] + for k in range(0, nL): + # C1: nm x d x d + # C2: nm x d x d + phic = phi[:, x, x.T, k] + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(obs, mu, C1) + dets = multiple_determinants(C2) + part2 = rpdf / (coef * np.sqrt(dets)) + + cval = -np.sum(part2) + vals.append(cval) + + idc = np.argmin(vals) + ct = cL[idc, :].reshape((1, p)) + tnew.append(ct) + + fE, tE, emu = impute_strategy(ct, x, fE, tE, emu, liar, self) + + cL = self.prior.rnd(nL, self.rand_stream) + + mu, S, cov, cvar = get_pred( + cL=cL, emu=emu, x=x, ttest=self.tmesh, reps=self.rep + ) + V1 = S + obsvar3d + + tnew = np.array(tnew).reshape((self.bnew, p)) + ivar = last_iteration(x, obs, obsvar, tE, fE, self) + self.tnew = tnew + self.ivar = ivar + return + + def simse(self): + + x = self.x + obs = self.obs + obsvar = self.obsvar + n_x = x.shape[0] + obsvar3d = obsvar.reshape(1, n_x, n_x) + + nm = self.tmesh.shape[0] + p = self.tmesh.shape[1] + + # Create a candidate list + nL = self.nL + cL = self.prior.rnd(self.nL, self.rand_stream) + + # Get emulator + emu = self.emu + + # cov : q x nm x nL + # cvar : q x nL + _, _, cov, cvar = get_pred(cL=cL, emu=emu, x=x, ttest=self.tmesh, reps=self.rep) + fE = emu._info["f"] + tE = emu._info["theta"] + + liar = np.mean(fE, axis=1) + + tnew = [] + for i in range(self.bnew): + G = emu._info["G"] + B = emu._info["B"] + GB = G @ B + BTG = B.T @ G + + d = G.shape[0] + q = B.shape[1] + + tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + for j in range(0, q): + tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + for k in range(0, nL): + phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + phidiag = np.diagonal(phi, axis1=1, axis2=2) + + vals = [] + for k in range(0, nL): + cval = -np.sum(phidiag[:, k, :]) + vals.append(cval) + + idc = np.argmin(vals) + ct = cL[idc, :].reshape((1, p)) + tnew.append(ct) + + fE, tE, emu = impute_strategy(ct, x, fE, tE, emu, liar, self) + + cL = self.prior.rnd(nL, self.rand_stream) + _, _, cov, cvar = get_pred( + cL=cL, emu=emu, x=x, ttest=self.tmesh, reps=self.rep + ) + tnew = np.array(tnew).reshape((self.bnew, p)) + ivar = last_iteration(x, obs, obsvar, tE, fE, self) + self.tnew = tnew + self.ivar = ivar + return + + def var(self): + x = self.x + obs = self.obs + obsvar = self.obsvar + n_x = x.shape[0] + # 1 x d x d + obsvar3d = obsvar.reshape(1, n_x, n_x) + + p = self.tmesh.shape[1] + + # Create a candidate list + nL = self.nL + cL = self.prior.rnd(self.nL, self.rand_stream) + + # Get emulator + emu = self.emu + + fE = emu._info["f"] + tE = emu._info["theta"] + + liar = np.mean(fE, axis=1) + + tnew = [] + det = np.linalg.det(obsvar) + + for i in range(self.bnew): + pcL = emu.predict(x=x, theta=cL) + mu, S = pcL._info["mean"].T, pcL._info["S"] + St = np.transpose(S, (2, 0, 1)) + + M = St + 0.5 * obsvar3d + N = St + obsvar3d + f = multiple_pdfs(obs, mu, M) + g = multiple_pdfs(obs, mu, N) + + coef = 1 / ((2**n_x) * (np.sqrt(np.pi) ** n_x) * np.sqrt(det)) + + vals = coef * f - g**2 + + idc = np.argmax(vals) + ct = cL[idc, :].reshape((1, p)) + tnew.append(ct) + + fE, tE, emu = impute_strategy(ct, x, fE, tE, emu, liar, self) + + cL = self.prior.rnd(nL, self.rand_stream) + + tnew = np.array(tnew).reshape((self.bnew, p)) + ivar = last_iteration(x, obs, obsvar, tE, fE, self) + self.tnew = tnew + self.ivar = ivar + return diff --git a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py new file mode 100644 index 0000000..fadc3a8 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py @@ -0,0 +1,122 @@ +"""Contains supplemental methods for acquisitionn funcs.""" + +import numpy as np +from PUQ.surrogate import emulator +from numpy.linalg import inv, det + +def impute(ct, x, fE, tE, reps, emu, rnd_str): + + # NEW PART + if fE.shape[0] > 1: + cpred = emu.predict(x=x, theta=ct) + cm = cpred.mean() + cS = cpred._info['S'] + cR = cpred._info['R'] + fnoise = rnd_str.multivariate_normal(mean=cm.flatten(), + cov=cS[:, :, 0] + cR[:, :, 0], + size=reps) + + tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) + fE = np.concatenate([fE, fnoise.T], axis=1) + else: + cpred = emu.predict(x=x, theta=ct) + cm = cpred.mean() + cv = cpred._info['var_noisy'] + fnoise = rnd_str.normal(loc=cm.flatten(), + scale=np.sqrt(cv.flatten()), + size=reps) + + tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) + fE = np.concatenate([fE, fnoise.reshape(1, reps)], axis=1) + + return fE, tE + +def impute_CL(ct, x, fE, tE, reps, liar): + + #print(fE) + d = fE.shape[0] + + tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) + fE = np.concatenate([fE, np.repeat(liar, reps).reshape(d, reps)], axis=1) + + #print(fE) + + return fE, tE + +def build_emulator(x, theta, f, pcset): + + print(theta.shape) + emu = emulator(x=x, + theta=theta, + f=f, + method="pcHetGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}, + 'pc_settings':pcset}) + return emu + +def compute_ivar(emu, ttest, x, obs, obsvar): + + testP = emu.predict(x=x, theta=ttest) + d = len(x) + obsvar3d = obsvar.reshape(1, d, d) + + # ntest x d + mu = testP._info['mean'].T + S = testP._info['S'] + St = np.transpose(S, (2, 0, 1)) + + # ntest x d x d + M = St + 0.5*obsvar3d + N = St + obsvar3d + + f = multiple_pdfs(obs, mu, M) + g = multiple_pdfs(obs, mu, N) + + coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(obsvar)))) + + # ivar compute + ivar = np.sum(coef*f - g**2) + return ivar + + +def multiple_pdfs(x, means, covs): + # Cite: http://gregorygundersen.com/blog/2020/12/12/group-multivariate-normal-pdf/ + + # NumPy broadcasts `eigh`. + vals, vecs = np.linalg.eigh(covs) + + # Compute the log determinants across the second axis. + logdets = np.sum(np.log(vals), axis=1) + + # Invert the eigenvalues. + valsinvs = 1.0 / vals + + # Add a dimension to `valsinvs` so that NumPy broadcasts appropriately. + Us = vecs * np.sqrt(valsinvs)[:, None] + devs = x - means + + # Use `einsum` for matrix-vector multiplications across the first dimension. + devUs = np.einsum("ni,nij->nj", devs, Us) + + # Compute the Mahalanobis distance by squaring each term and summing. + mahas = np.sum(np.square(devUs), axis=1) + + # Compute and broadcast scalar normalizers. + dim = len(vals[0]) + log2pi = np.log(2 * np.pi) + return np.exp(-0.5 * (dim * log2pi + mahas + logdets)) + + +def multiple_determinants(covs): + vals, vecs = np.linalg.eigh(covs) + # Compute the log determinants across the second axis. + # dets = np.prod(vals, axis=1) + logdets = np.sum(np.log(vals), axis=1) + return np.exp(logdets) # dets#np.exp(logdets) + diff --git a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py new file mode 100644 index 0000000..c49d50c --- /dev/null +++ b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py @@ -0,0 +1,279 @@ +import numpy as np +import scipy +from numpy.linalg import cholesky, inv, det +from PUQ.surrogatemethods.covariances import cov_gen +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + multiple_pdfs, + build_emulator, + impute, + compute_ivar +) + +class allocate: + def __init__(self, + budget, + emu_info, + func_cls, + theta_mesh, + prior, + method='imse', + trace=1, + alloc_settings={}, + rand_stream=None): + + self.budget = budget + self.emu = emu_info + self.x = func_cls.x + self.theta_mesh = theta_mesh + self.obs = func_cls.real_data + self.obsvar = func_cls.obsvar + self.method = method + self.use_Ki = alloc_settings.get('use_Ki') + self.eps = np.sqrt(np.finfo(float).eps) + self.trace = trace + self.func_cls = func_cls + self.rand_stream = rand_stream + + if alloc_settings.get('theta') is None: + if alloc_settings.get('gen') is False: + self.theta = self.emu._info['emulist'][0]['X0'] + else: + self.theta = prior.rnd(100, None) + self.theta = np.concatenate((self.emu._info['emulist'][0]['X0'], self.theta), axis=0) + else: + self.theta = alloc_settings['theta'] + + if alloc_settings.get('a0') is None: + if alloc_settings.get('gen') is False: + self.a0 = self.emu._info['emulist'][0]['mult'] + else: + self.a0 = np.concatenate((self.emu['emulist']._info[0]['mult'], np.repeat(0, 100))) + else: + self.a0 = alloc_settings['a0'] + + + def allocatereps(self): + + if self.trace == 1: + print("Allocation rule: ", self.method, " with ", self.emu._info['method']) + + if self.method == 'imse': + Cinfo = self.compute_imse_weights() + C = Cinfo.flatten() + elif self.method == 'ivar': + Cinfo = self.compute_ivar_weights() + C = Cinfo.flatten() + elif self.method == 'var': + Cinfo = self.compute_var_weights() + C = Cinfo.flatten() + + idneg = C < 0 + weight = np.sqrt(np.abs(C[idneg])) + a_frac = np.zeros(len(C)) + a_frac_ub = np.zeros(len(C)) + total_a = np.sum(self.a0) + self.budget + + # Find an upper bound + a_frac_ub[idneg] = total_a*weight/sum(weight) + a_ub = self.make_int(total_a, a_frac_ub) + a_ub = np.maximum(0, a_ub - self.a0) + + a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) + a_final = self.make_int(self.budget, a_frac) + a_final = np.array([int(a) for a in a_final]) + if np.sum(a_final) != self.budget: + raise ValueError("Budget constraint is not satisfied.") + + self.reps = a_final + return + + def compute_imse_weights(self): + + d = len(self.x) + n_integ = self.theta_mesh.shape[0] + n = self.theta.shape[0] + + pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + + # d x d + G = self.emu._info['G'] + # d x q + B = self.emu._info['B'] + # d x q + GB = G @ B + BTG = B.T @ G + + q = len(self.emu._info['emulist']) + Mb = np.zeros((n, n_integ, q, q)) + for i in range(0, n): + J = np.zeros((n, n)) + J[i, i] = 1 + for j in range(0, q): + emuinfo = self.emu._info['emulist'][j] + K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta']) + + if self.use_Ki: + Ki = emuinfo['Ki'] + else: + K = cov_gen(X1=self.theta, theta=emuinfo['theta']) + Ki = scipy.linalg.pinv(K, rcond=self.eps) + # Ki = scipy.linalg.pinv2(K, rcond=self.eps) + + A = Ki@J@Ki + if emuinfo['is_homGP']: + Mb[i, :, j, j] = -emuinfo['nu_hat']*emuinfo['g']*np.einsum('ji,jk,ki->i', K_s, A, K_s) + else: + Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + + C = np.zeros((n, 1)) + for i in range(0, n): + for l in range(0, n_integ): + par = GB @ Mb[i, l, :, :] @ BTG + C[i] += np.sum(np.diag(par)) + + return C + + + + def compute_ivar_weights(self): + + d = len(self.x) + n_integ = self.theta_mesh.shape[0] + n = self.theta.shape[0] + + pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + pred_mesh = self.emu.predict(x=self.x, theta=self.theta_mesh, thetaprime=None) + + obsvar3d = self.obsvar.reshape(1, d, d) + # ntest x d + mu = pred_mesh._info['mean'].T + S = pred_mesh._info['S'] + St = np.transpose(S, (2, 0, 1)) + + # ntest x d x d + Nb = St + 0.5*obsvar3d + N = St + obsvar3d + + f = multiple_pdfs(self.obs, mu, Nb) + g = multiple_pdfs(self.obs, mu, N) + + # ntest x 1 x d + h = (self.obs - mu).reshape(n_integ, 1, d) + + # ntest x d x d + Nbinv = inv(Nb) + + # ntest x d x d + Ninv = inv(N) + + # ntest x 1 x d + hNb = np.matmul(h, Nbinv) + hN = np.matmul(h, Ninv) + + # d x d + G = self.emu._info['G'] + # d x q + B = self.emu._info['B'] + # d x q + GB = G @ B + BTG = B.T @ G + + coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) + + q = len(self.emu._info['emulist']) + Mb = np.zeros((n, n_integ, q, q)) + + for i in range(0, n): + J = np.zeros((n, n)) + J[i, i] = 1 + for j in range(0, q): + emuinfo = self.emu._info['emulist'][j] + K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta']) + + if self.use_Ki: + Ki = emuinfo['Ki'] + else: + K = cov_gen(X1=self.theta, theta=emuinfo['theta']) + Ki = scipy.linalg.pinv(K, rcond=self.eps) + + A = Ki@J@Ki + if emuinfo['is_homGP']: + Mb[i, :, j, j] = -(emuinfo['g']*emuinfo['nu_hat'])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + else: + Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + + dlogfdai = np.zeros((n, n_integ)) + dloggdai = np.zeros((n, n_integ)) + C = np.zeros((n, 1)) + for i in range(0, n): + + M = GB @ Mb[i, :, :, :] @ BTG + part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) + part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) + part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) + part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) + + dlogfdai = part1f + part2f.flatten() + dloggdai = part1g + part2g.flatten() + C[i] = np.sum(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) + + return C + + def compute_var_weights(self): + + d = len(self.x) + pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + + obsvar3d = self.obsvar.reshape(1, d, d) + + # ntest x d + mu = pred_nugs._info['mean'].T + S = pred_nugs._info['S'] + St = np.transpose(S, (2, 0, 1)) + + # ntest x d x d + Nb = St + 0.5*obsvar3d + N = St + obsvar3d + + f = multiple_pdfs(self.obs, mu, Nb) + g = multiple_pdfs(self.obs, mu, N) + + coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) + + var = coef*f - g**2 + C = -1*var + + return C + + def make_int(self, bdg, n_frac): + # Make integer + n_floor = np.floor(n_frac) + remain = n_frac - n_floor + tot_remain = bdg - np.sum(n_floor) + sorted_indices = np.array(np.argsort(remain)[::-1]) + if tot_remain > 0: + idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] + n_floor[idx] = n_floor[idx] + 1 + + return n_floor + + def ivar_exploit(self, emu, pc_settings): + texploit = self.theta + rep = self.reps + x = self.x + tmesh = self.theta_mesh + obs = self.obs + obsvar = self.obsvar + + fE = emu._info['f'] + tE = emu._info['theta'] + for cid, ct in enumerate(texploit): + if rep[cid] > 0: + fE, tE = impute(ct=ct[None, :], x=x, fE=fE, tE=tE, reps=rep[cid], emu=emu, rnd_str=self.rand_stream) + + emu = build_emulator(x=x, theta=tE, f=fE, pcset=pc_settings) + ivar = compute_ivar(emu=emu, ttest=tmesh, x=x, obs=obs, obsvar=obsvar) + + return ivar + + \ No newline at end of file diff --git a/PUQ/designmethods/p_sto_bseq.py b/PUQ/designmethods/p_sto_bseq.py new file mode 100644 index 0000000..fa29c10 --- /dev/null +++ b/PUQ/designmethods/p_sto_bseq.py @@ -0,0 +1,413 @@ +import numpy as np +from PUQ.designmethods.gen_funcs.batch_allocate_reps import allocate +from PUQ.designmethods.gen_funcs.acquire_new import acquire +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + multiple_pdfs, + build_emulator, +) +from PUQ.designmethods.batch_support import load_H, update_arrays, create_arrays, pad_arrays, rebuild_condition +from libensemble.message_numbers import STOP_TAG, PERSIS_STOP, FINISHED_PERSISTENT_GEN_TAG, EVAL_GEN_TAG +from libensemble.tools.persistent_support import PersistentSupport +from libensemble.alloc_funcs.start_only_persistent import only_persistent_gens as alloc_f +from libensemble.libE import libE +from libensemble.tools import add_unique_random_streams +import copy +import time + +def fit(fitinfo, + data_cls, + acquisition, + theta=None, + prior=None, + batch_size=None, + data_test=None, + nworkers=None, + max_iter=None, + trace=0, + des_init={}, + des_add={}, + alloc_settings={}, + pc_settings={'standardize': True, 'latent': True}, + des_settings={}, + **kwargs): + + out = data_cls.out + sim = data_cls.sim + + sim_specs = { + 'sim_f': sim, + 'in': ['thetas'], + 'out': out, + 'user': { + 'sim_f': data_cls.sim_f + }, + } + + gen_out = [ + ('thetas', float, data_cls.p), + ('priority', int), + ('obs', float, (1,)), + ('obsvar', float, (1,)), + ('TV', float), + ('HD', float), + ('AE', float), + ('time', float), + ] + + gen_specs = { + 'gen_f': gen_f, + 'persis_in': [o[0] for o in gen_out] + ['f', 'sim_id'], + 'out': gen_out, + "user": { + "batch_size": batch_size, # No. of thetas to generate per step + "nworkers": nworkers, + "theta_init": des_init.get('theta'), + "f_init": des_init.get('f'), + "theta_add": des_add.get('theta'), + "synth_cls": data_cls, + "test_data": data_test, + "prior": prior, + "alloc_settings": alloc_settings, + "acquisition": acquisition, + "pc_settings": pc_settings, + "des_settings": des_settings, + "trace": trace, + }, + } + + alloc_specs = { + 'alloc_f': alloc_f, + 'user': { + 'init_sample_size': 0, + 'async_return': True, # True = Return results to gen as they come in (after sample) + 'active_recv_gen': True, # Persistent gen can handle irregular communications + }, + } + libE_specs = {'nworkers': nworkers, 'comms': 'local'} + #libE_specs = {'nworkers': nworkers, 'comms': 'local', 'sim_dirs_make': True, + # 'sim_dir_copy_files': [os.path.join(os.getcwd(), '48Ca_template.in')]} + + + persis_info = add_unique_random_streams({}, nworkers + 1) + for perid, per in enumerate(persis_info): + persis_info[perid]['rand_stream'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) + #persis_info[perid]['rand_stream'] = np.random.default_rng(perid) + + # Currently just allow gen to exit if mse goes below threshold value + exit_criteria = {'sim_max': max_iter} # Now just a set number of sims. + + # Perform the run + H, persis_info, flag = libE( + sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs=alloc_specs, libE_specs=libE_specs + ) + + if data_cls.d == 1: + fitinfo['f'] = H['f'][:, None] + else: + fitinfo['f'] = H['f'] + + if data_cls.p == 1: + fitinfo['theta'] = H['thetas']#[:, None] + else: + fitinfo['theta'] = H['thetas'] + + f_c = np.concatenate((des_init.get('f'), fitinfo['f'].T), axis=1) + print(des_init.get('theta').shape) + print(fitinfo['theta'].shape) + t_c = np.concatenate((des_init.get('theta'), fitinfo['theta']), axis=0) + + emu, TV = newiteration(t_c, f_c, data_cls.x, pc_settings, data_test, data_cls.obsvar, data_cls.real_data) + + fitinfo['theta'] = t_c + fitinfo['f'] = f_c + fitinfo['TViter'] = np.concatenate((H['TV'], np.repeat(TV, batch_size))) + fitinfo['theta0'] = emu._info['emulist'][0]['X0'] + fitinfo['reps0'] = emu._info['emulist'][0]['mult'] + + for key in persis_info.keys(): + if isinstance(persis_info[key], dict): + if 'additional' in persis_info[key].keys(): + fitinfo['TV'] = persis_info[key]['additional']['TVs'] + fitinfo['TV'].append(TV) + fitinfo['iter_explore'] = persis_info[key]['additional']['iter_explore'] + fitinfo['iter_exploit'] = persis_info[key]['additional']['iter_exploit'] + fitinfo['time'] = persis_info[key]['additional']['times'] + return + +def newiteration(theta, fevals, x, pc_settings, test_data, obsvar, obs): + + print("hay") + thetatest, ptest, ftest, priortest = None, None, None, None + if test_data is not None: + thetatest, ptest, ftest, priortest = ( + test_data["theta"], + test_data["p"], + test_data["f"], + test_data["p_prior"], + ) + + emu = build_emulator(x, theta, fevals, pc_settings) + + print("kkk") + d = len(x) + obsvar3d = obsvar.reshape(1, d, d) + + # ntest x d + pred = emu.predict(x=x, theta=thetatest) + mu = pred.mean().T + S = pred._info['S'] + St = np.transpose(S, (2, 0, 1)) + N = St + obsvar3d + phat = multiple_pdfs(obs, mu, N) + + print("hay") + + # Obtain the accuracy on the test set + if ptest is not None: + TV = np.mean(np.abs(ptest - phat)) + + return emu, TV + +def onebatch(b, + b_new, + r_new, + emu, + prior_func, + acqfunc, + data_cls, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + trace): + + ivar_exploit, ivar_explore = np.inf, np.inf + + emu_original_info = copy.deepcopy(emu._info) + + if is_exploit: + # Allocate existing ones + allocate_obj = allocate(budget=b, + emu_info=emu, + prior=prior_func, + func_cls=data_cls, + theta_mesh=test_data["theta"], + method=alloc_settings.get('method'), + alloc_settings=alloc_settings, + rand_stream=rand_stream, + trace=trace) + allocate_obj.allocatereps() + if not is_explore: + ivar_exploit = 0 + else: + ivar_exploit = allocate_obj.ivar_exploit(emu, pc_settings) + + r_exploit = allocate_obj.reps + theta_exploit = allocate_obj.theta + + emu._info = emu_original_info + + if is_explore: + # Find new ones + acquire_obj = acquire(bnew=b_new, + rep=r_new, + emu=emu, + func_cls=data_cls, + theta_mesh=test_data["theta"], + prior=prior_func, + method=acqfunc, + nL=des_settings.get('nL'), + pc_settings=pc_settings, + rand_stream=rand_stream, + impute_str=des_settings.get('impute_str'), + skip=not is_exploit) + + acquire_obj.acquire_new() + + ivar_explore = acquire_obj.ivar + theta_explore = acquire_obj.tnew + + if ivar_exploit <= ivar_explore: + new_theta = np.repeat(theta_exploit, r_exploit, axis=0) + iter_exploit += 1 + else: + new_theta = np.repeat(theta_explore, r_new, axis=0) + iter_explore += 1 + + return new_theta, iter_exploit, iter_explore + +def gen_f(H, persis_info, gen_specs, libE_info): + + """Generator to select and obviate parameters for calibration.""" + ps = PersistentSupport(libE_info, EVAL_GEN_TAG) + rand_stream = persis_info['rand_stream'] + + b = gen_specs["user"]["batch_size"] + n_workers = gen_specs["user"]["nworkers"] + + data_cls = gen_specs["user"]["synth_cls"] + test_data = gen_specs["user"]["test_data"] + prior_func = gen_specs["user"]["prior"] + theta_init = gen_specs["user"]["theta_init"] + f_init = gen_specs["user"]["f_init"] + theta_add = gen_specs["user"]["theta_add"] + + alloc_settings = gen_specs["user"]["alloc_settings"] + acqfunc = gen_specs["user"]["acquisition"] + pc_settings = gen_specs["user"]["pc_settings"] + des_settings = gen_specs["user"]["des_settings"] + trace = gen_specs["user"]["trace"] + + # data params + d = data_cls.d + x = data_cls.x + y = data_cls.real_data + Sigma = data_cls.obsvar + + # explore params + rho = alloc_settings.get('rho') + if rho is not None: + b_new = int(b*rho) + r_new = int(b/b_new) + + obs_offset, theta_offset = 0, 0 + + fevals, pending, prev_pending, complete, prev_complete = None, None, None, None, None + first_iter, update_model = True, False + tag = 0 + iter_explore, iter_exploit = 0, 0 + is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') + counter = 0 + TVs, times = [], [] + + print(f_init.shape) + + while tag not in [STOP_TAG, PERSIS_STOP]: + + if not first_iter: + # Update fevals from calc_in + update_arrays(d, + fevals, + pending, + complete, + calc_in, + obs_offset, + theta_offset) + update_model = rebuild_condition(complete, prev_complete, n_theta=b) + + if not update_model: + tag, Work, calc_in = ps.recv() + if tag in [STOP_TAG, PERSIS_STOP]: + break + + if update_model: + + if trace == 1: + print('Percentage Pending: %0.2f ( %d / %d)' % (100*np.round(np.mean(pending), 4), + np.sum(pending), + np.prod(pending.shape))) + print('Percentage Complete: %0.2f ( %d / %d)' % (100*np.round(np.mean(complete), 4), + np.sum(complete), + np.prod(pending.shape))) + + f_c = np.concatenate((f_init, fevals), axis=1) + t_c = np.concatenate((theta_init, theta), axis=0) + + print(f_c.shape) + start_emu = time.time() + emu, TV = newiteration(t_c, f_c, x, pc_settings, test_data, Sigma, y) + end_emu = time.time() + TVs.append(TV) + + prev_pending = pending.copy() + update_model = False + + if first_iter: + + + print("hey") + + start_emu = time.time() + emu, TV = newiteration(theta_init, f_init, x, pc_settings, test_data, Sigma, y) + end_emu = time.time() + TVs.append(TV) + + print("hey") + + n_init = n_workers - 1 + if theta_add is None: + start_batch = time.time() + theta, iter_exploit, iter_explore = onebatch(b, + b_new, + r_new, + emu, + prior_func, + acqfunc, + data_cls, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + trace) + end_batch = time.time() + times.append(end_batch - start_batch + end_emu - start_emu) + else: + cand_theta = theta_add[counter*b_new:(counter + 1)*b_new, :] + theta = np.repeat(cand_theta, r_new, axis=0) + + fevals, pending, prev_pending, complete, prev_complete = create_arrays(d, n_init) + H_o = np.zeros(len(theta), dtype=gen_specs['out']) + H_o = load_H(H_o, theta, TV) + tag, Work, calc_in = ps.send_recv(H_o) + first_iter = False + counter += 1 + + else: + if rebuild_condition(complete, prev_complete, n_theta=b): + + prev_complete = complete.copy() + if theta_add is None: + start_batch = time.time() + new_theta, iter_exploit, iter_explore = onebatch(b, + b_new, + r_new, + emu, + prior_func, + acqfunc, + data_cls, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + trace) + end_batch = time.time() + times.append(end_batch - start_batch + end_emu - start_emu) + else: + cand_theta = theta_add[counter*b_new:(counter + 1)*b_new, :] + new_theta = np.repeat(cand_theta, r_new, axis=0) + + theta, fevals, pending, prev_pending, complete, prev_complete = \ + pad_arrays(d, new_theta, theta, fevals, pending, prev_pending, complete, prev_complete) + + H_o = np.zeros(len(new_theta), dtype=gen_specs['out']) + H_o = load_H(H_o, new_theta, TV) + tag, Work, calc_in = ps.send_recv(H_o) + counter += 1 + + persis_info['additional'] = {'TVs':TVs, 'iter_exploit':iter_exploit, 'iter_explore':iter_explore, 'times':times} + return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/prior.py b/PUQ/prior.py index 73add02..2bcee66 100644 --- a/PUQ/prior.py +++ b/PUQ/prior.py @@ -9,61 +9,78 @@ def __init__(self, a, b): self.p = len(a) def rnd(self, n, seed=None): - if seed == None: - pass - else: - np.random.seed(seed) + # if seed == None: + # pass + # else: + # np.random.seed(seed) thlist = [] for i in range(self.p): - thlist.append(sps.uniform.rvs(self.a[i], self.b[i] - self.a[i], size=n)) + thlist.append(seed.uniform(self.a[i], self.b[i], size=n)) + #thlist.append(sps.uniform.rvs(self.a[i], self.b[i] - self.a[i], size=n)) return np.array(thlist).T - def pdf(self, theta): - ncnd = theta.shape[0] - thlist = np.ones(ncnd) - for i in range(self.p): - thlist *= sps.uniform.pdf(theta[:, i], self.a[i], self.b[i] - self.a[i]) - return np.array(thlist).T - - -class prior_truncnorm: - def __init__(self, a, b, loc, scale): - self.a = a - self.b = b - self.loc = loc - self.scale = scale - self.p = len(a) - - def rnd(self, n, seed=None): - if seed == None: - pass - else: - np.random.seed(seed) - thlist = [] - for i in range(self.p): - thlist.append( - sps.truncnorm.rvs( - a=self.a[i], - b=self.b[i], - loc=self.loc[i], - scale=self.scale[i], - size=n, - ) - ) - return np.array(thlist).T - - def pdf(self, theta): - ncnd = theta.shape[0] - thlist = np.ones(ncnd) - for i in range(self.p): - thlist *= sps.truncnorm.pdf( - theta[:, i], - a=self.a[i], - b=self.b[i], - loc=self.loc[i], - scale=self.scale[i], - ) - return np.array(thlist).T +# class prior_uniform: +# def __init__(self, a, b): +# self.a = a +# self.b = b +# self.p = len(a) + +# def rnd(self, n, seed=None): +# if seed == None: +# pass +# else: +# np.random.seed(seed) +# thlist = [] +# for i in range(self.p): +# thlist.append(sps.uniform.rvs(self.a[i], self.b[i] - self.a[i], size=n)) +# return np.array(thlist).T + +# def pdf(self, theta): +# ncnd = theta.shape[0] +# thlist = np.ones(ncnd) +# for i in range(self.p): +# thlist *= sps.uniform.pdf(theta[:, i], self.a[i], self.b[i] - self.a[i]) +# return np.array(thlist).T + + +# class prior_truncnorm: +# def __init__(self, a, b, loc, scale): +# self.a = a +# self.b = b +# self.loc = loc +# self.scale = scale +# self.p = len(a) + +# def rnd(self, n, seed=None): +# if seed == None: +# pass +# else: +# np.random.seed(seed) +# thlist = [] +# for i in range(self.p): +# thlist.append( +# sps.truncnorm.rvs( +# a=self.a[i], +# b=self.b[i], +# loc=self.loc[i], +# scale=self.scale[i], +# size=n, +# ) +# ) +# return np.array(thlist).T + +# def pdf(self, theta): +# ncnd = theta.shape[0] +# thlist = np.ones(ncnd) +# for i in range(self.p): +# thlist *= sps.truncnorm.pdf( +# theta[:, i], +# a=self.a[i], +# b=self.b[i], +# loc=self.loc[i], +# scale=self.scale[i], +# ) +# return np.array(thlist).T # if seed == None: # pass diff --git a/PUQ/surrogate.py b/PUQ/surrogate.py index 8fa40e8..ef4609c 100644 --- a/PUQ/surrogate.py +++ b/PUQ/surrogate.py @@ -1,7 +1,6 @@ """ This module contains a class that implements the main emulation method. """ - import numpy as np import importlib import copy @@ -14,6 +13,7 @@ def __init__( x=None, theta=None, f=None, + thetaprime=None, method="PCGP", passthroughfunc=None, args={}, @@ -85,6 +85,7 @@ def __init__( self.__ptf = passthroughfunc if self.__ptf is not None: return + self._args = copy.deepcopy(args) if f is not None: @@ -205,8 +206,8 @@ def __repr__(self): ) return strrepr - def __call__(self, x=None, theta=None, args=None): - return self.predict(x, theta, args) + def __call__(self, x=None, theta=None, thetaprime=None, args=None): + return self.predict(x, theta, thetaprime, args) def fit(self, args=None): """ @@ -221,15 +222,15 @@ def fit(self, args=None): Optional dictionary containing options you would like to pass to fit function. It will add/modify those in self._args. """ - if args is not None: argstemp = {**self._args, **copy.deepcopy(args)} else: argstemp = copy.copy(self._args) x, theta, f = self.__preprocess() + self.method.fit(self._info, x, theta, f, **argstemp) - def predict(self, x=None, theta=None, args={}): + def predict(self, x=None, theta=None, thetaprime=None, args={}): """ Fits an emulator or surrogate. @@ -336,12 +337,17 @@ def predict(self, x=None, theta=None, args={}): ) info = {} - self.method.predict(info, self._info, x, theta, **argstemp) + self.method.predict(info, self._info, x, theta, thetaprime, **argstemp) return prediction(info, self) def acquisition(self, x=None, theta1=None, theta2=None): return self.method.acquisition(self._info, x, theta1, theta2) + def computeC(self, x=None, theta1=None, realdata=None, realvar=None): + + return self.method.computeC(self._info, x, theta1, realdata, realvar) + + def supplement( self, size, diff --git a/PUQ/surrogatemethods/covariances.py b/PUQ/surrogatemethods/covariances.py new file mode 100644 index 0000000..2b6f4fd --- /dev/null +++ b/PUQ/surrogatemethods/covariances.py @@ -0,0 +1,57 @@ +#from distance import distance_cpp +import numpy as np + +### A) Gaussian covariance + +## Gaussian covariance function : K = exp(-(X1 - X2)^2/theta) +## @param X1 matrix of design locations +## @param X2 matrix of design locations if covariance is calculated between X1 and X2 +## @param theta vector of lengthscale parameters (either of size one if isotropic or of size d if anisotropic) + +def distance_cpp(X1, X2=None): + if X2 is None: + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X2**2, axis=1) - 2 * np.dot(X1, X2.T) + +def cov_gen(X1, X2=None, theta=None): + + if len(theta) == 1: + return np.exp(-distance_cpp(X1, X2) / theta) + + if X2 is None: + #np.exp(-distance_cpp(X1 * np.tile(1 / np.sqrt(theta), (X1.shape[0], len(theta))))) + return np.exp(-distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))))) + + # np.exp(-distance_cpp(X1 * np.repeat(1/np.sqrt(theta), np.repeat(X1.shape[0], len(theta))), + # X2 * np.repeat(1/np.sqrt(theta), np.repeat(X2.shape[0], len(theta))))) + + return np.exp(-distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))), + X2 * (1/np.sqrt(np.repeat(theta[None, :], X2.shape[0], axis=0))))) + + + +def partial_cov_gen(X1, theta, type="Gaussian", arg=None, X2=None, **kwargs): + + if X2 is None: + if type == "Gaussian": + if arg == "theta_k": + return partial_d_C_Gaussian_dtheta_k(X1=X1, theta=theta, **kwargs) + if arg == "k_theta_g": + return partial_d_Cg_Gaussian_d_k_theta_g(X1=X1, theta=theta, **kwargs) + if arg == "X_i_j": + return partial_d_C_Gaussian_dX_i_j(X1=X1, theta=theta, **kwargs) + + +def partial_d_C_Gaussian_dtheta_k(X1, theta): + return distance_cpp(X1) / theta**2 + +def partial_d_k_Gaussian_dtheta_k(X1, X2, theta): + return distance_cpp(X1, X2) / theta**2 + +def partial_d_Cg_Gaussian_d_k_theta_g(X1, theta, k_theta_g): + # 1-dimensional/isotropic case + if len(theta) == 1: + return distance_cpp(X1) / (theta * k_theta_g**2) + + return distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))))/k_theta_g**2 #distance_cpp(X1 * np.tile(1 / np.sqrt(theta), (X1.shape[0], len(theta)))) / k_theta_g**2 + diff --git a/PUQ/surrogatemethods/distance.py b/PUQ/surrogatemethods/distance.py new file mode 100644 index 0000000..cff7b95 --- /dev/null +++ b/PUQ/surrogatemethods/distance.py @@ -0,0 +1,141 @@ +import numpy as np + +# def distcpp(X1): +# nr = X1.shape[0] +# nc = X1.shape[1] +# s = np.zeros((nr, nr)) +# for i in range(1, nr): +# ptrs = s[:, i] +# ptrs2 = s[i, :] # symmetric +# for j in range(i): +# ptrX1 = X1[i, :] +# ptrX2 = X1[j, :] +# tmp = np.abs(ptrX1 - ptrX2) +# ptrs += tmp * tmp +# ptrs2[:] = ptrs +# ptrs2 += nr + +# print(np.round(s)) +# return s + +# def distcpp(X1): +# nr, nc = X1.shape +# s = np.zeros((nr, nr)) + +# for i in range(1, nr): +# ptrs = s[0:i, i] +# ptrs2 = s[i, 0:i] + +# for j in range(i): +# ptrX1 = X1[i, :] +# ptrX2 = X1[j, :] +# tmp = np.sum((ptrX1 - ptrX2) ** 2) +# ptrs += tmp +# ptrs2[j] = tmp + +# return s + +import numpy as np + +# def distcpp(X1): +# nr, nc = X1.shape +# s = np.zeros((nr, nr)) + +# for i in range(1, nr): +# ptrs = s[0:i, i] +# ptrs2 = s[i, 0:i] + +# for j in range(i): +# ptrX1 = X1[i, :] +# ptrX2 = X1[j, :] +# tmp = np.sum((ptrX1 - ptrX2) ** 2) +# ptrs[j] = tmp +# ptrs2[j] = tmp + +# # Make the matrix symmetric +# s = s + s.T - np.diag(s.diagonal()) + +# return s + +def distcpp(X1, X2=None): + if X2 is None: + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) + else: + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) + +def distcpp_2(X1, X2): + nr1 = X1.shape[0] + nr2 = X2.shape[0] + dim = X1.shape[1] + s = np.zeros((nr1, nr2)) + ptrs = s.flatten() + ptrX2 = X2.flatten() + ptrX1 = X1.flatten() + for i in range(nr2): + for j in range(nr1): + tmp = np.abs(ptrX1 - ptrX2) + ptrs += tmp * tmp + ptrX2 -= nr2 * dim + ptrX1 -= nr1 * dim - 1 + + ptrX2 += 1 + ptrX1 -= nr1 + + return s + +def distcppMaha(X1, m): + nr = X1.shape[0] + nc = X1.shape[1] + s = np.zeros((nr, nr)) + for i in range(1, nr): + ptrs = s[:, i] + ptrs2 = s[i, :] # symmetric + ptrX1 = X1[i, :] + ptrm = m + for j in range(i): + ptrX2 = X1[j, :] + tmp = np.abs(ptrX1 - ptrX2) + ptrs += tmp * tmp / ptrm + ptrX1 += nr + ptrX2 += nr + ptrm += 1 + + ptrs2[:] = ptrs + ptrs2 += nr + + return s + +def distcppMaha_2(X1, X2, m): + nr1 = X1.shape[0] + nr2 = X2.shape[0] + dim = X1.shape[1] + s = np.zeros((nr1, nr2)) + ptrs = s.flatten() + ptrX2 = X2.flatten() + ptrX1 = X1.flatten() + ptrm = m + for i in range(nr2): + for j in range(nr1): + tmp = np.abs(ptrX1 - ptrX2) + ptrs += tmp * tmp / ptrm + ptrX2 -= nr2 * dim + ptrX1 -= nr1 * dim - 1 + ptrm -= dim + + ptrX2 += 1 + ptrX1 -= nr1 + ptrm += dim + + return s + +def distance_cpp(X1, X2=None, m=None): + if X2 is not None: + if m is not None: + return distcppMaha_2(X1, X2, m) + else: + return distcpp_2(X1, X2) + else: + if m is not None: + return distcppMaha(X1, m) + else: + return distcpp(X1) diff --git a/PUQ/surrogatemethods/helpers.py b/PUQ/surrogatemethods/helpers.py new file mode 100644 index 0000000..27c3580 --- /dev/null +++ b/PUQ/surrogatemethods/helpers.py @@ -0,0 +1,108 @@ +import numpy as np +from scipy.optimize import minimize_scalar +#from distance import distance_cpp + +def distance_cpp(X1, X2=None): + if X2 is None: + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) + return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X2**2, axis=1) - 2 * np.dot(X1, X2.T) + +def find_corres(X0, X): + # X0: matrix of unique designs + # X: matrix of all designs + # Return: vector associating rows of X with those of X0 + + corres = np.zeros(X.shape[0], dtype=int) + + for i in range(X.shape[0]): + for j in range(X0.shape[0]): + if np.array_equal(X[i, :], X0[j, :]): + corres[i] = j + 1 + break + + return corres + +def find_reps(X, Z, return_Zlist=True, rescale=False, normalize=False, input_bounds=None): + + if np.ndim(X) == 1: + X = np.expand_dims(X, axis=1) + + if X.shape[0] == 1: + if return_Zlist: + return {'X0': X, 'Z0': Z, 'mult': np.array([1]), 'Z': Z, 'Zlist': [Z]} + return {'X0': X, 'Z0': Z, 'mult': np.array([1]), 'Z': Z} + + if rescale: + if input_bounds is None: + input_bounds = np.array([np.min(X, axis=0), np.max(X, axis=0)]) + X = (X - input_bounds[0, :]) @ np.diag(1 / (input_bounds[1, :] - input_bounds[0, :])) + + output_stats = None + if normalize: + output_stats = [np.mean(Z), np.var(Z)] + Z = (Z - output_stats[0]) / np.sqrt(output_stats[1]) + + X0, indices, inverse_indices = np.unique(X, axis=0, return_index=True, return_inverse=True) + X0 = X0[np.argsort(indices)] + corresp = find_corres(X0, X) - 1 + + Z0 = [np.mean(Z[corresp == i]) for i in range(len(X0))] + mult = np.array([np.sum(corresp == i) for i in range(len(X0))]) + Zlist = [Z[corresp == i] for i in range(len(X0))] + + flattened_list = [item for sublist in Zlist for item in sublist] + + if return_Zlist: + return {'X0': X0, 'Z0': np.array(Z0), 'mult': mult, 'Z': np.array(flattened_list), + 'Zlist': [Z[corres == i] for i in range(len(X0))], 'input_bounds': input_bounds, + 'output_stats': output_stats} + return {'X0': X0, 'Z0': np.array(Z0), 'mult': mult, 'Z': np.array(flattened_list), + 'input_bounds': input_bounds, 'output_stats': output_stats} + + + +def auto_bounds(X, min_cor=0.01, max_cor=0.5, covtype="Gaussian", p=0.05): + Xsc = find_reps(X, np.ones(X.shape[0]), rescale=True, return_Zlist=False) # rescaled distances + dists = distance_cpp(Xsc['X0']) # find 2 closest points + + repr_low_dist = np.quantile(dists[np.tril_indices(len(dists), k=-1)], p) + repr_lar_dist = np.quantile(dists[np.tril_indices(len(dists), k=-1)], 1 - p) + + if covtype == "Gaussian": + theta_min = -repr_low_dist / np.log(min_cor) + theta_max = -repr_lar_dist / np.log(max_cor) + return {'lower': theta_min * (Xsc['input_bounds'][1, :] - Xsc['input_bounds'][0, :]) ** 2, + 'upper': theta_max * (Xsc['input_bounds'][1, :] - Xsc['input_bounds'][0, :]) ** 2} + # else: + # def tmpfun(theta, repr_dist, covtype, value): + # cov_gen = cov_gen_matrix(np.sqrt(repr_dist / X.shape[1]), np.zeros((X.shape[1], X.shape[1])), covtype, theta) + # return cov_gen - value + + # theta_min = minimize_scalar(tmpfun, bounds=(np.sqrt(np.finfo(float).eps), 100), + # args=(repr_low_dist, covtype, min_cor), + # method='bounded', tol=np.sqrt(np.finfo(float).eps)).x + # if theta_min < np.sqrt(np.finfo(float).eps): + # warnings.warn("The automatic selection of lengthscales bounds was not successful. Perhaps provide lower and upper values.") + # theta_min = 1e-2 + + # theta_max = minimize_scalar(tmpfun, bounds=(np.sqrt(np.finfo(float).eps), 100), + # args=(repr_lar_dist, covtype, max_cor), + # method='bounded', tol=np.sqrt(np.finfo(float).eps)).x + # if theta_max < np.sqrt(np.finfo(float).eps): + # theta_max = 5 + + # return {'lower': theta_min * (Xsc['inputBounds'][1, :] - Xsc['inputBounds'][0, :]), + # 'upper': max(1, theta_max) * (Xsc['inputBounds'][1, :] - Xsc['inputBounds'][0, :])} + + + + +def trace_sym(A, B): + n = A.shape[0] + trace = 0.0 + + for i in range(n): + for j in range(i + 1): + trace += A[i, j] * B[i, j] + + return trace diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py new file mode 100644 index 0000000..35b676a --- /dev/null +++ b/PUQ/surrogatemethods/hetGP.py @@ -0,0 +1,1081 @@ +import numpy as np +from scipy.linalg import cholesky, inv +from PUQ.surrogatemethods.covariances import cov_gen, partial_cov_gen +import scipy.optimize as spo +from PUQ.surrogatemethods.helpers import auto_bounds, find_reps +from PUQ.surrogatemethods.homGP import fit as fitHomGP +from PUQ.surrogatemethods.homGP import predict as predictHomGP + +############################################################################### +## Heterogeneous GP with all options for the fit +############################################################################### + +## ' log-likelihood in the anisotropic case - one lengthscale by variable +## ' Model: K = nu2 * (C + Lambda) = nu using all observations using the replicates information +## ' nu2 is replaced by its plugin estimator in the likelihood +## ' @param X0 unique designs +## ' @param Z0 averaged observations +## ' @param Z replicated observations (sorted with respect to X0) +## ' @param mult number of replicates at each Xi +## ' @param Delta vector of nuggets corresponding to each X0i or pXi, that are smoothed to give Lambda +## ' @param logN should exponentiated variance be used +## ' @param SiNK should the smoothing come from the SiNK predictor instead of the kriging one +## ' @param theta scale parameter for the mean process, either one value (isotropic) or a vector (anistropic) +## ' @param k_theta_g constant used for linking nuggets lengthscale to mean process lengthscale, i.e., theta_g[k] = k_theta_g * theta[k], alternatively theta_g can be used +## ' @param theta_g either one value (isotropic) or a vector (anistropic), alternative to using k_theta_g +## ' @param g nugget of the nugget process +## ' @param pX matrix of pseudo inputs locations of the noise process for Delta (could be replaced by a vector to avoid double loop) +## ' @param beta0 mean, if not provided, the MLE estimator is used +## ' @param eps minimal value of elements of Lambda +## ' @param covtype covariance kernel type +## ' @param penalty should a penalty term on Delta be used? +## ' @param hom_ll reference homoskedastic likelihood +## ' @export + +def fit(fitinfo, x, theta, f, + lower=None, + upper=None, + noiseControl={'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + init={}, + known={}, + eps=np.sqrt(np.finfo(float).eps), + settings={"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}, + covtype = 'Gaussian', **kwargs): + + f = f.flatten() + model = mleHetGP(X = theta, + Z = f, + known=known, + noiseControl=noiseControl, + lower=lower, + upper=upper, + settings=settings, + init=init, + eps=eps, + covtype=covtype) + + fitinfo['Delta'] = model.get('Delta') + fitinfo['theta'] = model.get('theta') + fitinfo['g'] = model.get('g') + fitinfo['k_theta_g'] = model.get('k_theta_g') + fitinfo['theta_g'] = model.get('theta_g') + fitinfo['nmean'] = model.get('nmean') + fitinfo['Lambda'] = model.get('Lambda') + fitinfo['logN'] = model.get('logN') + fitinfo['nu_hat_var'] = model.get('nu_hat_var') + fitinfo['nu_hat'] = model.get('nu_hat') + fitinfo['Ki'] = model.get('Ki') + fitinfo['X0'] = model.get('X0') + fitinfo['Z0'] = model.get('Z0') + fitinfo['mult'] = model.get('mult') + fitinfo['beta0'] = model.get('beta0') + fitinfo['eps'] = model.get('eps') + fitinfo['trendtype'] = model.get('trendtype') + fitinfo['is_homGP'] = model.get('is_homGP') + return + + +def mleHetGP(X, Z, + lower=None, + upper=None, + known={}, + noiseControl={}, + init={}, + covtype="Gaussian", + maxit=100, + eps=np.sqrt(np.finfo(float).eps), + settings={}): + + + elem = find_reps(X, Z, return_Zlist=False) + + X0 = elem['X0'] + Z0 = elem['Z0'] + Z = elem['Z'] + mult = elem['mult'] + N = len(Z) + n = len(X0) + + if (lower is None or upper is None): + auto_thetas = auto_bounds(X=X0, covtype=covtype) + + if lower is None: + lower = auto_thetas["lower"] + if upper is None: + upper = auto_thetas["upper"] + + if len(lower) != len(upper): + print("upper and lower should have the same size") + sys.exit(1) + + + jointThetas, constrThetas = False, False + + # Check if known['theta_g'] is not None + if known.get('theta_g') is not None: + settings['linkThetas'] = False + + # Check if settings['linkThetas'] is None + if settings.get('linkThetas') is None: + jointThetas = True + else: + if settings['linkThetas'] == 'joint': + jointThetas = True + elif settings['linkThetas'] == 'constr': + constrThetas = True + + logN = True + if settings.get('logN') is not None: + logN = settings['logN'] + + if settings.get('return.matrices') is None: + settings['return.matrices'] = True + + if settings.get('return.hom') is None: + settings['return.hom'] = False + + if jointThetas and noiseControl.get('k_theta_g_bounds') is None: + noiseControl['k_theta_g_bounds'] = [1, 100] + + if settings.get('initStrategy') is None: + settings['initStrategy'] = 'residuals' + + if settings.get('factr') is None: + settings['factr'] = 1e9 + + penalty = True + if settings.get('penalty') is not None: + penalty = settings['penalty'] + + if settings.get('checkHom') is None: + settings['checkHom'] = True + + trace = 0 + if settings.get('trace') is not None: + trace = settings['trace'] + + components = [] + if known.get('theta') is None: + components.append('theta') + else: + init['theta'] = known['theta'] + + if known.get('Delta') is None: + components.append('Delta') + else: + init['Delta'] = known['Delta'] + + if jointThetas: + if known.get('k_theta_g') is None: + components.append('k_theta_g') + else: + init['k_theta_g'] = known['k_theta_g'] + + if not jointThetas and known.get('theta_g') is None: + components.append('theta_g') + else: + if not jointThetas: + init['theta_g'] = known['theta_g'] + + if known.get('g') is None: + components.append('g') + else: + init['g'] = known['g'] + + + trendtype = 'OK' + if known.get('beta0') is not None: + trendtype = 'SK' + # beta0 = known['beta0'] + + if noiseControl.get('g_bounds') is None: + noiseControl['g_bounds'] = [1e-6, 1] + + if len(components) == 0 and known.get('theta_g') is None: + known['theta_g'] = known['k_theta_g'] * known['theta'] + + # Automatic Initialization + modHom, modNugs = None, None + + if init.get('theta') is None or init.get('Delta') is None: + # A) homoskedastic mean process + if known.get('g_H') is not None: + g_init = None + else: + g_init = init.get('g_H') + + # Initial value for g of the homoskedastic process: based on the mean variance at replicates + # compared to the variance of Z0 + if any(mult > 5): + #mean_var_replicates = np.mean((fast_tUY2(mult, (Z - np.tile(Z0, mult))**2)/mult)[np.where(mult > 5)]) + v1 = (Z - np.repeat(Z0, mult))**2 + v2 = np.zeros(len(mult)) + counter = 0 + for mid, m in enumerate(mult): + v2[mid] = sum([v1[counter+i] for i in range(m)]) + counter += m + mean_var_replicates = np.mean((v2/mult)[mult > 5]) + + if g_init is None: + g_init = np.array([mean_var_replicates / np.var(Z0, ddof=1)]) + + if noiseControl.get('g_max') is None: + noiseControl['g_max'] = max(1e2, 100 * g_init) + + if noiseControl.get('g_min') is None: + noiseControl['g_min'] = eps + else: + if g_init is None: + g_init = np.array([0.1]) + + if noiseControl.get('g_max') is None: + noiseControl['g_max'] = 1e2 + + if noiseControl.get('g_min') is None: + noiseControl['g_min'] = eps + + rKI = True if settings['checkHom'] else False + + fitinfo_homGP = {} + fitHomGP(fitinfo=fitinfo_homGP, + x=np.array([[0]]), + theta={'X0':X0, 'Z0':Z0, 'mult':mult}, + f=Z, + lower=lower, + upper=upper, + known={'theta':known.get('theta'), + 'g':known.get('g_H'), + 'beta0':known.get('beta0')}, + init={'theta':init.get('theta'), + 'g':g_init}, + covtype=covtype, + maxit=maxit, + noiseControl={'g_bounds':(noiseControl['g_min'], noiseControl['g_max'])}, + eps=eps, + settings={'return.Ki':rKI}, + method="homGP") + + if known.get('theta') is None: + init["theta"] = fitinfo_homGP["theta"] + + if init.get('Delta') is None: + predinfo_homGP = {} + predictHomGP(predinfo=predinfo_homGP, + fitinfo=fitinfo_homGP, + x=np.array([[0]]), + theta=X0) + predHom = predinfo_homGP['mean'] + + # squared deviation from the homoskedastic prediction mean to the actual observations + nugs_est = (np.repeat(predHom, repeats=mult) - Z) ** 2 + nugs_est /= fitinfo_homGP["nu_hat"] + + if logN: + nugs_est = np.maximum(nugs_est, np.finfo(float).eps) # avoid problems on deterministic test functions + nugs_est = np.log(nugs_est) + + nugs_est0 = np.zeros(len(mult)) + counter = 0 + for mid, m in enumerate(mult): + nugs_est0[mid] = sum([nugs_est[counter+i] for i in range(m)])/m + counter += m + else: + nug_est0 = init['Delta'] + + if constrThetas: + noiseControl['lowerTheta_g'] = fitinfo_homGP['theta'] + + if settings['initStrategy'] == 'simple': + if logN: + init['Delta'] = np.repeat(np.log(fitinfo_homGP['g']), X0.shape[0]) + else: + init['Delta'] = np.repeat(fitinfo_homGP['g'], X0.shape[0]) + + if settings['initStrategy'] == 'residuals': + init['Delta'] = nugs_est0 + + if ((init.get('theta_g') is None and init.get('k_theta_g') is None) or init.get('g') is None): + # B) Homogeneous noise process + if jointThetas: + if init.get('k_theta_g') is None: + init['k_theta_g'] = 1 + init['theta_g'] = init['theta'] + else: + init['theta_g'] = init['k_theta_g'] * init['theta'] + + if noiseControl.get('lowerTheta_g') is None: + noiseControl['lowerTheta_g'] = init['theta_g'] - eps + + if noiseControl.get('upperTheta_g') is None: + noiseControl['upperTheta_g'] = init['theta_g'] + eps + + if not jointThetas and init.get('theta_g') is None: + init['theta_g'] = init['theta'] + + if noiseControl.get('lowerTheta_g') is None: + noiseControl['lowerTheta_g'] = lower + + if noiseControl.get('upperTheta_g') is None: + noiseControl['upperTheta_g'] = upper + + # If a homogeneous process of the mean has already been computed, it is used for estimating the parameters of the noise process + if 'nugs_est' in locals(): + if init.get('g') is None: + # mean_var_replicates_nugs = np.mean((fast_tUY2(mult, (nugs_est - np.repeat(nugs_est0, mult)))**2) / mult) + v1 = (nugs_est - np.repeat(nugs_est0, mult))**2 + v2 = np.zeros(len(mult)) + counter = 0 + for mid, m in enumerate(mult): + v2[mid] = sum([v1[counter+i] for i in range(m)]) + counter += m + mean_var_replicates_nugs = np.mean((v2/mult)) + init['g'] = np.array([mean_var_replicates_nugs / np.var(nugs_est0, ddof=1)]) + + fitnugs_homGP = {} + fitHomGP(fitinfo=fitnugs_homGP, + x=np.array([[0]]), + theta={'X0': X0, 'Z0': nugs_est0, 'mult': mult}, + f=nugs_est, + lower=noiseControl['lowerTheta_g'], + upper=noiseControl['upperTheta_g'], + init={'theta': init['theta_g'], 'g': init['g']}, + covtype=covtype, + noiseControl=noiseControl, + maxit=maxit, + eps=eps, + settings={'return.Ki': False}) + prednugs_homGP = {} + predictHomGP(predinfo=prednugs_homGP, + fitinfo=fitnugs_homGP, + x=np.array([[0]]), + theta=X0) + # prednugs = predinfo_homGP['mean'] + + else: + if 'nugs_est0' not in locals(): + nugs_est0 = init['Delta'] + + if init['g'] is None: + init['g'] = np.array([0.05]) + + fitnugs_homGP = {} + fitHomGP(fitinfo=fitnugs_homGP, + x=np.array([[0]]), + theta={'X0': X0, 'Z0': nugs_est0, 'mult': np.repeat(1, X0.shape[0])}, + f=nugs_est0, + lower=noiseControl['lowerTheta_g'], + upper=noiseControl['upperTheta_g'], + init={'theta': init['theta_g'], 'g': init['g']}, + covtype=covtype, + noiseControl=noiseControl, + maxit=maxit, + eps=eps, + settings={'return.Ki': False}) + prednugs_homGP = {} + predictHomGP(predinfo=prednugs_homGP, + fitinfo=fitnugs_homGP, + x=np.array([[0]]), + theta=X0) + # prednugs = predinfo_homGP['mean'] + + if settings['initStrategy'] == 'smoothed': + init['Delta'] = prednugs_homGP['mean'] + + if known.get('g') is None: + init['g'] = fitnugs_homGP['g'] + + if jointThetas and init.get('k_theta_g') is None: + init['k_theta_g'] = 1 + + if not jointThetas and init.get('theta_g') is None: + init['theta_g'] = fitnugs_homGP['theta'] + + if noiseControl.get('lowerTheta_g') is None: + noiseControl['lowerTheta_g'] = lower + + if noiseControl.get('upperTheta_g') is None: + noiseControl['upperTheta_g'] = upper + + def fn(par, X0, Z0, Z, mult, Delta=None, theta=None, g=None, k_theta_g=None, + theta_g=None, logN=False, beta0=None, hom_ll=None, env=None): + + idx = 0 # to store the first non-used element of par + + if theta is None: + idx = len(init['theta']) + theta = par[:idx] + + if Delta is None: + Delta = par[idx:idx + len(init['Delta'])] + idx += len(init['Delta']) + + if jointThetas and k_theta_g is None: + k_theta_g = par[idx] + idx += 1 + + if not jointThetas and theta_g is None: + theta_g = par[idx:idx + len(init['theta_g'])] + idx += len(init_theta_g) + + if g is None: + g = par[idx] + idx += 1 + + loglik = logLikHet(X0=X0, Z0=Z0, Z=Z, + mult=mult, + Delta=Delta, + theta=theta, + g=g, + k_theta_g=k_theta_g, + theta_g=theta_g, + logN=logN, + beta0=beta0, + hom_ll=hom_ll, + env=env, + eps=eps, + penalty=penalty, + trace=trace) + + # Update env with the maximum log-likelihood and corresponding parameters + if env is not None and not np.isnan(loglik): + if env.get('max_loglik') is None or loglik > env['max_loglik']: + env['max_loglik'] = loglik + env['arg_max'] = par + return -1*loglik + + def gr(par, X0, Z0, Z, mult, Delta=None, theta=None, g=None, k_theta_g=None, + theta_g=None, logN=False, beta0=None, hom_ll=None, env=None): + + idx = 0 # to store the first non-used element of par + + if theta is None: + theta = par[idx:idx + len(init['theta'])] + idx += len(init['theta']) + + if Delta is None: + Delta = par[idx:idx + len(init['Delta'])] + idx += len(init['Delta']) + + if jointThetas and k_theta_g is None: + k_theta_g = par[idx] + idx += 1 + + if not jointThetas and theta_g is None: + theta_g = par[idx:idx + len(init['theta_g'])] + idx += len(init['theta_g']) + + if g is None: + g = par[idx] + idx += 1 + + dloglik = dlogLikHet(X0=X0, Z0=Z0, Z=Z, + mult=mult, + Delta=Delta, + theta=theta, + g=g, + k_theta_g=k_theta_g, + theta_g=theta_g, + logN=logN, + beta0=beta0, + hom_ll=hom_ll, + env=env, + eps=eps, + penalty=penalty, + components=components) + + negdloglik = [-l for l in dloglik] + return negdloglik + + + parinit, lowerOpt, upperOpt = [], [], [] + + if trace > 2: + print("Initial value of the parameters:") + + if known.get("theta") is None: + parinit.extend(init["theta"]) + lowerOpt.extend(lower) + upperOpt.extend(upper) + if trace > 2: + print("Theta:", init["theta"]) + + if known.get("Delta") is None: + if noiseControl.get("lowerDelta") is None or len(noiseControl["lowerDelta"]) != n: + if logN: + noiseControl["lowerDelta"] = np.array([np.log(eps)]) # [np.log(eps)] * n + else: + noiseControl["lowerDelta"] = np.array([eps]) # [eps] * n + + if len(noiseControl["lowerDelta"]) == 1: + noiseControl["lowerDelta"] = np.repeat(noiseControl["lowerDelta"], n) #[noiseControl["lowerDelta"][0]] * n + + if noiseControl.get("g_max") is None: + noiseControl["g_max"] = 1e2 + + if noiseControl.get("upperDelta") is None or len(noiseControl["upperDelta"]) != n: + if logN: + noiseControl["upperDelta"] = np.array([np.log(noiseControl["g_max"])]) # [np.log(noiseControl["g_max"])] + else: + noiseControl["upperDelta"] = np.array([noiseControl["g_max"]]) # [noiseControl["g_max"]] + + if len(noiseControl["upperDelta"]) == 1: + noiseControl["upperDelta"] = np.repeat(noiseControl["upperDelta"], n) # [noiseControl["upperDelta"][0]] * n + + lowerOpt.extend(noiseControl["lowerDelta"]) + upperOpt.extend(noiseControl["upperDelta"]) + parinit.extend(init["Delta"]) + if trace > 2: + print("Delta:", init["Delta"]) + + if jointThetas and known.get("k_theta_g") is None: + parinit.extend([init["k_theta_g"]]) + lowerOpt.extend([noiseControl["k_theta_g_bounds"][0]]) + upperOpt.extend([noiseControl["k_theta_g_bounds"][1]]) + if trace > 2: + print("k_theta_g:", init["k_theta_g"]) + + if not jointThetas and known.get("theta_g") is None: + parinit.extend(init["theta_g"]) + lowerOpt.extend([noiseControl["lowerTheta_g"]]) + upperOpt.extend([noiseControl["upperTheta_g"]]) + if trace > 2: + print("theta_g:", init["theta_g"]) + + if known.get("g") is None: + parinit.extend([init["g"]]) + if trace > 2: + print("g:", init["g"]) + + lowerOpt.extend([noiseControl["g_bounds"][0]]) + upperOpt.extend([noiseControl["g_bounds"][1]]) + + mle_par = known.copy() # Store inferred and known parameters + + if components is not None: + if fitinfo_homGP is not None: + hom_ll = fitinfo_homGP['ll'] + else: + # Compute reference homoskedastic likelihood, with fixed theta for speed + modHom_tmp = mleHomGP(X={"X0": X0, "Z0": Z0, "mult": mult}, + Z=Z, + lower=lower, + upper=upper, + init={"theta": known["theta"], "g": known["g_H"], "beta0": known["beta0"]}, + covtype=covtype, + noiseControl={"g_bounds": [noiseControl["g_min"], noiseControl["g_max"]]}, + eps=eps, + settings={"return.Ki": False}) + + hom_ll = modHom_tmp.ll + + try: + # print("Starting") + envtemp = {} + opval = spo.minimize(fn, + parinit, + method='L-BFGS-B', + options={'gtol': 0.001, 'maxiter': 100}, + jac=gr, + bounds=spo.Bounds(lowerOpt, upperOpt), + args=(X0, Z0, Z, mult, known.get('Delta'), known.get('theta'), + known.get('g'), known.get('k_theta_g'), known.get('theta_g'), + logN, known.get('beta0'), hom_ll, envtemp)) + + out = 1*opval.x + out_ll = -1*opval.fun + except: + print('use best value so far') + out = envtemp["arg_max"] + out_ll = envtemp["max_loglik"] + + if trace > 1: + print("Name | Value | Lower bound | Upper bound") + + # Post-processing + idx = 0 + if known.get("theta") is None: + mle_par["theta"] = np.array(out[:len(init["theta"])]) + idx += len(init["theta"]) + if trace > 1: + print(f"Theta | {mle_par['theta']} | {lower} | {upper}") + + if known.get("Delta") is None: + mle_par["Delta"] = np.array(out[idx : (idx + len(init["Delta"]))]) + idx += len(init["Delta"]) + if trace > 1: + for ii in range(1, (len(mle_par["Delta"]) // 5) + 2): + i_tmp = np.arange(5 * (ii - 1), min(5 * ii, len(mle_par["Delta"]))) + if logN: + print( + f"Delta | {np.array(mle_par['Delta'])[i_tmp]} | {np.maximum(np.log(eps * mult[i_tmp]), init['Delta'][i_tmp] - np.log(1000))} | {init['Delta'][i_tmp] + np.log(100)}" + ) + if not logN: + print( + f"Delta | {np.array(mle_par['Delta'])[i_tmp]} | {np.maximum(mult[i_tmp] * eps, init['Delta'][i_tmp] / 1000)} | {init['Delta'][i_tmp] * 100}" + ) + if jointThetas: + if known.get('k_theta_g') is None: + mle_par['k_theta_g'] = np.array(out[idx]) + idx += 1 + mle_par['theta_g'] = mle_par['k_theta_g'] * mle_par['theta'] + + if trace > 1: + print(f"k_theta_g | {mle_par['k_theta_g']} | {noiseControl['k_theta_g_bounds'][0]} | {noiseControl['k_theta_g_bounds'][1]}") + + if not jointThetas and known.get("theta_g") is None: + mle_par['theta_g'] = np.array(out[idx : (idx + len(init["theta_g"]))]) + idx += len(init["theta_g"]) + if trace > 1: + print(f"theta_g | {mle_par['theta_g']} | {noiseControl['lowerTheta_g']} | {noiseControl['upperTheta_g']}") + + if known.get('g') is None: + mle_par['g'] = np.array(out[idx]) + idx += 1 + if trace > 1: + print(f"g | {mle_par['g']} | {noiseControl['g_bounds'][0]} | {noiseControl['g_bounds'][1]}") + + else: + out = {"message": "All hyperparameters given, no optimization \n", + "count": 0, + "value": None + } + + if penalty: + ll_non_pen = logLikHet(X0=X0, Z0=Z0, Z=Z, mult=mult, + Delta=mle_par['Delta'], + theta=mle_par['theta'], + g=mle_par['g'], + k_theta_g=mle_par['k_theta_g'], + theta_g=mle_par['theta_g'], + logN=logN, + beta0=mle_par.get('beta0'), + hom_ll=None, + env=None, + eps=eps, + penalty=False, + trace=trace) + + else: + ll_non_pen = 1*out_ll#-1*opval.fun + + + if fitinfo_homGP is not None: + if fitinfo_homGP['ll'] >= ll_non_pen: + if trace >= 1: + print(f"Homoskedastic model has higher log-likelihood: {fitinfo_homGP['ll']} compared to {ll_non_pen}") + + if settings['checkHom']: + if trace >= 1: + print("Return homoskedastic model") + fitinfo_homGP['is_homGP'] = True + return fitinfo_homGP + + + Cg = cov_gen(X1=X0, theta=mle_par['theta_g']) + Kg_c = cholesky(Cg + np.diag(eps + mle_par['g'] / mult)) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + + nmean = np.sum(Kgi @ mle_par['Delta']) / np.sum(Kgi) ## ordinary kriging mean + nu_hat_var = max(eps, np.dot((mle_par['Delta'] - nmean), Kgi @ (mle_par['Delta'] - nmean)) / len(mle_par['Delta'])) + M = np.dot(Cg, np.dot(Kgi, mle_par['Delta'] - nmean)) + + Lambda = nmean + M + if logN: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = eps + + LambdaN = np.repeat(Lambda, mult) + C = cov_gen(X1=X0, theta=mle_par['theta']) + Ki = cholesky(C + np.diag(Lambda / mult + eps)) + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if known.get('beta0') is None: + mle_par['beta0'] = np.sum(Ki @ Z0) / np.sum(Ki) + + psi_0 = np.dot(Z0 - mle_par['beta0'], Ki @ (Z0 - mle_par['beta0'])) + + nu2 = 1 / N * (np.dot((Z - mle_par['beta0']) / LambdaN, Z - mle_par['beta0']) - np.dot((Z0 - mle_par['beta0']) * mult / Lambda, Z0 - mle_par['beta0']) + psi_0) + + fitinfo = {} + fitinfo['Delta'] = mle_par['Delta'] + fitinfo['theta'] = mle_par['theta'] + fitinfo['g'] = mle_par['g'] + fitinfo['k_theta_g'] = mle_par['k_theta_g'] + fitinfo['theta_g'] = mle_par['theta_g'] + fitinfo['nmean'] = nmean + fitinfo['Lambda'] = Lambda + fitinfo['logN'] = logN + fitinfo['nu_hat_var'] = nu_hat_var + fitinfo['nu_hat'] = nu2 + fitinfo['Ki'] = Ki + fitinfo['Kgi'] = Kgi + fitinfo['X0'] = X0 + fitinfo['Z0'] = Z0 + fitinfo['mult'] = mult + fitinfo['beta0'] = mle_par['beta0'] + fitinfo['eps'] = eps + fitinfo['trendtype'] = trendtype + fitinfo['is_homGP'] = False + + return fitinfo + +def logLikHet(X0, Z0, Z, mult, Delta, theta, g, k_theta_g=None, theta_g=None, + logN=False, beta0=None, hom_ll=None, env=None, + eps=np.sqrt(np.finfo(float).eps), penalty=True, trace=0): + n = len(X0) + N = len(Z) + + if theta_g is None: + theta_g = k_theta_g * theta + + Cg = cov_gen(X1=X0, theta=theta_g) + Kg_c = cholesky(Cg + np.diag(eps + g / mult)) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + nmean = np.sum(Kgi @ Delta) / np.sum(Kgi) + M = Cg @ (Kgi @ (Delta - nmean)) + Lambda = nmean + M + + if logN: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = eps + + LambdaN = np.repeat(Lambda, mult) + + C = cov_gen(X1=X0, theta=theta) + if env is not None: + env['C'] = C + Ki = cholesky(C + np.diag(Lambda / mult + eps)) + ldetKi = -2 * np.sum(np.log(np.diag(Ki))) + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if env is not None: + env['Cg'] = Cg + env['Kg_c'] = Kg_c + env['Kgi'] = Kgi + env['ldetKi'] = ldetKi + env['Ki'] = Ki + + if beta0 is None: + beta0 = np.sum(Ki @ Z0) / np.sum(Ki) + + psi_0 = np.dot(Z0 - beta0, Ki @ (Z0 - beta0)) + + psi = 1 / N * (np.dot((Z - beta0) / LambdaN, Z - beta0) - np.dot((Z0 - beta0) * mult / Lambda, Z0 - beta0) + psi_0) + + loglik = -N / 2 * np.log(2 * np.pi) - N / 2 * np.log(psi) + 1 / 2 * ldetKi - 1 / 2 * np.sum( + (mult - 1) * np.log(Lambda) + np.log(mult)) - N / 2 + + if penalty: + nu_hat_var = np.dot((Delta - nmean), Kgi @ (Delta - nmean)) / len(Delta) + if nu_hat_var < eps: + return loglik + pen = -n / 2 * np.log(nu_hat_var) - np.sum(np.log(np.diag(Kg_c))) - n / 2 * np.log(2 * np.pi) - n / 2 + + if hom_ll is not None and loglik < hom_ll and pen > 0: + if trace > 0: + print("Penalty is deactivated when unpenalized likelihood is lower than its homGP equivalent") + return loglik + + return loglik + pen + + return loglik + +def dlogLikHet(X0, Z0, Z, mult, Delta, theta, g, k_theta_g=None, theta_g=None, + logN=True, beta0=None, hom_ll=None, env=None, + eps=np.sqrt(np.finfo(float).eps), penalty=True, components=None): + + # Verifications + if k_theta_g is None and theta_g is None: + print("Either k_theta_g or theta_g must be provided.") + return None + + # Initializations + if components is None: + components = ["theta", "Delta", "g"] + if k_theta_g is None: + components.append("theta_g") + else: + components.append("k_theta_g") + + if theta_g is None: + theta_g = k_theta_g * theta + + n = X0.shape[0] + N = len(Z) + + if env is not None: + Cg = env['Cg'] + Kg_c = env['Kg_c'] + Kgi = env['Kgi'] + M = (Kgi * (-eps - g / mult) + np.eye(n)).T + else: + Cg = cov_gen(X1=X0, theta=theta_g) + Kg_c = cholesky(Cg + np.diag(eps + g / mult)) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + M = (Kgi * (-eps - g / mult) + np.eye(n)).T + + # Precomputations for reuse + rSKgi = np.sum(Kgi, axis=1) + sKgi = np.sum(Kgi) + nmean = np.dot(rSKgi, Delta) / sKgi # ordinary kriging mean + + # Precomputations for reuse + KgiD = np.dot(Kgi, (Delta - nmean)) + Lambda = nmean + np.dot(M, (Delta - nmean)) + + if logN: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = eps + LambdaN = np.repeat(Lambda, repeats=mult) + + if env is not None: + C = env['C'] + Ki = env['Ki'] + ldetKi = env['ldetKi'] + else: + C = cov_gen(X1=X0, theta=theta) + Ki = cholesky(C + np.diag(Lambda / mult + eps)) + ldetKi = -2 * np.sum(np.log(np.diag(Ki))) # log determinant from Cholesky + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if beta0 is None: + beta0 = np.dot(np.sum(Ki, axis=0), Z0) / np.sum(Ki) + + # Precomputations for reuse + KiZ0 = np.dot(Ki, Z0 - beta0) + rsM = np.sum(M, axis=1) + + psi_0 = np.dot(KiZ0, Z0 - beta0) + psi = np.dot((Z - beta0) / LambdaN, Z - beta0) - np.dot((Z0 - beta0) * mult / Lambda, Z0 - beta0) + psi_0 + + if penalty: + nu_hat_var = np.dot(KgiD, Delta - nmean)/ len(Delta) + # To prevent numerical issues when Delta = nmean, resulting in divisions by zero + if nu_hat_var < eps: + penalty = False + else: + loglik = -N/2 * np.log(2*np.pi) - N/2 * np.log(psi/N) + 1/2 * ldetKi - 1/2 * np.sum((mult - 1) * np.log(Lambda) + np.log(mult)) - N/2 + pen = -n/2 * np.log(nu_hat_var) - np.sum(np.log(np.diag(Kg_c))) - n/2 * np.log(2*np.pi) - n/2 + + if loglik < hom_ll and pen > 0: + penalty = False + + + dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX = None, None, None, None, None, None + # First component, derivative of logL with respect to theta + if "theta" in components: + dLogL_dtheta = np.full(len(theta), np.nan) + for i in range(len(theta)): + if len(theta) == 1: + dC_dthetak = partial_cov_gen(X1=X0, theta=theta, arg="theta_k") * C + else: + dC_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=theta[i], arg="theta_k") * C + + if "k_theta_g" in components: + + if len(theta) == 1: + dCg_dthetak = partial_cov_gen(X1=X0, theta=k_theta_g * theta, arg="theta_k") * k_theta_g * Cg + else: + dCg_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=k_theta_g * theta[i], arg="theta_k") * k_theta_g * Cg + + # Derivative Lambda / theta_k (first part) + dLdtk = dCg_dthetak @ KgiD - M @ (dCg_dthetak @ KgiD) + + # (second part) + dLdtk -= (1 - rsM) * (np.dot(np.dot(rSKgi, dCg_dthetak), np.dot(Kgi, Delta)) * sKgi - np.dot(rSKgi, Delta) * np.dot(np.dot(rSKgi, dCg_dthetak), rSKgi)) / sKgi**2 + + if logN: + dLdtk *= Lambda + + dK_dthetak = np.add(dC_dthetak, np.diag(dLdtk) / mult) # dK/dtheta[k] + + term1 = N/2 * (np.dot(((Z - beta0) / LambdaN) * np.repeat(dLdtk, mult), (Z - beta0) / LambdaN) - np.dot(((Z0 - beta0) / Lambda * mult * dLdtk), (Z0 - beta0) / Lambda) + + np.dot(np.dot(KiZ0, dK_dthetak), KiZ0)) / psi - 0.5 * np.trace(np.dot(Ki.T, dK_dthetak)) + + term2 = 0.5 * np.sum((mult - 1) * dLdtk / Lambda) + dLogL_dtheta[i] = term1 - term2 + + if penalty: + dLogL_dtheta[i] += 0.5 * np.dot(np.dot(KgiD, dCg_dthetak), KgiD) / nu_hat_var - 0.5 * np.trace(np.dot(Kgi.T, dCg_dthetak)) + else: + # N/2 * crossprod(KiZ0, dC_dthetak) %*% KiZ0/psi + term1 = (N/2) * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / psi + # 1/2 * trace_sym(Ki, dC_dthetak) + term2 = 0.5 * np.trace(np.dot(Ki.T, dC_dthetak)) + dLogL_dtheta[i] = term1 - term2 + + + # Derivative of logL with respect to Lambda + if any(x in ["Delta", "g", "k_theta_g", "theta_g"] for x in components): + v1 = (Z - beta0) ** 2 + v2 = np.zeros(len(mult)) + counter = 0 + for mid, m in enumerate(mult): + v2[mid] = sum([v1[counter+i] for i in range(m)]) + counter += m + + dLogLdLambda = (N / 2 * ((v2 - (Z0 - beta0) ** 2 * mult) / Lambda ** 2 + + KiZ0 ** 2 / mult) / psi - (mult - 1) / (2 * Lambda) - 1 / (2 * mult) * np.diag(Ki)) + + if logN: + dLogLdLambda = Lambda * dLogLdLambda + + # Derivative of Lambda with respect to Delta + if "Delta" in components: + dLogL_dDelta = np.dot(M.T, dLogLdLambda) + rSKgi / sKgi * np.sum(dLogLdLambda) - rSKgi / sKgi * np.sum(np.dot(M.T, dLogLdLambda)) # chain rule + + # Derivative Lambda / k_theta_g + if "k_theta_g" in components: + dCg_dk = partial_cov_gen(X1=X0, theta=theta, k_theta_g=k_theta_g, arg="k_theta_g") * Cg + + dLogL_dkthetag = np.dot(dCg_dk, KgiD) - np.dot(M, np.dot(dCg_dk, KgiD)) - \ + (1 - rsM) * np.squeeze( + (rSKgi @ np.dot(dCg_dk, np.dot(Kgi, Delta)) * sKgi - rSKgi @ Delta * (rSKgi @ np.dot(dCg_dk, rSKgi))) / sKgi**2) + + dLogL_dkthetag = dLogL_dkthetag @ dLogLdLambda # chain rule + + # Derivative Lambda / theta_g + if "theta_g" in components: + dLogL_dthetag = np.full(len(theta_g), np.nan) + for i in range(len(theta_g)): + if len(theta_g) == 1: + dCg_dthetagk = partial_cov_gen(X1=X0, theta=theta_g, arg="theta_k") * Cg + else: + dCg_dthetagk = partial_cov_gen(X1=X0[:, i], theta=theta_g[i], arg="theta_k") * Cg + + dLogL_dthetag[i] = np.dot((dCg_dthetagk @ KgiD - M @ (dCg_dthetagk @ KgiD) - + ((1 - rsM) * (rSKgi @ (dCg_dthetagk @ Kgi @ Delta) * sKgi - + rSKgi @ Delta * (rSKgi @ (dCg_dthetagk @ rSKgi.T) @ rSKgi))))/ sKgi**2, dLogLdLambda) + + # Penalty term + if penalty: + dLogL_dthetag[i] += 1/2 * np.dot(np.dot(KgiD.T, dCg_dthetagk) @ KgiD, 1/nu_hat_var) - 0.5*np.trace(np.dot(Kgi, dCg_dthetagk)) #trace_sym(Kgi, dCg_dthetagk)/2 + + ## Derivative Lambda / g + if "g" in components: + dLogL_dg = np.dot(-M @ (KgiD/mult) - + (1 - rsM) * (np.dot(Delta, (Kgi @ (rSKgi/mult)) * sKgi) - rSKgi @ Delta * np.sum(rSKgi**2/mult))/sKgi**2, + dLogLdLambda) # chain rule + + + # Additional penalty terms on Delta + if penalty: + if "Delta" in components: + dLogL_dDelta = dLogL_dDelta - KgiD / nu_hat_var + if "k_theta_g" in components: + dLogL_dkthetag = dLogL_dkthetag + 1/2 * np.dot(KgiD, dCg_dk) @ KgiD / nu_hat_var - 0.5*np.trace(np.dot(Kgi, dCg_dk)) #trace_sym(Kgi, dCg_dk)/2 + if "g" in components: + dLogL_dg = dLogL_dg + 1/2 * np.dot(KgiD/mult, KgiD) / nu_hat_var - np.sum(np.diag(Kgi)/mult)/2 + + nested_list = [] + for der in [dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX]: + if der is not None: + if der.ndim < 1: + nested_list.extend(np.array([der])) + else: + nested_list.extend(der) + return nested_list #np.concatenate([dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX]) + + +def predict(predinfo, fitinfo, x, theta, thetaprime=None, rep_no=None, **kwargs): + + if fitinfo.get('Delta') is None: + return predictHomGP(predinfo, fitinfo, x, theta, thetaprime=thetaprime, rep_no=rep_no) + else: + return predicthetGP(predinfo, fitinfo, x, theta, thetaprime=thetaprime, noise_var=False, nugs_only=False, rep_no=rep_no) + + +def predicthetGP(predinfo, fitinfo, x, theta, thetaprime=None, noise_var=False, nugs_only=False, rep_no=None, **kwargs): + + if rep_no is not None: + mult = 1*rep_no + else: + mult = 1*fitinfo['mult'] + + if fitinfo.get('Kgi') is None: + Cg = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta_g']) + Kg_c = cholesky(Cg + np.diag(fitinfo['eps'] + fitinfo['g'] / mult)) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + + kg = cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta_g']) + + if fitinfo['Ki'] is None: + C = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta']) + Ki = cholesky(C + np.diag(fitinfo['Lambda'] / mult + fitinfo['eps'])) + Ki = np.linalg.inv((Ki)) + fitinfo['Ki'] = Ki @ Ki.T + + M = np.dot(kg, np.dot(Kgi, fitinfo['Delta'] - fitinfo['nmean'])) + + if fitinfo['logN']: + nugs = fitinfo['nu_hat'] * np.exp(fitinfo['nmean'] + M) + else: + nugs = fitinfo['nu_hat'] * np.maximum(0, fitinfo['nmean'] + M) + + if nugs_only: + return {'nugs': nugs} + + if noise_var: + if fitinfo['nu_hat_var'] is None: + fitinfo['nu_hat_var'] = max(fitinfo['eps'], np.dot(np.dot(fitinfo['Delta'] - fitinfo['nmean'], Kgi), fitinfo['Delta'] - fitinfo['nmean'])/len(fitinfo['Delta'])) # To avoid 0 variance + + # Assuming kg, object['Kgi'] are NumPy arrays + term1 = 1 - np.diag(np.dot(kg, np.dot(Kgi.T, kg))) + term2 = (1 - np.dot(np.sum(Kgi, axis=0), kg))**2 / np.sum(Kgi) + + sd2var = fitinfo['nu_hat'] * fitinfo['nu_hat_var'] * (term1 - term2) + else: + sd2var = None + + # theta.shape[0] x fitinfo['X0'].shape[0] + kx = cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta']) + if fitinfo['trendtype'] == 'SK': + sd2 = fitinfo['nu_hat'] * (1 - np.diag(np.dot(kx, np.dot(fitinfo['Ki'], kx.T)))) + else: + sd2 = fitinfo['nu_hat'] * (1 - np.diag(np.dot(kx, np.dot(fitinfo['Ki'], kx.T))) + (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T ).flatten()**2/np.sum(fitinfo['Ki']) ) + + if any(sd2 < 0): + sd2 = np.maximum(0, sd2) + + predinfo['mean'] = (fitinfo['beta0'] + np.dot(kx, np.dot(fitinfo['Ki'], fitinfo['Z0'] - fitinfo['beta0'])))[None, :] + predinfo['var'] = sd2[None, :] + predinfo['sdvar'] = sd2var + predinfo['nugs'] = nugs[None, :] + + if thetaprime is not None: + # fitinfo['X0'].shape[0] x thetaprime.shape[0] + kxprime = cov_gen(X1=fitinfo['X0'], X2=thetaprime, theta=fitinfo['theta']) + if fitinfo['trendtype'] == 'SK': + if theta.shape[0] < thetaprime.shape[0]: + cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki'] @ kxprime) + else: + cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki'] @ kxprime)) + else: + if theta.shape[0] < thetaprime.shape[0]: + + cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki'] @ kxprime + \ + ((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) + #np.dot(1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) + #((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) + + else: + cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki'] @ kxprime) + \ + np.dot((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) + #((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) + #np.dot((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) + + predinfo['covmat'] = cov + + return \ No newline at end of file diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py new file mode 100644 index 0000000..875ae39 --- /dev/null +++ b/PUQ/surrogatemethods/homGP.py @@ -0,0 +1,369 @@ +import sys +import numpy as np +from scipy.linalg import cholesky, inv +from PUQ.surrogatemethods.covariances import cov_gen, partial_cov_gen +import scipy.optimize as spo +from PUQ.surrogatemethods.helpers import auto_bounds, find_reps + +################################################################################ +## Homoskedastic noise +################################################################################ + +## Model: noisy observations with unknown homoskedastic noise +## K = nu^2 * (C + g * I) +# X0 unique designs matrix +# Z0 averaged observations at X0 +# Z observations vector (all observations) +# mult number of replicates at each unique design +# theta vector of lengthscale hyperparameters (or one for isotropy) +# g noise variance for the process +# beta0 trend + +def fit(fitinfo, x, theta, f, lower=None, upper=None, + known={}, + noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, + init={}, + covtype='Gaussian', + maxit=100, + eps=np.sqrt(np.finfo(float).eps), + settings={"return.Ki": True, "factr": 1e7}, + **kwargs): + + f = f.flatten() + model = mleHomGP(X = theta, + Z = f, + lower=lower, + upper=upper, + known=known, + noiseControl=noiseControl, + init=init, + covtype=covtype, + maxit=maxit, + eps=eps, + settings=settings) + + fitinfo['theta'] = model['theta'] + fitinfo['g'] = model['g'] + fitinfo['nu_hat'] = model['nu_hat'] + fitinfo['Ki'] = model['Ki'] + fitinfo['X0'] = model['X0'] + fitinfo['Z0'] = model['Z0'] + fitinfo['mult'] = model['mult'] + fitinfo['beta0'] = model['beta0'] + fitinfo['ll'] = model['ll'] + fitinfo['trendtype'] = model['trendtype'] + fitinfo['eps'] = model['eps'] + fitinfo['is_homGP'] = True + return + +def mleHomGP(X, Z, lower=None, upper=None, known={}, noiseControl={}, + init={}, covtype="Gaussian", + maxit=100, eps=np.sqrt(np.finfo(float).eps), + settings={}): + + + if isinstance(X, dict): + X0 = np.array(X.get('X0')) + Z0 = np.array(X.get('Z0')) + mult = np.array(X.get('mult')) + + if np.sum(mult) != len(Z): + raise ValueError("Length(Z) should be equal to sum(mult)") + + if X0.ndim == 1: + X0 = X0[:, np.newaxis] + + if len(Z0) != X0.shape[0]: + raise ValueError("Dimension mismatch between Z0 and X0") + + else: + elem = find_reps(X, Z, return_Zlist=False) + X0 = elem['X0'] + Z0 = elem['Z0'] + Z = elem['Z'] + mult = elem['mult'] + + N = len(Z) + + if (lower is None or upper is None): + auto_thetas = auto_bounds(X=X0, covtype=covtype) + if lower is None: + lower = auto_thetas["lower"] + if upper is None: + upper = auto_thetas["upper"] + + if known.get("theta") is None and init.get("theta") is None: + init['theta'] = np.sqrt(upper * lower) + + if len(lower) != len(upper): + print("upper and lower should have the same size") + sys.exit(1) + + if settings.get('return.Ki') is None: + settings['return.Ki'] = True + + if noiseControl.get("g_bounds") is None: + noiseControl["g_bounds"] = [np.sqrt(np.finfo(float).eps), 1e2] + + g_min = np.array([noiseControl["g_bounds"][0]]) + g_max = np.array([noiseControl["g_bounds"][1]]) + + beta0 = known.get("beta0") + + if (known.get("theta") is None and init.get("theta") is None): + init["theta"] = 0.9*lower + 0.1*upper + + if(known.get('g') is None and init.get('g') is None): + if(any(mult > 2)): + v1 = (Z - np.repeat(Z0, mult)) ** 2 + v2 = np.zeros(len(mult)) + counter = 0 + for mid, m in enumerate(mult): + v2[mid] = sum([v1[counter+i] for i in range(m)]) + counter += m + init['g'] = np.array([np.mean((v2/mult)[mult > 2])/np.var(Z0, ddof=1)]) + else: + init['g'] = np.array([0.1]) + + trendtype = 'OK' + if known.get("beta0") is not None: + trendtype = 'SK' + + def fn(par, X0, Z0, Z, mult, beta0, theta, g, env): + + idx = 0 # to store the first non-used element of par + + # If theta is not provided, extract it from par + if theta is None: + theta = par[:len(init['theta'])] + idx += len(init['theta']) + + # If g is not provided, extract it from par + if g is None: + g = par[idx] + + # Compute the log-likelihood using logLikHom function + loglik = logLikHom(X0, Z0, Z, mult, theta, g, beta0, covtype, eps, env) + + # Update env with the maximum log-likelihood and corresponding parameters + if env is not None and not np.isnan(loglik): + if env.get('max_loglik') is None or loglik > env['max_loglik']: + env['max_loglik'] = loglik + env['arg_max'] = par + + return -loglik + + def gr(par, X0, Z0, Z, mult, beta0, theta, g, env): + idx = 0 + components = [] + + if theta is None: + theta = par[:len(init['theta'])] + idx += len(init['theta']) + components.append("theta") + + if g is None: + g = par[idx] + components.append("g") + + gr_val = dlogLikHom(X0=X0, Z0=Z0, Z=Z, mult=mult, theta=theta, g=g, beta0=beta0, covtype=covtype, eps=eps, components=components, env=env) + + return -gr_val + + envtemp = {} + if known.get("g") is not None and known.get("theta") is not None: + theta_out = known['theta'] + g_out = known['g'] + out_ll = logLikHom(X0, Z0, Z, mult, theta_out, g_out, beta0, covtype, eps, None) + else: + parinit, lowerOpt, upperOpt = None, None, None + if known.get("theta") is None: + parinit = init['theta'] + lowerOpt = lower + upperOpt = upper + if known.get("g") is None: + + parinit = np.concatenate((parinit, init['g'])) + lowerOpt = np.concatenate((lowerOpt, g_min)) + upperOpt = np.concatenate((upperOpt, g_max)) + try: + opval = spo.minimize(fn, + parinit, + method='L-BFGS-B', + options={'gtol': 0.001}, + jac=gr, + bounds=spo.Bounds(lowerOpt, upperOpt), + args=(X0, Z0, Z, mult, known.get("theta"), known.get("g"), beta0, envtemp)) + + out = opval.x + out_ll = -1*opval.fun + except: + print("use best value so far") + out = envtemp["arg_max"] + out_ll = envtemp["max_loglik"] + + g_out = out[-1] if known.get("g") is None else known["g"] + theta_out = out[0:-1] if known.get("theta") is None else known["theta"] + + # Temporarily store Cholesky transform of K in Ki + C_out = cov_gen(X1=X0, theta=theta_out) + Ki = cholesky(C_out + np.diag(eps + g_out / mult)) + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if beta0 is None: + beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) + + psi_0 = np.dot(np.dot(Z0 - beta0, Ki), Z0 - beta0) + nu = 1/N * ((np.dot(Z - beta0, Z - beta0) - np.dot((Z0 - beta0) * mult, Z0 - beta0)) / g_out + psi_0) + + fitinfo = {} + fitinfo['theta'] = theta_out + fitinfo['g'] = g_out + fitinfo['nu_hat'] = nu + fitinfo['Ki'] = Ki + fitinfo['X0'] = X0 + fitinfo['Z0'] = Z0 + fitinfo['beta0'] = beta0 + fitinfo['ll'] = out_ll + fitinfo['mult'] = mult + fitinfo['trendtype'] = trendtype + fitinfo['eps'] = eps + + # print(logLikHom(X0, Z0, Z, mult, np.array([0.01951545099]), np.array([0.07451224610]), None, covtype, eps, None)) + # print(dlogLikHom(X0, Z0, Z, mult, np.array([0.01951545099]), np.array([0.07451224610]), beta0=None, covtype="Gaussian", + # eps=np.sqrt(np.finfo(float).eps), components=["theta", "g"], env=None)) + + return fitinfo + +# @return loglikelihood value + +def logLikHom(X0, Z0, Z, mult, theta, g, beta0=None, covtype="Gaussian", eps=np.sqrt(np.finfo(float).eps), env=None): + n = X0.shape[0] + N = len(Z) + + # Temporarily store Cholesky transform of K in Ki + C = cov_gen(X1=X0, theta=theta) + + if env is not None: + env['C'] = C + + Ki = cholesky(C + np.diag(eps + g / mult)) + + ldetKi = -2 * np.sum(np.log(np.diag(Ki))) # log determinant from Cholesky + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if env is not None: + env['Ki'] = Ki + + if beta0 is None: + #beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) + beta0 = np.sum(Ki, axis=0) @ Z0 / np.sum(Ki) + + psi_0 = np.dot(np.dot(Z0 - beta0, Ki), Z0 - beta0) + + psi = 1/N * ((np.dot(Z - beta0, Z - beta0) - np.dot((Z0 - beta0) * mult, Z0 - beta0)) / g + psi_0) + + loglik = -N/2 * np.log(2*np.pi) - N/2 * np.log(psi) + 1/2 * ldetKi - (N - n)/2 * np.log(g) - 1/2 * np.sum(np.log(mult)) - N/2 + + return loglik + +def dlogLikHom(X0, Z0, Z, mult, theta, g, beta0=None, covtype="Gaussian", + eps=np.sqrt(np.finfo(float).eps), components=["theta", "g"], env=None): + + k = len(Z) + n = X0.shape[0] + + if env is not None: + C = env['C'] + Ki = env['Ki'] + else: + C = cov_gen(X1=X0, theta=theta) + Ki = cholesky(C + np.diag(eps + g / mult)) + Ki = np.linalg.inv((Ki)) + Ki = Ki @ Ki.T + + if beta0 is None: + beta0 = np.sum(Ki, axis=0) @ Z0 / np.sum(Ki) + #beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) + + + Z0 = Z0 - beta0 + Z = Z - beta0 + + KiZ0 = np.dot(Ki, Z0) # to avoid recomputing + + psi = np.dot(Z0, KiZ0) + + tmp1, tmp2 = None, None + + # First component, derivative with respect to theta + if "theta" in components: + tmp1 = np.full(len(theta), np.nan) + + if len(theta) == 1: + dC_dthetak = partial_cov_gen(X1=X0, theta=theta, type=covtype, arg="theta_k") * C + tmp1[0] = k / 2 * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / ( + (np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - 1 / 2 * np.trace(Ki@dC_dthetak) + else: + + for i in range(len(theta)): + dC_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=theta[i], type=covtype, arg="theta_k") * C + tmp1[i] = k / 2 * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / ( + (np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - 1 / 2 * np.trace(Ki@dC_dthetak) #1 / 2 * trace_sym(Ki, dC_dthetak) + + # Second component derivative with respect to g + if "g" in components: + tmp2 = np.full(1, np.nan) + tmp2[0] = k / 2 * ((np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g ** 2 + np.sum(KiZ0 ** 2 / mult)) / ((np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - (k - n) / (2 * g) - 1 / 2 * np.sum(np.diag(Ki) / mult) + + return np.concatenate([tmp1, tmp2]) + + +def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): + + if fitinfo['Ki'] is None: + C = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta']) + Ki = cholesky(C + np.diag(fitinfo['eps'] + fitinfo['g'] / fitinfo['mult'])) + Ki = np.linalg.inv((Ki)) + fitinfo['Ki'] = Ki @ Ki.T + + fitinfo['Ki_scaled'] = fitinfo['Ki']/fitinfo['nu_hat'] + + kx = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta']) + + mean = fitinfo['beta0'] + kx @ (fitinfo['Ki_scaled'] @ (fitinfo['Z0'] - fitinfo['beta0'])) + + if fitinfo['trendtype'] == 'SK': + var = fitinfo['nu_hat'] - np.diag(kx @ (fitinfo['Ki_scaled'] @ kx.T)) + else: + var = fitinfo['nu_hat'] - np.diag(kx @ (fitinfo['Ki_scaled'] @ kx.T)) + (1 - np.sum(fitinfo['Ki_scaled'], axis=1) @ kx.T)**2/np.sum(fitinfo['Ki_scaled']) + + if any(var < 0): + var = np.maximum(0, var) + predinfo['mean'] = mean[None, :] + predinfo['var'] = var[None, :] + predinfo['nugs'] = np.repeat(fitinfo['nu_hat']*fitinfo['g'], theta.shape[0])[None, :] + + if thetaprime is not None: + # fitinfo['X0'].shape[0] x thetaprime.shape[0] + kxprime = fitinfo['nu_hat'] * cov_gen(X1=fitinfo['X0'], X2=thetaprime, theta=fitinfo['theta']) + if fitinfo['trendtype'] == 'SK': + if theta.shape[0] < thetaprime.shape[0]: + cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki_scaled'] @ kxprime + else: + cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki_scaled'] @ kxprime) + else: + + if theta.shape[0] < thetaprime.shape[0]: + cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki_scaled'] @ kxprime + \ + ((1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki_scaled']) + #np.dot(1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T, 1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki_scaled']) + else: + cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki_scaled'] @ kxprime) + \ + np.dot((1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki_scaled']) + + predinfo['covmat'] = cov + return + diff --git a/PUQ/surrogatemethods/pcHetGP.py b/PUQ/surrogatemethods/pcHetGP.py new file mode 100644 index 0000000..9b2e213 --- /dev/null +++ b/PUQ/surrogatemethods/pcHetGP.py @@ -0,0 +1,222 @@ +from PUQ.surrogate import emulator +from PUQ.surrogatemethods.hetGP import predict as predict_hetGP +import numpy as np +from PUQ.surrogatemethods.covariances import cov_gen +from scipy.linalg import cholesky, inv + + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, + init={}, + known={}, + settings={ + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + covtype="Gaussian", + pc_settings={"standardize": True, "latent": False}, + **kwargs +): + + numGPs = f.shape[0] + h = np.zeros((numGPs, 1)) + s = np.ones((numGPs)) + fs = np.zeros(f.shape) + + if pc_settings["standardize"]: + # print("Standardizing") + fitinfo["standardize"] = True + for k in range(0, numGPs): + h[k, 0] = np.mean(f[k, :]) + s[k] = np.std(f[k, :]) + + G = np.diag(s) + Gi = np.diag(1 / s) + fs = Gi @ (f - h) + B = np.eye(numGPs) + + if pc_settings["latent"]: + # print("Dimension reduction") + fitinfo["latent"] = True + + U, S, _ = np.linalg.svd(fs, full_matrices=False) + exp_var = np.cumsum(S**2) / np.cumsum(S**2)[-1] + numGPs = int(np.argwhere(np.array(exp_var) > 0.99)[0]) + 1 + + # d x q orthogonal matrix + B = U[:, 0:numGPs] + + # trasform q x n + W = B.T @ fs + + emulist = [dict() for x in range(0, numGPs)] + for i in range(0, numGPs): + if pc_settings["latent"]: + print("Latent surrogate") + fi = W[i, :][None, :] + else: + # print("On site surrogate") + fi = fs[i, :][None, :] + emu = emulator( + x=np.array([[i]]), + theta=theta, + f=fi, + method="hetGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + }, + ) + + emulist[i] = emu._info + + fitinfo["theta"] = theta + fitinfo["f"] = f + fitinfo["emulist"] = emulist + fitinfo["numGPs"] = numGPs + fitinfo["fs"] = fs + fitinfo["h"] = h + fitinfo["G"] = G + fitinfo["Gi"] = Gi + fitinfo["B"] = B + + return + + +def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): + + numGPs = fitinfo["numGPs"] + d = fitinfo["fs"].shape[0] + n = theta.shape[0] + + # calculate predictive mean and variance + mean_pc = np.full((numGPs, n), np.nan) + var_pc = np.full((numGPs, n), np.nan) + nugs_pc = np.full((numGPs, n), np.nan) + + if thetaprime is not None: + npr = thetaprime.shape[0] + covmat_pc = np.full((numGPs, n, npr), np.nan) + for i in range(0, numGPs): + predinfo_hetGP = {} + info = fitinfo["emulist"][i] + predict_hetGP( + predinfo=predinfo_hetGP, + fitinfo=info, + x=np.array([[i]]), + theta=theta, + thetaprime=thetaprime, + ) + + mean_pc[i, :] = predinfo_hetGP["mean"] + var_pc[i, :] = predinfo_hetGP["var"] + nugs_pc[i, :] = predinfo_hetGP["nugs"] + + if thetaprime is not None: + covmat_pc[i, :, :] = predinfo_hetGP["covmat"] + + # calculate predictive mean and variance + predinfo["mean_o"] = 1 * mean_pc + predinfo["var_o"] = 1 * var_pc + predinfo["nugs_o"] = 1 * nugs_pc + if thetaprime is not None: + predinfo["cov_o"] = 1 * covmat_pc + + predinfo["S"] = np.full((d, d, n), np.nan) + predinfo["R"] = np.full((d, d, n), np.nan) + predinfo["var"] = np.full((d, n), np.nan) + predinfo["nugs"] = np.full((d, n), np.nan) + predinfo["var_noisy"] = np.full((d, n), np.nan) + if thetaprime is not None: + predinfo["covmat"] = np.full((d, n, npr), np.nan) + predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) + + for i in range(0, theta.shape[0]): + C = np.diag(var_pc[:, i]) + R = np.diag(nugs_pc[:, i]) + predinfo["S"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["R"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) + predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) + + predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] + + return predinfo + + +def update(fitinfo, x, X0new=None, mult=None): + numGPs = fitinfo["numGPs"] + + Gi = fitinfo["Gi"] + h = fitinfo["h"] + pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) + mu_ct = pred_ct["mean"] + mu_ct = Gi @ (mu_ct - h) + Z0new = mu_ct.T + # print(mu_ct.shape) + + for i in range(0, numGPs): + info = fitinfo["emulist"][i] + + if info["is_homGP"] == True: + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T + else: + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + + kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) + M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) + + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) + # Updated + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) + Lambda = info["nmean"] + M + if info["logN"]: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = info["eps"] + + info["Lambda"] = Lambda + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T diff --git a/examples/Technometrics2025/Figure1_2.py b/examples/Technometrics2025/Figure1_2.py new file mode 100644 index 0000000..6fea602 --- /dev/null +++ b/examples/Technometrics2025/Figure1_2.py @@ -0,0 +1,125 @@ +import numpy as np +from PUQ.prior import prior_dist +#from PUQ.utils import parse_arguments +from PUQ.design import designer +from test_funcs import sinf +from utilities import test_data_gen_1d, Figure1, Figure2 +import matplotlib.pyplot as plt + +#args = parse_arguments() + +class Args: + pass + +args = Args() + +# # # # # +args.minibatch = 100 +example = "sinf" +args.seedmin = 13 +args.seedmax = 14 +# # # # # + +datalist = [] +n0 = 20 +rep0 = 5 +batch = args.minibatch +maxiter = 100 +worker = args.minibatch + 1 +rho = 1 / 2 +if __name__ == "__main__": + for s in np.arange(args.seedmin, args.seedmax): + + cls_func = eval(example)() + cls_func.realdata(s) + + theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + theta = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0)[ + :, None + ] + theta = np.repeat(theta, rep0, axis=0) + f = np.zeros((cls_func.d, rep0 * n0)) + for i in range(0, rep0 * n0): + f[:, i] = cls_func.sim_f(theta[i, :], persis_info=persis_info) + + theta_test, nugs, phat = Figure1( + f, + theta, + cls_func.x, + cls_func.obsvar, + cls_func.real_data, + theta_test, + f_test, + p_test, + cls_func, + ) + + fig, ax = plt.subplots(1, 2, figsize=(15, 3.5)) + fig.subplots_adjust(wspace=0.6) + + al_ivar = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="seivar", + args={ + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": worker, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta, "f": f}, + "alloc_settings": { + "method": "ivar", + "use_Ki": True, + "theta": None, + "a0": None, + "gen": False, + "rho": rho, + }, + "des_settings": {"is_explore": False, "is_exploit": True}, + "pc_settings": {"standardize": True, "latent": False}, + "trace": 0, + }, + ) + + Figure2(al_ivar, theta_test, nugs, phat, "ivar", ax[0]) + + al_imse = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="simse", + args={ + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": worker, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta, "f": f}, + "alloc_settings": { + "method": "imse", + "use_Ki": True, + "theta": None, + "a0": None, + "gen": False, + "rho": rho, + }, + "des_settings": {"is_explore": False, "is_exploit": True}, + "pc_settings": {"standardize": True, "latent": False}, + "trace": 0, + }, + ) + + Figure2(al_imse, theta_test, nugs, phat, "imse", ax[1]) + + plt.savefig("Figure2.png", bbox_inches="tight") + plt.show() diff --git a/examples/Technometrics2025/Figure3.py b/examples/Technometrics2025/Figure3.py new file mode 100644 index 0000000..a92dbde --- /dev/null +++ b/examples/Technometrics2025/Figure3.py @@ -0,0 +1,201 @@ +import numpy as np +from PUQ.prior import prior_dist +#from PUQ.utils import parse_arguments +from test_funcs import sinf +from utilities import test_data_gen_1d +import matplotlib.pyplot as plt +import scipy.stats as sps +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + build_emulator, + multiple_determinants, + multiple_pdfs, +) +from PUQ.designmethods.gen_funcs.acquire_new import get_pred + +#args = parse_arguments() + +class Args: + pass + +args = Args() + +# # # # # +args.funcname = "sinf" +args.seedmin = 3 # 13 +args.seedmax = 4 # 14 +# # # # # + +n0 = 6 +rep0 = 10 +nmesh = 50 +batch = 15 +reps = 5 +bnew = int(batch / reps) + +if __name__ == "__main__": + for s in np.arange(args.seedmin, args.seedmax): + cls_func = eval(args.funcname)() + cls_func.realdata(seed=s) + + theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + persis_info = {"rand_stream": np.random.default_rng(12345)} + + # Initial sample + theta0u = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0 + )[:, None] + f0mean = np.zeros(len(theta0u)) + p0 = np.zeros(len(theta0u)) + theta0 = np.repeat(theta0u, rep0, axis=0) + f0 = np.zeros(len(theta0)) + for tid, t in enumerate(theta0): + f0[tid] = cls_func.sim_f(t, persis_info=persis_info)[0] + + for tid, t in enumerate(theta0u): + f0mean[tid] = cls_func.function(t)[0] + rnd = sps.norm(loc=f0mean[tid], scale=np.sqrt(cls_func.obsvar)) + p0[tid] = rnd.pdf(cls_func.real_data)[0, 0] + + n_x, p, x = cls_func.x.shape[0], theta_test.shape[1], cls_func.x + obs, obsvar = cls_func.real_data, cls_func.obsvar + theta_acq, n_acq = None, None + obsvar3d = cls_func.obsvar.reshape(1, n_x, n_x) + is_cov = False + + # Create a candidate list + nL = 500 + nm = theta_test.shape[0] + pc_settings = {"standardize": True, "latent": False} + # plt.scatter(theta0, f0) + # plt.show() + + fig, ax = plt.subplots(2, 3, figsize=(15, 7), constrained_layout=True) + + emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=pc_settings) + for i in range(bnew): + ft = 18 + cL = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nL + )[:, None] + + mu, S, cov, cvar = get_pred( + cL=cL, emu=emu, x=x, ttest=theta_test, reps=reps + ) + V1 = S + obsvar3d + V2 = S + 0.5 * obsvar3d + + G = emu._info["G"] + B = emu._info["B"] + GB = G @ B + BTG = B.T @ G + + d = G.shape[0] + q = B.shape[1] + + tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) + + for j in range(0, q): + tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + for k in range(0, nL): + phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + vals = [] + for k in range(0, nL): + # C1: nm x d x d + # C2: nm x d x d + phic = phi[:, x, x.T, k] + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(obs, mu, C1) + dets = multiple_determinants(C2) + part2 = rpdf / np.sqrt(dets) + + rpdf2 = multiple_pdfs(obs, mu, V2) + denum2 = obsvar + + cval = coef * (np.sum(rpdf2 / np.sqrt(denum2)) - np.sum(part2)) + + vals.append(cval) + + idc = np.argmin(vals) + minacq = np.min(vals) + cu = cL[idc, :].reshape((1, p)) + + ax[0, i].plot(cL, vals, color="blue") + ax[0, i].set_xlabel(r"$\theta$", fontsize=ft) + ax[0, i].set_ylabel("IVAR", fontsize=ft) + ax[0, i].tick_params(axis="both", labelsize=ft) + ax[0, i].scatter(cu, minacq, color="green", marker="*", s=200) + + ctheta = np.repeat(cu, reps, axis=0) + cpred = emu.predict(x=x, theta=ctheta) + + cmean = cpred.mean() + cnoise = cpred._info["var_noisy"] + fnoise = persis_info["rand_stream"].normal( + loc=cmean[0], scale=np.sqrt(cnoise[0]), size=reps + ) + + theta0 = np.concatenate([theta0, ctheta], axis=0) + f0 = np.concatenate([f0, fnoise.flatten()]) + + fc = np.zeros(len(cu)) + pc = np.zeros(len(cu)) + for tid, t in enumerate(cu): + fc[tid] = cls_func.function(t) + rnd = sps.norm(loc=fc[tid], scale=np.sqrt(cls_func.obsvar)) + pc[tid] = rnd.pdf(cls_func.real_data) + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mu[tid, 0], scale=np.sqrt(obsvar + S[tid, 0, 0])) + phat[tid] = rnd.pdf(obs) + rnd = sps.norm( + loc=mu[tid, 0], scale=np.sqrt(0.5 * obsvar + S[tid, 0, 0]) + ) + pvar1[tid] = rnd.pdf(obs) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[ + tid + ] - phat[tid] ** 2 + + ax[1, i].plot(theta_test, p_test, color="red") + ax[1, i].plot( + theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5 + ) + ax[1, i].fill_between( + theta_test.flatten(), + (phat - np.sqrt(phatvar)).flatten(), + (phat + np.sqrt(phatvar)).flatten(), + color="blue", + alpha=0.1, + ) + ax[1, i].set_xlabel(r"$\theta$", fontsize=ft) + ax[1, i].set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax[1, i].tick_params(axis="both", labelsize=ft) + ax[1, i].scatter(theta0u, p0, color="black", s=100) + ax[1, i].scatter(cu, pc, color="green", marker="*", s=200) + + theta0u = np.concatenate([theta0u, cu], axis=0) + p0 = np.concatenate([p0, pc.flatten()]) + + print("Adding point:", np.round(ctheta, 2)) + + from PUQ.surrogatemethods.pcHetGP import update + + update(emu._info, x=x, X0new=cu, mult=reps) + + plt.savefig("Figure3.png", bbox_inches="tight") + plt.show() diff --git a/examples/Technometrics2025/bseq2d_exploit.py b/examples/Technometrics2025/bseq2d_exploit.py new file mode 100644 index 0000000..c6b9c62 --- /dev/null +++ b/examples/Technometrics2025/bseq2d_exploit.py @@ -0,0 +1,163 @@ +import numpy as np +from PUQ.prior import prior_dist +from PUQ.design import designer +from test_funcs import bimodal, banana, unimodal +from utilities import test_data_gen, twoD, heatmap +import pandas as pd + + +# args = parse_arguments() + +class Args: + pass + +args = Args() + +def create_entry(desobj, fname, method, s, b, w): + return [ + { + "MAD": MAD, + "t": t, + "rep": s, + "batch": b, + "worker": w, + "method": method, + "example": fname, + } + for t, MAD in enumerate(desobj._info["TViter"]) + ] + + +# # # # # +args.minibatch = 64 +# +args.funcname = "unimodal" + +args.seedmin = 0 +args.seedmax = 1 +# # # # # + +workers = args.minibatch + 1 +grid = 10 +rep0 = 2 +nmesh = 50 +rho = 1 / 2 +batch = args.minibatch +maxiter = 192 +dfl = [] +if __name__ == "__main__": + for s in np.arange(args.seedmin, args.seedmax): + + cls_func = eval(args.funcname)() + cls_func.realdata(seed=s) + + theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + heatmap(cls_func) + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Initial sample + theta1 = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], grid + ) + theta2 = np.linspace( + cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], grid + ) + th1, th2 = np.meshgrid(theta1, theta2) + theta0u = np.vstack([th1.ravel(), th2.ravel()]).T + theta0 = np.repeat(theta0u, rep0, axis=0) + f0 = np.zeros((cls_func.d, rep0 * (grid**2))) + for i in range(0, rep0 * (grid**2)): + f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) + + base_args = { + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": workers, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta0, "f": f0}, + "alloc_settings": { + "use_Ki": True, + "rho": rho, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": {"standardize": True, "latent": False}, + "des_settings": {"is_exploit": True, "is_explore": False}, + } + + methods = ["ivar", "imse"] + + args_list = [] + for method in methods: + args_ = base_args.copy() + args_["alloc_settings"] = args_["alloc_settings"].copy() + args_["alloc_settings"]["method"] = method + args_list.append(args_) + + al_ivar = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="seivar", + args=args_list[0], + ) + + # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) + twoD(al_ivar, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_ivar, args.funcname, "ivar", s, batch, workers)) + + al_imse = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="simse", + args=args_list[1], + ) + + # save_output(al_imse, cls_func.data_name, "imse", workers, batch, s) + twoD(al_imse, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_imse, args.funcname, "imse", s, batch, workers)) + + theta0r = theta0u[persis_info["rand_stream"].permutation(theta0u.shape[0]), :] + + al_unif = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition=None, + args={ + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": workers, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta0, "f": f0}, + "des_add": {"theta": theta0r}, + "alloc_settings": { + "use_Ki": True, + "rho": rho, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": {"standardize": True, "latent": False}, + "des_settings": {"is_exploit": True, "is_explore": True}, + }, + ) + + # save_output(al_unif, cls_func.data_name, "unif", workers, batch, s) + twoD(al_unif, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_unif, args.funcname, "unif", s, batch, workers)) + +from summary import lineplot + +df = pd.DataFrame(dfl) +lineplot(df, examples=[args.funcname], batches=[batch]) diff --git a/examples/Technometrics2025/bseq2d_explore.py b/examples/Technometrics2025/bseq2d_explore.py new file mode 100644 index 0000000..c36c74a --- /dev/null +++ b/examples/Technometrics2025/bseq2d_explore.py @@ -0,0 +1,182 @@ +import numpy as np +from PUQ.prior import prior_dist +# from PUQ.utils import parse_arguments, save_output +from PUQ.design import designer +from test_funcs import bimodal, banana, unimodal +from utilities import test_data_gen, twoD, heatmap +import pandas as pd +from smt.sampling_methods import LHS +import time + +# args = parse_arguments() + +class Args: + pass + +args = Args() + + +def create_entry(desobj, fname, method, s, b, w): + return [ + { + "MAD": MAD, + "t": t, + "rep": s, + "batch": b, + "worker": w, + "method": method, + "example": fname, + } + for t, MAD in enumerate(desobj._info["TViter"]) + ] + + +# # # # # +args.minibatch = 8 +args.funcname = "unimodal" +args.seedmin = 0 +args.seedmax = 1 + +# # # # # + +workers = args.minibatch + 1 +n0 = 15 +rep0 = 2 +nmesh = 50 +rho = 1 / 2 +batch = args.minibatch +maxiter = 256 +dfl, dfr = [], [] + +# Inputs to designer +pcset = {"standardize": True, "latent": False} +desset = {"is_exploit": True, "is_explore": True, "nL": 200, "impute_str": "update"} + +if __name__ == "__main__": + design_start = time.time() + for s in np.arange(args.seedmin, args.seedmax): + + cls_func = eval(args.funcname)() + cls_func.realdata(seed=s) + + theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # heatmap(cls_func) + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Initial sample + sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) + theta0 = sampling(n0) + theta0 = np.repeat(theta0, rep0, axis=0) + f0 = np.zeros((cls_func.d, n0 * rep0)) + for i in range(0, n0 * rep0): + f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) + + base_args = { + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": workers, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta0, "f": f0}, + "alloc_settings": { + "use_Ki": True, + "rho": rho, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": pcset, + "des_settings": desset, + } + + methods = ["ivar", "imse"] + + args_list = [] + for method in methods: + args_ = base_args.copy() + args_["alloc_settings"] = args_["alloc_settings"].copy() + args_["alloc_settings"]["method"] = method + args_list.append(args_) + + al_ivar = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="seivar", + args=args_list[0], + ) + + # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) + twoD(al_ivar, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_ivar, args.funcname, "ivar", s, batch, workers)) + + al_imse = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="simse", + args=args_list[1], + ) + + # save_output(al_imse, cls_func.data_name, "imse", workers, batch, s) + twoD(al_imse, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_imse, args.funcname, "imse", s, batch, workers)) + + rholhs = 1 / 4 + sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) + theta_lhs = sampling(int(maxiter * rholhs)) + + al_unif = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition=None, + args={ + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": workers, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta0, "f": f0}, + "des_add": {"theta": theta_lhs}, + "alloc_settings": { + "use_Ki": True, + "rho": rholhs, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": pcset, + "des_settings": {"is_exploit": True, "is_explore": True}, + }, + ) + + # save_output(al_unif, cls_func.data_name, "unif", workers, batch, s) + twoD(al_unif, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_unif, args.funcname, "unif", s, batch, workers)) + + al_var = designer( + data_cls=cls_func, + method="p_sto_bseq", + acquisition="var", + args=args_list[0], + ) + + # save_output(al_var, cls_func.data_name, "var", workers, batch, s) + twoD(al_var, Xpl, Ypl, p_test, nmesh) + dfl.extend(create_entry(al_var, args.funcname, "var", s, batch, workers)) + + design_end = time.time() + print("Elapsed time: " + str(round(design_end - design_start, 2))) + + +# from summary import lineplot + +# df = pd.DataFrame(dfl) +# lineplot(df, examples=[args.funcname], batches=[batch]) diff --git a/examples/Technometrics2025/test_funcs.py b/examples/Technometrics2025/test_funcs.py new file mode 100644 index 0000000..ad0d341 --- /dev/null +++ b/examples/Technometrics2025/test_funcs.py @@ -0,0 +1,562 @@ +import numpy as np +import scipy + + +class unimodal: + def __init__(self): + self.data_name = "unimodal" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.1]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, t1, t2): + t1 = -10 + t1 * 20 + t2 = -10 + t2 * 20 + f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.05, 0], [0, 0.05]]) + var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) + return 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class bimodal: + def __init__(self): + self.data_name = "bimodal" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.5, 0], [0, 0.5]]) + self.theta_true = np.array([8 / 12, 8 / 12]) + self.real_data = None + self.out = [("f", float, (2,))] + self.p = 2 + self.d = 2 + self.x = np.arange(0, self.d)[:, None] + + def function(self, t1, t2): + t1 = 12 * t1 - 6 + t2 = 12 * t2 - 4 + f = np.array([np.sqrt(0.2) * (t2 - t1**2), np.sqrt(0.75) * (t2 - t1)]) + return f + + def sim_f(self, thetas, persis_info): + + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].multivariate_normal( + np.array([0, 0]), np.diag(V.flatten()), 1 + ) + f += R.flatten() + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + V = np.repeat(0.5 + (theta[:, 0] ** 2 + theta[:, 1] ** 2) * 2, self.d) + V = V.reshape(self.d, theta.shape[0]) + return V + + def realdata(self, seed): + + M = np.array( + [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].multivariate_normal( + mean=[0, 0], cov=self.obsvar, size=1 + ) + self.real_data = M + R + + +class banana: + def __init__(self): + self.data_name = "banana" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.03, 0], [0, 0.5]]) + self.theta_true = np.array([0.5, 0.75]) + self.real_data = None + self.out = [("f", float, (2,))] + self.p = 2 + self.d = 2 + self.x = np.arange(0, self.d)[:, None] + + def function(self, t1, t2): + t1 = 40 * t1 - 20 + t2 = 15 * t2 - 15 + f = np.array([0.03 * t1, t2 + 0.06 * t1**2]) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].multivariate_normal( + np.array([0, 0]), np.diag(V.flatten()), 1 + ) + f += R.flatten() + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + + f = self.function(theta[0, 0], theta[0, 1]) + if theta[0, 0] < 0.5: + noise1 = 0.01 * np.abs(f[0]) + noise2 = 0.01 * np.abs(f[1]) + else: + noise1 = 0.1 * np.abs(f[0]) + noise2 = 0.2 * np.abs(f[1]) + return np.array([noise1, noise2]).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].multivariate_normal( + mean=[0, 0], cov=self.obsvar, size=1 + ) + self.real_data = M + R + + +class sinf: + def __init__(self): + self.data_name = "sinf" + self.thetalimits = np.array([[0, 1]]) + self.obsvar = np.array([[0.05]], dtype="float64") + self.theta_true = 0.5 + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 1 + self.x = np.arange(0, self.d)[:, None] + + def function(self, theta1): + return np.sin(10 * theta1) + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0]) + var_noise = self.noise(np.array([thetas[0]])[None, :]) + noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise[0]), 1) + f += noise + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"], persis_info) + return H_o, persis_info + + def realdata(self, seed): + + mean = np.array([[self.function(self.theta_true)]], dtype="float64") + if seed is None: + self.real_data = mean + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + noise = persis_info["rand_stream"].normal( + 0, np.sqrt(self.obsvar[0]), size=1 + ) + self.real_data = mean + noise + + def noise(self, theta): + rx = (1.1 + np.sin(2 * np.pi * theta)) * 0.05 + return rx.reshape(self.d, theta.shape[0]) + + +class branin: + def __init__(self): + self.data_name = "branin" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[10]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) + + def function(self, t1, t2): + t1 = -5 + 15 * t1 + t2 = 15 * t2 + f = ( + (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 + + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) + + 10 + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + return np.repeat(1, self.d * theta.shape[0]).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class himmelblau: + def __init__(self): + + self.data_name = "himmelblau" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[100]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.11085, 0.16]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = (theta1**2 + theta2 - 11) ** 2 + (theta1 + theta2**2 - 7) ** 2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.01, 0], [0, 0.01]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return 10 + 5 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class holder: + def __init__(self): + + self.data_name = "holder" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-10, 10], [-10, 10]]) + self.obsvar = np.array([[50]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.1, 0.01]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = -np.abs( + np.sin(theta1) + * np.cos(theta2) + * np.exp(np.abs(1 - (np.sqrt(theta1**2 + theta2**2) / np.pi))) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class easom: + def __init__(self): + + self.data_name = "easom" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-10, 10], [-10, 10]]) + self.obsvar = np.array([[0.001]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.75, 0.75]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = ( + -np.cos(theta1) + * np.cos(theta2) + * np.exp(-((theta1 - np.pi) ** 2 + (theta2 - np.pi) ** 2)) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class ackley: + def __init__(self): + + self.data_name = "ackley" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[10]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = ( + -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (theta1**2 + theta2**2))) + - np.exp(0.5 * (np.cos(2 * np.pi * theta1) + np.cos(2 * np.pi * theta2))) + + np.e + + 20 + ) + + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.85], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class sphere: + def __init__(self): + + self.data_name = "sphere" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[1]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, theta1, theta2): + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = theta1**2 + theta2**2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return 1 + 1 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R diff --git a/examples/Technometrics2025/utilities.py b/examples/Technometrics2025/utilities.py new file mode 100644 index 0000000..bfe0388 --- /dev/null +++ b/examples/Technometrics2025/utilities.py @@ -0,0 +1,818 @@ +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as sps +from smt.sampling_methods import LHS + + +def twoD(designobj, Xpl, Ypl, p_test, nmesh): + theta0 = designobj._info["theta0"] + reps0 = designobj._info["reps0"] + + fig, ax = plt.subplots(figsize=(5, 5)) + cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="RdGy") + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label == 2: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + weight="bold", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="blue", + weight="bold", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + +def test_data_gen(cls_func, nmesh): + xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh) + ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + theta_test = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + if cls_func.data_name in [ + "unimodal", + "branin", + "himmelblau", + "holder", + "easom", + "ackley", + "sphere", + ]: + f_test = np.zeros(theta_test.shape[0]) + else: + f_test = np.zeros((theta_test.shape[0], 2)) + p_test = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + if cls_func.data_name in [ + "unimodal", + "branin", + "himmelblau", + "holder", + "easom", + "ackley", + "sphere", + ]: + f_test[tid] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) + rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) + else: + f_test[tid, :] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) + rnd = sps.multivariate_normal(mean=f_test[tid, :], cov=(cls_func.obsvar)) + p_test[tid] = rnd.pdf(cls_func.real_data) + + return theta_test, p_test, f_test, Xpl, Ypl + + +def test_data_gen_1d(cls_func, nmesh): + + # Create test data + theta_test = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh + )[:, None] + f_test, p_test = np.zeros(theta_test.shape[0]), np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + f_test[tid] = cls_func.function(theta_test[tid, 0]) + rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) + p_test[tid] = rnd.pdf(cls_func.real_data) + + return theta_test, p_test, f_test + + +def oned(seqobject, x, obsvar, real_data, theta_test, f_test, p_test, cls_func, ninit): + from PUQ.surrogate import emulator + from scipy.stats import norm + import matplotlib.pyplot as plt + + pc_settings = {"standardize": True, "latent": True} + + theta = seqobject._info["theta"][0:ninit, :] + f = seqobject._info["f"][:, 0:ninit] + + theta_all = seqobject._info["theta"] + f_all = seqobject._info["f"] + + theta0 = seqobject._info["theta0"] + reps0 = seqobject._info["reps0"] + + emu = emulator( + x=x, + theta=theta, + f=f, + method="pcHetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + "pc_settings": pc_settings, + }, + ) + + emupred = emu.predict(x=x, theta=theta_test) + + mean = emupred.mean() + var = emupred.var() + var_noisy = emupred._info["var_noisy"] + + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + quantile = 0.975 + upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + quantile = 0.975 + upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + + ft = 20 + fig, ax = plt.subplots() + ax.plot(theta_test, f_test, color="red") + ax.plot( + theta_test.flatten(), + mean.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + theta_test.flatten(), + mean.flatten() - np.sqrt(var.flatten()), + mean.flatten() + np.sqrt(var.flatten()), + # lower_bound.flatten(), + # upper_bound.flatten(), + color="blue", + alpha=0.3, + linestyle="dotted", + ) + + # plt.fill_between( + # theta_test.flatten(), + # lower_bound_nug.flatten(), + # upper_bound_nug.flatten(), + # color='blue', + # alpha=0.1, + # linestyle="dotted", + # ) + ax.scatter( + theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$M(\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) + phat[tid] = rnd.pdf(real_data) + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) + pvar1[tid] = rnd.pdf(real_data) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ + tid + ] ** 2 + + fig, ax = plt.subplots() + ax.plot(theta_test, p_test, color="red") + ax.plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) + plt.fill_between( + theta_test.flatten(), + (phat - np.sqrt(phatvar)).flatten(), + (phat + np.sqrt(phatvar)).flatten(), + color="blue", + alpha=0.1, + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + p_alloc = np.zeros(theta0.shape[0]) + f_alloc = np.zeros((1, theta0.shape[0])) + for tid in range(0, len(theta0)): + f_alloc[0, tid] = cls_func.function(theta0[tid]) + rnd = sps.norm(loc=f_alloc[0, tid], scale=np.sqrt(cls_func.obsvar)) + p_alloc[tid] = rnd.pdf(cls_func.real_data) + + fig, ax = plt.subplots(figsize=(10, 6)) + ax.plot(theta_test, p_test, color="red", linewidth=2.5) + for label, x_count, pval in zip(reps0, theta0, p_alloc): + plt.annotate( + label, + xy=(x_count, pval), + xytext=(0, 0), + textcoords="offset points", + color="blue", + size=20, + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + fig, ax = plt.subplots() + ax.scatter(theta0.flatten(), reps0.flatten(), color="blue", marker="*", s=55) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"a", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + fig, ax = plt.subplots() + ax.plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + color="blue", + linestyle="dashed", + linewidth=2.5, + ) + ax.plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + ft = 15 + + def make_patch_spines_invisible(ax): + ax.set_frame_on(True) + ax.patch.set_visible(False) + for sp in ax.spines.values(): + sp.set_visible(False) + + fig, host = plt.subplots(figsize=(8, 4)) + fig.subplots_adjust(right=0.75) + + par1 = host.twinx() + par2 = host.twinx() + + # Offset the right spine of par2. The ticks and label have already been + # placed on the right by twinx above. + par2.spines["right"].set_position(("axes", 1.2)) + # Having been created by twinx, par2 has its frame off, so the line of its + # detached spine is invisible. First, activate the frame but make the patch + # and spines invisible. + make_patch_spines_invisible(par2) + # Second, show the right spine. + par2.spines["right"].set_visible(True) + + p1 = host.scatter( + theta0.flatten(), + reps0.flatten(), + color="black", + marker="*", + s=55, + label=r"$a_i$", + ) + (p2,) = par1.plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + "b", + linestyle="dashed", + label="Variance", + linewidth=2.5, + ) + (p3,) = par2.plot( + theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 + ) + + host.set_xlim(-0.1, 1.1) + host.set_ylim(0.9 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) + par1.set_ylim( + 0.9 * np.min(emupred._info["nugs"].flatten()), + 1.1 * np.max(emupred._info["nugs"].flatten()), + ) + par2.set_ylim(-0.01, 1.1 * np.max(phat)) + + host.set_xlabel(r"$\theta$", fontsize=ft) + host.set_ylabel(r"$a_i$", fontsize=ft) + par1.set_ylabel("Variance", fontsize=ft) + par2.set_ylabel("Likelihood", fontsize=ft) + host.tick_params(axis="both", labelsize=ft) + par1.tick_params(axis="both", labelsize=ft) + par2.tick_params(axis="both", labelsize=ft) + lines = [p1, p2, p3] + + host.legend( + lines, + [l.get_label() for l in lines], + loc="center", + bbox_to_anchor=(0.5, -0.3), + ncol=3, + prop={"size": ft}, + ) + + plt.show() + + +def twodpaper(cls_func, Xpl, Ypl, p_test, theta0, reps0, thetainit=None, name=None): + from matplotlib.ticker import MaxNLocator + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + + Pvar = P + else: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh, 2)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j, :] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + Pvar = np.sum(P, axis=2) + + fig, ax = plt.subplots() + cs = ax.contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) + # cbar = fig.colorbar(cs, pad=0.1) + cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") + + if thetainit is None: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label <= 2: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="black", + ) + else: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if np.array([x_count, y_count]) in thetainit: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="black", + ) + + ax.set_xticks([0, 0.5, 1]) # Custom tick locations for x-axis + ax.set_yticks([0, 0.5, 1]) # Custom tick locations for y-axis + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.savefig(name, bbox_inches="tight") + plt.show() + + +def twodpaperrev(cls_func, Xpl, Ypl, p_test, dictl, thetainit=None, name=None): + from matplotlib.ticker import MaxNLocator + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + + Pvar = P + else: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh, 2)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j, :] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + Pvar = np.sum(P, axis=2) + + fig, ax = plt.subplots(1, 3, figsize=(15, 4.5), constrained_layout=True) + + for i in range(0, 3): + if i == 0: + for el in dictl: + if el["method"] == "ivar": + reps0 = el["reps0"] + theta0 = el["theta0"] + elif i == 1: + for el in dictl: + if el["method"] == "var": + reps0 = el["reps0"] + theta0 = el["theta0"] + elif i == 2: + for el in dictl: + if el["method"] == "imse": + reps0 = el["reps0"] + theta0 = el["theta0"] + + cs = ax[i].contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) + if i == 2: + cbar = fig.colorbar(cs, ax=ax[i], pad=0.1) + cp = ax[i].contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") + ft = 14 + if thetainit is None: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label <= 2: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="cyan", + ) + else: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="black", + ) + else: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if np.array([x_count, y_count]) in thetainit: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="cyan", + ) + else: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="black", + ) + + if i == 0: + ax[i].set_yticks([0, 0.5, 1]) # Custom tick locations for x-axis + ax[i].set_ylabel(r"$\theta_2$", fontsize=16) + else: + ax[i].set_yticks([]) + ax[i].set_xticks([0, 0.5, 1]) + + ax[i].set_xlabel(r"$\theta_1$", fontsize=16) + + ax[i].tick_params(axis="both", labelsize=16) + + plt.savefig(name, bbox_inches="tight") + plt.show() + + +def heatmap(cls_func): + + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = 50 + a = np.arange(nmesh + 1) / nmesh + b = np.arange(nmesh + 1) / nmesh + X, Y = np.meshgrid(a, b) + Z = np.zeros((nmesh + 1, nmesh + 1)) + P = np.zeros((nmesh + 1, nmesh + 1)) + for i in range(nmesh + 1): + for j in range(nmesh + 1): + Z[i, j] = cls_func.function(X[i, j], Y[i, j]) + P[i, j] = cls_func.noise( + np.array([X[i, j], Y[i, j]])[None, :] + ).flatten() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P, cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z, colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + else: + nmesh = 50 + a = np.arange(nmesh + 1) / nmesh + b = np.arange(nmesh + 1) / nmesh + X, Y = np.meshgrid(a, b) + Z = np.zeros((nmesh + 1, nmesh + 1, 2)) + P = np.zeros((nmesh + 1, nmesh + 1, 2)) + + for i in range(nmesh + 1): + for j in range(nmesh + 1): + Z[i, j, :] = cls_func.function(X[i, j], Y[i, j]) + P[i, j, :] = cls_func.noise( + np.array([X[i, j], Y[i, j]])[None, :] + ).flatten() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P[:, :, 0], cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z[:, :, 0], colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P[:, :, 1], cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z[:, :, 1], colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + +def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func): + from PUQ.surrogate import emulator + from scipy.stats import norm + import matplotlib.pyplot as plt + + pc_settings = {"standardize": True, "latent": False} + + # theta = seqobject._info['theta'][0:ninit,:] + # f = seqobject._info['f'][:, 0:ninit] + + emu = emulator( + x=x, + theta=theta, + f=f, + method="pcHetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + "pc_settings": pc_settings, + }, + ) + + emupred = emu.predict(x=x, theta=theta_test) + + mean = emupred.mean() + var = emupred.var() + var_noisy = emupred._info["var_noisy"] + + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + quantile = 0.975 + upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + quantile = 0.975 + upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + + fig, ax = plt.subplots(1, 3, figsize=(15, 3.5), constrained_layout=True) + ft = 18 + ax[0].plot(theta_test, f_test, color="red") + ax[0].plot( + theta_test.flatten(), + mean.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + ax[0].fill_between( + theta_test.flatten(), + mean.flatten() - np.sqrt(var.flatten()), + mean.flatten() + np.sqrt(var.flatten()), + # lower_bound.flatten(), + # upper_bound.flatten(), + color="blue", + alpha=0.3, + linestyle="dotted", + ) + + ax[0].scatter( + theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" + ) + ax[0].set_xlabel(r"$\theta$", fontsize=ft) + ax[0].set_ylabel(r"$\zeta(\theta)$", fontsize=ft) + ax[0].tick_params(axis="both", labelsize=ft) + + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) + phat[tid] = rnd.pdf(real_data) + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) + pvar1[tid] = rnd.pdf(real_data) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ + tid + ] ** 2 + + ax[1].plot(theta_test, p_test, color="red") + ax[1].plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) + ax[1].fill_between( + theta_test.flatten(), + (phat - np.sqrt(phatvar)).flatten(), + (phat + np.sqrt(phatvar)).flatten(), + color="blue", + alpha=0.1, + ) + ax[1].set_xlabel(r"$\theta$", fontsize=ft) + ax[1].set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax[1].tick_params(axis="both", labelsize=ft) + + ax[2].plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + color="blue", + linestyle="dashed", + linewidth=2.5, + ) + ax[2].plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") + ax[2].set_xlabel(r"$\theta$", fontsize=ft) + ax[2].set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) + ax[2].tick_params(axis="both", labelsize=ft) + plt.savefig("Figure1.png", bbox_inches="tight") + plt.show() + + return theta_test, emupred._info["nugs"].flatten(), phat + + +def Figure2(desobject, theta_test, nugs, phat, method, axs): + + theta0 = desobject._info["theta0"] + reps0 = desobject._info["reps0"] + + ft = 15 + + def make_patch_spines_invisible(ax): + ax.set_frame_on(True) + ax.patch.set_visible(False) + for sp in ax.spines.values(): + sp.set_visible(False) + + par1 = axs.twinx() + par2 = axs.twinx() + + par2.spines["right"].set_position(("axes", 1.25)) + make_patch_spines_invisible(par2) + par2.spines["right"].set_visible(True) + + p1 = axs.scatter( + theta0.flatten(), + reps0.flatten(), + color="black", + marker="*", + s=55, + label=r"$a_i$", + ) + (p2,) = par1.plot( + theta_test.flatten(), + nugs, + "b", + linestyle="dashed", + label="Variance", + linewidth=2.5, + ) + (p3,) = par2.plot( + theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 + ) + + axs.set_xlim(-0.1, 1.1) + axs.set_ylim(0.8 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) + par1.set_ylim(0.8 * np.min(nugs), 1.1 * np.max(nugs)) + par2.set_ylim(-0.1, 1.1 * np.max(phat)) + + axs.set_xlabel(r"$\theta$", fontsize=ft) + axs.set_ylabel(r"$a_i$", fontsize=ft) + par1.set_ylabel("Variance", fontsize=ft) + par2.set_ylabel("Likelihood", fontsize=ft) + axs.tick_params(axis="both", labelsize=ft) + par1.tick_params(axis="both", labelsize=ft) + par2.tick_params(axis="both", labelsize=ft) + lines = [p1, p2, p3] + + axs.legend( + lines, + [l.get_label() for l in lines], + loc="center", + bbox_to_anchor=(0.5, -0.3), + ncol=3, + prop={"size": ft}, + ) + From 387b4b090a5c2c1063b465cae60fe604f05807a4 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 27 Aug 2025 10:34:56 -0400 Subject: [PATCH 003/103] Update Readme --- examples/README.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/examples/README.rst b/examples/README.rst index 97175ca..2e782d7 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -10,6 +10,10 @@ The available examples include: * `JQT2024 `_ : These examples reproduce the results from Sürer’s (2024) paper `Simulation Experiment Design for Calibration via Active Learning `_ . -* `IJOC2024+ `_ : These examples replicate results from the paper 'Performance Analysis of Sequential Experimental Design for Calibration in Parallel Computing Environments' by Sürer and Wild (2024). +* `NeurIPS2024 `_ : These examples replicate results from the paper 'An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations' by Sürer and Wild (2024). + +* `Technometrics2025 `_ : These examples replicate the results from the paper `Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models `_ by Sürer (2025). + +* `2025+ `_ : These examples replicate the results from the paper `Active Learning for Data-Efficient Calibration of Stochastic Simulation Models` by Sürer (2025). * `fresco_example `_ : This example showcases the application of PUQ with the physics reaction code `fresco`. From b6ce252bc3b26de9e9fb1aa73e68ed064e76a339 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 27 Aug 2025 10:39:16 -0400 Subject: [PATCH 004/103] Update examples --- examples/{IJOC2024+ => NeurIPS2024}/Fig1.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig10.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig2.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig3.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig4_5_12_13.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig6_14.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig7.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig8.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Fig9.py | 0 examples/{IJOC2024+ => NeurIPS2024}/README.rst | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS0.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS0_run.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS1_real.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS1_synth.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS2_real.py | 0 examples/{IJOC2024+ => NeurIPS2024}/Workshop_files/WS2_synth.py | 0 examples/{IJOC2024+ => NeurIPS2024}/ci_example.py | 0 examples/{IJOC2024+ => NeurIPS2024}/compile_results.py | 0 examples/{IJOC2024+ => NeurIPS2024}/plotutils.py | 0 examples/{IJOC2024+ => NeurIPS2024}/plotv0/Fig10.py | 0 examples/{IJOC2024+ => NeurIPS2024}/plotv0/Fig8.py | 0 examples/{IJOC2024+ => NeurIPS2024}/plotv0/Fig9.py | 0 examples/{IJOC2024+ => NeurIPS2024}/result_read.py | 0 examples/{IJOC2024+ => NeurIPS2024}/run_test_funcs.py | 0 examples/{IJOC2024+ => NeurIPS2024}/test_funcs.py | 0 examples/README.rst | 2 +- 26 files changed, 1 insertion(+), 1 deletion(-) rename examples/{IJOC2024+ => NeurIPS2024}/Fig1.py (100%) rename examples/{IJOC2024+ => NeurIPS2024}/Fig10.py (100%) rename examples/{IJOC2024+ => NeurIPS2024}/Fig2.py (100%) rename examples/{IJOC2024+ => NeurIPS2024}/Fig3.py (100%) rename examples/{IJOC2024+ => NeurIPS2024}/Fig4_5_12_13.py (100%) rename examples/{IJOC2024+ => NeurIPS2024}/Fig6_14.py 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a/examples/IJOC2024+/Fig4_5_12_13.py b/examples/NeurIPS2024/Fig4_5_12_13.py similarity index 100% rename from examples/IJOC2024+/Fig4_5_12_13.py rename to examples/NeurIPS2024/Fig4_5_12_13.py diff --git a/examples/IJOC2024+/Fig6_14.py b/examples/NeurIPS2024/Fig6_14.py similarity index 100% rename from examples/IJOC2024+/Fig6_14.py rename to examples/NeurIPS2024/Fig6_14.py diff --git a/examples/IJOC2024+/Fig7.py b/examples/NeurIPS2024/Fig7.py similarity index 100% rename from examples/IJOC2024+/Fig7.py rename to examples/NeurIPS2024/Fig7.py diff --git a/examples/IJOC2024+/Fig8.py b/examples/NeurIPS2024/Fig8.py similarity index 100% rename from examples/IJOC2024+/Fig8.py rename to examples/NeurIPS2024/Fig8.py diff --git a/examples/IJOC2024+/Fig9.py b/examples/NeurIPS2024/Fig9.py similarity index 100% rename from examples/IJOC2024+/Fig9.py rename to examples/NeurIPS2024/Fig9.py diff --git a/examples/IJOC2024+/README.rst b/examples/NeurIPS2024/README.rst similarity index 100% rename from examples/IJOC2024+/README.rst rename to examples/NeurIPS2024/README.rst diff --git a/examples/IJOC2024+/Workshop_files/WS0.py b/examples/NeurIPS2024/Workshop_files/WS0.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS0.py rename to examples/NeurIPS2024/Workshop_files/WS0.py diff --git a/examples/IJOC2024+/Workshop_files/WS0_run.py b/examples/NeurIPS2024/Workshop_files/WS0_run.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS0_run.py rename to examples/NeurIPS2024/Workshop_files/WS0_run.py diff --git a/examples/IJOC2024+/Workshop_files/WS1_real.py b/examples/NeurIPS2024/Workshop_files/WS1_real.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS1_real.py rename to examples/NeurIPS2024/Workshop_files/WS1_real.py diff --git a/examples/IJOC2024+/Workshop_files/WS1_synth.py b/examples/NeurIPS2024/Workshop_files/WS1_synth.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS1_synth.py rename to examples/NeurIPS2024/Workshop_files/WS1_synth.py diff --git a/examples/IJOC2024+/Workshop_files/WS2_real.py b/examples/NeurIPS2024/Workshop_files/WS2_real.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS2_real.py rename to examples/NeurIPS2024/Workshop_files/WS2_real.py diff --git a/examples/IJOC2024+/Workshop_files/WS2_synth.py b/examples/NeurIPS2024/Workshop_files/WS2_synth.py similarity index 100% rename from examples/IJOC2024+/Workshop_files/WS2_synth.py rename to examples/NeurIPS2024/Workshop_files/WS2_synth.py diff --git a/examples/IJOC2024+/ci_example.py b/examples/NeurIPS2024/ci_example.py similarity index 100% rename from examples/IJOC2024+/ci_example.py rename to examples/NeurIPS2024/ci_example.py diff --git a/examples/IJOC2024+/compile_results.py b/examples/NeurIPS2024/compile_results.py similarity index 100% rename from examples/IJOC2024+/compile_results.py rename to examples/NeurIPS2024/compile_results.py diff --git a/examples/IJOC2024+/plotutils.py b/examples/NeurIPS2024/plotutils.py similarity index 100% rename from examples/IJOC2024+/plotutils.py rename to examples/NeurIPS2024/plotutils.py diff --git a/examples/IJOC2024+/plotv0/Fig10.py b/examples/NeurIPS2024/plotv0/Fig10.py similarity index 100% rename from examples/IJOC2024+/plotv0/Fig10.py rename to examples/NeurIPS2024/plotv0/Fig10.py diff --git a/examples/IJOC2024+/plotv0/Fig8.py b/examples/NeurIPS2024/plotv0/Fig8.py similarity index 100% rename from examples/IJOC2024+/plotv0/Fig8.py rename to examples/NeurIPS2024/plotv0/Fig8.py diff --git a/examples/IJOC2024+/plotv0/Fig9.py b/examples/NeurIPS2024/plotv0/Fig9.py similarity index 100% rename from examples/IJOC2024+/plotv0/Fig9.py rename to examples/NeurIPS2024/plotv0/Fig9.py diff --git a/examples/IJOC2024+/result_read.py b/examples/NeurIPS2024/result_read.py similarity index 100% rename from examples/IJOC2024+/result_read.py rename to examples/NeurIPS2024/result_read.py diff --git a/examples/IJOC2024+/run_test_funcs.py b/examples/NeurIPS2024/run_test_funcs.py similarity index 100% rename from examples/IJOC2024+/run_test_funcs.py rename to examples/NeurIPS2024/run_test_funcs.py diff --git a/examples/IJOC2024+/test_funcs.py b/examples/NeurIPS2024/test_funcs.py similarity index 100% rename from examples/IJOC2024+/test_funcs.py rename to examples/NeurIPS2024/test_funcs.py diff --git a/examples/README.rst b/examples/README.rst index 2e782d7..410a5c1 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -10,7 +10,7 @@ The available examples include: * `JQT2024 `_ : These examples reproduce the results from Sürer’s (2024) paper `Simulation Experiment Design for Calibration via Active Learning `_ . -* `NeurIPS2024 `_ : These examples replicate results from the paper 'An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations' by Sürer and Wild (2024). +* `NeurIPS2024 `_ : These examples replicate results from the paper `An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations `_ by Sürer and Wild (2024). * `Technometrics2025 `_ : These examples replicate the results from the paper `Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models `_ by Sürer (2025). From 8496783fa3078d3f08573fa931109b4c0b05d588 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 3 Sep 2025 10:42:19 -0400 Subject: [PATCH 005/103] include alternative slns --- PUQ/designmethods/batch_sequential.py | 219 ++++++++++++++++++++++++++ PUQ/designmethods/p_sto_bseq.py | 13 +- PUQ/surrogatemethods/highdimHetGP.py | 214 +++++++++++++++++++++++++ 3 files changed, 434 insertions(+), 12 deletions(-) create mode 100644 PUQ/designmethods/batch_sequential.py create mode 100644 PUQ/surrogatemethods/highdimHetGP.py diff --git a/PUQ/designmethods/batch_sequential.py b/PUQ/designmethods/batch_sequential.py new file mode 100644 index 0000000..ca82ddf --- /dev/null +++ b/PUQ/designmethods/batch_sequential.py @@ -0,0 +1,219 @@ +import numpy as np +from PUQ.designmethods.gen_funcs.batch_allocate_reps import allocate +from PUQ.designmethods.gen_funcs.acquire_new import acquire +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + multiple_pdfs, + build_emulator, +) +from joblib import Parallel, delayed +import copy + +class batch_sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.x = self.cls_func.x + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + + + def build_design(self, t0, f0, af, args={}): + # data params + # d = data_cls.d + # x = data_cls.x + # y = data_cls.real_data + # Sigma = data_cls.obsvar + + + b = args["batch_size"] + max_iter = args["max_iter"] + alloc_settings = args["alloc_settings"] + des_settings = args["des_settings"] + pc_settings = args["pc_settings"] + test_data = args["data_test"] + prior_func = args["prior"] + # explore params + rho = alloc_settings.get('rho') + if rho is not None: + b_new = int(b*rho) + r_new = int(b/b_new) + + seed = 1 + + persis_info = {"rand_stream": np.random.default_rng(1)} + + persis_info = {i: {} for i in range(b)} # add_unique_random_streams({}, b) + for perid, per in enumerate(persis_info): + persis_info[perid]['rand_stream'] = np.random.default_rng(seed*b + perid) + + rand_stream = np.random.default_rng(seed*(b+1)) #np.random.Generator(np.random.PCG64()) + + f_c, t_c = f0, t0 + + print("here") + print(f_c.shape) + print(t_c.shape) + + iter_explore, iter_exploit = 0, 0 + is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') + + for iteration in range(max_iter): + + emu, TV = self.newiteration(t_c, f_c, self.x, pc_settings, test_data, self.Sigma, self.y) + + theta, iter_exploit, iter_explore = self.onebatch(b, + b_new, + r_new, + emu, + prior_func, + af, + self.cls_func, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + self.trace) + + + fevals = Parallel(n_jobs=b)( + delayed(self.cls_func.sim_f)(theta[i], persis_info[i]) for i in range(b) + ) + print(fevals) + fevals = np.array(fevals).T + print("here 2") + + f_c = np.concatenate((f_c, fevals), axis=1) + t_c = np.concatenate((t_c, theta), axis=0) + + print(f_c[:, -4:]) + print(t_c.shape) + + + + self.f = f_c + self.theta = t_c + + + def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): + + thetatest, ptest, ftest, priortest = None, None, None, None + if test_data is not None: + thetatest, ptest, ftest, priortest = ( + test_data["theta"], + test_data["p"], + test_data["f"], + test_data["p_prior"], + ) + + emu = build_emulator(x, theta, fevals, pc_settings) + + d = len(x) + obsvar3d = obsvar.reshape(1, d, d) + + # ntest x d + pred = emu.predict(x=x, theta=thetatest) + mu = pred.mean().T + S = pred._info['S'] + St = np.transpose(S, (2, 0, 1)) + N = St + obsvar3d + phat = multiple_pdfs(obs, mu, N) + + # Obtain the accuracy on the test set + if ptest is not None: + TV = np.mean(np.abs(ptest - phat)) + + return emu, TV + + def onebatch(self, + b, + b_new, + r_new, + emu, + prior_func, + acqfunc, + data_cls, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + trace): + + ivar_exploit, ivar_explore = np.inf, np.inf + + emu_original_info = copy.deepcopy(emu._info) + + if is_exploit: + # Allocate existing ones + allocate_obj = allocate(budget=b, + emu_info=emu, + prior=prior_func, + func_cls=data_cls, + theta_mesh=test_data["theta"], + method=alloc_settings.get('method'), + alloc_settings=alloc_settings, + rand_stream=rand_stream, + trace=trace) + allocate_obj.allocatereps() + if not is_explore: + ivar_exploit = 0 + else: + ivar_exploit = allocate_obj.ivar_exploit(emu, pc_settings) + + r_exploit = allocate_obj.reps + theta_exploit = allocate_obj.theta + + emu._info = emu_original_info + + if is_explore: + # Find new ones + acquire_obj = acquire(bnew=b_new, + rep=r_new, + emu=emu, + func_cls=data_cls, + theta_mesh=test_data["theta"], + prior=prior_func, + method=acqfunc, + nL=des_settings.get('nL'), + pc_settings=pc_settings, + rand_stream=rand_stream, + impute_str=des_settings.get('impute_str'), + skip=not is_exploit) + + acquire_obj.acquire_new() + + ivar_explore = acquire_obj.ivar + theta_explore = acquire_obj.tnew + + if ivar_exploit <= ivar_explore: + new_theta = np.repeat(theta_exploit, r_exploit, axis=0) + iter_exploit += 1 + else: + new_theta = np.repeat(theta_explore, r_new, axis=0) + iter_explore += 1 + + return new_theta, iter_exploit, iter_explore + \ No newline at end of file diff --git a/PUQ/designmethods/p_sto_bseq.py b/PUQ/designmethods/p_sto_bseq.py index fa29c10..4daebf2 100644 --- a/PUQ/designmethods/p_sto_bseq.py +++ b/PUQ/designmethods/p_sto_bseq.py @@ -112,8 +112,6 @@ def fit(fitinfo, fitinfo['theta'] = H['thetas'] f_c = np.concatenate((des_init.get('f'), fitinfo['f'].T), axis=1) - print(des_init.get('theta').shape) - print(fitinfo['theta'].shape) t_c = np.concatenate((des_init.get('theta'), fitinfo['theta']), axis=0) emu, TV = newiteration(t_c, f_c, data_cls.x, pc_settings, data_test, data_cls.obsvar, data_cls.real_data) @@ -136,7 +134,6 @@ def fit(fitinfo, def newiteration(theta, fevals, x, pc_settings, test_data, obsvar, obs): - print("hay") thetatest, ptest, ftest, priortest = None, None, None, None if test_data is not None: thetatest, ptest, ftest, priortest = ( @@ -148,7 +145,6 @@ def newiteration(theta, fevals, x, pc_settings, test_data, obsvar, obs): emu = build_emulator(x, theta, fevals, pc_settings) - print("kkk") d = len(x) obsvar3d = obsvar.reshape(1, d, d) @@ -160,8 +156,6 @@ def newiteration(theta, fevals, x, pc_settings, test_data, obsvar, obs): N = St + obsvar3d phat = multiple_pdfs(obs, mu, N) - print("hay") - # Obtain the accuracy on the test set if ptest is not None: TV = np.mean(np.abs(ptest - phat)) @@ -328,16 +322,11 @@ def gen_f(H, persis_info, gen_specs, libE_info): update_model = False if first_iter: - - - print("hey") - + start_emu = time.time() emu, TV = newiteration(theta_init, f_init, x, pc_settings, test_data, Sigma, y) end_emu = time.time() TVs.append(TV) - - print("hey") n_init = n_workers - 1 if theta_add is None: diff --git a/PUQ/surrogatemethods/highdimHetGP.py b/PUQ/surrogatemethods/highdimHetGP.py new file mode 100644 index 0000000..2b783ce --- /dev/null +++ b/PUQ/surrogatemethods/highdimHetGP.py @@ -0,0 +1,214 @@ +from PUQ.surrogate import emulator +from PUQ.surrogatemethods.hetGP import predict as predict_hetGP +import numpy as np +from PUQ.surrogatemethods.covariances import cov_gen +from scipy.linalg import cholesky, inv +from hetgpy import hetGP + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, + init={}, + known={}, + settings={ + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + covtype="Gaussian", + pc_settings={"standardize": True, "latent": False}, + **kwargs +): + + numGPs = f.shape[0] + h = np.zeros((numGPs, 1)) + s = np.ones((numGPs)) + fs = np.zeros(f.shape) + + if pc_settings["standardize"]: + # print("Standardizing") + fitinfo["standardize"] = True + for k in range(0, numGPs): + h[k, 0] = np.mean(f[k, :]) + s[k] = np.std(f[k, :]) + + G = np.diag(s) + Gi = np.diag(1 / s) + fs = Gi @ (f - h) + B = np.eye(numGPs) + + emulist = [dict() for x in range(0, numGPs)] + for i in range(0, numGPs): + print("On site surrogate") + fi = fs[i, :][None, :] + # hetGP + model = hetGP() + print(theta.shape) + print(fi.shape) + model.mle(X=theta, + Z=fi.flatten(), + covtype="Gaussian", + known={"beta0":np.mean(fi)}) + + # emu = emulator( + # x=np.array([[i]]), + # theta=theta, + # f=fi, + # method="hetGP", + # args={ + # "noiseControl": noiseControl, + # "lower": lower, + # "upper": upper, + # "settings": settings, + # "init": init, + # "known": known, + # "covtype": covtype, + # }, + # ) + + emulist[i] = model.copy()#emu._info + + fitinfo["theta"] = theta + fitinfo["f"] = f + fitinfo["emulist"] = emulist + fitinfo["numGPs"] = numGPs + fitinfo["fs"] = fs + fitinfo["h"] = h + fitinfo["G"] = G + fitinfo["Gi"] = Gi + fitinfo["B"] = B + + return + + +def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): + + numGPs = fitinfo["numGPs"] + d = fitinfo["fs"].shape[0] + n = theta.shape[0] + + # calculate predictive mean and variance + mean_pc = np.full((numGPs, n), np.nan) + var_pc = np.full((numGPs, n), np.nan) + nugs_pc = np.full((numGPs, n), np.nan) + + if thetaprime is not None: + npr = thetaprime.shape[0] + covmat_pc = np.full((numGPs, n, npr), np.nan) + for i in range(0, numGPs): + # predinfo_hetGP = {} + info = fitinfo["emulist"][i] + # predict_hetGP( + # predinfo=predinfo_hetGP, + # fitinfo=info, + # x=np.array([[i]]), + # theta=theta, + # thetaprime=thetaprime, + # ) + pr = info.predict(x=theta, xprime=thetaprime) + + mean_pc[i, :] = pr["mean"] # predinfo_hetGP["mean"] + var_pc[i, :] = pr["sd2"] # predinfo_hetGP["var"] + nugs_pc[i, :] = pr["nugs"] # predinfo_hetGP["nugs"] + + if thetaprime is not None: + covmat_pc[i, :, :] = pr["cov"] # predinfo_hetGP["covmat"] + + # calculate predictive mean and variance + predinfo["mean_o"] = 1 * mean_pc + predinfo["var_o"] = 1 * var_pc + predinfo["nugs_o"] = 1 * nugs_pc + if thetaprime is not None: + predinfo["cov_o"] = 1 * covmat_pc + + predinfo["S"] = np.full((d, d, n), np.nan) + predinfo["R"] = np.full((d, d, n), np.nan) + predinfo["var"] = np.full((d, n), np.nan) + predinfo["nugs"] = np.full((d, n), np.nan) + predinfo["var_noisy"] = np.full((d, n), np.nan) + if thetaprime is not None: + predinfo["covmat"] = np.full((d, n, npr), np.nan) + predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) + + for i in range(0, theta.shape[0]): + C = np.diag(var_pc[:, i]) + R = np.diag(nugs_pc[:, i]) + predinfo["S"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["R"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) + predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) + + predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] + + return predinfo + + +def update(fitinfo, x, X0new=None, mult=None): + numGPs = fitinfo["numGPs"] + + Gi = fitinfo["Gi"] + h = fitinfo["h"] + pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) + mu_ct = pred_ct["mean"] + mu_ct = Gi @ (mu_ct - h) + Z0new = mu_ct.T + # print(mu_ct.shape) + + for i in range(0, numGPs): + info = fitinfo["emulist"][i] + + if info["is_homGP"] == True: + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T + else: + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + + kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) + M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) + + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) + # Updated + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) + Lambda = info["nmean"] + M + if info["logN"]: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = info["eps"] + + info["Lambda"] = Lambda + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T From 22125682ac68cfdc8efcd7d00aa08a9f197e8912 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Sat, 13 Sep 2025 23:19:08 -0400 Subject: [PATCH 006/103] Including Techno2024 paper with homGP --- .../gen_funcs/acquisition_deterministic.py | 308 ++++++++++++++++++ PUQ/designmethods/sequential_deterministic.py | 114 +++++++ PUQ/surrogatemethods/pcHomGP.py | 223 +++++++++++++ examples/Technometrics2024/figure1b_new.py | 83 +++++ examples/Technometrics2024/figure2_new.py | 150 +++++++++ examples/Technometrics2024/figure6_new.py | 135 ++++++++ examples/Technometrics2024/test_funcs_new.py | 109 +++++++ 7 files changed, 1122 insertions(+) create mode 100644 PUQ/designmethods/gen_funcs/acquisition_deterministic.py create mode 100644 PUQ/designmethods/sequential_deterministic.py create mode 100644 PUQ/surrogatemethods/pcHomGP.py create mode 100644 examples/Technometrics2024/figure1b_new.py create mode 100644 examples/Technometrics2024/figure2_new.py create mode 100644 examples/Technometrics2024/figure6_new.py create mode 100644 examples/Technometrics2024/test_funcs_new.py diff --git a/PUQ/designmethods/gen_funcs/acquisition_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_deterministic.py new file mode 100644 index 0000000..8bef438 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition_deterministic.py @@ -0,0 +1,308 @@ +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from hetgpy.IMSE import crit_IMSPE +import emcee + +def generate_neighborhood(acq): + N = int(acq.nL) + sampling = LHS(xlimits=acq.tlim, random_state=acq.seed) + L = sampling(N) + return L + +def get_pred(cL, emu, x, ttest, reps): + + cP = emu.predict(x=x, theta=cL) + var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps + + testP = emu.predict(x=x, theta=ttest, thetaprime=cL) + mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] + + mut = mu.T + St = np.transpose(S, (2, 0, 1)) + + return mut, St, cov, var_cand + + +class acquisition_function: + def __init__(self, model, cls_func, args): + self.model = model #deepcopy(model) + self.cls_func = cls_func + #self.persis_info = persis_info + self.args = args + self.x = self.cls_func.x + self.d = self.cls_func.d + self.p = self.cls_func.p + self.dx = self.cls_func.dx + self.dt = self.cls_func.dt + + self.tlim = cls_func.thetalimits + self.y = self.cls_func.real_data + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) + self.twopiddet = ( + (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + ) + + def acquire_new(self): + return eval("self.evaluate")( + self.args.get("new", True), return_pseudo=False + ) + + def gen_pred(self, model, x, t, return_id=False, return_flat=False): + + # predict at mesh + meshPr = model.predict(theta=t, thetaprime=t) + + # ntot, ntot x ntot, ntot + mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["S"], meshPr._info["var"] + muT = mu.T + S = Sn.transpose(2, 0, 1) + + return muT, S, t + + def crit_pvar(self, model, x, t): + + # posterior variance + mu, S, z = self.gen_pred(model, x, t) + + M = S + 0.5 * self.Sigma3d + N = S + self.Sigma3d + + f = multiple_pdfs(self.y, mu, M) + g = multiple_pdfs(self.y, mu, N) + + vals = (1 / self.twopiddet) * f - g**2 + + return vals + + def total_var(self, model, x, t): + vals = self.crit_pvar(model=model, x=x, t=t) + return np.mean(self.weights * vals) + + +class rnd(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + new_input = self.args["prior"].rnd(1, + self.args["rand_stream"]) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + +class var(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", True) + self.nL = args.get("nL", 100) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L) + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + +class imse(acquisition_function): + # ASK THIS FUNCTION TO DAVID + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + +class exp(acquisition_function): + # ASK THIS FUNCTION TO DAVID + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + +class ivar(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + + nm, nL = self.t_ref.shape[0], self.nL + vals = np.zeros(nL) + + mu, S, cov, cvar = get_pred( + cL=L, emu=self.model, x=self.x, ttest=self.t_ref, reps=1 + ) + + V1 = S + self.Sigma3d + + G = self.model._info["G"] + B = self.model._info["B"] + GB = G @ B + BTG = B.T @ G + + d = G.shape[0] + q = B.shape[1] + + tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) + + for j in range(0, q): + tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + for k in range(0, nL): + phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + for k in range(0, self.nL): + # C1: nm x d x d + # C2: nm x d x d + phic = phi[:, self.x, self.x.T, k] + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(self.y, mu, C1) + dets = multiple_determinants(C2) + part2 = rpdf / (coef * np.sqrt(dets)) + + vals[k] = np.sum(self.weights * part2) + + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + + return new_input + diff --git a/PUQ/designmethods/sequential_deterministic.py b/PUQ/designmethods/sequential_deterministic.py new file mode 100644 index 0000000..ff9b08b --- /dev/null +++ b/PUQ/designmethods/sequential_deterministic.py @@ -0,0 +1,114 @@ +import numpy as np +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from PUQ.designmethods.gen_funcs.acquisition_deterministic import ivar, var, imse, rnd, exp +import time +from PUQ.surrogate import emulator + +class sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + def build_design(self, z0, f0, T, af, test=None, args={}): + + self.test = test + args["rand_stream"] = np.random.default_rng(args["seed"]) + H = [] + timel = [] + metric_sum = {} + print(T) + for t in range(0, T): + # print(f"t: {t}") if self.trace else None + print(t) + tic = time.time() + + model = emulator(x=self.cls_func.x, + theta=z0, + f=f0, + method="pcHomGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + + toc = time.time() + timel.append(toc - tic) + + args["seed"] += 1 + + if test is not None: + metric_sum = self.eval_perf(model) + + acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) + + acq_func.acquire_new() + tnew = acq_func.znew + + fnew = self.cls_func.function(tnew.flatten()[0], tnew.flatten()[1]).reshape( + 1, self.cls_func.d + ) + + f0 = np.concatenate((f0, fnew), axis=0) + z0 = np.concatenate((z0, tnew), axis=0) + + H.append( + { + "t": t, + "f": fnew, + "z": tnew, + "MAD": metric_sum.get("MAD", None) + } + ) + + unique_rows, counts = np.unique(z0, axis=0, return_counts=True) + + self.t = unique_rows + self.reps = counts + self.fs = f0 + self.zs = z0 + self.time = timel + self.H = H + + return self + + + def eval_perf(self, model): + + nm, d = self.test["theta"].shape[0], self.cls_func.d + + # predict at mesh + pr = model.predict(theta=self.test["theta"], thetaprime=self.test["theta"]) + + # ntot, ntot x ntot, ntot + mu, Sn = pr._info["mean"], pr._info["S"] + + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) # Sn[self.idr, self.idc].reshape(nm, d, d) + + N = S + self.Sigma3d + g = multiple_pdfs(self.y, muT, N) + + MSE = np.mean(((g.flatten() - self.test["p"].flatten()) ** 2)) + MAD = np.mean(np.abs(g.flatten() - self.test["p"].flatten())) + + metric_sum = {"MAD": MAD, "MSE": MSE} + return metric_sum diff --git a/PUQ/surrogatemethods/pcHomGP.py b/PUQ/surrogatemethods/pcHomGP.py new file mode 100644 index 0000000..3044748 --- /dev/null +++ b/PUQ/surrogatemethods/pcHomGP.py @@ -0,0 +1,223 @@ +from PUQ.surrogate import emulator +from PUQ.surrogatemethods.hetGP import predict as predict_hetGP +import numpy as np +from PUQ.surrogatemethods.covariances import cov_gen +from scipy.linalg import cholesky, inv + + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, + init={}, + known={}, + settings={ + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + covtype="Gaussian", + pc_settings={"standardize": True, "latent": False}, + **kwargs +): + + numGPs = f.shape[0] + h = np.zeros((numGPs, 1)) + s = np.ones((numGPs)) + fs = np.zeros(f.shape) + + if pc_settings["standardize"]: + # print("Standardizing") + fitinfo["standardize"] = True + for k in range(0, numGPs): + h[k, 0] = np.mean(f[k, :]) + s[k] = np.std(f[k, :]) + + G = np.diag(s) + Gi = np.diag(1 / s) + fs = Gi @ (f - h) + B = np.eye(numGPs) + + if pc_settings["latent"]: + # print("Dimension reduction") + fitinfo["latent"] = True + + U, S, _ = np.linalg.svd(fs, full_matrices=False) + exp_var = np.cumsum(S**2) / np.cumsum(S**2)[-1] + numGPs = int(np.argwhere(np.array(exp_var) > 0.99)[0]) + 1 + + # d x q orthogonal matrix + B = U[:, 0:numGPs] + + # trasform q x n + W = B.T @ fs + + emulist = [dict() for x in range(0, numGPs)] + for i in range(0, numGPs): + if pc_settings["latent"]: + print("Latent surrogate") + fi = W[i, :][None, :] + else: + # print("On site surrogate") + fi = fs[i, :][None, :] + emu = emulator( + x=np.array([[i]]), + theta=theta, + f=fi, + method="homGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + }, + ) + + emulist[i] = emu._info + + fitinfo["theta"] = theta + fitinfo["f"] = f + fitinfo["emulist"] = emulist + fitinfo["numGPs"] = numGPs + fitinfo["fs"] = fs + fitinfo["h"] = h + fitinfo["G"] = G + fitinfo["Gi"] = Gi + fitinfo["B"] = B + + return + + +def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): + + numGPs = fitinfo["numGPs"] + d = fitinfo["fs"].shape[0] + n = theta.shape[0] + + # calculate predictive mean and variance + mean_pc = np.full((numGPs, n), np.nan) + var_pc = np.full((numGPs, n), np.nan) + nugs_pc = np.full((numGPs, n), np.nan) + + if thetaprime is not None: + npr = thetaprime.shape[0] + covmat_pc = np.full((numGPs, n, npr), np.nan) + for i in range(0, numGPs): + predinfo_hetGP = {} + info = fitinfo["emulist"][i] + predict_hetGP( + predinfo=predinfo_hetGP, + fitinfo=info, + x=np.array([[i]]), + theta=theta, + thetaprime=thetaprime, + ) + + mean_pc[i, :] = predinfo_hetGP["mean"] + var_pc[i, :] = predinfo_hetGP["var"] + nugs_pc[i, :] = predinfo_hetGP["nugs"] + + if thetaprime is not None: + covmat_pc[i, :, :] = predinfo_hetGP["covmat"] + + # calculate predictive mean and variance + predinfo["mean_o"] = 1 * mean_pc + predinfo["var_o"] = 1 * var_pc + predinfo["nugs_o"] = 1 * nugs_pc + if thetaprime is not None: + predinfo["cov_o"] = 1 * covmat_pc + + + predinfo["S"] = np.full((d, d, n), np.nan) + predinfo["R"] = np.full((d, d, n), np.nan) + predinfo["var"] = np.full((d, n), np.nan) + predinfo["nugs"] = np.full((d, n), np.nan) + predinfo["var_noisy"] = np.full((d, n), np.nan) + if thetaprime is not None: + predinfo["covmat"] = np.full((d, n, npr), np.nan) + predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) + + for i in range(0, theta.shape[0]): + C = np.diag(var_pc[:, i]) + R = np.diag(nugs_pc[:, i]) + predinfo["S"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["R"][:, :, i] = ( + fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] + ) + predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) + predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) + + predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] + + return predinfo + + +def update(fitinfo, x, X0new=None, mult=None): + numGPs = fitinfo["numGPs"] + + Gi = fitinfo["Gi"] + h = fitinfo["h"] + pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) + mu_ct = pred_ct["mean"] + mu_ct = Gi @ (mu_ct - h) + Z0new = mu_ct.T + # print(mu_ct.shape) + + for i in range(0, numGPs): + info = fitinfo["emulist"][i] + + if info["is_homGP"] == True: + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T + else: + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + + kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) + M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) + + info["X0"] = np.concatenate((info["X0"], X0new), axis=0) + info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) + info["mult"] = np.concatenate((info["mult"], np.array([mult]))) + info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) + # Updated + Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) + Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) + Kgi = np.linalg.inv((Kg_c)) + Kgi = Kgi @ Kgi.T + M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) + Lambda = info["nmean"] + M + if info["logN"]: + Lambda = np.exp(Lambda) + else: + Lambda[Lambda <= 0] = info["eps"] + + info["Lambda"] = Lambda + + C = cov_gen(X1=info["X0"], theta=info["theta"]) + Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) + Ki = np.linalg.inv((Ki)) + info["Ki"] = Ki @ Ki.T diff --git a/examples/Technometrics2024/figure1b_new.py b/examples/Technometrics2024/figure1b_new.py new file mode 100644 index 0000000..9b6a2ea --- /dev/null +++ b/examples/Technometrics2024/figure1b_new.py @@ -0,0 +1,83 @@ +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as sps +from PUQ.designmethods.utils import parse_arguments +from PUQ.prior import prior_dist +from PUQ.designmethods.sequential_deterministic import sequential_design +from smt.sampling_methods import LHS +from test_funcs_new import unimodal + +if __name__ == "__main__": + args = parse_arguments() + cls_unimodal = unimodal() + + # # # Create a mesh for test set # # # + xpl = np.linspace( + cls_unimodal.thetalimits[0][0], cls_unimodal.thetalimits[0][1], 50 + ) + ypl = np.linspace( + cls_unimodal.thetalimits[1][0], cls_unimodal.thetalimits[1][1], 50 + ) + Xpl, Ypl = np.meshgrid(xpl, ypl) + thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + ftest = np.zeros((thetatest.shape[0], 1)) + for i in range(thetatest.shape[0]): + ftest[i, 0] = cls_unimodal.function(thetatest[i,0], thetatest[i,1]) + + ptest = sps.norm.pdf( + cls_unimodal.real_data - ftest, 0, np.sqrt(cls_unimodal.obsvar) + ) + + test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} + # # # # # # # # # # # # # # # # # # # # # + prior_func = prior_dist(dist="uniform")( + a=cls_unimodal.thetalimits[:, 0], b=cls_unimodal.thetalimits[:, 1] + ) + + # Initial sample + n0 = 10 + s = 1 + sampling = LHS(xlimits=cls_unimodal.thetalimits, random_state=int(s)) + t0 = sampling(n0) + f0 = np.zeros((t0.shape[0], 1)) + for i in range(t0.shape[0]): + f0[i, 0] = cls_unimodal.function(t0[i,0], t0[i,1]) + + for af in ["rnd", "var", "ivar"]: + des_obj = sequential_design(cls_unimodal) + des_obj.build_design(z0=t0, + f0=f0, + T=50, + test=test_data, + af=af, + args={ + "mini_batch": 1, + "n_init_thetas": 10, + "nworkers": 2, + "seed_n0": 1, + "prior": prior_func, + "data_test": test_data, + "max_evals": 60, + "type_init": None, + "seed": 0, + "integral": "LHS" + }) + + + theta_al = des_obj.zs + + fig, ax = plt.subplots() + cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") + ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) + ax.scatter( + theta_al[0:n0, 0], + theta_al[0:n0, 1], + zorder=2, + marker="o", + facecolors="none", + edgecolors="blue", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.savefig("Figure1b.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure2_new.py b/examples/Technometrics2024/figure2_new.py new file mode 100644 index 0000000..24ab87d --- /dev/null +++ b/examples/Technometrics2024/figure2_new.py @@ -0,0 +1,150 @@ +import scipy.stats as sps +import numpy as np +import matplotlib.pyplot as plt +from PUQ.surrogate import emulator +from PUQ.posterior import posterior + + +class sinlinear: + def __init__(self): + self.data_name = "sinlinear" + self.thetalimits = np.array([[-10, 10]]) + self.obsvar = np.array([[1]], dtype="float64") + self.real_data = np.array([[0]], dtype="float64") + self.out = [("f", float)] + self.p = 1 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + + def function(self, theta): + f = np.sin(theta) + 0.1 * theta + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + """ + Wraps the sin() function + """ + function = sim_specs["user"]["function"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + theta = H["thetas"][0] + H_o["f"] = function(theta) + + return H_o, persis_info + + +if __name__ == "__main__": + cls_sinlin = sinlinear() + + # Generate training data + theta = np.array([-10, -8.7, -7.5, -5, -2.5, -1, 1, 2.5, 5, 7.5, 8.7, 10])[:, None] + f = cls_sinlin.function(theta) + ptr = sps.norm.pdf(cls_sinlin.real_data, f, np.sqrt(cls_sinlin.obsvar)) + + # Fit an emulator + # emu = emulator(cls_sinlin.x, theta, f, method="PCGP") + + emu = emulator(x=cls_sinlin.x, + theta=theta, + f=f, + method="pcHomGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + post = posterior(data_cls=cls_sinlin, emulator=emu) + + # Generate test data + thetatest = np.arange(-10, 10, 0.0025)[:, None] + ftest = cls_sinlin.function(thetatest) + ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) + + # Predict via the emulator + emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) + emupred_tr = emu.predict(x=cls_sinlin.x, theta=theta) + + posttesthat, posttestvar = post.predict(thetatest) + posttrhat, posttrvar = post.predict(theta) + + # Figure 2 (a) + ft = 20 + fig, ax = plt.subplots() + ax.plot(thetatest, ftest, color="black") + ax.plot( + thetatest.flatten(), + emupred_test.mean().flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + thetatest.flatten(), + (emupred_test.mean() - np.sqrt(emupred_test.var())).flatten(), + (emupred_test.mean() + np.sqrt(emupred_test.var())).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, emupred_tr.mean(), color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$\eta(\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2a.png", bbox_inches="tight") + + # Figure 2 (b) + fig, ax = plt.subplots() + ax.plot(thetatest, ptest, color="black") + ax.plot(thetatest, posttesthat, color="blue", linestyle="dashed", linewidth=2.5) + plt.fill_between( + thetatest.flatten(), + (posttesthat - np.sqrt(posttestvar)).flatten(), + (posttesthat + np.sqrt(posttestvar)).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, posttrhat, color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2b.png", bbox_inches="tight") + + # Figure 2 (c) + # Fit an emulator for posterior + # emu = emulator(cls_sinlin.x, theta, ptr, method="PCGP") + emu = emulator(x=cls_sinlin.x, + theta=theta, + f=ptr, + method="pcHomGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) + emumean_test = emupred_test.mean() + emumean_var = emupred_test.var() + + fig, ax = plt.subplots() + ax.plot(thetatest, ptest, color="black") + ax.plot( + thetatest.flatten(), + emumean_test.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + thetatest.flatten(), + (emumean_test - np.sqrt(emumean_var)).flatten(), + (emumean_test + np.sqrt(emumean_var)).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, ptr, color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2c.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure6_new.py b/examples/Technometrics2024/figure6_new.py new file mode 100644 index 0000000..a188969 --- /dev/null +++ b/examples/Technometrics2024/figure6_new.py @@ -0,0 +1,135 @@ +import pandas as pd +import numpy as np +import scipy.stats as sps +from PUQ.designmethods.sequential_deterministic import sequential_design +from PUQ.designmethods.utils import parse_arguments +from PUQ.prior import prior_dist +from plots import plotline +from test_funcs_new import unimodal, banana, bimodal, unidentifiable +import time +from smt.sampling_methods import LHS +import matplotlib.pyplot as plt + +if __name__ == "__main__": + design_start = time.time() + args = parse_arguments() + print("Running function: " + args.funcname) + args.funcname = "bimodal" + # Choose the test function + if args.funcname == "unimodal": + cls_func = unimodal() + elif args.funcname == "banana": + cls_func = banana() + elif args.funcname == "bimodal": + cls_func = bimodal() + elif args.funcname == "unidentifiable": + cls_func = unidentifiable() + + # Create a mesh for test set + xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], 50) + ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], 50) + Xpl, Ypl = np.meshgrid(xpl, ypl) + thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + ftest = np.zeros((thetatest.shape[0], cls_func.d)) + for i in range(thetatest.shape[0]): + ftest[i, :] = cls_func.function(thetatest[i,0], thetatest[i,1]) + + ptest = np.zeros(thetatest.shape[0]) + if cls_func.data_name == "unimodal": + ptest = sps.norm.pdf(cls_func.real_data - ftest, 0, np.sqrt(cls_func.obsvar)) + else: + for i in range(ftest.shape[0]): + mean = ftest[i, :] + rnd = sps.multivariate_normal(mean=mean, cov=cls_func.obsvar) + ptest[i] = rnd.pdf(cls_func.real_data) + test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Define acquisition functions + # acq_funcs = ["eivar", "rnd", "maxvar", "maxexp"] + acq_funcs = ["ivar", "rnd", "var"] + datalist = [] + rep_no = 2 + n0 = 10 + # Run over 50 replications + for seed_id in range(1, rep_no + 1): + # Initial sample + n0 = 10 + sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(seed_id)) + t0 = sampling(n0) + f0 = np.zeros((t0.shape[0], cls_func.d)) + for i in range(t0.shape[0]): + f0[i, :] = cls_func.function(t0[i,0], t0[i,1]) + + for func in acq_funcs: + print("Running " + func + " with seed " + str(seed_id)) + des_obj = sequential_design(cls_func) + des_obj.build_design(z0=t0, + f0=f0, + T=75, + test=test_data, + af=func, + args={ + "mini_batch": 1, + "n_init_thetas": 10, + "nworkers": 2, + "prior": prior_func, + "data_test": test_data, + "seed": seed_id, + "integral": "LHS" + }) + + + theta_al = des_obj.zs + + fig, ax = plt.subplots() + cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") + ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) + ax.scatter( + theta_al[0:n0, 0], + theta_al[0:n0, 1], + zorder=2, + marker="o", + facecolors="none", + edgecolors="blue", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + # TV = al_unimodal._info["TV"] + + for ms_id, ms in enumerate(des_obj.H): + d = { + "TV": ms["MAD"], + "TV_id": ms_id, + "rep": seed_id, + "batch": 1, + "methods": func, + "worker": 2, + "synth": args.funcname, + "theta_id": ms_id, + } + + datalist.append(d) + + datalist.append(d) + df = pd.DataFrame(datalist) + + + # Create the plot and save it + plotline( + df, + acq_funcs, + rep_no, + w=2, + b=1, + s=args.funcname, + ylim=[0.000005, 0.00001], + idstart=0, + ) diff --git a/examples/Technometrics2024/test_funcs_new.py b/examples/Technometrics2024/test_funcs_new.py new file mode 100644 index 0000000..8b2c038 --- /dev/null +++ b/examples/Technometrics2024/test_funcs_new.py @@ -0,0 +1,109 @@ +import numpy as np +import scipy.stats as sps + + +class unimodal: + def __init__(self): + self.data_name = "unimodal" + self.thetalimits = np.array([[-4, 4], [-4, 4]]) + self.obsvar = np.array([[4]], dtype="float64") + self.real_data = np.array([[-6]], dtype="float64") + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + #### + self.dx = 1 + self.dt = 1 + + def function(self, theta1, theta2): + """ + Wraps the unimodal function + """ + thetas = np.array([theta1, theta2]).reshape((1, 2)) + S = np.array([[1, 0.5], [0.5, 1]]) + f = (thetas @ S) @ thetas.T + return f + + + +class banana: + def __init__(self): + self.data_name = "banana" + self.thetalimits = np.array([[-20, 20], [-10, 5]]) + self.obsvar = np.array([[10**2, 0], [0, 1]]) + self.real_data = np.array([[1, 3]], dtype="float64") + self.out = [("f", float, (2,))] + self.p = 2 + self.d = 2 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + #### + self.dx = 1 + self.dt = 1 + + def function(self, theta1, theta2): + f = np.array([theta1, theta2 + 0.03 * theta1**2]) + return f + + + +class unidentifiable: + def __init__(self): + self.data_name = "unidentifiable" + self.thetalimits = np.array([[-8, 8], [-8, 8]]) + self.obsvar = np.array([[1 / 0.01, 0], [0, 1]]) + self.real_data = np.array([[0, 0]], dtype="float64") + self.out = [("f", float, (2,))] + self.d = 2 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + #### + self.dx = 1 + self.dt = 1 + + def function(self, theta1, theta2): + f = np.array([theta1, theta2]) + return f + + + +class bimodal: + def __init__(self): + self.data_name = "bimodal" + self.thetalimits = np.array([[-6, 6], [-4, 8]]) + self.obsvar = np.array([[1 / np.sqrt(0.2), 0], [0, 1 / np.sqrt(0.75)]]) + self.real_data = np.array([[0, 2]], dtype="float64") + self.out = [("f", float, (2,))] + self.d = 2 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + #### + self.dx = 1 + self.dt = 1 + + def function(self, theta1, theta2): + f = np.array([theta2 - theta1**2, theta2 - theta1]) + return f + + + +def create_test_data(al_test, cls_func): + ftest = al_test._info["f"] + thetatest = al_test._info["theta"] + ptest = np.zeros(thetatest.shape[0]) + + if cls_func.data_name == "unimodal": + ptest = sps.norm.pdf(cls_func.real_data - ftest, 0, np.sqrt(cls_func.obsvar)) + else: + for i in range(ftest.shape[0]): + mean = ftest[i, :] + rnd = sps.multivariate_normal(mean=mean, cov=cls_func.obsvar) + ptest[i] = rnd.pdf(cls_func.real_data) + + test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} + + return test_data From d14b95469c6a87ea921651271c2dbf086d910d9d Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 15 Sep 2025 09:48:57 -0500 Subject: [PATCH 007/103] add homGP wrapper and test --- PUQ/surrogatemethods/homGP.py | 392 +++++++-------------------------- tests/unit_tests/test_homGP.py | 82 +++++++ 2 files changed, 162 insertions(+), 312 deletions(-) create mode 100644 tests/unit_tests/test_homGP.py diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 875ae39..8cd4173 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -1,9 +1,7 @@ -import sys import numpy as np -from scipy.linalg import cholesky, inv -from PUQ.surrogatemethods.covariances import cov_gen, partial_cov_gen -import scipy.optimize as spo -from PUQ.surrogatemethods.helpers import auto_bounds, find_reps +from hetgpy import homGP +from hetgpy.auto_bounds import auto_bounds +from hetgpy.find_reps import find_reps ################################################################################ ## Homoskedastic noise @@ -28,9 +26,48 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, eps=np.sqrt(np.finfo(float).eps), settings={"return.Ki": True, "factr": 1e7}, **kwargs): - + r''' + Wrapper function for hetgpy.homGP.mleHomGP + + Arguments + --------- + fitinfo: dictionary/class of results + x: nxd design matrix (must have at least one column) + f: output array for training + theta: not used + lower,upper : ndarray_like + optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). + In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) + noiseControl : dict + dict with element: + - ``g_bounds`` vector providing minimal and maximal noise to signal ratio (default to ``(sqrt(MACHINE_DOUBLE_EPS), 100)``). + settings : dict + dict for options about the general modeling procedure, with elements: + - ``return_Ki`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). + - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + eps : float + jitter used in the inversion of the covariance matrix for numerical stability + known : dict + optional dict of known parameters (e.g. ``beta0``, ``theta``, ``g``) + init : dict + optional lists of starting values for mle optimization: + - ``theta_init`` initial value of the theta parameters to be optimized over (default to 10% of the range determined with ``lower`` and ``upper``) + - ``g_init`` vector of nugget parameter to be optimized over + covtype : str + covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` + maxit : int + maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization + + + + Returns + ------- + fitinfo: dictionary containing fit results + + ''' f = f.flatten() - model = mleHomGP(X = theta, + model = homGP() + model.mleHomGP(X = x, Z = f, lower=lower, upper=upper, @@ -48,322 +85,53 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, fitinfo['Ki'] = model['Ki'] fitinfo['X0'] = model['X0'] fitinfo['Z0'] = model['Z0'] + fitinfo['Z'] = model['Z'] fitinfo['mult'] = model['mult'] fitinfo['beta0'] = model['beta0'] - fitinfo['ll'] = model['ll'] + fitinfo['ll'] = model['ll'] + fitinfo['covtype'] = model['covtype'] + fitinfo['nu_hat'] = model['nu_hat'] fitinfo['trendtype'] = model['trendtype'] fitinfo['eps'] = model['eps'] fitinfo['is_homGP'] = True return -def mleHomGP(X, Z, lower=None, upper=None, known={}, noiseControl={}, - init={}, covtype="Gaussian", - maxit=100, eps=np.sqrt(np.finfo(float).eps), - settings={}): - - - if isinstance(X, dict): - X0 = np.array(X.get('X0')) - Z0 = np.array(X.get('Z0')) - mult = np.array(X.get('mult')) - - if np.sum(mult) != len(Z): - raise ValueError("Length(Z) should be equal to sum(mult)") - - if X0.ndim == 1: - X0 = X0[:, np.newaxis] - - if len(Z0) != X0.shape[0]: - raise ValueError("Dimension mismatch between Z0 and X0") - - else: - elem = find_reps(X, Z, return_Zlist=False) - X0 = elem['X0'] - Z0 = elem['Z0'] - Z = elem['Z'] - mult = elem['mult'] - - N = len(Z) - - if (lower is None or upper is None): - auto_thetas = auto_bounds(X=X0, covtype=covtype) - if lower is None: - lower = auto_thetas["lower"] - if upper is None: - upper = auto_thetas["upper"] - - if known.get("theta") is None and init.get("theta") is None: - init['theta'] = np.sqrt(upper * lower) - - if len(lower) != len(upper): - print("upper and lower should have the same size") - sys.exit(1) - - if settings.get('return.Ki') is None: - settings['return.Ki'] = True - - if noiseControl.get("g_bounds") is None: - noiseControl["g_bounds"] = [np.sqrt(np.finfo(float).eps), 1e2] - - g_min = np.array([noiseControl["g_bounds"][0]]) - g_max = np.array([noiseControl["g_bounds"][1]]) - - beta0 = known.get("beta0") - - if (known.get("theta") is None and init.get("theta") is None): - init["theta"] = 0.9*lower + 0.1*upper - - if(known.get('g') is None and init.get('g') is None): - if(any(mult > 2)): - v1 = (Z - np.repeat(Z0, mult)) ** 2 - v2 = np.zeros(len(mult)) - counter = 0 - for mid, m in enumerate(mult): - v2[mid] = sum([v1[counter+i] for i in range(m)]) - counter += m - init['g'] = np.array([np.mean((v2/mult)[mult > 2])/np.var(Z0, ddof=1)]) - else: - init['g'] = np.array([0.1]) - - trendtype = 'OK' - if known.get("beta0") is not None: - trendtype = 'SK' - def fn(par, X0, Z0, Z, mult, beta0, theta, g, env): +class homGPWrapper(homGP): + ''' + A class that converts the information in fitinfo (from the fit and predict methods) to a class so + it can be used to make predictions with hetgpy.homGP - idx = 0 # to store the first non-used element of par + ''' + def __init__(self,fitinfo): - # If theta is not provided, extract it from par - if theta is None: - theta = par[:len(init['theta'])] - idx += len(init['theta']) - - # If g is not provided, extract it from par - if g is None: - g = par[idx] - - # Compute the log-likelihood using logLikHom function - loglik = logLikHom(X0, Z0, Z, mult, theta, g, beta0, covtype, eps, env) + keys_to_transfer = [ + 'X0','Z0','Z', # data + 'covtype', # kernel + 'theta', 'g', 'beta0','trendtype', # hyperparameters + 'Ki', # inverse covariance matrix + 'eps', # for numeric stability + 'nu_hat' # output from maximum likelihood + ] - # Update env with the maximum log-likelihood and corresponding parameters - if env is not None and not np.isnan(loglik): - if env.get('max_loglik') is None or loglik > env['max_loglik']: - env['max_loglik'] = loglik - env['arg_max'] = par - - return -loglik - - def gr(par, X0, Z0, Z, mult, beta0, theta, g, env): - idx = 0 - components = [] - - if theta is None: - theta = par[:len(init['theta'])] - idx += len(init['theta']) - components.append("theta") - - if g is None: - g = par[idx] - components.append("g") - - gr_val = dlogLikHom(X0=X0, Z0=Z0, Z=Z, mult=mult, theta=theta, g=g, beta0=beta0, covtype=covtype, eps=eps, components=components, env=env) - - return -gr_val - - envtemp = {} - if known.get("g") is not None and known.get("theta") is not None: - theta_out = known['theta'] - g_out = known['g'] - out_ll = logLikHom(X0, Z0, Z, mult, theta_out, g_out, beta0, covtype, eps, None) - else: - parinit, lowerOpt, upperOpt = None, None, None - if known.get("theta") is None: - parinit = init['theta'] - lowerOpt = lower - upperOpt = upper - if known.get("g") is None: - - parinit = np.concatenate((parinit, init['g'])) - lowerOpt = np.concatenate((lowerOpt, g_min)) - upperOpt = np.concatenate((upperOpt, g_max)) - try: - opval = spo.minimize(fn, - parinit, - method='L-BFGS-B', - options={'gtol': 0.001}, - jac=gr, - bounds=spo.Bounds(lowerOpt, upperOpt), - args=(X0, Z0, Z, mult, known.get("theta"), known.get("g"), beta0, envtemp)) - - out = opval.x - out_ll = -1*opval.fun - except: - print("use best value so far") - out = envtemp["arg_max"] - out_ll = envtemp["max_loglik"] - - g_out = out[-1] if known.get("g") is None else known["g"] - theta_out = out[0:-1] if known.get("theta") is None else known["theta"] - - # Temporarily store Cholesky transform of K in Ki - C_out = cov_gen(X1=X0, theta=theta_out) - Ki = cholesky(C_out + np.diag(eps + g_out / mult)) - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if beta0 is None: - beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) - - psi_0 = np.dot(np.dot(Z0 - beta0, Ki), Z0 - beta0) - nu = 1/N * ((np.dot(Z - beta0, Z - beta0) - np.dot((Z0 - beta0) * mult, Z0 - beta0)) / g_out + psi_0) - - fitinfo = {} - fitinfo['theta'] = theta_out - fitinfo['g'] = g_out - fitinfo['nu_hat'] = nu - fitinfo['Ki'] = Ki - fitinfo['X0'] = X0 - fitinfo['Z0'] = Z0 - fitinfo['beta0'] = beta0 - fitinfo['ll'] = out_ll - fitinfo['mult'] = mult - fitinfo['trendtype'] = trendtype - fitinfo['eps'] = eps - - # print(logLikHom(X0, Z0, Z, mult, np.array([0.01951545099]), np.array([0.07451224610]), None, covtype, eps, None)) - # print(dlogLikHom(X0, Z0, Z, mult, np.array([0.01951545099]), np.array([0.07451224610]), beta0=None, covtype="Gaussian", - # eps=np.sqrt(np.finfo(float).eps), components=["theta", "g"], env=None)) - - return fitinfo - -# @return loglikelihood value - -def logLikHom(X0, Z0, Z, mult, theta, g, beta0=None, covtype="Gaussian", eps=np.sqrt(np.finfo(float).eps), env=None): - n = X0.shape[0] - N = len(Z) - - # Temporarily store Cholesky transform of K in Ki - C = cov_gen(X1=X0, theta=theta) - - if env is not None: - env['C'] = C - - Ki = cholesky(C + np.diag(eps + g / mult)) - - ldetKi = -2 * np.sum(np.log(np.diag(Ki))) # log determinant from Cholesky - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if env is not None: - env['Ki'] = Ki - - if beta0 is None: - #beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) - beta0 = np.sum(Ki, axis=0) @ Z0 / np.sum(Ki) - - psi_0 = np.dot(np.dot(Z0 - beta0, Ki), Z0 - beta0) - - psi = 1/N * ((np.dot(Z - beta0, Z - beta0) - np.dot((Z0 - beta0) * mult, Z0 - beta0)) / g + psi_0) - - loglik = -N/2 * np.log(2*np.pi) - N/2 * np.log(psi) + 1/2 * ldetKi - (N - n)/2 * np.log(g) - 1/2 * np.sum(np.log(mult)) - N/2 - - return loglik - -def dlogLikHom(X0, Z0, Z, mult, theta, g, beta0=None, covtype="Gaussian", - eps=np.sqrt(np.finfo(float).eps), components=["theta", "g"], env=None): - - k = len(Z) - n = X0.shape[0] - - if env is not None: - C = env['C'] - Ki = env['Ki'] - else: - C = cov_gen(X1=X0, theta=theta) - Ki = cholesky(C + np.diag(eps + g / mult)) - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if beta0 is None: - beta0 = np.sum(Ki, axis=0) @ Z0 / np.sum(Ki) - #beta0 = np.sum(np.dot(Ki, Z0)) / np.sum(Ki) - - - Z0 = Z0 - beta0 - Z = Z - beta0 - - KiZ0 = np.dot(Ki, Z0) # to avoid recomputing - - psi = np.dot(Z0, KiZ0) - - tmp1, tmp2 = None, None - - # First component, derivative with respect to theta - if "theta" in components: - tmp1 = np.full(len(theta), np.nan) - - if len(theta) == 1: - dC_dthetak = partial_cov_gen(X1=X0, theta=theta, type=covtype, arg="theta_k") * C - tmp1[0] = k / 2 * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / ( - (np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - 1 / 2 * np.trace(Ki@dC_dthetak) - else: - - for i in range(len(theta)): - dC_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=theta[i], type=covtype, arg="theta_k") * C - tmp1[i] = k / 2 * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / ( - (np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - 1 / 2 * np.trace(Ki@dC_dthetak) #1 / 2 * trace_sym(Ki, dC_dthetak) - - # Second component derivative with respect to g - if "g" in components: - tmp2 = np.full(1, np.nan) - tmp2[0] = k / 2 * ((np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g ** 2 + np.sum(KiZ0 ** 2 / mult)) / ((np.dot(Z, Z) - np.dot(Z0 * mult, Z0)) / g + psi) - (k - n) / (2 * g) - 1 / 2 * np.sum(np.diag(Ki) / mult) - - return np.concatenate([tmp1, tmp2]) + # model hyperparameters + for key in keys_to_transfer: + self[key] = fitinfo[key] def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): - - if fitinfo['Ki'] is None: - C = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta']) - Ki = cholesky(C + np.diag(fitinfo['eps'] + fitinfo['g'] / fitinfo['mult'])) - Ki = np.linalg.inv((Ki)) - fitinfo['Ki'] = Ki @ Ki.T - - fitinfo['Ki_scaled'] = fitinfo['Ki']/fitinfo['nu_hat'] - - kx = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta']) - - mean = fitinfo['beta0'] + kx @ (fitinfo['Ki_scaled'] @ (fitinfo['Z0'] - fitinfo['beta0'])) - - if fitinfo['trendtype'] == 'SK': - var = fitinfo['nu_hat'] - np.diag(kx @ (fitinfo['Ki_scaled'] @ kx.T)) - else: - var = fitinfo['nu_hat'] - np.diag(kx @ (fitinfo['Ki_scaled'] @ kx.T)) + (1 - np.sum(fitinfo['Ki_scaled'], axis=1) @ kx.T)**2/np.sum(fitinfo['Ki_scaled']) - - if any(var < 0): - var = np.maximum(0, var) - predinfo['mean'] = mean[None, :] - predinfo['var'] = var[None, :] - predinfo['nugs'] = np.repeat(fitinfo['nu_hat']*fitinfo['g'], theta.shape[0])[None, :] - - if thetaprime is not None: - # fitinfo['X0'].shape[0] x thetaprime.shape[0] - kxprime = fitinfo['nu_hat'] * cov_gen(X1=fitinfo['X0'], X2=thetaprime, theta=fitinfo['theta']) - if fitinfo['trendtype'] == 'SK': - if theta.shape[0] < thetaprime.shape[0]: - cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki_scaled'] @ kxprime - else: - cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki_scaled'] @ kxprime) - else: - - if theta.shape[0] < thetaprime.shape[0]: - cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki_scaled'] @ kxprime + \ - ((1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki_scaled']) - #np.dot(1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T, 1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki_scaled']) - else: - cov = fitinfo['nu_hat'] * cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki_scaled'] @ kxprime) + \ - np.dot((1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki_scaled'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki_scaled']) - - predinfo['covmat'] = cov - return - + r''' + Wrapper method for hetgpy.homGP.predict + ''' + GP = fitinfo.get('model') + if GP is None: + # use wrapper class to instantiate trained GP + GP = homGPWrapper(fitinfo=fitinfo) + + + preds = GP.predict(x=x,xprime=thetaprime) + # ensure naming consistency + predinfo['mean'] = preds['mean'] + predinfo['var'] = preds['sd2'] + predinfo['nugs'] = preds['nugs'] + predinfo['covmat'] = preds['cov'] diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py new file mode 100644 index 0000000..60712bd --- /dev/null +++ b/tests/unit_tests/test_homGP.py @@ -0,0 +1,82 @@ +'''Tests whether the emulator with homGP gives the same result as just using homGP''' + +import sys +sys.path.append('./') +import numpy as np +from hetgpy import homGP +from PUQ.surrogate import emulator +# generate some test data +rng = np.random.default_rng(123) + +X = np.linspace(0,2*np.pi,20).reshape(-1,1) +# add some replicates +reps = rng.choice(len(X),size=100,replace=True) +X = X[reps,] +Ytrue = np.sin(X) +noise = 0.5 * rng.normal(size = (len(Ytrue),1)) + +Y = Ytrue + noise + +# prediction grid (interpolation) +Xgrid = np.linspace(X.min(),X.max(),100).reshape(-1,1) + +def test_fit(): + + reference_model = homGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model.fit() + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + + +def test_predict(): + # refits models and then tests predictions + + reference_model = homGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model.fit() + + # test predictions + preds = reference_model.predict(Xgrid,xprime=Xgrid) + + test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid) + assert np.allclose(test_preds._info['mean'],preds['mean']) + assert np.allclose(test_preds._info['var'],preds['sd2']) + assert np.allclose(test_preds._info['nugs'],preds['nugs']) + assert np.allclose(test_preds._info['covmat'],preds['cov']) + + +def test_matern(): + # test a different covariance type and define some input args + COVTYPE = "Matern5_2" + MAXIT = 50 + SETTINGS = {"return.Ki": False, "factr": 1e5} + + + reference_model = homGP() + reference_model.mle(X,Y.flatten(),covtype=COVTYPE,maxit=MAXIT,settings=SETTINGS) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='homGP', + args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + test_model.fit() + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + + +if __name__ == "__main__": + test_predict() + From f622a4e9902babdf060f27f62d911460cbc40bce Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 15 Sep 2025 14:47:55 -0500 Subject: [PATCH 008/103] add hetGP wrapper --- PUQ/surrogatemethods/hetGP.py | 1082 ++------------------------------ tests/unit_tests/test_hetGP.py | 82 +++ 2 files changed, 145 insertions(+), 1019 deletions(-) create mode 100644 tests/unit_tests/test_hetGP.py diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 35b676a..42445a9 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -1,10 +1,5 @@ import numpy as np -from scipy.linalg import cholesky, inv -from PUQ.surrogatemethods.covariances import cov_gen, partial_cov_gen -import scipy.optimize as spo -from PUQ.surrogatemethods.helpers import auto_bounds, find_reps -from PUQ.surrogatemethods.homGP import fit as fitHomGP -from PUQ.surrogatemethods.homGP import predict as predictHomGP +from hetgpy import hetGP ############################################################################### ## Heterogeneous GP with all options for the fit @@ -45,17 +40,19 @@ def fit(fitinfo, x, theta, f, covtype = 'Gaussian', **kwargs): f = f.flatten() - model = mleHetGP(X = theta, - Z = f, - known=known, - noiseControl=noiseControl, - lower=lower, - upper=upper, - settings=settings, - init=init, - eps=eps, - covtype=covtype) - + model = hetGP() + model.mle(X = x, + Z = f, + known=known, + noiseControl=noiseControl, + lower=lower, + upper=upper, + settings=settings, + init=init, + eps=eps, + covtype=covtype + ) + fitinfo['ll'] = model.get('ll') fitinfo['Delta'] = model.get('Delta') fitinfo['theta'] = model.get('theta') fitinfo['g'] = model.get('g') @@ -66,1016 +63,63 @@ def fit(fitinfo, x, theta, f, fitinfo['logN'] = model.get('logN') fitinfo['nu_hat_var'] = model.get('nu_hat_var') fitinfo['nu_hat'] = model.get('nu_hat') + fitinfo['Kgi'] = model.get('Kgi') + fitinfo['SiNK'] = model.get('SiNK') fitinfo['Ki'] = model.get('Ki') fitinfo['X0'] = model.get('X0') fitinfo['Z0'] = model.get('Z0') + fitinfo['Z'] = model.get('Z') fitinfo['mult'] = model.get('mult') + fitinfo['covtype'] = model.get('covtype') fitinfo['beta0'] = model.get('beta0') fitinfo['eps'] = model.get('eps') fitinfo['trendtype'] = model.get('trendtype') fitinfo['is_homGP'] = model.get('is_homGP') return - -def mleHetGP(X, Z, - lower=None, - upper=None, - known={}, - noiseControl={}, - init={}, - covtype="Gaussian", - maxit=100, - eps=np.sqrt(np.finfo(float).eps), - settings={}): - - - elem = find_reps(X, Z, return_Zlist=False) - - X0 = elem['X0'] - Z0 = elem['Z0'] - Z = elem['Z'] - mult = elem['mult'] - N = len(Z) - n = len(X0) - - if (lower is None or upper is None): - auto_thetas = auto_bounds(X=X0, covtype=covtype) - - if lower is None: - lower = auto_thetas["lower"] - if upper is None: - upper = auto_thetas["upper"] - - if len(lower) != len(upper): - print("upper and lower should have the same size") - sys.exit(1) - - - jointThetas, constrThetas = False, False - - # Check if known['theta_g'] is not None - if known.get('theta_g') is not None: - settings['linkThetas'] = False - - # Check if settings['linkThetas'] is None - if settings.get('linkThetas') is None: - jointThetas = True - else: - if settings['linkThetas'] == 'joint': - jointThetas = True - elif settings['linkThetas'] == 'constr': - constrThetas = True - - logN = True - if settings.get('logN') is not None: - logN = settings['logN'] - - if settings.get('return.matrices') is None: - settings['return.matrices'] = True - - if settings.get('return.hom') is None: - settings['return.hom'] = False - - if jointThetas and noiseControl.get('k_theta_g_bounds') is None: - noiseControl['k_theta_g_bounds'] = [1, 100] - - if settings.get('initStrategy') is None: - settings['initStrategy'] = 'residuals' - - if settings.get('factr') is None: - settings['factr'] = 1e9 - - penalty = True - if settings.get('penalty') is not None: - penalty = settings['penalty'] - - if settings.get('checkHom') is None: - settings['checkHom'] = True - - trace = 0 - if settings.get('trace') is not None: - trace = settings['trace'] - - components = [] - if known.get('theta') is None: - components.append('theta') - else: - init['theta'] = known['theta'] - - if known.get('Delta') is None: - components.append('Delta') - else: - init['Delta'] = known['Delta'] - - if jointThetas: - if known.get('k_theta_g') is None: - components.append('k_theta_g') - else: - init['k_theta_g'] = known['k_theta_g'] - - if not jointThetas and known.get('theta_g') is None: - components.append('theta_g') - else: - if not jointThetas: - init['theta_g'] = known['theta_g'] - - if known.get('g') is None: - components.append('g') - else: - init['g'] = known['g'] - - - trendtype = 'OK' - if known.get('beta0') is not None: - trendtype = 'SK' - # beta0 = known['beta0'] - - if noiseControl.get('g_bounds') is None: - noiseControl['g_bounds'] = [1e-6, 1] - - if len(components) == 0 and known.get('theta_g') is None: - known['theta_g'] = known['k_theta_g'] * known['theta'] - - # Automatic Initialization - modHom, modNugs = None, None - - if init.get('theta') is None or init.get('Delta') is None: - # A) homoskedastic mean process - if known.get('g_H') is not None: - g_init = None - else: - g_init = init.get('g_H') - - # Initial value for g of the homoskedastic process: based on the mean variance at replicates - # compared to the variance of Z0 - if any(mult > 5): - #mean_var_replicates = np.mean((fast_tUY2(mult, (Z - np.tile(Z0, mult))**2)/mult)[np.where(mult > 5)]) - v1 = (Z - np.repeat(Z0, mult))**2 - v2 = np.zeros(len(mult)) - counter = 0 - for mid, m in enumerate(mult): - v2[mid] = sum([v1[counter+i] for i in range(m)]) - counter += m - mean_var_replicates = np.mean((v2/mult)[mult > 5]) - - if g_init is None: - g_init = np.array([mean_var_replicates / np.var(Z0, ddof=1)]) - - if noiseControl.get('g_max') is None: - noiseControl['g_max'] = max(1e2, 100 * g_init) - - if noiseControl.get('g_min') is None: - noiseControl['g_min'] = eps - else: - if g_init is None: - g_init = np.array([0.1]) - - if noiseControl.get('g_max') is None: - noiseControl['g_max'] = 1e2 - - if noiseControl.get('g_min') is None: - noiseControl['g_min'] = eps - - rKI = True if settings['checkHom'] else False - - fitinfo_homGP = {} - fitHomGP(fitinfo=fitinfo_homGP, - x=np.array([[0]]), - theta={'X0':X0, 'Z0':Z0, 'mult':mult}, - f=Z, - lower=lower, - upper=upper, - known={'theta':known.get('theta'), - 'g':known.get('g_H'), - 'beta0':known.get('beta0')}, - init={'theta':init.get('theta'), - 'g':g_init}, - covtype=covtype, - maxit=maxit, - noiseControl={'g_bounds':(noiseControl['g_min'], noiseControl['g_max'])}, - eps=eps, - settings={'return.Ki':rKI}, - method="homGP") - - if known.get('theta') is None: - init["theta"] = fitinfo_homGP["theta"] - - if init.get('Delta') is None: - predinfo_homGP = {} - predictHomGP(predinfo=predinfo_homGP, - fitinfo=fitinfo_homGP, - x=np.array([[0]]), - theta=X0) - predHom = predinfo_homGP['mean'] - - # squared deviation from the homoskedastic prediction mean to the actual observations - nugs_est = (np.repeat(predHom, repeats=mult) - Z) ** 2 - nugs_est /= fitinfo_homGP["nu_hat"] - - if logN: - nugs_est = np.maximum(nugs_est, np.finfo(float).eps) # avoid problems on deterministic test functions - nugs_est = np.log(nugs_est) - - nugs_est0 = np.zeros(len(mult)) - counter = 0 - for mid, m in enumerate(mult): - nugs_est0[mid] = sum([nugs_est[counter+i] for i in range(m)])/m - counter += m - else: - nug_est0 = init['Delta'] - - if constrThetas: - noiseControl['lowerTheta_g'] = fitinfo_homGP['theta'] - - if settings['initStrategy'] == 'simple': - if logN: - init['Delta'] = np.repeat(np.log(fitinfo_homGP['g']), X0.shape[0]) - else: - init['Delta'] = np.repeat(fitinfo_homGP['g'], X0.shape[0]) - - if settings['initStrategy'] == 'residuals': - init['Delta'] = nugs_est0 - - if ((init.get('theta_g') is None and init.get('k_theta_g') is None) or init.get('g') is None): - # B) Homogeneous noise process - if jointThetas: - if init.get('k_theta_g') is None: - init['k_theta_g'] = 1 - init['theta_g'] = init['theta'] - else: - init['theta_g'] = init['k_theta_g'] * init['theta'] - - if noiseControl.get('lowerTheta_g') is None: - noiseControl['lowerTheta_g'] = init['theta_g'] - eps - - if noiseControl.get('upperTheta_g') is None: - noiseControl['upperTheta_g'] = init['theta_g'] + eps - - if not jointThetas and init.get('theta_g') is None: - init['theta_g'] = init['theta'] - - if noiseControl.get('lowerTheta_g') is None: - noiseControl['lowerTheta_g'] = lower - - if noiseControl.get('upperTheta_g') is None: - noiseControl['upperTheta_g'] = upper - - # If a homogeneous process of the mean has already been computed, it is used for estimating the parameters of the noise process - if 'nugs_est' in locals(): - if init.get('g') is None: - # mean_var_replicates_nugs = np.mean((fast_tUY2(mult, (nugs_est - np.repeat(nugs_est0, mult)))**2) / mult) - v1 = (nugs_est - np.repeat(nugs_est0, mult))**2 - v2 = np.zeros(len(mult)) - counter = 0 - for mid, m in enumerate(mult): - v2[mid] = sum([v1[counter+i] for i in range(m)]) - counter += m - mean_var_replicates_nugs = np.mean((v2/mult)) - init['g'] = np.array([mean_var_replicates_nugs / np.var(nugs_est0, ddof=1)]) - - fitnugs_homGP = {} - fitHomGP(fitinfo=fitnugs_homGP, - x=np.array([[0]]), - theta={'X0': X0, 'Z0': nugs_est0, 'mult': mult}, - f=nugs_est, - lower=noiseControl['lowerTheta_g'], - upper=noiseControl['upperTheta_g'], - init={'theta': init['theta_g'], 'g': init['g']}, - covtype=covtype, - noiseControl=noiseControl, - maxit=maxit, - eps=eps, - settings={'return.Ki': False}) - prednugs_homGP = {} - predictHomGP(predinfo=prednugs_homGP, - fitinfo=fitnugs_homGP, - x=np.array([[0]]), - theta=X0) - # prednugs = predinfo_homGP['mean'] - - else: - if 'nugs_est0' not in locals(): - nugs_est0 = init['Delta'] - - if init['g'] is None: - init['g'] = np.array([0.05]) - - fitnugs_homGP = {} - fitHomGP(fitinfo=fitnugs_homGP, - x=np.array([[0]]), - theta={'X0': X0, 'Z0': nugs_est0, 'mult': np.repeat(1, X0.shape[0])}, - f=nugs_est0, - lower=noiseControl['lowerTheta_g'], - upper=noiseControl['upperTheta_g'], - init={'theta': init['theta_g'], 'g': init['g']}, - covtype=covtype, - noiseControl=noiseControl, - maxit=maxit, - eps=eps, - settings={'return.Ki': False}) - prednugs_homGP = {} - predictHomGP(predinfo=prednugs_homGP, - fitinfo=fitnugs_homGP, - x=np.array([[0]]), - theta=X0) - # prednugs = predinfo_homGP['mean'] - - if settings['initStrategy'] == 'smoothed': - init['Delta'] = prednugs_homGP['mean'] - - if known.get('g') is None: - init['g'] = fitnugs_homGP['g'] - - if jointThetas and init.get('k_theta_g') is None: - init['k_theta_g'] = 1 - - if not jointThetas and init.get('theta_g') is None: - init['theta_g'] = fitnugs_homGP['theta'] - - if noiseControl.get('lowerTheta_g') is None: - noiseControl['lowerTheta_g'] = lower - - if noiseControl.get('upperTheta_g') is None: - noiseControl['upperTheta_g'] = upper - - def fn(par, X0, Z0, Z, mult, Delta=None, theta=None, g=None, k_theta_g=None, - theta_g=None, logN=False, beta0=None, hom_ll=None, env=None): - - idx = 0 # to store the first non-used element of par - - if theta is None: - idx = len(init['theta']) - theta = par[:idx] - - if Delta is None: - Delta = par[idx:idx + len(init['Delta'])] - idx += len(init['Delta']) - - if jointThetas and k_theta_g is None: - k_theta_g = par[idx] - idx += 1 - - if not jointThetas and theta_g is None: - theta_g = par[idx:idx + len(init['theta_g'])] - idx += len(init_theta_g) - - if g is None: - g = par[idx] - idx += 1 - - loglik = logLikHet(X0=X0, Z0=Z0, Z=Z, - mult=mult, - Delta=Delta, - theta=theta, - g=g, - k_theta_g=k_theta_g, - theta_g=theta_g, - logN=logN, - beta0=beta0, - hom_ll=hom_ll, - env=env, - eps=eps, - penalty=penalty, - trace=trace) - - # Update env with the maximum log-likelihood and corresponding parameters - if env is not None and not np.isnan(loglik): - if env.get('max_loglik') is None or loglik > env['max_loglik']: - env['max_loglik'] = loglik - env['arg_max'] = par - return -1*loglik - - def gr(par, X0, Z0, Z, mult, Delta=None, theta=None, g=None, k_theta_g=None, - theta_g=None, logN=False, beta0=None, hom_ll=None, env=None): - - idx = 0 # to store the first non-used element of par - - if theta is None: - theta = par[idx:idx + len(init['theta'])] - idx += len(init['theta']) - - if Delta is None: - Delta = par[idx:idx + len(init['Delta'])] - idx += len(init['Delta']) - - if jointThetas and k_theta_g is None: - k_theta_g = par[idx] - idx += 1 - - if not jointThetas and theta_g is None: - theta_g = par[idx:idx + len(init['theta_g'])] - idx += len(init['theta_g']) - - if g is None: - g = par[idx] - idx += 1 - - dloglik = dlogLikHet(X0=X0, Z0=Z0, Z=Z, - mult=mult, - Delta=Delta, - theta=theta, - g=g, - k_theta_g=k_theta_g, - theta_g=theta_g, - logN=logN, - beta0=beta0, - hom_ll=hom_ll, - env=env, - eps=eps, - penalty=penalty, - components=components) - - negdloglik = [-l for l in dloglik] - return negdloglik - - - parinit, lowerOpt, upperOpt = [], [], [] - - if trace > 2: - print("Initial value of the parameters:") - - if known.get("theta") is None: - parinit.extend(init["theta"]) - lowerOpt.extend(lower) - upperOpt.extend(upper) - if trace > 2: - print("Theta:", init["theta"]) - - if known.get("Delta") is None: - if noiseControl.get("lowerDelta") is None or len(noiseControl["lowerDelta"]) != n: - if logN: - noiseControl["lowerDelta"] = np.array([np.log(eps)]) # [np.log(eps)] * n - else: - noiseControl["lowerDelta"] = np.array([eps]) # [eps] * n - - if len(noiseControl["lowerDelta"]) == 1: - noiseControl["lowerDelta"] = np.repeat(noiseControl["lowerDelta"], n) #[noiseControl["lowerDelta"][0]] * n - - if noiseControl.get("g_max") is None: - noiseControl["g_max"] = 1e2 - - if noiseControl.get("upperDelta") is None or len(noiseControl["upperDelta"]) != n: - if logN: - noiseControl["upperDelta"] = np.array([np.log(noiseControl["g_max"])]) # [np.log(noiseControl["g_max"])] - else: - noiseControl["upperDelta"] = np.array([noiseControl["g_max"]]) # [noiseControl["g_max"]] - - if len(noiseControl["upperDelta"]) == 1: - noiseControl["upperDelta"] = np.repeat(noiseControl["upperDelta"], n) # [noiseControl["upperDelta"][0]] * n - - lowerOpt.extend(noiseControl["lowerDelta"]) - upperOpt.extend(noiseControl["upperDelta"]) - parinit.extend(init["Delta"]) - if trace > 2: - print("Delta:", init["Delta"]) - - if jointThetas and known.get("k_theta_g") is None: - parinit.extend([init["k_theta_g"]]) - lowerOpt.extend([noiseControl["k_theta_g_bounds"][0]]) - upperOpt.extend([noiseControl["k_theta_g_bounds"][1]]) - if trace > 2: - print("k_theta_g:", init["k_theta_g"]) - - if not jointThetas and known.get("theta_g") is None: - parinit.extend(init["theta_g"]) - lowerOpt.extend([noiseControl["lowerTheta_g"]]) - upperOpt.extend([noiseControl["upperTheta_g"]]) - if trace > 2: - print("theta_g:", init["theta_g"]) - - if known.get("g") is None: - parinit.extend([init["g"]]) - if trace > 2: - print("g:", init["g"]) - - lowerOpt.extend([noiseControl["g_bounds"][0]]) - upperOpt.extend([noiseControl["g_bounds"][1]]) - - mle_par = known.copy() # Store inferred and known parameters - - if components is not None: - if fitinfo_homGP is not None: - hom_ll = fitinfo_homGP['ll'] - else: - # Compute reference homoskedastic likelihood, with fixed theta for speed - modHom_tmp = mleHomGP(X={"X0": X0, "Z0": Z0, "mult": mult}, - Z=Z, - lower=lower, - upper=upper, - init={"theta": known["theta"], "g": known["g_H"], "beta0": known["beta0"]}, - covtype=covtype, - noiseControl={"g_bounds": [noiseControl["g_min"], noiseControl["g_max"]]}, - eps=eps, - settings={"return.Ki": False}) - - hom_ll = modHom_tmp.ll - - try: - # print("Starting") - envtemp = {} - opval = spo.minimize(fn, - parinit, - method='L-BFGS-B', - options={'gtol': 0.001, 'maxiter': 100}, - jac=gr, - bounds=spo.Bounds(lowerOpt, upperOpt), - args=(X0, Z0, Z, mult, known.get('Delta'), known.get('theta'), - known.get('g'), known.get('k_theta_g'), known.get('theta_g'), - logN, known.get('beta0'), hom_ll, envtemp)) - - out = 1*opval.x - out_ll = -1*opval.fun - except: - print('use best value so far') - out = envtemp["arg_max"] - out_ll = envtemp["max_loglik"] - - if trace > 1: - print("Name | Value | Lower bound | Upper bound") - - # Post-processing - idx = 0 - if known.get("theta") is None: - mle_par["theta"] = np.array(out[:len(init["theta"])]) - idx += len(init["theta"]) - if trace > 1: - print(f"Theta | {mle_par['theta']} | {lower} | {upper}") - - if known.get("Delta") is None: - mle_par["Delta"] = np.array(out[idx : (idx + len(init["Delta"]))]) - idx += len(init["Delta"]) - if trace > 1: - for ii in range(1, (len(mle_par["Delta"]) // 5) + 2): - i_tmp = np.arange(5 * (ii - 1), min(5 * ii, len(mle_par["Delta"]))) - if logN: - print( - f"Delta | {np.array(mle_par['Delta'])[i_tmp]} | {np.maximum(np.log(eps * mult[i_tmp]), init['Delta'][i_tmp] - np.log(1000))} | {init['Delta'][i_tmp] + np.log(100)}" - ) - if not logN: - print( - f"Delta | {np.array(mle_par['Delta'])[i_tmp]} | {np.maximum(mult[i_tmp] * eps, init['Delta'][i_tmp] / 1000)} | {init['Delta'][i_tmp] * 100}" - ) - if jointThetas: - if known.get('k_theta_g') is None: - mle_par['k_theta_g'] = np.array(out[idx]) - idx += 1 - mle_par['theta_g'] = mle_par['k_theta_g'] * mle_par['theta'] - - if trace > 1: - print(f"k_theta_g | {mle_par['k_theta_g']} | {noiseControl['k_theta_g_bounds'][0]} | {noiseControl['k_theta_g_bounds'][1]}") - - if not jointThetas and known.get("theta_g") is None: - mle_par['theta_g'] = np.array(out[idx : (idx + len(init["theta_g"]))]) - idx += len(init["theta_g"]) - if trace > 1: - print(f"theta_g | {mle_par['theta_g']} | {noiseControl['lowerTheta_g']} | {noiseControl['upperTheta_g']}") - - if known.get('g') is None: - mle_par['g'] = np.array(out[idx]) - idx += 1 - if trace > 1: - print(f"g | {mle_par['g']} | {noiseControl['g_bounds'][0]} | {noiseControl['g_bounds'][1]}") - - else: - out = {"message": "All hyperparameters given, no optimization \n", - "count": 0, - "value": None - } - - if penalty: - ll_non_pen = logLikHet(X0=X0, Z0=Z0, Z=Z, mult=mult, - Delta=mle_par['Delta'], - theta=mle_par['theta'], - g=mle_par['g'], - k_theta_g=mle_par['k_theta_g'], - theta_g=mle_par['theta_g'], - logN=logN, - beta0=mle_par.get('beta0'), - hom_ll=None, - env=None, - eps=eps, - penalty=False, - trace=trace) - - else: - ll_non_pen = 1*out_ll#-1*opval.fun - - - if fitinfo_homGP is not None: - if fitinfo_homGP['ll'] >= ll_non_pen: - if trace >= 1: - print(f"Homoskedastic model has higher log-likelihood: {fitinfo_homGP['ll']} compared to {ll_non_pen}") - - if settings['checkHom']: - if trace >= 1: - print("Return homoskedastic model") - fitinfo_homGP['is_homGP'] = True - return fitinfo_homGP - - - Cg = cov_gen(X1=X0, theta=mle_par['theta_g']) - Kg_c = cholesky(Cg + np.diag(eps + mle_par['g'] / mult)) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - - nmean = np.sum(Kgi @ mle_par['Delta']) / np.sum(Kgi) ## ordinary kriging mean - nu_hat_var = max(eps, np.dot((mle_par['Delta'] - nmean), Kgi @ (mle_par['Delta'] - nmean)) / len(mle_par['Delta'])) - M = np.dot(Cg, np.dot(Kgi, mle_par['Delta'] - nmean)) - - Lambda = nmean + M - if logN: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = eps - - LambdaN = np.repeat(Lambda, mult) - C = cov_gen(X1=X0, theta=mle_par['theta']) - Ki = cholesky(C + np.diag(Lambda / mult + eps)) - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if known.get('beta0') is None: - mle_par['beta0'] = np.sum(Ki @ Z0) / np.sum(Ki) - - psi_0 = np.dot(Z0 - mle_par['beta0'], Ki @ (Z0 - mle_par['beta0'])) - - nu2 = 1 / N * (np.dot((Z - mle_par['beta0']) / LambdaN, Z - mle_par['beta0']) - np.dot((Z0 - mle_par['beta0']) * mult / Lambda, Z0 - mle_par['beta0']) + psi_0) - - fitinfo = {} - fitinfo['Delta'] = mle_par['Delta'] - fitinfo['theta'] = mle_par['theta'] - fitinfo['g'] = mle_par['g'] - fitinfo['k_theta_g'] = mle_par['k_theta_g'] - fitinfo['theta_g'] = mle_par['theta_g'] - fitinfo['nmean'] = nmean - fitinfo['Lambda'] = Lambda - fitinfo['logN'] = logN - fitinfo['nu_hat_var'] = nu_hat_var - fitinfo['nu_hat'] = nu2 - fitinfo['Ki'] = Ki - fitinfo['Kgi'] = Kgi - fitinfo['X0'] = X0 - fitinfo['Z0'] = Z0 - fitinfo['mult'] = mult - fitinfo['beta0'] = mle_par['beta0'] - fitinfo['eps'] = eps - fitinfo['trendtype'] = trendtype - fitinfo['is_homGP'] = False - - return fitinfo - -def logLikHet(X0, Z0, Z, mult, Delta, theta, g, k_theta_g=None, theta_g=None, - logN=False, beta0=None, hom_ll=None, env=None, - eps=np.sqrt(np.finfo(float).eps), penalty=True, trace=0): - n = len(X0) - N = len(Z) - - if theta_g is None: - theta_g = k_theta_g * theta - - Cg = cov_gen(X1=X0, theta=theta_g) - Kg_c = cholesky(Cg + np.diag(eps + g / mult)) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - nmean = np.sum(Kgi @ Delta) / np.sum(Kgi) - M = Cg @ (Kgi @ (Delta - nmean)) - Lambda = nmean + M - - if logN: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = eps - - LambdaN = np.repeat(Lambda, mult) - - C = cov_gen(X1=X0, theta=theta) - if env is not None: - env['C'] = C - Ki = cholesky(C + np.diag(Lambda / mult + eps)) - ldetKi = -2 * np.sum(np.log(np.diag(Ki))) - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if env is not None: - env['Cg'] = Cg - env['Kg_c'] = Kg_c - env['Kgi'] = Kgi - env['ldetKi'] = ldetKi - env['Ki'] = Ki - - if beta0 is None: - beta0 = np.sum(Ki @ Z0) / np.sum(Ki) - - psi_0 = np.dot(Z0 - beta0, Ki @ (Z0 - beta0)) - - psi = 1 / N * (np.dot((Z - beta0) / LambdaN, Z - beta0) - np.dot((Z0 - beta0) * mult / Lambda, Z0 - beta0) + psi_0) - - loglik = -N / 2 * np.log(2 * np.pi) - N / 2 * np.log(psi) + 1 / 2 * ldetKi - 1 / 2 * np.sum( - (mult - 1) * np.log(Lambda) + np.log(mult)) - N / 2 - - if penalty: - nu_hat_var = np.dot((Delta - nmean), Kgi @ (Delta - nmean)) / len(Delta) - if nu_hat_var < eps: - return loglik - pen = -n / 2 * np.log(nu_hat_var) - np.sum(np.log(np.diag(Kg_c))) - n / 2 * np.log(2 * np.pi) - n / 2 - - if hom_ll is not None and loglik < hom_ll and pen > 0: - if trace > 0: - print("Penalty is deactivated when unpenalized likelihood is lower than its homGP equivalent") - return loglik - - return loglik + pen - - return loglik - -def dlogLikHet(X0, Z0, Z, mult, Delta, theta, g, k_theta_g=None, theta_g=None, - logN=True, beta0=None, hom_ll=None, env=None, - eps=np.sqrt(np.finfo(float).eps), penalty=True, components=None): - - # Verifications - if k_theta_g is None and theta_g is None: - print("Either k_theta_g or theta_g must be provided.") - return None - - # Initializations - if components is None: - components = ["theta", "Delta", "g"] - if k_theta_g is None: - components.append("theta_g") - else: - components.append("k_theta_g") - - if theta_g is None: - theta_g = k_theta_g * theta - - n = X0.shape[0] - N = len(Z) - - if env is not None: - Cg = env['Cg'] - Kg_c = env['Kg_c'] - Kgi = env['Kgi'] - M = (Kgi * (-eps - g / mult) + np.eye(n)).T - else: - Cg = cov_gen(X1=X0, theta=theta_g) - Kg_c = cholesky(Cg + np.diag(eps + g / mult)) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - M = (Kgi * (-eps - g / mult) + np.eye(n)).T - - # Precomputations for reuse - rSKgi = np.sum(Kgi, axis=1) - sKgi = np.sum(Kgi) - nmean = np.dot(rSKgi, Delta) / sKgi # ordinary kriging mean - - # Precomputations for reuse - KgiD = np.dot(Kgi, (Delta - nmean)) - Lambda = nmean + np.dot(M, (Delta - nmean)) - - if logN: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = eps - LambdaN = np.repeat(Lambda, repeats=mult) - - if env is not None: - C = env['C'] - Ki = env['Ki'] - ldetKi = env['ldetKi'] - else: - C = cov_gen(X1=X0, theta=theta) - Ki = cholesky(C + np.diag(Lambda / mult + eps)) - ldetKi = -2 * np.sum(np.log(np.diag(Ki))) # log determinant from Cholesky - Ki = np.linalg.inv((Ki)) - Ki = Ki @ Ki.T - - if beta0 is None: - beta0 = np.dot(np.sum(Ki, axis=0), Z0) / np.sum(Ki) - - # Precomputations for reuse - KiZ0 = np.dot(Ki, Z0 - beta0) - rsM = np.sum(M, axis=1) - - psi_0 = np.dot(KiZ0, Z0 - beta0) - psi = np.dot((Z - beta0) / LambdaN, Z - beta0) - np.dot((Z0 - beta0) * mult / Lambda, Z0 - beta0) + psi_0 - - if penalty: - nu_hat_var = np.dot(KgiD, Delta - nmean)/ len(Delta) - # To prevent numerical issues when Delta = nmean, resulting in divisions by zero - if nu_hat_var < eps: - penalty = False - else: - loglik = -N/2 * np.log(2*np.pi) - N/2 * np.log(psi/N) + 1/2 * ldetKi - 1/2 * np.sum((mult - 1) * np.log(Lambda) + np.log(mult)) - N/2 - pen = -n/2 * np.log(nu_hat_var) - np.sum(np.log(np.diag(Kg_c))) - n/2 * np.log(2*np.pi) - n/2 - - if loglik < hom_ll and pen > 0: - penalty = False - - - dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX = None, None, None, None, None, None - # First component, derivative of logL with respect to theta - if "theta" in components: - dLogL_dtheta = np.full(len(theta), np.nan) - for i in range(len(theta)): - if len(theta) == 1: - dC_dthetak = partial_cov_gen(X1=X0, theta=theta, arg="theta_k") * C - else: - dC_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=theta[i], arg="theta_k") * C - - if "k_theta_g" in components: - - if len(theta) == 1: - dCg_dthetak = partial_cov_gen(X1=X0, theta=k_theta_g * theta, arg="theta_k") * k_theta_g * Cg - else: - dCg_dthetak = partial_cov_gen(X1=X0[:, i][:, None], theta=k_theta_g * theta[i], arg="theta_k") * k_theta_g * Cg - - # Derivative Lambda / theta_k (first part) - dLdtk = dCg_dthetak @ KgiD - M @ (dCg_dthetak @ KgiD) - - # (second part) - dLdtk -= (1 - rsM) * (np.dot(np.dot(rSKgi, dCg_dthetak), np.dot(Kgi, Delta)) * sKgi - np.dot(rSKgi, Delta) * np.dot(np.dot(rSKgi, dCg_dthetak), rSKgi)) / sKgi**2 - - if logN: - dLdtk *= Lambda - - dK_dthetak = np.add(dC_dthetak, np.diag(dLdtk) / mult) # dK/dtheta[k] - - term1 = N/2 * (np.dot(((Z - beta0) / LambdaN) * np.repeat(dLdtk, mult), (Z - beta0) / LambdaN) - np.dot(((Z0 - beta0) / Lambda * mult * dLdtk), (Z0 - beta0) / Lambda) + - np.dot(np.dot(KiZ0, dK_dthetak), KiZ0)) / psi - 0.5 * np.trace(np.dot(Ki.T, dK_dthetak)) - - term2 = 0.5 * np.sum((mult - 1) * dLdtk / Lambda) - dLogL_dtheta[i] = term1 - term2 - - if penalty: - dLogL_dtheta[i] += 0.5 * np.dot(np.dot(KgiD, dCg_dthetak), KgiD) / nu_hat_var - 0.5 * np.trace(np.dot(Kgi.T, dCg_dthetak)) - else: - # N/2 * crossprod(KiZ0, dC_dthetak) %*% KiZ0/psi - term1 = (N/2) * np.dot(np.dot(KiZ0, dC_dthetak), KiZ0) / psi - # 1/2 * trace_sym(Ki, dC_dthetak) - term2 = 0.5 * np.trace(np.dot(Ki.T, dC_dthetak)) - dLogL_dtheta[i] = term1 - term2 - - - # Derivative of logL with respect to Lambda - if any(x in ["Delta", "g", "k_theta_g", "theta_g"] for x in components): - v1 = (Z - beta0) ** 2 - v2 = np.zeros(len(mult)) - counter = 0 - for mid, m in enumerate(mult): - v2[mid] = sum([v1[counter+i] for i in range(m)]) - counter += m - - dLogLdLambda = (N / 2 * ((v2 - (Z0 - beta0) ** 2 * mult) / Lambda ** 2 - + KiZ0 ** 2 / mult) / psi - (mult - 1) / (2 * Lambda) - 1 / (2 * mult) * np.diag(Ki)) - - if logN: - dLogLdLambda = Lambda * dLogLdLambda - - # Derivative of Lambda with respect to Delta - if "Delta" in components: - dLogL_dDelta = np.dot(M.T, dLogLdLambda) + rSKgi / sKgi * np.sum(dLogLdLambda) - rSKgi / sKgi * np.sum(np.dot(M.T, dLogLdLambda)) # chain rule - - # Derivative Lambda / k_theta_g - if "k_theta_g" in components: - dCg_dk = partial_cov_gen(X1=X0, theta=theta, k_theta_g=k_theta_g, arg="k_theta_g") * Cg - - dLogL_dkthetag = np.dot(dCg_dk, KgiD) - np.dot(M, np.dot(dCg_dk, KgiD)) - \ - (1 - rsM) * np.squeeze( - (rSKgi @ np.dot(dCg_dk, np.dot(Kgi, Delta)) * sKgi - rSKgi @ Delta * (rSKgi @ np.dot(dCg_dk, rSKgi))) / sKgi**2) - - dLogL_dkthetag = dLogL_dkthetag @ dLogLdLambda # chain rule - - # Derivative Lambda / theta_g - if "theta_g" in components: - dLogL_dthetag = np.full(len(theta_g), np.nan) - for i in range(len(theta_g)): - if len(theta_g) == 1: - dCg_dthetagk = partial_cov_gen(X1=X0, theta=theta_g, arg="theta_k") * Cg - else: - dCg_dthetagk = partial_cov_gen(X1=X0[:, i], theta=theta_g[i], arg="theta_k") * Cg - - dLogL_dthetag[i] = np.dot((dCg_dthetagk @ KgiD - M @ (dCg_dthetagk @ KgiD) - - ((1 - rsM) * (rSKgi @ (dCg_dthetagk @ Kgi @ Delta) * sKgi - - rSKgi @ Delta * (rSKgi @ (dCg_dthetagk @ rSKgi.T) @ rSKgi))))/ sKgi**2, dLogLdLambda) - - # Penalty term - if penalty: - dLogL_dthetag[i] += 1/2 * np.dot(np.dot(KgiD.T, dCg_dthetagk) @ KgiD, 1/nu_hat_var) - 0.5*np.trace(np.dot(Kgi, dCg_dthetagk)) #trace_sym(Kgi, dCg_dthetagk)/2 - - ## Derivative Lambda / g - if "g" in components: - dLogL_dg = np.dot(-M @ (KgiD/mult) - - (1 - rsM) * (np.dot(Delta, (Kgi @ (rSKgi/mult)) * sKgi) - rSKgi @ Delta * np.sum(rSKgi**2/mult))/sKgi**2, - dLogLdLambda) # chain rule - - - # Additional penalty terms on Delta - if penalty: - if "Delta" in components: - dLogL_dDelta = dLogL_dDelta - KgiD / nu_hat_var - if "k_theta_g" in components: - dLogL_dkthetag = dLogL_dkthetag + 1/2 * np.dot(KgiD, dCg_dk) @ KgiD / nu_hat_var - 0.5*np.trace(np.dot(Kgi, dCg_dk)) #trace_sym(Kgi, dCg_dk)/2 - if "g" in components: - dLogL_dg = dLogL_dg + 1/2 * np.dot(KgiD/mult, KgiD) / nu_hat_var - np.sum(np.diag(Kgi)/mult)/2 - - nested_list = [] - for der in [dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX]: - if der is not None: - if der.ndim < 1: - nested_list.extend(np.array([der])) - else: - nested_list.extend(der) - return nested_list #np.concatenate([dLogL_dtheta, dLogL_dDelta, dLogL_dkthetag, dLogL_dthetag, dLogL_dg, dLogL_dpX]) - - -def predict(predinfo, fitinfo, x, theta, thetaprime=None, rep_no=None, **kwargs): - - if fitinfo.get('Delta') is None: - return predictHomGP(predinfo, fitinfo, x, theta, thetaprime=thetaprime, rep_no=rep_no) - else: - return predicthetGP(predinfo, fitinfo, x, theta, thetaprime=thetaprime, noise_var=False, nugs_only=False, rep_no=rep_no) - - -def predicthetGP(predinfo, fitinfo, x, theta, thetaprime=None, noise_var=False, nugs_only=False, rep_no=None, **kwargs): - - if rep_no is not None: - mult = 1*rep_no - else: - mult = 1*fitinfo['mult'] - - if fitinfo.get('Kgi') is None: - Cg = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta_g']) - Kg_c = cholesky(Cg + np.diag(fitinfo['eps'] + fitinfo['g'] / mult)) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - - kg = cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta_g']) - - if fitinfo['Ki'] is None: - C = cov_gen(X1=fitinfo['X0'], theta=fitinfo['theta']) - Ki = cholesky(C + np.diag(fitinfo['Lambda'] / mult + fitinfo['eps'])) - Ki = np.linalg.inv((Ki)) - fitinfo['Ki'] = Ki @ Ki.T - - M = np.dot(kg, np.dot(Kgi, fitinfo['Delta'] - fitinfo['nmean'])) - - if fitinfo['logN']: - nugs = fitinfo['nu_hat'] * np.exp(fitinfo['nmean'] + M) - else: - nugs = fitinfo['nu_hat'] * np.maximum(0, fitinfo['nmean'] + M) - - if nugs_only: - return {'nugs': nugs} - - if noise_var: - if fitinfo['nu_hat_var'] is None: - fitinfo['nu_hat_var'] = max(fitinfo['eps'], np.dot(np.dot(fitinfo['Delta'] - fitinfo['nmean'], Kgi), fitinfo['Delta'] - fitinfo['nmean'])/len(fitinfo['Delta'])) # To avoid 0 variance - - # Assuming kg, object['Kgi'] are NumPy arrays - term1 = 1 - np.diag(np.dot(kg, np.dot(Kgi.T, kg))) - term2 = (1 - np.dot(np.sum(Kgi, axis=0), kg))**2 / np.sum(Kgi) - - sd2var = fitinfo['nu_hat'] * fitinfo['nu_hat_var'] * (term1 - term2) - else: - sd2var = None - - # theta.shape[0] x fitinfo['X0'].shape[0] - kx = cov_gen(X1=theta, X2=fitinfo['X0'], theta=fitinfo['theta']) - if fitinfo['trendtype'] == 'SK': - sd2 = fitinfo['nu_hat'] * (1 - np.diag(np.dot(kx, np.dot(fitinfo['Ki'], kx.T)))) - else: - sd2 = fitinfo['nu_hat'] * (1 - np.diag(np.dot(kx, np.dot(fitinfo['Ki'], kx.T))) + (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T ).flatten()**2/np.sum(fitinfo['Ki']) ) - - if any(sd2 < 0): - sd2 = np.maximum(0, sd2) - - predinfo['mean'] = (fitinfo['beta0'] + np.dot(kx, np.dot(fitinfo['Ki'], fitinfo['Z0'] - fitinfo['beta0'])))[None, :] - predinfo['var'] = sd2[None, :] - predinfo['sdvar'] = sd2var - predinfo['nugs'] = nugs[None, :] - - if thetaprime is not None: - # fitinfo['X0'].shape[0] x thetaprime.shape[0] - kxprime = cov_gen(X1=fitinfo['X0'], X2=thetaprime, theta=fitinfo['theta']) - if fitinfo['trendtype'] == 'SK': - if theta.shape[0] < thetaprime.shape[0]: - cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki'] @ kxprime) - else: - cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki'] @ kxprime)) - else: - if theta.shape[0] < thetaprime.shape[0]: - - cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ fitinfo['Ki'] @ kxprime + \ - ((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) - #np.dot(1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) - #((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) - - else: - cov = fitinfo['nu_hat'] * (cov_gen(X1=theta, X2=thetaprime, theta=fitinfo['theta']) - kx @ (fitinfo['Ki'] @ kxprime) + \ - np.dot((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) - #((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T @ (1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime))/np.sum(fitinfo['Ki'])) - #np.dot((1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kx.T).T, 1 - np.sum(fitinfo['Ki'], axis=0)[None, :] @ kxprime)/np.sum(fitinfo['Ki'])) - - predinfo['covmat'] = cov - +class hetGPWrapper(hetGP): + ''' + A class that converts the information in fitinfo (from the fit and predict methods) to a class so + it can be used to make predictions with hetgpy.hetGP + + ''' + def __init__(self,fitinfo): + + keys_to_transfer = [ + 'X0','Z0','Z', # data + 'covtype', # kernel + 'theta', 'g', 'beta0','trendtype', # hyperparameters + 'Lambda', 'Delta', # latent noise + 'theta_g','k_theta_g', # noise hyperparameters + 'nu_hat_var', # noise variance + 'Kgi', # noise covariance inverse + 'SiNK', + 'nmean', + 'logN', + 'g', # nugget + 'Ki', # inverse covariance matrix + 'eps', # for numeric stability + 'nu_hat' # output from maximum likelihood + ] + + # model hyperparameters + for key in keys_to_transfer: + self[key] = fitinfo[key] + +def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): + r''' + Wrapper method for hetgpy.hetGP.predict + ''' + GP = fitinfo.get('model') + if GP is None: + # use wrapper class to instantiate trained GP + GP = hetGPWrapper(fitinfo=fitinfo) + + + preds = GP.predict(x=x,xprime=thetaprime) + # ensure naming consistency + predinfo['mean'] = preds['mean'] + predinfo['var'] = preds['sd2'] + predinfo['nugs'] = preds['nugs'] + predinfo['covmat'] = preds['cov'] return \ No newline at end of file diff --git a/tests/unit_tests/test_hetGP.py b/tests/unit_tests/test_hetGP.py new file mode 100644 index 0000000..1faf962 --- /dev/null +++ b/tests/unit_tests/test_hetGP.py @@ -0,0 +1,82 @@ +'''Tests whether the emulator with hetGP gives the same result as just using hetGP''' + +import sys +sys.path.append('./') +import numpy as np +from hetgpy import hetGP +from PUQ.surrogate import emulator +# generate some test data +rng = np.random.default_rng(123) + +X = np.linspace(0,2*np.pi,20).reshape(-1,1) +# add some replicates +reps = rng.choice(len(X),size=100,replace=True) +X = X[reps,] +Ytrue = np.sin(X) +noise = 0.5 * rng.normal(size = (len(Ytrue),1)) + +Y = Ytrue + noise + +# prediction grid (interpolation) +Xgrid = np.linspace(X.min(),X.max(),100).reshape(-1,1) + +def test_fit(): + reference_model = hetGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model.fit() + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + assert np.allclose(test_model._info['Delta'],reference_model['Delta']) + assert np.allclose(test_model._info['Lambda'],reference_model['Lambda']) + + +def test_matern(): + # test a different covariance type and define some input args + COVTYPE = "Matern5_2" + MAXIT = 50 + SETTINGS = {"return.Ki": False, "factr": 1e5} + + + reference_model = hetGP() + reference_model.mle(X,Y.flatten(),covtype=COVTYPE,maxit=MAXIT,settings=SETTINGS) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='hetGP', + args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + test_model.fit() + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + assert np.allclose(test_model._info['Delta'],reference_model['Delta']) + assert np.allclose(test_model._info['Lambda'],reference_model['Lambda']) + +def test_predict(): + # refits models and then tests predictions + + reference_model = hetGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model.fit() + + # test predictions + preds = reference_model.predict(Xgrid,xprime=Xgrid) + + test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid) + assert np.allclose(test_preds._info['mean'],preds['mean']) + assert np.allclose(test_preds._info['var'],preds['sd2']) + assert np.allclose(test_preds._info['nugs'],preds['nugs']) + assert np.allclose(test_preds._info['covmat'],preds['cov']) + +if __name__ == "__main__": + test_predict() From f62feb3dfae7669c096a7aa130a58afcbb6a5d86 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Tue, 16 Sep 2025 15:37:30 -0500 Subject: [PATCH 009/103] add hetGP documentation --- PUQ/surrogatemethods/hetGP.py | 56 +++++++++++++++++++++++++++++++++++ 1 file changed, 56 insertions(+) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 42445a9..373eb72 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -38,6 +38,62 @@ def fit(fitinfo, x, theta, f, "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, "return.hom": False, "factr": 1e9}, covtype = 'Gaussian', **kwargs): + r''' + Wrapper function for hetgpy.hetGP.mleHetGP + + Arguments + --------- + fitinfo: dictionary/class holding emulator results + x : ndarray_like + matrix of all designs, one per row, or list with elements: + - ``X0`` matrix of unique design locations, one point per row + - ``Z0`` vector of averaged observations, of length ``len(X0)`` + - ``mult`` number of replicates at designs in ``X0``, of length ``len(X0)`` + theta: not used + f : ndarray_like + Z vector of all observations. If using a list with ``X``, ``Z`` has to be ordered with respect to ``X0``, and of length ``sum(mult)`` + lower,upper : ndarray_like + optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). + In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) + noiseControl : dict + dict with elements related to optimization of the noise process parameters: + - ``g_min``, ``g_max`` minimal and maximal noise to signal ratio (of the mean process) + - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) + - ``lowerpX``, ``upperpX`` optional vectors of bounds of the input domain if `pX` is used. + - ``lowerTheta_g``, ``upperTheta_g`` optional vectors of bounds for the lengthscales of the noise process if ``linkThetas == 'none'``. Same as for ``theta`` if not provided. + - ``k_theta_g_bounds`` if ``linkThetas == 'joint'``, vector with minimal and maximal values for ``k_theta_g`` (default to ``(1, 100)``). See Notes. + - ``g_bounds`` vector for minimal and maximal noise to signal ratios for the noise of the noise process, i.e., the smoothing parameter for the noise process. (default to ``(1e-6, 1)``). + settings : dict + dict for options about the general modeling procedure, with elements: + - ``linkThetas`` defines the relation between lengthscales of the mean and noise processes. Either ``'none'``, ``'joint'``(default) or ``'constr'``, see Notes. + - ``logN``, when ``True`` (default), the log-noise process is modeled. + - ``initStrategy`` one of ``'simple'``, ``'residuals'`` (default) and ``'smoothed'`` to obtain starting values for ``Delta``, see Notes + - ``penalty`` when ``True``, the penalized version of the likelihood is used (i.e., the sum of the log-likelihoods of the mean and variance processes, see References). + - ``hardpenalty`` is ``True``, the log-likelihood from the noise GP is taken into account only if negative (default if ``maxit > 1000``). + - ``checkHom`` when ``True``, if the log-likelihood with a homoskedastic model is better, then return it. + - ``trace`` optional scalar (default to ``0``). If negative, fit silently. If ``0``, only high level information is given. If ``1``, information is given about the result of the heterogeneous model optimization. Level ``2`` gives more details. Level ``3`` additionaly displays all details about initialization of hyperparameters. + - ``return_matrices`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). + - ``return_hom`` boolean to include homoskedastic GP models used for initialization (i.e., ``modHom`` and ``modNugs``). + - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + eps : float + jitter used in the inversion of the covariance matrix for numerical stability + init,known : dict + optional lists of starting values for mle optimization or that should not be optimized over, respectively. + Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. + One can set one or several of the following: + - ``theta`` lengthscale parameter(s) for the mean process either one value (isotropic) or a vector (anistropic) + - ``Delta`` vector of nuggets corresponding to each design in ``X0``, that are smoothed to give ``Lambda`` (as the global covariance matrix depends on ``Delta`` and ``nu_hat``, it is recommended to also pass values for ``theta``) + - ``beta0`` constant trend of the mean process + - ``k_theta_g`` constant used for link mean and noise processes lengthscales, when ``settings['linkThetas'] == 'joint'`` + - ``theta_g`` either one value (isotropic) or a vector (anistropic) for lengthscale parameter(s) of the noise process, when ``settings['linkThetas'] != 'joint'`` + - ``g`` scalar nugget of the noise process + - ``g_H`` scalar homoskedastic nugget for the initialisation with a :func: homGP.mleHomGP. See Notes. + - ``pX`` matrix of fixed pseudo inputs locations of the noise process corresponding to Delta + covtype : str + covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` + maxit : int + maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization + ''' f = f.flatten() model = hetGP() From 26d396ad6ec55e927e5d07d918e32a4d946d0a45 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Thu, 18 Sep 2025 22:43:12 -0400 Subject: [PATCH 010/103] Integrating homGP and hetGP --- .../gen_funcs/acquisition_1d_stochastic.py | 764 ++++++++++++++++++ ...tic.py => acquisition_md_deterministic.py} | 0 PUQ/designmethods/sequential_1d_stochastic.py | 192 +++++ ...stic.py => sequential_md_deterministic.py} | 2 +- PUQ/surrogate.py | 602 -------------- PUQ/surrogatemethods/pcHetGP.py | 8 +- PUQ/surrogatemethods/pcHomGP.py | 13 +- examples/Technometrics2024/figure1b_new.py | 2 +- examples/Technometrics2024/figure2_new.py | 40 +- examples/Technometrics2024/figure6_new.py | 2 +- 10 files changed, 1005 insertions(+), 620 deletions(-) create mode 100644 PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py rename PUQ/designmethods/gen_funcs/{acquisition_deterministic.py => acquisition_md_deterministic.py} (100%) create mode 100644 PUQ/designmethods/sequential_1d_stochastic.py rename PUQ/designmethods/{sequential_deterministic.py => sequential_md_deterministic.py} (97%) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py new file mode 100644 index 0000000..eeac25f --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -0,0 +1,764 @@ +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from hetgpy.covariance_functions import cov_gen +from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult +import emcee +from PUQ.designmethods.gen_funcs.allocate_reps import allocate +from copy import deepcopy + +def generate_neighborhood(acq): + + rand = np.random.default_rng(acq.seed) + neigh_type = acq.args.get("neighbor", None) + + if neigh_type == "LHS": + N = int(acq.nL) + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L = sampling(N) + else: + N = int(acq.nL * 0.5) + + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L_explore = sampling(N) + + sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) + Lt = sampling(N) + + num_options = len(acq.x) + + # Distribute the rows as evenly as possible + counts = np.full(num_options, N // num_options) # Base count for each row type + counts[ + rand.choice(num_options, N % num_options, replace=False) + ] += 1 # Assign extra rows randomly + + # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) + Lx = np.vstack( + [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] + ) + + # Shuffle the rows randomly + rand.shuffle(Lx) + + L_exploit = np.concatenate((Lx, Lt), axis=1) + + L = np.concatenate((L_explore, L_exploit)) + + return L + + +class acquisition_function: + def __init__(self, model, cls_func, args): + self.model = model#deepcopy(model) # model.copy() + self.cls_func = cls_func + #self.persis_info = persis_info + self.args = args + self.x = self.cls_func.x + self.d = self.cls_func.d + self.p = self.cls_func.p + self.dx = self.cls_func.dx + self.dt = self.cls_func.dt + self.zlim = cls_func.zlim + self.xlim = cls_func.zlim[0 : cls_func.dx, :] + self.tlim = cls_func.zlim[cls_func.dx : cls_func.p, :] + self.y = self.cls_func.real_data + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) + self.twopiddet = ( + (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + ) + + def acquire_new(self): + + if isinstance(self, lookahead): + return eval("self.evaluate")() + + else: + return eval("self.evaluate")( + self.args.get("new", True), return_pseudo=False + ) + + def gen_pred(self, model, x, t, return_id=False, return_flat=False): + nm, d = t.shape[0], x.shape[0] + ntot = nm * d + + # print(t.shape) + # (ntot, d) + x_tiled = np.tile(x, (t.shape[0], 1)) + # (ntot, p-d) + t_repeated = np.repeat(t, x.shape[0], axis=0) + # (ntot, p) + z = np.hstack([x_tiled, t_repeated]) + + # to construct S matrix + id_row = np.arange(0, ntot) + id_col = np.arange(0, ntot).reshape(nm, d) + id_col = np.repeat(id_col, repeats=d, axis=0) + + # predict at mesh + meshPr = model.predict(x=z, thetaprime=z) + + # ntot, ntot x ntot, ntot + # mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] + mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["covmat"], meshPr._info["var"] + + muT = mu.reshape(nm, d) + S = Sn[id_row[:, None], id_col].reshape(nm, d, d) + + if return_id: + return muT, S, z, id_row[:, None], id_col, mu[:, None] + else: + if return_flat: + return muT, S, z, mu[:, None] + else: + return muT, S, z + + def crit_pvar(self, model, x, t): + + # posterior variance + mu, S, z = self.gen_pred(model, x, t) + + M = S + 0.5 * self.Sigma3d + N = S + self.Sigma3d + + f = multiple_pdfs(self.y, mu, M) + g = multiple_pdfs(self.y, mu, N) + + vals = (1 / self.twopiddet) * f - g**2 + + return vals + + def total_var(self, model, x, t): + vals = self.crit_pvar(model=model, x=x, t=t) + return np.mean(self.weights * vals) + + +class var(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", True) + self.nL = args.get("nL", 100) + + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + if new: + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + + def evaluate_exploit(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + + +class imse(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + if new: + sampling = LHS(xlimits=self.zlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + def evaluate_exploit(self, L): + IMSPE_grid = np.array( + [crit_IMSPE(id=[x_id], model=self.model) for x_id, x in enumerate(L)] + ) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + +class ivar(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + + # import pandas as pd + # import seaborn as sns + # import matplotlib.pyplot as plt + # df = pd.DataFrame(self.t_ref) + # sns.pairplot(df, diag_kind="kde") # Use KDE for diagonal histograms + # plt.show() + # print(self.t_ref.shape) + # import matplotlib.pyplot as plt + # if self.t_ref.shape[1] > 1: + # plt.scatter(self.t_ref[:, 0], self.t_ref[:, 1]) + # plt.show() + # else: + # plt.hist(self.t_ref) + # plt.show() + + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + if new: + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + else: + L = self.model["X0"] + new_input = self.evaluate_exploit(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + + + nm = self.t_ref.shape[0] + + vals = np.zeros(self.nL) + + mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) + + V1 = S + self.Sigma3d + + # for i in np.arange(S.shape[0]): + # print(np.diag(S[i, :, :])) + + # predict at candidates + candPr = self.model.predict(x=L, thetaprime=z) + + # nL, nL, nL x ntot + # candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] + candvar, candnugs, candcov = candPr._info["var"], candPr._info["nugs"], candPr._info["covmat"] + candtotvat = candvar + candnugs + + for k in range(0, self.nL): + # (mn x d x 1) + covc = candcov[k, :].reshape(nm, self.d, 1) + + # (mn x 1 x d) + covcT = np.transpose(covc, (0, 2, 1)) + + # (nm, d, d) + phic = (covc * covcT) / candtotvat[k] + + # C1: nm x d x d, C2: nm x d x d + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(self.y, mu, C1) + dets = multiple_determinants(C2) + + part2 = (1 / self.twopid) * (rpdf / np.sqrt(dets)) + + vals[k] = np.sum(self.weights * part2) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + + return new_input + + def evaluate_exploit(self, L): + + nL = L.shape[0] + + nm = self.t_ref.shape[0] + vals = np.zeros(nL) + nrep = 1 + + mu, S, z, idr, idc, muf = self.gen_pred( + self.model, self.x, self.t_ref, return_id=True + ) + + V1 = S + self.Sigma3d + V2 = S + 0.5 * self.Sigma3d + + Ki, mult = self.model.Ki, self.model.mult + + candPr = self.model.predict(x=self.model["X0"]) + candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] + smean = self.model["Z0"] + + # compute B + if self.model.get("Lambda") is None: + tmp = self.model.g + tmp = np.repeat(tmp, nL) + else: + tmp = self.model.Lambda + + denom = (mult * (mult + nrep)) / (nrep * tmp) - np.diag(Ki) + Ki3D = Ki[:, :, None] * Ki[:, None, :] + B = Ki3D / denom[:, None, None] + KB = self.model.Ki + B + + # ntot x ntr + kx = cov_gen( + X1=z, X2=self.model.X0, theta=self.model.theta, type=self.model.covtype + ) + + # coefficients to be used + ap1 = mult + 1 + mean_coefs = (candmean - smean) / ap1 + var_coefs = (candvar + candnugs) / (ap1**2) + smean = smean[:, None] + + KBe_all = KB[np.arange(nL), :, np.arange(nL)] + kKBe_all = KBe_all @ kx.T + + B_kx = np.einsum("ijk, bk -> ijb", B, kx) + phi_all = self.model.nu_hat * np.einsum("aj, ijb -> aib", kx, B_kx) + + Bs = B @ smean + c1, c2 = (1 / self.twopiddet), (1 / self.twopid) + for k in range(0, nL): + + phi_k = phi_all[idr, k, idc].reshape(nm, self.d, self.d) + + KBe_k = KB[k, :, k : k + 1] + mu_k = muf + kx @ (mean_coefs[k] * KBe_k + Bs[k, :, :]) + mu_kT = mu_k.reshape(nm, self.d) + + kKBe = kKBe_all[k, :] + covc = kKBe.reshape(nm, self.d, 1) + covcT = np.transpose(covc, (0, 2, 1)) + + # nm x d x d + gamma_k = var_coefs[k] * (covc * covcT) + + M = V2 - phi_k + gamma_k + part1 = c1 * multiple_pdfs(self.y, mu_kT, M) + + C1 = V1 - phi_k + N = 0.5 * C1 + gamma_k + part2 = multiple_pdfs(self.y, mu_kT, N) + + dets1 = multiple_determinants(C1) + part2 = c2 * (1 / np.sqrt(dets1)) * part2 + + vals[k] = np.sum(self.weights * (part1 - part2)) + + # print(np.argmin(vals)) + new_input = L[np.argmin(vals), :].reshape(1, self.p) + + # import matplotlib.pyplot as plt + # plt.scatter(L[:, 1], L[:, 2], c=vals) + # plt.scatter(L[np.argmin(vals), :][1], L[np.argmin(vals), :][2], marker="*") + # plt.show() + + return new_input + + +class lookahead(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.method = args.get("method", "ivar") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + + def determine_h(self): + horizon = self.args.get("horizon", None) + # print(horizon) + if horizon.get("method") == "target": + if horizon.get("previous_ratio") is None: + horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + self.h = horizon.get("h0") + else: + target = horizon.get("target_ratio") + previous_ratio = horizon.get("previous_ratio") + current_horizon = horizon.get("h0") + + ratio = len(self.model.Z0) / len(self.model.Z) + + # Ratio increased while too small + if ratio < target and ratio < previous_ratio: + self.h = max(-1, current_horizon - 1) + + # Ratio decreased while too high + elif ratio > target and ratio > previous_ratio: + self.h = current_horizon + 1 + else: + self.h = current_horizon + + horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + horizon["h0"] = self.h + + elif horizon.get("method") == "adaptive": + budget = np.sum(self.model._info["mult"]) + + if self.method == "imse": + mult_star = allocate_mult(model=self.model, N=budget).astype(int) + + + + else: + alloc_obj = allocate(budget, + self.model, + self.cls_func, + {"theta":self.t_ref, "weight":self.weights}) + + alloc_obj.allocatereps() + mult_star = alloc_obj.reps + + tab_input = mult_star - self.model.mult + tab_input[tab_input < 0] = 0 + + # import matplotlib.pyplot as plt + # for label, x_count, y_count in zip(tab_input, self.model.X0[:, 1], self.model.X0[:, 2]): + # col = "blue" + # plt.annotate( + # label, + # xy=(x_count, y_count), + # xytext=(0, 0), + # textcoords="offset points", + # fontsize=12, + # color=col + # ) + # plt.show() + + rand = np.random.default_rng(self.seed) + u, counts = np.unique(tab_input, return_counts=True) + self.h = rand.choice(u, p=counts / counts.sum()) + + elif horizon.get("h0") == -1: + self.h = -1 + else: + self.h = horizon.get("h0") + + def crit_eval(self, model): + if self.method == "imse": + value = IMSPE(model) + else: + value = self.total_var(model, self.x, self.t_ref) + return value + + def evaluate(self): + + if self.args.get("long"): + self.evaluate_vl() + else: + self.evaluate_vq() + + def evaluate_vl(self): + + acq_obj = eval(self.method)( + model=self.model, + cls_func=self.cls_func, + args=self.args, + ) + + self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( + acq_obj, "weights", None + ) + + # define horizon + self.determine_h() + + if self.h == -1: + acq_obj.evaluate(new=True, return_pseudo=True) + self.znew, self.new = acq_obj.znew, True + elif self.h == 0: + acq_obj.evaluate(new=True, return_pseudo=True) + z_A = acq_obj.znew + acq_obj.model.update(Xnew=z_A, Znew=acq_obj.fnew, maxit=0) + model_A = acq_obj.model.copy() + IVAR_A1 = self.crit_eval(model_A) + + acq_obj.model = self.model.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + z_B = acq_obj.znew + acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) + + model_B = acq_obj.model.copy() + IVAR_B1 = self.crit_eval(model_B) + + if IVAR_A1 < IVAR_B1: + self.znew, self.new = z_A, True + return + else: + self.znew, self.new = z_B, False + return + else: + depth = self.h + 1 + paths = np.eye(depth, dtype=int).tolist() + inputs, values = [], [] + if self.args.get("parallel", None) is None: + for pid, path in enumerate(paths): + znew, vals = self.eval_branch(acq_obj, path) + inputs.append(znew) + values.append(vals) + # print(np.round(vals*1000000)) + + vals_id = np.argmin(values) + new_input = inputs[vals_id][0] + a = paths[vals_id][0] + self.znew = new_input + self.new = True if a == 1 else False + return self + else: + from joblib import Parallel, delayed + + results = Parallel(n_jobs=len(paths))( + delayed(self.eval_branch)(acq_obj, path) + for pid, path in enumerate(paths) + ) + inputs, vals = zip(*results) + vals_id = np.argmin(vals) + return inputs[vals_id][0] + + def eval_branch(self, acq_obj, action): + + acq_obj.model = self.model.copy() + + # Initialize actions + input_action = [] + + for a in action: + + new = True if a == 1 else False + + acq_obj.evaluate(new=new, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + input_action.append(acq_obj.znew) + + value = self.crit_eval(acq_obj.model) + + return input_action, value + + def evaluate_vq(self): + designs = [] + + acq_obj = eval(self.method)( + model=self.model, + cls_func=self.cls_func, + args=self.args, + ) + + self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( + acq_obj, "weights", None + ) + + # define horizon + self.determine_h() + + acq_obj.evaluate(new=True, return_pseudo=True) + z_A, f_A = acq_obj.znew, acq_obj.fnew + path_A = [{"par": z_A, "new": True}] + + if self.h == -1: + self.znew, self.new = z_A, True + return + else: + acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) + model_A = acq_obj.model.copy() + IVAR_A1 = self.crit_eval(model_A) + + if self.h > 0: + + acq_obj.model = model_A.copy() + for i in range(0, self.h): + acq_obj.evaluate(new=False, return_pseudo=True) + path_A.append({"par": acq_obj.znew, "new": False}) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + + IVAR_A = self.crit_eval(acq_obj.model) + + designs.append({"input": z_A, "path": path_A, "value": IVAR_A}) + + if self.h == 0: + acq_obj.model = self.model.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + z_B = acq_obj.znew + acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) + + model_B = acq_obj.model.copy() + IVAR_B1 = self.crit_eval(model_B) + + if IVAR_A1 < IVAR_B1: + self.znew, self.new = z_A, True + return + else: + self.znew, self.new = z_B, False + return + + else: + newmodelB = self.model.copy() + for i in range(self.h): + # Choose a new replicate + acq_obj.model = newmodelB.copy() + acq_obj.evaluate(new=False, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + + if i == 0: + z0 = 1 * acq_obj.znew + path_B = [] + + path_B.append({"par": acq_obj.znew, "new": False}) + newmodelB = acq_obj.model.copy() + + # Choose a new design + acq_obj.evaluate(new=True, return_pseudo=True) + acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + path_C = [{"par": acq_obj.znew, "new": True}] + + for j in range(i, self.h - 1): + # Remaining replicates + acq_obj.evaluate(new=False, return_pseudo=True) + acq_obj.model.update( + Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0 + ) + path_C.append({"par": acq_obj.znew, "new": False}) + + IVAR_C = self.crit_eval(acq_obj.model) + designs.append( + {"input": z0, "path": path_B + path_C, "value": IVAR_C} + ) + + if IVAR_C < IVAR_A: + self.znew, self.new = z0, False + return + + self.znew, self.new = z_A, True + return diff --git a/PUQ/designmethods/gen_funcs/acquisition_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py similarity index 100% rename from PUQ/designmethods/gen_funcs/acquisition_deterministic.py rename to PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py diff --git a/PUQ/designmethods/sequential_1d_stochastic.py b/PUQ/designmethods/sequential_1d_stochastic.py new file mode 100644 index 0000000..56e8764 --- /dev/null +++ b/PUQ/designmethods/sequential_1d_stochastic.py @@ -0,0 +1,192 @@ +from hetgpy import hetGP +import numpy as np +import matplotlib.pyplot as plt +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from PUQ.designmethods.gen_funcs.acquisition_1d_stochastic import var, ivar, imse, lookahead +import time +from PUQ.surrogate import emulator + +class sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): + + if test is not None: + self.test_data_gen(test) + + H = [] + timel = [] + metric_sum = {} + for t in range(0, T): + # print(f"t: {t}") if self.trace else None + print(t) + tic = time.time() + + # hetGP + # model = hetGP() + # model.mle(X=z0, + # Z=f0, + # covtype="Gaussian", + # known={"beta0":np.mean(f0)}) + + model = emulator(x=z0, + theta=np.array([0]), + f=f0, + method='hetGP', + args={"known": {"beta0":np.mean(f0)}}) + model.fit() + + + toc = time.time() + timel.append(toc - tic) + print(toc - tic) + args["seed"] += 1 + + if test is not None: + metric_sum = self.eval_perf(model, args.get("extra_metric", True)) + + acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) + + acq_func.acquire_new() + znew = acq_func.znew + fnew = self.cls_func.sim_f(znew.flatten(), persis_info=persis_info).reshape( + 1, 1 + ) + + f0 = np.concatenate((f0, fnew), axis=0) + z0 = np.concatenate((z0, znew), axis=0) + + H.append( + { + "t": t, + "new": acq_func.new, + "h": getattr(acq_func, "h", None), + "f": fnew, + "z": znew, + "MSE": metric_sum.get("MSE", None), + "MAD": metric_sum.get("MAD", None), + "VAR": metric_sum.get("VAR", None), + "MSEy": metric_sum.get("MSEy", None), + "MADy": metric_sum.get("MADy", None), + "MSEn": metric_sum.get("MSEn", None), + "MADn": metric_sum.get("MADn", None), + "summary_metric": metric_sum.get("sum_metric", None), + } + ) + + unique_rows, counts = np.unique(z0, axis=0, return_counts=True) + + self.xt = unique_rows + self.reps = counts + self.fs = f0 + self.zs = z0 + self.time = timel + self.H = H + + return self + + def test_data_gen(self, test): + + # Gen test data + self.ttest, self.ptest, self.wtest, self.ntest, self.ftest = ( + test["theta"], + test["p"], + test["w"], + test["noise"], + test["f"], + ) + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ftest) + # plt.show() + + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ntest) + # plt.show() + + x_tiled = np.tile(self.cls_func.x, (self.ttest.shape[0], 1)) + t_repeated = np.repeat(self.ttest, self.cls_func.x.shape[0], axis=0) + self.ztest = np.hstack([x_tiled, t_repeated]) + + # To be used for performance evaluations + ntot, nm, d = self.ztest.shape[0], self.ttest.shape[0], self.cls_func.d + + # to construct S matrix + self.idr = np.arange(0, ntot)[:, None] + idc = np.arange(0, ntot).reshape(nm, d) + self.idc = np.repeat(idc, repeats=d, axis=0) + + def eval_perf(self, model, extra_metric=False): + + nm, d = self.ttest.shape[0], self.cls_func.d + # sc = plt.scatter(self.ztest[:, 1], self.ztest[:, 2], c=self.wtest, cmap="viridis") # Use any colormap + # plt.colorbar(sc, label="wtest values") # Add a colorbar + # plt.show() + + # predict at mesh + pr = model.predict(x=self.ztest, thetaprime=self.ztest) + + # ntot, ntot x ntot, ntot + # mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] + mu, Sn, nugs = pr._info["mean"], pr._info["covmat"], pr._info["nugs"] + + muT = mu.reshape(nm, d) + S = Sn[self.idr, self.idc].reshape(nm, d, d) + + N = S + self.Sigma3d + g = multiple_pdfs(self.y, muT, N) + + MSE = np.mean(((g.flatten() - self.ptest.flatten()) ** 2) * self.wtest) + MAD = np.mean(np.abs(g.flatten() - self.ptest.flatten()) * self.wtest) + + M = S + 0.5 * self.Sigma3d + f = multiple_pdfs(self.y, muT, M) + + twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) + + if extra_metric: + diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) + diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) + + + MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) + MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) + + MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) + MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) + + # Weighted absolute differences + f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) + n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) + + # Unweighted absolute differences + f_wh = np.mean(np.abs(diff_y), axis=0) + n_wh = np.mean(np.abs(diff_n), axis=0) + summary = {} + for di in range(d): + summary[f"f{di+1}w"] = f_w[di] + summary[f"f{di+1}"] = f_wh[di] + summary[f"n{di+1}w"] = n_w[di] + summary[f"n{di+1}"] = n_wh[di] + else: + MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 + + metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, + "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, + "summary": summary} + return metric_sum diff --git a/PUQ/designmethods/sequential_deterministic.py b/PUQ/designmethods/sequential_md_deterministic.py similarity index 97% rename from PUQ/designmethods/sequential_deterministic.py rename to PUQ/designmethods/sequential_md_deterministic.py index ff9b08b..925ed3a 100644 --- a/PUQ/designmethods/sequential_deterministic.py +++ b/PUQ/designmethods/sequential_md_deterministic.py @@ -1,6 +1,6 @@ import numpy as np from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from PUQ.designmethods.gen_funcs.acquisition_deterministic import ivar, var, imse, rnd, exp +from PUQ.designmethods.gen_funcs.acquisition_md_deterministic import ivar, var, imse, rnd, exp import time from PUQ.surrogate import emulator diff --git a/PUQ/surrogate.py b/PUQ/surrogate.py index ef4609c..22619f8 100644 --- a/PUQ/surrogate.py +++ b/PUQ/surrogate.py @@ -348,437 +348,6 @@ def computeC(self, x=None, theta1=None, realdata=None, realvar=None): return self.method.computeC(self._info, x, theta1, realdata, realvar) - def supplement( - self, - size, - x=None, - xchoices=None, - theta=None, - thetachoices=None, - choicescost=None, - cal=None, - args=None, - overwrite=False, - removereps=None, - ): - """ - Chooses new theta or x to be investigated. - - .. important:: - A user must provide either x or theta (or cal). - - :Example: - .. code-block:: python - - emulator.supplement(size=size, theta=theta) - - Parameters - ---------- - size : int - The number of new supplements to return. - - .. note:: - - If only theta is supplied, returns at most size of those. - - If only x is supplied, returns at most size of those. - - If both x and theta are supplied, then size will be less - than the product of the number of returned theta and the - number of x. - - x : numpy.ndarray, optional - An array of parameters where to predict. - The default is None. - xchoices : numpy.ndarray, optional - An array of inputs to select from. - If not provided, a subset of x is used. The default is None. - - .. warning:: self.__suppx has not developed yet. - - theta : numpy.ndarray, optional - An array of parameters where to predict. The default is None. - thetachoices : numpy.ndarray, optional - An array of parameters to select from. - If not provided, a subset of x is used. The default is None. - choicescost : numpy.ndarray, optional - An array of positive cost of each element in choice. - The default is None. - cal : surmise.calibration.calibrator, optional - A calibrator object that contains information about calibration. - The default is None. - args : dict, optional - A dictionary containing options to pass to the method. - The default is None. If not provided, defaults to the one used to - build the emulator. - overwrite : boolean, optional - True if an existing supplement is replaced. If False, and one - exists, returns without doing anything. The default is False. - removereps : boolean, optional - True if any replications existing supplement is removed. - The default is None. If not provided, defaults to the one used to - build the emulator. - - Raises - ------ - ValueError - If the dimensions do not match the fitted emulator - - Returns - ------- - numpy.ndarray - self.__thetasupp (or self.__x) - numpy.ndarray - suppinfo - - """ - - if args is not None: - argstemp = {**self._args, **args} - else: - argstemp = self._args - - if removereps is None: - if x is not None: - removereps = not self.__options["xreps"] - if theta is not None: - removereps = not self.__options["thetareps"] - - if size < 1: - if size == 0: - if self.__supptheta is None: - raise ValueError("No self.__supptheta exists.") - else: - print("Returning self.__supptheta.") - return copy.deepcopy(self.__supptheta) - else: - raise ValueError("Size should be a positive integer.") - - if cal is not None: - try: - if theta is None: - theta = cal.theta(1000) - if self.__theta.shape[1] != theta.shape[1]: - raise ValueError("cal.theta(n) produces the wrong " "shape.") - except Exception: - raise ValueError("cal.theta(2000) failed.") - - if (theta is None) and (x is None): - raise ValueError("Provide either x or (theta or cal).") - else: - if x is not None: - if theta is not None: - raise ValueError("Provide either x or (theta or cal).") - else: - raise ValueError("Supplement x has not supported yet.") - elif theta is not None: - if self.__theta.shape[1] == theta.shape[1]: - if thetachoices is None: - if theta.shape[0] > 30 * size: - thetachoices = theta[ - np.random.choice( - theta.shape[0], 30 * size, replace=False - ), - :, - ] - else: - thetachoices = copy.copy(theta) - else: - if thetachoices.shape[1] != theta.shape[1]: - raise ValueError( - "Dimensions of choices and " - "predictions are not aligning." - ) - if choicescost is None: - choicescost = np.ones(thetachoices.shape[0]) - else: - if thetachoices.shape[0] != choicescost.shape[0]: - raise ValueError("choicecost is not the right " "shape.") - else: - raise ValueError( - "theta has the wrong shape, it does not " - "match emu._emulator__theta." - ) - - try: - supptheta, suppinfo = self.method.supplementtheta( - self._info, - copy.copy(size), - copy.copy(theta), - copy.copy(thetachoices), - copy.copy(choicescost), - copy.copy(cal), - **argstemp - ) - except Exception: - raise ValueError("supplementtheta does not exist.") - - if supptheta is not None: - if removereps: - nctheta, ctheta, rtheta = _matrixmatching(self.__theta, supptheta) - if nctheta.shape[0] < 0.5: - supptheta = None - raise ValueError( - "supptheta is a complete replication of " "self.__theta." - ) - else: - if nctheta.shape[0] < supptheta.shape[0]: - print("Removing replications from supptheta.") - supptheta = supptheta[nctheta, :] - - if (self.__supptheta is not None) and (not overwrite): - raise ValueError( - "Either evaluate self.__supptheta or " "select overwrite = True." - ) - else: - self.__supptheta = copy.copy(supptheta) - return copy.copy(self.__supptheta), suppinfo - else: - raise ValueError("method.supplementtheta provides None.") - - def update(self, x=None, theta=None, f=None, args=None, options=None): - """ - Updates and refits the emulator. - - :Example: - .. code-block:: python - - emulator.update(x=x) # Replace self.__x with x - - emulator.update(theta=theta) # Replace self.__theta with theta - - emulator.update(f=f) # Replace self.__f with f if - # self.__supptheta is None. Otherwise, - # update self.__theta - # with (self.__theta, self.__supptheta) - # and self.__f with (self.__f, f) - - emulator.update(x=x, f=f) # Update self.__x with (self.__x, x) - # and self.__f with (self.__f, f) - - emulator.update(theta=theta, f=f) # Update self.__theta with - # (self.__theta, theta) and - # self.__f with (self.__f, f) - - .. warning:: self.__suppx has not developed yet. - - Parameters - ---------- - x : numpy.ndarray, optional - xs you would like to append. The default is None. - - theta : numpy.ndarray, optional - thetas you would like to append. Defaults to emu.__supptheta. - The default is None. - - f : numpy.ndarray, optional - An array of responses. The default is None. - - args : dict, optional - A dictionary containing options you would like to pass to - [method].update(f, theta, x, args). - Defaults to the one used to build the emulator. - - options : dict, optional - A dictionary containing options to build the emulator. - Modify with update when you want to change it. - The default is None. - - Raises - ------ - ValueError - If the dimensions of inputs do not match with the existing - emulator. - - Returns - ------- - None. - - """ - - if options is not None: - self.__optionsset(copy.copy(options)) - if args is not None: - self._args = {**self._args, **copy.deepcopy(args)} - - if (theta is not None) and (x is not None): - # provide either x or theta for now - raise ValueError( - "Adding new theta and x at once is currently not " - "supported. Supply either theta or x." - ) - elif f is not None: - if (theta is None) and (x is None): - if f.shape[0] == self.__f.shape[0]: - # no of rows of f (and x) is still the same - if self.__supptheta is not None: - if f.shape[1] == self.__supptheta.shape[0]: - if self.__options["thetareps"]: - # update with self.__supptheta and f - self.__theta = np.vstack( - (self.__theta, self.__supptheta) - ) - self.__f = np.hstack((self.__f, f)) - self.__supptheta = None - else: - # identify matches - nc, c, r = _matrixmatching( - self.__theta, self.__supptheta - ) - # update __f with f for the matches - self.__f[:, r] = f[:, c] - if nc.shape[0] > 0.5: - f = f[:, nc] - supptheta = self.__supptheta[nc, :] - self.__f = np.hstack((self.__f, f)) - self.__theta = np.vstack((self.__theta, supptheta)) - self.__supptheta = None - else: - raise ValueError( - "Could not resolve absense of " - " theta, please provide theta." - ) - elif f.shape[1] == self.__theta.shape[0]: - # just updating f - self.__f = f - else: - raise ValueError( - "Could not resolve absense of theta, " - "please provide theta" - ) - else: - # no of rows of f (and x) is not the same - raise ValueError("Could not resolve absense of x. Provide " "x.") - elif theta is not None: - if ( - (f.shape[0] == self.__f.shape[0]) - and (f.shape[1] == theta.shape[0]) - and (theta.shape[1] == self.__theta.shape[1]) - ): - if self.__options["thetareps"]: - # if replicated thetas are allowed - self.__theta = np.vstack((self.__theta, theta)) - self.__f = np.hstack((self.__f, f)) - else: - # if replicated thetas are not allowed - nc, c, r = _matrixmatching(self.__theta, theta) - self.__f[:, r] = f[:, c] - if nc.shape[0] > 0.5: - # append the unmatched thetas - f = f[:, nc] - theta = theta[nc, :] - self.__f = np.hstack((self.__f, f)) - self.__theta = np.vstack((self.__theta, theta)) - else: - raise ValueError( - "Check the dimensions of theta and f. " - "Possible solutions: " - "1) Use emu.update(theta=theta) to " - "update the emulator first. " - "2) Provide x for alignment." - ) - - elif x is not None: - if ( - (f.shape[1] == self.__f.shape[1]) - and (f.shape[0] == x.shape[0]) - and (x.shape[1] == self.__x.shape[1]) - ): - if options["xreps"]: - # if replicated xs are allowed - self.__x = np.vstack((self.__x, x)) - self.__f = np.vstack((self.__f, f)) - else: - # if replicated xs are not allowed - nc, c, r = _matrixmatching(self.__x, x) - self.__f[r, :] = f[c, :] - if nc.shape[0] > 0.5: - # append the unmatched xs - f = f[nc, :] - x = x[nc, :] - self.__f = np.vstack((self.__f, f)) - self.__x = np.vstack((self.__x, x)) - else: - raise ValueError( - "Check the dimensions of x and f. " - "Possible solutions: " - "1) Use emu.update(x=x) to " - "update the emulator first. " - "2) Provide theta for alignment." - ) - elif x is not None: # theta None, f None - # Update self.__x with x - if x.shape[0] != self.__f.shape[0]: - raise ValueError( - "Number of rows of x is changed but new f is " "not provided." - ) - else: - self.__x = x - elif theta is not None: # x None, f None - # Update self.__theta with theta - if theta.shape[0] != self.__f.shape[1]: - raise ValueError( - "Number of rows of theta is changed but new " "f is not provided." - ) - else: - self.__theta = theta - - if "update" in dir(self.method): - x, theta, f = self.__preprocess() - self.method.update(self._info, x, theta, f, **self._args) - else: - if self.__options["autofit"]: - self.fit() - - return - - def remove(self, x=None, theta=None, cal=None, options=None): - """ - Removes either x or theta, and the corresponding f values from - the fitted emulator, and refits the emulator. - - :Example: - - .. code-block:: python - - emlator.remove(theta=theta) - - Parameters - ---------- - x : numpy.ndarray, optional - x to remove from self.__x. The default is None. - theta : numpy.ndarray, optional - theta to remove from self.__theta. The default is None. - cal : surmise.calibration.calibrator, optional - A calibrator class instance as defined in surmise.calibration. - The default is None. - options : dict, optional - A dictionary containing options to build the emulator. - The default is None. - - Returns - ------- - None. - - """ - - if cal is not None: - totalseen = np.where( - np.mean(np.logical_not(np.isfinite(self.__f)), 0) - < self.__options["thetarmnan"] - )[0] - lpdf_ex = cal.theta.lpdf(self.__theta[totalseen, :]) - thetasort = np.argsort(lpdf_ex) - m_cutoff = max(lpdf_ex.shape[0] - 10 * self.__theta.shape[1], 0) - numcutoff = np.minimum(-500, lpdf_ex[thetasort[m_cutoff]]) - if any(lpdf_ex < numcutoff): - rmtheta = totalseen[np.where(lpdf_ex < numcutoff)[0]] - theta = self.__theta[rmtheta, :] - print("removing %d thetas" % rmtheta.shape[0]) - if theta is not None: - nc, c, r = _matrixmatching(theta, self.__theta) - self.__theta = self.__theta[nc, :] - self.__f = self.__f[:, nc] - if self.__options["autofit"]: - self.fit() - return def __optionsset(self, options=None): options = copy.deepcopy(options) @@ -1093,19 +662,6 @@ def mean(self, args=None): else: raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - def mean_gradtheta(self, args=None): - """ - Returns the gradient of the mean at theta and x with respect to theta - when building the prediction. - """ - - pfstr = "predict" # prefix string - opstr = "mean_gradtheta" # operation string - if opstr in self._info.keys(): - return self._info[opstr] - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - def var(self, args=None): """ Returns the pointwise variance at theta and x when building @@ -1126,161 +682,3 @@ def var(self, args=None): return copy.deepcopy(np.var(self._info["rnd"], 0)) else: raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def covx(self, args=None): - """ - Returns the covariance matrix at theta and x when building - the prediction. - """ - - pfstr = "predict" # prefix string - opstr = "covx" # operation string - if (self.emu._emulator__ptf is None) and ( - (pfstr + opstr) in dir(self.emu.method) - ): - if args is None: - args = self.emu._args - return copy.deepcopy(self.emu.method.predictcov(self._info, **args)) - elif opstr in self._info.keys(): - return copy.deepcopy(self._info[opstr]) - elif "covxhalf" in self._info.keys(): - if self._info["covxhalf"].ndim == 2: - return self._info["covxhalf"] @ self._info["covxhalf"].T - else: - am = self._info["covxhalf"].shape - covx = np.ones((am[0], am[1], am[0])) - for k in range(0, self._info["covxhalf"].shape[1]): - A = self._info["covxhalf"][:, k, :] - covx[:, k, :] = A @ A.T - self._info["covx"] = covx - return copy.deepcopy(self._info[opstr]) - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def covxhalf(self, args=None): - """ - Returns the sqrt of the covariance matrix at theta and x when building - the prediction. - That is, if this returns A = predict.covhalf(.)[k], - then A.T @ A = predict.cov(.)[k] - """ - - pfstr = "predict" # prefix string - opstr = "covxhalf" # operation string - if (self.emu._emulator__ptf is None) and ( - (pfstr + opstr) in dir(self.emu.method) - ): - if args is None: - args = self.emu._args - return copy.deepcopy(self.emu.method.predictcov(self._info, **args)) - elif opstr in self._info.keys(): - return copy.deepcopy(self._info[opstr]) - elif "covx" in self._info.keys(): - covxhalf = np.ones(self._info["covx"].shape) - if self._info["covx"].ndim == 2: - W, V = np.linalg.eigh(self._info["covx"]) - covxhalf = V @ (np.sqrt(np.abs(W)) * V.T) - else: - for k in range(0, self._info["covx"].shape[0]): - W, V = np.linalg.eigh(self._info["covx"][k]) - covxhalf[k, :, :] = V @ (np.sqrt(np.abs(W)) * V.T) - self._info["covxhalf"] = covxhalf - return copy.deepcopy(self._info[opstr]) - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def covxhalf_gradtheta(self, args=None): - """ - Returns the gradient of the covxhalf matrix at theta and x when - building the prediction. - """ - - pfstr = "predict" # prefix string - opstr = "covxhalf_gradtheta" # operation string - if opstr in self._info.keys(): - return self._info[opstr] - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def rnd(self, s=100, args=None): - """ - Returns a rnd draws of size s at theta and x - """ - pfstr = "predict" # prefix string - opstr = "rnd" # operation string - - if (self.emu._emulator__ptf is None) and ( - (pfstr + opstr) in dir(self.emu.method) - ): - if args is None: - args = self.emu._args - return copy.deepcopy(self.emu.method.ldf(self._info, **args)) - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def lpdf(self, f=None, args=None): - """ - Returns a log pdf at theta and x - """ - - pfstr = "predict" # prefix string - opstr = "lpdf" # operation string - if (self.emu._emulator__ptf is None) and ( - (pfstr + opstr) in dir(self.emu.method) - ): - if args is None: - args = self.emu._args - return copy.deepcopy(self.emu.method.predictlpdf(self._info, f, **args)) - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - def lpdf_gradtheta(self, f=None, args=None): - """ - Returns a log pdf at theta and x - """ - pfstr = "predict" # prefix string - opstr = "lpdf_gradtheta" # operation string - if (self.emu._emulator__ptf is None) and ( - (pfstr + opstr) in dir(self.emu.method) - ): - if args is None: - args = self.emu._args - return copy.deepcopy( - self.emu.method.predictlpdf_gradtheta(self._info, f, **args) - ) - else: - raise ValueError(self.__methodnotfoundstr(pfstr, opstr)) - - -def _matrixmatching(mat1, mat2): - """ - This is an internal function to do matching between two vectors - """ - # This is an internal function to do matching between two vectors - # it just came up alot - # It returns the where each row of mat2 is first found in mat1 - # If a row of mat2 is never found in mat1, then 'nan' is in that location - - if (mat1.shape[0] > (10 ** (4))) or (mat2.shape[0] > (10 ** (4))): - raise ValueError( - "too many matchings attempted." "Don" "t make the method work so hard!" - ) - if mat1.ndim != mat2.ndim: - raise ValueError("Somehow sent non-matching information to" " _matrixmatching") - if mat1.ndim == 1: - matchingmatrix = np.isclose(mat1[:, None].astype("float"), mat2.astype("float")) - else: - matchingmatrix = np.isclose( - mat1[:, 0][:, None].astype("float"), mat2[:, 0].astype("float") - ) - for k in range(1, mat2.shape[1]): - try: - matchingmatrix *= np.isclose( - mat1[:, k][:, None].astype("float"), mat2[:, k].astype("float") - ) - except Exception: - matchingmatrix *= np.equal(mat1[:, k], mat2[:, k]) - r, c = np.where(matchingmatrix.cumsum(axis=0).cumsum(axis=0) == 1) - - nc = np.array(list(set(range(0, mat2.shape[0])) - set(c))).astype("int") - return nc, c, r diff --git a/PUQ/surrogatemethods/pcHetGP.py b/PUQ/surrogatemethods/pcHetGP.py index 9b2e213..8ad8fa0 100644 --- a/PUQ/surrogatemethods/pcHetGP.py +++ b/PUQ/surrogatemethods/pcHetGP.py @@ -71,8 +71,8 @@ def fit( # print("On site surrogate") fi = fs[i, :][None, :] emu = emulator( - x=np.array([[i]]), - theta=theta, + x=theta, + theta=np.array([[i]]), f=fi, method="hetGP", args={ @@ -121,8 +121,8 @@ def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): predict_hetGP( predinfo=predinfo_hetGP, fitinfo=info, - x=np.array([[i]]), - theta=theta, + x=theta, + theta=np.array([[i]]), thetaprime=thetaprime, ) diff --git a/PUQ/surrogatemethods/pcHomGP.py b/PUQ/surrogatemethods/pcHomGP.py index 3044748..acd4b74 100644 --- a/PUQ/surrogatemethods/pcHomGP.py +++ b/PUQ/surrogatemethods/pcHomGP.py @@ -1,5 +1,5 @@ from PUQ.surrogate import emulator -from PUQ.surrogatemethods.hetGP import predict as predict_hetGP +from PUQ.surrogatemethods.homGP import predict as predict_homGP import numpy as np from PUQ.surrogatemethods.covariances import cov_gen from scipy.linalg import cholesky, inv @@ -71,8 +71,8 @@ def fit( # print("On site surrogate") fi = fs[i, :][None, :] emu = emulator( - x=np.array([[i]]), - theta=theta, + x=theta, + theta=np.array([[i]]), f=fi, method="homGP", args={ @@ -115,14 +115,15 @@ def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): if thetaprime is not None: npr = thetaprime.shape[0] covmat_pc = np.full((numGPs, n, npr), np.nan) + for i in range(0, numGPs): predinfo_hetGP = {} info = fitinfo["emulist"][i] - predict_hetGP( + predict_homGP( predinfo=predinfo_hetGP, fitinfo=info, - x=np.array([[i]]), - theta=theta, + x=theta, + theta=np.array([[i]]), thetaprime=thetaprime, ) diff --git a/examples/Technometrics2024/figure1b_new.py b/examples/Technometrics2024/figure1b_new.py index 9b6a2ea..a505a09 100644 --- a/examples/Technometrics2024/figure1b_new.py +++ b/examples/Technometrics2024/figure1b_new.py @@ -3,7 +3,7 @@ import scipy.stats as sps from PUQ.designmethods.utils import parse_arguments from PUQ.prior import prior_dist -from PUQ.designmethods.sequential_deterministic import sequential_design +from PUQ.designmethods.sequential_md_deterministic import sequential_design from smt.sampling_methods import LHS from test_funcs_new import unimodal diff --git a/examples/Technometrics2024/figure2_new.py b/examples/Technometrics2024/figure2_new.py index 24ab87d..e98baf6 100644 --- a/examples/Technometrics2024/figure2_new.py +++ b/examples/Technometrics2024/figure2_new.py @@ -2,7 +2,7 @@ import numpy as np import matplotlib.pyplot as plt from PUQ.surrogate import emulator -from PUQ.posterior import posterior +from PUQ.posterior import multiple_pdfs, compute_postvar class sinlinear: @@ -56,19 +56,49 @@ def sim(self, H, persis_info, sim_specs, libE_info): "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, "return.hom": False, "factr": 1e9}}) - post = posterior(data_cls=cls_sinlin, emulator=emu) - # Generate test data thetatest = np.arange(-10, 10, 0.0025)[:, None] ftest = cls_sinlin.function(thetatest) ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) + + # predict at mesh + nm, d = thetatest.shape[0], cls_sinlin.d + pr_test = emu.predict(theta=thetatest, thetaprime=thetatest) + mu, Sn = pr_test._info["mean"], pr_test._info["S"] + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) + Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) + N = S + Sigma3d + M = S + 0.5*Sigma3d + posttesthat = multiple_pdfs(cls_sinlin.real_data, muT, N) + + diags = np.diag(cls_sinlin.obsvar[cls_sinlin.x, cls_sinlin.x.T]) + coef = (2**cls_sinlin.d) * (np.sqrt(np.pi) ** cls_sinlin.d) * np.sqrt(np.prod(diags)) + posttestvar = compute_postvar(cls_sinlin.real_data, + muT, + N, + M, + coef) + + + # predict at mesh + nm, d = theta.shape[0], cls_sinlin.d + pr_test = emu.predict(theta=theta, thetaprime=theta) + mu, Sn = pr_test._info["mean"], pr_test._info["S"] + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) + Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) + N = S + Sigma3d + posttrhat = multiple_pdfs(cls_sinlin.real_data, muT, N) + + # post = posterior(data_cls=cls_sinlin, emulator=emu) # Predict via the emulator emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) emupred_tr = emu.predict(x=cls_sinlin.x, theta=theta) - posttesthat, posttestvar = post.predict(thetatest) - posttrhat, posttrvar = post.predict(theta) + # posttesthat, posttestvar = post.predict(thetatest) + # posttrhat, posttrvar = post.predict(theta) # Figure 2 (a) ft = 20 diff --git a/examples/Technometrics2024/figure6_new.py b/examples/Technometrics2024/figure6_new.py index a188969..b508da1 100644 --- a/examples/Technometrics2024/figure6_new.py +++ b/examples/Technometrics2024/figure6_new.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np import scipy.stats as sps -from PUQ.designmethods.sequential_deterministic import sequential_design +from PUQ.designmethods.sequential_md_deterministic import sequential_design from PUQ.designmethods.utils import parse_arguments from PUQ.prior import prior_dist from plots import plotline From 2b29f08064c46f8e509a89c28c32a05e0208e035 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Thu, 18 Sep 2025 23:12:27 -0400 Subject: [PATCH 011/103] Cleaning performance model --- PUQ/designmethods/p_sto_bseq.py | 3 +- examples/NeurIPS2024/Fig1.py | 95 ------- examples/NeurIPS2024/Fig10.py | 262 ------------------ examples/NeurIPS2024/Fig2.py | 133 --------- examples/NeurIPS2024/Fig3.py | 176 ------------ examples/NeurIPS2024/Fig4_5_12_13.py | 85 ------ examples/NeurIPS2024/Fig6_14.py | 78 ------ examples/NeurIPS2024/Fig7.py | 53 ---- examples/NeurIPS2024/Fig8.py | 134 --------- examples/NeurIPS2024/Fig9.py | 131 --------- .../WS1_synth.py => Figure2.py} | 0 .../WS2_synth.py => Figure3.py} | 0 examples/NeurIPS2024/README.rst | 41 +-- .../NeurIPS2024/{Workshop_files => }/WS0.py | 0 .../{Workshop_files => }/WS0_run.py | 0 .../{Workshop_files => }/WS1_real.py | 0 .../{Workshop_files => }/WS2_real.py | 0 examples/NeurIPS2024/ci_example.py | 66 ----- examples/NeurIPS2024/compile_results.py | 183 ------------ examples/NeurIPS2024/plotv0/Fig10.py | 182 ------------ examples/NeurIPS2024/plotv0/Fig8.py | 131 --------- examples/NeurIPS2024/plotv0/Fig9.py | 122 -------- examples/NeurIPS2024/result_read.py | 50 ---- examples/NeurIPS2024/run_test_funcs.py | 101 ------- 24 files changed, 7 insertions(+), 2019 deletions(-) delete mode 100644 examples/NeurIPS2024/Fig1.py delete mode 100644 examples/NeurIPS2024/Fig10.py delete mode 100644 examples/NeurIPS2024/Fig2.py delete mode 100644 examples/NeurIPS2024/Fig3.py delete mode 100644 examples/NeurIPS2024/Fig4_5_12_13.py delete mode 100644 examples/NeurIPS2024/Fig6_14.py delete mode 100644 examples/NeurIPS2024/Fig7.py delete mode 100644 examples/NeurIPS2024/Fig8.py delete mode 100644 examples/NeurIPS2024/Fig9.py rename examples/NeurIPS2024/{Workshop_files/WS1_synth.py => Figure2.py} (100%) rename examples/NeurIPS2024/{Workshop_files/WS2_synth.py => Figure3.py} (100%) rename examples/NeurIPS2024/{Workshop_files => }/WS0.py (100%) rename examples/NeurIPS2024/{Workshop_files => }/WS0_run.py (100%) rename examples/NeurIPS2024/{Workshop_files => }/WS1_real.py (100%) rename examples/NeurIPS2024/{Workshop_files => }/WS2_real.py (100%) delete mode 100644 examples/NeurIPS2024/ci_example.py delete mode 100644 examples/NeurIPS2024/compile_results.py delete mode 100644 examples/NeurIPS2024/plotv0/Fig10.py delete mode 100644 examples/NeurIPS2024/plotv0/Fig8.py delete mode 100644 examples/NeurIPS2024/plotv0/Fig9.py delete mode 100644 examples/NeurIPS2024/result_read.py delete mode 100644 examples/NeurIPS2024/run_test_funcs.py diff --git a/PUQ/designmethods/p_sto_bseq.py b/PUQ/designmethods/p_sto_bseq.py index 4daebf2..a666d56 100644 --- a/PUQ/designmethods/p_sto_bseq.py +++ b/PUQ/designmethods/p_sto_bseq.py @@ -90,7 +90,8 @@ def fit(fitinfo, persis_info = add_unique_random_streams({}, nworkers + 1) for perid, per in enumerate(persis_info): - persis_info[perid]['rand_stream'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) + #persis_info[perid]['rand_stream'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) + persis_info[perid]['x'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) #persis_info[perid]['rand_stream'] = np.random.default_rng(perid) # Currently just allow gen to exit if mse goes below threshold value diff --git a/examples/NeurIPS2024/Fig1.py b/examples/NeurIPS2024/Fig1.py deleted file mode 100644 index a753fe3..0000000 --- a/examples/NeurIPS2024/Fig1.py +++ /dev/null @@ -1,95 +0,0 @@ -from PUQ.performance import performanceModel -from PUQ.performanceutils.utils import ( - plot_acc, - plot_acqtime, - plot_endtime, - plot_errorend, -) -import numpy as np -from result_read import get_rep_data -import matplotlib.pyplot as plt - -n = 2048 -w = 2 -b = 1 -rep = 30 -example = "himmelblau_ex" -s = "himmelblau" -label = ["hybrid_ei_c1000/", "hybrid_ei_c100/", "hybrid_ei_c10/", "rnd/"] -labelf = ["hybrid_ei", "hybrid_ei", "hybrid_ei", "rnd"] -path = "/Users/ozgesurer/Desktop/GithubRepos/parallelUQ/" - -acclevel = 0.01 -result = [] -worker = 1 -for mid, m in enumerate(label): - - PM = performanceModel(worker=1, batch=1, n=n, n0=0) - - # Read from existing experimental data - filename = ( - path - + "performanceAnalytics/new_fun_all/new_examples/" - + example - + "/" - + label[mid] - ) - avgae, avgtime = get_rep_data(s, w, b, rep, filename, labelf[mid]) - - # Gen acq and sim time - xt = np.arange(0, len(avgtime)) - xtest = np.arange(0, n) - PM.gen_acqtime(xt, avgtime, xtest, typeGen="regress") - PM.gen_simtime(0.0001, 0.0001, 0, typeSim="normal") - - # Fit a progress curve - minl = np.min(avgae) - maxl = np.max(avgae) - lnew = [(litem - 0) / (maxl - 0) for litem in avgae] - - x_a = np.log(np.arange(1, len(lnew) + 1)) - y_a = np.log(lnew) - xtest_a = np.log(np.arange(1, n + 1)) - PM.gen_curve(x_a, y_a, xtest_a, typeAcc="regress") - PM.acc = np.exp(PM.acc) - - PM.simulate() - PM.summarize() - PM.complete(acclevel) - result.append(PM) - - -lbl = [r"$\mathcal{A}_1$", r"$\mathcal{A}_2$", r"$\mathcal{A}_3$", r"$\mathcal{A}_4$"] -ft = 25 -fig, axes = plt.subplots(1, 3, figsize=(24, 6)) -plot_acc(axes[0], n, acclevel, result, labellist=lbl, logscale=True, fontsize=ft, n0=1) -plot_endtime( - axes[1], - n, - acclevel, - result, - labellist=lbl, - worker=worker, - logscale=True, - fontsize=ft, -) -plot_errorend( - axes[2], - n, - acclevel, - result, - labellist=lbl, - worker=worker, - logscale=True, - fontsize=ft, -) -axes[1].legend( - loc="upper center", - bbox_to_anchor=(0.5, -0.2), - fancybox=True, - shadow=True, - ncol=4, - fontsize=ft, -) -plt.savefig("Figure1.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/Fig10.py b/examples/NeurIPS2024/Fig10.py deleted file mode 100644 index 39d7b5e..0000000 --- a/examples/NeurIPS2024/Fig10.py +++ /dev/null @@ -1,262 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -import time -import matplotlib.colors as mcolors -from mpl_toolkits.axes_grid1 import make_axes_locatable -from matplotlib.gridspec import GridSpec - -start = time.time() - -repno = 10 -varlist = [1] -acclevel = 0.2 -n = 2560 -workers = [1, 2, 4, 8, 16, 32, 64, 128, 256] -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2**0, 2**3, 2**6] -accparams = [ - [-1, 0.15], - [-1, 0.18], - [-1, 0.21], - [-1, 0.24], - [-1, 0.27], - [-1, 0.30], - [-1, 0.33], - [-1, 0.36], - [-1, 0.39], -] -am = 2 ** (-1) - -ft = 25 -lw = 5 -ms = 15 -me = 200 - -fig = plt.figure(figsize=(20, 15)) -gs = GridSpec(3, 6, width_ratios=[1, 0.04, 1, 0.04, 1, 0.04]) - -for sid, sm in enumerate(s_mean): - result = [] - for id_b, b in enumerate(batches): - for r in range(repno): - for id_w, w in enumerate(workers): - if b <= w: - PM = performanceModel(worker=w, batch=b, n=n, n0=w) - PM.gen_acqtime(am, am, 2 ** (-6), typeGen="linear") - PM.gen_simtime(sm, sm, 0.001, typeSim="normal", seed=r) - PM.gen_curve( - accparams[id_b][0], accparams[id_b][1], typeAcc="exponential" - ) - PM.simulate() - # PM.summarize() - PM.complete(acclevel) - result.append( - { - "r": r, - "b": b, - "var": 1, - "w": w, - "sm": sm, - "res": PM, - } - ) - - idle = np.zeros((len(batches), len(workers))) - computing = np.zeros((len(batches), len(workers))) - endtime = np.zeros((len(batches), len(workers))) - endtime_temp = np.zeros((len(batches), len(workers))) - for wid, w in enumerate(workers): - for bid, b in enumerate(batches): - if b <= w: - res_c = [res for res in result if ((res["w"] == w) & (res["b"] == b))] - idle[bid, wid] = np.mean( - [res_c[i]["res"].avg_idle_time for i in range(0, repno)] - ) - computing[bid, wid] = np.mean( - [res_c[i]["res"].computing_hours for i in range(0, repno)] - ) - endtime[bid, wid] = np.mean( - [res_c[i]["res"].complete_time for i in range(0, repno)] - ) - endtime_temp[bid, wid] = np.mean( - [res_c[i]["res"].complete_time for i in range(0, repno)] - ) - else: - idle[bid, wid] = np.nan - computing[bid, wid] = np.nan - endtime[bid, wid] = np.nan - endtime_temp[bid, wid] = np.nan - - for bid, b in enumerate(batches): - for wid, w in enumerate(workers): - if b <= w: - endtime[bid, wid] = endtime[bid, wid] / endtime_temp[bid, bid] - - bidle, bcomph, bend = ( - np.zeros(len(workers)), - np.zeros(len(workers)), - np.zeros(len(workers)), - ) - for wid, w in enumerate(workers): - bidle[wid] = np.nanargmin(idle[:, wid]) - bcomph[wid] = np.nanargmin(computing[:, wid]) - bend[wid] = np.nanargmin(endtime_temp[:, wid]) - - widle, wcomph, wend = ( - np.zeros(len(workers)), - np.zeros(len(workers)), - np.zeros(len(workers)), - ) - for bid, b in enumerate(batches): - widle[bid] = np.nanargmin(idle[bid, :]) - wcomph[bid] = np.nanargmin(computing[bid, :]) - wend[bid] = np.nanargmin(endtime_temp[bid, :]) - - if sid == 0: - colid = 0 - elif sid == 1: - colid = 2 - elif sid == 2: - colid = 4 - - # AXIS 1 - subplot_ax = fig.add_subplot(gs[1, colid]) - masked_data = np.ma.masked_invalid(idle) - norm = mcolors.LogNorm(vmin=np.nanmin(idle), vmax=np.nanmax(idle)) - im = subplot_ax.imshow(masked_data, norm=norm, cmap="YlOrRd") - - cbar_ax = fig.add_subplot(gs[1, colid + 1]) - cbar = fig.colorbar(im, cax=cbar_ax) - cbar.ax.tick_params(labelsize=ft - 8) - cbar.ax.yaxis.set_ticks_position("left") - cbar.ax.yaxis.set_tick_params(width=2, color="black") - - if sid == 2: - cbar.ax.set_ylabel("Idle time", rotation=-90, va="bottom", fontsize=ft) - - # # Show all ticks and label them with the respective list entries - subplot_ax.set_yticks(np.arange(len(batches)), labels=batches) - subplot_ax.set_xticks(np.arange(len(workers)), labels=workers) - subplot_ax.tick_params(axis="both", which="major", labelsize=ft - 5) - plt.setp( - subplot_ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" - ) - if sid == 0: - subplot_ax.set_ylabel(r"Batch size ($b$)", fontsize=ft) - - # AXIS 2 - subplot_ax = fig.add_subplot(gs[2, colid]) - masked_data = np.ma.masked_invalid(computing) - norm = mcolors.LogNorm(vmin=np.nanmin(computing), vmax=np.nanmax(computing)) - im = subplot_ax.imshow(masked_data, norm=norm, cmap="YlOrRd") - cbar_ax = fig.add_subplot(gs[2, colid + 1]) - cbar = fig.colorbar(im, cax=cbar_ax) - cbar.ax.tick_params(labelsize=ft - 5) - if sid == 2: - cbar.ax.set_ylabel("Computing hours", rotation=-90, va="bottom", fontsize=ft) - # Adjust ticks to be on the left side - cbar.ax.yaxis.set_ticks_position("left") - cbar.ax.yaxis.set_tick_params(width=2, color="black") - # # Show all ticks and label them with the respective list entries - subplot_ax.set_yticks(np.arange(len(batches)), labels=batches) - subplot_ax.set_xticks(np.arange(len(workers)), labels=workers) - if sid == 0: - subplot_ax.set_ylabel(r"Batch size ($b$)", fontsize=ft) - subplot_ax.tick_params(axis="both", which="major", labelsize=ft - 5) - subplot_ax.set_xlabel(r"# of workers ($w$)", fontsize=ft) - plt.setp( - subplot_ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" - ) - # AXIS 0 - subplot_ax = fig.add_subplot(gs[0, colid]) - masked_data = np.ma.masked_invalid(endtime) - - bounds = [ - 0.0, - 2 ** (-9), - 2 ** (-8), - 2 ** (-7), - 2 ** (-6), - 2 ** (-5), - 2 ** (-4), - 2 ** (-3), - 2 ** (-2), - 2 ** (-1), - 2**0, - ] # bounds to differentiate colors - cmap = plt.get_cmap("YlOrRd", 11) - norm = mcolors.BoundaryNorm(bounds, cmap.N) # Normalize colors based on bounds - - cbar_ax = fig.add_subplot(gs[0, colid + 1]) - im = subplot_ax.imshow(masked_data, cmap=cmap, norm=norm) - cbar = fig.colorbar(im, cax=cbar_ax, orientation="vertical") - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.yaxis.set_ticks_position("left") - cbar.ax.yaxis.set_tick_params(width=2, color="black") - cbar.set_ticks( - [ - 0.0, - 2 ** (-9), - 2 ** (-8), - 2 ** (-7), - 2 ** (-6), - 2 ** (-5), - 2 ** (-4), - 2 ** (-3), - 2 ** (-2), - 2 ** (-1), - 2**0, - ] - ) - cbar.set_ticklabels( - [ - r"$0$", - r"$2^{-9}$", - r"$2^{-8}$", - r"$2^{-7}$", - r"$2^{-6}$", - r"$2^{-5}$", - r"$2^{-4}$", - r"$2^{-3}$", - r"$2^{-2}$", - r"$2^{-1}$", - r"$2^{0}$", - ] - ) - if sid == 2: - cbar.ax.set_ylabel( - "Relative wall-clock time", rotation=-90, va="bottom", fontsize=ft - ) - - # # Show all ticks and label them with the respective list entries - subplot_ax.set_yticks(np.arange(len(batches)), labels=batches) - subplot_ax.set_xticks(np.arange(len(workers)), labels=workers) - if sid == 0: - subplot_ax.set_ylabel(r"Batch size ($b$)", fontsize=ft) - subplot_ax.tick_params(axis="both", which="major", labelsize=ft - 5) - plt.setp( - subplot_ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" - ) - - if sid == 0: - if colid == 0: - subplot_ax.set_title(r"$\breve{a}^S = 2^0$", fontsize=ft) - if sid == 1: - if colid == 2: - subplot_ax.set_title(r"$\breve{a}^S = 2^3$", fontsize=ft) - if sid == 2: - if colid == 4: - subplot_ax.set_title(r"$\breve{a}^S = 2^6$", fontsize=ft) - # for i in range(len(batches)): - # for j in range(len(workers)): - # if batches[i] <= workers[j]: - # text = ax[0, sid].text(j, i, np.round(endtime[i, j], 1), - # ha="center", va="center", color="black", fontsize=ft-10) - - -fig.suptitle("Simulation time increases \u2192", fontsize=ft) -fig.subplots_adjust(top=0.9, bottom=0.1) -# fig.tight_layout() -plt.savefig("Figure10.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/Fig2.py b/examples/NeurIPS2024/Fig2.py deleted file mode 100644 index a0af7f8..0000000 --- a/examples/NeurIPS2024/Fig2.py +++ /dev/null @@ -1,133 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -import scipy.stats as sps -from PUQ.prior import prior_dist -from test_funcs import himmelblau -import matplotlib.pyplot as plt -import time - -if __name__ == "__main__": - start = time.time() - - args = parse_arguments() - - example = "himmelblau" - cls_data = eval(example)() - - # # # Create a mesh for test set # # # - xpl = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 50) - ypl = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 50) - Xpl, Ypl = np.meshgrid(xpl, ypl) - th = np.vstack([Xpl.ravel(), Ypl.ravel()]) - setattr(cls_data, "theta", th.T) - - ftest = np.zeros(2500) - for tid, t in enumerate(th.T): - ftest[tid] = cls_data.function(t[0], t[1]) - thetatest = th.T - ptest = np.zeros(thetatest.shape[0]) - for i in range(ftest.shape[0]): - mean = ftest[i] - rnd = sps.multivariate_normal(mean=mean, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - # # # # # # # # # # # # # # # # # # # # # - prior_func = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - # # # # # # # # # # # # # # # # # # # # # - - init_seeds = 2 - final_seeds = 3 - - n_init = 10 - for s in np.arange(init_seeds, final_seeds): - - thetainit = prior_func.rnd(n_init, s) - finit = np.zeros(n_init) - for tid, t in enumerate(thetainit): - finit[tid] = cls_data.function(t[0], t[1]) - test_data["thetainit"] = thetainit - test_data["finit"] = finit[None, :] - - al_data_ei = designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": 1, - "nworkers": 2, - "AL": "ei", - "seed_n0": int(s), - "prior": prior_func, - "data_test": test_data, - "max_evals": 200, - "candsize": args.candsize, - "refsize": args.refsize, - "believer": args.believer, - }, - ) - - al_data_hyb = designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": 1, - "nworkers": 2, - "AL": "hybrid_ei", - "seed_n0": int(s), - "prior": prior_func, - "data_test": test_data, - "max_evals": 200, - "candsize": args.candsize, - "refsize": args.refsize, - "believer": args.believer, - }, - ) - - show = True - ft = 20 - ms = 50 - if show: - theta_ei = al_data_ei._info["theta"] - theta_hyb = al_data_hyb._info["theta"] - - fig, ax = plt.subplots(1, 2, figsize=(16, 6)) - cp = ax[0].contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax[0].scatter( - theta_ei[:, 0], theta_ei[:, 1], c="black", marker="+", s=ms, zorder=2 - ) - ax[0].scatter( - thetainit[:, 0], - thetainit[:, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax[0].set_xlabel(r"$\theta_1$", fontsize=ft) - ax[0].set_ylabel(r"$\theta_2$", fontsize=ft) - ax[0].tick_params(axis="both", labelsize=ft) - - cp = ax[1].contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax[1].scatter( - theta_hyb[:, 0], theta_hyb[:, 1], c="black", marker="+", s=ms, zorder=2 - ) - ax[1].scatter( - thetainit[:, 0], - thetainit[:, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax[1].set_xlabel(r"$\theta_1$", fontsize=ft) - ax[1].set_ylabel(r"$\theta_2$", fontsize=ft) - ax[1].tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2.jpg", format="jpeg", bbox_inches="tight", dpi=500) - plt.show() - - end = time.time() - print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/Fig3.py b/examples/NeurIPS2024/Fig3.py deleted file mode 100644 index b7bb857..0000000 --- a/examples/NeurIPS2024/Fig3.py +++ /dev/null @@ -1,176 +0,0 @@ -import matplotlib.pyplot as plt -import matplotlib.gridspec as gridspec -import numpy as np -from PUQ.performance import performanceModel -import matplotlib.patches as patches - -# Create a figure -fig = plt.figure(figsize=(24, 6)) - -# Define a GridSpec layout -gs = gridspec.GridSpec(1, 3) # 1 row, 3 columns - -# Create subplots with varying spaces between them by adjusting the positions -ax1 = fig.add_subplot(gs[0]) # First subplot -ax2 = fig.add_subplot(gs[1]) # Second subplot -ax3 = fig.add_subplot(gs[2]) # Third subplot - -# Adjust positions manually for different spacing -ax1.set_position([0.1, 0.1, 0.25, 0.8]) # [left, bottom, width, height] -ax2.set_position([0.43, 0.1, 0.25, 0.8]) # Less width, more space on the left -ax3.set_position([0.76, 0.1, 0.25, 0.8]) # Normal width, more space between ax2 and ax3 - -# Plot data on the subplots -acclevel = [0.1, 0.2, 0.25] -n = 1280 -ft = 25 -worker = 128 -n0 = 0 -batches = [1, 64, 128] -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] -level = 0.3 -for bid, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=n0) - PM.gen_curve(-1, acclevel[bid], typeAcc="exponential") - PM.gen_acqtime(1, 1, 0.25, typeGen="linear") - PM.gen_simtime(1, 1, 0.1, typeSim="normal", seed=1) - PM.simulate() - # PM.summarize() - - PM.complete(level) - - ax1.plot(PM.acc[0:500], linestyle=linelist[bid], linewidth=5.0, color=clist[bid]) - - ax1.hlines(y=level, xmin=0, xmax=500, linewidth=5, color="k") - - ax1.vlines( - x=PM.complete_no, - ymin=0, - ymax=level, - linewidth=5, - color=clist[bid], - linestyles=(0, (2, 5)), - ) - -# Annotating outside of ax1 using figure-relative coordinates -ax1.annotate( - r"$\alpha$", - xy=(0.3, 0.34), - xycoords="figure fraction", - fontsize=ft, - fontweight="bold", - color="black", -) - -# # Define arrow properties -# arrow = patches.FancyArrowPatch( -# (0.35, 0.35), # Start point in figure coordinates -# (0.38, 0.35), # End point in figure coordinates -# mutation_scale=50, # Size of the arrow -# arrowstyle="->", # Arrow style -# color="black", -# linewidth=5, -# transform=fig.transFigure, # Use figure coordinates -# ) - -# # Add the arrow to the figure -# fig.patches.append(arrow) - -ax1.set_xlabel("# of parameters", fontsize=ft) -ax1.set_ylabel("Error", fontsize=ft) -ax1.tick_params(axis="both", which="major", labelsize=ft - 5) -ax1.set_xlim(0, 500) -ax1.set_ylim(0, 1.1) - -# AXIS 2 -# Generate random data -xmax = 25 -np.random.seed(1) -means = [5, 10, 15, 20] - -datas = [] -for m in means: - rnddat = np.random.normal(m, 2, 30) - rnddat = [0.1 if r < 0 else r for r in rnddat] - datas.append(rnddat) - -# Set the number of bins -bins = np.linspace(-2, xmax, 30) - -# Plot histograms horizontally with vertical shifts -for i, data in enumerate(datas): - counts, _ = np.histogram(data, bins=bins) - # Plot histogram as a horizontal bar plot - ax2.barh( - bins[:-1], - counts, - height=0.8, - color="red", - alpha=0.7, - label=i, - edgecolor="none", - align="center", - left=i * 10, - ) - -# Add labels and title -ax2.set_xlabel("t", fontsize=ft) -ax2.set_ylabel("Acquisition time", fontsize=ft) - -# Set custom x-axis ticks and labels -tick_positions = [0, 10, 20, 30] -tick_labels = ["0", "1", "2", "3"] - -ax2.set_xticks(tick_positions) -ax2.set_xticklabels(tick_labels) -ax2.plot([0, 10, 20, 30], [5, 10, 15, 20], color="black", linestyle="--", linewidth=5) -ax2.scatter([0, 10, 20, 30], [5, 10, 15, 20], color="black", marker="*", s=1000) -ax2.tick_params(axis="both", which="major", labelsize=ft - 5) -ax2.set_ylim(0, 25) - -# AXIS 3 -ax3.hist( - np.random.normal(5, 2, 100), bins=30, edgecolor="white", color="blue", alpha=0.5 -) -ax3.set_xlabel("Simulation time", fontsize=ft) -ax3.set_ylabel("Frequency", fontsize=ft) -ax3.tick_params(axis="both", which="major", labelsize=ft - 5) -# ax3.vlines(x=5, ymin=0, ymax=9, color="black", linestyle="--", linewidth=5) - -# Set titles to distinguish the plots -ax1.set_title(r"Find $n_k(b, \alpha)$ for worker size $w$", fontsize=ft) -ax2.set_title("Histogram of $a_{\omega,k}(b,t)$", fontsize=ft) -ax3.set_title("Histogram of $s_{\omega,j}$", fontsize=ft) -ax3.set_xlim(0, 11) -# plt.annotate( -# "1", -# (0.05, 1), -# xycoords="figure fraction", -# bbox={"boxstyle": "circle", "color": "lightgrey"}, -# fontsize=ft, -# color="black", -# ) - -# plt.annotate( -# "2", -# (0.4, 1), -# xycoords="figure fraction", -# bbox={"boxstyle": "circle", "color": "lightgrey"}, -# fontsize=ft, -# color="black", -# ) - -# plt.annotate( -# "3", -# (0.7, 1), -# xycoords="figure fraction", -# bbox={"boxstyle": "circle", "color": "lightgrey"}, -# fontsize=ft, -# color="black", -# ) - -plt.gca().set_aspect("equal", adjustable="box") -plt.savefig("Figure3.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/Fig4_5_12_13.py b/examples/NeurIPS2024/Fig4_5_12_13.py deleted file mode 100644 index a553e46..0000000 --- a/examples/NeurIPS2024/Fig4_5_12_13.py +++ /dev/null @@ -1,85 +0,0 @@ -from plotutils import plotresult, plotparams -import matplotlib.pyplot as plt -import numpy as np - -# Generates Fig 3, 4, 11, 12 -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] - -lw = 5 -ms = 15 -me = 200 - -labelsb = ["EI", "EIVAR", "HYBRID", "RND"] -method = ["ei", "eivar", "hybrid_ei", "rnd"] -batch = 1 -worker = 2 -rep = 30 -fonts = 25 - -path = "/Users/ozgesurer/Desktop/sh_files/" -figs = [["himmelblau", "holder", "easom"], ["sphere", "matyas", "ackley"]] -for ex_id, example_name in enumerate(figs): - for metric in ["AE", "MAD"]: - fig, axes = plt.subplots(1, 3, figsize=(24, 6)) - for exid, ex in enumerate(example_name): - for mid, m in enumerate(method): - out = ex + "_" + m - avgAE, avgtime, avgTV = plotresult( - path, out, ex, worker, batch, rep, m, n0=0, nf=1000 - ) - # print(avgAE[0:10]) - if metric == "AE": - axes[exid].plot( - np.arange(len(avgAE)), - avgAE, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=lw, - marker=mlist[mid], - markersize=ms, - markevery=me, - ) - else: - axes[exid].plot( - np.arange(len(avgTV)), - avgTV, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=lw, - marker=mlist[mid], - markersize=ms, - markevery=me, - ) - axes[exid].set_yscale("log") - axes[exid].set_xlabel("# of parameters", fontsize=fonts) - if exid == 0: - if metric == "AE": - axes[exid].set_ylabel(r"$\delta$", fontsize=fonts) - else: - axes[exid].set_ylabel(r"MAD", fontsize=fonts) - axes[exid].tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes[1].legend( - loc="upper center", - bbox_to_anchor=(0.5, -0.2), - fancybox=True, - shadow=True, - ncol=4, - fontsize=fonts, - ) - if ex_id == 0: - if metric == "AE": - plt.savefig("Figure4.jpg", format="jpeg", bbox_inches="tight", dpi=500) - else: - plt.savefig("Figure5.jpg", format="jpeg", bbox_inches="tight", dpi=500) - else: - if metric == "AE": - plt.savefig("Figure12.jpg", format="jpeg", bbox_inches="tight", dpi=500) - else: - plt.savefig("Figure13.jpg", format="jpeg", bbox_inches="tight", dpi=500) - - plt.show() diff --git a/examples/NeurIPS2024/Fig6_14.py b/examples/NeurIPS2024/Fig6_14.py deleted file mode 100644 index f3f8fd2..0000000 --- a/examples/NeurIPS2024/Fig6_14.py +++ /dev/null @@ -1,78 +0,0 @@ -from plotutils import plotresult, plotparams -import matplotlib.pyplot as plt -import numpy as np - - -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] - - -labelsb = ["b=1", "b=5", "b=25", "b=125"] -method = ["hybrid_ei"] -batch_sizes = [1, 5, 25, 125] -worker = 126 -rep = 30 -fonts = 25 -nf = 1000 -lw = 5 -ms = 15 -me = 200 - -path = "/Users/ozgesurer/Desktop/batch_mode/" -figs = [["sphere", "matyas", "ackley"], ["himmelblau", "holder", "easom"]] -for figno, example_name in enumerate(figs): - for metric in ["AE"]: - fig, axes = plt.subplots(1, 3, figsize=(24, 6)) - for exid, ex in enumerate(example_name): - for bid, b in enumerate(batch_sizes): - for mid, m in enumerate(method): - out = ex + "_" + m + "_" + "b" + str(b) + "_" + "w125" - avgAE, avgtime, avgTV = plotresult( - path, out, ex, worker, b, rep, m, n0=123, nf=nf - ) - if metric == "AE": - axes[exid].plot( - np.arange(len(avgAE)), - avgAE, - label=labelsb[bid], - color=clist[bid], - linestyle=linelist[bid], - linewidth=lw, - marker=mlist[bid], - markersize=ms, - markevery=me, - ) - else: - axes[exid].plot( - np.arange(len(avgTV)), - avgTV, - label=labelsb[bid], - color=clist[bid], - linestyle=linelist[bid], - linewidth=lw, - marker=mlist[bid], - markersize=ms, - markevery=me, - ) - axes[exid].set_yscale("log") - axes[exid].set_xlabel("# of parameters", fontsize=fonts) - if exid == 0: - if metric == "AE": - axes[exid].set_ylabel(r"$\delta$", fontsize=fonts) - else: - axes[exid].set_ylabel(r"MAD", fontsize=fonts) - axes[exid].tick_params(axis="both", which="major", labelsize=fonts - 5) - axes[1].legend( - loc="upper center", - bbox_to_anchor=(0.5, -0.2), - fancybox=True, - shadow=True, - ncol=4, - fontsize=fonts, - ) - if figno == 0: - plt.savefig("Figure6.jpg", format="jpeg", bbox_inches="tight", dpi=500) - else: - plt.savefig("Figure14.jpg", format="jpeg", bbox_inches="tight", dpi=500) - plt.show() diff --git a/examples/NeurIPS2024/Fig7.py b/examples/NeurIPS2024/Fig7.py deleted file mode 100644 index 9ce9731..0000000 --- a/examples/NeurIPS2024/Fig7.py +++ /dev/null @@ -1,53 +0,0 @@ -from PUQ.performance import performanceModel -from PUQ.performanceutils.utils import ( - plot_acc, - plot_endtime, - plot_errorend, -) -import matplotlib.pyplot as plt -import time - -start = time.time() -scale_list = [1, 1.1, 1.2] -acclevel = [0.1, 0.2, 0.25] -result = [] -n = 1280 -ft = 25 -worker = 128 -n0 = 0 -batches = [1, 64, 128] -level = 0.1 -for bid, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=n0) - PM.gen_acqtime(1, 1, 0.25, typeGen="linear") - PM.gen_simtime(1, 1, 0.1, typeSim="normal", seed=1) - PM.gen_curve(-1, acclevel[bid], typeAcc="exponential") - - PM.simulate() - # PM.summarize() - - PM.complete(level) - result.append(PM) - -labs = ["$b=1$", "$b=64$", "$b=128$"] -fig, axes = plt.subplots(1, 3, figsize=(24, 6)) -plot_acc(axes[0], n, level, result, labellist=labs, logscale=False, fontsize=ft, n0=n0) -plot_endtime( - axes[1], n, level, result, labellist=labs, worker=worker, logscale=True, fontsize=ft -) -plot_errorend( - axes[2], n, level, result, labellist=labs, worker=worker, logscale=True, fontsize=ft -) -axes[1].legend( - loc="upper center", - bbox_to_anchor=(0.5, -0.2), - fancybox=True, - shadow=True, - ncol=4, - fontsize=ft, -) -plt.savefig("Figure7.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() - -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/Fig8.py b/examples/NeurIPS2024/Fig8.py deleted file mode 100644 index 96a1edb..0000000 --- a/examples/NeurIPS2024/Fig8.py +++ /dev/null @@ -1,134 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -import time -from matplotlib.ticker import FixedFormatter -from matplotlib.gridspec import GridSpec - -start = time.time() - -repno = 10 -n = 2560 -varlist = [0.1, 10] - -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2**3, 2**6, 2**9] -a_mean = [2 ** (-2), 2 ** (-1), 2**0, 2**1, 2**2, 2**3, 2**4, 2**5] -a_tick = [ - r"$2^{-2}$", - r"$2^{-1}$", - r"$2^0$", - r"$2^1$", - r"$2^2$", - r"$2^3$", - r"$2^4$", - r"$2^5$", -] -accparams = [ - [-1, 0.15], - [-1, 0.18], - [-1, 0.21], - [-1, 0.24], - [-1, 0.27], - [-1, 0.30], - [-1, 0.33], - [-1, 0.36], - [-1, 0.39], -] -acclevel = 0.2 -ft = 25 - - -fig = plt.figure(figsize=(20, 10)) -gs = GridSpec(2, 4, width_ratios=[1, 1, 1, 0.05]) - -for vid, var in enumerate(varlist): - for sid, sm in enumerate(s_mean): - result = [] - for aid, am in enumerate(a_mean): - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 0.001, typeGen="linear") - PM.gen_simtime(sm, sm * var, 0.01, typeSim="normal", seed=r) - PM.gen_curve(-1, accparams[id_b][1], typeAcc="exponential") - PM.simulate() - # PM.summarize() - PM.complete(acclevel) - result.append( - { - "r": r, - "b": b, - "am": am, - "var": var, - "sm": sm, - "res": PM, - } - ) - # print(PM.complete_no) - - timemat = np.zeros((len(a_mean), len(batches))) - for aid, am in enumerate(a_mean): - for bid, b in enumerate(batches): - res_c = [res for res in result if ((res["am"] == am) & (res["b"] == b))] - timemat[aid, bid] = np.mean( - [res_c[i]["res"].complete_time for i in range(0, len(res_c))] - ) - - subplot_ax = fig.add_subplot(gs[vid, sid]) - bo = np.argsort(np.argsort(timemat, axis=1), axis=1) - im = subplot_ax.imshow(bo, aspect="auto", cmap="YlOrRd") - if sid == 2: - cbar_ax = fig.add_subplot(gs[vid, 3]) - cbar = fig.colorbar( - im, - cax=cbar_ax, - ticks=np.array([0.0, 0.5, 1.0]) * bo.max(), - format=FixedFormatter(["lowest", "middle", "highest"]), - ) - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.set_ylabel( - "Wall-clock time", rotation=-90, va="bottom", fontsize=ft - ) - - # # Show all ticks and label them with the respective list entries - subplot_ax.set_xticks(np.arange(len(batches)), labels=batches) - subplot_ax.set_yticks(np.arange(len(a_mean)), labels=a_tick) - - # Rotate the tick labels and set their alignment. - plt.setp( - subplot_ax.get_xticklabels(), - rotation=45, - ha="right", - rotation_mode="anchor", - ) - - if vid == 1: - subplot_ax.set_xlabel(r"Batch size ($b$)", fontsize=ft) - if sid == 0: - subplot_ax.set_ylabel(r"Acquisition time ($\breve{a}^A$)", fontsize=ft) - - subplot_ax.tick_params(axis="both", which="major", labelsize=ft - 5) - - if vid == 0: - if sid == 0: - subplot_ax.set_title(r"$\breve{a}^S = 2^3$", fontsize=ft) - elif sid == 1: - subplot_ax.set_title(r"$\breve{a}^S = 2^6$", fontsize=ft) - elif sid == 2: - subplot_ax.set_title(r"$\breve{a}^S = 2^9$", fontsize=ft) - - # for aid, am in enumerate(a_mean): - # for bid, b in enumerate(batches): - # text = ax[vid, sid].text(bid, aid, np.round(timemat[aid, bid]/np.min(timemat), 1), - # ha="center", va="center", color="black", fontsize=ft-10) - - -fig.suptitle("Simulation time increases \u2192", fontsize=ft) -plt.savefig("Figure8.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() - - -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/Fig9.py b/examples/NeurIPS2024/Fig9.py deleted file mode 100644 index bf9a4cb..0000000 --- a/examples/NeurIPS2024/Fig9.py +++ /dev/null @@ -1,131 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from mpl_toolkits.axes_grid1 import make_axes_locatable -import matplotlib.colors as mcolors -from matplotlib.gridspec import GridSpec - -repno = 10 -n = 2560 -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2**0, 2**1, 2**2, 2**3, 2**4, 2**5] -a_mean = [2 ** (-3), 2 ** (-2), 2 ** (-1), 2**0, 2**1, 2**2] -sim_tick = [r"$2^0$", r"$2^1$", r"$2^2$", r"$2^3$", r"$2^4$", r"$2^5$"] -acq_tick = [r"$2^{-3}$", r"$2^{-2}$", r"$2^{-1}$", r"$2^{0}$", r"$2^1$", r"$2^2$"] -acclevel = 0.2 -accparams = [[-1, 0.1], [-1, 0.3], [-1, 0.5]] - -fig = plt.figure(figsize=(20, 10)) -gs = GridSpec(1, 4, width_ratios=[1, 1, 1, 0.05]) - -for accid, acc in enumerate(accparams): - result = [] - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 2 ** (-5), typeGen="linear") - PM.gen_simtime(sm, sm, 0.01, typeSim="normal", seed=r) - PM.gen_curve(acc[0], acc[1] + id_b * 0.02, typeAcc="exponential") - PM.simulate() - # PM.summarize() - PM.complete(acclevel) - result.append( - { - "r": r, - "b": b, - "am": am, - "var": 1, - "sm": sm, - "res": PM, - } - ) - # print(PM.complete_no) - - timemat = np.zeros((len(a_mean), len(s_mean))) - bmat = np.zeros((len(a_mean), len(s_mean))) - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - res_c = [res for res in result if ((res["am"] == am) & (res["sm"] == sm))] - - rmin = np.inf - bmin = np.inf - for bid, b in enumerate(batches): - cs = [rs["res"].complete_time for rs in res_c if rs["b"] == b] - if np.mean(cs) < rmin: - rmin = np.mean(cs) - bmin = b - - timemat[aid, sid] = rmin - bmat[aid, sid] = bmin - - ft = 25 - subplot_ax = fig.add_subplot(gs[accid]) - # Show all ticks and label them with the respective list entries - subplot_ax.set_xticks(np.arange(len(s_mean)), labels=sim_tick) - subplot_ax.set_yticks(np.arange(len(a_mean)), labels=acq_tick) - subplot_ax.tick_params(axis="both", which="major", labelsize=ft - 5) - # Rotate the tick labels and set their alignment. - plt.setp( - subplot_ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor" - ) - - # Loop over data dimensions and create text annotations. - for i in range(len(a_mean)): - for j in range(len(s_mean)): - if bmat[i, j] >= 64: - text = subplot_ax.text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="white", - fontsize=ft - 5, - ) - else: - text = subplot_ax.text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="black", - fontsize=ft - 5, - ) - - subplot_ax.grid(which="minor", color="w", linestyle="-", linewidth=3) - subplot_ax.set_xlabel(r"Simulation time ($\breve{a}^S$)", fontsize=ft) - if accid == 0: - subplot_ax.set_ylabel(r"Acquisition time ($\breve{a}^A$)", fontsize=ft) - - # bounds to differentiate colors - bounds = [0.5, 1.5, 3.5, 7.5, 15.5, 31.5, 63.5, 127.5, 255.5, 512] - cmap = plt.get_cmap("YlOrRd", len(batches)) - # Normalize colors based on bounds - norm = mcolors.BoundaryNorm(bounds, cmap.N) - im = subplot_ax.imshow(bmat, cmap=cmap, norm=norm) - - if accid == 2: - cbar_ax = fig.add_subplot(gs[3]) - - # Heatmap - # divider = make_axes_locatable(cbar_ax) - # cax = divider.append_axes('right', size='5%', pad=0.2) - cbar = fig.colorbar(im, cax=cbar_ax, orientation="vertical") - cbar_ax.set_aspect(20) - cbar.ax.tick_params(labelsize=ft - 5) - cbar.set_ticks(batches) - cbar.ax.set_ylabel(r"Batch size ($b$)", rotation=-90, va="bottom", fontsize=ft) - - if accid == 0: - subplot_ax.set_title(r"$\mathcal{A}_1$", fontsize=ft) - elif accid == 1: - subplot_ax.set_title(r"$\mathcal{A}_2$", fontsize=ft) - elif accid == 2: - subplot_ax.set_title(r"$\mathcal{A}_3$", fontsize=ft) - -plt.savefig("Figure9.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/Workshop_files/WS1_synth.py b/examples/NeurIPS2024/Figure2.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS1_synth.py rename to examples/NeurIPS2024/Figure2.py diff --git a/examples/NeurIPS2024/Workshop_files/WS2_synth.py b/examples/NeurIPS2024/Figure3.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS2_synth.py rename to examples/NeurIPS2024/Figure3.py diff --git a/examples/NeurIPS2024/README.rst b/examples/NeurIPS2024/README.rst index e41f85a..c3d2132 100644 --- a/examples/NeurIPS2024/README.rst +++ b/examples/NeurIPS2024/README.rst @@ -1,13 +1,13 @@ Examples ~~~~~~~~ -These examples replicate the results presented in the paper titled 'Performance Analysis of -Sequential Experimental Design for Calibration in Parallel Computing Environments' +These examples replicate the results presented in the paper titled 'An Active Learning Performance Model +for Parallel Bayesian Calibration of Expensive Simulations ' by Sürer and Wild (2024). **Instructions for running the illustrative examples with performance model** -To replicate Figures~3 and 7--10: +To replicate Figures~2 and 3: 1) Go to the ``examples/`` directory. @@ -15,38 +15,7 @@ To replicate Figures~3 and 7--10: .. code-block:: python - python Fig3.py - python Fig7.py - python Fig8.py - python Fig9.py - python Fig10.py + python Figure2.py + python Figure3.py Running each script should not take more than 90 sec. See the figures (jpeg files) saved under ``examples/`` directory. - -**Instructions for running the sequential design using different synthetic simulation models** - -To replicate Figure~2, execute the following from the command line: - -.. code-block:: python - - python Fig2.py - -Running this script takes about a minute on a personal Mac laptop. - -To collect ``-max_eval`` simulation outputs from parameters acquired with -different acquisition functions (``-al_func``) and synthetic simulation models (``-funcname``), -one can use the script ``run_test_funcs.py``. - -As an example, execute any of the following from the command line: - -.. code-block:: python - - python run_test_funcs.py -funcname ackley -al_func ei -max_eval 200 - python run_test_funcs.py -funcname easom -al_func ei -max_eval 200 - python run_test_funcs.py -funcname himmelblau -al_func eivar -max_eval 200 - python run_test_funcs.py -funcname holder -al_func eivar -max_eval 200 - python run_test_funcs.py -funcname matyas -al_func hybrid_ei -max_eval 200 - python run_test_funcs.py -funcname sphere -al_func hybrid_ei -max_eval 200 - -Once completed, ``Figure_funcname.jpg`` is saved under ``examples/`` directory. -Running each script should not take more than one minute on a personal Mac laptop. \ No newline at end of file diff --git a/examples/NeurIPS2024/Workshop_files/WS0.py b/examples/NeurIPS2024/WS0.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS0.py rename to examples/NeurIPS2024/WS0.py diff --git a/examples/NeurIPS2024/Workshop_files/WS0_run.py b/examples/NeurIPS2024/WS0_run.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS0_run.py rename to examples/NeurIPS2024/WS0_run.py diff --git a/examples/NeurIPS2024/Workshop_files/WS1_real.py b/examples/NeurIPS2024/WS1_real.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS1_real.py rename to examples/NeurIPS2024/WS1_real.py diff --git a/examples/NeurIPS2024/Workshop_files/WS2_real.py b/examples/NeurIPS2024/WS2_real.py similarity index 100% rename from examples/NeurIPS2024/Workshop_files/WS2_real.py rename to examples/NeurIPS2024/WS2_real.py diff --git a/examples/NeurIPS2024/ci_example.py b/examples/NeurIPS2024/ci_example.py deleted file mode 100644 index 2e7a95c..0000000 --- a/examples/NeurIPS2024/ci_example.py +++ /dev/null @@ -1,66 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -import scipy.stats as sps -from PUQ.prior import prior_dist -from test_funcs import holder, ackley, easom, sphere, matyas, himmelblau - -if __name__ == "__main__": - args = parse_arguments() - - cls_data = eval(args.funcname)() - - # # # Create a mesh for test set # # # - xpl = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 50) - ypl = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 50) - Xpl, Ypl = np.meshgrid(xpl, ypl) - th = np.vstack([Xpl.ravel(), Ypl.ravel()]) - setattr(cls_data, "theta", th.T) - - ftest = np.zeros(2500) - for tid, t in enumerate(th.T): - ftest[tid] = cls_data.function(t[0], t[1]) - thetatest = th.T - ptest = np.zeros(thetatest.shape[0]) - for i in range(ftest.shape[0]): - mean = ftest[i] - rnd = sps.multivariate_normal(mean=mean, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - # # # # # # # # # # # # # # # # # # # # # - prior_func = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - # # # # # # # # # # # # # # # # # # # # # - - init_seeds = args.init_seeds - final_seeds = args.final_seeds - - n_init = 10 - for s in np.arange(init_seeds, final_seeds): - - thetainit = prior_func.rnd(n_init, s) - finit = np.zeros(n_init) - for tid, t in enumerate(thetainit): - finit[tid] = cls_data.function(t[0], t[1]) - test_data["thetainit"] = thetainit - test_data["finit"] = finit[None, :] - - al_data = designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": 1, - "nworkers": 2, - "AL": args.al_func, - "seed_n0": int(s), - "prior": prior_func, - "data_test": test_data, - "max_evals": args.max_eval, - "candsize": args.candsize, - "refsize": args.refsize, - "believer": args.believer, - }, - ) diff --git a/examples/NeurIPS2024/compile_results.py b/examples/NeurIPS2024/compile_results.py deleted file mode 100644 index 24dd9e7..0000000 --- a/examples/NeurIPS2024/compile_results.py +++ /dev/null @@ -1,183 +0,0 @@ -from plotutils import plotresult, plotparams -import matplotlib.pyplot as plt -import numpy as np - -clist = ["b", "r", "g", "m", "y", "c", "pink", "purple"] -mlist = ["P", "p", "*", "o", "s", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] - -labelsb = ["EI", "EIVAR", "HYBRID", "RND"] -method = ["ei", "eivar", "hybrid_ei", "rnd"] -example_name = ["sphere", "matyas", "ackley"] -example_name = ["himmelblau", "holder", "easom"] - -batch = 1 -worker = 2 -rep = 30 -fonts = 22 - -path = "/Users/ozgesurer/Desktop/sh_files/" - -for metric in ["AE", "MAD"]: - fig, axes = plt.subplots(1, 3, figsize=(22, 5)) - for exid, ex in enumerate(example_name): - for mid, m in enumerate(method): - out = ex + "_" + m - avgAE, avgtime, avgTV = plotresult( - path, out, ex, worker, batch, rep, m, n0=10, nf=1000 - ) - if metric == "AE": - axes[exid].plot( - np.arange(len(avgAE)), - avgAE, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - ) - else: - axes[exid].plot( - np.arange(len(avgTV)), - avgTV, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - ) - axes[exid].set_yscale("log") - # axes[exid].set_xscale('log') - axes[exid].set_xlabel("# of parameters", fontsize=fonts) - if exid == 0: - if metric == "AE": - axes[exid].set_ylabel(r"$\delta$", fontsize=fonts) - else: - axes[exid].set_ylabel(r"MAD", fontsize=fonts) - axes[exid].tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes[1].legend(bbox_to_anchor=(1.3, -0.2), ncol=4, fontsize=fonts) - plt.show() - -show1 = False -if show1: - labelsb = ["10", "100", "1000", "RND"] - method = ["hybrid_ei", "hybrid_ei", "hybrid_ei", "rnd"] - outs = [10, 100, 1000] - example_name = ["himmelblau"] - - for metric in ["AE"]: - fig, axes = plt.subplots(1, 1, figsize=(5, 5)) - for exid, ex in enumerate(example_name): - for mid, m in enumerate(method): - if mid < 3: - out = ex + "_" + m + "_" + str(outs[mid]) - else: - out = ex + "_" + m - avgAE, avgtime, avgTV = plotresult( - path, out, ex, worker, batch, rep, m, n0=10, nf=1000 - ) - if metric == "AE": - axes.plot( - np.arange(len(avgAE)), - avgAE, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - ) - else: - axes.plot( - np.arange(len(avgTV)), - avgTV, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - ) - axes.set_yscale("log") - axes.set_xscale("log") - axes.set_xlabel("# of parameters", fontsize=fonts) - if exid == 0: - if metric == "AE": - axes.set_ylabel(r"$\delta$", fontsize=fonts) - else: - axes.set_ylabel(r"MAD", fontsize=fonts) - axes.tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes.legend(bbox_to_anchor=(1.3, -0.2), ncol=4, fontsize=fonts) - plt.show() - - -show2 = True -if show2: - - clist = ["b", "r", "g", "m", "y", "c", "pink", "purple"] - mlist = ["P", "p", "*", "o", "s", "h"] - linelist = ["-", "--", "-.", ":", "-.", ":"] - - labelsb = ["b=1", "b=32", "b=64", "b=128"] - method = ["ei"] - batch_sizes = [1, 32, 64, 128] - batch_sizes = [1, 5, 25, 125] - example_name = ["sphere", "matyas", "ackley"] - # example_name = ['matyas'] - # example_name = ['himmelblau', 'holder', 'easom'] - worker = 126 - rep = 28 - fonts = 22 - - path = "/Users/ozgesurer/Desktop/sh_files/batch_mode/" - - for metric in ["AE", "MAD"]: - fig, axes = plt.subplots(1, 3, figsize=(22, 5)) - for exid, ex in enumerate(example_name): - for bid, b in enumerate(batch_sizes): - for mid, m in enumerate(method): - out = ex + "_" + m + "_" + "b" + str(b) + "_" + "w128" - avgAE, avgtime, avgTV = plotresult( - path, out, ex, worker, b, rep, m, n0=128, nf=1000 - ) - if metric == "AE": - axes[exid].plot( - np.arange(len(avgAE)), - avgAE, - label=labelsb[bid], - color=clist[bid], - linestyle=linelist[bid], - ) - else: - axes[exid].plot( - np.arange(len(avgTV)), - avgTV, - label=labelsb[bid], - color=clist[bid], - linestyle=linelist[bid], - ) - axes[exid].set_yscale("log") - # axes[exid].set_xscale('log') - axes[exid].set_xlabel("# of parameters", fontsize=fonts) - if exid == 0: - if metric == "AE": - axes[exid].set_ylabel(r"$\delta$", fontsize=fonts) - else: - axes[exid].set_ylabel(r"MAD", fontsize=fonts) - axes[exid].tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes[1].legend(bbox_to_anchor=(1.3, -0.2), ncol=4, fontsize=fonts) - plt.show() - - -show3 = False -if show3: - - clist = ["b", "r", "g", "m", "y", "c", "pink", "purple"] - mlist = ["P", "p", "*", "o", "s", "h"] - linelist = ["-", "--", "-.", ":", "-.", ":"] - - labelsb = ["b=1", "b=32", "b=64", "b=128"] - m = "ei" - - ex = "easom" - worker = 129 - rep = 30 - fonts = 22 - - path = "/Users/ozgesurer/Desktop/sh_files/batch_mode/" - b = 32 - out = ex + "_" + m + "_" + "b" + str(b) + "_" + "w128" - plotparams(path, out, ex, worker, b, rep, m, n0=128, nf=1000) diff --git a/examples/NeurIPS2024/plotv0/Fig10.py b/examples/NeurIPS2024/plotv0/Fig10.py deleted file mode 100644 index 368f7a6..0000000 --- a/examples/NeurIPS2024/plotv0/Fig10.py +++ /dev/null @@ -1,182 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from PUQ.performanceutils.utils import ( - plot_acqtime, - plot_endtime, - plot_errorend, -) -import time - -start = time.time() - -repno = 2 -varlist = [1] -acclevel = 0.2 -n = 2048 -workers = [16, 32, 64, 128, 256, 512] -batches = [4, 8, 16] -simmeans = [2, 4, 16] -accparams = [[-1, 0.2], [-1, 0.21], [-1, 0.22]] -genparams = [[0.2, 0.2]] - -result = [] -ft = 25 -lw = 5 -ms = 15 -me = 200 -for sid, sim_mean in enumerate(simmeans): - for varid, var in enumerate(varlist): - for id_b, b in enumerate(batches): - res = [] - for r in range(repno): - for id_w, w in enumerate(workers): - PM = performanceModel(worker=w, batch=b, n=n, n0=w) - PM.gen_acqtime(genparams[0][0], 0.001, typeGen="constant") - PM.gen_simtime( - sim_mean, sim_mean * var, 0.001, typeSim="normal", seed=r - ) - PM.gen_curve( - accparams[id_b][0], accparams[id_b][1], typeAcc="exponential" - ) - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "var": var, - "w": w, - "simmean": sim_mean, - "res": PM, - } - ) - -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] - -labs = ["b=4", "b=8", "b=16"] -for varid, var in enumerate(varlist): - fig, axes = plt.subplots(3, len(simmeans), figsize=(24, 18)) - for sid, sim_mean in enumerate(simmeans): - - res_c = [ - res - for res in result - if ((res["simmean"] == sim_mean) & (res["var"] == var)) - ] - for bid, b in enumerate(batches): - endtime = [] - idletime = [] - computetime = [] - for wid, w in enumerate(workers): - endtime.append( - np.mean( - [ - res["res"].complete_time - for res in res_c - if ((res["w"] == w) & (res["b"] == b)) - ] - ) - ) - idletime.append( - np.mean( - [ - res["res"].avg_idle_time - for res in res_c - if ((res["w"] == w) & (res["b"] == b)) - ] - ) - ) - computetime.append( - np.mean( - [ - res["res"].computing_hours - for res in res_c - if ((res["w"] == w) & (res["b"] == b)) - ] - ) - ) - - endtimescaled = [e / (endtime[0]) for e in endtime] - axes[0, sid].plot( - workers, - endtimescaled, - marker=mlist[bid], - markersize=ms, - linestyle=linelist[bid], - linewidth=lw, - label=labs[bid], - color=clist[bid], - ) - axes[1, sid].plot( - workers, - idletime, - marker=mlist[bid], - markersize=ms, - linestyle=linelist[bid], - linewidth=lw, - label=labs[bid], - color=clist[bid], - ) - axes[2, sid].plot( - workers, - computetime, - marker=mlist[bid], - markersize=ms, - linestyle=linelist[bid], - linewidth=lw, - label=labs[bid], - color=clist[bid], - ) - - axes[0, sid].set_xscale("log") - axes[0, sid].set_yscale("log") - axes[0, sid].set_xticks(workers) - axes[0, sid].set_xticklabels(workers) - axes[0, sid].tick_params(axis="both", which="major", labelsize=ft - 5) - - axes[1, sid].set_xscale("log") - axes[1, sid].set_yscale("log") - axes[1, sid].set_xticks(workers) - axes[1, sid].set_xticklabels(workers) - axes[1, sid].tick_params(axis="both", which="major", labelsize=ft - 5) - - axes[2, sid].set_xscale("log") - axes[2, sid].set_yscale("log") - axes[2, sid].set_xticks(workers) - axes[2, sid].set_xticklabels(workers) - axes[2, sid].tick_params(axis="both", which="major", labelsize=ft - 5) - axes[2, sid].set_xlabel("# of workers", fontsize=ft) - - axes[0, sid].plot( - workers, - [1, 1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32], - color="black", - linestyle=linelist[4], - linewidth=4, - ) - axes[0, 0].set_ylabel("Wall-clock time (scaled)", fontsize=ft) - axes[1, 0].set_ylabel("Idle time", fontsize=ft) - axes[2, 0].set_ylabel("Computing hours", fontsize=ft) - if varid == len(varlist) - 1: - handles, labels = axes[0, 0].get_legend_handles_labels() - fig.legend( - handles, - labels, - loc="upper center", - title_fontsize=ft, - bbox_to_anchor=(0.5, 0.06), - ncol=4, - prop={"size": ft}, - fancybox=True, - shadow=True, - ) - plt.savefig("Figure9.jpg", format="jpeg", bbox_inches="tight", dpi=500) - plt.show() - -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/plotv0/Fig8.py b/examples/NeurIPS2024/plotv0/Fig8.py deleted file mode 100644 index 95ff46e..0000000 --- a/examples/NeurIPS2024/plotv0/Fig8.py +++ /dev/null @@ -1,131 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from PUQ.performanceutils.utils import ( - plot_acqtime, - plot_endtime, - plot_errorend, -) -import time - -start = time.time() - -repno = 1 -n = 2560 -varlist = [0.1, 10] -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -simmeans = [0.1, 1, 10] -acqscale = [0.01, 0.1, 1] -accparams = [ - [-1, 0.2], - [-1, 0.22], - [-1, 0.24], - [-1, 0.26], - [-1, 0.28], - [-1, 0.3], - [-1, 0.32], - [-1, 0.34], - [-1, 0.36], -] -acclevel = 0.2 -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] -thrlist = [] - -lab = ["b=1", "b=2", "b=4", "b=8", "b=16", "b=32", "b=64", "b=128", "b=256"] -lw = 5 -ms = 15 -me = 200 - -result = [] -for scaleid, scale in enumerate(acqscale): - for varid, var in enumerate(varlist): - for sid, sim_mean in enumerate(simmeans): - res = [] - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(scale, scale, 0.001, typeGen="linear") - PM.gen_simtime( - sim_mean, sim_mean * var, 0.01, typeSim="normal", seed=r - ) - PM.gen_curve(-1, accparams[id_b][1], typeAcc="exponential") - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "scale": scale, - "var": var, - "simmean": sim_mean, - "res": PM, - } - ) - -ft = 25 -fig, axes = plt.subplots(2, len(acqscale), figsize=(24, 12)) -for varid, var in enumerate(varlist): - - for scaleid, scale in enumerate(acqscale): - - res_c = [ - res for res in result if ((res["scale"] == scale) & (res["var"] == var)) - ] - for sid, s in enumerate(simmeans): - endtime = [] - - for bid, b in enumerate(batches): - endtime.append( - np.mean( - [ - res["res"].complete_time - for res in res_c - if ((res["b"] == b) & (res["simmean"] == s)) - ] - ) - ) - - axes[varid, scaleid].plot( - batches, - endtime, - marker=mlist[sid], - markersize=ms, - linestyle=linelist[sid], - linewidth=lw, - label=str(s), - color=clist[sid], - ) - axes[varid, scaleid].set_xscale("log") - axes[varid, scaleid].set_yscale("log") - axes[varid, scaleid].set_xticks(batches) - axes[varid, scaleid].set_xticklabels(batches) - axes[varid, scaleid].tick_params( - axis="both", which="major", labelsize=ft - 5 - ) - axes[varid, scaleid].set_xlabel("b", fontsize=ft) - axes[varid, 0].set_ylabel("Wall-clock time", fontsize=ft) - if varid == len(varlist) - 1: - handles, labels = axes[varid, 0].get_legend_handles_labels() - - labels = [r"$\tilde{a}$=" + l for l in labels] - fig.legend( - handles, - labels, - loc="upper center", - title_fontsize=ft, - bbox_to_anchor=(0.5, 0.06), - ncol=4, - prop={"size": ft}, - fancybox=True, - shadow=True, - ) -plt.savefig("Figure7.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() - -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/plotv0/Fig9.py b/examples/NeurIPS2024/plotv0/Fig9.py deleted file mode 100644 index d1f7f5f..0000000 --- a/examples/NeurIPS2024/plotv0/Fig9.py +++ /dev/null @@ -1,122 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from PUQ.performanceutils.utils import ( - plot_acqtime, - plot_endtime, - plot_errorend, -) -import time - -start = time.time() - -### ### ### ### ### ### - -repno = 10 -varlist = [1] -acclevel = 0.2 -n = 2048 -worker = 256 -n0 = worker -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -simmeans = [4, 16, 64] -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] -lab = ["b=1", "b=2", "b=4", "b=8", "b=16", "b=32", "b=64", "b=128", "b=256"] -accparams = [[-1, 0.2], [-1, 0.3], [-1, 0.4]] -genparams = [1, 0.5, 0.25] -result = [] -ft = 25 -lw = 5 -ms = 15 -me = 200 - -for sid, sim_mean in enumerate(simmeans): - for varid, var in enumerate(varlist): - for aid, acc in enumerate(accparams): - res = [] - for r in range(repno): - - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=n0) - PM.gen_acqtime(genparams[aid], 0.001, typeGen="constant") - PM.gen_simtime( - simmeans[sid], simmeans[sid], 0.001, typeSim="normal", seed=r - ) - PM.gen_curve(acc[0], acc[1] + id_b * 0.01, typeAcc="exponential") - - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "var": var, - "acc": aid, - "simmean": simmeans[sid], - "res": PM, - } - ) - -labs = [r"$\mathcal{A}_1$", r"$\mathcal{A}_2$", r"$\mathcal{A}_3$"] -for varid, var in enumerate(varlist): - fig, axes = plt.subplots(1, len(simmeans), figsize=(24, 6)) - for sid, sim_mean in enumerate(simmeans): - - res_c = [ - res - for res in result - if ((res["simmean"] == sim_mean) & (res["var"] == var)) - ] - for aid, acc in enumerate(accparams): - endtime = [] - - for bid, b in enumerate(batches): - endtime.append( - np.mean( - [ - res["res"].complete_time - for res in res_c - if ((res["b"] == b) & (res["acc"] == aid)) - ] - ) - ) - - axes[sid].plot( - batches, - endtime, - marker=mlist[aid], - markersize=ms, - linestyle=linelist[aid], - linewidth=lw, - label=labs[aid], - color=clist[aid], - ) - axes[sid].set_xscale("log") - axes[sid].set_yscale("log") - axes[sid].set_xticks(batches) - axes[sid].set_xticklabels(batches) - axes[sid].tick_params(axis="both", which="major", labelsize=ft - 5) - axes[sid].set_xlabel("b", fontsize=ft) - axes[0].set_ylabel("Wall-clock time", fontsize=ft) - if varid == len(varlist) - 1: - handles, labels = axes[0].get_legend_handles_labels() - fig.legend( - handles, - labels, - loc="upper center", - title_fontsize=ft, - bbox_to_anchor=(0.5, 0.01), - ncol=4, - prop={"size": ft}, - fancybox=True, - shadow=True, - ) - plt.savefig("Figure8.jpg", format="jpeg", bbox_inches="tight", dpi=500) - plt.show() - -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/result_read.py b/examples/NeurIPS2024/result_read.py deleted file mode 100644 index 63734af..0000000 --- a/examples/NeurIPS2024/result_read.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import dill as pickle -from PUQ.designmethods.utils import read_output - - -def get_rep_data(s, w, b, rep, filename, method): - - avgtime = 0 - avgAE = 0 - avgTV = 0 - avgbestTV = 0 - - AElist = [] - TVlist = [] - timelist = [] - for i in range(1, 1 + rep): - design_saved = read_output(filename, s, method, w, b, i) - - theta_al = design_saved._info["theta"] - TV = design_saved._info["TV"] - HD = design_saved._info["HD"] - AE = design_saved._info["AE"] - time = design_saved._info["time"] - - if method == "rnd": - time = np.repeat(0.1, len(time)) - - bestTV = np.zeros(TV.shape) - for i in range(len(TV)): - bestTV[i] = np.min(TV[0 : (i + 1)]) - - AElist.append(AE) - timelist.append(time) - TVlist.append(bestTV) - - avgtime += time - avgAE += AE - avgTV += TV - avgbestTV += bestTV - - avgtime = np.mean(np.array(timelist), 0) - sdtime = np.std(np.array(timelist), 0) / np.sqrt(30) - - avgAE = np.mean(np.array(AElist), 0) - sdAE = np.std(np.array(AElist), 0) / np.sqrt(30) - - avgTV = np.mean(np.array(TVlist), 0) - sdTV = np.std(np.array(TVlist), 0) / np.sqrt(30) - - return avgAE[10:], avgtime[20:] diff --git a/examples/NeurIPS2024/run_test_funcs.py b/examples/NeurIPS2024/run_test_funcs.py deleted file mode 100644 index 9330929..0000000 --- a/examples/NeurIPS2024/run_test_funcs.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -import scipy.stats as sps -from PUQ.prior import prior_dist -from test_funcs import holder, ackley, easom, sphere, matyas, himmelblau -import matplotlib.pyplot as plt -import time - -if __name__ == "__main__": - start = time.time() - - args = parse_arguments() - - cls_data = eval(args.funcname)() - - # # # Create a mesh for test set # # # - xpl = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 50) - ypl = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 50) - Xpl, Ypl = np.meshgrid(xpl, ypl) - th = np.vstack([Xpl.ravel(), Ypl.ravel()]) - setattr(cls_data, "theta", th.T) - - ftest = np.zeros(2500) - for tid, t in enumerate(th.T): - ftest[tid] = cls_data.function(t[0], t[1]) - thetatest = th.T - ptest = np.zeros(thetatest.shape[0]) - for i in range(ftest.shape[0]): - mean = ftest[i] - rnd = sps.multivariate_normal(mean=mean, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - # # # # # # # # # # # # # # # # # # # # # - prior_func = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - # # # # # # # # # # # # # # # # # # # # # - - init_seeds = args.init_seeds - final_seeds = args.final_seeds - - n_init = 10 - for s in np.arange(init_seeds, final_seeds): - - thetainit = prior_func.rnd(n_init, s) - finit = np.zeros(n_init) - for tid, t in enumerate(thetainit): - finit[tid] = cls_data.function(t[0], t[1]) - test_data["thetainit"] = thetainit - test_data["finit"] = finit[None, :] - - al_data = designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": 1, - "nworkers": 2, - "AL": args.al_func, - "seed_n0": int(s), - "prior": prior_func, - "data_test": test_data, - "max_evals": args.max_eval, - "candsize": args.candsize, - "refsize": args.refsize, - "believer": args.believer, - }, - ) - - theta_al = al_data._info["theta"] - ft = 20 - ms = 50 - fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax.scatter( - theta_al[:, 0], theta_al[:, 1], c="black", marker="+", s=ms, zorder=2 - ) - ax.scatter( - thetainit[:, 0], - thetainit[:, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=ft) - ax.set_ylabel(r"$\theta_2$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - - plt.savefig( - "Figure_" + args.funcname + ".jpg", - format="jpeg", - bbox_inches="tight", - dpi=500, - ) - plt.show() - - end = time.time() - print("Elapsed time =", round(end - start, 3)) From 9e77b5c71fe774acc8c04cbb65ba29d035a55771 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Fri, 19 Sep 2025 12:49:34 -0500 Subject: [PATCH 012/103] add nugs option for predict --- PUQ/surrogatemethods/homGP.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 8cd4173..02d6a31 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -127,11 +127,17 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): if GP is None: # use wrapper class to instantiate trained GP GP = homGPWrapper(fitinfo=fitinfo) + # handle kws + kws = {} + eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] + for key in eligible_keys: + if key in kwargs.keys(): + kws[key] = kwargs.get('nugs_only') - preds = GP.predict(x=x,xprime=thetaprime) + preds = GP.predict(x=x,xprime=thetaprime,**kws) # ensure naming consistency - predinfo['mean'] = preds['mean'] - predinfo['var'] = preds['sd2'] - predinfo['nugs'] = preds['nugs'] - predinfo['covmat'] = preds['cov'] + predinfo['mean'] = preds.get('mean') + predinfo['var'] = preds.get('sd2') + predinfo['nugs'] = preds.get('nugs') + predinfo['covmat'] = preds.get('cov') From dfadfe7def0d2993442c3a5cd5b2428b041cd26f Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Fri, 19 Sep 2025 12:49:47 -0500 Subject: [PATCH 013/103] add nugs option for predict --- PUQ/surrogatemethods/hetGP.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 373eb72..95c8d92 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -170,12 +170,19 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): if GP is None: # use wrapper class to instantiate trained GP GP = hetGPWrapper(fitinfo=fitinfo) + + # handle kws + kws = {} + eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] + for key in eligible_keys: + if key in kwargs.keys(): + kws[key] = kwargs.get('nugs_only') - preds = GP.predict(x=x,xprime=thetaprime) + preds = GP.predict(x=x,xprime=thetaprime,**kws) # ensure naming consistency - predinfo['mean'] = preds['mean'] - predinfo['var'] = preds['sd2'] - predinfo['nugs'] = preds['nugs'] - predinfo['covmat'] = preds['cov'] + predinfo['mean'] = preds.get('mean') + predinfo['var'] = preds.get('sd2') + predinfo['nugs'] = preds.get('nugs') + predinfo['covmat'] = preds.get('cov') return \ No newline at end of file From 12c0c3fe5f5ba94a5e9bb8464d10dee73d8e3d2c Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Fri, 19 Sep 2025 12:50:13 -0500 Subject: [PATCH 014/103] add prediction tests --- tests/unit_tests/test_hetGP.py | 19 ++++++++++++++++++- tests/unit_tests/test_homGP.py | 19 ++++++++++++++++++- 2 files changed, 36 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_hetGP.py b/tests/unit_tests/test_hetGP.py index 1faf962..6b60190 100644 --- a/tests/unit_tests/test_hetGP.py +++ b/tests/unit_tests/test_hetGP.py @@ -78,5 +78,22 @@ def test_predict(): assert np.allclose(test_preds._info['nugs'],preds['nugs']) assert np.allclose(test_preds._info['covmat'],preds['cov']) +def test_predict_nugs_only(): + + reference_model = hetGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model.fit() + + # test predictions + preds = reference_model.predict(Xgrid,xprime=Xgrid,nugs_only=True) + + test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid,args=dict(nugs_only=True)) + for key in ['mean','var','covmat']: + assert test_preds._info.get(key) is None + assert np.allclose(test_preds._info['nugs'],preds['nugs']) + if __name__ == "__main__": - test_predict() + test_predict_nugs_only() diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py index 60712bd..4276ddf 100644 --- a/tests/unit_tests/test_homGP.py +++ b/tests/unit_tests/test_homGP.py @@ -55,6 +55,23 @@ def test_predict(): assert np.allclose(test_preds._info['covmat'],preds['cov']) +def test_predict_nugs_only(): + + reference_model = homGP() + reference_model.mle(X,Y.flatten()) + + + test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model.fit() + + # test predictions + preds = reference_model.predict(Xgrid,xprime=Xgrid,nugs_only=True) + + test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid,args=dict(nugs_only=True)) + for key in ['mean','var','covmat']: + assert test_preds._info.get(key) is None + assert np.allclose(test_preds._info['nugs'],preds['nugs']) + def test_matern(): # test a different covariance type and define some input args COVTYPE = "Matern5_2" @@ -78,5 +95,5 @@ def test_matern(): if __name__ == "__main__": - test_predict() + test_predict_nugs_only() From 8de301393c650167f7d84807ea24a6b9eaf97b5e Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 14:18:13 -0400 Subject: [PATCH 015/103] An example to illustrate JQT2024 --- .../gen_funcs/acquisition_1d_deterministic.py | 349 ++++++++++++++++++ .../sequential_1d_deterministic.py | 185 ++++++++++ examples/Example2/README.rst | 23 ++ examples/Example2/ex2.png | Bin 0 -> 1539794 bytes examples/Example2/example.py | 71 ++++ examples/Example2/ptest_funcs.py | 295 +++++++++++++++ 6 files changed, 923 insertions(+) create mode 100644 PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py create mode 100644 PUQ/designmethods/sequential_1d_deterministic.py create mode 100644 examples/Example2/README.rst create mode 100644 examples/Example2/ex2.png create mode 100644 examples/Example2/example.py create mode 100644 examples/Example2/ptest_funcs.py diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py new file mode 100644 index 0000000..d8e0a93 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py @@ -0,0 +1,349 @@ +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult +import emcee +from copy import deepcopy + +def generate_neighborhood(acq): + + rand = np.random.default_rng(acq.seed) + neigh_type = acq.args.get("neighbor", None) + + if neigh_type == "LHS": + N = int(acq.nL) + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L = sampling(N) + else: + N = int(acq.nL * 0.5) + + sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) + L_explore = sampling(N) + + sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) + Lt = sampling(N) + + num_options = len(acq.x) + + # Distribute the rows as evenly as possible + counts = np.full(num_options, N // num_options) # Base count for each row type + counts[ + rand.choice(num_options, N % num_options, replace=False) + ] += 1 # Assign extra rows randomly + + # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) + Lx = np.vstack( + [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] + ) + + # Shuffle the rows randomly + rand.shuffle(Lx) + + L_exploit = np.concatenate((Lx, Lt), axis=1) + + L = np.concatenate((L_explore, L_exploit)) + + return L + + +class acquisition_function: + def __init__(self, model, cls_func, args): + self.model = model#deepcopy(model) # model.copy() + self.cls_func = cls_func + #self.persis_info = persis_info + self.args = args + self.x = self.cls_func.x + self.d = self.cls_func.d + self.p = self.cls_func.p + self.dx = self.cls_func.dx + self.dt = self.cls_func.dt + self.zlim = cls_func.zlim + self.xlim = cls_func.zlim[0 : cls_func.dx, :] + self.tlim = cls_func.zlim[cls_func.dx : cls_func.p, :] + self.y = self.cls_func.real_data + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) + self.twopiddet = ( + (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + ) + + def acquire_new(self): + + return eval("self.evaluate")( + self.args.get("new", True), return_pseudo=False + ) + + def gen_pred(self, model, x, t, return_id=False, return_flat=False): + nm, d = t.shape[0], x.shape[0] + ntot = nm * d + + # print(t.shape) + # (ntot, d) + x_tiled = np.tile(x, (t.shape[0], 1)) + # (ntot, p-d) + t_repeated = np.repeat(t, x.shape[0], axis=0) + # (ntot, p) + z = np.hstack([x_tiled, t_repeated]) + + # to construct S matrix + id_row = np.arange(0, ntot) + id_col = np.arange(0, ntot).reshape(nm, d) + id_col = np.repeat(id_col, repeats=d, axis=0) + + # predict at mesh + meshPr = model.predict(x=z, thetaprime=z) + + # ntot, ntot x ntot, ntot + # mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] + mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["covmat"], meshPr._info["var"] + + muT = mu.reshape(nm, d) + S = Sn[id_row[:, None], id_col].reshape(nm, d, d) + + if return_id: + return muT, S, z, id_row[:, None], id_col, mu[:, None] + else: + if return_flat: + return muT, S, z, mu[:, None] + else: + return muT, S, z + + def crit_pvar(self, model, x, t): + + # posterior variance + mu, S, z = self.gen_pred(model, x, t) + + M = S + 0.5 * self.Sigma3d + N = S + self.Sigma3d + + f = multiple_pdfs(self.y, mu, M) + g = multiple_pdfs(self.y, mu, N) + + vals = (1 / self.twopiddet) * f - g**2 + + return vals + + def total_var(self, model, x, t): + vals = self.crit_pvar(model=model, x=x, t=t) + return np.mean(self.weights * vals) + + +class var(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", True) + self.nL = args.get("nL", 100) + + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + + + +class imse(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + sampling = LHS(xlimits=self.zlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + +class ivar(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + + + nm = self.t_ref.shape[0] + + vals = np.zeros(self.nL) + + mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) + + V1 = S + self.Sigma3d + + # predict at candidates + candPr = self.model.predict(x=L, thetaprime=z) + + # nL, nL, nL x ntot + # candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] + candvar, candnugs, candcov = candPr._info["var"], candPr._info["nugs"], candPr._info["covmat"] + candtotvat = candvar + candnugs + + for k in range(0, self.nL): + # (mn x d x 1) + covc = candcov[k, :].reshape(nm, self.d, 1) + + # (mn x 1 x d) + covcT = np.transpose(covc, (0, 2, 1)) + + # (nm, d, d) + phic = (covc * covcT) / candtotvat[k] + + # C1: nm x d x d, C2: nm x d x d + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(self.y, mu, C1) + dets = multiple_determinants(C2) + + part2 = (1 / self.twopid) * (rpdf / np.sqrt(dets)) + + vals[k] = np.sum(self.weights * part2) + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + + return new_input + diff --git a/PUQ/designmethods/sequential_1d_deterministic.py b/PUQ/designmethods/sequential_1d_deterministic.py new file mode 100644 index 0000000..0594d62 --- /dev/null +++ b/PUQ/designmethods/sequential_1d_deterministic.py @@ -0,0 +1,185 @@ +import numpy as np +import matplotlib.pyplot as plt +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from PUQ.designmethods.gen_funcs.acquisition_1d_deterministic import var, ivar, imse +import time +from PUQ.surrogate import emulator + +class sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + def build_design(self, z0, f0, T, af, test=None, args={}): + + # if test is not None: + # self.test_data_gen(test) + + H = [] + timel = [] + metric_sum = {} + for t in range(0, T): + # print(f"t: {t}") if self.trace else None + print(t) + print(z0.shape) + print(f0.shape) + tic = time.time() + model = emulator(x=z0, + theta=np.array([0]), + f=f0, + method='homGP', + args={"known": {"beta0":np.mean(f0)}}) + model.fit() + + + toc = time.time() + timel.append(toc - tic) + print(toc - tic) + args["seed"] += 1 + + # if test is not None: + # metric_sum = self.eval_perf(model, args.get("extra_metric", True)) + + acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) + + acq_func.acquire_new() + znew = acq_func.znew + fnew = self.cls_func.function(znew.flatten()[0], znew.flatten()[1]).reshape( + 1, 1 + ) + + f0 = np.concatenate((f0, fnew), axis=0) + z0 = np.concatenate((z0, znew), axis=0) + + H.append( + { + "t": t, + "new": acq_func.new, + "h": getattr(acq_func, "h", None), + "f": fnew, + "z": znew, + "MSE": metric_sum.get("MSE", None), + "MAD": metric_sum.get("MAD", None), + "VAR": metric_sum.get("VAR", None), + "MSEy": metric_sum.get("MSEy", None), + "MADy": metric_sum.get("MADy", None), + "MSEn": metric_sum.get("MSEn", None), + "MADn": metric_sum.get("MADn", None), + "summary_metric": metric_sum.get("sum_metric", None), + } + ) + + unique_rows, counts = np.unique(z0, axis=0, return_counts=True) + + self.xt = unique_rows + self.reps = counts + self.fs = f0 + self.zs = z0 + self.time = timel + self.H = H + + return self + + def test_data_gen(self, test): + + # Gen test data + self.ttest, self.ptest, self.wtest, self.ntest, self.ftest = ( + test["theta"], + test["p"], + test["w"], + test["noise"], + test["f"], + ) + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ftest) + # plt.show() + + # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ntest) + # plt.show() + + x_tiled = np.tile(self.cls_func.x, (self.ttest.shape[0], 1)) + t_repeated = np.repeat(self.ttest, self.cls_func.x.shape[0], axis=0) + self.ztest = np.hstack([x_tiled, t_repeated]) + + # To be used for performance evaluations + ntot, nm, d = self.ztest.shape[0], self.ttest.shape[0], self.cls_func.d + + # to construct S matrix + self.idr = np.arange(0, ntot)[:, None] + idc = np.arange(0, ntot).reshape(nm, d) + self.idc = np.repeat(idc, repeats=d, axis=0) + + def eval_perf(self, model, extra_metric=False): + + nm, d = self.ttest.shape[0], self.cls_func.d + # sc = plt.scatter(self.ztest[:, 1], self.ztest[:, 2], c=self.wtest, cmap="viridis") # Use any colormap + # plt.colorbar(sc, label="wtest values") # Add a colorbar + # plt.show() + + # predict at mesh + pr = model.predict(x=self.ztest, thetaprime=self.ztest) + + # ntot, ntot x ntot, ntot + # mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] + mu, Sn, nugs = pr._info["mean"], pr._info["covmat"], pr._info["nugs"] + + muT = mu.reshape(nm, d) + S = Sn[self.idr, self.idc].reshape(nm, d, d) + + N = S + self.Sigma3d + g = multiple_pdfs(self.y, muT, N) + + MSE = np.mean(((g.flatten() - self.ptest.flatten()) ** 2) * self.wtest) + MAD = np.mean(np.abs(g.flatten() - self.ptest.flatten()) * self.wtest) + + M = S + 0.5 * self.Sigma3d + f = multiple_pdfs(self.y, muT, M) + + twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) + + if extra_metric: + diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) + diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) + + + MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) + MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) + + MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) + MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) + + # Weighted absolute differences + f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) + n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) + + # Unweighted absolute differences + f_wh = np.mean(np.abs(diff_y), axis=0) + n_wh = np.mean(np.abs(diff_n), axis=0) + summary = {} + for di in range(d): + summary[f"f{di+1}w"] = f_w[di] + summary[f"f{di+1}"] = f_wh[di] + summary[f"n{di+1}w"] = n_w[di] + summary[f"n{di+1}"] = n_wh[di] + else: + MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 + + metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, + "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, + "summary": summary} + return metric_sum diff --git a/examples/Example2/README.rst b/examples/Example2/README.rst new file mode 100644 index 0000000..125b810 --- /dev/null +++ b/examples/Example2/README.rst @@ -0,0 +1,23 @@ + +Examples +~~~~~~~~ + +This example demonstrates how to apply the proposed method from Sürer (2024), +Simulation Experiment Design for Calibration via Active Learning, to a deterministic simulation model with one-dimensional outputs. + + +Instructions for running the illustrative examples +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +To replicate the figures below, respectively: + +1) Go to the ``examples/Example2`` directory. + +2) Execute the followings from the command line:: + + python3 example.py + +Running this script should not take more than 60 sec. 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= sinfunc() + dt = len(cex.true_theta) + x_obs = np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None] + cex.realdata(x=x_obs, seed=s) + + + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + s = 1 + th_vec = [np.pi / 7, np.pi / 6, np.pi / 5, np.pi / 4] + thlabel = [7, 6, 5, 4] + x_vec = (np.arange(0, 100, 1) / 100)[:, None] + fvec = np.zeros((len(th_vec), len(x_vec))) + colors = ["blue", "orange", "red", "green", "purple"] + for t_id, t in enumerate(th_vec): + for x_id, x in enumerate(x_vec): + fvec[t_id, x_id] = cex.function(x, t)[0] + ax[0].plot( + x_vec, + fvec[t_id, :], + label=r"$\theta=\pi/$" + str(thlabel[t_id]), + color=colors[t_id], + linewidth=3, + ) + for d_id in range(len(cex.x)): + ax[0].scatter( + cex.x[d_id, 0], cex.real_data[0, d_id], color="black", s=50 + ) + ft = 16 + ax[0].set_xlabel(r"$x$", fontsize=ft) + ax[0].set_ylabel(r"$\eta(x, \theta)$", fontsize=ft) + ax[0].set_xticks([0.1, 0.3, 0.5, 0.7, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]) + ax[0].tick_params(labelsize=ft - 2) + ax[0].legend(bbox_to_anchor=(1.1, -0.2), fontsize=ft - 2, ncol=4) + + # Generate initial sample + n0, nmax = 10, 30 + sampling = LHS(xlimits=cex.zlim, random_state=int(s)) + z0 = sampling(n0) + f0 = np.array([cex.function(z0[i, 0], z0[i, 1]) for i in range(n0)]) + + # Generate design + des_obj = sequential_design(cex) + des_obj.build_design(z0=z0, + f0=f0[:, None], + T=nmax, + af="ivar", + args={"nL":200, + "seed":s, + "integral":"importance"}) + + + ax[1].scatter(des_obj.zs[n0:, 0], des_obj.zs[n0:, 1], marker="+", c="red", s=200, linewidth=3) + ax[1].hlines(cex.true_theta, 0, 1, linestyles="dotted", linewidth=3, colors="orange",) + for xitem in x_obs: + ax[1].vlines(xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1) + ax[1].set_xlabel(r"$x$", fontsize=ft) + ax[1].set_ylabel(r"$\theta$", fontsize=ft) + ax[1].tick_params(labelsize=ft) + ax[1].set_xlim(0, 1) + ax[1].set_ylim(0, 1) + plt.savefig("ex2.png", format="jpeg", bbox_inches="tight", dpi=1000) + plt.show() \ No newline at end of file diff --git a/examples/Example2/ptest_funcs.py b/examples/Example2/ptest_funcs.py new file mode 100644 index 0000000..afbc110 --- /dev/null +++ b/examples/Example2/ptest_funcs.py @@ -0,0 +1,295 @@ +import numpy as np + + +class sinfunc: + def __init__(self): + self.data_name = "sinfunc" + self.zlim = np.array([[0, 1], [0, 1]]) + self.true_theta = np.array([np.pi / 5]) + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.dx = 1 + self.dt = 1 + self.x = None + self.real_data = None + self.sigma2 = 0.2**2 + self.nodata = True + + def function(self, x, theta): + f = np.sin(10 * x - 5 * theta) + return f + + def realdata(self, x, seed, isbias=False): + self.x = x + self.d = len(x) + self.nodata = False + self.obsvar = np.diag(np.repeat(self.sigma2, len(self.x))) + + np.random.seed(seed) + fevals = np.zeros(len(x)) + for xid, x in enumerate(self.x): + fevals[xid] = self.genobsdata(x, isbias) + + self.real_data = np.array([fevals], dtype="float64") + + def genobsdata(self, x, isbias=False): + return self.function(x[0], self.true_theta[0]) + np.random.normal( + 0, np.sqrt(self.sigma2), 1 + ) + + +class pritam: + def __init__(self): + self.data_name = "pritam" + self.thetalimits = np.array([[0, 1], [0, 1], [0, 1]]) + self.true_theta = np.array([0.5]) + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.x = None + self.real_data = None + self.dx = 2 + self.sigma2 = 0.5**2 + self.nodata = True + + def function(self, x1, x2, theta1): + f = (30 + 5 * x1 * np.sin(5 * x1)) * (6 * theta1 + 1 + np.exp(-5 * x2)) + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["function"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1], H["thetas"][0][2]) + return H_o, persis_info + + def realdata(self, x, seed, isbias=False): + self.x = x + self.nodata = False + self.obsvar = np.diag(np.repeat(self.sigma2, len(self.x))) + + np.random.seed(seed) + fevals = np.zeros(len(x)) + for xid, x in enumerate(self.x): + fevals[xid] = self.genobsdata(x, isbias) + + self.real_data = np.array([fevals], dtype="float64") + + def genobsdata(self, x, isbias=False): + if isbias: + return ( + self.function(x[0], x[1], self.true_theta[0]) + + self.bias(x[0], x[1]) + + np.random.normal(0, np.sqrt(self.sigma2), 1) + ) + else: + return self.function(x[0], x[1], self.true_theta[0]) + np.random.normal( + 0, np.sqrt(self.sigma2), 1 + ) + + def bias(self, x1, x2): + return -50 * (np.exp(-0.2 * x1 - 0.1 * x2)) + + +class highdim2: + def __init__(self): + self.data_name = "highdim" + self.thetalimits = np.array( + [ + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + [0, 1], + ] + ) + self.true_theta = None + self.out = [("f", float)] + self.d = 1 + self.p = 12 + self.x = None + self.real_data = None + self.dx = None + self.sigma2 = None # 1#5#10 + self.nodata = True + + def function( + self, + theta1, + theta2, + theta3, + theta4, + theta5, + theta6, + theta7, + theta8, + theta9, + theta10, + theta11, + theta12, + ): + + a = -5 + b = 10 + if self.dx == 2: + # print(self.dx) + theta3 = a + b * theta3 + theta4 = a + b * theta4 + theta5 = a + b * theta5 + theta6 = a + b * theta6 + theta7 = a + b * theta7 + theta8 = a + b * theta8 + theta9 = a + b * theta9 + theta10 = a + b * theta10 + theta11 = a + b * theta11 + theta12 = a + b * theta12 + elif self.dx == 6: + # print(self.dx) + theta7 = a + b * theta7 + theta8 = a + b * theta8 + theta9 = a + b * theta9 + theta10 = a + b * theta10 + theta11 = a + b * theta11 + theta12 = a + b * theta12 + elif self.dx == 10: + # print(self.dx) + theta11 = a + b * theta11 + theta12 = a + b * theta12 + + if self.x.shape[1] == 2: + f = np.sqrt(np.abs(theta1 + theta2)) * np.power( + theta3 + + theta4 + + theta5 + + theta6 + + theta7 + + theta8 + + theta9 + + theta10 + + theta11 + + theta12, + 2, + ) + # f = np.sqrt(np.abs(theta1 + theta2))*((theta3 + theta4)**2 + (theta5 + theta6)**2 + (theta7 + theta8)**2 + (theta9 + theta10)**2 + (theta11 + theta12)**2) + elif self.x.shape[1] == 6: + f = np.sqrt( + np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6) + ) * np.power(theta7 + theta8 + theta9 + theta10 + theta11 + theta12, 2) + # f = np.sqrt(np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6))*((theta7 + theta8)**2 + (theta9 + theta10)**2 + (theta11 + theta12)**2) + elif self.x.shape[1] == 10: + f = np.sqrt( + np.abs( + theta1 + + theta2 + + theta3 + + theta4 + + theta5 + + theta6 + + theta7 + + theta8 + + theta9 + + theta10 + ) + ) * np.power(theta11 + theta12, 2) + # f = np.sqrt(np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6 + theta7 + theta8 + theta9 + theta10))*((theta11 + theta12)**2) + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["function"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function( + H["thetas"][0][0], + H["thetas"][0][1], + H["thetas"][0][2], + H["thetas"][0][3], + H["thetas"][0][4], + H["thetas"][0][5], + H["thetas"][0][6], + H["thetas"][0][7], + H["thetas"][0][8], + H["thetas"][0][9], + H["thetas"][0][10], + H["thetas"][0][11], + ) + return H_o, persis_info + + def realdata(self, x, seed, isbias=False): + self.x = x + self.dx = x.shape[1] + + if self.dx == 2: + self.sigma2 = 25 + elif self.dx == 6: + self.sigma2 = 5 + elif self.dx == 10: + self.sigma2 = 1 + + self.true_theta = np.repeat(0.5, self.p - self.dx) + self.nodata = False + self.obsvar = np.diag(np.repeat(self.sigma2, x.shape[0])) + + np.random.seed(seed) + fevals = np.zeros(len(x)) + for xid, x in enumerate(self.x): + fevals[xid] = self.genobsdata(x, isbias) + + self.real_data = np.array([fevals], dtype="float64") + + def genobsdata(self, x, isbias=False): + + if len(x) == 2: + print("Obs:", len(x)) + return self.function( + x[0], + x[1], + self.true_theta[0], + self.true_theta[1], + self.true_theta[2], + self.true_theta[3], + self.true_theta[4], + self.true_theta[5], + self.true_theta[6], + self.true_theta[7], + self.true_theta[8], + self.true_theta[9], + ) + np.random.normal(0, np.sqrt(self.sigma2), 1) + elif len(x) == 6: + print("Obs:", len(x)) + return self.function( + x[0], + x[1], + x[2], + x[3], + x[4], + x[5], + self.true_theta[0], + self.true_theta[1], + self.true_theta[2], + self.true_theta[3], + self.true_theta[4], + self.true_theta[5], + ) + np.random.normal(0, np.sqrt(self.sigma2), 1) + + elif len(x) == 10: + print("Obs:", len(x)) + return self.function( + x[0], + x[1], + x[2], + x[3], + x[4], + x[5], + x[6], + x[7], + x[8], + x[9], + self.true_theta[0], + self.true_theta[1], + ) + np.random.normal(0, np.sqrt(self.sigma2), 1) From f6c3ec8077c1b45e79ebd4e54a137ed878fe67c5 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 14:20:22 -0400 Subject: [PATCH 016/103] trying to show fig --- examples/Example2/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/Example2/README.rst b/examples/Example2/README.rst index 125b810..00602a9 100644 --- a/examples/Example2/README.rst +++ b/examples/Example2/README.rst @@ -20,4 +20,4 @@ To replicate the figures below, respectively: Running this script should not take more than 60 sec. See the figures (png files) saved under the directory. -![Illustration of PUQ with the example](https://github.com/parallelUQ/PUQ/blob/dev/PUQ_with_hetGPy/examples/Example2/ex2.png) +![Illustration of PUQ with the example](https://github.com/parallelUQ/PUQ/tree/dev/PUQ_with_hetGPy/examples/Example2/ex2.png) From 8296324ad797147d7a2ab3cd614872e084b94127 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 14:22:45 -0400 Subject: [PATCH 017/103] fig --- examples/Example2/README.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/Example2/README.rst b/examples/Example2/README.rst index 00602a9..1a1fc46 100644 --- a/examples/Example2/README.rst +++ b/examples/Example2/README.rst @@ -20,4 +20,7 @@ To replicate the figures below, respectively: Running this script should not take more than 60 sec. See the figures (png files) saved under the directory. -![Illustration of PUQ with the example](https://github.com/parallelUQ/PUQ/tree/dev/PUQ_with_hetGPy/examples/Example2/ex2.png) +.. image:: ex2.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 \ No newline at end of file From 775576ace707dceb3ebd27166eec6bc1122dc39c Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 15:09:21 -0400 Subject: [PATCH 018/103] Example 1 Update --- examples/Example1/README.rst | 24 ++++++++ examples/Example1/ex1.png | Bin 0 -> 1326909 bytes examples/Example1/example.py | 85 +++++++++++++++++++++++++ examples/Example1/test_func.py | 109 +++++++++++++++++++++++++++++++++ 4 files changed, 218 insertions(+) create mode 100644 examples/Example1/README.rst create mode 100644 examples/Example1/ex1.png create mode 100644 examples/Example1/example.py create mode 100644 examples/Example1/test_func.py diff --git a/examples/Example1/README.rst b/examples/Example1/README.rst new file mode 100644 index 0000000..9fd8c54 --- /dev/null +++ b/examples/Example1/README.rst @@ -0,0 +1,24 @@ + +Examples +~~~~~~~~ + +This example demonstrates how to apply the proposed method from Sürer, Plumlee, and Wild (2024), +Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models, to a deterministic simulation model with high-dimensional outputs. + + +**Instructions for running the illustrative examples** + +To replicate the figures below, respectively: + +1) Go to the ``examples/Example1`` directory. + +2) Execute the followings from the command line:: + + python3 example.py + +Running this script should not take more than 120 sec. 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zW6$%3GABeeuaVMwY`yPE_{CQ7@AT>P0CdE%R=S`LYGY6*@c3n*CYs>|^@cBUkN1GI zSJ**)UuYUf3KS;DI$;^_0pGyGe&8~|XD~Rr#NW@{e;JyYFSiVY$WZ6FAv2u&(D;gB zXiyK2@B$QAY~K?~nBN1AOhHN#u{5qwd~FkJBWczh3NIoIQT4(pDPVo=1CY{#XKf-v zQ+K|)m_DkS0GR)H`0~2IV$*x-s@<~ROO8lb2JD9?^T1Py;0a9snE|a;Bq(1Q(zN8} z{K2lmFIcu3AKFsif(@N*Fc74_j(|j7#drD{MtC%K%n~va-H}{1ZH(j}YXf*gYe-C) zJ_)EzQis#%E{yx3WwP+t?*3z~DYFh!tv{WZ-c{&rbwie>?DFR`zwnj`|7L$616npq z>EMV<3pfcy^F&98TH Date: Fri, 19 Sep 2025 15:28:14 -0400 Subject: [PATCH 019/103] Example 3 --- examples/Example3/README.rst | 31 ++++++++ examples/Example3/ex3_a.jpg | Bin 0 -> 548391 bytes examples/Example3/ex3_b.jpg | Bin 0 -> 796324 bytes examples/Example3/example_a.py | 92 ++++++++++++++++++++++ examples/Example3/example_b.py | 137 +++++++++++++++++++++++++++++++++ 5 files changed, 260 insertions(+) create mode 100644 examples/Example3/README.rst create mode 100644 examples/Example3/ex3_a.jpg create mode 100644 examples/Example3/ex3_b.jpg create mode 100644 examples/Example3/example_a.py create mode 100644 examples/Example3/example_b.py diff --git a/examples/Example3/README.rst b/examples/Example3/README.rst new file mode 100644 index 0000000..a313987 --- /dev/null +++ b/examples/Example3/README.rst @@ -0,0 +1,31 @@ +Examples +~~~~~~~~ + +This example demonstrates how to use the performance model from Sürer and Wild (2024), +An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations. + + +**Instructions for running the illustrative examples with performance model** + +To replicate the figures below, respectively: + +1) Go to the ``examples/Example3`` directory. + +2) Execute any of the following from the command line: + +.. code-block:: python + + python example_a.py + python example_b.py + +Running each script should not take more than 60 sec. See the figures (jpeg files) saved under ``examples/`` directory. + +.. image:: ex3_a.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 + +.. image:: ex3_b.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 diff --git a/examples/Example3/ex3_a.jpg b/examples/Example3/ex3_a.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0dd5e60cdddbc6cffbb1b78db1abd6e6a30f7231 GIT binary patch literal 548391 zcmeFa2{@Gf`!{@PXo{HZOPGWxNejs~LnR@p$P_V^vW9HgjgmDILaC4?$}-6=X6$RS zQ}#7GGZ@CK@8!OKzu&+6{yqQaeV+gEJnwrv@0;Vej>a{YneTEgpYwB`=Qrke<`}S7 z@4W7LfCct8eb+q}5JdF^0jg|I@m=f$$a1H2&I ztZWj;ck*amf!=oJJ#gaDF1|C-@A9j6OP;(+;J@S2$u1zJI4(`xp4uNX`yZRwEd#%0;I|C?mVw_g@LL9c%fN3L_$>p! zW#G3A{FZ^=GVogle#^jb8Tc&&|1JaPZ+*NG%Dz)q=Fv^;Gn!w9C&vXrR9&0|-|_o8<~E zyHzZke1S^)yYIW;k*>Uq4hBcir|0wCQP-DxU^K6K-+aSxVJnCH^Q+3K9}Wf}Qld7V z671h7B^AKGm^IR+lydJ8+pAD_<>m{_j?c;zJ@@~(3mZGty>iFA>&DOlO5sK5>c<|I 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ztvQ|tH*8VKYRQOJ^J(~TuXQ0@J6WO?_fG*Vbrt#q2f*Yp!Z!W38QEi*?1+usmN)de zxJ3OIH0$|nzdSSj9yW+{Z5jOVlri65-|Me9;ie3aBe7RV9T&Cs~FGg=+uypVC?$B`4vcoHwOCS3DvYH3ymiA>waWFwioh}@<0(xK<&0d*DeUQWa^ zi-~KBSG_^)@Yg=k{BU>f$M;Sl`6TISq(}hoL;-D*?<^4gNG&x2#gsFUMBXa4{NkC7 zdiTKjQ288MF?S&dygx(j$q{0mzCpXJJZ%wP8fAK#!X`dvwGagT6fHDcdOzM`F%SF1 z^=(vwXm9rc^oibo5J1003;!{qg+JDNzd!#S@4sW= 64: + text = ax[vid].text( + j, + i, + int(timemat[i, j]), + ha="center", + va="center", + color="white", + fontsize=ft - 5, + ) + else: + text = ax[vid].text( + j, + i, + int(timemat[i, j]), + ha="center", + va="center", + color="black", + fontsize=ft - 5, + ) + + ax[vid].grid(which="minor", color="w", linestyle="-", linewidth=3) + ax[vid].set_xlabel("Simulation Time", fontsize=ft) + ax[vid].set_ylabel("Acquisition Time", fontsize=ft) + cbar = fig.colorbar(im, cax=cax, orientation="vertical") + cbar.ax.tick_params(labelsize=ft - 5) + cbar.ax.set_ylabel("Batch Size", rotation=-90, va="bottom", fontsize=ft - 5) + cbar.set_ticks(batches) +fig.suptitle("Variability in Simulation Time Increases \u2192", fontsize=ft) +fig.tight_layout() +plt.savefig("ex3_b.jpg", format="jpeg", bbox_inches="tight", dpi=500) +plt.show() From 5584b5f5f00ec97c0240eba12646dbb265d78902 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 15:30:37 -0400 Subject: [PATCH 020/103] update png files --- examples/Example3/{ex3_a.jpg => ex3_a.png} | Bin examples/Example3/{ex3_b.jpg => ex3_b.png} | Bin 2 files changed, 0 insertions(+), 0 deletions(-) rename examples/Example3/{ex3_a.jpg => ex3_a.png} (100%) rename examples/Example3/{ex3_b.jpg => ex3_b.png} (100%) diff --git a/examples/Example3/ex3_a.jpg b/examples/Example3/ex3_a.png similarity index 100% rename from examples/Example3/ex3_a.jpg rename to examples/Example3/ex3_a.png diff --git a/examples/Example3/ex3_b.jpg b/examples/Example3/ex3_b.png similarity index 100% rename from examples/Example3/ex3_b.jpg rename to examples/Example3/ex3_b.png From 114ed59fb5cce2b914ea1579433e5e925afa05ba Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:34:25 -0400 Subject: [PATCH 021/103] paper ci test --- .github/workflows/paper-ci.yml | 16 ++++++---------- 1 file changed, 6 insertions(+), 10 deletions(-) diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml index fd90967..b9f7941 100644 --- a/.github/workflows/paper-ci.yml +++ b/.github/workflows/paper-ci.yml @@ -2,9 +2,9 @@ name: CI Papers on: push: - branches: [ "main", "develop"] + branches: [ "dev/PUQ_with_hetGPy"] pull_request: - branches: [ "main", "develop"] + branches: [ "dev/PUQ_with_hetGPy"] jobs: run-full-tests: @@ -13,7 +13,7 @@ jobs: strategy: matrix: os: [ubuntu-latest, macos-latest] - python-version: ["3.9", "3.10", "3.11", "3.12"] + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v2 @@ -28,11 +28,7 @@ jobs: pip install -r requirements.txt pip install -e . - name: Test with pytest on main branch (an example from Technometrics paper) - if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/develop' + if: github.ref == 'refs/heads/dev/PUQ_with_hetGPy' run: | - python ./examples/Technometrics2024/figure1b.py - - - name: Test with pytest on main branch (an example from IJOC paper) - if: github.ref == 'refs/heads/main' || github.ref == 'refs/heads/develop' - run: | - python ./examples/IJOC2024+/ci_example.py -funcname "holder" + python ./examples/Example1/example.py + From d91c4f48bf8a0429cc00f6abf6a7a59a65b4b474 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:37:09 -0400 Subject: [PATCH 022/103] update requirements --- requirements.txt | 3 +-- setup.py | 8 -------- 2 files changed, 1 insertion(+), 10 deletions(-) diff --git a/requirements.txt b/requirements.txt index d2cabb9..b9dae0a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ -numpy<2.0.0 +numpy scipy -Cython>=3.0.10 wheel sphinx>=7.2 sphinx-rtd-theme==1.3.0 diff --git a/setup.py b/setup.py index 95a64e2..bb42a2a 100644 --- a/setup.py +++ b/setup.py @@ -20,16 +20,8 @@ install_requires=[ "pandas", "matplotlib", - "libensemble==1.4.2", - "torch", "scikit-learn", "smt", ], - ext_modules=[ - Extension( - "PUQ.surrogatesupport.matern_covmat", - sources=["PUQ/surrogatesupport/matern_covmat.pyx"], - ), - ], include_dirs=[numpy.get_include()], ) From a3414b8d8caefd7cb52fec78871764025080b27b Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:42:52 -0400 Subject: [PATCH 023/103] update ci --- .github/workflows/paper-ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml index b9f7941..d4d4848 100644 --- a/.github/workflows/paper-ci.yml +++ b/.github/workflows/paper-ci.yml @@ -24,9 +24,9 @@ jobs: - name: Install dependencies run: | python -m ensurepip --upgrade - python -m pip install --upgrade setuptools + python -m pip install --upgrade pip setuptools wheel pip install -r requirements.txt - pip install -e . + pip install -e . --use-pep517 - name: Test with pytest on main branch (an example from Technometrics paper) if: github.ref == 'refs/heads/dev/PUQ_with_hetGPy' run: | From b900154d5dc0a4d94aec948e374a2db243327299 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:46:54 -0400 Subject: [PATCH 024/103] toml --- pyproject.toml | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 pyproject.toml diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..69363a4 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,7 @@ +[build-system] +requires = [ + "setuptools>=64", + "wheel", + "numpy>=1.25" # required during build +] +build-backend = "setuptools.build_meta" \ No newline at end of file From 7b776b8b3f96b2aac36e6457d39f7e033d568f94 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:48:41 -0400 Subject: [PATCH 025/103] add hetpy to requirements --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index b9dae0a..9dad890 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,4 +4,5 @@ wheel sphinx>=7.2 sphinx-rtd-theme==1.3.0 docutils<0.19 -dill \ No newline at end of file +dill +hetGPy \ No newline at end of file From 71318ae95871696ca60e1f18bf93fae879b3170c Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 20:50:03 -0400 Subject: [PATCH 026/103] adding emcee --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 9dad890..e06e0d4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,5 @@ sphinx>=7.2 sphinx-rtd-theme==1.3.0 docutils<0.19 dill -hetGPy \ No newline at end of file +hetGPy +emcee \ No newline at end of file From b4b69af8ad60e3071cc25fbe1ff512a22ddf131f Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 21:00:17 -0400 Subject: [PATCH 027/103] adding windows --- .github/workflows/paper-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml index d4d4848..0162d56 100644 --- a/.github/workflows/paper-ci.yml +++ b/.github/workflows/paper-ci.yml @@ -12,7 +12,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, macos-latest] + os: [ubuntu-latest, windows-latest, macos-14] python-version: ["3.10", "3.11", "3.12"] steps: From a505ad0904b1029d67a94d751d54baf31091c87f Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 21:04:18 -0400 Subject: [PATCH 028/103] numpy scipy --- .github/workflows/paper-ci.yml | 1 + requirements.txt | 2 -- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml index 0162d56..a8c514b 100644 --- a/.github/workflows/paper-ci.yml +++ b/.github/workflows/paper-ci.yml @@ -25,6 +25,7 @@ jobs: run: | python -m ensurepip --upgrade python -m pip install --upgrade pip setuptools wheel + pip install numpy scipy pip install -r requirements.txt pip install -e . --use-pep517 - name: Test with pytest on main branch (an example from Technometrics paper) diff --git a/requirements.txt b/requirements.txt index e06e0d4..7fba7cb 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,3 @@ -numpy -scipy wheel sphinx>=7.2 sphinx-rtd-theme==1.3.0 From 5177983411f0036599e16e0d418a2fcc0e388c79 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 21:14:04 -0400 Subject: [PATCH 029/103] paper-ci --- .github/workflows/paper-ci.yml | 2 +- setup.py | 8 +------- 2 files changed, 2 insertions(+), 8 deletions(-) diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml index a8c514b..931a88b 100644 --- a/.github/workflows/paper-ci.yml +++ b/.github/workflows/paper-ci.yml @@ -25,7 +25,7 @@ jobs: run: | python -m ensurepip --upgrade python -m pip install --upgrade pip setuptools wheel - pip install numpy scipy + pip install numpy scipy matplotlib pandas scikit-learn smt pip install -r requirements.txt pip install -e . --use-pep517 - name: Test with pytest on main branch (an example from Technometrics paper) diff --git a/setup.py b/setup.py index bb42a2a..6aff491 100644 --- a/setup.py +++ b/setup.py @@ -1,5 +1,5 @@ import setuptools -from setuptools import setup, Extension +from setuptools import setup import numpy @@ -17,11 +17,5 @@ "Operating System :: OS Independent", ], python_requires=">=3.6", - install_requires=[ - "pandas", - "matplotlib", - "scikit-learn", - "smt", - ], include_dirs=[numpy.get_include()], ) From e2749739684bedf9842a14a1944c32565c005cfd Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 21:20:00 -0400 Subject: [PATCH 030/103] update puq ci --- .github/workflows/paper-ci.yml | 35 --------- .github/workflows/puq-ci.yml | 11 +-- tests/unit_tests/test_UQ_ijoc.py | 111 ----------------------------- tests/unit_tests/test_UQ_techno.py | 78 -------------------- 4 files changed, 6 insertions(+), 229 deletions(-) delete mode 100644 .github/workflows/paper-ci.yml delete mode 100644 tests/unit_tests/test_UQ_ijoc.py delete mode 100644 tests/unit_tests/test_UQ_techno.py diff --git a/.github/workflows/paper-ci.yml b/.github/workflows/paper-ci.yml deleted file mode 100644 index 931a88b..0000000 --- a/.github/workflows/paper-ci.yml +++ /dev/null @@ -1,35 +0,0 @@ -name: CI Papers - -on: - push: - branches: [ "dev/PUQ_with_hetGPy"] - pull_request: - branches: [ "dev/PUQ_with_hetGPy"] - -jobs: - run-full-tests: - - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-latest, windows-latest, macos-14] - python-version: ["3.10", "3.11", "3.12"] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m ensurepip --upgrade - python -m pip install --upgrade pip setuptools wheel - pip install numpy scipy matplotlib pandas scikit-learn smt - pip install -r requirements.txt - pip install -e . --use-pep517 - - name: Test with pytest on main branch (an example from Technometrics paper) - if: github.ref == 'refs/heads/dev/PUQ_with_hetGPy' - run: | - python ./examples/Example1/example.py - diff --git a/.github/workflows/puq-ci.yml b/.github/workflows/puq-ci.yml index 580c37c..2bf8145 100644 --- a/.github/workflows/puq-ci.yml +++ b/.github/workflows/puq-ci.yml @@ -5,9 +5,9 @@ name: PUQ CI on: push: - branches: ["main", "develop"] + branches: ["dev/PUQ_with_hetGPy"] pull_request: - branches: ["main", "develop"] + branches: ["dev/PUQ_with_hetGPy"] jobs: run-full-tests: @@ -15,8 +15,8 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, macos-latest] - python-version: ["3.9", "3.10", "3.11", "3.12"] + os: [windows-latest, ubuntu-latest, macos-latest] + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v2 @@ -27,7 +27,8 @@ jobs: - name: Install dependencies run: | python -m ensurepip --upgrade - python -m pip install --upgrade setuptools + python -m pip install --upgrade pip setuptools wheel + pip install numpy scipy matplotlib pandas scikit-learn smt pip install -r requirements.txt python -m pip install flake8 pytest pytest-cov Cython pip install -e . diff --git a/tests/unit_tests/test_UQ_ijoc.py b/tests/unit_tests/test_UQ_ijoc.py deleted file mode 100644 index bd25fb9..0000000 --- a/tests/unit_tests/test_UQ_ijoc.py +++ /dev/null @@ -1,111 +0,0 @@ -import numpy as np -import pytest -from contextlib import contextmanager -from PUQ.design import designer -from PUQ.prior import prior_dist -import scipy.stats as sps - - -class himmelblau: - def __init__(self): - - self.data_name = "himmelblau" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = np.array([[1]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = (theta1**2 + theta2 - 11) ** 2 + (theta1 + theta2**2 - 7) ** 2 - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the himmelblau function - """ - - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - return H_o, persis_info - - -example = "himmelblau" -cls_data = eval(example)() - -# # # Create a mesh for test set # # # -xpl = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 50) -ypl = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 50) -Xpl, Ypl = np.meshgrid(xpl, ypl) -th = np.vstack([Xpl.ravel(), Ypl.ravel()]) -setattr(cls_data, "theta", th.T) - -ftest = np.zeros(2500) -for tid, t in enumerate(th.T): - ftest[tid] = cls_data.function(t[0], t[1]) -thetatest = th.T -ptest = np.zeros(thetatest.shape[0]) -for i in range(ftest.shape[0]): - mean = ftest[i] - rnd = sps.multivariate_normal(mean=mean, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - -test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - -# # # # # # # # # # # # # # # # # # # # # -prior_func = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] -) - -n_init = 10 -s = 0 -thetainit = prior_func.rnd(n_init, s) -finit = np.zeros(n_init) -for tid, t in enumerate(thetainit): - finit[tid] = cls_data.function(t[0], t[1]) -test_data["thetainit"] = thetainit -test_data["finit"] = finit[None, :] - - -@contextmanager -def does_not_raise(): - yield - - -@pytest.mark.parametrize( - "input1,expectation", - [("ei", does_not_raise()), ("hybrid_ei", does_not_raise())], -) -def test_none_input(input1, expectation): - with expectation: - assert ( - designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": 1, - "nworkers": 2, - "AL": input1, - "seed_n0": 0, - "prior": prior_func, - "data_test": test_data, - "max_evals": 50, - "candsize": 100, - "refsize": 100, - "believer": 0, - }, - ) - is not None - ) diff --git a/tests/unit_tests/test_UQ_techno.py b/tests/unit_tests/test_UQ_techno.py deleted file mode 100644 index 24a22de..0000000 --- a/tests/unit_tests/test_UQ_techno.py +++ /dev/null @@ -1,78 +0,0 @@ -import numpy as np -import pytest -from contextlib import contextmanager -from PUQ.design import designer -from PUQ.prior import prior_dist - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[-4, 4], [-4, 4]]) - self.obsvar = np.array([[4]], dtype="float64") - self.real_data = np.array([[-6]], dtype="float64") - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - """ - Wraps the unimodal function - """ - thetas = np.array([theta1, theta2]).reshape((1, 2)) - S = np.array([[1, 0.5], [0.5, 1]]) - f = (thetas @ S) @ thetas.T - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the simulator - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -cls_unimodal = unimodal() -prior_func = prior_dist(dist="uniform")( - a=cls_unimodal.thetalimits[:, 0], b=cls_unimodal.thetalimits[:, 1] -) - - -@contextmanager -def does_not_raise(): - yield - - -@pytest.mark.parametrize( - "input1,expectation", - [ - ("maxvar", does_not_raise()), - ("maxexp", does_not_raise()), - ("rnd", does_not_raise()), - ], -) -def test_none_input(input1, expectation): - with expectation: - assert ( - designer( - data_cls=cls_unimodal, - method="SEQCAL", - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "AL": input1, - "seed_n0": 1, - "prior": prior_func, - "data_test": None, - "max_evals": 60, - "type_init": None, - }, - ) - is not None - ) From e209573daefc022326ffae274568c9684206f466 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 22:00:50 -0400 Subject: [PATCH 031/103] include design for 1d deterministic --- tests/unit_tests/test_1d_deter.py | 78 +++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 tests/unit_tests/test_1d_deter.py diff --git a/tests/unit_tests/test_1d_deter.py b/tests/unit_tests/test_1d_deter.py new file mode 100644 index 0000000..d3cb89d --- /dev/null +++ b/tests/unit_tests/test_1d_deter.py @@ -0,0 +1,78 @@ +'''Tests whether the emulator with homGP gives the same result as just using homGP''' + +import sys +sys.path.append('./') +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential_1d_deterministic import sequential_design + + +class sinfunc: + def __init__(self): + self.data_name = "sinfunc" + self.zlim = np.array([[0, 1], [0, 1]]) + self.true_theta = np.array([np.pi / 5]) + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.dx = 1 + self.dt = 1 + self.x = None + self.real_data = None + self.sigma2 = 0.2**2 + self.nodata = True + + def function(self, x, theta): + f = np.sin(10 * x - 5 * theta) + return f + + def realdata(self, x, seed, isbias=False): + self.x = x + self.d = len(x) + self.nodata = False + self.obsvar = np.diag(np.repeat(self.sigma2, len(self.x))) + + np.random.seed(seed) + fevals = np.zeros(len(x)) + for xid, x in enumerate(self.x): + fevals[xid] = self.genobsdata(x, isbias) + + self.real_data = np.array([fevals], dtype="float64") + + def genobsdata(self, x, isbias=False): + return self.function(x[0], self.true_theta[0]) + np.random.normal( + 0, np.sqrt(self.sigma2), 1 + ) + +cex = sinfunc() +dt = len(cex.true_theta) +x_obs = np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None] +cex.realdata(x=x_obs, seed=1) + + +# Generate initial sample +n0, nmax = 10, 30 +sampling = LHS(xlimits=cex.zlim, random_state=1) +z0 = sampling(n0) +f0 = np.array([cex.function(z0[i, 0], z0[i, 1]) for i in range(n0)]) + +def test_build_design(): + + # Generate design + des_obj = sequential_design(cex) + des_obj.build_design(z0=z0, + f0=f0[:, None], + T=nmax, + af="ivar", + args={"nL":200, + "seed":1, + "integral":"importance"}) + + assert des_obj.zs.shape == (40, 2) + assert des_obj.fs.shape == (40, 1) + + + +if __name__ == "__main__": + test_fit() + From 7e4ea4bc7d1188635b5f5d524c22343f43ce99a9 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Fri, 19 Sep 2025 22:08:55 -0400 Subject: [PATCH 032/103] updating test --- tests/unit_tests/test_1d_deter.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tests/unit_tests/test_1d_deter.py b/tests/unit_tests/test_1d_deter.py index d3cb89d..75c38b3 100644 --- a/tests/unit_tests/test_1d_deter.py +++ b/tests/unit_tests/test_1d_deter.py @@ -1,4 +1,4 @@ -'''Tests whether the emulator with homGP gives the same result as just using homGP''' +'''Tests whether the we obtain a design for a one-dimensional deterministic simulation model''' import sys sys.path.append('./') @@ -68,11 +68,11 @@ def test_build_design(): "seed":1, "integral":"importance"}) - assert des_obj.zs.shape == (40, 2) - assert des_obj.fs.shape == (40, 1) + assert des_obj.zs.shape == (n0 + nmax, 2) + assert des_obj.fs.shape == (n0 + nmax, 1) if __name__ == "__main__": - test_fit() + test_build_design() From 44d793b1695500a516b56ce8ec4fd6dc3101b70d Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 09:57:32 -0500 Subject: [PATCH 033/103] use setattr to improve wrapper --- PUQ/surrogatemethods/hetGP.py | 2 +- PUQ/surrogatemethods/homGP.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 95c8d92..e414885 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -160,7 +160,7 @@ def __init__(self,fitinfo): # model hyperparameters for key in keys_to_transfer: - self[key] = fitinfo[key] + setattr(self,key,fitinfo[key]) def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): r''' diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 02d6a31..9bff0dd 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -116,7 +116,7 @@ def __init__(self,fitinfo): # model hyperparameters for key in keys_to_transfer: - self[key] = fitinfo[key] + setattr(self,key,fitinfo[key]) def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): From 1ef192273d8073cee0dc9d9a65b743808ca1bcd8 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 10:16:00 -0500 Subject: [PATCH 034/103] copy all attributes during fit and in wrapper classes --- PUQ/surrogatemethods/hetGP.py | 47 +++-------------------------------- PUQ/surrogatemethods/homGP.py | 32 ++++++------------------ 2 files changed, 11 insertions(+), 68 deletions(-) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index e414885..a10f21c 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -108,29 +108,9 @@ def fit(fitinfo, x, theta, f, eps=eps, covtype=covtype ) - fitinfo['ll'] = model.get('ll') - fitinfo['Delta'] = model.get('Delta') - fitinfo['theta'] = model.get('theta') - fitinfo['g'] = model.get('g') - fitinfo['k_theta_g'] = model.get('k_theta_g') - fitinfo['theta_g'] = model.get('theta_g') - fitinfo['nmean'] = model.get('nmean') - fitinfo['Lambda'] = model.get('Lambda') - fitinfo['logN'] = model.get('logN') - fitinfo['nu_hat_var'] = model.get('nu_hat_var') - fitinfo['nu_hat'] = model.get('nu_hat') - fitinfo['Kgi'] = model.get('Kgi') - fitinfo['SiNK'] = model.get('SiNK') - fitinfo['Ki'] = model.get('Ki') - fitinfo['X0'] = model.get('X0') - fitinfo['Z0'] = model.get('Z0') - fitinfo['Z'] = model.get('Z') - fitinfo['mult'] = model.get('mult') - fitinfo['covtype'] = model.get('covtype') - fitinfo['beta0'] = model.get('beta0') - fitinfo['eps'] = model.get('eps') - fitinfo['trendtype'] = model.get('trendtype') - fitinfo['is_homGP'] = model.get('is_homGP') + for key in model.__dict__.keys(): + fitinfo[key] = model.get(key) + fitinfo['is_homGP'] = False return class hetGPWrapper(hetGP): @@ -140,26 +120,7 @@ class hetGPWrapper(hetGP): ''' def __init__(self,fitinfo): - - keys_to_transfer = [ - 'X0','Z0','Z', # data - 'covtype', # kernel - 'theta', 'g', 'beta0','trendtype', # hyperparameters - 'Lambda', 'Delta', # latent noise - 'theta_g','k_theta_g', # noise hyperparameters - 'nu_hat_var', # noise variance - 'Kgi', # noise covariance inverse - 'SiNK', - 'nmean', - 'logN', - 'g', # nugget - 'Ki', # inverse covariance matrix - 'eps', # for numeric stability - 'nu_hat' # output from maximum likelihood - ] - - # model hyperparameters - for key in keys_to_transfer: + for key in fitinfo.keys(): setattr(self,key,fitinfo[key]) def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 9bff0dd..a7b658e 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -79,20 +79,8 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, eps=eps, settings=settings) - fitinfo['theta'] = model['theta'] - fitinfo['g'] = model['g'] - fitinfo['nu_hat'] = model['nu_hat'] - fitinfo['Ki'] = model['Ki'] - fitinfo['X0'] = model['X0'] - fitinfo['Z0'] = model['Z0'] - fitinfo['Z'] = model['Z'] - fitinfo['mult'] = model['mult'] - fitinfo['beta0'] = model['beta0'] - fitinfo['ll'] = model['ll'] - fitinfo['covtype'] = model['covtype'] - fitinfo['nu_hat'] = model['nu_hat'] - fitinfo['trendtype'] = model['trendtype'] - fitinfo['eps'] = model['eps'] + for key in model.__dict__.keys(): + fitinfo[key] = model.get(key) fitinfo['is_homGP'] = True return @@ -104,18 +92,8 @@ class homGPWrapper(homGP): ''' def __init__(self,fitinfo): - - keys_to_transfer = [ - 'X0','Z0','Z', # data - 'covtype', # kernel - 'theta', 'g', 'beta0','trendtype', # hyperparameters - 'Ki', # inverse covariance matrix - 'eps', # for numeric stability - 'nu_hat' # output from maximum likelihood - ] - # model hyperparameters - for key in keys_to_transfer: + for key in fitinfo.keys(): setattr(self,key,fitinfo[key]) @@ -141,3 +119,7 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): predinfo['var'] = preds.get('sd2') predinfo['nugs'] = preds.get('nugs') predinfo['covmat'] = preds.get('cov') + +def update(fitinfo, x): + + return From 858415943d8d2e9a4346430345b85c9256e2fc20 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 15:23:46 -0500 Subject: [PATCH 035/103] add update kwarg handling and delete GP --- PUQ/surrogatemethods/homGP.py | 32 ++++++++++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index a7b658e..61e04ff 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -82,6 +82,7 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, for key in model.__dict__.keys(): fitinfo[key] = model.get(key) fitinfo['is_homGP'] = True + del model return @@ -119,7 +120,34 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): predinfo['var'] = preds.get('sd2') predinfo['nugs'] = preds.get('nugs') predinfo['covmat'] = preds.get('cov') + del GP -def update(fitinfo, x): - +def update(fitinfo, x,Y = None,**kwargs): + r''' + Update function for homGP + + Parameters + ---------- + fitinfo: dictionary that contains the fit information for a hetgpy.homGP object + x: array of new design locations + Y: new response. If None, then + kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: + ''' + # validate kwargs + valid_kws = ('ginit','lower','upper', + 'noiseControl','settings','known','maxit') + for kw in kwargs.keys(): + if kw not in valid_kws: + raise ValueError(f"{kw} not found, must be one of {valid_kws}") + GP = homGPWrapper(fitinfo) + if Y is None: + maxit = 0 # impute mean response and do not update hyperparams + Y = GP.predict(x)['mean'] + else: + maxit = kwargs.get('maxit',100) + kwargs['maxit'] = maxit + GP.update(Xnew=x,Znew=Y,**kwargs) + for key in GP.__dict__.keys(): + fitinfo[key] = GP.get(key) + del GP return From 94cde5e9c745290a7925b5b97ceadf38d30296a2 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 15:25:50 -0500 Subject: [PATCH 036/103] add kriging believer test --- tests/unit_tests/test_homGP.py | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py index 4276ddf..8f413eb 100644 --- a/tests/unit_tests/test_homGP.py +++ b/tests/unit_tests/test_homGP.py @@ -54,7 +54,6 @@ def test_predict(): assert np.allclose(test_preds._info['nugs'],preds['nugs']) assert np.allclose(test_preds._info['covmat'],preds['cov']) - def test_predict_nugs_only(): reference_model = homGP() @@ -94,6 +93,30 @@ def test_matern(): assert test_model._info['beta0'] == reference_model['beta0'] +def test_homGP_update_kriging_believer(): + reference_model = homGP() + reference_model.mle(X,Y.flatten()) + + Xnew = X.mean().reshape(-1,1) + Ypred = reference_model.predict(Xnew)['mean'] + reference_model.predict(Xnew,Ypred) + reference_model_checkpoint = reference_model.copy() + reference_model.update(Xnew,Ypred,maxit=0) + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='homGP') + test_model.fit() + test_model.update(Xnew) + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + + +#def test_homGP_update_() + + if __name__ == "__main__": - test_predict_nugs_only() + test_homGP_update_kriging_believer() From 8bb9acb80db27d4971e3cb9a823af2ffa50a7afe Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 15:26:17 -0500 Subject: [PATCH 037/103] add update to surrogate --- PUQ/surrogate.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/PUQ/surrogate.py b/PUQ/surrogate.py index 22619f8..04f1575 100644 --- a/PUQ/surrogate.py +++ b/PUQ/surrogate.py @@ -339,6 +339,9 @@ def predict(self, x=None, theta=None, thetaprime=None, args={}): info = {} self.method.predict(info, self._info, x, theta, thetaprime, **argstemp) return prediction(info, self) + def update(self,x=None,Y=None,**kwargs): + + self.method.update(self._info,x=x,Y=Y,**kwargs) def acquisition(self, x=None, theta1=None, theta2=None): return self.method.acquisition(self._info, x, theta1, theta2) From 92807fc80f91cd4effd5e7920d59e3d6223ea2f2 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 15:38:38 -0500 Subject: [PATCH 038/103] test update is passing --- tests/unit_tests/test_homGP.py | 21 ++++++++++++++++++--- 1 file changed, 18 insertions(+), 3 deletions(-) diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py index 8f413eb..7f98110 100644 --- a/tests/unit_tests/test_homGP.py +++ b/tests/unit_tests/test_homGP.py @@ -100,7 +100,6 @@ def test_homGP_update_kriging_believer(): Xnew = X.mean().reshape(-1,1) Ypred = reference_model.predict(Xnew)['mean'] reference_model.predict(Xnew,Ypred) - reference_model_checkpoint = reference_model.copy() reference_model.update(Xnew,Ypred,maxit=0) test_model = emulator(x=X,theta=np.array([0]),f=Y, @@ -113,10 +112,26 @@ def test_homGP_update_kriging_believer(): assert test_model._info['g'] == reference_model['g'] assert test_model._info['beta0'] == reference_model['beta0'] +def test_homGP_update(): + reference_model = homGP() + reference_model.mle(X,Y.flatten()) + + Xnew = X.mean().reshape(-1,1) + Ypred = reference_model.predict(Xnew)['mean'] + reference_model.predict(Xnew,Ypred) + reference_model.update(Xnew,Ypred) + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='homGP') + test_model.fit() + test_model.update(x=Xnew,Y=Ypred) -#def test_homGP_update_() + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] if __name__ == "__main__": - test_homGP_update_kriging_believer() + test_homGP_update() From 602f3741e0b7a9ac8b764af5e7a49589f04fc759 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 17:18:57 -0500 Subject: [PATCH 039/103] fix typo --- tests/unit_tests/test_homGP.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py index 7f98110..23f2d15 100644 --- a/tests/unit_tests/test_homGP.py +++ b/tests/unit_tests/test_homGP.py @@ -99,7 +99,6 @@ def test_homGP_update_kriging_believer(): Xnew = X.mean().reshape(-1,1) Ypred = reference_model.predict(Xnew)['mean'] - reference_model.predict(Xnew,Ypred) reference_model.update(Xnew,Ypred,maxit=0) test_model = emulator(x=X,theta=np.array([0]),f=Y, @@ -118,7 +117,6 @@ def test_homGP_update(): Xnew = X.mean().reshape(-1,1) Ypred = reference_model.predict(Xnew)['mean'] - reference_model.predict(Xnew,Ypred) reference_model.update(Xnew,Ypred) test_model = emulator(x=X,theta=np.array([0]),f=Y, From ea9f0b12adee4c268199c2c183e0d7f9319d5096 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 17:19:08 -0500 Subject: [PATCH 040/103] add update test --- tests/unit_tests/test_hetGP.py | 38 +++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/tests/unit_tests/test_hetGP.py b/tests/unit_tests/test_hetGP.py index 6b60190..052b98b 100644 --- a/tests/unit_tests/test_hetGP.py +++ b/tests/unit_tests/test_hetGP.py @@ -95,5 +95,41 @@ def test_predict_nugs_only(): assert test_preds._info.get(key) is None assert np.allclose(test_preds._info['nugs'],preds['nugs']) +def test_hetGP_update_kriging_believer(): + reference_model = hetGP() + reference_model.mle(X,Y.flatten()) + + Xnew = X.mean().reshape(-1,1) + Ypred = reference_model.predict(Xnew)['mean'] + reference_model.update(Xnew,Ypred,maxit=0) + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='hetGP') + test_model.fit() + test_model.update(Xnew) + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + +def test_hetGP_update(): + reference_model = hetGP() + reference_model.mle(X,Y.flatten()) + + Xnew = X.mean().reshape(-1,1) + Ypred = reference_model.predict(Xnew)['mean'] + reference_model.update(Xnew,Ypred) + + test_model = emulator(x=X,theta=np.array([0]),f=Y, + method='hetGP') + test_model.fit() + test_model.update(x=Xnew,Y=Ypred) + + assert test_model._info['ll'] == reference_model['ll'] + assert test_model._info['theta'] == reference_model['theta'] + assert test_model._info['g'] == reference_model['g'] + assert test_model._info['beta0'] == reference_model['beta0'] + if __name__ == "__main__": - test_predict_nugs_only() + test_hetGP_update_kriging_believer() From 3412f3af5f0ae3959611d4452e31c7120e6222a3 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 17:19:28 -0500 Subject: [PATCH 041/103] add update wrapper --- PUQ/surrogatemethods/hetGP.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index a10f21c..6423338 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -146,4 +146,33 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): predinfo['var'] = preds.get('sd2') predinfo['nugs'] = preds.get('nugs') predinfo['covmat'] = preds.get('cov') + return +def update(fitinfo, x,Y = None,**kwargs): + r''' + Update function for homGP + + Parameters + ---------- + fitinfo: dictionary that contains the fit information for a hetgpy.homGP object + x: array of new design locations + Y: new response. If None, then + kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: + ''' + # validate kwargs + valid_kws = ('ginit','lower','upper', + 'noiseControl','settings','known','maxit','method') + for kw in kwargs.keys(): + if kw not in valid_kws: + raise ValueError(f"{kw} not found, must be one of {valid_kws}") + GP = hetGPWrapper(fitinfo) + if Y is None: + maxit = 0 # impute mean response and do not update hyperparams + Y = GP.predict(x)['mean'] + else: + maxit = kwargs.get('maxit',100) + kwargs['maxit'] = maxit + GP.update(Xnew=x,Znew=Y,**kwargs) + for key in GP.__dict__.keys(): + fitinfo[key] = GP.get(key) + del GP return \ No newline at end of file From c871628679f281de9c93342897d25ac3f5ee5ee6 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 17:30:59 -0500 Subject: [PATCH 042/103] finish docstring --- PUQ/surrogatemethods/hetGP.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 6423338..f7f4291 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -156,7 +156,8 @@ def update(fitinfo, x,Y = None,**kwargs): fitinfo: dictionary that contains the fit information for a hetgpy.homGP object x: array of new design locations Y: new response. If None, then - kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: + kwargs: key-value pairs that get passed to hetgpy.hetGP.update. + Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit, method ''' # validate kwargs valid_kws = ('ginit','lower','upper', From 35742410156fdc65f4f8a3d0d2d344365064d456 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Mon, 22 Sep 2025 17:31:34 -0500 Subject: [PATCH 043/103] finish docstring --- PUQ/surrogatemethods/homGP.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 61e04ff..5ec92c3 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -131,7 +131,8 @@ def update(fitinfo, x,Y = None,**kwargs): fitinfo: dictionary that contains the fit information for a hetgpy.homGP object x: array of new design locations Y: new response. If None, then - kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: + kwargs: key-value pairs that get passed to hetgpy.homGP.update. + Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit ''' # validate kwargs valid_kws = ('ginit','lower','upper', From 8c8a02bcf8af0cd81e520cdb38bcc06b915abffc Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Wed, 24 Sep 2025 16:47:53 -0500 Subject: [PATCH 044/103] initial draft of multihomGP --- PUQ/surrogatemethods/multihomGP.py | 64 ++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 PUQ/surrogatemethods/multihomGP.py diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py new file mode 100644 index 0000000..acf351e --- /dev/null +++ b/PUQ/surrogatemethods/multihomGP.py @@ -0,0 +1,64 @@ +''' +Set of functions to fit a GP to each dimension of output data +''' +import numpy as np +from hetgpy import homGP +from PUQ.surrogate import emulator + +def fit(fitinfo, x, theta, f, lower=None, upper=None, + known={}, + noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, + init={}, + covtype='Gaussian', + maxit=100, + eps=np.sqrt(np.finfo(float).eps), + settings={"return.Ki": True, "factr": 1e7}, + **kwargs): + + numGPs = f.shape[1] + emulist = [dict() for x in range(0, numGPs)] + for i in range(numGPs): + emu = emulator( + x=x, + theta=np.array([[i]]), + f=f[:,i:i+1], + method="homGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + "maxit": maxit, + "eps": eps + } + ) + emulist[i] = emu + fitinfo["f"] = f + fitinfo["emulist"] = emulist + fitinfo["numGPs"] = numGPs + return + +def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): + numGPs = fitinfo['numGPs'] + emulist = [dict() for x in range(0, numGPs)] + + # instantiate outputs + nr, nc = (x.shape[0],numGPs) + for key in ('mean','var'): + predinfo[key] = np.zeros(shape=(nr,nc),dtype=float) + predinfo['covmat'] = [np.zeros(shape=(nr,nr),dtype=float) + for i in range(numGPs) + ] + for i in range(numGPs): + preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) + predinfo['mean'][:,i] = preds._info['mean'] + predinfo['var'][:,i] = preds._info['var'] + predinfo['covmat'][i] = preds._info['covmat'] + + return + +def update(): + return \ No newline at end of file From bf4a54439131976e8acff8144e2f9b05757679c1 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Wed, 24 Sep 2025 16:48:15 -0500 Subject: [PATCH 045/103] add test for multihomGP --- tests/unit_tests/test_multihomGP.py | 67 +++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 tests/unit_tests/test_multihomGP.py diff --git a/tests/unit_tests/test_multihomGP.py b/tests/unit_tests/test_multihomGP.py new file mode 100644 index 0000000..370ba1c --- /dev/null +++ b/tests/unit_tests/test_multihomGP.py @@ -0,0 +1,67 @@ +import sys +sys.path.append('./') +import numpy as np +from scipy.stats import qmc +from PUQ.surrogate import emulator +from hetgpy import homGP +# create some noise 2D data +rng = np.random.default_rng(1) +lhs = qmc.LatinHypercube(d=2,rng=rng) + +X = lhs.random(n=50) +# prediction grid +Xp = lhs.random(n=100) +Y = 0*X +Y[:,0] = np.sin(X[:,0]) +Y[:,1] = np.cos(X[:,1]) +noise = rng.normal(size=Y.shape) + +Y+= noise + +COVTYPE = "Matern5_2" + +def test_fit(): + SETTINGS = {"return.Ki": False, "factr": 1e9} + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + + GPlist = [ + homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, + settings=SETTINGS) + for i in range(Y.shape[1]) + ] + emulator_keys = ['ll','theta','beta0'] + for i in range(len(GPlist)): + GP = GPlist[i] + for key in emulator_keys: + + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + + return + +def test_predict(): + SETTINGS = {"return.Ki": False, "factr": 1e9} + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + preds = multiGP.predict(x=Xp,thetaprime=Xp) + GPlist = [ + homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE, + maxit=50,settings=SETTINGS + ).predict(Xp,xprime=Xp) + for i in range(Y.shape[1]) + ] + em2GPkey = {'mean':'mean','var':'sd2'} + for i in range(len(GPlist)): + GP = GPlist[i] + for em_key, GP_key in em2GPkey.items(): + + assert np.allclose(preds._info[em_key][:,i],GP[GP_key]) + + # test cov and covmat + for i in range(len(GPlist)): + assert np.allclose(preds._info['covmat'][i],GPlist[i]['cov']) + + return + +if __name__ == "__main__": + test_predict() \ No newline at end of file From 4fda978bd1ef2127b4e3dec321c188312670116c Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 24 Sep 2025 20:06:57 -0400 Subject: [PATCH 046/103] Update based on new gps --- .../gen_funcs/acquisition_1d_stochastic.py | 6 ++-- PUQ/designmethods/gen_funcs/allocate_reps.py | 30 +++++++++++-------- examples/Example3/example_a.py | 2 +- examples/Example3/example_b.py | 2 +- 4 files changed, 23 insertions(+), 17 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py index eeac25f..7f30ce6 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -347,7 +347,8 @@ def evaluate(self, new, return_pseudo): if return_pseudo: pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] + # fnew = np.array([pnew["mean"]])[None, :] + fnew = np.array([pnew._info["mean"]])[None, :] self.fnew = fnew return self @@ -542,7 +543,8 @@ def determine_h(self): alloc_obj.allocatereps() mult_star = alloc_obj.reps - tab_input = mult_star - self.model.mult + # tab_input = mult_star - self.model.mult + tab_input = mult_star - self.model._info["mult"] tab_input[tab_input < 0] = 0 # import matplotlib.pyplot as plt diff --git a/PUQ/designmethods/gen_funcs/allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps.py index f16eda1..ee272cc 100644 --- a/PUQ/designmethods/gen_funcs/allocate_reps.py +++ b/PUQ/designmethods/gen_funcs/allocate_reps.py @@ -26,8 +26,8 @@ def __init__(self, self.trace = trace self.func_cls = func_cls self.rand_stream = rand_stream - self.z = self.model['X0'] - self.a0 = self.model['mult'] + self.z = self.model._info['X0'] # self.model['X0'] + self.a0 = self.model._info['mult'] # self.model['mult'] @@ -87,10 +87,12 @@ def compute_ivar_weights(self): id_col = np.repeat(id_col, repeats=d, axis=0) # predict at mesh - meshPr = self.model.predict(x=z, xprime=z) + # meshPr = self.model.predict(x=z, xprime=z) + meshPr = self.model.predict(x=z, thetaprime=z) # ntot, ntot x ntot, ntot - mu, Sn = meshPr["mean"], meshPr["cov"] + # mu, Sn = meshPr["mean"], meshPr["cov"] + mu, Sn = meshPr._info["mean"], meshPr._info["covmat"] muT = mu.reshape(n_integ, d) S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) @@ -119,13 +121,14 @@ def compute_ivar_weights(self): Mb = np.zeros((n, n_integ, d, d), dtype=np.float64) - pred_nugs = self.model.predict(x=self.z, nugs_only=True) - + pred_nugs = self.model.predict(x=self.z, args=dict(nugs_only=True)) #nugs_only=True) # n x n if self.use_Ki: - Ki = self.model['Ki'] + # Ki = self.model['Ki'] + Ki = self.model._info['Ki'] else: - K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) + # K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) + K = cov_gen(X1=self.z, theta=self.model._info['theta'], type = self.model._info['covtype']) Ki = scipy.linalg.pinv(K, rcond=self.eps) ids = np.arange(0, len(z), d) @@ -141,17 +144,18 @@ def compute_ivar_weights(self): zo = z[ids + j] # n x n_integ - kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) + # kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) + kv = cov_gen(X1=self.z, X2=zo, theta=self.model._info['theta'], type = self.model._info['covtype']) for jp in range(0, d): # n_integ x 1 zop = z[ids + jp] # n x n_integ - kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) - - Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) - + # kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) + kvp = cov_gen(X1=self.z, X2=zop, theta=self.model._info['theta'], type = self.model._info['covtype']) + # Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + Mb[i, :, j, jp] = -pred_nugs._info['nugs'][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) dlogfdai = np.zeros((n, n_integ)) dloggdai = np.zeros((n, n_integ)) diff --git a/examples/Example3/example_a.py b/examples/Example3/example_a.py index 4e64194..3893083 100644 --- a/examples/Example3/example_a.py +++ b/examples/Example3/example_a.py @@ -88,5 +88,5 @@ ax[sid].tick_params(axis="both", which="major", labelsize=ft - 5) fig.suptitle("Simulation Time Increases \u2192", fontsize=ft) fig.tight_layout() -plt.savefig("ex3_a.jpg", format="jpeg", bbox_inches="tight", dpi=500) +plt.savefig("ex3_a.png", format="jpeg", bbox_inches="tight", dpi=500) plt.show() diff --git a/examples/Example3/example_b.py b/examples/Example3/example_b.py index 14cc1b2..ef1ec44 100644 --- a/examples/Example3/example_b.py +++ b/examples/Example3/example_b.py @@ -133,5 +133,5 @@ cbar.set_ticks(batches) fig.suptitle("Variability in Simulation Time Increases \u2192", fontsize=ft) fig.tight_layout() -plt.savefig("ex3_b.jpg", format="jpeg", bbox_inches="tight", dpi=500) +plt.savefig("ex3_b.png", format="jpeg", bbox_inches="tight", dpi=500) plt.show() From d4448a759fa28a0eb2b82de8f4816cde0e98208b Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 11:59:46 -0500 Subject: [PATCH 047/103] handle case when Y is not None in update --- PUQ/surrogatemethods/multihomGP.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py index acf351e..6cb77be 100644 --- a/PUQ/surrogatemethods/multihomGP.py +++ b/PUQ/surrogatemethods/multihomGP.py @@ -60,5 +60,13 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): return -def update(): +def update(fitinfo, x,Y = None,**kwargs): + numGPs = fitinfo['numGPs'] + for i in range(numGPs): + emu = fitinfo['emulist'][i] + if Y is not None: + Yi = Y[:,i] + else: + Yi = None + emu.update(x=x,Y=Yi,**kwargs) return \ No newline at end of file From 14bed20d14df38e567c02b82b2d28d4344368d58 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 12:00:04 -0500 Subject: [PATCH 048/103] add update tests --- tests/unit_tests/test_multihomGP.py | 62 ++++++++++++++++++++++++++++- 1 file changed, 60 insertions(+), 2 deletions(-) diff --git a/tests/unit_tests/test_multihomGP.py b/tests/unit_tests/test_multihomGP.py index 370ba1c..284840c 100644 --- a/tests/unit_tests/test_multihomGP.py +++ b/tests/unit_tests/test_multihomGP.py @@ -4,6 +4,7 @@ from scipy.stats import qmc from PUQ.surrogate import emulator from hetgpy import homGP +from copy import deepcopy # create some noise 2D data rng = np.random.default_rng(1) lhs = qmc.LatinHypercube(d=2,rng=rng) @@ -24,7 +25,7 @@ def test_fit(): SETTINGS = {"return.Ki": False, "factr": 1e9} multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) - + multiGP.fit() GPlist = [ homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, settings=SETTINGS) @@ -43,6 +44,7 @@ def test_predict(): SETTINGS = {"return.Ki": False, "factr": 1e9} multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP.fit() preds = multiGP.predict(x=Xp,thetaprime=Xp) GPlist = [ homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE, @@ -63,5 +65,61 @@ def test_predict(): return +def test_update_kriging_believer(): + SETTINGS = {"return.Ki": False, "factr": 1e9} + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP.fit() + initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = deepcopy(initial_lls) + GPlist = [ + homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, + settings=SETTINGS) + for i in range(Y.shape[1]) + ] + GPlist_initial = deepcopy(GPlist) + Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + multiGP.update(x=Xnew) + em_keys = ['ll','theta','beta0'] + for GP in GPlist: + Ynew = GP.predict(Xnew)['mean'] + GP.update(Xnew,Ynew,maxit=0) + for i in range(multiGP._info['numGPs']): + GP = GPlist[i] + for key in em_keys: + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + +def test_update(): + '''Test update and that log likelihood changes''' + SETTINGS = {"return.Ki": False, "factr": 1e9} + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP.fit() + initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = deepcopy(initial_lls) + + GPlist = [ + homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, + settings=SETTINGS) + for i in range(Y.shape[1]) + ] + GPlist_initial = deepcopy(GPlist) + Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + mpreds = multiGP.predict(x=Xnew) + Ynew = mpreds._info['mean'] + multiGP.update(x=Xnew,Y=Ynew) + em_keys = ['ll','theta','beta0'] + for GP in GPlist: + Ynew = GP.predict(Xnew)['mean'] + GP.update(Xnew,Ynew,maxit=50) + for i in range(multiGP._info['numGPs']): + GP = GPlist[i] + for key in em_keys: + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + # also check that likelihoods changed after update + for i in range(len(initial_lls)): + old_ll = initial_lls[i] + assert not np.allclose(old_ll,multiGP._info['emulist'][i]._info['ll']) + if __name__ == "__main__": - test_predict() \ No newline at end of file + test_update_kriging_believer() \ No newline at end of file From 914c2196d6fb4923fa0f2225dfc74bcad971d9dc Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Thu, 25 Sep 2025 15:53:18 -0400 Subject: [PATCH 049/103] Update surrogate with __deepcopy__ --- PUQ/surrogate.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/PUQ/surrogate.py b/PUQ/surrogate.py index 04f1575..50d0a59 100644 --- a/PUQ/surrogate.py +++ b/PUQ/surrogate.py @@ -3,9 +3,8 @@ """ import numpy as np import importlib -import copy import warnings - +import copy, types class emulator(object): def __init__( @@ -351,6 +350,22 @@ def computeC(self, x=None, theta1=None, realdata=None, realvar=None): return self.method.computeC(self._info, x, theta1, realdata, realvar) + def __deepcopy__(self, memo): + # create a blank instance without calling __init__ + new = self.__class__.__new__(self.__class__) + memo[id(self)] = new + + for k, v in self.__dict__.items(): + if isinstance(v, types.ModuleType): + # leave module references as-is + setattr(new, k, v) + elif k == "_info": + # force deep copy of _info so updates won't affect the original + setattr(new, k, copy.deepcopy(v, memo)) + else: + # default deepcopy for everything else + setattr(new, k, copy.deepcopy(v, memo)) + return new def __optionsset(self, options=None): options = copy.deepcopy(options) From 20a7a23cfda52296e13f52122e99561d597edfca Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 16:08:14 -0500 Subject: [PATCH 050/103] remove explicit import of homGP --- PUQ/surrogatemethods/multihomGP.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py index 6cb77be..3fe9b1e 100644 --- a/PUQ/surrogatemethods/multihomGP.py +++ b/PUQ/surrogatemethods/multihomGP.py @@ -1,8 +1,7 @@ ''' -Set of functions to fit a GP to each dimension of output data +Set of functions to fit a homoskedastic GP to each dimension of output data ''' import numpy as np -from hetgpy import homGP from PUQ.surrogate import emulator def fit(fitinfo, x, theta, f, lower=None, upper=None, From 5aa12dde69317d22322cc372898990bc0ceb78d1 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 16:13:10 -0500 Subject: [PATCH 051/103] clarify return statement in fit --- PUQ/surrogatemethods/homGP.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 5ec92c3..031e012 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -62,8 +62,8 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, Returns ------- - fitinfo: dictionary containing fit results - + None, but fitinfo is updated with maximum likelihood estimates for theta and beta0 + ''' f = f.flatten() model = homGP() From 1f14e6b00b97d9758299462f0fa4ef9a13926108 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Thu, 25 Sep 2025 17:30:56 -0400 Subject: [PATCH 052/103] Example 5 --- .../gen_funcs/acquisition_1d_stochastic.py | 65 +- .../gen_funcs/acquisition_allocate_reps.py | 194 +++++ PUQ/designmethods/gen_funcs/allocate_reps.py | 25 +- examples/Example5/README.rst | 25 + examples/Example5/ex5.png | Bin 0 -> 540504 bytes examples/Example5/example.py | 94 +++ examples/Example5/paper_figs.py | 731 ++++++++++++++++++ examples/Example5/test_functions.py | 249 ++++++ examples/Example5/utils.py | 344 +++++++++ examples/Example5/utils_sample.py | 143 ++++ 10 files changed, 1823 insertions(+), 47 deletions(-) create mode 100644 PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py create mode 100644 examples/Example5/README.rst create mode 100644 examples/Example5/ex5.png create mode 100644 examples/Example5/example.py create mode 100644 examples/Example5/paper_figs.py create mode 100644 examples/Example5/test_functions.py create mode 100644 examples/Example5/utils.py create mode 100644 examples/Example5/utils_sample.py diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py index 7f30ce6..452e709 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -4,8 +4,9 @@ from hetgpy.covariance_functions import cov_gen from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult import emcee -from PUQ.designmethods.gen_funcs.allocate_reps import allocate +from PUQ.designmethods.gen_funcs.acquisition_allocate_reps import allocate from copy import deepcopy +import copy def generate_neighborhood(acq): @@ -50,7 +51,7 @@ def generate_neighborhood(acq): class acquisition_function: def __init__(self, model, cls_func, args): - self.model = model#deepcopy(model) # model.copy() + self.model = copy.deepcopy(model)#deepcopy(model) # model.copy() self.cls_func = cls_func #self.persis_info = persis_info self.args = args @@ -340,7 +341,7 @@ def evaluate(self, new, return_pseudo): L = generate_neighborhood(self) new_input = self.evaluate_explore(L) else: - L = self.model["X0"] + L = self.model._info["X0"] # self.model["X0"] new_input = self.evaluate_exploit(L) self.znew, self.new = new_input, new @@ -397,7 +398,6 @@ def evaluate_explore(self, L): vals[k] = np.sum(self.weights * part2) new_input = L[np.argmax(vals), :].reshape(1, self.p) - return new_input def evaluate_exploit(self, L): @@ -415,27 +415,30 @@ def evaluate_exploit(self, L): V1 = S + self.Sigma3d V2 = S + 0.5 * self.Sigma3d - Ki, mult = self.model.Ki, self.model.mult + # Ki, mult = self.model.Ki, self.model.mult + Ki, mult = self.model._info["Ki"], self.model._info["mult"] - candPr = self.model.predict(x=self.model["X0"]) - candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] - smean = self.model["Z0"] + candPr = self.model.predict(x=self.model._info["X0"]) # self.model.predict(x=self.model["X0"]) + # candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] + candmean, candvar, candnugs = candPr._info["mean"], candPr._info["var"], candPr._info["nugs"] + smean = self.model._info["Z0"] # self.model["Z0"] # compute B - if self.model.get("Lambda") is None: + # if self.model.get("Lambda") is None: + if self.model._info.get("Lambda") is None: tmp = self.model.g tmp = np.repeat(tmp, nL) else: - tmp = self.model.Lambda + tmp = self.model._info["Lambda"] denom = (mult * (mult + nrep)) / (nrep * tmp) - np.diag(Ki) Ki3D = Ki[:, :, None] * Ki[:, None, :] B = Ki3D / denom[:, None, None] - KB = self.model.Ki + B + KB = self.model._info["Ki"] + B # ntot x ntr kx = cov_gen( - X1=z, X2=self.model.X0, theta=self.model.theta, type=self.model.covtype + X1=z, X2=self.model._info["X0"], theta=self.model._info["theta"], type=self.model._info["covtype"] ) # coefficients to be used @@ -448,7 +451,7 @@ def evaluate_exploit(self, L): kKBe_all = KBe_all @ kx.T B_kx = np.einsum("ijk, bk -> ijb", B, kx) - phi_all = self.model.nu_hat * np.einsum("aj, ijb -> aib", kx, B_kx) + phi_all = self.model._info["nu_hat"] * np.einsum("aj, ijb -> aib", kx, B_kx) Bs = B @ smean c1, c2 = (1 / self.twopiddet), (1 / self.twopid) @@ -504,14 +507,14 @@ def determine_h(self): # print(horizon) if horizon.get("method") == "target": if horizon.get("previous_ratio") is None: - horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + horizon["previous_ratio"] = len(self.model._info["Z0"]) / len(self.model._info["Z"]) self.h = horizon.get("h0") else: target = horizon.get("target_ratio") previous_ratio = horizon.get("previous_ratio") current_horizon = horizon.get("h0") - ratio = len(self.model.Z0) / len(self.model.Z) + ratio = len(self.model._info["Z0"]) / len(self.model._info["Z"]) # Ratio increased while too small if ratio < target and ratio < previous_ratio: @@ -523,7 +526,7 @@ def determine_h(self): else: self.h = current_horizon - horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) + horizon["previous_ratio"] = len(self.model._info["Z0"]) / len(self.model._info["Z"]) horizon["h0"] = self.h elif horizon.get("method") == "adaptive": @@ -684,7 +687,7 @@ def evaluate_vq(self): # define horizon self.determine_h() - + acq_obj.evaluate(new=True, return_pseudo=True) z_A, f_A = acq_obj.znew, acq_obj.fnew path_A = [{"par": z_A, "new": True}] @@ -693,29 +696,29 @@ def evaluate_vq(self): self.znew, self.new = z_A, True return else: - acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) - model_A = acq_obj.model.copy() + # acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) + acq_obj.model.update(x=z_A, Y=f_A, maxit=0) + model_A = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() IVAR_A1 = self.crit_eval(model_A) if self.h > 0: - acq_obj.model = model_A.copy() + acq_obj.model = copy.deepcopy(model_A) # model_A.copy() for i in range(0, self.h): acq_obj.evaluate(new=False, return_pseudo=True) path_A.append({"par": acq_obj.znew, "new": False}) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + acq_obj.model.update(x=acq_obj.znew, Y=acq_obj.fnew, maxit=0) IVAR_A = self.crit_eval(acq_obj.model) designs.append({"input": z_A, "path": path_A, "value": IVAR_A}) if self.h == 0: - acq_obj.model = self.model.copy() + acq_obj.model = copy.deepcopy(self.model) # self.model.copy() acq_obj.evaluate(new=False, return_pseudo=True) z_B = acq_obj.znew - acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) - - model_B = acq_obj.model.copy() + acq_obj.model.update(x=z_B, Y=acq_obj.fnew, maxit=0) + model_B = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() IVAR_B1 = self.crit_eval(model_B) if IVAR_A1 < IVAR_B1: @@ -726,30 +729,30 @@ def evaluate_vq(self): return else: - newmodelB = self.model.copy() + newmodelB = copy.deepcopy(self.model) # self.model.copy() for i in range(self.h): # Choose a new replicate - acq_obj.model = newmodelB.copy() + acq_obj.model = copy.deepcopy(newmodelB) # newmodelB.copy() acq_obj.evaluate(new=False, return_pseudo=True) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + acq_obj.model.update(x=acq_obj.znew, Y=acq_obj.fnew, maxit=0) if i == 0: z0 = 1 * acq_obj.znew path_B = [] path_B.append({"par": acq_obj.znew, "new": False}) - newmodelB = acq_obj.model.copy() + newmodelB = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() # Choose a new design acq_obj.evaluate(new=True, return_pseudo=True) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) + acq_obj.model.update(x=acq_obj.znew, Y=acq_obj.fnew, maxit=0) path_C = [{"par": acq_obj.znew, "new": True}] for j in range(i, self.h - 1): # Remaining replicates acq_obj.evaluate(new=False, return_pseudo=True) acq_obj.model.update( - Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0 + x=acq_obj.znew, Y=acq_obj.fnew, maxit=0 ) path_C.append({"par": acq_obj.znew, "new": False}) diff --git a/PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py b/PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py new file mode 100644 index 0000000..ee272cc --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py @@ -0,0 +1,194 @@ +import numpy as np +import scipy +from numpy.linalg import cholesky, inv, det +from hetgpy.covariance_functions import cov_gen +from PUQ.designmethods.support import multiple_pdfs + +class allocate: + def __init__(self, + budget, + model, + func_cls, + mesh, + trace=1, + alloc_settings={}, + rand_stream=None): + + self.budget = budget + self.model = model + self.x = func_cls.x + self.theta_mesh = mesh["theta"] + self.w_mesh = mesh["weight"] + self.obs = func_cls.real_data + self.obsvar = func_cls.obsvar + self.use_Ki = alloc_settings.get('use_Ki', True) + self.eps = np.sqrt(np.finfo(float).eps) + self.trace = trace + self.func_cls = func_cls + self.rand_stream = rand_stream + self.z = self.model._info['X0'] # self.model['X0'] + self.a0 = self.model._info['mult'] # self.model['mult'] + + + + def allocatereps(self): + + Cinfo = self.compute_ivar_weights() + C = Cinfo.flatten() + + idneg = C < 0 + weight = np.sqrt(np.abs(C[idneg])) + a_frac = np.zeros(len(C)) + a_frac_ub = np.zeros(len(C)) + total_a = np.sum(self.a0) + self.budget + + # Find an upper bound + a_frac_ub[idneg] = total_a*weight/sum(weight) + a_ub = self.make_int(total_a, a_frac_ub) + a_ub = np.maximum(0, a_ub - self.a0) + + a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) + a_final = self.make_int(self.budget, a_frac) + a_final = np.array([int(a) for a in a_final]) + if np.sum(a_final) != self.budget: + raise ValueError("Budget constraint is not satisfied.") + + self.reps = a_final + return + + + def compute_ivar_weights(self): + + d = len(self.x) + n_integ = self.theta_mesh.shape[0] + n = self.z.shape[0] + + #pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + #pred_mesh = self.model.predict(x=self.theta_mesh) + + # obsvar3d = self.obsvar.reshape(1, d, d) + # # ntest x d + # mu = pred_mesh._info['mean'].T + # S = pred_mesh._info['S'] + # St = np.transpose(S, (2, 0, 1)) + + obsvar3d = self.obsvar.reshape(1, d, d) + + x = self.x + ntot = n_integ*d + t = self.theta_mesh + x_tiled = np.tile(x, (t.shape[0], 1)) + t_repeated = np.repeat(t, x.shape[0], axis=0) + z = np.hstack([x_tiled, t_repeated]) + + # to construct S matrix + id_row = np.arange(0, ntot) + id_col = np.arange(0, ntot).reshape(n_integ, d) + id_col = np.repeat(id_col, repeats=d, axis=0) + + # predict at mesh + # meshPr = self.model.predict(x=z, xprime=z) + meshPr = self.model.predict(x=z, thetaprime=z) + + # ntot, ntot x ntot, ntot + # mu, Sn = meshPr["mean"], meshPr["cov"] + mu, Sn = meshPr._info["mean"], meshPr._info["covmat"] + + muT = mu.reshape(n_integ, d) + S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) + + # ntest x d x d + Nb = S + 0.5*obsvar3d + N = S + obsvar3d + + f = multiple_pdfs(self.obs, muT, Nb) + g = multiple_pdfs(self.obs, muT, N) + + # ntest x 1 x d + h = (self.obs - muT).reshape(n_integ, 1, d) + + # ntest x d x d + Nbinv = inv(Nb) + + # ntest x d x d + Ninv = inv(N) + + # ntest x 1 x d + hNb = np.matmul(h, Nbinv) + hN = np.matmul(h, Ninv) + + coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) + + Mb = np.zeros((n, n_integ, d, d), dtype=np.float64) + + pred_nugs = self.model.predict(x=self.z, args=dict(nugs_only=True)) #nugs_only=True) + # n x n + if self.use_Ki: + # Ki = self.model['Ki'] + Ki = self.model._info['Ki'] + else: + # K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) + K = cov_gen(X1=self.z, theta=self.model._info['theta'], type = self.model._info['covtype']) + Ki = scipy.linalg.pinv(K, rcond=self.eps) + + ids = np.arange(0, len(z), d) + for i in range(0, n): + J = np.zeros((n, n)) + J[i, i] = 1 + + # n x n + A = Ki@J@Ki + + for j in range(0, d): + # n_integ x 1 + zo = z[ids + j] + + # n x n_integ + # kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) + kv = cov_gen(X1=self.z, X2=zo, theta=self.model._info['theta'], type = self.model._info['covtype']) + + for jp in range(0, d): + # n_integ x 1 + zop = z[ids + jp] + + # n x n_integ + # kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) + kvp = cov_gen(X1=self.z, X2=zop, theta=self.model._info['theta'], type = self.model._info['covtype']) + # Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + Mb[i, :, j, jp] = -pred_nugs._info['nugs'][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + + dlogfdai = np.zeros((n, n_integ)) + dloggdai = np.zeros((n, n_integ)) + C = np.zeros((n, 1)) + for i in range(0, n): + + M = Mb[i, :, :, :] + + part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) + part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) + part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) + part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) + + dlogfdai = part1f + part2f.flatten() + dloggdai = part1g + part2g.flatten() + subC = self.w_mesh*(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) + + C[i] = np.sum(subC) + + return C + + + def make_int(self, bdg, n_frac): + # Make integer + n_floor = np.floor(n_frac) + remain = n_frac - n_floor + tot_remain = bdg - np.sum(n_floor) + sorted_indices = np.array(np.argsort(remain)[::-1]) + if tot_remain > 0: + idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] + n_floor[idx] = n_floor[idx] + 1 + + return n_floor + + + \ No newline at end of file diff --git a/PUQ/designmethods/gen_funcs/allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps.py index ee272cc..251b1bc 100644 --- a/PUQ/designmethods/gen_funcs/allocate_reps.py +++ b/PUQ/designmethods/gen_funcs/allocate_reps.py @@ -26,8 +26,8 @@ def __init__(self, self.trace = trace self.func_cls = func_cls self.rand_stream = rand_stream - self.z = self.model._info['X0'] # self.model['X0'] - self.a0 = self.model._info['mult'] # self.model['mult'] + self.z = self.model['X0'] + self.a0 = self.model['mult'] @@ -87,12 +87,10 @@ def compute_ivar_weights(self): id_col = np.repeat(id_col, repeats=d, axis=0) # predict at mesh - # meshPr = self.model.predict(x=z, xprime=z) - meshPr = self.model.predict(x=z, thetaprime=z) + meshPr = self.model.predict(x=z, xprime=z) # ntot, ntot x ntot, ntot - # mu, Sn = meshPr["mean"], meshPr["cov"] - mu, Sn = meshPr._info["mean"], meshPr._info["covmat"] + mu, Sn = meshPr["mean"], meshPr["cov"] muT = mu.reshape(n_integ, d) S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) @@ -124,11 +122,9 @@ def compute_ivar_weights(self): pred_nugs = self.model.predict(x=self.z, args=dict(nugs_only=True)) #nugs_only=True) # n x n if self.use_Ki: - # Ki = self.model['Ki'] - Ki = self.model._info['Ki'] + Ki = self.model['Ki'] else: - # K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) - K = cov_gen(X1=self.z, theta=self.model._info['theta'], type = self.model._info['covtype']) + K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) Ki = scipy.linalg.pinv(K, rcond=self.eps) ids = np.arange(0, len(z), d) @@ -144,18 +140,15 @@ def compute_ivar_weights(self): zo = z[ids + j] # n x n_integ - # kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) - kv = cov_gen(X1=self.z, X2=zo, theta=self.model._info['theta'], type = self.model._info['covtype']) + kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) for jp in range(0, d): # n_integ x 1 zop = z[ids + jp] # n x n_integ - # kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) - kvp = cov_gen(X1=self.z, X2=zop, theta=self.model._info['theta'], type = self.model._info['covtype']) - # Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) - Mb[i, :, j, jp] = -pred_nugs._info['nugs'][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) + Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) dlogfdai = np.zeros((n, n_integ)) dloggdai = np.zeros((n, n_integ)) diff --git a/examples/Example5/README.rst b/examples/Example5/README.rst new file mode 100644 index 0000000..731759a --- /dev/null +++ b/examples/Example5/README.rst @@ -0,0 +1,25 @@ +Examples +~~~~~~~~ + +This example demonstrates how to use the performance model from Sürer (2025), +Active Learning for Data-Efficient Calibration of Stochastic Simulation Models. + + +**Instructions for running the illustrative examples with the active learning procedurel** + +To replicate the figures below, respectively: + +1) Go to the ``examples/Example5`` directory. + +2) Execute any of the following from the command line: + +.. code-block:: python + + python example.py + +Running this script should not take more than 5 min. 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zf);2d_^f6mJoxH|k$4>{QQQO=O>qGeLQVfgSE`OfpJgL^w{)QQA%j%f1ALFiA4U}Z z@8XE}|KC0aeLn{FV_-i9_G4f_2KHlMKL++=U_S=-V_-i9_G4f_2KHlMKL++=U_S=- VV_-i9_G4f_2L7Lp0ft}0{|EV=p7a0! literal 0 HcmV?d00001 diff --git a/examples/Example5/example.py b/examples/Example5/example.py new file mode 100644 index 0000000..0132c63 --- /dev/null +++ b/examples/Example5/example.py @@ -0,0 +1,94 @@ +import numpy as np +from utils import fig6 +from utils_sample import test_data_gen +import matplotlib.pyplot as plt +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential_1d_stochastic import sequential_design +from test_functions import unimodalx, bimodalx, braninx + +T, s = 100, 1 +# Methods to iterate over +dict_meth = { + "method": ["ivar"], + "horizon": [ + {"method": "target", "h0": 0, "target_ratio": 0.2}, + ], + "labels": ["target"], +} +fig, ax = plt.subplots(1, 3, figsize=(12, 3), constrained_layout=True) + +if __name__ == "__main__": + + for eid, example in enumerate(["unimodalx", "bimodalx", "braninx"]): + + cex = eval(example)() + cex.realdata(x=np.array([0.5])[:, None], seed=None) + + tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl = test_data_gen(cex, sample=True) + + tdat = { + "f": f_s, + "theta": t_s, + "xt": None, + "p": p_s, + "noise": n_s, + "w": w_s, + "p_prior": 1, + } + + gdat = {"f": fg, "theta": tg, "xt": zg, "p": pg, "noise": ng, "X": Xpl, "Y": Ypl} + + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Initial sample + n0, rep0 = 30, 5 + sampling = LHS(xlimits=cex.zlim, random_state=int(s)) + z0u = sampling(n0) + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array( + [cex.sim_f(z0[i, :], persis_info=persis_info) for i in range(n0 * rep0)] + ) + + for mid, method in enumerate(dict_meth["method"]): + print(method) + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Set random stream for initial design + des_obj = sequential_design(cex) + des_obj.build_design( + z0=z0, + f0=f0, + T=T, + persis_info=persis_info, + test=tdat, + af="lookahead", + args={ + "horizon": dict_meth["horizon"][mid], + "long": False, + "nL": 300, + "seed": s, + "method": method, + "t_grid": None, + "integral": "importance", + }, + ) + + + fig6( + des_obj, + z0[:, 1:3], + rep0, + gdat["X"], + gdat["Y"], + gdat["p"], + gdat["noise"], + ax[eid], + {}, + fig, + ) + +plt.savefig("ex5.png", format="jpeg", bbox_inches="tight", dpi=500) +plt.show() \ No newline at end of file diff --git a/examples/Example5/paper_figs.py b/examples/Example5/paper_figs.py new file mode 100644 index 0000000..3d527a7 --- /dev/null +++ b/examples/Example5/paper_figs.py @@ -0,0 +1,731 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue May 20 14:45:08 2025 + +@author: surero +""" +import seaborn as sns +import matplotlib.pyplot as plt +from utils import read_data, read_output, read_summary_metric +import matplotlib as mpl +import pandas as pd +import numpy as np +from matplotlib.colors import ListedColormap + +# Custom yellow-orange colormap +yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), + (1, 0.9, 0), + (1, 0.8, 0), + (1, 0.6, 0), + (1, 0.4, 0), + (1, 0.2, 0), +] +yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + +custom_palette = {"ivar": "red", "imse": "blue", "var": "green", "varx": "magenta"} +hue_order = ["ivar", "imse", "var"] + +hatch_color_map = { + (0.875, 0.125, 0.125, 1): "//", + (0.125, 0.125, 0.875, 1): "\\", + (0.06274509803921569, 0.4392156862745098, 0.06274509803921569, 1): "xx", +} + + + + +def lineplot( + df, + examples, + metric="TV", + ci=None, + label=None, + custom_labels=None, + ax=None, + hue=None, + figset={}, + scaled=True, +): + ifLeg = figset.get("ifLeg", True) + sety = figset.get("sety", True) + ft = figset.get("ft", 12) + lw = figset.get("lw", 3) + + for i, example in enumerate(examples): + df1 = df.loc[df["example"] == example] + + if scaled: + group_cols = ["t", hue, "h"] + mean_df = df1.groupby(group_cols)[metric].mean().reset_index() + max_val = mean_df[metric].max() + mean_df[metric] = mean_df[metric] / max_val + data = mean_df.copy() + else: + data = df1.copy() + + sns.lineplot( + data=data, + x="t", + y=metric, + hue=hue, + style="h", + palette=custom_palette, + errorbar=ci, + linewidth=lw, + ax=ax, + ) + + lgd = ax.legend( + loc="upper center", + bbox_to_anchor=(1.3, 1), + fancybox=True, + shadow=True, + ncol=1, + fontsize=ft, + ) + + ax.set_yscale("log") + ax.set_xlabel("t", fontsize=ft) + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel(metric, fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + if ifLeg == False: + ax.legend().remove() + + +def boxplot(df, ax, x, hue, figset={}): + ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) + ft = figset.get("ft", 12) + + sns.boxplot( + x=x, + y="percent", + hue="method", + data=df, + ax=ax, + showfliers=False, + palette=custom_palette, + hue_order=hue_order, + ) + + # select the correct patches + patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch] + + # iterate through the patches for each subplot + for patch in patches: + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + # # # Fix legend to match hatches and colors + handles, labels = ax.get_legend_handles_labels() + legend = ax.legend( + handles, + labels, + title="method", + loc="center left", + bbox_to_anchor=(1.02, 0.5), + borderaxespad=0.0, + ) + + for patch in legend.get_patches(): + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel("Percent of exploration", fontsize=ft) + + plt.xticks(fontsize=ft) + + if ifLeg == False: + ax.legend().remove() + + +def plot_experiment_figure( + examples, + path, + ms, + ls, + lg, + ly, + reps=(1, 31), + metric="MAD", + layout="2xN", + figsize=(12, 6), + ft=10, + r0=5, + n0=150, + title=None, +): + + single_example = len(examples) == 1 + if layout == "1x2" and single_example: + fig, ax = plt.subplots(1, 2, figsize=figsize) + elif layout == "2xN": + fig, ax = plt.subplots(2, len(examples), figsize=figsize) + else: + raise ValueError("Unsupported layout or mismatched number of examples.") + + for i, ex in enumerate(examples): + print(ex) + figset = {"ifLeg": lg[i], "sety": ly[i], "ft": ft, "r0": r0, "n0": n0} + df1 = read_data( + rep0=reps[0], + repf=reps[1], + methods=ms, + examples=[ex], + folderpath=path, + label=ls, + ) + print(df1["s"]) + df2 = ( + df1.groupby(["s", "h", "example", "method"])["new"] + .mean() + .mul(100) + .reset_index() + ) + df2.rename(columns={"new": "percent"}, inplace=True) + + if layout == "1x2": + lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0], figset=figset) + boxplot_alternative(path, ms, ls, ex, ax=ax[1], figset=figset) + #boxplot(df=df2, ax=ax[1], x="h", hue="method", figset=figset) + else: # layout == "2xN" + lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0, i], figset=figset) + boxplot_alternative(path, ms, ls, ex, ax=ax[1, i], figset=figset) + #boxplot(df=df2, ax=ax[1, i], x="h", hue="method", figset=figset) + + plt.tight_layout(pad=0.2) + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + + + +def plot_SIR_experiment_figure( + path, ms, ls, reps=(1, 31), metric="MAD", figsize=(12, 6), ft=10, figset={}, title=None, +): + usual_boxplot = figset.get("usual_boxplot", True) + shift, scaled = figset.get("shift_x", 0), figset.get("scaled", True) + + fig, ax = plt.subplots( + 1, 4, figsize=figsize, gridspec_kw={"wspace": 0.25} + ) + #, "width_ratios": [1.25, 1.25, 1, 1] + lw = 3 + + df1 = read_data(rep0=reps[0], repf=reps[1], methods=ms, examples=["SIR"], folderpath=path, label=ls) + print(df1["s"]) + df3 = df1.loc[df1["t"] == 199] + + + if scaled: + group_cols = ["t", "method", "h"] + mean_df = df1.groupby(group_cols)[metric].mean().reset_index() + max_val = mean_df[metric].max() + mean_df[metric] = mean_df[metric] / max_val + data = mean_df.copy() + else: + data = df1.copy() + + # Lineplot + sns.lineplot( + data=data, + x="t", + y=metric, + hue="method", + style="h", + palette=custom_palette, + errorbar=None, + linewidth=lw, + ax=ax[0], + ) + ax[0].legend( + loc="upper center", + bbox_to_anchor=(0.5, -0.2), + fancybox=True, + shadow=True, + ncol=2, + fontsize=ft, + ) + ax[0].set_yscale("log") + + if usual_boxplot: + df2 = ( + df1.groupby(["s", "h", "example", "method"])["new"] + .mean() + .mul(100) + .reset_index() + ) + df2.rename(columns={"new": "percent"}, inplace=True) + box_data = [ + (df2, "percent", ax[1], "percent"), + (df3, "MADy", ax[2], r"$MAD^y$"), + (df3, "MADn", ax[3], r"$MAD^n$"), + ] + else: + figset["legend_right"] = False + boxplot_alternative(path, ms, ls, "SIR", ax=ax[1], figset=figset)#{"legend_right":False}) + box_data = [ + (df3, "MADy", ax[2], r"$MAD^y$"), + (df3, "MADn", ax[3], r"$MAD^n$"), + ] + + for boxid, box in enumerate(box_data): + sns.boxplot( + x=box[1], + y="h", + hue="method", + data=box[0], + showfliers=False, + palette=custom_palette, + hue_order=hue_order, + ax=box[2], + ) + + # select the correct patches + patches = [ + patch for patch in box[2].patches if type(patch) == mpl.patches.PathPatch + ] + + # iterate through the patches for each subplot + for patch in patches: + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + box[2].legend_.remove() + + # # # Fix legend to match hatches and colors + if boxid == 1: + handles, labels = box[2].get_legend_handles_labels() + legend = box[2].legend( + handles, + labels, + title="method", + loc="lower center", + bbox_to_anchor=(0.5+shift, -0.5), + ncol=3, + ) + box[2].set_yticks([]) + + for patch in legend.get_patches(): + fc = patch.get_facecolor() + patch.set_edgecolor(fc) + patch.set_facecolor("none") + patch.set_hatch(hatch_color_map[fc]) + + for label in box[2].get_yticklabels(): + label.set_rotation(90) + + box[2].set_ylabel("") + box[2].set_xlabel(box[3], fontsize=ft) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + + + +def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1,31)): + + df = read_summary_metric(path, examples, methods, ls, rep) + + for metric in metrics: + fig, ax = plt.subplots(1, 1, figsize=(6, 4), constrained_layout=True) + sns.lineplot( + data=df, + x="t", + y=metric, + hue="method", + style="h", + ax=ax, + ) + ax.set_yscale("log") + plt.show() + + + +def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, repid=(1, 2), title=None): + + # Prepare data + from utils_SIR import test_gen_SIR + from SIR_funcs import SIRx + + cls_func = SIRx() + cls_func.realdata(x=np.array([[0.25, 0.25], [0.75, 0.75]]), seed=None) + p_grid, t_grid, noise_grid, Xpl, Ypl = test_gen_SIR(cls_func, return_XY=False) + + + for r in np.arange(repid[0], repid[1]): + # Plotting + fig, ax = plt.subplots(1, len(methods), figsize=(11, 3), constrained_layout=True) + + for mid, method in enumerate(methods): + desobj = read_output(path, "SIR", method, r, label=label) + + # Contour plots + ax[mid].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm", zorder=2) + cs = ax[mid].contourf( + Xpl, + Ypl, + np.sum(noise_grid, axis=1).reshape(50, 50), + cmap=yellow_cmap, + alpha=0.75, + zorder=1, + ) + + # Add colorbar only to the last axis + if mid == len(methods) - 1: + fig.colorbar(cs, ax=ax[mid], pad=0.1) + + # Annotate sample locations and frequency + unique_rows, counts = np.unique( + desobj.zs[200:, 2:4], axis=0, return_counts=True + ) + print(counts) + for lbl, x, y in zip(counts, unique_rows[:, 0], unique_rows[:, 1]): + ax[mid].annotate( + lbl, + xy=(x, y), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="blue", + zorder=3, + ) + + # Axis formatting + ax[mid].set_xticks([0, 0.5, 1]) + ax[mid].set_xlabel(r"$\theta_1$", fontsize=ft + 2) + ax[mid].tick_params(axis="both", labelsize=ft + 2) + if mid == 0: + ax[mid].set_yticks([0, 0.5, 1]) + ax[mid].set_ylabel(r"$\theta_2$", fontsize=ft + 2) + else: + ax[mid].set_yticks([]) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + +def plot_pritam(path, repid=1, label="target", title=None): + from test_functions import pritam + from utils_sample import test_data_gen_pri + + # Plot settings + ft = 12 + ms = ["ivar", "imse", "var"] + n0 = 150 + + + # Define test points + x = np.array([0.2, 0.8]) + y = np.array([0.2, 0.8]) + xr = np.array([[xx, yy] for xx in x for yy in y]) + + # Instantiate test function object and generate real data + cex = pritam() + cex.realdata(x=xr, seed=None) + + # Generate test grid + nmesh = 50 + x1 = np.linspace(cex.zlim[0][0], cex.zlim[0][1], nmesh) + x2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], nmesh) + X1, X2 = np.meshgrid(x1, x2) + Xg = np.vstack([X1.ravel(), X2.ravel()]).T + + # Evaluate noise and function values on the grid + ng = np.array([cex.noise(x[0], x[1], cex.theta_true[0]) for x in Xg]) + fg = np.array([cex.function(x[0], x[1], cex.theta_true[0]) for x in Xg]) + + # Create subplots + fig, ax = plt.subplots(2, 3, figsize=(9, 5), constrained_layout=True) + + for mid, m in enumerate(ms): + desobj = read_output(path, "pritam", m, repid, label="target") + + # Top row: histogram of theta samples + ax[0, mid].hist( + desobj.zs[n0:, 2], + bins=30, + edgecolor="black", + alpha=0.75, + color="blue" + ) + ax[0, mid].set_xlim(0, 1) + ax[0, mid].set_xlabel(r"$\vartheta$", fontsize=ft) + if mid == 0: + ax[0, mid].set_ylabel("Frequency", fontsize=ft) + + # Bottom row: scatter plot of x-samples with contour background + ax[1, mid].scatter( + desobj.zs[n0:, 0], + desobj.zs[n0:, 1], + marker="o", + facecolors="none", + edgecolors="blue", + zorder=2 + ) + + # Overlay test design points + for xpt in xr: + ax[1, mid].scatter(xpt[0], xpt[1], marker="x", color="black", s=100, zorder=3) + + # Contour of noise + cs = ax[1, mid].contourf( + X1, X2, ng.reshape(nmesh, nmesh), + cmap=yellow_cmap, + alpha=0.75, + zorder=1 + ) + + ax[1, mid].set_xlabel(r"$x_1$", fontsize=ft) + ax[1, mid].set_ylabel(r"$x_2$", fontsize=ft) + ax[1, mid].set_xticks([0, 0.5, 1]) + ax[1, mid].set_yticks([0, 0.5, 1]) + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + plt.show() + + +def plot_horizon_progress( + path, + example="SIR", + method="ivar", + single_seed=1, + labels=("target", "adapt"), + reps=(1, 31), + figsize=(8, 2), + colors=("gray", "orange"), + title=None, +): + + fig, ax = plt.subplots(1, 2, figsize=figsize) + + for mid, label in enumerate(labels): + # Read and filter data + df = read_data( + rep0=reps[0], + repf=reps[1], + methods=[method], + examples=[example], + folderpath=path, + label=[label], + ) + + df_label = df.loc[df["h"] == label].copy() + df_label["new_cumsum"] = df_label.groupby("s")["new"].cumsum() + + # Plot for a single seed + df_seed1 = df_label[df_label["s"] == single_seed] + ax[0].plot(df_seed1["t"], df_seed1["horizon"], color=colors[mid], linestyle=':') + + # Median horizon across reps + df_median = df_label.groupby("t", as_index=False)["horizon"].mean() + ax[0].plot(df_median["t"], df_median["horizon"], color=colors[mid], label=label, linewidth=3) + + # Mean cumulative new per t + df_cumsum_avg = df_label.groupby("t", as_index=False)["new_cumsum"].mean() + ax[1].plot( + df_cumsum_avg["t"], + df_cumsum_avg["new_cumsum"] / np.arange(1, len(df_cumsum_avg) + 1), + color=colors[mid], + linewidth=3, + label=label, + ) + + # Axis labels and legends + ax[0].set_xlabel("t") + ax[0].set_ylabel("Horizon") + ax[0].legend() + + ax[1].set_xlabel("t") + ax[1].set_ylabel(r"$n_t / \sum_{i=1}^{n_t} a_i$") + ax[1].legend() + plt.tight_layout() + + if title is not None: + plt.savefig(title, dpi=300, bbox_inches="tight") + + plt.show() + + +def plot_pairwise_from_output(path, example, ms, n0=90, rep0=5, d=2, label="h=-1"): + + for m in ms: + desobj = read_output(path, example, m, 1, label=label) + df = pd.DataFrame(desobj.zs[(n0 * rep0):, d:]) + sns.pairplot(df, diag_kind="kde") + plt.suptitle(f"m = {m}", y=1.02) + plt.tight_layout() + plt.show() + + +def boxplot_alternative(path, methods, horizons, example, ax, figset={}): + ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) + legend_right = figset.get("legend_right", True) + ft = figset.get("ft", 10) + r0, n0 = figset.get("r0", 5), figset.get("n0", 50) + + lst = [] + for m in methods: + for hor in horizons: + for i in np.arange(1, 31): + desobj = read_output(path, example, m, i, label=hor) + + # Eliminate the initial sample + initial_des = desobj.zs[0:n0, :] + unique_init, counts_init = np.unique(initial_des, axis=0, return_counts=True) + + # Convert unique_init to a set of tuple rows for fast lookup + unique_init_set = set(map(tuple, unique_init)) + + count_reps = [] + for xtid, xt in enumerate(desobj.xt): + xt_tuple = tuple(xt) + if (xt_tuple in unique_init_set) and (desobj.reps[xtid] == r0): + continue # skip this one + count_reps.append(desobj.reps[xtid]) + + count_reps = np.array(count_reps) + total = len(count_reps) + # Count percentages + percent_1 = np.sum(count_reps == 1) / total * 100 + percent_2 = np.sum(count_reps == 2) / total * 100 + percent_3 = np.sum(count_reps == 3) / total * 100 + percent_4 = np.sum(count_reps == 4) / total * 100 + percent_5 = np.sum(count_reps >= 5) / total * 100 + + lst.append({ + "1": percent_1, + "2": percent_2, + "3": percent_3, + "4": percent_4, + "5": percent_5, + "r": i, + "method": m, + "h": hor + }) + + df = pd.DataFrame(lst) + + # Average over r before plotting + components = ['1', '2', '3', '4', '5'] + + df_avg = df.groupby(['h', 'method'])[components].mean().reset_index() + + # Prepare for plotting + h_values = sorted(df_avg['h'].unique()) + methods_u = df_avg['method'].unique() + num_methods = len(methods_u) + + bar_width = 0.1 + group_spacing = 0.1 + hatches = ['/', '\\', 'x', 'o', '.'] + colors = ['blue', 'green', 'orange', 'red', 'purple'] + + # Create x positions + x_positions = [] + current_x = 0 + grouped = df_avg.sort_values(['h', 'method']).reset_index(drop=True) + + for i, h in enumerate(h_values): + methods_in_group = grouped[grouped['h'] == h] + for _ in range(len(methods_in_group)): + x_positions.append(current_x) + current_x += bar_width + current_x += group_spacing + + # Plot + #fig, ax = plt.subplots(figsize=(6, 6)) + bottoms = np.zeros(len(grouped)) + + # First pass: fill with white so we can hatch over it + for i, comp in enumerate(components): + ax.bar( + x_positions, + grouped[comp], + bottom=bottoms, + width=bar_width, + color='white', + edgecolor='black' + ) + bottoms += grouped[comp].values + + # Second pass: hatch overlay with colored edges + bottoms = np.zeros(len(grouped)) + for i, comp in enumerate(components): + ax.bar( + x_positions, + grouped[comp], + bottom=bottoms, + width=bar_width, + fill=False, + hatch=hatches[i % len(hatches)], + edgecolor=colors[i % len(colors)], + linewidth=1.5, + label=f'{comp}' + ) + bottoms += grouped[comp].values + + # Method labels on x-axis + ax.set_xticks(x_positions) + ax.set_xticklabels(grouped['method'].values, rotation=45, ha='right') + + # h group labels below the x-axis + for i, h in enumerate(h_values): + group_start = i * (num_methods * bar_width + group_spacing) + group_mid = group_start + (num_methods - 1) * bar_width / 2 + ax.text( + group_mid, -0.002 * ax.get_ylim()[1], + f'h = {h}', + ha='center', + va='top', + fontsize=ft, + transform=ax.get_xaxis_transform() + ) + + if legend_right: + ax.legend( + loc="upper center", + bbox_to_anchor=(1.3, 1), + fancybox=True, + shadow=True, + ncol=1, + fontsize=ft) + else: + ax.legend( + loc="lower center", + bbox_to_anchor=(0.5, -0.6), + fancybox=True, + shadow=True, + ncol=3, + fontsize=ft) + + if sety == False: + ax.set_ylabel("") + else: + ax.set_ylabel("Percentage (%)", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + if ifLeg == False: + ax.legend().remove() diff --git a/examples/Example5/test_functions.py b/examples/Example5/test_functions.py new file mode 100644 index 0000000..1566078 --- /dev/null +++ b/examples/Example5/test_functions.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Wed Feb 12 13:13:23 2025 + +@author: surero +""" +import numpy as np +import scipy + +class sinfunc: + def __init__(self): + self.data_name = "sinfunc" + self.zlim = np.array([[0, 1], [0, 1]]) + self.theta_true = np.array([0.5]) # np.array([np.pi / 5]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.dx = 1 + self.dt = 1 + self.x = None + self.sigma2 = 0.1 + + def function(self, x, theta): + f = np.sin(10 * x - 5 * theta) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + var_noise = self.noise(thetas[0], thetas[1]) + noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise), 1) + f += noise + return f + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + + M = np.array([self.function(x, self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + def noise(self, x, theta): + alpha = 10 + min_value = 0.01#0.05 + max_value = 0.1#0.3 + + weight = 1 / (1 + np.exp(-alpha * (x - 0.5))) # Sigmoid transition + value = min_value + (max_value - min_value) * weight # Scale between 0.1 and 10 + return value + + +class unimodalx: + def __init__(self): + self.data_name = "unimodalx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + self.sigma2 = 0.1 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + t1 = -10 + t1 * 20 + t2 = -10 + t2 * 20 + f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 + (2 * x - 1) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + V = 0.01 + 1.2 * (t1**2 + t2**2) * 2 + V = V + return V + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + +class bimodalx: + def __init__(self): + self.data_name = "bimodalx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.theta_true = np.array([0.35, 0.35]) + self.real_data = None + self.out = [("f", float)] + self.p = 3 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.sigma2 = 0.05 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + mu1 = (0.35, 0.35) + mu2 = (0.65, 0.65) + sigma = 0.15 + term1 = np.exp(-((t1 - mu1[0]) ** 2 + (t2 - mu1[1]) ** 2) / sigma**2) + term2 = np.exp(-((t1 - mu2[0]) ** 2 + (t2 - mu2[1]) ** 2) / sigma**2) + f = (term1 + term2) + (2 * x - 1) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + cov = np.array([[0.05, 0], [0, 0.05]]) + var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) + return 0.1*var.pdf(np.array([t1, t2])) + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + +class braninx: + def __init__(self): + self.data_name = "braninx" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) + self.sigma2 = 5 + self.dx = 1 + self.dt = 2 + + def function(self, x, t1, t2): + t1 = -5 + 15 * t1 + t2 = 15 * t2 + f = ( + (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 + + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) + + 10 + + (2 * x - 1) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def noise(self, x, t1, t2): + alpha = 10 + min_value = 0.1 + max_value = 15 + + weight = 1 / (1 + np.exp(-alpha * (t2 - 0.5))) + value = min_value + (max_value - min_value) * weight + + return value + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + +class pritam: + def __init__(self): + self.data_name = "pritam" + self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) + self.theta_true = np.array([0.5]) + self.out = [("f", float)] + self.d = 1 + self.p = 3 + self.real_data = None + self.dx = 2 + self.sigma2 = 10 #0.5**2 + self.dx = 2 + self.dt = 1 + + def function(self, x1, x2, theta1): + f = (30 + 5 * x1 * np.sin(5 * x1)) * (6 * theta1 + 1 + np.exp(-5 * x2)) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1], thetas[2]) + V = self.noise(thetas[0], thetas[1], thetas[2]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) + f += R + return f + + def realdata(self, x, seed): + self.x = x + self.d = len(x) + self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) + M = np.array([self.function(x[0], x[1], self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + if seed is not None: + rand_stream = np.random.default_rng(seed) + R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) + self.real_data = M + R + else: + self.real_data = M + + def noise(self, x1, x2, t1): + # return 5 + + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return (15*t1)*var.pdf(np.array([x1, x2])) + diff --git a/examples/Example5/utils.py b/examples/Example5/utils.py new file mode 100644 index 0000000..a296d44 --- /dev/null +++ b/examples/Example5/utils.py @@ -0,0 +1,344 @@ +import matplotlib.pyplot as plt +import numpy as np +from matplotlib.colors import ListedColormap +import pandas as pd +import matplotlib as mpl +import matplotlib.patches as mpatches + +yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + (1, 0, 0), # very dark orange +] +yellow_cmap = ListedColormap(yellow_colors, name="yellow") + +def heatmap(Xpl, Ypl, noise_grid, f_grid, p_grid, t_sample=None): + + + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + cs = ax[0].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax[0].contour(Xpl, Ypl, f_grid.reshape(50, 50), colors="black") + ax[0].clabel(CS, inline=True, fontsize=10) + ax[0].set_xlabel(r"$\theta_1$", fontsize=16) + ax[0].set_ylabel(r"$\theta_2$", fontsize=16) + ax[0].tick_params(axis="both", labelsize=16) + + cs = ax[1].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax[1].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm") + if t_sample is not None: + ax[1].scatter(t_sample[:, 0], t_sample[:, 1]) + ax[1].clabel(CS, inline=True, fontsize=10) + ax[1].set_xlabel(r"$\theta_1$", fontsize=16) + ax[1].set_ylabel(r"$\theta_2$", fontsize=16) + ax[1].tick_params(axis="both", labelsize=16) + plt.show() + +def heatmap_pritam(cls_data): + # test data + nmesh = 50 + x1 = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + x2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + X1, X2 = np.meshgrid(x1, x2) + Xg = np.vstack([X1.ravel(), X2.ravel()]).T + #cls_data.theta_true[0] + ng = np.array([cls_data.noise(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) + fg = np.array([cls_data.function(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) + + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) + cs = ax[0].contourf(X1, X2, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + #ax[1].clabel(CS, inline=True, fontsize=10) + for x in cls_data.x: + ax[0].scatter(x[0], x[1], marker="*", color="black") + ax[0].set_xlabel(r"$x_1$", fontsize=16) + ax[0].set_ylabel(r"$x_2$", fontsize=16) + ax[0].tick_params(axis="both", labelsize=16) + + cs = ax[1].contourf(X1, X2, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + #ax[0].clabel(CS, inline=True, fontsize=10) + for x in cls_data.x: + ax[1].scatter(x[0], x[1], marker="*", color="black") + ax[1].set_xlabel(r"$x_1$", fontsize=16) + ax[1].set_ylabel(r"$x_2$", fontsize=16) + ax[1].tick_params(axis="both", labelsize=16) + plt.show() + + + + + + + +def toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info): + + from mpl_toolkits.axes_grid1 import make_axes_locatable + + ft = 12 + fig, axs = plt.subplots(1, 3, figsize=(14, 3.5), constrained_layout=False) # Disable constrained_layout + fig.subplots_adjust(wspace=0.4, left=0.1, right=0.9) # Adjust space between subplots + + # Contour plot for axs[1] + divider = make_axes_locatable(axs[0]) + cax1 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cs1 = axs[0].contourf(Xpl, Ypl, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + fig.colorbar(cs1, cax=cax1) + + xt_joint = np.column_stack((cls_data.x, np.repeat(cls_data.theta_true, len(cls_data.x)))) + axs[0].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") + axs[0].set_xlabel(r"$x$", fontsize=ft) + axs[0].set_ylabel(r"$\vartheta$", fontsize=ft) + axs[0].tick_params(axis="both", labelsize=ft) + + # Contour plot for axs[2] + divider = make_axes_locatable(axs[1]) + cax2 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cs2 = axs[1].contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + fig.colorbar(cs2, cax=cax2) + + axs[1].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") + axs[1].set_xlabel(r"$x$", fontsize=ft) + axs[1].set_ylabel(r"$\vartheta$", fontsize=ft) + axs[1].tick_params(axis="both", labelsize=ft) + + # Scatter plot for axs[0] + xs = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], 100) + for k in range(10): + axs[2].scatter(xs, + np.array([cls_data.sim_f(np.array([x, cls_data.theta_true[0]]), persis_info) for x in xs]), + facecolors="none", edgecolors="green", s=80, linewidth=2) + axs[2].plot(xs, + np.array([cls_data.function(x, cls_data.theta_true[0]) for x in xs]), + linestyle="dotted", linewidth=3, color="black") + axs[2].set_xlabel(r"$x$", fontsize=ft) + #axs[2].set_ylabel(r"$\theta$", fontsize=ft) + axs[2].tick_params(axis="both", labelsize=ft) + + plt.show() + + + +def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): + + z, reps = des_obj.xt, des_obj.reps + + ft = 14 + fig, ax = plt.subplots(figsize=(5, 4)) + + for xitem in cls_data.x: + ax.vlines( + xitem, 0, 1, linestyles="dotted", colors="green", linewidth=3, zorder=5 + ) + for label, x_count, y_count in zip(reps, z[:, 0], z[:, 1]): + if new: + if np.any(np.all(z0u == np.array([x_count, y_count]), axis=1)): + col = "cyan" + else: + col = "blue" + else: + if label > rep0: + col = "blue" + else: + col = "cyan" + + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft-2, + color=col, + weight="bold", + zorder=6, + ) + + ax.contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.set_xlabel(r"$x$", fontsize=ft) + ax.set_ylabel(r"$\theta$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + ax2 = ax.twiny() + ax2.plot(pg, tg, color='black', label="Y-axis scatter") + ax2.set_xlabel(r"$p(y|\theta)$", fontsize=ft) + ax2.tick_params(axis="both", labelsize=ft) + plt.show() + + +def fig6(desobj, theta0, rep0, Xpl, Ypl, pg, ng, ax, figset={}, fig={}, exp=True): + + th, reps = desobj.xt[:, 1:3], desobj.reps + + xtick, ytick, cbarf = figset.get("xtick", True), figset.get("ytick", True), figset.get("cbar", True) + + ft = 12 + nm = Xpl.shape[0] + cs = ax.contourf(Xpl, Ypl, ng.reshape(nm, nm), cmap=yellow_cmap, alpha=0.75) + cp = ax.contour(Xpl, Ypl, pg.reshape(nm, nm), cmap="coolwarm") + + + if cbarf: + cbar = fig.colorbar(cs, ax=ax, pad=0.1) + + for label, x_count, y_count in zip(reps, th[:, 0], th[:, 1]): + + if exp: + if np.any(np.all(theta0 == np.array([x_count, y_count]), axis=1)): + col = "cyan" + else: + col = "blue" + else: + if label > rep0: + col = "blue" + else: + col = "cyan" + + ax.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color=col + ) + + if xtick: + ax.set_xticks([0, 0.5, 1]) + ax.set_xlabel(r"$\vartheta_1$", fontsize=ft) + else: + ax.set_xticks([]) + + if ytick: + ax.set_yticks([0, 0.5, 1]) + ax.set_ylabel(r"$\vartheta_2$", fontsize=ft) + else: + ax.set_yticks([]) + + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.tick_params(axis="both", labelsize=ft) + + + +def visual_pritam(des_obj, z0u): + ft = 16 + xtu, repu = des_obj.xt, des_obj.reps + for xtid, xt in enumerate(xtu): + # print(xt) + if xt in z0u: + col = "cyan" + else: + col = "blue" + + plt.annotate( + repu[xtid], + xy=(xt[0], xt[1]), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color=col, + weight="bold", + ) + + plt.xlabel(r"$x_1$") + plt.ylabel(r"$x_2$") + plt.show() + + ut = [] + for xtid, xt in enumerate(xtu): + if xt not in z0u: + ut.append(xt[2]) + plt.hist(ut) + plt.show() + +import os +import dill as pickle +def save_output(desing_obj, name, al_func, seedno, path=None, label=None): + + if path is None: + if not os.path.isdir('output'): + os.mkdir('output') + + design_path = 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + else: + design_path = path + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + + with open(design_path, 'wb') as file: + pickle.dump(desing_obj, file) + +def read_output(path1, name, al_func, seedno, label=None): + + design_path = path1 + 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + with open(design_path, 'rb') as file: + design_obj = pickle.load(file) + + return design_obj + + + +def create_entry(desobj, fname, method, s, h): + return [ + {"MSE": he["MSE"], + "MAD": he["MAD"], + "VAR": he["VAR"], + "MSEy": he["MSEy"], + "MADy": he["MADy"], + "MSEn": he["MSEn"], + "MADn": he["MADn"], + "t": he["t"], + "new": he["new"], + "method": method, + "example": fname, + "s": s, + "h": h, + "horizon": he["h"]} + for he in desobj["H"] + ] + +def read_data(rep0=0, + repf=10, + methods=["ivar", "imse", "unif"], + examples=["unimodal", "banana", "bimodal"], + folderpath=None, + label=None, + ): + + datalist = [] + for eid, example in enumerate(examples): + for mid, m in enumerate(methods): + for r in range(rep0, repf): + + desobj = read_output(folderpath, example, m, r, label=label[mid]) + entry = create_entry(desobj, example, m, r, label[mid]) + datalist.extend(entry) + + df = pd.DataFrame(datalist) + return df + +def read_summary_metric(folderpath, examples, methods, ls, rep=(1,31)): + dfl = [] + for eid, example in enumerate(examples): + for mid, m in enumerate(methods): + for r in range(rep[0], rep[1]): + + desobj = read_output(folderpath, example, m, r, label=ls[mid]) + for heid, he in enumerate(desobj.H): + sm = he["summary_metric"] + sm["r"] = r + sm["method"] = m + sm["h"] = ls[mid] + sm["t"] = heid + dfl.append(sm) + + df = pd.DataFrame(dfl) + return df diff --git a/examples/Example5/utils_sample.py b/examples/Example5/utils_sample.py new file mode 100644 index 0000000..2f2c7ce --- /dev/null +++ b/examples/Example5/utils_sample.py @@ -0,0 +1,143 @@ +import numpy as np +import scipy.stats as sps +from utils import heatmap, heatmap_pritam +import matplotlib.pyplot as plt +import sys + +def sample_from_posterior(cls_data, seed): + import emcee + import scipy.stats as sps + from smt.sampling_methods import LHS + + discard = 250 + nsteps = 1000 + thin = 30 + nwalkers = 40 + seed = int(seed) + + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + if cls_data.dx == 1: + feval = np.array([cls_data.function(x, *ctheta) for x in cls_data.x]).squeeze() + elif cls_data.dx == 2: + feval = np.array([cls_data.function(x[0], x[1], *ctheta) for x in cls_data.x]).squeeze() + + + rnd = sps.multivariate_normal(mean=feval, cov=cls_data.obsvar) + pvar = rnd.pdf(cls_data.real_data) + sys.float_info.epsilon + return np.log(pvar) + + def sample(ndim, nwalkers, seed): + np.random.seed(seed) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + sampling = LHS( + xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed + ) + loc0 = sampling(nwalkers) + sampler.run_mcmc(initial_state=loc0, nsteps=nsteps, progress=False) + samples = sampler.get_chain(discard=discard, thin=thin, flat=True) + return samples + + return sample(cls_data.dt, nwalkers, seed) + +def test_data_gen(cls_data, sample=False): + + # test data + nmesh = 50 + xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + tg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + pg = np.array( + [ + sps.multivariate_normal( + mean=np.array([cls_data.function(x, *t) for x in cls_data.x]), + cov=cls_data.obsvar, + ).pdf(cls_data.real_data) + for t in tg + ] + )[:, None] + + zg = np.column_stack((np.tile(cls_data.x, (tg.shape[0], 1)), tg)) + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + if sample: + t_s = sample_from_posterior(cls_data, 1234) + p_s = np.zeros((t_s.shape[0], 1)) + f_s = np.zeros((t_s.shape[0], 1)) + n_s = np.zeros((t_s.shape[0], 1)) + for t_id, t in enumerate(t_s): + f_s[t_id, 0] = np.array([cls_data.function(x, t[0], t[1]) for x in cls_data.x]) + n_s[t_id, 0] = np.array([cls_data.noise(x, t[0], t[1]) for x in cls_data.x]) + rnd = sps.multivariate_normal(mean=f_s[t_id, 0], cov=cls_data.obsvar) + p_s[t_id, 0] = rnd.pdf(cls_data.real_data) + + p_se = p_s + sys.float_info.epsilon + w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() + + + # heatmap(Xpl, Ypl, ng, fg, pg, t_s) + + return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl + else: + return tg, fg, pg, zg, ng, Xpl, Ypl + + +def test_data_gen_pri(cls_data, sample=False): + + nt = 100 + tg = np.linspace(cls_data.zlim[2][0], cls_data.zlim[2][1], nt)[:, None] + pg = np.array( + [ + sps.multivariate_normal( + mean=np.array( + [cls_data.function(x[0], x[1], t) for x in cls_data.x] + ).squeeze(), + cov=cls_data.obsvar, + ).pdf(cls_data.real_data) + for t in tg + ] + ) + + + + heatmap_pritam(cls_data) + + # (ntot, d) + x_tiled = np.tile(cls_data.x, (tg.shape[0], 1)) + # (ntot, p-d) + t_repeated = np.repeat(tg, cls_data.x.shape[0], axis=0) + # (ntot, p) + zg = np.hstack([x_tiled, t_repeated]) + + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + if sample: + t_s = sample_from_posterior(cls_data, 1234) + #t_s = np.linspace(0, 1, 900)[:, None] + p_s = np.zeros((t_s.shape[0], 1)) + f_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) + n_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) + + for t_id, t in enumerate(t_s): + feval = np.array([cls_data.function(x[0], x[1], t) for x in cls_data.x]) + f_s[t_id*4 : (t_id + 1)*4, 0] = feval.flatten() + n_s[t_id*4 : (t_id + 1)*4, 0] = np.array([cls_data.noise(x[0], x[1], t) for x in cls_data.x]).flatten() + + rnd = sps.multivariate_normal(mean=feval.flatten(), cov=cls_data.obsvar) + p_s[t_id, 0] = rnd.pdf(cls_data.real_data) + + p_se = p_s + sys.float_info.epsilon + w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() + + plt.scatter(tg, pg, alpha=0.5, marker="+", color="blue", s=50) + plt.scatter(t_s, p_s, alpha=0.1, marker="*", color="red", s=10) + plt.show() + + return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s + else: + return tg, fg, pg, zg, ng From e4dd64a7ac4dfefc03e629100313df5f68ff3a80 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 16:47:03 -0500 Subject: [PATCH 053/103] improve docstrings --- PUQ/surrogatemethods/homGP.py | 27 ++++++++++- PUQ/surrogatemethods/multihomGP.py | 78 ++++++++++++++++++++++++++++++ 2 files changed, 103 insertions(+), 2 deletions(-) diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 031e012..64e0048 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -62,7 +62,7 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, Returns ------- - None, but fitinfo is updated with maximum likelihood estimates for theta and beta0 + None, but fitinfo is updated with maximum likelihood estimates ''' f = f.flatten() @@ -101,6 +101,25 @@ def __init__(self,fitinfo): def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): r''' Wrapper method for hetgpy.homGP.predict + + Parameters + ---------- + predinfo: dict + (empty) dictionary that will hold prediction results + fitinfo: dict + dictionary with hetgpy.homGP-trained hyperparameters and inverse covariance matrices. fitinfo is converted back into a hetgpy.homGP object for prediction + x: ndarray + nxd numpy array for prediction. Must match same number of columns as supplied to `fitinfo["X0"]` + theta: ndarray + Deprecated, but used to specify output dimension + thetaprime: ndarray + nxd numpy array for calculating covariance matrix + kwargs: dict + additional keyword arguments passed to hetgpy.homGP.predict + + Returns + ------- + None, but predinfo is populated with `mean`, `variance`, and `covmat` fields ''' GP = fitinfo.get('model') if GP is None: @@ -130,9 +149,13 @@ def update(fitinfo, x,Y = None,**kwargs): ---------- fitinfo: dictionary that contains the fit information for a hetgpy.homGP object x: array of new design locations - Y: new response. If None, then + Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit + + Returns + ------- + None, but fitinfo is updated in place ''' # validate kwargs valid_kws = ('ginit','lower','upper', diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py index 3fe9b1e..67d0d71 100644 --- a/PUQ/surrogatemethods/multihomGP.py +++ b/PUQ/surrogatemethods/multihomGP.py @@ -13,6 +13,45 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, eps=np.sqrt(np.finfo(float).eps), settings={"return.Ki": True, "factr": 1e7}, **kwargs): + r''' + Fit a homoskedastic GP to each dimension of output data. + + Parameters + ---------- + fitinfo: dictionary/class of results + x: ndarray + nxd design matrix (must have at least one column) + f: ndarray + output array for training. One GP is trained for each output column. + theta: not used + lower,upper : ndarray_like + optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). + In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) + noiseControl : dict + dict with element: + - ``g_bounds`` vector providing minimal and maximal noise to signal ratio (default to ``(sqrt(MACHINE_DOUBLE_EPS), 100)``). + settings : dict + dict for options about the general modeling procedure, with elements: + - ``return_Ki`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). + - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + eps : float + jitter used in the inversion of the covariance matrix for numerical stability + known : dict + optional dict of known parameters (e.g. ``beta0``, ``theta``, ``g``) + init : dict + optional lists of starting values for mle optimization: + - ``theta_init`` initial value of the theta parameters to be optimized over (default to 10% of the range determined with ``lower`` and ``upper``) + - ``g_init`` vector of nugget parameter to be optimized over + covtype : str + covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` + maxit : int + maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization + + Returns + ------- + None, but fitinfo is updated with maximum likelihood estimates. Individual emulators are accessed via fitinfo["emulist"] + + ''' numGPs = f.shape[1] emulist = [dict() for x in range(0, numGPs)] @@ -41,6 +80,30 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, return def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): + r''' + Wrapper method for homGP.predict + + Parameters + ---------- + predinfo: dict + (empty) dictionary that will hold prediction results + fitinfo: dict + dictionary with hetgpy.homGP-trained hyperparameters and inverse covariance matrices. fitinfo is converted back into a hetgpy.homGP object for prediction + x: ndarray + nxd numpy array for prediction. Must match same number of columns as supplied to `fitinfo["X0"]` + theta: ndarray + Deprecated, but used to specify output dimension + thetaprime: ndarray + nxd numpy array for calculating covariance matrix + kwargs: dict + additional keyword arguments passed to hetgpy.homGP.predict + + Returns + ------- + None, but predinfo is populated with `mean`, `variance`, and `covmat` fields. + `mean` and `variance` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. + `covmat` is stored as a list of matrices with the ith element corresponding to the ith output column + ''' numGPs = fitinfo['numGPs'] emulist = [dict() for x in range(0, numGPs)] @@ -60,6 +123,21 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): return def update(fitinfo, x,Y = None,**kwargs): + r''' + Update function for homGP + + Parameters + ---------- + fitinfo: dictionary that contains the fit information for a hetgpy.homGP object + x: array of new design locations + Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location + kwargs: key-value pairs that get passed to hetgpy.homGP.update. + Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit + + Returns + ------- + None, but fitinfo is updated in place and individual emulator are accessed via fitinfo["emulist"] + ''' numGPs = fitinfo['numGPs'] for i in range(numGPs): emu = fitinfo['emulist'][i] From 1fd9083818be2d853156366a0ec014c4668397d5 Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 17:39:11 -0500 Subject: [PATCH 054/103] put maxit in hetGP inputs --- PUQ/surrogatemethods/hetGP.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index f7f4291..961ab18 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -30,6 +30,7 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, + maxit=100, noiseControl={'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, init={}, known={}, @@ -103,6 +104,7 @@ def fit(fitinfo, x, theta, f, noiseControl=noiseControl, lower=lower, upper=upper, + maxit=maxit, settings=settings, init=init, eps=eps, From e0e879ddf325a09946397383af43f4734af02e2c Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Thu, 25 Sep 2025 21:26:02 -0500 Subject: [PATCH 055/103] add multihetGP and test --- PUQ/surrogatemethods/multihetGP.py | 168 ++++++++++++++++++++++++++++ tests/unit_tests/test_multihetGP.py | 136 ++++++++++++++++++++++ 2 files changed, 304 insertions(+) create mode 100644 PUQ/surrogatemethods/multihetGP.py create mode 100644 tests/unit_tests/test_multihetGP.py diff --git a/PUQ/surrogatemethods/multihetGP.py b/PUQ/surrogatemethods/multihetGP.py new file mode 100644 index 0000000..60bf6c1 --- /dev/null +++ b/PUQ/surrogatemethods/multihetGP.py @@ -0,0 +1,168 @@ +''' +Set of functions to fit a heteroskedastic GP to each dimension of output data +''' +import numpy as np +from PUQ.surrogate import emulator + +def fit(fitinfo, x, theta, f, + lower=None, + upper=None, + maxit=100, + noiseControl={'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + init={}, + known={}, + eps=np.sqrt(np.finfo(float).eps), + settings={"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}, + covtype = 'Gaussian', **kwargs): + r''' + Wrapper function for hetgpy.hetGP.mleHetGP + + Arguments + --------- + fitinfo: dictionary/class holding emulator results + x : ndarray_like + matrix of all designs, one per row, or list with elements: + - ``X0`` matrix of unique design locations, one point per row + - ``Z0`` vector of averaged observations, of length ``len(X0)`` + - ``mult`` number of replicates at designs in ``X0``, of length ``len(X0)`` + theta: not used + f : ndarray_like + output array for training. One GP is trained for each output column. + lower,upper : ndarray_like + optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). + In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) + noiseControl : dict + dict with elements related to optimization of the noise process parameters: + - ``g_min``, ``g_max`` minimal and maximal noise to signal ratio (of the mean process) + - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) + - ``lowerpX``, ``upperpX`` optional vectors of bounds of the input domain if `pX` is used. + - ``lowerTheta_g``, ``upperTheta_g`` optional vectors of bounds for the lengthscales of the noise process if ``linkThetas == 'none'``. Same as for ``theta`` if not provided. + - ``k_theta_g_bounds`` if ``linkThetas == 'joint'``, vector with minimal and maximal values for ``k_theta_g`` (default to ``(1, 100)``). See Notes. + - ``g_bounds`` vector for minimal and maximal noise to signal ratios for the noise of the noise process, i.e., the smoothing parameter for the noise process. (default to ``(1e-6, 1)``). + settings : dict + dict for options about the general modeling procedure, with elements: + - ``linkThetas`` defines the relation between lengthscales of the mean and noise processes. Either ``'none'``, ``'joint'``(default) or ``'constr'``, see Notes. + - ``logN``, when ``True`` (default), the log-noise process is modeled. + - ``initStrategy`` one of ``'simple'``, ``'residuals'`` (default) and ``'smoothed'`` to obtain starting values for ``Delta``, see Notes + - ``penalty`` when ``True``, the penalized version of the likelihood is used (i.e., the sum of the log-likelihoods of the mean and variance processes, see References). + - ``hardpenalty`` is ``True``, the log-likelihood from the noise GP is taken into account only if negative (default if ``maxit > 1000``). + - ``checkHom`` when ``True``, if the log-likelihood with a homoskedastic model is better, then return it. + - ``trace`` optional scalar (default to ``0``). If negative, fit silently. If ``0``, only high level information is given. If ``1``, information is given about the result of the heterogeneous model optimization. Level ``2`` gives more details. Level ``3`` additionaly displays all details about initialization of hyperparameters. + - ``return_matrices`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). + - ``return_hom`` boolean to include homoskedastic GP models used for initialization (i.e., ``modHom`` and ``modNugs``). + - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + eps : float + jitter used in the inversion of the covariance matrix for numerical stability + init,known : dict + optional lists of starting values for mle optimization or that should not be optimized over, respectively. + Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. + One can set one or several of the following: + - ``theta`` lengthscale parameter(s) for the mean process either one value (isotropic) or a vector (anistropic) + - ``Delta`` vector of nuggets corresponding to each design in ``X0``, that are smoothed to give ``Lambda`` (as the global covariance matrix depends on ``Delta`` and ``nu_hat``, it is recommended to also pass values for ``theta``) + - ``beta0`` constant trend of the mean process + - ``k_theta_g`` constant used for link mean and noise processes lengthscales, when ``settings['linkThetas'] == 'joint'`` + - ``theta_g`` either one value (isotropic) or a vector (anistropic) for lengthscale parameter(s) of the noise process, when ``settings['linkThetas'] != 'joint'`` + - ``g`` scalar nugget of the noise process + - ``g_H`` scalar homoskedastic nugget for the initialisation with a :func: homGP.mleHomGP. See Notes. + - ``pX`` matrix of fixed pseudo inputs locations of the noise process corresponding to Delta + covtype : str + covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` + maxit : int + maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization + ''' + numGPs = f.shape[1] + emulist = [dict() for x in range(0, numGPs)] + for i in range(numGPs): + emu = emulator( + x=x, + theta=np.array([[0]]), + f=f[:,i:i+1], + method="hetGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + "maxit": maxit, + "eps": eps + } + ) + emulist[i] = emu + fitinfo["f"] = f + fitinfo["emulist"] = emulist + fitinfo["numGPs"] = numGPs + return +def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): + r''' + Wrapper method for hetGP.predict + + Parameters + ---------- + predinfo: dict + (empty) dictionary that will hold prediction results + fitinfo: dict + dictionary with hetgpy.hetGP-trained hyperparameters and inverse covariance matrices. fitinfo is converted back into a hetgpy.hetGP object for prediction + x: ndarray + nxd numpy array for prediction. Must match same number of columns as supplied to `fitinfo["X0"]` + theta: ndarray + Deprecated, but used to specify output dimension + thetaprime: ndarray + nxd numpy array for calculating covariance matrix + kwargs: dict + additional keyword arguments passed to hetgpy.hetGP.predict + + Returns + ------- + None, but predinfo is populated with `mean`, `variance`,`nugs`, and `covmat` fields. + `mean`, `variance`, and `nugs` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. + `covmat` is stored as a list of matrices with the ith element corresponding to the ith output column + ''' + numGPs = fitinfo['numGPs'] + emulist = [dict() for x in range(0, numGPs)] + + # instantiate outputs + nr, nc = (x.shape[0],numGPs) + for key in ('mean','var','nugs'): + predinfo[key] = np.zeros(shape=(nr,nc),dtype=float) + predinfo['covmat'] = [np.zeros(shape=(nr,nr),dtype=float) + for i in range(numGPs) + ] + for i in range(numGPs): + preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) + predinfo['mean'][:,i] = preds._info['mean'] + predinfo['var'][:,i] = preds._info['var'] + predinfo['nugs'][:,i] = preds._info['nugs'] + predinfo['covmat'][i] = preds._info['covmat'] + + return + +def update(fitinfo, x,Y = None,**kwargs): + r''' + Update function for hetGP + + Parameters + ---------- + fitinfo: dictionary that contains the fit information for a hetgpy.hetGP object + x: array of new design locations + Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location + kwargs: key-value pairs that get passed to hetgpy.hetGP.update. + Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit + + Returns + ------- + None, but fitinfo is updated in place and individual emulator are accessed via fitinfo["emulist"] + ''' + numGPs = fitinfo['numGPs'] + for i in range(numGPs): + emu = fitinfo['emulist'][i] + if Y is not None: + Yi = Y[:,i] + else: + Yi = None + emu.update(x=x,Y=Yi,**kwargs) + return \ No newline at end of file diff --git a/tests/unit_tests/test_multihetGP.py b/tests/unit_tests/test_multihetGP.py new file mode 100644 index 0000000..ff51393 --- /dev/null +++ b/tests/unit_tests/test_multihetGP.py @@ -0,0 +1,136 @@ +import sys +sys.path.append('./') +import numpy as np +from scipy.stats import qmc +from PUQ.surrogate import emulator +from hetgpy import hetGP +from copy import deepcopy +# create some noise 2D data +rng = np.random.default_rng(1) +lhs = qmc.LatinHypercube(d=2,rng=rng) + +X = lhs.random(n=100) +reps = rng.choice(len(X),size=100,replace=True) +X = X[reps,] +# prediction grid +Xp = lhs.random(n=100) +Y = 0*X +Y[:,0] = np.sin(X[:,0]) +Y[:,1] = np.cos(X[:,1]) +# varying noise field +noise = rng.normal(size=Y.shape) * np.exp(-X**2) +noise *=2 + +Y+= noise + +COVTYPE = "Matern5_2" + +# module wide settings +SETTINGS = {"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9} +NOISECONTROL = {'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)} + +MAXIT = 100 +def test_fit(): + GPlist = [ + hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, + settings=SETTINGS,noiseControl=NOISECONTROL) + for i in range(Y.shape[1]) + ] + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS,'noiseControl':NOISECONTROL}) + #multiGP.fit() + + emulator_keys = ['ll','theta','beta0','Delta'] + for i in range(len(GPlist)): + GP = GPlist[i] + for key in emulator_keys: + + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + + return + +def test_predict(): + + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + multiGP.fit() + preds = multiGP.predict(x=Xp,thetaprime=Xp) + GPlist = [ + hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE, + maxit=MAXIT,settings=SETTINGS + ).predict(Xp,xprime=Xp) + for i in range(Y.shape[1]) + ] + em2GPkey = {'mean':'mean','var':'sd2','nugs':'nugs'} + for i in range(len(GPlist)): + GP = GPlist[i] + for em_key, GP_key in em2GPkey.items(): + + assert np.allclose(preds._info[em_key][:,i],GP[GP_key]) + + # test cov and covmat + for i in range(len(GPlist)): + assert np.allclose(preds._info['covmat'][i],GPlist[i]['cov']) + + return + +def test_update_kriging_believer(): + + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + multiGP.fit() + initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = deepcopy(initial_lls) + GPlist = [ + hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, + settings=SETTINGS) + for i in range(Y.shape[1]) + ] + GPlist_initial = deepcopy(GPlist) + Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + multiGP.update(x=Xnew) + em_keys = ['ll','theta','beta0','Delta'] + for GP in GPlist: + Ynew = GP.predict(Xnew)['mean'] + GP.update(Xnew,Ynew,maxit=0) + for i in range(multiGP._info['numGPs']): + GP = GPlist[i] + for key in em_keys: + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + +def test_update(): + '''Test update and that log likelihood changes''' + + multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, + method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + multiGP.fit() + initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = deepcopy(initial_lls) + + GPlist = [ + hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, + settings=SETTINGS) + for i in range(Y.shape[1]) + ] + GPlist_initial = deepcopy(GPlist) + Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + mpreds = multiGP.predict(x=Xnew) + Ynew = mpreds._info['mean'] + multiGP.update(x=Xnew,Y=Ynew) + em_keys = ['ll','theta','beta0','Delta'] + for GP in GPlist: + Ynew = GP.predict(Xnew)['mean'] + GP.update(Xnew,Ynew,maxit=MAXIT) + for i in range(multiGP._info['numGPs']): + GP = GPlist[i] + for key in em_keys: + assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + # also check that likelihoods changed after update + for i in range(len(initial_lls)): + old_ll = initial_lls[i] + assert not np.allclose(old_ll,multiGP._info['emulist'][i]._info['ll']) + +if __name__ == "__main__": + test_fit() \ No newline at end of file From 2747656f850d55b38c66d518f41efdf9c7625440 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 30 Sep 2025 16:50:59 -0400 Subject: [PATCH 056/103] Updating multidim emulators --- .../gen_funcs/acquisition_md_deterministic.py | 30 +- .../acquisition_md_deterministic_old.py | 306 +++++++ PUQ/surrogatemethods/multihetGP.py | 31 +- PUQ/surrogatemethods/multihomGP.py | 26 +- examples/Example4/emulator_check.py | 53 ++ examples/Example4/example.py | 150 ++++ examples/Example4/test_funcs.py | 562 ++++++++++++ examples/Example4/utilities.py | 818 ++++++++++++++++++ tests/unit_tests/test_multihetGP.py | 8 +- tests/unit_tests/test_multihomGP.py | 6 +- 10 files changed, 1945 insertions(+), 45 deletions(-) create mode 100644 PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py create mode 100644 examples/Example4/emulator_check.py create mode 100644 examples/Example4/example.py create mode 100644 examples/Example4/test_funcs.py create mode 100644 examples/Example4/utilities.py diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py index 8bef438..c02ec3d 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py @@ -12,12 +12,10 @@ def generate_neighborhood(acq): def get_pred(cL, emu, x, ttest, reps): - cP = emu.predict(x=x, theta=cL) - var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps - - testP = emu.predict(x=x, theta=ttest, thetaprime=cL) - mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] - + cP = emu.predict(x=cL) + var_cand = cP._info["var"] + cP._info["nugs"] / reps + testP = emu.predict(x=ttest, thetaprime=cL) + mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["covmat"] mut = mu.T St = np.transpose(S, (2, 0, 1)) @@ -54,8 +52,8 @@ def acquire_new(self): def gen_pred(self, model, x, t, return_id=False, return_flat=False): # predict at mesh - meshPr = model.predict(theta=t, thetaprime=t) - + meshPr = model.predict(x=t, thetaprime=t) + # ntot, ntot x ntot, ntot mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["S"], meshPr._info["var"] muT = mu.T @@ -73,7 +71,7 @@ def crit_pvar(self, model, x, t): f = multiple_pdfs(self.y, mu, M) g = multiple_pdfs(self.y, mu, N) - + vals = (1 / self.twopiddet) * f - g**2 return vals @@ -269,24 +267,14 @@ def evaluate_explore(self, L): V1 = S + self.Sigma3d - G = self.model._info["G"] - B = self.model._info["B"] - GB = G @ B - BTG = B.T @ G - - d = G.shape[0] - q = B.shape[1] + q, d = self.model._info['numGPs'], mu.shape[1] - tau = np.zeros((nm, q, q, nL)) phi = np.zeros((nm, d, d, nL)) coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) for j in range(0, q): - tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] - - for k in range(0, nL): - phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + phi[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] for k in range(0, self.nL): # C1: nm x d x d diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py new file mode 100644 index 0000000..65f9d39 --- /dev/null +++ b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py @@ -0,0 +1,306 @@ +import numpy as np +from smt.sampling_methods import LHS +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from hetgpy.IMSE import crit_IMSPE +import emcee + +def generate_neighborhood(acq): + N = int(acq.nL) + sampling = LHS(xlimits=acq.tlim, random_state=acq.seed) + L = sampling(N) + return L + +def get_pred(cL, emu, x, ttest, reps): + + cP = emu.predict(x=x, theta=cL) + var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps + testP = emu.predict(x=x, theta=ttest, thetaprime=cL) + mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] + mut = mu.T + St = np.transpose(S, (2, 0, 1)) + + return mut, St, cov, var_cand + + +class acquisition_function: + def __init__(self, model, cls_func, args): + self.model = model #deepcopy(model) + self.cls_func = cls_func + #self.persis_info = persis_info + self.args = args + self.x = self.cls_func.x + self.d = self.cls_func.d + self.p = self.cls_func.p + self.dx = self.cls_func.dx + self.dt = self.cls_func.dt + + self.tlim = cls_func.thetalimits + self.y = self.cls_func.real_data + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) + self.twopiddet = ( + (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) + ) + + def acquire_new(self): + return eval("self.evaluate")( + self.args.get("new", True), return_pseudo=False + ) + + def gen_pred(self, model, x, t, return_id=False, return_flat=False): + + # predict at mesh + meshPr = model.predict(theta=t, thetaprime=t) + + # ntot, ntot x ntot, ntot + mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["S"], meshPr._info["var"] + muT = mu.T + S = Sn.transpose(2, 0, 1) + + return muT, S, t + + def crit_pvar(self, model, x, t): + + # posterior variance + mu, S, z = self.gen_pred(model, x, t) + + M = S + 0.5 * self.Sigma3d + N = S + self.Sigma3d + + f = multiple_pdfs(self.y, mu, M) + g = multiple_pdfs(self.y, mu, N) + + vals = (1 / self.twopiddet) * f - g**2 + + return vals + + def total_var(self, model, x, t): + vals = self.crit_pvar(model=model, x=x, t=t) + return np.mean(self.weights * vals) + + +class rnd(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + new_input = self.args["prior"].rnd(1, + self.args["rand_stream"]) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + +class var(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", True) + self.nL = args.get("nL", 100) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + vals = self.crit_pvar(model=self.model, x=self.x, t=L) + new_input = L[np.argmax(vals), :].reshape(1, self.p) + return new_input + +class imse(acquisition_function): + # ASK THIS FUNCTION TO DAVID + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + +class exp(acquisition_function): + # ASK THIS FUNCTION TO DAVID + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + self.t_grid = args.get("t_grid", None) + + def evaluate(self, new, return_pseudo): + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + L = sampling(self.nL) + new_input = self.evaluate_explore(L) + + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) + new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) + return new_input + + +class ivar(acquisition_function): + def __init__(self, model, cls_func, args): + super().__init__(model, cls_func, args) + self.seed = args.get("seed", None) + self.explore = args.get("explore", "both") + self.nL = args.get("nL", 100) + if args.get("integral") == "importance": + # print("Importance sampling") + self.epsilon = args.get("epsilon", 10 ** (-10)) + self.discard = args.get("discard", 100) + self.nsteps = args.get("nsteps", 200) + self.thin = args.get("thin", 20) + self.nwalkers = args.get("nwalkers", 20) + self.reference_set() + elif args.get("integral") == "LHS": + print("LHS") + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + self.t_ref = sampling(500) + self.weights = 1 + else: + self.t_ref = args.get("t_grid") + self.weights = 1 + + def reference_set(self): + def log_probability(ctheta): + if np.any((ctheta < 0) | (ctheta > 1)): + return -np.inf + else: + pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) + + pvar = max(pvar, 0) + return np.log(pvar) + + def sample(ndim, nwalkers): + np.random.seed(int(self.seed)) + sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) + + sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) + loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) + + samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) + return samples + + def importance_weight(theta): + pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) + pvar = pvar + self.epsilon + unnorm_weight = 1 / pvar + weight = unnorm_weight / np.sum(unnorm_weight) + return weight + + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) + self.weights = importance_weight(theta=self.t_ref) + + def evaluate(self, new, return_pseudo): + + L = generate_neighborhood(self) + new_input = self.evaluate_explore(L) + self.znew, self.new = new_input, new + + if return_pseudo: + pnew = self.model.predict(x=self.znew) + fnew = np.array([pnew["mean"]])[None, :] + self.fnew = fnew + + return self + + def evaluate_explore(self, L): + + nm, nL = self.t_ref.shape[0], self.nL + vals = np.zeros(nL) + + mu, S, cov, cvar = get_pred( + cL=L, emu=self.model, x=self.x, ttest=self.t_ref, reps=1 + ) + + V1 = S + self.Sigma3d + + G = self.model._info["G"] + B = self.model._info["B"] + GB = G @ B + BTG = B.T @ G + + d = G.shape[0] + q = B.shape[1] + + tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) + + for j in range(0, q): + tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + for k in range(0, nL): + phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + for k in range(0, self.nL): + # C1: nm x d x d + # C2: nm x d x d + phic = phi[:, self.x, self.x.T, k] + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(self.y, mu, C1) + dets = multiple_determinants(C2) + part2 = rpdf / (coef * np.sqrt(dets)) + + vals[k] = np.sum(self.weights * part2) + + + new_input = L[np.argmax(vals), :].reshape(1, self.p) + + return new_input + diff --git a/PUQ/surrogatemethods/multihetGP.py b/PUQ/surrogatemethods/multihetGP.py index 60bf6c1..8c552a1 100644 --- a/PUQ/surrogatemethods/multihetGP.py +++ b/PUQ/surrogatemethods/multihetGP.py @@ -93,7 +93,8 @@ def fit(fitinfo, x, theta, f, } ) emulist[i] = emu - fitinfo["f"] = f + fitinfo["f"] = f.T + fitinfo["theta"] = x fitinfo["emulist"] = emulist fitinfo["numGPs"] = numGPs return @@ -128,17 +129,29 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): # instantiate outputs nr, nc = (x.shape[0],numGPs) for key in ('mean','var','nugs'): - predinfo[key] = np.zeros(shape=(nr,nc),dtype=float) - predinfo['covmat'] = [np.zeros(shape=(nr,nr),dtype=float) - for i in range(numGPs) - ] + predinfo[key] = np.zeros(shape=(nc,nr),dtype=float) + + if thetaprime is None: + predinfo['covmat'] = np.zeros(shape=(nc,nr,nr),dtype=float) + else: + predinfo['covmat'] = np.zeros(shape=(nc,nr,thetaprime.shape[0]),dtype=float) + for i in range(numGPs): preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) - predinfo['mean'][:,i] = preds._info['mean'] - predinfo['var'][:,i] = preds._info['var'] - predinfo['nugs'][:,i] = preds._info['nugs'] - predinfo['covmat'][i] = preds._info['covmat'] + predinfo['mean'][i,:] = preds._info['mean'] + predinfo['var'][i,:] = preds._info['var'] + predinfo['nugs'][i,:] = preds._info['nugs'] + predinfo['covmat'][i,:,:] = preds._info['covmat'] + + predinfo["S"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) + predinfo["R"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) + for i in range(0, x.shape[0]): + C = np.diag(predinfo['var'][:,i]) + R = np.diag(predinfo['nugs'][:,i]) + predinfo["S"][:, :, i] = C + predinfo["R"][:, :, i] = R + return def update(fitinfo, x,Y = None,**kwargs): diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py index 67d0d71..f62197a 100644 --- a/PUQ/surrogatemethods/multihomGP.py +++ b/PUQ/surrogatemethods/multihomGP.py @@ -109,16 +109,26 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): # instantiate outputs nr, nc = (x.shape[0],numGPs) - for key in ('mean','var'): - predinfo[key] = np.zeros(shape=(nr,nc),dtype=float) - predinfo['covmat'] = [np.zeros(shape=(nr,nr),dtype=float) - for i in range(numGPs) - ] + for key in ('mean','var','nugs'): + predinfo[key] = np.zeros(shape=(nc,nr),dtype=float) + + if thetaprime is None: + predinfo['covmat'] = np.zeros(shape=(nc,nr,nr),dtype=float) + else: + predinfo['covmat'] = np.zeros(shape=(nc,nr,thetaprime.shape[0]),dtype=float) + for i in range(numGPs): preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) - predinfo['mean'][:,i] = preds._info['mean'] - predinfo['var'][:,i] = preds._info['var'] - predinfo['covmat'][i] = preds._info['covmat'] + predinfo['mean'][i,:] = preds._info['mean'] + predinfo['var'][i,:] = preds._info['var'] + predinfo['nugs'][i,:] = preds._info['nugs'] + predinfo['covmat'][i,:,:] = preds._info['covmat'] + + + predinfo["S"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) + for i in range(0, x.shape[0]): + C = np.diag(predinfo['var'][:,i]) + predinfo["S"][:, :, i] = C return diff --git a/examples/Example4/emulator_check.py b/examples/Example4/emulator_check.py new file mode 100644 index 0000000..c9b2dd0 --- /dev/null +++ b/examples/Example4/emulator_check.py @@ -0,0 +1,53 @@ +import numpy as np +from PUQ.prior import prior_dist +from test_funcs import bimodal, banana, unimodal +from smt.sampling_methods import LHS +from PUQ.surrogate import emulator + +n0 = 15 +rep0 = 2 + +if __name__ == "__main__": + + for s in np.arange(0, 1): + + cls_func = eval("banana")() + cls_func.realdata(seed=s) + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Initial sample + sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) + theta0 = sampling(n0) + theta0 = np.repeat(theta0, rep0, axis=0) + f0 = np.zeros((cls_func.d, n0 * rep0)) + for i in range(0, n0 * rep0): + f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) + + + d = f0.shape[0] + md = np.arange(d).reshape(d, 1) + emu = emulator(x=theta0, + theta=md, + f=f0, + method="multihetGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + nmesh = 50 + xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh) + ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + theta_test = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + pre_obj = emu.predict(x=theta_test) \ No newline at end of file diff --git a/examples/Example4/example.py b/examples/Example4/example.py new file mode 100644 index 0000000..1e89c6c --- /dev/null +++ b/examples/Example4/example.py @@ -0,0 +1,150 @@ +import numpy as np +from PUQ.prior import prior_dist +# from PUQ.utils import parse_arguments, save_output +from PUQ.design import designer +from test_funcs import bimodal, banana, unimodal +from utilities import test_data_gen, twoD, heatmap +import pandas as pd +from smt.sampling_methods import LHS +import time +from PUQ.designmethods.batch_sequential import batch_sequential_design +# args = parse_arguments() + +class Args: + pass + +args = Args() + + +def create_entry(desobj, fname, method, s, b, w): + return [ + { + "MAD": MAD, + "t": t, + "rep": s, + "batch": b, + "worker": w, + "method": method, + "example": fname, + } + for t, MAD in enumerate(desobj._info["TViter"]) + ] + + +# # # # # +args.minibatch = 8 +args.funcname = "bimodal" +args.seedmin = 0 +args.seedmax = 1 + +# # # # # + +workers = args.minibatch + 1 +n0 = 15 +rep0 = 2 +nmesh = 50 +rho = 1 / 2 +batch = args.minibatch +maxiter = 20 +dfl, dfr = [], [] + +# Inputs to designer +pcset = {"standardize": True, "latent": False} +desset = {"is_exploit": True, "is_explore": True, "nL": 200, "impute_str": "update"} + +if __name__ == "__main__": + design_start = time.time() + for s in np.arange(args.seedmin, args.seedmax): + + cls_func = eval(args.funcname)() + cls_func.realdata(seed=s) + + theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # heatmap(cls_func) + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Initial sample + sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) + theta0 = sampling(n0) + theta0 = np.repeat(theta0, rep0, axis=0) + f0 = np.zeros((cls_func.d, n0 * rep0)) + for i in range(0, n0 * rep0): + f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) + + base_args = { + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": workers, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta0, "f": f0}, + "alloc_settings": { + "use_Ki": True, + "rho": rho, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": pcset, + "des_settings": desset, + } + + methods = ["ivar"] + + args_list = [] + for method in methods: + args_ = base_args.copy() + args_["alloc_settings"] = args_["alloc_settings"].copy() + args_["alloc_settings"]["method"] = method + args_list.append(args_) + + des_obj = batch_sequential_design(cls_func) + des_obj.build_design(t0=theta0, f0=f0, af="seivar", args=args_list[0]) + + + + theta = des_obj["theta0"] + reps = des_obj["rep0"] + import matplotlib.pyplot as plt + + fig, ax = plt.subplots(figsize=(5, 5)) + cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="RdGy") + for label, x_count, y_count in zip(reps, theta[:, 0], theta[:, 1]): + row = np.array([x_count, y_count]) + exists = np.any(np.all(theta0 == row, axis=1)) + if exists: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + weight="bold", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="blue", + weight="bold", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) + # twoD(des_obj, Xpl, Ypl, p_test, nmesh) diff --git a/examples/Example4/test_funcs.py b/examples/Example4/test_funcs.py new file mode 100644 index 0000000..ad0d341 --- /dev/null +++ b/examples/Example4/test_funcs.py @@ -0,0 +1,562 @@ +import numpy as np +import scipy + + +class unimodal: + def __init__(self): + self.data_name = "unimodal" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.1]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, t1, t2): + t1 = -10 + t1 * 20 + t2 = -10 + t2 * 20 + f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.05, 0], [0, 0.05]]) + var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) + return 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class bimodal: + def __init__(self): + self.data_name = "bimodal" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.5, 0], [0, 0.5]]) + self.theta_true = np.array([8 / 12, 8 / 12]) + self.real_data = None + self.out = [("f", float, (2,))] + self.p = 2 + self.d = 2 + self.x = np.arange(0, self.d)[:, None] + + def function(self, t1, t2): + t1 = 12 * t1 - 6 + t2 = 12 * t2 - 4 + f = np.array([np.sqrt(0.2) * (t2 - t1**2), np.sqrt(0.75) * (t2 - t1)]) + return f + + def sim_f(self, thetas, persis_info): + + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].multivariate_normal( + np.array([0, 0]), np.diag(V.flatten()), 1 + ) + f += R.flatten() + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + V = np.repeat(0.5 + (theta[:, 0] ** 2 + theta[:, 1] ** 2) * 2, self.d) + V = V.reshape(self.d, theta.shape[0]) + return V + + def realdata(self, seed): + + M = np.array( + [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].multivariate_normal( + mean=[0, 0], cov=self.obsvar, size=1 + ) + self.real_data = M + R + + +class banana: + def __init__(self): + self.data_name = "banana" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[0.03, 0], [0, 0.5]]) + self.theta_true = np.array([0.5, 0.75]) + self.real_data = None + self.out = [("f", float, (2,))] + self.p = 2 + self.d = 2 + self.x = np.arange(0, self.d)[:, None] + + def function(self, t1, t2): + t1 = 40 * t1 - 20 + t2 = 15 * t2 - 15 + f = np.array([0.03 * t1, t2 + 0.06 * t1**2]) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].multivariate_normal( + np.array([0, 0]), np.diag(V.flatten()), 1 + ) + f += R.flatten() + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + + f = self.function(theta[0, 0], theta[0, 1]) + if theta[0, 0] < 0.5: + noise1 = 0.01 * np.abs(f[0]) + noise2 = 0.01 * np.abs(f[1]) + else: + noise1 = 0.1 * np.abs(f[0]) + noise2 = 0.2 * np.abs(f[1]) + return np.array([noise1, noise2]).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].multivariate_normal( + mean=[0, 0], cov=self.obsvar, size=1 + ) + self.real_data = M + R + + +class sinf: + def __init__(self): + self.data_name = "sinf" + self.thetalimits = np.array([[0, 1]]) + self.obsvar = np.array([[0.05]], dtype="float64") + self.theta_true = 0.5 + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 1 + self.x = np.arange(0, self.d)[:, None] + + def function(self, theta1): + return np.sin(10 * theta1) + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0]) + var_noise = self.noise(np.array([thetas[0]])[None, :]) + noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise[0]), 1) + f += noise + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"], persis_info) + return H_o, persis_info + + def realdata(self, seed): + + mean = np.array([[self.function(self.theta_true)]], dtype="float64") + if seed is None: + self.real_data = mean + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + noise = persis_info["rand_stream"].normal( + 0, np.sqrt(self.obsvar[0]), size=1 + ) + self.real_data = mean + noise + + def noise(self, theta): + rx = (1.1 + np.sin(2 * np.pi * theta)) * 0.05 + return rx.reshape(self.d, theta.shape[0]) + + +class branin: + def __init__(self): + self.data_name = "branin" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.obsvar = np.array([[10]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.d = 1 + self.p = 2 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) + + def function(self, t1, t2): + t1 = -5 + 15 * t1 + t2 = 15 * t2 + f = ( + (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 + + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) + + 10 + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + return np.repeat(1, self.d * theta.shape[0]).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class himmelblau: + def __init__(self): + + self.data_name = "himmelblau" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[100]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.11085, 0.16]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = (theta1**2 + theta2 - 11) ** 2 + (theta1 + theta2**2 - 7) ** 2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.01, 0], [0, 0.01]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return 10 + 5 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class holder: + def __init__(self): + + self.data_name = "holder" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-10, 10], [-10, 10]]) + self.obsvar = np.array([[50]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.1, 0.01]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = -np.abs( + np.sin(theta1) + * np.cos(theta2) + * np.exp(np.abs(1 - (np.sqrt(theta1**2 + theta2**2) / np.pi))) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class easom: + def __init__(self): + + self.data_name = "easom" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-10, 10], [-10, 10]]) + self.obsvar = np.array([[0.001]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.75, 0.75]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = ( + -np.cos(theta1) + * np.cos(theta2) + * np.exp(-((theta1 - np.pi) ** 2 + (theta2 - np.pi) ** 2)) + ) + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class ackley: + def __init__(self): + + self.data_name = "ackley" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[10]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, theta1, theta2): + + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = ( + -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (theta1**2 + theta2**2))) + - np.exp(0.5 * (np.cos(2 * np.pi * theta1) + np.cos(2 * np.pi * theta2))) + + np.e + + 20 + ) + + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.85], cov=cov) + return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R + + +class sphere: + def __init__(self): + + self.data_name = "sphere" + self.thetalimits = np.array([[0, 1], [0, 1]]) + self.truelimits = np.array([[-5, 5], [-5, 5]]) + self.obsvar = np.array([[1]], dtype="float64") + self.real_data = None + self.out = [("f", float)] + self.p = 2 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.theta_true = np.array([0.5, 0.5]) + + def function(self, theta1, theta2): + theta1 = self.truelimits[0][0] + theta1 * ( + self.truelimits[0][1] - self.truelimits[0][0] + ) + theta2 = self.truelimits[1][0] + theta2 * ( + self.truelimits[1][1] - self.truelimits[1][0] + ) + f = theta1**2 + theta2**2 + return f + + def sim_f(self, thetas, persis_info): + f = self.function(thetas[0], thetas[1]) + V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) + R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) + f += R + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + function = sim_specs["user"]["sim_f"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + H_o["f"] = function(H["thetas"][0], persis_info) + + return H_o, persis_info + + def noise(self, theta): + cov = np.array([[0.1, 0], [0, 0.1]]) + var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) + return 1 + 1 * var.pdf(theta).reshape(self.d, theta.shape[0]) + + def realdata(self, seed): + + M = np.array( + self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" + ) + if seed is None: + self.real_data = M + else: + persis_info = {"rand_stream": np.random.default_rng(seed)} + R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) + self.real_data = M + R diff --git a/examples/Example4/utilities.py b/examples/Example4/utilities.py new file mode 100644 index 0000000..bfe0388 --- /dev/null +++ b/examples/Example4/utilities.py @@ -0,0 +1,818 @@ +import numpy as np +import matplotlib.pyplot as plt +import scipy.stats as sps +from smt.sampling_methods import LHS + + +def twoD(designobj, Xpl, Ypl, p_test, nmesh): + theta0 = designobj._info["theta0"] + reps0 = designobj._info["reps0"] + + fig, ax = plt.subplots(figsize=(5, 5)) + cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="RdGy") + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label == 2: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + weight="bold", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="blue", + weight="bold", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + +def test_data_gen(cls_func, nmesh): + xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh) + ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + theta_test = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + if cls_func.data_name in [ + "unimodal", + "branin", + "himmelblau", + "holder", + "easom", + "ackley", + "sphere", + ]: + f_test = np.zeros(theta_test.shape[0]) + else: + f_test = np.zeros((theta_test.shape[0], 2)) + p_test = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + if cls_func.data_name in [ + "unimodal", + "branin", + "himmelblau", + "holder", + "easom", + "ackley", + "sphere", + ]: + f_test[tid] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) + rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) + else: + f_test[tid, :] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) + rnd = sps.multivariate_normal(mean=f_test[tid, :], cov=(cls_func.obsvar)) + p_test[tid] = rnd.pdf(cls_func.real_data) + + return theta_test, p_test, f_test, Xpl, Ypl + + +def test_data_gen_1d(cls_func, nmesh): + + # Create test data + theta_test = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh + )[:, None] + f_test, p_test = np.zeros(theta_test.shape[0]), np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + f_test[tid] = cls_func.function(theta_test[tid, 0]) + rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) + p_test[tid] = rnd.pdf(cls_func.real_data) + + return theta_test, p_test, f_test + + +def oned(seqobject, x, obsvar, real_data, theta_test, f_test, p_test, cls_func, ninit): + from PUQ.surrogate import emulator + from scipy.stats import norm + import matplotlib.pyplot as plt + + pc_settings = {"standardize": True, "latent": True} + + theta = seqobject._info["theta"][0:ninit, :] + f = seqobject._info["f"][:, 0:ninit] + + theta_all = seqobject._info["theta"] + f_all = seqobject._info["f"] + + theta0 = seqobject._info["theta0"] + reps0 = seqobject._info["reps0"] + + emu = emulator( + x=x, + theta=theta, + f=f, + method="pcHetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + "pc_settings": pc_settings, + }, + ) + + emupred = emu.predict(x=x, theta=theta_test) + + mean = emupred.mean() + var = emupred.var() + var_noisy = emupred._info["var_noisy"] + + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + quantile = 0.975 + upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + quantile = 0.975 + upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + + ft = 20 + fig, ax = plt.subplots() + ax.plot(theta_test, f_test, color="red") + ax.plot( + theta_test.flatten(), + mean.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + theta_test.flatten(), + mean.flatten() - np.sqrt(var.flatten()), + mean.flatten() + np.sqrt(var.flatten()), + # lower_bound.flatten(), + # upper_bound.flatten(), + color="blue", + alpha=0.3, + linestyle="dotted", + ) + + # plt.fill_between( + # theta_test.flatten(), + # lower_bound_nug.flatten(), + # upper_bound_nug.flatten(), + # color='blue', + # alpha=0.1, + # linestyle="dotted", + # ) + ax.scatter( + theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$M(\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) + phat[tid] = rnd.pdf(real_data) + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) + pvar1[tid] = rnd.pdf(real_data) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ + tid + ] ** 2 + + fig, ax = plt.subplots() + ax.plot(theta_test, p_test, color="red") + ax.plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) + plt.fill_between( + theta_test.flatten(), + (phat - np.sqrt(phatvar)).flatten(), + (phat + np.sqrt(phatvar)).flatten(), + color="blue", + alpha=0.1, + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + p_alloc = np.zeros(theta0.shape[0]) + f_alloc = np.zeros((1, theta0.shape[0])) + for tid in range(0, len(theta0)): + f_alloc[0, tid] = cls_func.function(theta0[tid]) + rnd = sps.norm(loc=f_alloc[0, tid], scale=np.sqrt(cls_func.obsvar)) + p_alloc[tid] = rnd.pdf(cls_func.real_data) + + fig, ax = plt.subplots(figsize=(10, 6)) + ax.plot(theta_test, p_test, color="red", linewidth=2.5) + for label, x_count, pval in zip(reps0, theta0, p_alloc): + plt.annotate( + label, + xy=(x_count, pval), + xytext=(0, 0), + textcoords="offset points", + color="blue", + size=20, + ) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + fig, ax = plt.subplots() + ax.scatter(theta0.flatten(), reps0.flatten(), color="blue", marker="*", s=55) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"a", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + fig, ax = plt.subplots() + ax.plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + color="blue", + linestyle="dashed", + linewidth=2.5, + ) + ax.plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.show() + + ft = 15 + + def make_patch_spines_invisible(ax): + ax.set_frame_on(True) + ax.patch.set_visible(False) + for sp in ax.spines.values(): + sp.set_visible(False) + + fig, host = plt.subplots(figsize=(8, 4)) + fig.subplots_adjust(right=0.75) + + par1 = host.twinx() + par2 = host.twinx() + + # Offset the right spine of par2. The ticks and label have already been + # placed on the right by twinx above. + par2.spines["right"].set_position(("axes", 1.2)) + # Having been created by twinx, par2 has its frame off, so the line of its + # detached spine is invisible. First, activate the frame but make the patch + # and spines invisible. + make_patch_spines_invisible(par2) + # Second, show the right spine. + par2.spines["right"].set_visible(True) + + p1 = host.scatter( + theta0.flatten(), + reps0.flatten(), + color="black", + marker="*", + s=55, + label=r"$a_i$", + ) + (p2,) = par1.plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + "b", + linestyle="dashed", + label="Variance", + linewidth=2.5, + ) + (p3,) = par2.plot( + theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 + ) + + host.set_xlim(-0.1, 1.1) + host.set_ylim(0.9 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) + par1.set_ylim( + 0.9 * np.min(emupred._info["nugs"].flatten()), + 1.1 * np.max(emupred._info["nugs"].flatten()), + ) + par2.set_ylim(-0.01, 1.1 * np.max(phat)) + + host.set_xlabel(r"$\theta$", fontsize=ft) + host.set_ylabel(r"$a_i$", fontsize=ft) + par1.set_ylabel("Variance", fontsize=ft) + par2.set_ylabel("Likelihood", fontsize=ft) + host.tick_params(axis="both", labelsize=ft) + par1.tick_params(axis="both", labelsize=ft) + par2.tick_params(axis="both", labelsize=ft) + lines = [p1, p2, p3] + + host.legend( + lines, + [l.get_label() for l in lines], + loc="center", + bbox_to_anchor=(0.5, -0.3), + ncol=3, + prop={"size": ft}, + ) + + plt.show() + + +def twodpaper(cls_func, Xpl, Ypl, p_test, theta0, reps0, thetainit=None, name=None): + from matplotlib.ticker import MaxNLocator + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + + Pvar = P + else: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh, 2)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j, :] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + Pvar = np.sum(P, axis=2) + + fig, ax = plt.subplots() + cs = ax.contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) + # cbar = fig.colorbar(cs, pad=0.1) + cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") + + if thetainit is None: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label <= 2: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="black", + ) + else: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if np.array([x_count, y_count]) in thetainit: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="cyan", + ) + else: + plt.annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=12, + color="black", + ) + + ax.set_xticks([0, 0.5, 1]) # Custom tick locations for x-axis + ax.set_yticks([0, 0.5, 1]) # Custom tick locations for y-axis + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.savefig(name, bbox_inches="tight") + plt.show() + + +def twodpaperrev(cls_func, Xpl, Ypl, p_test, dictl, thetainit=None, name=None): + from matplotlib.ticker import MaxNLocator + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + + Pvar = P + else: + nmesh = len(Xpl) + P = np.zeros((nmesh, nmesh, 2)) + for i in range(nmesh): + for j in range(nmesh): + P[i, j, :] = cls_func.noise( + np.array([Xpl[i, j], Ypl[i, j]])[None, :] + ).flatten() + Pvar = np.sum(P, axis=2) + + fig, ax = plt.subplots(1, 3, figsize=(15, 4.5), constrained_layout=True) + + for i in range(0, 3): + if i == 0: + for el in dictl: + if el["method"] == "ivar": + reps0 = el["reps0"] + theta0 = el["theta0"] + elif i == 1: + for el in dictl: + if el["method"] == "var": + reps0 = el["reps0"] + theta0 = el["theta0"] + elif i == 2: + for el in dictl: + if el["method"] == "imse": + reps0 = el["reps0"] + theta0 = el["theta0"] + + cs = ax[i].contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) + if i == 2: + cbar = fig.colorbar(cs, ax=ax[i], pad=0.1) + cp = ax[i].contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") + ft = 14 + if thetainit is None: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if label <= 2: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="cyan", + ) + else: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="black", + ) + else: + for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): + if np.array([x_count, y_count]) in thetainit: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="cyan", + ) + else: + ax[i].annotate( + label, + xy=(x_count, y_count), + xytext=(0, 0), + textcoords="offset points", + fontsize=ft, + color="black", + ) + + if i == 0: + ax[i].set_yticks([0, 0.5, 1]) # Custom tick locations for x-axis + ax[i].set_ylabel(r"$\theta_2$", fontsize=16) + else: + ax[i].set_yticks([]) + ax[i].set_xticks([0, 0.5, 1]) + + ax[i].set_xlabel(r"$\theta_1$", fontsize=16) + + ax[i].tick_params(axis="both", labelsize=16) + + plt.savefig(name, bbox_inches="tight") + plt.show() + + +def heatmap(cls_func): + + from matplotlib.colors import ListedColormap + + yellow_colors = [ + (1, 1, 1), + (1, 1, 0.8), # light yellow + (1, 1, 0.6), + (1, 1, 0.4), + (1, 1, 0.2), + (1, 1, 0), # yellow + (1, 0.9, 0), # dark yellow + (1, 0.8, 0), # yellow-orange + (1, 0.6, 0), # orange + (1, 0.4, 0), # dark orange + (1, 0.2, 0), # very dark orange + ] + yellow_cmap = ListedColormap(yellow_colors, name="yellow") + + if cls_func.data_name in ["unimodal", "branin"]: + nmesh = 50 + a = np.arange(nmesh + 1) / nmesh + b = np.arange(nmesh + 1) / nmesh + X, Y = np.meshgrid(a, b) + Z = np.zeros((nmesh + 1, nmesh + 1)) + P = np.zeros((nmesh + 1, nmesh + 1)) + for i in range(nmesh + 1): + for j in range(nmesh + 1): + Z[i, j] = cls_func.function(X[i, j], Y[i, j]) + P[i, j] = cls_func.noise( + np.array([X[i, j], Y[i, j]])[None, :] + ).flatten() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P, cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z, colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + else: + nmesh = 50 + a = np.arange(nmesh + 1) / nmesh + b = np.arange(nmesh + 1) / nmesh + X, Y = np.meshgrid(a, b) + Z = np.zeros((nmesh + 1, nmesh + 1, 2)) + P = np.zeros((nmesh + 1, nmesh + 1, 2)) + + for i in range(nmesh + 1): + for j in range(nmesh + 1): + Z[i, j, :] = cls_func.function(X[i, j], Y[i, j]) + P[i, j, :] = cls_func.noise( + np.array([X[i, j], Y[i, j]])[None, :] + ).flatten() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P[:, :, 0], cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z[:, :, 0], colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + fig, ax = plt.subplots() + cs = ax.contourf(X, Y, P[:, :, 1], cmap=yellow_cmap, alpha=0.75) + cbar = fig.colorbar(cs) + CS = ax.contour(X, Y, Z[:, :, 1], colors="black") + ax.clabel(CS, inline=True, fontsize=10) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() + + +def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func): + from PUQ.surrogate import emulator + from scipy.stats import norm + import matplotlib.pyplot as plt + + pc_settings = {"standardize": True, "latent": False} + + # theta = seqobject._info['theta'][0:ninit,:] + # f = seqobject._info['f'][:, 0:ninit] + + emu = emulator( + x=x, + theta=theta, + f=f, + method="pcHetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + "pc_settings": pc_settings, + }, + ) + + emupred = emu.predict(x=x, theta=theta_test) + + mean = emupred.mean() + var = emupred.var() + var_noisy = emupred._info["var_noisy"] + + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + quantile = 0.975 + upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) + # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) + quantile = 0.025 + lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + quantile = 0.975 + upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) + + fig, ax = plt.subplots(1, 3, figsize=(15, 3.5), constrained_layout=True) + ft = 18 + ax[0].plot(theta_test, f_test, color="red") + ax[0].plot( + theta_test.flatten(), + mean.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + ax[0].fill_between( + theta_test.flatten(), + mean.flatten() - np.sqrt(var.flatten()), + mean.flatten() + np.sqrt(var.flatten()), + # lower_bound.flatten(), + # upper_bound.flatten(), + color="blue", + alpha=0.3, + linestyle="dotted", + ) + + ax[0].scatter( + theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" + ) + ax[0].set_xlabel(r"$\theta$", fontsize=ft) + ax[0].set_ylabel(r"$\zeta(\theta)$", fontsize=ft) + ax[0].tick_params(axis="both", labelsize=ft) + + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) + phat[tid] = rnd.pdf(real_data) + rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) + pvar1[tid] = rnd.pdf(real_data) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ + tid + ] ** 2 + + ax[1].plot(theta_test, p_test, color="red") + ax[1].plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) + ax[1].fill_between( + theta_test.flatten(), + (phat - np.sqrt(phatvar)).flatten(), + (phat + np.sqrt(phatvar)).flatten(), + color="blue", + alpha=0.1, + ) + ax[1].set_xlabel(r"$\theta$", fontsize=ft) + ax[1].set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax[1].tick_params(axis="both", labelsize=ft) + + ax[2].plot( + theta_test.flatten(), + emupred._info["nugs"].flatten(), + color="blue", + linestyle="dashed", + linewidth=2.5, + ) + ax[2].plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") + ax[2].set_xlabel(r"$\theta$", fontsize=ft) + ax[2].set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) + ax[2].tick_params(axis="both", labelsize=ft) + plt.savefig("Figure1.png", bbox_inches="tight") + plt.show() + + return theta_test, emupred._info["nugs"].flatten(), phat + + +def Figure2(desobject, theta_test, nugs, phat, method, axs): + + theta0 = desobject._info["theta0"] + reps0 = desobject._info["reps0"] + + ft = 15 + + def make_patch_spines_invisible(ax): + ax.set_frame_on(True) + ax.patch.set_visible(False) + for sp in ax.spines.values(): + sp.set_visible(False) + + par1 = axs.twinx() + par2 = axs.twinx() + + par2.spines["right"].set_position(("axes", 1.25)) + make_patch_spines_invisible(par2) + par2.spines["right"].set_visible(True) + + p1 = axs.scatter( + theta0.flatten(), + reps0.flatten(), + color="black", + marker="*", + s=55, + label=r"$a_i$", + ) + (p2,) = par1.plot( + theta_test.flatten(), + nugs, + "b", + linestyle="dashed", + label="Variance", + linewidth=2.5, + ) + (p3,) = par2.plot( + theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 + ) + + axs.set_xlim(-0.1, 1.1) + axs.set_ylim(0.8 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) + par1.set_ylim(0.8 * np.min(nugs), 1.1 * np.max(nugs)) + par2.set_ylim(-0.1, 1.1 * np.max(phat)) + + axs.set_xlabel(r"$\theta$", fontsize=ft) + axs.set_ylabel(r"$a_i$", fontsize=ft) + par1.set_ylabel("Variance", fontsize=ft) + par2.set_ylabel("Likelihood", fontsize=ft) + axs.tick_params(axis="both", labelsize=ft) + par1.tick_params(axis="both", labelsize=ft) + par2.tick_params(axis="both", labelsize=ft) + lines = [p1, p2, p3] + + axs.legend( + lines, + [l.get_label() for l in lines], + loc="center", + bbox_to_anchor=(0.5, -0.3), + ncol=3, + prop={"size": ft}, + ) + diff --git a/tests/unit_tests/test_multihetGP.py b/tests/unit_tests/test_multihetGP.py index ff51393..9b194d4 100644 --- a/tests/unit_tests/test_multihetGP.py +++ b/tests/unit_tests/test_multihetGP.py @@ -68,11 +68,11 @@ def test_predict(): GP = GPlist[i] for em_key, GP_key in em2GPkey.items(): - assert np.allclose(preds._info[em_key][:,i],GP[GP_key]) + assert np.allclose(preds._info[em_key][i,:],GP[GP_key]) # test cov and covmat for i in range(len(GPlist)): - assert np.allclose(preds._info['covmat'][i],GPlist[i]['cov']) + assert np.allclose(preds._info['covmat'][i,:,:],GPlist[i]['cov']) return @@ -117,7 +117,7 @@ def test_update(): GPlist_initial = deepcopy(GPlist) Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) mpreds = multiGP.predict(x=Xnew) - Ynew = mpreds._info['mean'] + Ynew = mpreds._info['mean'].T multiGP.update(x=Xnew,Y=Ynew) em_keys = ['ll','theta','beta0','Delta'] for GP in GPlist: @@ -133,4 +133,4 @@ def test_update(): assert not np.allclose(old_ll,multiGP._info['emulist'][i]._info['ll']) if __name__ == "__main__": - test_fit() \ No newline at end of file + test_update() \ No newline at end of file diff --git a/tests/unit_tests/test_multihomGP.py b/tests/unit_tests/test_multihomGP.py index 284840c..2112d24 100644 --- a/tests/unit_tests/test_multihomGP.py +++ b/tests/unit_tests/test_multihomGP.py @@ -57,11 +57,11 @@ def test_predict(): GP = GPlist[i] for em_key, GP_key in em2GPkey.items(): - assert np.allclose(preds._info[em_key][:,i],GP[GP_key]) + assert np.allclose(preds._info[em_key][i,:],GP[GP_key]) # test cov and covmat for i in range(len(GPlist)): - assert np.allclose(preds._info['covmat'][i],GPlist[i]['cov']) + assert np.allclose(preds._info['covmat'][i,:,:],GPlist[i]['cov']) return @@ -106,7 +106,7 @@ def test_update(): GPlist_initial = deepcopy(GPlist) Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) mpreds = multiGP.predict(x=Xnew) - Ynew = mpreds._info['mean'] + Ynew = mpreds._info['mean'].T multiGP.update(x=Xnew,Y=Ynew) em_keys = ['ll','theta','beta0'] for GP in GPlist: From b4183d6319933f3e768949a74c5fb21fc47e4763 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 30 Sep 2025 17:28:01 -0400 Subject: [PATCH 057/103] WIP on dev/PUQ_with_hetGPy Updates about Example4 --- PUQ/designmethods/batch_sequential.py | 20 ++- PUQ/designmethods/gen_funcs/acquire_new.py | 44 ++++--- .../batch_acquisition_funcs_support.py | 34 ++++-- .../gen_funcs/batch_allocate_reps.py | 36 +++--- .../sequential_md_deterministic.py | 32 ++--- .../sequential_md_deterministic_old.py | 114 ++++++++++++++++++ examples/Example1/ex1.png | Bin 1326909 -> 1326237 bytes 7 files changed, 224 insertions(+), 56 deletions(-) create mode 100644 PUQ/designmethods/sequential_md_deterministic_old.py diff --git a/PUQ/designmethods/batch_sequential.py b/PUQ/designmethods/batch_sequential.py index ca82ddf..9e8f19c 100644 --- a/PUQ/designmethods/batch_sequential.py +++ b/PUQ/designmethods/batch_sequential.py @@ -70,7 +70,9 @@ def build_design(self, t0, f0, af, args={}): iter_explore, iter_exploit = 0, 0 is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') - + + print("max_iter") + print(max_iter) for iteration in range(max_iter): emu, TV = self.newiteration(t_c, f_c, self.x, pc_settings, test_data, self.Sigma, self.y) @@ -108,9 +110,11 @@ def build_design(self, t0, f0, af, args={}): print(t_c.shape) - + unique_rows, counts = np.unique(t_c, axis=0, return_counts=True) self.f = f_c self.theta = t_c + self.theta0 = unique_rows + self.rep0 = counts def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): @@ -130,7 +134,11 @@ def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): obsvar3d = obsvar.reshape(1, d, d) # ntest x d - pred = emu.predict(x=x, theta=thetatest) + # pred = emu.predict(x=x, theta=thetatest) + print("ttte") + print(thetatest.shape) + pred = emu.predict(x=thetatest) + print("dblkdb") mu = pred.mean().T S = pred._info['S'] St = np.transpose(S, (2, 0, 1)) @@ -185,6 +193,12 @@ def onebatch(self, r_exploit = allocate_obj.reps theta_exploit = allocate_obj.theta + import matplotlib.pyplot as plt + plt.scatter(theta_exploit[:,0], theta_exploit[:,1]) + for (x, y, label) in zip(theta_exploit[:, 0], theta_exploit[:, 1], r_exploit): + plt.annotate(str(label), (x, y), xytext=(5, 5), textcoords="offset points") + + plt.show() emu._info = emu_original_info diff --git a/PUQ/designmethods/gen_funcs/acquire_new.py b/PUQ/designmethods/gen_funcs/acquire_new.py index ed77294..2196ca7 100644 --- a/PUQ/designmethods/gen_funcs/acquire_new.py +++ b/PUQ/designmethods/gen_funcs/acquire_new.py @@ -9,17 +9,21 @@ multiple_determinants, multiple_pdfs, ) -from PUQ.surrogatemethods.pcHetGP import update +# from PUQ.surrogatemethods.pcHetGP import update def get_pred(cL, emu, x, ttest, reps): - cP = emu.predict(x=x, theta=cL) - var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps - - testP = emu.predict(x=x, theta=ttest, thetaprime=cL) - mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] + # cP = emu.predict(x=x, theta=cL) + # var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps + cP = emu.predict(x=cL) + var_cand = cP._info["var"] + cP._info["nugs"] / reps + # testP = emu.predict(x=x, theta=ttest, thetaprime=cL) + testP = emu.predict(x=ttest, thetaprime=cL) + # mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] + mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["covmat"] + mut = mu.T St = np.transpose(S, (2, 0, 1)) @@ -44,7 +48,8 @@ def impute_strategy(ct, x, fE, tE, emu, liar, des_obj): pc_settings = des_obj.pc_settings if impute_str == "update": - update(emu._info, x=x, X0new=ct, mult=rep) + # update(emu._info, x=x, X0new=ct, mult=rep) + emu.update(x=ct) fE, tE = impute( ct=ct, x=x, fE=fE, tE=tE, reps=rep, emu=emu, rnd_str=rand_stream ) @@ -139,25 +144,30 @@ def seivar(self): liar = np.mean(fE, axis=1) tnew = [] + + q, d = self.emu._info['numGPs'], mu.shape[1] for i in range(self.bnew): - G = emu._info["G"] - B = emu._info["B"] - GB = G @ B - BTG = B.T @ G + # G = emu._info["G"] + # B = emu._info["B"] + # GB = G @ B + # BTG = B.T @ G - d = G.shape[0] - q = B.shape[1] + # d = G.shape[0] + # q = B.shape[1] - tau = np.zeros((nm, q, q, nL)) + # tau = np.zeros((nm, q, q, nL)) phi = np.zeros((nm, d, d, nL)) coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) for j in range(0, q): - tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + phi[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + # for j in range(0, q): + # tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] - for k in range(0, nL): - phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + # for k in range(0, nL): + # phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG vals = [] for k in range(0, nL): diff --git a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py index fadc3a8..d7b5307 100644 --- a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py +++ b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py @@ -8,7 +8,8 @@ def impute(ct, x, fE, tE, reps, emu, rnd_str): # NEW PART if fE.shape[0] > 1: - cpred = emu.predict(x=x, theta=ct) + # cpred = emu.predict(x=x, theta=ct) + cpred = emu.predict(x=ct) cm = cpred.mean() cS = cpred._info['S'] cR = cpred._info['R'] @@ -19,9 +20,11 @@ def impute(ct, x, fE, tE, reps, emu, rnd_str): tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) fE = np.concatenate([fE, fnoise.T], axis=1) else: - cpred = emu.predict(x=x, theta=ct) + # cpred = emu.predict(x=x, theta=ct) + cpred = emu.predict(x=ct) cm = cpred.mean() - cv = cpred._info['var_noisy'] + # cv = cpred._info['var_noisy'] + cv = cpred._info['var'] + cpred._info['nugs'] fnoise = rnd_str.normal(loc=cm.flatten(), scale=np.sqrt(cv.flatten()), size=reps) @@ -46,10 +49,25 @@ def impute_CL(ct, x, fE, tE, reps, liar): def build_emulator(x, theta, f, pcset): print(theta.shape) - emu = emulator(x=x, - theta=theta, + # emu = emulator(x=x, + # theta=theta, + # f=f, + # method="pcHetGP", + # args={'lower':None, 'upper':None, + # 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + # 'init':{}, + # 'known':{}, + # 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + # "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + # "return.hom": False, "factr": 1e9}, + # 'pc_settings':pcset}) + print(f.shape) + d = f.shape[0] + md = np.arange(d).reshape(d, 1) + emu = emulator(x=theta, + theta=md, f=f, - method="pcHetGP", + method="multihetGP", args={'lower':None, 'upper':None, 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, 'init':{}, @@ -58,11 +76,13 @@ def build_emulator(x, theta, f, pcset): "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, "return.hom": False, "factr": 1e9}, 'pc_settings':pcset}) + return emu def compute_ivar(emu, ttest, x, obs, obsvar): - testP = emu.predict(x=x, theta=ttest) + # testP = emu.predict(x=x, theta=ttest) + testP = emu.predict(x=ttest) d = len(x) obsvar3d = obsvar.reshape(1, d, d) diff --git a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py index c49d50c..6f24039 100644 --- a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py +++ b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py @@ -36,7 +36,8 @@ def __init__(self, if alloc_settings.get('theta') is None: if alloc_settings.get('gen') is False: - self.theta = self.emu._info['emulist'][0]['X0'] + # self.theta = self.emu._info['emulist'][0]['X0'] + self.theta = self.emu._info['emulist'][0]._info["X0"] else: self.theta = prior.rnd(100, None) self.theta = np.concatenate((self.emu._info['emulist'][0]['X0'], self.theta), axis=0) @@ -45,7 +46,8 @@ def __init__(self, if alloc_settings.get('a0') is None: if alloc_settings.get('gen') is False: - self.a0 = self.emu._info['emulist'][0]['mult'] + # self.a0 = self.emu._info['emulist'][0]['mult'] + self.a0 = self.emu._info['emulist'][0]._info['mult'] else: self.a0 = np.concatenate((self.emu['emulist']._info[0]['mult'], np.repeat(0, 100))) else: @@ -141,8 +143,11 @@ def compute_ivar_weights(self): n_integ = self.theta_mesh.shape[0] n = self.theta.shape[0] - pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) - pred_mesh = self.emu.predict(x=self.x, theta=self.theta_mesh, thetaprime=None) + # pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + # pred_mesh = self.emu.predict(x=self.x, theta=self.theta_mesh, thetaprime=None) + + pred_nugs = self.emu.predict(x=self.theta, thetaprime=None) + pred_mesh = self.emu.predict(x=self.theta_mesh, thetaprime=None) obsvar3d = self.obsvar.reshape(1, d, d) # ntest x d @@ -170,13 +175,13 @@ def compute_ivar_weights(self): hNb = np.matmul(h, Nbinv) hN = np.matmul(h, Ninv) - # d x d - G = self.emu._info['G'] - # d x q - B = self.emu._info['B'] - # d x q - GB = G @ B - BTG = B.T @ G + # # d x d + # G = self.emu._info['G'] + # # d x q + # B = self.emu._info['B'] + # # d x q + # GB = G @ B + # BTG = B.T @ G coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) @@ -187,7 +192,8 @@ def compute_ivar_weights(self): J = np.zeros((n, n)) J[i, i] = 1 for j in range(0, q): - emuinfo = self.emu._info['emulist'][j] + # emuinfo = self.emu._info['emulist'][j] + emuinfo = self.emu._info['emulist'][j]._info K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta']) if self.use_Ki: @@ -200,14 +206,16 @@ def compute_ivar_weights(self): if emuinfo['is_homGP']: Mb[i, :, j, j] = -(emuinfo['g']*emuinfo['nu_hat'])*np.einsum('ji,jk,ki->i', K_s, A, K_s) else: - Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + # Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + Mb[i, :, j, j] = -(pred_nugs._info['nugs'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) dlogfdai = np.zeros((n, n_integ)) dloggdai = np.zeros((n, n_integ)) C = np.zeros((n, 1)) for i in range(0, n): - M = GB @ Mb[i, :, :, :] @ BTG + #M = GB @ Mb[i, :, :, :] @ BTG + M = Mb[i, :, :, :] part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) diff --git a/PUQ/designmethods/sequential_md_deterministic.py b/PUQ/designmethods/sequential_md_deterministic.py index 925ed3a..e7022d3 100644 --- a/PUQ/designmethods/sequential_md_deterministic.py +++ b/PUQ/designmethods/sequential_md_deterministic.py @@ -31,24 +31,25 @@ def build_design(self, z0, f0, T, af, test=None, args={}): H = [] timel = [] metric_sum = {} - print(T) + + md = np.arange(f0.shape[1]).reshape(f0.shape[1], 1) for t in range(0, T): # print(f"t: {t}") if self.trace else None print(t) tic = time.time() - model = emulator(x=self.cls_func.x, - theta=z0, - f=f0, - method="pcHomGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - + model = emulator(x=z0, + theta=md, + f=f0, + method="multihomGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + toc = time.time() timel.append(toc - tic) @@ -96,8 +97,8 @@ def eval_perf(self, model): nm, d = self.test["theta"].shape[0], self.cls_func.d # predict at mesh - pr = model.predict(theta=self.test["theta"], thetaprime=self.test["theta"]) - + pr = model.predict(x=self.test["theta"], thetaprime=self.test["theta"]) + # ntot, ntot x ntot, ntot mu, Sn = pr._info["mean"], pr._info["S"] @@ -107,6 +108,7 @@ def eval_perf(self, model): N = S + self.Sigma3d g = multiple_pdfs(self.y, muT, N) + MSE = np.mean(((g.flatten() - self.test["p"].flatten()) ** 2)) MAD = np.mean(np.abs(g.flatten() - self.test["p"].flatten())) diff --git a/PUQ/designmethods/sequential_md_deterministic_old.py b/PUQ/designmethods/sequential_md_deterministic_old.py new file mode 100644 index 0000000..4a447a1 --- /dev/null +++ b/PUQ/designmethods/sequential_md_deterministic_old.py @@ -0,0 +1,114 @@ +import numpy as np +from PUQ.designmethods.support import multiple_pdfs, multiple_determinants +from PUQ.designmethods.gen_funcs.acquisition_md_deterministic_old import ivar, var, imse, rnd, exp +import time +from PUQ.surrogate import emulator + +class sequential_design: + def __init__(self, cls_func, trace=True): + self.cls_func = cls_func + self.trace = trace + self.y = self.cls_func.real_data + self.d = self.cls_func.d + self.Sigma = self.cls_func.obsvar + self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) + self.detSigma = np.linalg.det(self.Sigma) + return + + def __getitem__(self, key): + return self.__dict__[key] + + def __setitem__(self, item, value): + self.__dict__[item] = value + + def get(self, key): + return self.__dict__.get(key) + + def build_design(self, z0, f0, T, af, test=None, args={}): + + self.test = test + args["rand_stream"] = np.random.default_rng(args["seed"]) + H = [] + timel = [] + metric_sum = {} + print(T) + for t in range(0, T): + # print(f"t: {t}") if self.trace else None + print(t) + tic = time.time() + + model = emulator(x=self.cls_func.x, + theta=z0, + f=f0, + method="pcHomGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + toc = time.time() + timel.append(toc - tic) + + args["seed"] += 1 + + if test is not None: + metric_sum = self.eval_perf(model) + + acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) + + acq_func.acquire_new() + tnew = acq_func.znew + + fnew = self.cls_func.function(tnew.flatten()[0], tnew.flatten()[1]).reshape( + 1, self.cls_func.d + ) + + f0 = np.concatenate((f0, fnew), axis=0) + z0 = np.concatenate((z0, tnew), axis=0) + + H.append( + { + "t": t, + "f": fnew, + "z": tnew, + "MAD": metric_sum.get("MAD", None) + } + ) + + unique_rows, counts = np.unique(z0, axis=0, return_counts=True) + + self.t = unique_rows + self.reps = counts + self.fs = f0 + self.zs = z0 + self.time = timel + self.H = H + + return self + + + def eval_perf(self, model): + + nm, d = self.test["theta"].shape[0], self.cls_func.d + + # predict at mesh + pr = model.predict(theta=self.test["theta"], thetaprime=self.test["theta"]) + + # ntot, ntot x ntot, ntot + mu, Sn = pr._info["mean"], pr._info["S"] + + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) # Sn[self.idr, self.idc].reshape(nm, d, d) + + N = S + self.Sigma3d + g = multiple_pdfs(self.y, muT, N) + + + MSE = np.mean(((g.flatten() - self.test["p"].flatten()) ** 2)) + MAD = np.mean(np.abs(g.flatten() - self.test["p"].flatten())) + + metric_sum = {"MAD": MAD, "MSE": MSE} + return metric_sum diff --git a/examples/Example1/ex1.png b/examples/Example1/ex1.png index 6701789ddd21d589604d50bd510975bc8d2822a9..6f5e3820191cde7b07b2c547a7bf5465a92c8c9f 100644 GIT binary patch delta 94867 zcmZs?cTiJd+w~hn1rdoTAUz5q2qKE0(h?A9D$=Akl@0>ZI~zqn5QsDp5Q21Rk={Y1 z3etOr(0f7&A*6iU=XvM-&Y5%m*fW{TBsI3bE(;v=! zIQxOFoc@C;FnhQg?VA`PDKOx8@agH-?V|ZphSy*Ae2xFMI6Z1klE^8cLLQNkWQy;; zIqa@8`|Rc6TNZm!4$0|I_0dM*Iqz<>QopGCvvVQA%yqoyd3}hZsXo?7D&$%LjJSg$ zUPSAppHZ6LXv}3j*x+lA`1oB{8A1zTIL`RvH8+(D9A#?}BZ#kq7=$wZ=PK6d2$ZbC zh9)LV-OhZ-H+z{1`CfIxf4IIGbe7P8VMac|p1vZ_M_ 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PUQ/designmethods/SEQCALOPT.py | 390 ----------------- PUQ/designmethods/SEQCALsupport.py | 144 ------- PUQ/designmethods/SEQDES.py | 307 ------------- PUQ/designmethods/SEQDESsupport.py | 226 ---------- PUQ/designmethods/SEQUNIFORM.py | 208 --------- PUQ/designmethods/p_sto_bseq.py | 403 ------------------ PUQ/designmethods/sequential.py | 183 -------- .../sequential_md_deterministic_old.py | 114 ----- 9 files changed, 2244 deletions(-) delete mode 100644 PUQ/designmethods/SEQCAL.py delete mode 100644 PUQ/designmethods/SEQCALOPT.py delete mode 100644 PUQ/designmethods/SEQCALsupport.py delete mode 100644 PUQ/designmethods/SEQDES.py delete mode 100644 PUQ/designmethods/SEQDESsupport.py delete mode 100644 PUQ/designmethods/SEQUNIFORM.py delete mode 100644 PUQ/designmethods/p_sto_bseq.py delete mode 100644 PUQ/designmethods/sequential.py delete mode 100644 PUQ/designmethods/sequential_md_deterministic_old.py diff --git a/PUQ/designmethods/SEQCAL.py b/PUQ/designmethods/SEQCAL.py deleted file mode 100644 index 337d44b..0000000 --- a/PUQ/designmethods/SEQCAL.py +++ /dev/null @@ -1,269 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.acquisition_funcs import ( - maxvar, - eivar, - maxexp, - rnd, - imse, -) -from PUQ.designmethods.SEQCALsupport import ( - fit_emulator, - load_H, - update_arrays, - create_arrays, - pad_arrays, - select_condition, - rebuild_condition, -) -from libensemble.message_numbers import ( - STOP_TAG, - PERSIS_STOP, - FINISHED_PERSISTENT_GEN_TAG, - EVAL_GEN_TAG, -) -from libensemble.tools.persistent_support import PersistentSupport -from libensemble.alloc_funcs.start_only_persistent import ( - only_persistent_gens as alloc_f, -) -from libensemble.libE import libE -from libensemble.tools import parse_args, save_libE_output, add_unique_random_streams -from PUQ.posterior import posterior - - -def fit(fitinfo, data_cls, args): - mini_batch = args["mini_batch"] - n_init_thetas = args["n_init_thetas"] - nworkers = args["nworkers"] - AL_type = args["AL"] - seed_n0 = args["seed_n0"] - prior = args["prior"] - max_evals = args["max_evals"] - test_data = args["data_test"] - - out = data_cls.out - sim_f = data_cls.sim - - sim_specs = { - "sim_f": sim_f, - "in": ["thetas"], - "out": out, - "user": {"function": data_cls.function}, - } - - gen_out = [ - ("thetas", float, data_cls.p), - ("priority", int), - ("obs", float, (1,)), - ("obsvar", float, (1,)), - ("TV", float), - ("HD", float), - ] - - gen_specs = { - "gen_f": gen_f, - "persis_in": [o[0] for o in gen_out] + ["f", "sim_id"], - "out": gen_out, - "user": { - "n_init_thetas": n_init_thetas, # Num thetas in initial batch - "mini_batch": mini_batch, # No. of thetas to generate per step - "nworkers": nworkers, - "AL": AL_type, - "seed_n0": seed_n0, - "synth_cls": data_cls, - "test_data": test_data, - "prior": prior, - "type_init": args["type_init"], - }, - } - - alloc_specs = { - "alloc_f": alloc_f, - "user": { - "init_sample_size": 0, - "async_return": True, # True = Return results to gen as they come in (after sample) - "active_recv_gen": True, # Persistent gen can handle irregular communications - }, - } - libE_specs = {"nworkers": nworkers, "comms": "local"} - # libE_specs = {'nworkers': nworkers, 'comms': 'local', 'sim_dirs_make': True, - # 'sim_dir_copy_files': [os.path.join(os.getcwd(), '48Ca_template.in')]} - - persis_info = add_unique_random_streams({}, nworkers + 1) - - # Currently just allow gen to exit if mse goes below threshold value - exit_criteria = {"sim_max": max_evals} # Now just a set number of sims. - - # Perform the run - H, persis_info, flag = libE( - sim_specs, - gen_specs, - exit_criteria, - persis_info, - alloc_specs=alloc_specs, - libE_specs=libE_specs, - ) - - fitinfo["f"] = H["f"] - fitinfo["theta"] = H["thetas"] - fitinfo["TV"] = H["TV"] - fitinfo["HD"] = H["HD"] - return - - -def gen_f(H, persis_info, gen_specs, libE_info): - """Generator to select and obviate parameters for calibration.""" - ps = PersistentSupport(libE_info, EVAL_GEN_TAG) - rand_stream = persis_info["rand_stream"] - n0 = gen_specs["user"]["n_init_thetas"] - mini_batch = gen_specs["user"]["mini_batch"] - n_workers = gen_specs["user"]["nworkers"] - AL = gen_specs["user"]["AL"] - seed = gen_specs["user"]["seed_n0"] - synth_info = gen_specs["user"]["synth_cls"] - test_data = gen_specs["user"]["test_data"] - prior_func = gen_specs["user"]["prior"] - type_init = gen_specs["user"]["type_init"] - - obsvar = synth_info.obsvar - data = synth_info.real_data - theta_limits = synth_info.thetalimits - - thetatest, posttest, ftest, priortest = None, None, None, None - if test_data is not None: - thetatest, posttest, ftest, priortest = ( - test_data["theta"], - test_data["p"], - test_data["f"], - test_data["p_prior"], - ) - - true_fevals = np.reshape(data[0, :], (1, data.shape[1])) - n_x = synth_info.d - x = synth_info.x - real_x = synth_info.real_x - - obs_offset, theta_offset, generated_no = 0, 0, 0 - TV, HD = 1000, 1000 - fevals, pending, prev_pending, complete, prev_complete = ( - None, - None, - None, - None, - None, - ) - first_iter = True - tag = 0 - update_model = False - acquisition_f = eval(AL) - list_id = [] - - theta = 0 - - while tag not in [STOP_TAG, PERSIS_STOP]: - if not first_iter: - # Update fevals from calc_in - update_arrays( - n_x, - fevals, - pending, - complete, - calc_in, - obs_offset, - theta_offset, - list_id, - ) - update_model = rebuild_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ) - - if not update_model: - tag, Work, calc_in = ps.recv() - if tag in [STOP_TAG, PERSIS_STOP]: - break - - if update_model: - # print('Updating model...\n') - - # print('Percentage Pending: %0.2f ( %d / %d)' % (100*np.round(np.mean(pending), 4), - # np.sum(pending), - # np.prod(pending.shape))) - # print('Percentage Complete: %0.2f ( %d / %d)' % (100*np.round(np.mean(complete), 4), - # np.sum(complete), - # np.prod(pending.shape))) - - emu = fit_emulator(x, theta, fevals, theta_limits) - prev_pending = pending.copy() - update_model = False - - # Obtain the accuracy on the test set - if test_data is not None: - post = posterior(data_cls=synth_info, emulator=emu) - posttesthat, posttestvar = post.predict(thetatest) - TV = np.mean(np.abs(posttest - posttesthat * priortest)) - HD = np.sqrt( - 0.5 * np.mean((np.sqrt(posttesthat) - np.sqrt(posttest)) ** 2) - ) - - if first_iter: - # print('Selecting theta for the first iteration...\n') - - n_init = max(n_workers - 1, n0) - theta = prior_func.rnd(n_init, seed) - - fevals, pending, prev_pending, complete, prev_complete = create_arrays( - n_x, n_init - ) - - H_o = np.zeros(len(theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - first_iter = False - generated_no += n_workers - 1 - - else: - if select_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ): - # print('Selecting theta...\n') - - prev_complete = complete.copy() - new_theta = acquisition_f( - mini_batch, - x, - real_x, - emu, - theta, - fevals, - true_fevals, - obsvar, - theta_limits, - prior_func, - thetatest, - priortest, - type_init, - ) - - ( - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) = pad_arrays( - n_x, - new_theta, - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) - - H_o = np.zeros(len(new_theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, new_theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - generated_no += mini_batch - - return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/designmethods/SEQCALOPT.py b/PUQ/designmethods/SEQCALOPT.py deleted file mode 100644 index 2cc258d..0000000 --- a/PUQ/designmethods/SEQCALOPT.py +++ /dev/null @@ -1,390 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - get_emuvar, - multiple_pdfs, -) -from PUQ.designmethods.gen_funcs.EIVAR import eivar -from PUQ.designmethods.gen_funcs.MAXEXP import maxexp -from PUQ.designmethods.gen_funcs.MAXVAR import maxvar -from PUQ.designmethods.gen_funcs.EI import ei -from PUQ.designmethods.gen_funcs.PI import pi -from PUQ.designmethods.gen_funcs.HYBRID_EI import hybrid_ei -from PUQ.designmethods.gen_funcs.RND import rnd -from PUQ.designmethods.SEQCALsupport import ( - fit_emulator, - load_H_opt, - update_arrays, - create_arrays, - pad_arrays, - rebuild_condition_opt, -) -from libensemble.message_numbers import ( - STOP_TAG, - PERSIS_STOP, - FINISHED_PERSISTENT_GEN_TAG, - EVAL_GEN_TAG, -) -from libensemble.tools.persistent_support import PersistentSupport -from libensemble.alloc_funcs.start_only_persistent import ( - only_persistent_gens as alloc_f, -) -from libensemble.libE import libE -from libensemble.tools import parse_args, save_libE_output, add_unique_random_streams -from smt.sampling_methods import LHS -from PUQ.posterior import posterior -import time - - -def fit(fitinfo, data_cls, args): - - mini_batch = args["mini_batch"] - nworkers = args["nworkers"] - AL_type = args["AL"] - seed_n0 = args["seed_n0"] - prior = args["prior"] - max_evals = args["max_evals"] - test_data = args["data_test"] - - if AL_type in ["ei", "pi", "hybrid_ei", "hybrid_pi", "eivar"]: - candsize = args["candsize"] - refsize = args["refsize"] - believer = args["believer"] - else: - candsize, refsize, believer = None, None, None - - out = data_cls.out - sim_f = data_cls.sim - - sim_specs = { - "sim_f": sim_f, - "in": ["thetas"], - "out": out, - "user": {"function": data_cls.function}, - } - - gen_out = [ - ("thetas", float, data_cls.p), - ("priority", int), - ("obs", float, (1,)), - ("obsvar", float, (1,)), - ("TV", float), - ("HD", float), - ("AE", float), - ("time", float), - ] - - gen_specs = { - "gen_f": gen_f, - "persis_in": [o[0] for o in gen_out] + ["f", "sim_id"], - "out": gen_out, - "user": { - "mini_batch": mini_batch, # No. of thetas to generate per step - "nworkers": nworkers, - "AL": AL_type, - "seed_n0": seed_n0, - "synth_cls": data_cls, - "test_data": test_data, - "prior": prior, - "candsize": candsize, - "refsize": refsize, - "believer": believer, - }, - } - - alloc_specs = { - "alloc_f": alloc_f, - "user": { - "init_sample_size": 0, - "async_return": True, # True = Return results to gen as they come in (after sample) - "active_recv_gen": True, # Persistent gen can handle irregular communications - }, - } - libE_specs = {"nworkers": nworkers, "comms": "local"} - # libE_specs = {'nworkers': nworkers, 'comms': 'local', 'sim_dirs_make': True, - # 'sim_dir_copy_files': [os.path.join(os.getcwd(), '48Ca_template.in')]} - - persis_info = add_unique_random_streams({}, nworkers + 1) - - # Currently just allow gen to exit if mse goes below threshold value - exit_criteria = {"sim_max": max_evals} # Now just a set number of sims. - - # Perform the run - H, persis_info, flag = libE( - sim_specs, - gen_specs, - exit_criteria, - persis_info, - alloc_specs=alloc_specs, - libE_specs=libE_specs, - ) - - emu = fit_emulator(data_cls.x, H["thetas"], H["f"][None, :], data_cls.thetalimits) - - TV, HD, AE = collect_data( - emu, - data_cls.x, - H["f"][None, :], - data_cls.x, - test_data["theta"], - test_data["p_prior"], - test_data["p"], - np.reshape(data_cls.real_data[0, :], (1, data_cls.real_data.shape[1])), - data_cls.obsvar.reshape(1, data_cls.d, data_cls.d), - ) - - fitinfo["f"] = H["f"] - fitinfo["theta"] = H["thetas"] - fitinfo["TV"] = np.concatenate((H["TV"], np.array([TV]))) - fitinfo["HD"] = np.concatenate((H["HD"], np.array([HD]))) - fitinfo["AE"] = np.concatenate((H["AE"], np.array([AE]))) - - fitinfo["time"] = H["time"] - return - - -def compute_timepass(start_time, new_theta): - end_time = time.time() - timepass = end_time - start_time - timepassvec = np.zeros(new_theta.shape[0]) - timepassvec[0] = timepass - return timepassvec - - -def collect_data( - emu, x, fevals, real_x, thetatest, priortest, posttest, true_fevals, obsvar3d -): - - emupredict = emu.predict(x=x, theta=thetatest) - emumean = emupredict.mean() - emuvar, is_cov = get_emuvar(emupredict) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - - posttesthat = multiple_pdfs( - true_fevals, emumeanT[:, real_x.flatten()], var_obsvar1[:, real_x, real_x.T] - ) - - TV = np.mean(np.abs(posttest - posttesthat * priortest)) - HD = np.sqrt(0.5 * np.mean((np.sqrt(posttesthat) - np.sqrt(posttest)) ** 2)) - idnan = np.isnan(fevals).any(axis=0).flatten() - fevals_c = fevals[:, ~idnan] - AE = np.min(np.sum(np.abs(true_fevals - fevals_c.T), axis=1)) - - return TV, HD, AE - - -def gen_f(H, persis_info, gen_specs, libE_info): - """Generator to select and obviate parameters for calibration.""" - ps = PersistentSupport(libE_info, EVAL_GEN_TAG) - rand_stream = persis_info["rand_stream"] - mini_batch = gen_specs["user"]["mini_batch"] - n_workers = gen_specs["user"]["nworkers"] - AL = gen_specs["user"]["AL"] - seed = gen_specs["user"]["seed_n0"] - synth_info = gen_specs["user"]["synth_cls"] - test_data = gen_specs["user"]["test_data"] - prior_func = gen_specs["user"]["prior"] - candsize = gen_specs["user"]["candsize"] - refsize = gen_specs["user"]["refsize"] - believer = gen_specs["user"]["believer"] - - obsvar = synth_info.obsvar - data = synth_info.real_data - theta_limits = synth_info.thetalimits - - thetatest, posttest, ftest, priortest = None, None, None, None - if test_data is not None: - thetatest, posttest, ftest, priortest, thetainit, finit = ( - test_data["theta"], - test_data["p"], - test_data["f"], - test_data["p_prior"], - test_data["thetainit"], - test_data["finit"], - ) - - true_fevals = np.reshape(data[0, :], (1, data.shape[1])) - n_x, x, real_x = synth_info.d, synth_info.x, synth_info.real_x - obsvar3d = obsvar.reshape(1, n_x, n_x) - obs_offset, theta_offset, generated_no = 0, 0, 0 - TV, HD, AE, time_pass = 1000, 1000, 1000, 0 - fevals, pending, prev_pending, complete, prev_complete = ( - None, - None, - None, - None, - None, - ) - first_iter = True - tag = 0 - update_model = False - acquisition_f = eval(AL) - list_id = [] - theta = 0 - - while tag not in [STOP_TAG, PERSIS_STOP]: - starttime = time.time() - if not first_iter: - # Update fevals from calc_in - update_arrays( - n_x, - fevals, - pending, - complete, - calc_in, - obs_offset, - theta_offset, - list_id, - ) - update_model = rebuild_condition_opt( - complete, prev_complete, n_theta=mini_batch - ) - - if not update_model: - tag, Work, calc_in = ps.recv() - if tag in [STOP_TAG, PERSIS_STOP]: - break - - if update_model: - starttime = time.time() - - # if len(theta) % 50 == 0: - # print("Updating model...\n") - - # print( - # "Percentage Pending: %0.2f ( %d / %d)" - # % ( - # 100 * np.round(np.mean(pending), 4), - # np.sum(pending), - # np.prod(pending.shape), - # ) - # ) - # print( - # "Percentage Complete: %0.2f ( %d / %d)" - # % ( - # 100 * np.round(np.mean(complete), 4), - # np.sum(complete), - # np.prod(pending.shape), - # ) - # ) - - fcomb = np.concatenate((finit, fevals), axis=1) - thetacomb = np.concatenate((thetainit, theta), axis=0) - emu = fit_emulator(x, thetacomb, fcomb, theta_limits) - prev_pending = pending.copy() - update_model = False - - # Obtain the accuracy on the test set - if test_data is not None: - TV, HD, AE = collect_data( - emu, - x, - fcomb, - real_x, - thetatest, - priortest, - posttest, - true_fevals, - obsvar3d, - ) - - if first_iter: - emuinit = fit_emulator(x, thetainit, finit, theta_limits) - TV, HD, AE = collect_data( - emuinit, - x, - finit, - real_x, - thetatest, - priortest, - posttest, - true_fevals, - obsvar3d, - ) - n_init = n_workers - 1 - - theta = acquisition_f( - n_init, - x, - real_x, - emuinit, - thetainit, - finit, - true_fevals, - obsvar, - theta_limits, - prior_func, - thetatest, - priortest, - None, - believer=believer, - candsize=candsize, - refsize=refsize, - ) - - fevals, pending, prev_pending, complete, prev_complete = create_arrays( - n_x, n_init - ) - time_pass = compute_timepass(starttime, theta) - H_o = np.zeros(len(theta), dtype=gen_specs["out"]) - H_o = load_H_opt( - H_o, theta, TV, HD, AE, time_pass, generated_no, set_priorities=True - ) - tag, Work, calc_in = ps.send_recv(H_o) - first_iter = False - generated_no += n_init - - else: - if rebuild_condition_opt(complete, prev_complete, n_theta=mini_batch): - - prev_complete = complete.copy() - new_theta = acquisition_f( - mini_batch, - x, - real_x, - emu, - thetacomb, - fcomb, - true_fevals, - obsvar, - theta_limits, - prior_func, - thetatest, - priortest, - None, - believer=believer, - candsize=candsize, - refsize=refsize, - ) - - theta, fevals, pending, prev_pending, complete, prev_complete = ( - pad_arrays( - n_x, - new_theta, - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) - ) - - time_pass = compute_timepass(starttime, new_theta) - - H_o = np.zeros(len(new_theta), dtype=gen_specs["out"]) - H_o = load_H_opt( - H_o, - new_theta, - TV, - HD, - AE, - time_pass, - generated_no, - set_priorities=True, - ) - tag, Work, calc_in = ps.send_recv(H_o) - generated_no += mini_batch - - return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/designmethods/SEQCALsupport.py b/PUQ/designmethods/SEQCALsupport.py deleted file mode 100644 index 05ffe36..0000000 --- a/PUQ/designmethods/SEQCALsupport.py +++ /dev/null @@ -1,144 +0,0 @@ -import numpy as np -from PUQ.surrogate import emulator -import pyximport - -pyximport.install(setup_args={"include_dirs": np.get_include()}, reload_support=True) - - -def select_condition(complete, prev_complete, n_theta=2, n_initial=10): - # return False if np.sum(complete) - np.sum(prev_complete) < n_theta else True - if (np.sum(complete) - np.sum(prev_complete) < n_theta) or ( - np.sum(complete) < n_initial - ): - nflag = False - else: - nflag = True - return nflag - - -def rebuild_condition(complete, prev_complete, n_theta=2, n_initial=10): - if (np.sum(complete) - np.sum(prev_complete) < n_theta) or ( - np.sum(complete) < n_initial - ): - nflag = False - else: - nflag = True - return nflag - - -def rebuild_condition_opt(complete, prev_complete, n_theta=2): - - if np.sum(complete) - np.sum(prev_complete) < n_theta: - nflag = False - else: - nflag = True - return nflag - - -def create_arrays(n_x, n_thetas): - """Create 2D (point * rows) arrays fevals, pending and complete""" - - fevals = np.full((n_x, n_thetas), np.nan) - pending = np.full((1, n_thetas), True) - prev_pending = pending.copy() - complete = np.full((1, n_thetas), False) - prev_complete = np.full((1, n_thetas), False) - - return fevals, pending, prev_pending, complete, prev_complete - - -def pad_arrays( - n_x, thetanew, theta, fevals, pending, prev_pending, complete, prev_complete -): - """Extend arrays to appropriate sizes.""" - n_thetanew = len(thetanew) - theta = np.vstack((theta, thetanew)) - fevals = np.hstack((fevals, np.full((n_x, n_thetanew), np.nan))) - pending = np.hstack((pending, np.full((1, n_thetanew), True))) - prev_pending = np.hstack((prev_pending, np.full((1, n_thetanew), True))) - complete = np.hstack((complete, np.full((1, n_thetanew), False))) - prev_complete = np.hstack((prev_complete, np.full((1, n_thetanew), False))) - - return theta, fevals, pending, prev_pending, complete, prev_complete - - -def update_arrays( - n_x, fevals, pending, complete, calc_in, obs_offset, theta_offset, list_id -): - """Unpack from calc_in into 2D (point * rows) fevals""" - - sim_id = calc_in["sim_id"] - list_id.append(sim_id) - r = np.repeat(0, len(sim_id - obs_offset)) - c = sim_id - obs_offset - - if n_x < 2: - fevals[r, c + theta_offset] = calc_in["f"] - else: - rc = [i for j in range(len(sim_id - obs_offset)) for i in range(n_x)] - cc = np.repeat(c, n_x) - fevals[rc, cc] = calc_in["f"].flatten() - - pending[r, c + theta_offset] = False - complete[r, c + theta_offset] = True - return - - -def assign_priority(n_thetas, generated_no): - """Assign priorities to points.""" - priority = np.arange(generated_no, generated_no + n_thetas) - return priority - - -def load_H(H, thetas, mse, hd, generated_no, offset=0, set_priorities=False): - """Fill inputs into H0. - There will be num_points x num_thetas entries - """ - n_thetas = len(thetas) - start = offset * n_thetas - - if thetas.shape[1] < 2: - H["thetas"][start : start + n_thetas] = thetas.flatten() - else: - H["thetas"][start : start + n_thetas] = thetas - - H["TV"][start : start + n_thetas] = np.repeat(mse, n_thetas) - H["HD"][start : start + n_thetas] = np.repeat(hd, n_thetas) - if set_priorities: - H["priority"] = assign_priority(n_thetas, generated_no) - - return H - - -def load_H_opt( - H, thetas, mse, hd, ae, time, generated_no, offset=0, set_priorities=False -): - """Fill inputs into H0. - There will be num_points x num_thetas entries - """ - n_thetas = len(thetas) - start = offset * n_thetas - - if thetas.shape[1] < 2: - H["thetas"][start : start + n_thetas] = thetas.flatten() - else: - H["thetas"][start : start + n_thetas] = thetas - - H["TV"][start : start + n_thetas] = np.repeat(mse, n_thetas) - H["HD"][start : start + n_thetas] = np.repeat(hd, n_thetas) - H["AE"][start : start + n_thetas] = np.repeat(ae, n_thetas) - H["time"][start : start + n_thetas] = time - if set_priorities: - H["priority"] = assign_priority(n_thetas, generated_no) - - return H - - -def fit_emulator(x, theta, fevals, thetalimits): - idnan = np.isnan(fevals).any(axis=0).flatten() - fevals_c = fevals[:, ~idnan] - theta_c = theta[~idnan, :] - - emu = emulator(x, theta_c, fevals_c, method="PCGP") - - return emu diff --git a/PUQ/designmethods/SEQDES.py b/PUQ/designmethods/SEQDES.py deleted file mode 100644 index f5cde87..0000000 --- a/PUQ/designmethods/SEQDES.py +++ /dev/null @@ -1,307 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.CIMSPE import imspe -from PUQ.designmethods.gen_funcs.CMAXVAR import maxvar -from PUQ.designmethods.gen_funcs.CEIVAR import ceivar -from PUQ.designmethods.gen_funcs.CEIVARX import ceivarx -from PUQ.designmethods.SEQCALsupport import ( - load_H, - update_arrays, - create_arrays, - pad_arrays, - select_condition, - rebuild_condition, -) -from PUQ.designmethods.SEQDESsupport import collect_data, fit_emulator1d, find_mle -from libensemble.message_numbers import ( - STOP_TAG, - PERSIS_STOP, - FINISHED_PERSISTENT_GEN_TAG, - EVAL_GEN_TAG, -) -from libensemble.tools.persistent_support import PersistentSupport -from libensemble.alloc_funcs.start_only_persistent import ( - only_persistent_gens as alloc_f, -) -from libensemble.libE import libE -from libensemble.tools import parse_args, save_libE_output, add_unique_random_streams - - -def fit(fitinfo, data_cls, args): - - mini_batch = args["mini_batch"] - n_init_thetas = args["n_init_thetas"] - nworkers = args["nworkers"] - AL_type = args["AL"] - seed_n0 = args["seed_n0"] - prior = args["prior"] - max_evals = args["max_evals"] - test_data = args["data_test"] - theta_torun = args["theta_torun"] - is_thetamle = args["is_thetamle"] - - out = data_cls.out - sim_f = data_cls.sim - - sim_specs = { - "sim_f": sim_f, - "in": ["thetas"], - "out": out, - "user": {"function": data_cls.function}, - } - - gen_out = [ - ("thetas", float, data_cls.p), - ("priority", int), - ("obs", float, (1,)), - ("obsvar", float, (1,)), - ("TV", float), - ("HD", float), - ("thetamle", float, (1,)), - ] - - gen_specs = { - "gen_f": gen_f, - "persis_in": [o[0] for o in gen_out] + ["f", "sim_id"], - "out": gen_out, - "user": { - "n_init_thetas": n_init_thetas, # Num thetas in initial batch - "mini_batch": mini_batch, # No. of thetas to generate per step - "nworkers": nworkers, - "AL": AL_type, - "seed_n0": seed_n0, - "synth_cls": data_cls, - "test_data": test_data, - "prior": prior, - "theta_torun": theta_torun, - "is_thetamle": is_thetamle, - }, - } - - alloc_specs = { - "alloc_f": alloc_f, - "user": { - "init_sample_size": 0, - "async_return": True, # True = Return results to gen as they come in (after sample) - "active_recv_gen": True, # Persistent gen can handle irregular communications - }, - } - libE_specs = {"nworkers": nworkers, "comms": "local"} - # libE_specs = {'nworkers': nworkers, 'comms': 'local', 'sim_dirs_make': True, - # 'sim_dir_copy_files': [os.path.join(os.getcwd(), '48Ca_template.in')]} - - persis_info = add_unique_random_streams({}, nworkers + 1) - - # Currently just allow gen to exit if mse goes below threshold value - exit_criteria = {"sim_max": max_evals} # Now just a set number of sims. - - # Perform the run - H, persis_info, flag = libE( - sim_specs, - gen_specs, - exit_criteria, - persis_info, - alloc_specs=alloc_specs, - libE_specs=libE_specs, - ) - - fitinfo["f"] = H["f"] - fitinfo["theta"] = H["thetas"] - fitinfo["TV"] = H["TV"] - fitinfo["HD"] = H["HD"] - - for key in persis_info.keys(): - # print(type(persis_info[key])) - if isinstance(persis_info[key], dict): - if "thetamle" in persis_info[key].keys(): - fitinfo["thetamle"] = persis_info[key]["thetamle"] - - return - - -def gen_f(H, persis_info, gen_specs, libE_info): - """Generator to select and obviate parameters for calibration.""" - ps = PersistentSupport(libE_info, EVAL_GEN_TAG) - rand_stream = persis_info["rand_stream"] - n0 = gen_specs["user"]["n_init_thetas"] - mini_batch = gen_specs["user"]["mini_batch"] - n_workers = gen_specs["user"]["nworkers"] - AL = gen_specs["user"]["AL"] - seed = gen_specs["user"]["seed_n0"] - theta_torun = gen_specs["user"]["theta_torun"] - is_thetamle = gen_specs["user"]["is_thetamle"] - # Prior functions - prior_func_all = gen_specs["user"]["prior"] - prior_func, prior_func_x, prior_func_t = ( - prior_func_all["prior"], - prior_func_all["priorx"], - prior_func_all["priort"], - ) - - # Simulation info - synth_info = gen_specs["user"]["synth_cls"] - obsvar, data, theta_limits, dim, x = ( - synth_info.obsvar, - synth_info.real_data, - synth_info.thetalimits, - synth_info.d, - synth_info.x, - ) - - # Test data - test_data = gen_specs["user"]["test_data"] - thetatest, posttest, ftest, priortest = None, None, None, None - if test_data is not None: - thetatest, th_mesh, x_mesh, ptest, ftest, priortest, ytest = ( - test_data["theta"], - test_data["th"], - test_data["xmesh"], - test_data["p"], - test_data["f"], - test_data["p_prior"], - test_data["y"], - ) - - # Additional set - true_fevals = np.reshape(data[0, :], (1, data.shape[1])) - x_emu = np.arange(0, 1)[:, None] - dx, dt, nmesh = x.shape[1], th_mesh.shape[1], len(x_mesh) - obs_offset, theta_offset, generated_no = 0, 0, 0 - TV, HD, tag, theta = 1000, 1000, 0, 0 - fevals, pending, prev_pending, complete, prev_complete = ( - None, - None, - None, - None, - None, - ) - first_iter, update_model = True, False - list_id, mlelist = [], [] - - if AL == None: - pass - else: - acquisition_f = eval(AL) - - while tag not in [STOP_TAG, PERSIS_STOP]: - if not first_iter: - # Update fevals from calc_in - update_arrays( - dim, - fevals, - pending, - complete, - calc_in, - obs_offset, - theta_offset, - list_id, - ) - update_model = rebuild_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ) - - if not update_model: - tag, Work, calc_in = ps.recv() - if tag in [STOP_TAG, PERSIS_STOP]: - break - - if update_model: - - emu = fit_emulator1d(x_emu, theta, fevals) - if is_thetamle: - theta_mle = np.array(synth_info.true_theta)[None, :] - else: - theta_mle = find_mle( - emu, x, x_emu, true_fevals, obsvar, dx, dt, theta_limits, False - ) - - mlelist.append(theta_mle) - - if len(theta) % 10 == 0: - print("mle:", theta_mle) - - TV, HD = collect_data( - emu, - None, - x_emu, - theta_mle, - dt, - x_mesh, - thetatest, - nmesh, - ytest, - ptest, - x, - true_fevals, - obsvar, - synth_info, - ) - prev_pending = pending.copy() - update_model = False - - if first_iter: - n_init = max(n_workers - 1, n0) - theta = prior_func.rnd(n_init, seed) - fevals, pending, prev_pending, complete, prev_complete = create_arrays( - dim, n_init - ) - - H_o = np.zeros(len(theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - first_iter = False - generated_no += n_init - - else: - if select_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ): - - prev_complete = complete.copy() - - if AL == None: - new_theta = theta_torun[ - (generated_no - n_init) : (generated_no - n_init + mini_batch), - :, - ] - else: - new_theta = acquisition_f( - mini_batch, - x, - None, - emu, - theta, - fevals, - true_fevals, - obsvar, - theta_limits, - prior_func, - prior_func_t, - thetatest, - x_mesh, - th_mesh, - priortest, - None, - synth_info, - theta_mle, - ) - - theta, fevals, pending, prev_pending, complete, prev_complete = ( - pad_arrays( - dim, - new_theta, - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) - ) - - H_o = np.zeros(len(new_theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, new_theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - generated_no += mini_batch - - persis_info["thetamle"] = mlelist - return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/designmethods/SEQDESsupport.py b/PUQ/designmethods/SEQDESsupport.py deleted file mode 100644 index 0d96e7f..0000000 --- a/PUQ/designmethods/SEQDESsupport.py +++ /dev/null @@ -1,226 +0,0 @@ -import numpy as np -from PUQ.surrogate import emulator -import pyximport -import scipy.optimize as spo -from sklearn.linear_model import LinearRegression -from PUQ.surrogatemethods.PCGPexp import postpred, postpredbias - -pyximport.install(setup_args={"include_dirs": np.get_include()}, reload_support=True) - - -def fit_emulator1d(x_emu, theta, fevals): - - emu = emulator(x_emu, theta, fevals, method="PCGPexp") - - return emu - - -def compute_diff(x, parameter, nx, emu, x_emu, obs, is_bias): - # obs : 1 x n_x - # emumean : 1 x n_x - # bias : n_x x 1 - dt = len(parameter) - dx = x.shape[1] - xp = [ - np.concatenate([xc.reshape(1, dx), parameter.reshape(1, dt)], axis=1) - for xc in x - ] - xp = np.array([m for mesh in xp for m in mesh]) - # xp = np.concatenate((x, np.repeat(parameter, nx).reshape(nx, len(parameter))), axis=1) - - # Predict computer model - emupred = emu.predict(x=x_emu, theta=xp) - emumean = emupred.mean() - - # Predict linear bias mean - bias = (obs - emumean).T - - if is_bias: - model = LinearRegression().fit(x, bias) - mu_bias = model.predict(x) - diff = bias - mu_bias - return diff - else: - return bias - - -def obj_mle(parameter, args): - emu, x, x_emu, obs, obsvar, is_bias = ( - args[0], - args[1], - args[2], - args[3], - args[4], - args[5], - ) - nx = len(x) - diff = compute_diff(x, parameter, nx, emu, x_emu, obs, is_bias) - ll = diff.T @ diff - return ll.flatten() - - -def find_mle(emu, x, x_emu, obs, obsvar, dx, dt, theta_limits, is_bias): - - bnd = () - theta_init = [] - for i in range(dx, dx + dt): - bnd += ((theta_limits[i][0], theta_limits[i][1]),) - theta_init.append((theta_limits[i][0] + theta_limits[i][1]) / 2) - - opval = spo.minimize( - obj_mle, - theta_init, - method="L-BFGS-B", - options={"gtol": 0.01}, - bounds=bnd, - args=([emu, x, x_emu, obs, obsvar, is_bias]), - ) - - theta_mle = opval.x - theta_mle = theta_mle.reshape(1, dt) - return theta_mle - - -def collect_data( - emu, - emubias, - x_emu, - theta_mle, - dt, - xmesh, - xtmesh, - nmesh, - ytest, - ptest, - x, - obs, - obsvar, - synth_info, -): - - dx = xmesh.shape[1] - xtrue_test = [ - np.concatenate([xc.reshape(1, dx), theta_mle], axis=1) for xc in xmesh - ] - xtrue_test = np.array([m for mesh in xtrue_test for m in mesh]) - predobj = emu.predict(x=x_emu, theta=xtrue_test) - - fmeanhat, fvarhat = predobj.mean(), predobj.var() - - if emubias == None: - pred_error = np.mean(np.abs(fmeanhat - ytest)) - pmeanhat, pvarhat = postpred(emu._info, x, xtmesh, obs, obsvar) - post_error = np.mean(np.abs(pmeanhat - ptest)) - else: - bmeanhat = emubias.predict(xmesh) - pred_error = np.mean(np.abs(fmeanhat + bmeanhat - ytest)) - bmeanhat = emubias.predict(x) - pmeanhat, pvarhat = postpredbias(emu._info, x, xtmesh, obs, obsvar, bmeanhat) - post_error = np.mean(np.abs(pmeanhat - ptest)) - - return pred_error, post_error - - -def find_abs_diff(nx, d, x): - # diff = np.zeros((nx, nx)) - # for i in range(nx): - # for j in range(nx): - # for k in range(d): - # diff[i, j] += np.abs(x[i, k] - x[j, k]) - diff = np.sum(np.abs(x[:, None, :] - x[None, :, :]), axis=-1) - - return diff - - -def gen_cov(sigmae_sq, sigmab_sq, lambdap, nx, abs_dist): - return np.diag(np.repeat(sigmae_sq, nx)) + sigmab_sq * np.exp(-lambdap * abs_dist) - - -def obj_covmle(parameter, args): - - x, biasdiff = args[0], args[1] - nx, d = x.shape[0], x.shape[1] - - abs_dist = find_abs_diff(nx, d, x) - # Generate cov - covmat = gen_cov( - sigmae_sq=parameter[0], - sigmab_sq=parameter[1], - lambdap=parameter[2], - nx=nx, - abs_dist=abs_dist, - ) - - # Inverse of covmat - covmatinv = np.linalg.inv(covmat) - - # Negative likelihood - ll = np.log(np.linalg.det(covmat)) + biasdiff @ covmatinv @ biasdiff.T - - return ll.flatten() - - -def find_covparam(x, biasdiff): - bnd = () - theta_init = [] - limits = [0.0001, 0.5] - # limits = [0.0001, 50] - for i in range(0, 3): - bnd += ((limits[0], limits[1]),) - theta_init.append((limits[1]) / 2) - - opval = spo.minimize( - obj_covmle, - theta_init, - method="L-BFGS-B", - options={"gtol": 0.01}, - bounds=bnd, - args=([x, biasdiff]), - ) - - return opval.x[0], opval.x[1], opval.x[2] - - -def bias_predict(emu, theta_mle, x_emu, x, obs, unknowncov=False): - - nx = len(x) - dx = x.shape[1] - xp = [np.concatenate([xc.reshape(1, dx), theta_mle], axis=1) for xc in x] - xp = np.array([m for mesh in xp for m in mesh]) - - # Predict computer model - emupred = emu.predict(x=x_emu, theta=xp) - emumean = emupred.mean() - - # Predict linear bias mean - bias = (obs - emumean).T - model = LinearRegression().fit(x, bias) - mu_bias = model.predict(x) - diff = bias - mu_bias - - if unknowncov: - sigmae_sq, sigmab_sq, lambdap = find_covparam(x, diff.T) - - class biaspred: - def __init__(self, model): - self.model = model - - def predict(self, xnew): - return self.model.predict(xnew).T - - if unknowncov: - - def predictcov(self, xnew): - nx, d = xnew.shape[0], xnew.shape[1] - abs_dist = find_abs_diff(nx, d, xnew) - covmat = gen_cov( - sigmae_sq=sigmae_sq, - sigmab_sq=sigmab_sq, - lambdap=lambdap, - nx=nx, - abs_dist=abs_dist, - ) - return covmat - - biasobj = biaspred(model) - return biasobj diff --git a/PUQ/designmethods/SEQUNIFORM.py b/PUQ/designmethods/SEQUNIFORM.py deleted file mode 100644 index 2b9da27..0000000 --- a/PUQ/designmethods/SEQUNIFORM.py +++ /dev/null @@ -1,208 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - get_emuvar, - multiple_pdfs, -) -from PUQ.designmethods.gen_funcs.acquisition_funcs import maxvar, eivar, maxexp, rnd -from PUQ.designmethods.SEQCALsupport import ( - fit_emulator, - load_H, - update_arrays, - create_arrays, - pad_arrays, - select_condition, - rebuild_condition, -) -from libensemble.message_numbers import ( - STOP_TAG, - PERSIS_STOP, - FINISHED_PERSISTENT_GEN_TAG, - EVAL_GEN_TAG, -) -from libensemble.tools.persistent_support import PersistentSupport -from libensemble.alloc_funcs.start_only_persistent import ( - only_persistent_gens as alloc_f, -) -from libensemble.libE import libE -from libensemble.tools import parse_args, save_libE_output, add_unique_random_streams -from PUQ.prior import prior_dist - - -def fit(fitinfo, data_cls, args): - mini_batch = args["mini_batch"] - n_init_thetas = args["n_init_thetas"] - nworkers = args["nworkers"] - max_evals = args["max_evals"] - - out = data_cls.out - sim_f = data_cls.sim - - sim_specs = { - "sim_f": sim_f, - "in": ["thetas"], - "out": out, - "user": {"function": data_cls.function}, - } - - gen_out = [ - ("thetas", float, data_cls.p), - ("priority", int), - ("obs", float, (1,)), - ("obsvar", float, (1,)), - ("TV", float), - ("HD", float), - ] - - gen_specs = { - "gen_f": gen_f, - "persis_in": [o[0] for o in gen_out] + ["f", "sim_id"], - "out": gen_out, - "user": { - "n_init_thetas": n_init_thetas, # Num thetas in initial batch - "mini_batch": mini_batch, # No. of thetas to generate per step - "nworkers": nworkers, - "synth_cls": data_cls, - }, - } - - alloc_specs = { - "alloc_f": alloc_f, - "user": { - "init_sample_size": 0, - "async_return": True, # True = Return results to gen as they come in (after sample) - "active_recv_gen": True, # Persistent gen can handle irregular communications - }, - } - libE_specs = {"nworkers": nworkers, "comms": "local"} - - persis_info = add_unique_random_streams({}, nworkers + 1) - - # Currently just allow gen to exit if mse goes below threshold value - exit_criteria = {"sim_max": max_evals} # Now just a set number of sims. - - # Perform the run - H, persis_info, flag = libE( - sim_specs, - gen_specs, - exit_criteria, - persis_info, - alloc_specs=alloc_specs, - libE_specs=libE_specs, - ) - - fitinfo["f"] = H["f"] - fitinfo["theta"] = H["thetas"] - fitinfo["TV"] = H["TV"] - fitinfo["HD"] = H["HD"] - return - - -def gen_f(H, persis_info, gen_specs, libE_info): - """Generator to select and obviate parameters for calibration.""" - ps = PersistentSupport(libE_info, EVAL_GEN_TAG) - rand_stream = persis_info["rand_stream"] - n0 = gen_specs["user"]["n_init_thetas"] - mini_batch = gen_specs["user"]["mini_batch"] - n_workers = gen_specs["user"]["nworkers"] - synth_info = gen_specs["user"]["synth_cls"] - - theta_torun = synth_info.theta - obsvar = synth_info.obsvar - data = synth_info.real_data - theta_limits = synth_info.thetalimits - - true_fevals = np.reshape(data[0, :], (1, data.shape[1])) - - n_x = synth_info.d - n_realx = true_fevals.shape[1] - x = synth_info.x - real_x = synth_info.real_x - - obs_offset, theta_offset, generated_no = 0, 0, 0 - TV, HD = 1000, 1000 - fevals, pending, prev_pending, complete, prev_complete = ( - None, - None, - None, - None, - None, - ) - first_iter = True - tag = 0 - - obsvar3d = obsvar.reshape(1, n_x, n_x) - update_model = False - list_id = [] - - theta = 0 - - while tag not in [STOP_TAG, PERSIS_STOP]: - if not first_iter: - # Update fevals from calc_in - update_arrays( - n_x, - fevals, - pending, - complete, - calc_in, - obs_offset, - theta_offset, - list_id, - ) - update_model = rebuild_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ) - - if not update_model: - tag, Work, calc_in = ps.recv() - if tag in [STOP_TAG, PERSIS_STOP]: - break - - if first_iter: - # print('Selecting theta for the first iteration...\n') - - n_init = max(n_workers - 1, n0) - theta = theta_torun[0:n_init, :] - fevals, pending, prev_pending, complete, prev_complete = create_arrays( - n_x, n_init - ) - - H_o = np.zeros(len(theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - first_iter = False - generated_no += n_init - - else: - if select_condition( - complete, prev_complete, n_theta=mini_batch, n_initial=n0 - ): - # print('Selecting theta...\n') - - prev_complete = complete.copy() - new_theta = theta_torun[generated_no : (generated_no + mini_batch), :] - - ( - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) = pad_arrays( - n_x, - new_theta, - theta, - fevals, - pending, - prev_pending, - complete, - prev_complete, - ) - - H_o = np.zeros(len(new_theta), dtype=gen_specs["out"]) - H_o = load_H(H_o, new_theta, TV, HD, generated_no, set_priorities=True) - tag, Work, calc_in = ps.send_recv(H_o) - generated_no += mini_batch - - return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/designmethods/p_sto_bseq.py b/PUQ/designmethods/p_sto_bseq.py deleted file mode 100644 index a666d56..0000000 --- a/PUQ/designmethods/p_sto_bseq.py +++ /dev/null @@ -1,403 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.batch_allocate_reps import allocate -from PUQ.designmethods.gen_funcs.acquire_new import acquire -from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( - multiple_pdfs, - build_emulator, -) -from PUQ.designmethods.batch_support import load_H, update_arrays, create_arrays, pad_arrays, rebuild_condition -from libensemble.message_numbers import STOP_TAG, PERSIS_STOP, FINISHED_PERSISTENT_GEN_TAG, EVAL_GEN_TAG -from libensemble.tools.persistent_support import PersistentSupport -from libensemble.alloc_funcs.start_only_persistent import only_persistent_gens as alloc_f -from libensemble.libE import libE -from libensemble.tools import add_unique_random_streams -import copy -import time - -def fit(fitinfo, - data_cls, - acquisition, - theta=None, - prior=None, - batch_size=None, - data_test=None, - nworkers=None, - max_iter=None, - trace=0, - des_init={}, - des_add={}, - alloc_settings={}, - pc_settings={'standardize': True, 'latent': True}, - des_settings={}, - **kwargs): - - out = data_cls.out - sim = data_cls.sim - - sim_specs = { - 'sim_f': sim, - 'in': ['thetas'], - 'out': out, - 'user': { - 'sim_f': data_cls.sim_f - }, - } - - gen_out = [ - ('thetas', float, data_cls.p), - ('priority', int), - ('obs', float, (1,)), - ('obsvar', float, (1,)), - ('TV', float), - ('HD', float), - ('AE', float), - ('time', float), - ] - - gen_specs = { - 'gen_f': gen_f, - 'persis_in': [o[0] for o in gen_out] + ['f', 'sim_id'], - 'out': gen_out, - "user": { - "batch_size": batch_size, # No. of thetas to generate per step - "nworkers": nworkers, - "theta_init": des_init.get('theta'), - "f_init": des_init.get('f'), - "theta_add": des_add.get('theta'), - "synth_cls": data_cls, - "test_data": data_test, - "prior": prior, - "alloc_settings": alloc_settings, - "acquisition": acquisition, - "pc_settings": pc_settings, - "des_settings": des_settings, - "trace": trace, - }, - } - - alloc_specs = { - 'alloc_f': alloc_f, - 'user': { - 'init_sample_size': 0, - 'async_return': True, # True = Return results to gen as they come in (after sample) - 'active_recv_gen': True, # Persistent gen can handle irregular communications - }, - } - libE_specs = {'nworkers': nworkers, 'comms': 'local'} - #libE_specs = {'nworkers': nworkers, 'comms': 'local', 'sim_dirs_make': True, - # 'sim_dir_copy_files': [os.path.join(os.getcwd(), '48Ca_template.in')]} - - - persis_info = add_unique_random_streams({}, nworkers + 1) - for perid, per in enumerate(persis_info): - #persis_info[perid]['rand_stream'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) - persis_info[perid]['x'] = np.random.default_rng(des_init.get('seed')*(nworkers + 1) + perid) - #persis_info[perid]['rand_stream'] = np.random.default_rng(perid) - - # Currently just allow gen to exit if mse goes below threshold value - exit_criteria = {'sim_max': max_iter} # Now just a set number of sims. - - # Perform the run - H, persis_info, flag = libE( - sim_specs, gen_specs, exit_criteria, persis_info, alloc_specs=alloc_specs, libE_specs=libE_specs - ) - - if data_cls.d == 1: - fitinfo['f'] = H['f'][:, None] - else: - fitinfo['f'] = H['f'] - - if data_cls.p == 1: - fitinfo['theta'] = H['thetas']#[:, None] - else: - fitinfo['theta'] = H['thetas'] - - f_c = np.concatenate((des_init.get('f'), fitinfo['f'].T), axis=1) - t_c = np.concatenate((des_init.get('theta'), fitinfo['theta']), axis=0) - - emu, TV = newiteration(t_c, f_c, data_cls.x, pc_settings, data_test, data_cls.obsvar, data_cls.real_data) - - fitinfo['theta'] = t_c - fitinfo['f'] = f_c - fitinfo['TViter'] = np.concatenate((H['TV'], np.repeat(TV, batch_size))) - fitinfo['theta0'] = emu._info['emulist'][0]['X0'] - fitinfo['reps0'] = emu._info['emulist'][0]['mult'] - - for key in persis_info.keys(): - if isinstance(persis_info[key], dict): - if 'additional' in persis_info[key].keys(): - fitinfo['TV'] = persis_info[key]['additional']['TVs'] - fitinfo['TV'].append(TV) - fitinfo['iter_explore'] = persis_info[key]['additional']['iter_explore'] - fitinfo['iter_exploit'] = persis_info[key]['additional']['iter_exploit'] - fitinfo['time'] = persis_info[key]['additional']['times'] - return - -def newiteration(theta, fevals, x, pc_settings, test_data, obsvar, obs): - - thetatest, ptest, ftest, priortest = None, None, None, None - if test_data is not None: - thetatest, ptest, ftest, priortest = ( - test_data["theta"], - test_data["p"], - test_data["f"], - test_data["p_prior"], - ) - - emu = build_emulator(x, theta, fevals, pc_settings) - - d = len(x) - obsvar3d = obsvar.reshape(1, d, d) - - # ntest x d - pred = emu.predict(x=x, theta=thetatest) - mu = pred.mean().T - S = pred._info['S'] - St = np.transpose(S, (2, 0, 1)) - N = St + obsvar3d - phat = multiple_pdfs(obs, mu, N) - - # Obtain the accuracy on the test set - if ptest is not None: - TV = np.mean(np.abs(ptest - phat)) - - return emu, TV - -def onebatch(b, - b_new, - r_new, - emu, - prior_func, - acqfunc, - data_cls, - test_data, - alloc_settings, - pc_settings, - des_settings, - iter_exploit, - iter_explore, - is_exploit, - is_explore, - rand_stream, - trace): - - ivar_exploit, ivar_explore = np.inf, np.inf - - emu_original_info = copy.deepcopy(emu._info) - - if is_exploit: - # Allocate existing ones - allocate_obj = allocate(budget=b, - emu_info=emu, - prior=prior_func, - func_cls=data_cls, - theta_mesh=test_data["theta"], - method=alloc_settings.get('method'), - alloc_settings=alloc_settings, - rand_stream=rand_stream, - trace=trace) - allocate_obj.allocatereps() - if not is_explore: - ivar_exploit = 0 - else: - ivar_exploit = allocate_obj.ivar_exploit(emu, pc_settings) - - r_exploit = allocate_obj.reps - theta_exploit = allocate_obj.theta - - emu._info = emu_original_info - - if is_explore: - # Find new ones - acquire_obj = acquire(bnew=b_new, - rep=r_new, - emu=emu, - func_cls=data_cls, - theta_mesh=test_data["theta"], - prior=prior_func, - method=acqfunc, - nL=des_settings.get('nL'), - pc_settings=pc_settings, - rand_stream=rand_stream, - impute_str=des_settings.get('impute_str'), - skip=not is_exploit) - - acquire_obj.acquire_new() - - ivar_explore = acquire_obj.ivar - theta_explore = acquire_obj.tnew - - if ivar_exploit <= ivar_explore: - new_theta = np.repeat(theta_exploit, r_exploit, axis=0) - iter_exploit += 1 - else: - new_theta = np.repeat(theta_explore, r_new, axis=0) - iter_explore += 1 - - return new_theta, iter_exploit, iter_explore - -def gen_f(H, persis_info, gen_specs, libE_info): - - """Generator to select and obviate parameters for calibration.""" - ps = PersistentSupport(libE_info, EVAL_GEN_TAG) - rand_stream = persis_info['rand_stream'] - - b = gen_specs["user"]["batch_size"] - n_workers = gen_specs["user"]["nworkers"] - - data_cls = gen_specs["user"]["synth_cls"] - test_data = gen_specs["user"]["test_data"] - prior_func = gen_specs["user"]["prior"] - theta_init = gen_specs["user"]["theta_init"] - f_init = gen_specs["user"]["f_init"] - theta_add = gen_specs["user"]["theta_add"] - - alloc_settings = gen_specs["user"]["alloc_settings"] - acqfunc = gen_specs["user"]["acquisition"] - pc_settings = gen_specs["user"]["pc_settings"] - des_settings = gen_specs["user"]["des_settings"] - trace = gen_specs["user"]["trace"] - - # data params - d = data_cls.d - x = data_cls.x - y = data_cls.real_data - Sigma = data_cls.obsvar - - # explore params - rho = alloc_settings.get('rho') - if rho is not None: - b_new = int(b*rho) - r_new = int(b/b_new) - - obs_offset, theta_offset = 0, 0 - - fevals, pending, prev_pending, complete, prev_complete = None, None, None, None, None - first_iter, update_model = True, False - tag = 0 - iter_explore, iter_exploit = 0, 0 - is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') - counter = 0 - TVs, times = [], [] - - print(f_init.shape) - - while tag not in [STOP_TAG, PERSIS_STOP]: - - if not first_iter: - # Update fevals from calc_in - update_arrays(d, - fevals, - pending, - complete, - calc_in, - obs_offset, - theta_offset) - update_model = rebuild_condition(complete, prev_complete, n_theta=b) - - if not update_model: - tag, Work, calc_in = ps.recv() - if tag in [STOP_TAG, PERSIS_STOP]: - break - - if update_model: - - if trace == 1: - print('Percentage Pending: %0.2f ( %d / %d)' % (100*np.round(np.mean(pending), 4), - np.sum(pending), - np.prod(pending.shape))) - print('Percentage Complete: %0.2f ( %d / %d)' % (100*np.round(np.mean(complete), 4), - np.sum(complete), - np.prod(pending.shape))) - - f_c = np.concatenate((f_init, fevals), axis=1) - t_c = np.concatenate((theta_init, theta), axis=0) - - print(f_c.shape) - start_emu = time.time() - emu, TV = newiteration(t_c, f_c, x, pc_settings, test_data, Sigma, y) - end_emu = time.time() - TVs.append(TV) - - prev_pending = pending.copy() - update_model = False - - if first_iter: - - start_emu = time.time() - emu, TV = newiteration(theta_init, f_init, x, pc_settings, test_data, Sigma, y) - end_emu = time.time() - TVs.append(TV) - - n_init = n_workers - 1 - if theta_add is None: - start_batch = time.time() - theta, iter_exploit, iter_explore = onebatch(b, - b_new, - r_new, - emu, - prior_func, - acqfunc, - data_cls, - test_data, - alloc_settings, - pc_settings, - des_settings, - iter_exploit, - iter_explore, - is_exploit, - is_explore, - rand_stream, - trace) - end_batch = time.time() - times.append(end_batch - start_batch + end_emu - start_emu) - else: - cand_theta = theta_add[counter*b_new:(counter + 1)*b_new, :] - theta = np.repeat(cand_theta, r_new, axis=0) - - fevals, pending, prev_pending, complete, prev_complete = create_arrays(d, n_init) - H_o = np.zeros(len(theta), dtype=gen_specs['out']) - H_o = load_H(H_o, theta, TV) - tag, Work, calc_in = ps.send_recv(H_o) - first_iter = False - counter += 1 - - else: - if rebuild_condition(complete, prev_complete, n_theta=b): - - prev_complete = complete.copy() - if theta_add is None: - start_batch = time.time() - new_theta, iter_exploit, iter_explore = onebatch(b, - b_new, - r_new, - emu, - prior_func, - acqfunc, - data_cls, - test_data, - alloc_settings, - pc_settings, - des_settings, - iter_exploit, - iter_explore, - is_exploit, - is_explore, - rand_stream, - trace) - end_batch = time.time() - times.append(end_batch - start_batch + end_emu - start_emu) - else: - cand_theta = theta_add[counter*b_new:(counter + 1)*b_new, :] - new_theta = np.repeat(cand_theta, r_new, axis=0) - - theta, fevals, pending, prev_pending, complete, prev_complete = \ - pad_arrays(d, new_theta, theta, fevals, pending, prev_pending, complete, prev_complete) - - H_o = np.zeros(len(new_theta), dtype=gen_specs['out']) - H_o = load_H(H_o, new_theta, TV) - tag, Work, calc_in = ps.send_recv(H_o) - counter += 1 - - persis_info['additional'] = {'TVs':TVs, 'iter_exploit':iter_exploit, 'iter_explore':iter_explore, 'times':times} - return None, persis_info, FINISHED_PERSISTENT_GEN_TAG diff --git a/PUQ/designmethods/sequential.py b/PUQ/designmethods/sequential.py deleted file mode 100644 index 862be45..0000000 --- a/PUQ/designmethods/sequential.py +++ /dev/null @@ -1,183 +0,0 @@ -from hetgpy import hetGP -import numpy as np -import matplotlib.pyplot as plt -from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from PUQ.designmethods.gen_funcs.acquisition import var, ivar, imse, lookahead -import time - - -class sequential_design: - def __init__(self, cls_func, trace=True): - self.cls_func = cls_func - self.trace = trace - self.y = self.cls_func.real_data - self.d = self.cls_func.d - self.Sigma = self.cls_func.obsvar - self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) - self.detSigma = np.linalg.det(self.Sigma) - return - - def __getitem__(self, key): - return self.__dict__[key] - - def __setitem__(self, item, value): - self.__dict__[item] = value - - def get(self, key): - return self.__dict__.get(key) - - def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): - - if test is not None: - self.test_data_gen(test) - - H = [] - timel = [] - metric_sum = {} - for t in range(0, T): - # print(f"t: {t}") if self.trace else None - print(t) - tic = time.time() - - # hetGP - model = hetGP() - model.mle(X=z0, - Z=f0, - covtype="Gaussian", - known={"beta0":np.mean(f0)}) - - toc = time.time() - timel.append(toc - tic) - print(toc - tic) - args["seed"] += 1 - - if test is not None: - metric_sum = self.eval_perf(model, args.get("extra_metric", True)) - - acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) - - acq_func.acquire_new() - znew = acq_func.znew - fnew = self.cls_func.sim_f(znew.flatten(), persis_info=persis_info).reshape( - 1, 1 - ) - - f0 = np.concatenate((f0, fnew), axis=0) - z0 = np.concatenate((z0, znew), axis=0) - - H.append( - { - "t": t, - "new": acq_func.new, - "h": getattr(acq_func, "h", None), - "f": fnew, - "z": znew, - "MSE": metric_sum.get("MSE", None), - "MAD": metric_sum.get("MAD", None), - "VAR": metric_sum.get("VAR", None), - "MSEy": metric_sum.get("MSEy", None), - "MADy": metric_sum.get("MADy", None), - "MSEn": metric_sum.get("MSEn", None), - "MADn": metric_sum.get("MADn", None), - "summary_metric": metric_sum.get("sum_metric", None), - } - ) - - unique_rows, counts = np.unique(z0, axis=0, return_counts=True) - - self.xt = unique_rows - self.reps = counts - self.fs = f0 - self.zs = z0 - self.time = timel - self.H = H - - return self - - def test_data_gen(self, test): - - # Gen test data - self.ttest, self.ptest, self.wtest, self.ntest, self.ftest = ( - test["theta"], - test["p"], - test["w"], - test["noise"], - test["f"], - ) - # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ftest) - # plt.show() - - # plt.scatter(self.ttest[:, 0], self.ttest[:, 1], c=self.ntest) - # plt.show() - - x_tiled = np.tile(self.cls_func.x, (self.ttest.shape[0], 1)) - t_repeated = np.repeat(self.ttest, self.cls_func.x.shape[0], axis=0) - self.ztest = np.hstack([x_tiled, t_repeated]) - - # To be used for performance evaluations - ntot, nm, d = self.ztest.shape[0], self.ttest.shape[0], self.cls_func.d - - # to construct S matrix - self.idr = np.arange(0, ntot)[:, None] - idc = np.arange(0, ntot).reshape(nm, d) - self.idc = np.repeat(idc, repeats=d, axis=0) - - def eval_perf(self, model, extra_metric=False): - - nm, d = self.ttest.shape[0], self.cls_func.d - # sc = plt.scatter(self.ztest[:, 1], self.ztest[:, 2], c=self.wtest, cmap="viridis") # Use any colormap - # plt.colorbar(sc, label="wtest values") # Add a colorbar - # plt.show() - - # predict at mesh - pr = model.predict(x=self.ztest, xprime=self.ztest) - - # ntot, ntot x ntot, ntot - mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] - - muT = mu.reshape(nm, d) - S = Sn[self.idr, self.idc].reshape(nm, d, d) - - N = S + self.Sigma3d - g = multiple_pdfs(self.y, muT, N) - - MSE = np.mean(((g.flatten() - self.ptest.flatten()) ** 2) * self.wtest) - MAD = np.mean(np.abs(g.flatten() - self.ptest.flatten()) * self.wtest) - - M = S + 0.5 * self.Sigma3d - f = multiple_pdfs(self.y, muT, M) - - twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) - VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) - - if extra_metric: - diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) - diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) - - - MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) - MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) - - MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) - MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) - - # Weighted absolute differences - f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) - n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) - - # Unweighted absolute differences - f_wh = np.mean(np.abs(diff_y), axis=0) - n_wh = np.mean(np.abs(diff_n), axis=0) - summary = {} - for di in range(d): - summary[f"f{di+1}w"] = f_w[di] - summary[f"f{di+1}"] = f_wh[di] - summary[f"n{di+1}w"] = n_w[di] - summary[f"n{di+1}"] = n_wh[di] - else: - MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 - - metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, - "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, - "summary": summary} - return metric_sum diff --git a/PUQ/designmethods/sequential_md_deterministic_old.py b/PUQ/designmethods/sequential_md_deterministic_old.py deleted file mode 100644 index 4a447a1..0000000 --- a/PUQ/designmethods/sequential_md_deterministic_old.py +++ /dev/null @@ -1,114 +0,0 @@ -import numpy as np -from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from PUQ.designmethods.gen_funcs.acquisition_md_deterministic_old import ivar, var, imse, rnd, exp -import time -from PUQ.surrogate import emulator - -class sequential_design: - def __init__(self, cls_func, trace=True): - self.cls_func = cls_func - self.trace = trace - self.y = self.cls_func.real_data - self.d = self.cls_func.d - self.Sigma = self.cls_func.obsvar - self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) - self.detSigma = np.linalg.det(self.Sigma) - return - - def __getitem__(self, key): - return self.__dict__[key] - - def __setitem__(self, item, value): - self.__dict__[item] = value - - def get(self, key): - return self.__dict__.get(key) - - def build_design(self, z0, f0, T, af, test=None, args={}): - - self.test = test - args["rand_stream"] = np.random.default_rng(args["seed"]) - H = [] - timel = [] - metric_sum = {} - print(T) - for t in range(0, T): - # print(f"t: {t}") if self.trace else None - print(t) - tic = time.time() - - model = emulator(x=self.cls_func.x, - theta=z0, - f=f0, - method="pcHomGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - - toc = time.time() - timel.append(toc - tic) - - args["seed"] += 1 - - if test is not None: - metric_sum = self.eval_perf(model) - - acq_func = eval(af)(model=model, cls_func=self.cls_func, args=args) - - acq_func.acquire_new() - tnew = acq_func.znew - - fnew = self.cls_func.function(tnew.flatten()[0], tnew.flatten()[1]).reshape( - 1, self.cls_func.d - ) - - f0 = np.concatenate((f0, fnew), axis=0) - z0 = np.concatenate((z0, tnew), axis=0) - - H.append( - { - "t": t, - "f": fnew, - "z": tnew, - "MAD": metric_sum.get("MAD", None) - } - ) - - unique_rows, counts = np.unique(z0, axis=0, return_counts=True) - - self.t = unique_rows - self.reps = counts - self.fs = f0 - self.zs = z0 - self.time = timel - self.H = H - - return self - - - def eval_perf(self, model): - - nm, d = self.test["theta"].shape[0], self.cls_func.d - - # predict at mesh - pr = model.predict(theta=self.test["theta"], thetaprime=self.test["theta"]) - - # ntot, ntot x ntot, ntot - mu, Sn = pr._info["mean"], pr._info["S"] - - muT = mu.reshape(nm, d) - S = Sn.transpose(2, 0, 1) # Sn[self.idr, self.idc].reshape(nm, d, d) - - N = S + self.Sigma3d - g = multiple_pdfs(self.y, muT, N) - - - MSE = np.mean(((g.flatten() - self.test["p"].flatten()) ** 2)) - MAD = np.mean(np.abs(g.flatten() - self.test["p"].flatten())) - - metric_sum = {"MAD": MAD, "MSE": MSE} - return metric_sum From 5c52e33215ec4329a39409bcbce3107662876f31 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 30 Sep 2025 18:00:01 -0400 Subject: [PATCH 059/103] Cleaning redundant emulator and acquisition scripts --- PUQ/designmethods/gen_funcs/CEIVAR.py | 176 --- PUQ/designmethods/gen_funcs/CEIVARX.py | 219 ---- PUQ/designmethods/gen_funcs/CIMSPE.py | 46 - PUQ/designmethods/gen_funcs/CMAXVAR.py | 56 - PUQ/designmethods/gen_funcs/EI.py | 104 -- PUQ/designmethods/gen_funcs/EIVAR.py | 105 -- PUQ/designmethods/gen_funcs/HYBRID_EI.py | 120 -- PUQ/designmethods/gen_funcs/MAXEXP.py | 90 -- PUQ/designmethods/gen_funcs/MAXVAR.py | 85 -- PUQ/designmethods/gen_funcs/PI.py | 89 -- PUQ/designmethods/gen_funcs/RND.py | 21 - PUQ/designmethods/gen_funcs/acquisition.py | 762 ------------ .../acquisition_md_deterministic_old.py | 306 ----- PUQ/surrogatemethods/PCGP.py | 833 ------------- PUQ/surrogatemethods/PCGPexp.py | 1059 ----------------- PUQ/surrogatemethods/highdimHetGP.py | 214 ---- PUQ/surrogatemethods/pcHetGP.py | 222 ---- PUQ/surrogatemethods/pcHomGP.py | 224 ---- PUQ/surrogatesupport/__init__.py | 0 PUQ/surrogatesupport/matern_covmat.pyx | 60 - 20 files changed, 4791 deletions(-) delete mode 100644 PUQ/designmethods/gen_funcs/CEIVAR.py delete mode 100644 PUQ/designmethods/gen_funcs/CEIVARX.py delete mode 100644 PUQ/designmethods/gen_funcs/CIMSPE.py delete mode 100644 PUQ/designmethods/gen_funcs/CMAXVAR.py delete mode 100644 PUQ/designmethods/gen_funcs/EI.py delete mode 100644 PUQ/designmethods/gen_funcs/EIVAR.py delete mode 100644 PUQ/designmethods/gen_funcs/HYBRID_EI.py delete mode 100644 PUQ/designmethods/gen_funcs/MAXEXP.py delete mode 100644 PUQ/designmethods/gen_funcs/MAXVAR.py delete mode 100644 PUQ/designmethods/gen_funcs/PI.py delete mode 100644 PUQ/designmethods/gen_funcs/RND.py delete mode 100644 PUQ/designmethods/gen_funcs/acquisition.py delete mode 100644 PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py delete mode 100644 PUQ/surrogatemethods/PCGP.py delete mode 100644 PUQ/surrogatemethods/PCGPexp.py delete mode 100644 PUQ/surrogatemethods/highdimHetGP.py delete mode 100644 PUQ/surrogatemethods/pcHetGP.py delete mode 100644 PUQ/surrogatemethods/pcHomGP.py delete mode 100644 PUQ/surrogatesupport/__init__.py delete mode 100644 PUQ/surrogatesupport/matern_covmat.pyx diff --git a/PUQ/designmethods/gen_funcs/CEIVAR.py b/PUQ/designmethods/gen_funcs/CEIVAR.py deleted file mode 100644 index adf8759..0000000 --- a/PUQ/designmethods/gen_funcs/CEIVAR.py +++ /dev/null @@ -1,176 +0,0 @@ -import numpy as np -from PUQ.surrogatemethods.PCGPexp import temp_postphimat, postphimat -from smt.sampling_methods import LHS -from numpy.random import rand -import scipy.stats as sps - - -def ceivar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - xmesh=None, - thetamesh=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, -): - """ - Smat3D : ncand x nfield x nfield (3D) Emulator Covariance matrix - pred_mean : ncand x nfield (2D) Emulator Mean matrix - rVh_1_3d : ncand x nfield x nacquired (3D) matrix - """ - - p = theta.shape[1] - dt = thetamesh.shape[1] - dx = x.shape[1] - n_x = x.shape[0] - - # REMOVE THESE LINES - # sampling = LHS(xlimits=thetalimits[dx:, :]) - # thetamesh = sampling(synth_info.meshsize) - # print('mesh size') - # print(thetamesh.shape) - - xuniq = np.unique(x, axis=0) - if synth_info.data_name == "covid19": - clist = construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t) - elif synth_info.data_name == "highdim": - clist = construct_candlist_high(thetalimits, xuniq, prior_func, prior_func_t) - else: - clist = construct_candlist(thetalimits, xuniq, prior_func, prior_func_t) - - xt_ref = np.array([np.concatenate([xc, th]) for th in thetamesh for xc in x]) - Smat3D, rVh_1_3d, pred_mean = temp_postphimat(emu._info, n_x, xt_ref, obs, obsvar) - - eivar_val = np.zeros(len(clist)) - for xt_id, xt_c in enumerate(clist): - eivar_val[xt_id] = postphimat( - emu._info, - n_x, - xt_ref, - obs, - obsvar, - xt_c.reshape(1, p), - Smat3D, - rVh_1_3d, - pred_mean, - ) - - th_cand = clist[np.argmax(eivar_val), :].reshape(1, p) - - return th_cand - - -def ceivarbias( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - xmesh=None, - thetamesh=None, - posttest=None, - emubias=None, - synth_info=None, - theta_mle=None, - unknowncov=None, -): - - p = theta.shape[1] - dt = thetamesh.shape[1] - dx = x.shape[1] - n_x = x.shape[0] - x_emu = np.arange(0, 1)[:, None] - - bias_mean = emubias.predict(x) - if unknowncov: - bias_var = emubias.predictcov(x) - else: - bias_var = 1 * obsvar - - xuniq = np.unique(x, axis=0) - # Create a candidate list - if synth_info.data_name == "covid19": - clist = construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t) - else: - clist = construct_candlist(thetalimits, xuniq, prior_func, prior_func_t) - - thetatest = np.array([np.concatenate([xc, th]) for th in thetamesh for xc in x]) - - Smat3D, rVh_1_3d, pred_mean = temp_postphimat( - emu._info, n_x, thetatest, obs, bias_var - ) - eivar_val = np.zeros(len(clist)) - - for xt_id, xt_c in enumerate(clist): - eivar_val[xt_id] = postphimat( - emu._info, - n_x, - thetatest, - obs - bias_mean, - bias_var, - xt_c.reshape(1, p), - Smat3D, - rVh_1_3d, - pred_mean, - ) - th_cand = clist[np.argmax(eivar_val), :].reshape(1, p) - - return th_cand - - -def construct_candlist(thetalimits, xuniq, prior_func, prior_func_t): - - n0 = 100 - n_clist = n0 * len(xuniq) - t_unif = prior_func_t.rnd(n0, None) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - clist2 = prior_func.rnd(n_clist, None) - clist = np.concatenate((clist1, clist2), axis=0) - return clist - - -def construct_candlist_high(thetalimits, xuniq, prior_func, prior_func_t): - d_x = xuniq.shape[1] - sampling = LHS(xlimits=thetalimits[d_x:, :]) - t_unif = sampling(500) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - sampling = LHS(xlimits=thetalimits) - clist2 = sampling(1000) - clist = np.concatenate((clist1, clist2), axis=0) - return clist - - -def construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t): - - # 1000 = 100 x nx - n0 = 100 - nx = len(xuniq) - t_unif = prior_func_t.rnd(n0, None) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - - # 1000 = 100 x nx - xref_sample = np.random.choice(a=189, size=nx, replace=False)[:, None] / 188 - t_unif = prior_func_t.rnd(n0, None) - clist2 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xref_sample]) - clist = np.concatenate((clist1, clist2), axis=0) - - return clist diff --git a/PUQ/designmethods/gen_funcs/CEIVARX.py b/PUQ/designmethods/gen_funcs/CEIVARX.py deleted file mode 100644 index a92de7f..0000000 --- a/PUQ/designmethods/gen_funcs/CEIVARX.py +++ /dev/null @@ -1,219 +0,0 @@ -import numpy as np -from PUQ.surrogatemethods.PCGPexp import temp_postphimat, postphimat -from smt.sampling_methods import LHS -from numpy.random import rand -import scipy.stats as sps - - -def ceivarx( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - x_ref=None, - theta_ref=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, -): - - x_emu = np.arange(0, 1)[:, None] - xuniq = np.unique(x, axis=0) - - # Create a candidate list - if synth_info.data_name == "covid19": - clist = construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t) - elif synth_info.data_name == "highdim": - clist = construct_candlist_high(thetalimits, xuniq, prior_func, prior_func_t) - else: - clist = construct_candlist(thetalimits, xuniq, prior_func, prior_func_t) - - dx = x_ref.shape[1] - nx_ref = x_ref.shape[0] - nt_ref = theta_ref.shape[0] - dt = theta_ref.shape[1] - nf = x.shape[0] - # Get estimate for real data at theta_mle for reference x - # nx_ref x (d_x + d_t) - xt_ref = [np.concatenate([xc.reshape(1, dx), theta_mle], axis=1) for xc in x_ref] - xt_ref = np.array([m for mesh in xt_ref for m in mesh]) - - # 1 x nx_ref - y_ref = emu.predict(x=x_emu, theta=xt_ref).mean() - # nx_ref x nf - f_temp_rep = np.repeat(obs, nx_ref, axis=0) - - # nx_ref x (nf + 1) - f_field_rep = np.concatenate((f_temp_rep, y_ref.T), axis=1) - - xs = [np.concatenate([x, xc.reshape(1, dx)], axis=0) for xc in x_ref] - ts = [np.repeat(theta_mle.reshape(1, dt), nf + 1, axis=0)] - mesh_grid = [np.concatenate([xc, th], axis=1).tolist() for xc in xs for th in ts] - mesh_grid = np.array([m for mesh in mesh_grid for m in mesh]) - - n_x = nf + 1 - - # Construct obsvar - obsvar3D = np.zeros(shape=(nx_ref, n_x, n_x)) - for i in range(nx_ref): - obsvar3D[i, :, :] = np.diag(np.repeat(synth_info.sigma2, n_x)) - - Smat3D, rVh_1_3d, pred_mean = temp_postphimat( - emu._info, n_x, mesh_grid, f_field_rep, obsvar3D - ) - eivar_val = np.zeros(len(clist)) - for xt_id, x_c in enumerate(clist): - xt_cand = x_c.reshape(1, dx + dt) - eivar_val[xt_id] = postphimat( - emu._info, - n_x, - mesh_grid, - f_field_rep, - obsvar3D, - xt_cand, - Smat3D, - rVh_1_3d, - pred_mean, - ) - - maxid = np.argmax(eivar_val) - xnew = clist[maxid].reshape(1, dx + dt) - return xnew - - -def ceivarxbias( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - x_ref=None, - theta_ref=None, - posttest=None, - emubias=None, - synth_info=None, - theta_mle=None, - unknowncov=None, -): - - x_emu = np.arange(0, 1)[:, None] - xuniq = np.unique(x, axis=0) - - # Create a candidate list - if synth_info.data_name == "covid19": - clist = construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t) - else: - clist = construct_candlist(thetalimits, xuniq, prior_func, prior_func_t) - - nx_ref = x_ref.shape[0] - dx = x_ref.shape[1] - nt_ref = theta_ref.shape[0] - dt = theta_ref.shape[1] - nf = x.shape[0] - - # Get estimate for real data at theta_mle for reference x - # nx_ref x (d_x + d_t) - xt_ref = [np.concatenate([xc.reshape(1, dx), theta_mle], axis=1) for xc in x_ref] - xt_ref = np.array([m for mesh in xt_ref for m in mesh]) - - # 1 x nx_ref - y_ref = emu.predict(x=x_emu, theta=xt_ref).mean() - - # nx_ref x nf - bias_mean = emubias.predict(x) - f_temp_rep = np.repeat(obs - bias_mean, nx_ref, axis=0) - # nx_ref x (nf + 1) - f_field_rep = np.concatenate((f_temp_rep, (y_ref).T), axis=1) - - xs = [np.concatenate([x, xc.reshape(1, dx)], axis=0) for xc in x_ref] - ts = [np.repeat(theta_mle.reshape(1, dt), nf + 1, axis=0)] - mesh_grid = [np.concatenate([xc, th], axis=1).tolist() for xc in xs for th in ts] - mesh_grid = np.array([m for mesh in mesh_grid for m in mesh]) - - n_x = nf + 1 - - # Construct obsvar - obsvar3D = np.zeros(shape=(nx_ref, n_x, n_x)) - for i in range(nx_ref): - xnew = np.concatenate((x, x_ref[i].reshape(1, dx)), axis=0) - if unknowncov: - obsvar3D[i, :, :] = emubias.predictcov(xnew) - else: - obsvar3D[i, :, :] = np.diag(np.repeat(synth_info.sigma2, n_x)) - - Smat3D, rVh_1_3d, pred_mean = temp_postphimat( - emu._info, n_x, mesh_grid, f_field_rep, obsvar3D - ) - - eivar_val = np.zeros(len(clist)) - for xt_id, x_c in enumerate(clist): - xt_cand = x_c.reshape(1, dx + dt) - eivar_val[xt_id] = postphimat( - emu._info, - n_x, - mesh_grid, - f_field_rep, - obsvar3D, - xt_cand, - Smat3D, - rVh_1_3d, - pred_mean, - ) - - maxid = np.argmax(eivar_val) - xnew = clist[maxid].reshape(1, dx + dt) - return xnew - - -def construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t): - - # 1000 = 100 x nx - n0 = 100 - nx = len(xuniq) - t_unif = prior_func_t.rnd(n0, None) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - - # 1000 = 100 x nx - xref_sample = np.random.choice(a=189, size=nx, replace=False)[:, None] / 188 - t_unif = prior_func_t.rnd(n0, None) - clist2 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xref_sample]) - clist = np.concatenate((clist1, clist2), axis=0) - - return clist - - -def construct_candlist(thetalimits, xuniq, prior_func, prior_func_t): - n0 = 100 - n_clist = n0 * len(xuniq) - t_unif = prior_func_t.rnd(n0, None) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - clist2 = prior_func.rnd(n_clist, None) - clist = np.concatenate((clist1, clist2), axis=0) - return clist - - -def construct_candlist_high(thetalimits, xuniq, prior_func, prior_func_t): - d_x = xuniq.shape[1] - sampling = LHS(xlimits=thetalimits[d_x:, :]) - t_unif = sampling(500) - clist1 = np.array([np.concatenate([xc, th]) for th in t_unif for xc in xuniq]) - sampling = LHS(xlimits=thetalimits) - clist2 = sampling(1000) - clist = np.concatenate((clist1, clist2), axis=0) - return clist diff --git a/PUQ/designmethods/gen_funcs/CIMSPE.py b/PUQ/designmethods/gen_funcs/CIMSPE.py deleted file mode 100644 index 4e75b65..0000000 --- a/PUQ/designmethods/gen_funcs/CIMSPE.py +++ /dev/null @@ -1,46 +0,0 @@ -import numpy as np -from PUQ.surrogatemethods.PCGPexp import imspe_acq -from smt.sampling_methods import LHS - - -def imspe( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - x_ref=None, - theta_ref=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, -): - - # Create a candidate list - sampling = LHS(xlimits=thetalimits) - clist = sampling(1500) - - # Create grid - sampling = LHS(xlimits=thetalimits) - mesh_grid = sampling(1500) - - dx = x_ref.shape[1] - dt = theta_ref.shape[1] - - imspe_val = np.zeros(len(clist)) - for xt_id, x_c in enumerate(clist): - xt_cand = x_c.reshape(1, dx + dt) - imspe_val[xt_id] = imspe_acq(emu._info, mesh_grid, xt_cand) - - maxid = np.argmax(imspe_val) - xnew = clist[maxid].reshape(1, dx + dt) - - return xnew diff --git a/PUQ/designmethods/gen_funcs/CMAXVAR.py b/PUQ/designmethods/gen_funcs/CMAXVAR.py deleted file mode 100644 index 6d61570..0000000 --- a/PUQ/designmethods/gen_funcs/CMAXVAR.py +++ /dev/null @@ -1,56 +0,0 @@ -import numpy as np -from PUQ.surrogatemethods.PCGPexp import temp_postphimat, postphimat -from smt.sampling_methods import LHS -from numpy.random import rand -import scipy.stats as sps - - -def maxvar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - xmesh=None, - thetamesh=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, -): - """ - Smat3D : ncand x nfield x nfield (3D) Emulator Covariance matrix - pred_mean : ncand x nfield (2D) Emulator Mean matrix - rVh_1_3d : ncand x nfield x nacquired (3D) matrix - """ - - p = theta.shape[1] - dt = thetamesh.shape[1] - dx = x.shape[1] - n_x = x.shape[0] - - xuniq = np.unique(x, axis=0) - # if synth_info.data_name == 'covid19': - # clist = construct_candlist_covid(thetalimits, xuniq, prior_func, prior_func_t) - # else: - clist = construct_candlist(thetalimits, xuniq, prior_func, prior_func_t) - - emupred = emu.predict(x=np.arange(0, 1)[:, None], theta=clist) - emuvar = emupred.var() - th_cand = clist[np.argmax(emuvar.flatten()), :].reshape(1, p) - return th_cand - - -def construct_candlist(thetalimits, xuniq, prior_func, prior_func_t): - - # Create a candidate list - sampling = LHS(xlimits=thetalimits) - clist = sampling(1500) - return clist diff --git a/PUQ/designmethods/gen_funcs/EI.py b/PUQ/designmethods/gen_funcs/EI.py deleted file mode 100644 index 99447e3..0000000 --- a/PUQ/designmethods/gen_funcs/EI.py +++ /dev/null @@ -1,104 +0,0 @@ -import numpy as np -import scipy - - -def eifunc(clist, x, obs, obsvar, emu, delta): - - ei_val = np.zeros(len(clist)) - pp = emu.predict(x, clist) - ppmean = pp.mean() - ppvar = pp.var() - - for l in range(ppmean.shape[0]): - diff = obs[0, l] - ppmean[l, :] - sumvar = np.diag(obsvar)[l] + ppvar[l, :] - - p2 = 1 - scipy.stats.norm.cdf((delta[l] - diff) / np.sqrt(sumvar), 0, 1) - i2 = diff - delta[l] - pdfval = scipy.stats.norm.pdf((delta[l] - diff) / np.sqrt(sumvar), 0, 1) - r2 = np.sqrt(sumvar) * pdfval - - p1 = scipy.stats.norm.cdf((-delta[l] - diff) / np.sqrt(sumvar), 0, 1) - i1 = -diff - delta[l] - pdfval = scipy.stats.norm.pdf((-delta[l] - diff) / np.sqrt(sumvar), 0, 1) - r1 = np.sqrt(sumvar) * pdfval - - ei_val += -1 * ((p1 * i1 + r1) + (p2 * i2 + r2)) - - return ei_val - - -def ei( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=0, - candsize=100, - refsize=100, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - fevals_c = fevals[:, ~idnan] - - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - p = theta.shape[1] - theta_acq, f_acq = [], [] - - # Best error - error = np.sum(np.abs(obs - fevals_c.T), axis=1) - delta = np.abs(obs - fevals_c.T)[np.argmin(error)] - liar = np.mean(fevals_c) - - for i in range(n): - clist = prior_func.rnd(candsize, None) - acq_val = eifunc(clist, x, obs, obsvar, emu, delta) - - if n == 1: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - break - else: - if believer == 0: - idc = np.argsort(acq_val)[::-1][:n] - ctheta = clist[idc, :].reshape((n, p)) - theta_acq.append(ctheta) - break - - elif believer == 1: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - elif believer == 2: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Constant-liar strategy - fevalsnew = liar.reshape(1, 1) - emu.update(theta=ctheta, f=fevalsnew) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/EIVAR.py b/PUQ/designmethods/gen_funcs/EIVAR.py deleted file mode 100644 index 8d353f0..0000000 --- a/PUQ/designmethods/gen_funcs/EIVAR.py +++ /dev/null @@ -1,105 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - compute_postvar, - compute_eivar, - multiple_pdfs, - get_emuvar, -) -from smt.sampling_methods import LHS -import scipy - - -def eivar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=None, - candsize=None, - refsize=None, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - fevals_c = fevals[:, ~idnan] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq = [] - n_x, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, n_x, n_x) - - liar = np.mean(fevals_c) - - # print(fevals_c.shape) - - for i in range(n): - clist = prior_func.rnd(candsize, None) - - emupredict = emu.predict(x, thetatest) - emumean = emupredict.mean() - emumeanT = emumean.T - emuvar, is_cov = get_emuvar(emupredict) - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - - # Get the n_ref x d x d x n_cand phi matrix - emuphi4d = emu.acquisition(x=x, theta1=thetatest, theta2=clist) - acq_func = [] - - # Pass over all the candidates - for c_id in range(len(clist)): - posteivar = compute_eivar( - var_obsvar1[:, real_x, real_x.T], - emuphi4d[:, real_x, real_x.T, c_id], - emumeanT[:, real_x.flatten()], - emuvar[real_x, :, real_x.T], - obs, - is_cov, - posttest, - ) - acq_func.append(posteivar) - - if n == 1: - idc = np.argmin(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - break - else: - if believer == 1: - idc = np.argmin(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - elif believer == 2: - idc = np.argmin(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - - # Constant liar strategy - fevalsnew = liar.reshape(1, 1) - emu.update(theta=ctheta, f=fevalsnew) - - theta_acq.append(ctheta) - - # clist = np.delete(clist, idc, 0) - # Kriging believer strategy - # fevalsnew = emu.predict(x=x, theta=ctheta) - # emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/HYBRID_EI.py b/PUQ/designmethods/gen_funcs/HYBRID_EI.py deleted file mode 100644 index a3132fa..0000000 --- a/PUQ/designmethods/gen_funcs/HYBRID_EI.py +++ /dev/null @@ -1,120 +0,0 @@ -import numpy as np -import scipy -from PUQ.designmethods.gen_funcs.EI import eifunc -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - compute_postvar, - compute_eivar, - multiple_pdfs, - get_emuvar, -) -from smt.sampling_methods import LHS - - -def eivarfunc(clist, x, real_x, thetatest, refsize, obs, obsvar3d, emu, priortest): - ids = np.random.choice(len(thetatest), refsize, replace=False) - thetaref = thetatest[ids, :] - emupredict = emu.predict(x, thetaref) - emumean = emupredict.mean() - emumeanT = emumean.T - emuvar, is_cov = get_emuvar(emupredict) - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - - # Get the n_ref x d x d x n_cand phi matrix - emuphi4d = emu.acquisition(x=x, theta1=thetaref, theta2=clist) - acq_func = [] - - # Pass over all the candidates - for c_id in range(len(clist)): - posteivar = compute_eivar( - var_obsvar1[:, real_x, real_x.T], - emuphi4d[:, real_x, real_x.T, c_id], - emumeanT[:, real_x.flatten()], - emuvar[real_x, :, real_x.T], - obs, - is_cov, - priortest, - ) - acq_func.append(-1 * posteivar) - - return acq_func - - -def hybrid_ei( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=0, - candsize=100, - refsize=100, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - fevals_c = fevals[:, ~idnan] - - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - n_x, p = x.shape[0], theta.shape[1] - theta_acq = [] - obsvar3d = obsvar.reshape(1, n_x, n_x) - - theta_acq, f_acq = [], [] - is_ei = True if (len(theta) % 2) == 0 else False - - # Best error - error = np.sum(np.abs(obs - fevals_c.T), axis=1) - delta = np.abs(obs - fevals_c.T)[np.argmin(error)] - liar = np.mean(fevals_c) - for i in range(n): - clist = prior_func.rnd(candsize, None) - - if is_ei: - acq_val = eifunc(clist, x, obs, obsvar, emu, delta) - is_ei = False - else: - acq_val = eivarfunc( - clist, x, real_x, thetatest, refsize, obs, obsvar3d, emu, posttest - ) - is_ei = True - - if n == 1: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - break - else: - if believer == 1: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - elif believer == 2: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - - # Constant liar strategy - fevalsnew = liar.reshape(1, 1) - emu.update(theta=ctheta, f=fevalsnew) - - theta_acq.append(ctheta) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/MAXEXP.py b/PUQ/designmethods/gen_funcs/MAXEXP.py deleted file mode 100644 index cd65bdc..0000000 --- a/PUQ/designmethods/gen_funcs/MAXEXP.py +++ /dev/null @@ -1,90 +0,0 @@ -import numpy as np -from sklearn.metrics import pairwise_distances -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - compute_postvar, - compute_eivar, - multiple_pdfs, - get_emuvar, -) -from smt.sampling_methods import LHS -import scipy - - -def maxexp( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=None, - candsize=None, - refsize=None, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq, f_acq = [], [] - d, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, d, d) - - # Create a candidate list. - n_clist = 100 * n - if type_init == "LHS": - sampling = LHS(xlimits=thetalimits) - clist = sampling(n_clist) - else: - clist = prior_func.rnd(n_clist, None) - - theta_sd = (theta - thetalimits[:, 0]) / (thetalimits[:, 1] - thetalimits[:, 0]) - theta_cand_sd = (clist - thetalimits[:, 0]) / ( - thetalimits[:, 1] - thetalimits[:, 0] - ) - - for j in range(n): - - emupredict = emu.predict(x, clist) - emumean = emupredict.mean() - emuvar, is_cov = get_emuvar(emupredict) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - postmean = multiple_pdfs( - obs, emumeanT[:, real_x.flatten()], var_obsvar1[:, real_x, real_x.T] - ) - logpostmean = np.log(postmean) - - # Diversity term - d = pairwise_distances(theta_sd, theta_cand_sd, metric="euclidean") - min_dist = np.min(d, axis=0) - - # Acquisition function - acq_func = logpostmean + np.log(min_dist) - - # New theta - idc = np.argmax(acq_func) - theta_sd = np.concatenate((theta_sd, theta_cand_sd[idc][None, :]), axis=0) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - f_acq.append(postmean[idc]) - clist = np.delete(clist, idc, 0) - theta_cand_sd = np.delete(theta_cand_sd, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/MAXVAR.py b/PUQ/designmethods/gen_funcs/MAXVAR.py deleted file mode 100644 index 9d65156..0000000 --- a/PUQ/designmethods/gen_funcs/MAXVAR.py +++ /dev/null @@ -1,85 +0,0 @@ -import numpy as np -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - compute_postvar, - multiple_pdfs, - get_emuvar, -) -from smt.sampling_methods import LHS -import scipy - - -def maxvar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=None, - candsize=None, - refsize=None, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq, f_acq = [], [] - d, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, d, d) - diags = np.diag(obsvar[real_x, real_x.T]) - d_real = real_x.shape[0] - coef = (2**d_real) * (np.sqrt(np.pi) ** d_real) * np.sqrt(np.prod(diags)) - - # Create a candidate list. - n_clist = 100 * n - if type_init == "LHS": - sampling = LHS(xlimits=thetalimits) - clist = sampling(n_clist) - else: - clist = prior_func.rnd(n_clist, None) - - # Acquire n thetas. - for i in range(n): - emupredict = emu.predict(x, clist) - emumean = emupredict.mean() - emuvar, is_cov = get_emuvar(emupredict) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - var_obsvar2 = emuvarT + 0.5 * obsvar3d - postmean = multiple_pdfs( - obs, emumeanT[:, real_x.flatten()], var_obsvar1[:, real_x, real_x.T] - ) - postvar = compute_postvar( - obs, - emumeanT[:, real_x.flatten()], - var_obsvar1[:, real_x, real_x.T], - var_obsvar2[:, real_x, real_x.T], - coef, - ) - - acq_func = postvar.copy() - idc = np.argmax(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - f_acq.append(postmean[idc]) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/PI.py b/PUQ/designmethods/gen_funcs/PI.py deleted file mode 100644 index 0345645..0000000 --- a/PUQ/designmethods/gen_funcs/PI.py +++ /dev/null @@ -1,89 +0,0 @@ -import numpy as np -import scipy - - -def pifunc(clist, x, obs, obsvar, emu, delta): - pi_val = np.zeros(len(clist)) - pp = emu.predict(x, clist) - ppmean = pp.mean() - ppvar = pp.var() - - for l in range(ppmean.shape[0]): - diff = obs[0, l] - ppmean[l, :] - sumvar = np.diag(obsvar)[l] + ppvar[l, :] - - part1 = scipy.stats.norm.cdf((delta[l] - diff) / np.sqrt(sumvar), 0, 1) - part2 = scipy.stats.norm.cdf((-delta[l] - diff) / np.sqrt(sumvar), 0, 1) - pi_val += part1 - part2 - - return pi_val - - -def pi( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=0, - candsize=100, - refsize=100, -): - - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - fevals_c = fevals[:, ~idnan] - - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - p = theta.shape[1] - theta_acq, f_acq = [], [] - - # Best error - error = np.sum(np.abs(obs - fevals_c.T), axis=1) - delta = np.abs(obs - fevals_c.T)[np.argmin(error)] - liar = np.mean(fevals_c) - - for i in range(n): - clist = prior_func.rnd(candsize, None) - - acq_val = pifunc(clist, x, obs, obsvar, emu, delta) - if believer == 0: - idc = np.argsort(acq_val)[::-1][:n] - ctheta = clist[idc, :].reshape((n, p)) - theta_acq.append(ctheta) - break - - elif believer == 1: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - elif believer == 2: - idc = np.argmax(acq_val) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = liar.reshape(1, 1) - emu.update(theta=ctheta, f=fevalsnew) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/RND.py b/PUQ/designmethods/gen_funcs/RND.py deleted file mode 100644 index 3255c0c..0000000 --- a/PUQ/designmethods/gen_funcs/RND.py +++ /dev/null @@ -1,21 +0,0 @@ -def rnd( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, - believer=None, - candsize=None, - refsize=None, -): - - theta_acq = prior_func.rnd(n, None) - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/acquisition.py b/PUQ/designmethods/gen_funcs/acquisition.py deleted file mode 100644 index 2220a8a..0000000 --- a/PUQ/designmethods/gen_funcs/acquisition.py +++ /dev/null @@ -1,762 +0,0 @@ -import numpy as np -from smt.sampling_methods import LHS -from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from hetgpy.covariance_functions import cov_gen -from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult -import emcee -from PUQ.designmethods.gen_funcs.allocate_reps import allocate - - -def generate_neighborhood(acq): - - rand = np.random.default_rng(acq.seed) - neigh_type = acq.args.get("neighbor", None) - - if neigh_type == "LHS": - N = int(acq.nL) - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L = sampling(N) - else: - N = int(acq.nL * 0.5) - - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L_explore = sampling(N) - - sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) - Lt = sampling(N) - - num_options = len(acq.x) - - # Distribute the rows as evenly as possible - counts = np.full(num_options, N // num_options) # Base count for each row type - counts[ - rand.choice(num_options, N % num_options, replace=False) - ] += 1 # Assign extra rows randomly - - # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) - Lx = np.vstack( - [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] - ) - - # Shuffle the rows randomly - rand.shuffle(Lx) - - L_exploit = np.concatenate((Lx, Lt), axis=1) - - L = np.concatenate((L_explore, L_exploit)) - - return L - - -class acquisition_function: - def __init__(self, model, cls_func, args): - self.model = model.copy() - self.cls_func = cls_func - #self.persis_info = persis_info - self.args = args - self.x = self.cls_func.x - self.d = self.cls_func.d - self.p = self.cls_func.p - self.dx = self.cls_func.dx - self.dt = self.cls_func.dt - self.zlim = cls_func.zlim - self.xlim = cls_func.zlim[0 : cls_func.dx, :] - self.tlim = cls_func.zlim[cls_func.dx : cls_func.p, :] - self.y = self.cls_func.real_data - self.Sigma = self.cls_func.obsvar - self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) - self.detSigma = np.linalg.det(self.Sigma) - self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) - self.twopiddet = ( - (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) - ) - - def acquire_new(self): - - if isinstance(self, lookahead): - return eval("self.evaluate")() - - else: - return eval("self.evaluate")( - self.args.get("new", True), return_pseudo=False - ) - - def gen_pred(self, model, x, t, return_id=False, return_flat=False): - nm, d = t.shape[0], x.shape[0] - ntot = nm * d - - # print(t.shape) - # (ntot, d) - x_tiled = np.tile(x, (t.shape[0], 1)) - # (ntot, p-d) - t_repeated = np.repeat(t, x.shape[0], axis=0) - # (ntot, p) - z = np.hstack([x_tiled, t_repeated]) - - # to construct S matrix - id_row = np.arange(0, ntot) - id_col = np.arange(0, ntot).reshape(nm, d) - id_col = np.repeat(id_col, repeats=d, axis=0) - - # predict at mesh - meshPr = model.predict(x=z, xprime=z) - - # ntot, ntot x ntot, ntot - mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] - - muT = mu.reshape(nm, d) - S = Sn[id_row[:, None], id_col].reshape(nm, d, d) - - if return_id: - return muT, S, z, id_row[:, None], id_col, mu[:, None] - else: - if return_flat: - return muT, S, z, mu[:, None] - else: - return muT, S, z - - def crit_pvar(self, model, x, t): - - # posterior variance - mu, S, z = self.gen_pred(model, x, t) - - M = S + 0.5 * self.Sigma3d - N = S + self.Sigma3d - - f = multiple_pdfs(self.y, mu, M) - g = multiple_pdfs(self.y, mu, N) - - vals = (1 / self.twopiddet) * f - g**2 - - return vals - - def total_var(self, model, x, t): - vals = self.crit_pvar(model=model, x=x, t=t) - return np.mean(self.weights * vals) - - -class var(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", True) - self.nL = args.get("nL", 100) - - if args.get("integral") == "importance": - # print("Importance sampling") - self.epsilon = args.get("epsilon", 10 ** (-10)) - self.discard = args.get("discard", 100) - self.nsteps = args.get("nsteps", 200) - self.thin = args.get("thin", 20) - self.nwalkers = args.get("nwalkers", 20) - self.reference_set() - elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) - self.weights = 1 - else: - self.t_ref = args.get("t_grid") - self.weights = 1 - - def reference_set(self): - def log_probability(ctheta): - if np.any((ctheta < 0) | (ctheta > 1)): - return -np.inf - else: - pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) - - pvar = max(pvar, 0) - return np.log(pvar) - - def sample(ndim, nwalkers): - np.random.seed(int(self.seed)) - sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) - - sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) - - samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) - return samples - - def importance_weight(theta): - pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) - pvar = pvar + self.epsilon - unnorm_weight = 1 / pvar - weight = unnorm_weight / np.sum(unnorm_weight) - return weight - - self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) - self.weights = importance_weight(theta=self.t_ref) - - def evaluate(self, new, return_pseudo): - - if new: - L = generate_neighborhood(self) - new_input = self.evaluate_explore(L) - else: - L = self.model["X0"] - new_input = self.evaluate_exploit(L) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) - - new_input = L[np.argmax(vals), :].reshape(1, self.p) - return new_input - - def evaluate_exploit(self, L): - vals = self.crit_pvar(model=self.model, x=self.x, t=L[:, self.dx : self.p]) - - new_input = L[np.argmax(vals), :].reshape(1, self.p) - return new_input - - -class imse(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - self.t_grid = args.get("t_grid", None) - - def evaluate(self, new, return_pseudo): - - if new: - sampling = LHS(xlimits=self.zlim, random_state=int(self.seed)) - L = sampling(self.nL) - new_input = self.evaluate_explore(L) - else: - L = self.model["X0"] - new_input = self.evaluate_exploit(L) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) - new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) - return new_input - - def evaluate_exploit(self, L): - IMSPE_grid = np.array( - [crit_IMSPE(id=[x_id], model=self.model) for x_id, x in enumerate(L)] - ) - new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) - return new_input - - -class ivar(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - if args.get("integral") == "importance": - # print("Importance sampling") - self.epsilon = args.get("epsilon", 10 ** (-10)) - self.discard = args.get("discard", 100) - self.nsteps = args.get("nsteps", 200) - self.thin = args.get("thin", 20) - self.nwalkers = args.get("nwalkers", 20) - self.reference_set() - elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) - self.weights = 1 - else: - self.t_ref = args.get("t_grid") - self.weights = 1 - - def reference_set(self): - def log_probability(ctheta): - if np.any((ctheta < 0) | (ctheta > 1)): - return -np.inf - else: - pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) - - pvar = max(pvar, 0) - return np.log(pvar) - - def sample(ndim, nwalkers): - np.random.seed(int(self.seed)) - sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) - - sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) - - samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) - return samples - - def importance_weight(theta): - pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) - pvar = pvar + self.epsilon - unnorm_weight = 1 / pvar - weight = unnorm_weight / np.sum(unnorm_weight) - return weight - - self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) - - # import pandas as pd - # import seaborn as sns - # import matplotlib.pyplot as plt - # df = pd.DataFrame(self.t_ref) - # sns.pairplot(df, diag_kind="kde") # Use KDE for diagonal histograms - # plt.show() - # print(self.t_ref.shape) - # import matplotlib.pyplot as plt - # if self.t_ref.shape[1] > 1: - # plt.scatter(self.t_ref[:, 0], self.t_ref[:, 1]) - # plt.show() - # else: - # plt.hist(self.t_ref) - # plt.show() - - self.weights = importance_weight(theta=self.t_ref) - - def evaluate(self, new, return_pseudo): - - if new: - L = generate_neighborhood(self) - new_input = self.evaluate_explore(L) - else: - L = self.model["X0"] - new_input = self.evaluate_exploit(L) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - - - nm = self.t_ref.shape[0] - - vals = np.zeros(self.nL) - - mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) - - V1 = S + self.Sigma3d - - # for i in np.arange(S.shape[0]): - # print(np.diag(S[i, :, :])) - - # predict at candidates - candPr = self.model.predict(x=L, xprime=z) - - # nL, nL, nL x ntot - candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] - candtotvat = candvar + candnugs - - for k in range(0, self.nL): - # (mn x d x 1) - covc = candcov[k, :].reshape(nm, self.d, 1) - - # (mn x 1 x d) - covcT = np.transpose(covc, (0, 2, 1)) - - # (nm, d, d) - phic = (covc * covcT) / candtotvat[k] - - # C1: nm x d x d, C2: nm x d x d - C1 = (V1 + phic) * 0.5 - C2 = V1 - phic - - rpdf = multiple_pdfs(self.y, mu, C1) - dets = multiple_determinants(C2) - - part2 = (1 / self.twopid) * (rpdf / np.sqrt(dets)) - - vals[k] = np.sum(self.weights * part2) - - new_input = L[np.argmax(vals), :].reshape(1, self.p) - - return new_input - - def evaluate_exploit(self, L): - - nL = L.shape[0] - - nm = self.t_ref.shape[0] - vals = np.zeros(nL) - nrep = 1 - - mu, S, z, idr, idc, muf = self.gen_pred( - self.model, self.x, self.t_ref, return_id=True - ) - - V1 = S + self.Sigma3d - V2 = S + 0.5 * self.Sigma3d - - Ki, mult = self.model.Ki, self.model.mult - - candPr = self.model.predict(x=self.model["X0"]) - candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] - smean = self.model["Z0"] - - # compute B - if self.model.get("Lambda") is None: - tmp = self.model.g - tmp = np.repeat(tmp, nL) - else: - tmp = self.model.Lambda - - denom = (mult * (mult + nrep)) / (nrep * tmp) - np.diag(Ki) - Ki3D = Ki[:, :, None] * Ki[:, None, :] - B = Ki3D / denom[:, None, None] - KB = self.model.Ki + B - - # ntot x ntr - kx = cov_gen( - X1=z, X2=self.model.X0, theta=self.model.theta, type=self.model.covtype - ) - - # coefficients to be used - ap1 = mult + 1 - mean_coefs = (candmean - smean) / ap1 - var_coefs = (candvar + candnugs) / (ap1**2) - smean = smean[:, None] - - KBe_all = KB[np.arange(nL), :, np.arange(nL)] - kKBe_all = KBe_all @ kx.T - - B_kx = np.einsum("ijk, bk -> ijb", B, kx) - phi_all = self.model.nu_hat * np.einsum("aj, ijb -> aib", kx, B_kx) - - Bs = B @ smean - c1, c2 = (1 / self.twopiddet), (1 / self.twopid) - for k in range(0, nL): - - phi_k = phi_all[idr, k, idc].reshape(nm, self.d, self.d) - - KBe_k = KB[k, :, k : k + 1] - mu_k = muf + kx @ (mean_coefs[k] * KBe_k + Bs[k, :, :]) - mu_kT = mu_k.reshape(nm, self.d) - - kKBe = kKBe_all[k, :] - covc = kKBe.reshape(nm, self.d, 1) - covcT = np.transpose(covc, (0, 2, 1)) - - # nm x d x d - gamma_k = var_coefs[k] * (covc * covcT) - - M = V2 - phi_k + gamma_k - part1 = c1 * multiple_pdfs(self.y, mu_kT, M) - - C1 = V1 - phi_k - N = 0.5 * C1 + gamma_k - part2 = multiple_pdfs(self.y, mu_kT, N) - - dets1 = multiple_determinants(C1) - part2 = c2 * (1 / np.sqrt(dets1)) * part2 - - vals[k] = np.sum(self.weights * (part1 - part2)) - - # print(np.argmin(vals)) - new_input = L[np.argmin(vals), :].reshape(1, self.p) - - # import matplotlib.pyplot as plt - # plt.scatter(L[:, 1], L[:, 2], c=vals) - # plt.scatter(L[np.argmin(vals), :][1], L[np.argmin(vals), :][2], marker="*") - # plt.show() - - return new_input - - -class lookahead(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.method = args.get("method", "ivar") - self.nL = args.get("nL", 100) - self.t_grid = args.get("t_grid", None) - - - def determine_h(self): - horizon = self.args.get("horizon", None) - # print(horizon) - if horizon.get("method") == "target": - if horizon.get("previous_ratio") is None: - horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) - self.h = horizon.get("h0") - else: - target = horizon.get("target_ratio") - previous_ratio = horizon.get("previous_ratio") - current_horizon = horizon.get("h0") - - ratio = len(self.model.Z0) / len(self.model.Z) - - # Ratio increased while too small - if ratio < target and ratio < previous_ratio: - self.h = max(-1, current_horizon - 1) - - # Ratio decreased while too high - elif ratio > target and ratio > previous_ratio: - self.h = current_horizon + 1 - else: - self.h = current_horizon - - horizon["previous_ratio"] = len(self.model.Z0) / len(self.model.Z) - horizon["h0"] = self.h - - elif horizon.get("method") == "adaptive": - budget = np.sum(self.model.mult) - - if self.method == "imse": - mult_star = allocate_mult(model=self.model, N=budget).astype(int) - - - - else: - alloc_obj = allocate(budget, - self.model, - self.cls_func, - {"theta":self.t_ref, "weight":self.weights}) - - alloc_obj.allocatereps() - mult_star = alloc_obj.reps - - tab_input = mult_star - self.model.mult - tab_input[tab_input < 0] = 0 - - # import matplotlib.pyplot as plt - # for label, x_count, y_count in zip(tab_input, self.model.X0[:, 1], self.model.X0[:, 2]): - # col = "blue" - # plt.annotate( - # label, - # xy=(x_count, y_count), - # xytext=(0, 0), - # textcoords="offset points", - # fontsize=12, - # color=col - # ) - # plt.show() - - rand = np.random.default_rng(self.seed) - u, counts = np.unique(tab_input, return_counts=True) - self.h = rand.choice(u, p=counts / counts.sum()) - - elif horizon.get("h0") == -1: - self.h = -1 - else: - self.h = horizon.get("h0") - - def crit_eval(self, model): - if self.method == "imse": - value = IMSPE(model) - else: - value = self.total_var(model, self.x, self.t_ref) - return value - - def evaluate(self): - - if self.args.get("long"): - self.evaluate_vl() - else: - self.evaluate_vq() - - def evaluate_vl(self): - - acq_obj = eval(self.method)( - model=self.model, - cls_func=self.cls_func, - args=self.args, - ) - - self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( - acq_obj, "weights", None - ) - - # define horizon - self.determine_h() - - if self.h == -1: - acq_obj.evaluate(new=True, return_pseudo=True) - self.znew, self.new = acq_obj.znew, True - elif self.h == 0: - acq_obj.evaluate(new=True, return_pseudo=True) - z_A = acq_obj.znew - acq_obj.model.update(Xnew=z_A, Znew=acq_obj.fnew, maxit=0) - model_A = acq_obj.model.copy() - IVAR_A1 = self.crit_eval(model_A) - - acq_obj.model = self.model.copy() - acq_obj.evaluate(new=False, return_pseudo=True) - z_B = acq_obj.znew - acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) - - model_B = acq_obj.model.copy() - IVAR_B1 = self.crit_eval(model_B) - - if IVAR_A1 < IVAR_B1: - self.znew, self.new = z_A, True - return - else: - self.znew, self.new = z_B, False - return - else: - depth = self.h + 1 - paths = np.eye(depth, dtype=int).tolist() - inputs, values = [], [] - if self.args.get("parallel", None) is None: - for pid, path in enumerate(paths): - znew, vals = self.eval_branch(acq_obj, path) - inputs.append(znew) - values.append(vals) - # print(np.round(vals*1000000)) - - vals_id = np.argmin(values) - new_input = inputs[vals_id][0] - a = paths[vals_id][0] - self.znew = new_input - self.new = True if a == 1 else False - return self - else: - from joblib import Parallel, delayed - - results = Parallel(n_jobs=len(paths))( - delayed(self.eval_branch)(acq_obj, path) - for pid, path in enumerate(paths) - ) - inputs, vals = zip(*results) - vals_id = np.argmin(vals) - return inputs[vals_id][0] - - def eval_branch(self, acq_obj, action): - - acq_obj.model = self.model.copy() - - # Initialize actions - input_action = [] - - for a in action: - - new = True if a == 1 else False - - acq_obj.evaluate(new=new, return_pseudo=True) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) - input_action.append(acq_obj.znew) - - value = self.crit_eval(acq_obj.model) - - return input_action, value - - def evaluate_vq(self): - designs = [] - - acq_obj = eval(self.method)( - model=self.model, - cls_func=self.cls_func, - args=self.args, - ) - - self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( - acq_obj, "weights", None - ) - - # define horizon - self.determine_h() - - acq_obj.evaluate(new=True, return_pseudo=True) - z_A, f_A = acq_obj.znew, acq_obj.fnew - path_A = [{"par": z_A, "new": True}] - - if self.h == -1: - self.znew, self.new = z_A, True - return - else: - acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) - model_A = acq_obj.model.copy() - IVAR_A1 = self.crit_eval(model_A) - - if self.h > 0: - - acq_obj.model = model_A.copy() - for i in range(0, self.h): - acq_obj.evaluate(new=False, return_pseudo=True) - path_A.append({"par": acq_obj.znew, "new": False}) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) - - IVAR_A = self.crit_eval(acq_obj.model) - - designs.append({"input": z_A, "path": path_A, "value": IVAR_A}) - - if self.h == 0: - acq_obj.model = self.model.copy() - acq_obj.evaluate(new=False, return_pseudo=True) - z_B = acq_obj.znew - acq_obj.model.update(Xnew=z_B, Znew=acq_obj.fnew, maxit=0) - - model_B = acq_obj.model.copy() - IVAR_B1 = self.crit_eval(model_B) - - if IVAR_A1 < IVAR_B1: - self.znew, self.new = z_A, True - return - else: - self.znew, self.new = z_B, False - return - - else: - newmodelB = self.model.copy() - for i in range(self.h): - # Choose a new replicate - acq_obj.model = newmodelB.copy() - acq_obj.evaluate(new=False, return_pseudo=True) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) - - if i == 0: - z0 = 1 * acq_obj.znew - path_B = [] - - path_B.append({"par": acq_obj.znew, "new": False}) - newmodelB = acq_obj.model.copy() - - # Choose a new design - acq_obj.evaluate(new=True, return_pseudo=True) - acq_obj.model.update(Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0) - path_C = [{"par": acq_obj.znew, "new": True}] - - for j in range(i, self.h - 1): - # Remaining replicates - acq_obj.evaluate(new=False, return_pseudo=True) - acq_obj.model.update( - Xnew=acq_obj.znew, Znew=acq_obj.fnew, maxit=0 - ) - path_C.append({"par": acq_obj.znew, "new": False}) - - IVAR_C = self.crit_eval(acq_obj.model) - designs.append( - {"input": z0, "path": path_B + path_C, "value": IVAR_C} - ) - - if IVAR_C < IVAR_A: - self.znew, self.new = z0, False - return - - self.znew, self.new = z_A, True - return diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py deleted file mode 100644 index 65f9d39..0000000 --- a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic_old.py +++ /dev/null @@ -1,306 +0,0 @@ -import numpy as np -from smt.sampling_methods import LHS -from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from hetgpy.IMSE import crit_IMSPE -import emcee - -def generate_neighborhood(acq): - N = int(acq.nL) - sampling = LHS(xlimits=acq.tlim, random_state=acq.seed) - L = sampling(N) - return L - -def get_pred(cL, emu, x, ttest, reps): - - cP = emu.predict(x=x, theta=cL) - var_cand = cP._info["var_o"] + cP._info["nugs_o"] / reps - testP = emu.predict(x=x, theta=ttest, thetaprime=cL) - mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] - mut = mu.T - St = np.transpose(S, (2, 0, 1)) - - return mut, St, cov, var_cand - - -class acquisition_function: - def __init__(self, model, cls_func, args): - self.model = model #deepcopy(model) - self.cls_func = cls_func - #self.persis_info = persis_info - self.args = args - self.x = self.cls_func.x - self.d = self.cls_func.d - self.p = self.cls_func.p - self.dx = self.cls_func.dx - self.dt = self.cls_func.dt - - self.tlim = cls_func.thetalimits - self.y = self.cls_func.real_data - self.Sigma = self.cls_func.obsvar - self.Sigma3d = self.Sigma.reshape(1, self.d, self.d) - self.detSigma = np.linalg.det(self.Sigma) - self.twopid = (2**self.d) * (np.sqrt(np.pi) ** self.d) - self.twopiddet = ( - (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) - ) - - def acquire_new(self): - return eval("self.evaluate")( - self.args.get("new", True), return_pseudo=False - ) - - def gen_pred(self, model, x, t, return_id=False, return_flat=False): - - # predict at mesh - meshPr = model.predict(theta=t, thetaprime=t) - - # ntot, ntot x ntot, ntot - mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["S"], meshPr._info["var"] - muT = mu.T - S = Sn.transpose(2, 0, 1) - - return muT, S, t - - def crit_pvar(self, model, x, t): - - # posterior variance - mu, S, z = self.gen_pred(model, x, t) - - M = S + 0.5 * self.Sigma3d - N = S + self.Sigma3d - - f = multiple_pdfs(self.y, mu, M) - g = multiple_pdfs(self.y, mu, N) - - vals = (1 / self.twopiddet) * f - g**2 - - return vals - - def total_var(self, model, x, t): - vals = self.crit_pvar(model=model, x=x, t=t) - return np.mean(self.weights * vals) - - -class rnd(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - self.t_grid = args.get("t_grid", None) - - def evaluate(self, new, return_pseudo): - - new_input = self.args["prior"].rnd(1, - self.args["rand_stream"]) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - -class var(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", True) - self.nL = args.get("nL", 100) - - def evaluate(self, new, return_pseudo): - - L = generate_neighborhood(self) - new_input = self.evaluate_explore(L) - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - vals = self.crit_pvar(model=self.model, x=self.x, t=L) - new_input = L[np.argmax(vals), :].reshape(1, self.p) - return new_input - -class imse(acquisition_function): - # ASK THIS FUNCTION TO DAVID - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - self.t_grid = args.get("t_grid", None) - - def evaluate(self, new, return_pseudo): - - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - L = sampling(self.nL) - new_input = self.evaluate_explore(L) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) - new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) - return new_input - -class exp(acquisition_function): - # ASK THIS FUNCTION TO DAVID - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - self.t_grid = args.get("t_grid", None) - - def evaluate(self, new, return_pseudo): - - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - L = sampling(self.nL) - new_input = self.evaluate_explore(L) - - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) - new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) - return new_input - - -class ivar(acquisition_function): - def __init__(self, model, cls_func, args): - super().__init__(model, cls_func, args) - self.seed = args.get("seed", None) - self.explore = args.get("explore", "both") - self.nL = args.get("nL", 100) - if args.get("integral") == "importance": - # print("Importance sampling") - self.epsilon = args.get("epsilon", 10 ** (-10)) - self.discard = args.get("discard", 100) - self.nsteps = args.get("nsteps", 200) - self.thin = args.get("thin", 20) - self.nwalkers = args.get("nwalkers", 20) - self.reference_set() - elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) - self.weights = 1 - else: - self.t_ref = args.get("t_grid") - self.weights = 1 - - def reference_set(self): - def log_probability(ctheta): - if np.any((ctheta < 0) | (ctheta > 1)): - return -np.inf - else: - pvar = self.crit_pvar(model=self.model, x=self.x, t=ctheta[None, :]) - - pvar = max(pvar, 0) - return np.log(pvar) - - def sample(ndim, nwalkers): - np.random.seed(int(self.seed)) - sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) - - sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) - - samples = sampler.get_chain(discard=self.discard, thin=self.thin, flat=True) - return samples - - def importance_weight(theta): - pvar = self.crit_pvar(model=self.model, x=self.x, t=theta) - pvar = pvar + self.epsilon - unnorm_weight = 1 / pvar - weight = unnorm_weight / np.sum(unnorm_weight) - return weight - - self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) - self.weights = importance_weight(theta=self.t_ref) - - def evaluate(self, new, return_pseudo): - - L = generate_neighborhood(self) - new_input = self.evaluate_explore(L) - self.znew, self.new = new_input, new - - if return_pseudo: - pnew = self.model.predict(x=self.znew) - fnew = np.array([pnew["mean"]])[None, :] - self.fnew = fnew - - return self - - def evaluate_explore(self, L): - - nm, nL = self.t_ref.shape[0], self.nL - vals = np.zeros(nL) - - mu, S, cov, cvar = get_pred( - cL=L, emu=self.model, x=self.x, ttest=self.t_ref, reps=1 - ) - - V1 = S + self.Sigma3d - - G = self.model._info["G"] - B = self.model._info["B"] - GB = G @ B - BTG = B.T @ G - - d = G.shape[0] - q = B.shape[1] - - tau = np.zeros((nm, q, q, nL)) - phi = np.zeros((nm, d, d, nL)) - - coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) - - for j in range(0, q): - tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] - - for k in range(0, nL): - phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG - - for k in range(0, self.nL): - # C1: nm x d x d - # C2: nm x d x d - phic = phi[:, self.x, self.x.T, k] - C1 = (V1 + phic) * 0.5 - C2 = V1 - phic - - rpdf = multiple_pdfs(self.y, mu, C1) - dets = multiple_determinants(C2) - part2 = rpdf / (coef * np.sqrt(dets)) - - vals[k] = np.sum(self.weights * part2) - - - new_input = L[np.argmax(vals), :].reshape(1, self.p) - - return new_input - diff --git a/PUQ/surrogatemethods/PCGP.py b/PUQ/surrogatemethods/PCGP.py deleted file mode 100644 index 96e21db..0000000 --- a/PUQ/surrogatemethods/PCGP.py +++ /dev/null @@ -1,833 +0,0 @@ -"""PCGP (Higdon et al., 2008). """ - -import numpy as np -import scipy.optimize as spo -import scipy.linalg as spla -import copy -from PUQ.surrogatesupport.matern_covmat import covmat as __covmat -import torch - - -def fit( - fitinfo, - x, - theta, - f, - epsilonPC=0.001, - lognugmean=-10, - lognugLB=-20, - varconstant=None, - dampalpha=0.3, - eta=10, - standardpcinfo=None, - verbose=0, - **kwargs -): - """ - The purpose of fit is to take information and plug all of our fit - information into fitinfo, which is a python dictionary. - - Parameters - ---------- - fitinfo : dict - A dictionary including the emulation fitting information once - complete. - The dictionary is passed by reference, so it returns None. - x : numpy.ndarray - An array of inputs. Each row should correspond to a row in f. - theta : numpy.ndarray - An array of parameters. Each row should correspond to a column in f. - f : numpy.ndarray - An array of responses. Each column in f should correspond to a row in - theta. Each row in f should correspond to a row in x. - epsilonPC : scalar - A parameter to control the number of PCs used. The suggested range for - epsilonPC is (0.001, 0.1). The larger epsilonPC is, the fewer PCs will be - used. Note that epsilonPC here is *not* the unexplained variance in - typical principal component analysis. - lognugmean : scalar - A parameter to control the log of the nugget used in fitting the GPs. - The suggested range for lognugmean is (-12, -4). The nugget is estimated, - and this parameter is used to guide the estimation. - lognugLB : scalar - A parameter to control the lower bound of the log of the nugget. The - suggested range for lognugLB is (-24, -12). - varconstant : scalar - A multiplying constant to control the inflation (deflation) of additional - variances if missing values are present. Default is None, the parameter will - be optimized in such case. A general working range is (np.exp(-4), np.exp(4)). - dampalpha : scalar - A parameter to control the rate of increase of variance as amount of missing - values increases. Default is 0.3, otherwise an appropriate range is (0, 0.5). - Values larger than 0.5 are permitted but it leads to poor empirical performance. - eta : scalar - A parameter as an upper bound for the additional variance term. Default is 10. - standardpcinfo : dict - A dictionary user supplies that contains information for standardization of `f`, - in the following format, such that fs = (f - offset) / scale, U are the - orthogonal basis vectors, and S are the singular values from SVD of `fs`. - The entry extravar contains the average squared residual for each column (x). - {'offset': offset, - 'scale': scale, - 'fs': fs, - 'extravar': extravar, - 'U': U, # optional - 'S': S # optional - } - - verbose : scalar - A parameter to suppress in-method console output. Use 0 to suppress output, - use 1 to show output. - - - kwargs : dict, optional - A dictionary containing options. The default is None. - - Returns - ------- - None. - - """ - # print("Fitting...") - f = f.T - fitinfo["epsilonPC"] = epsilonPC - hyp1 = lognugmean - hyp2 = lognugLB - hypvarconst = np.log(varconstant) if varconstant is not None else None - - fitinfo["dampalpha"] = dampalpha - fitinfo["eta"] = eta - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["x"] = x - - # Standardize the function evaluations f - if standardpcinfo is None: - __standardizef(fitinfo) - else: - fitinfo["standardpcinfo"] = standardpcinfo - - # Construct principal components - __PCs(fitinfo) - numpcs = fitinfo["pc"].shape[1] - - if verbose > 0: - print(fitinfo["method"], "considering ", numpcs, "PCs") - - # Fit emulators for all PCs - emulist = __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, hypvarconst) - fitinfo["varc_status"] = "fixed" if varconstant is not None else "optimized" - fitinfo["logvarc"] = np.array([emulist[i]["hypvarconst"] for i in range(numpcs)]) - fitinfo["pcstdvar"] = np.exp(fitinfo["logvarc"]) * fitinfo["unscaled_pcstdvar"] - fitinfo["emulist"] = emulist - - return - - -def update(fitinfo, x, theta, f, **kwargs): - # print("Updating...") - f = f.T - # print(fitinfo['f'].shape) - # print(f.shape) - # print(fitinfo['pc'].shape) - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["x"] = x - - standardpcinfo = fitinfo["standardpcinfo"] - offset = standardpcinfo["offset"] - scale = standardpcinfo["scale"] - fs = (f - offset) / scale - standardpcinfo["fs"] = fs - fitinfo["pc"] = fs @ fitinfo["pct"] - emulist = fitinfo["emulist"] - numpcs = fitinfo["pc"].shape[1] - - for pcanum in range(0, numpcs): - subinfo = emulist[pcanum] - R = __covmat(theta, theta, subinfo["hypcov"]) - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - # sig2ofconst = subinfo['sig2ofconst'] - g = fitinfo["pc"][:, pcanum] - # fcenter = Vh.T @ g - subinfo["Vh"] = Vh - # n = subinfo['R'].shape[0] - # subinfo['sig2'] = (np.mean(fcenter ** 2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = V @ np.diag(1 / W) @ V.T - subinfo["pw"] = subinfo["Rinv"] @ g - - -def predict(predinfo, fitinfo, x, theta, **kwargs): - r""" - Finds prediction at theta and x given the dictionary fitinfo. - This [emulationpredictdocstring] automatically filled by docinfo.py when - running updatedocs.py - - Parameters - ---------- - predinfo : dict - An arbitary dictionary where you should place all of your prediction - information once complete. This dictionary is pass by reference, so - there is no reason to return anything. Keep only stuff that will be - used by predict. Key elements are - - - `predinfo['mean']` : `predinfo['mean'][k]` is mean of the prediction - at all x at `theta[k]`. - - `predinfo['var']` : `predinfo['var'][k]` is variance of the - prediction at all x at `theta[k]`. - - `predinfo['cov']` : `predinfo['cov'][k]` is covariance matrix of the prediction - at all x at `theta[k]`. - - `predinfo['covhalf']` : if `A = predinfo['covhalf'][k]` then - `A.T @ A = predinfo['cov'][k]`. - - fitinfo : dict - An arbitary dictionary where you placed all your important fitting - information from the fit function above. - - x : array of objects - An matrix (vector) of inputs for prediction. - - theta : array of objects - An matrix (vector) of parameters to prediction. - - kwargs : dict - A dictionary containing additional options - """ - return_grad = False - if ( - (kwargs is not None) - and ("return_grad" in kwargs.keys()) - and (kwargs["return_grad"] is True) - ): - return_grad = True - return_covx = True - if ( - (kwargs is not None) - and ("return_covx" in kwargs.keys()) - and (kwargs["return_covx"] is False) - ): - return_covx = False - infos = fitinfo["emulist"] - predvecs = np.zeros((theta.shape[0], len(infos))) - predvars = np.zeros((theta.shape[0], len(infos))) - - if return_grad: - predvecs_gradtheta = np.zeros((theta.shape[0], len(infos), theta.shape[1])) - predvars_gradtheta = np.zeros((theta.shape[0], len(infos), theta.shape[1])) - drsave = np.array(np.ones(len(infos)), dtype=object) - if predvecs.ndim < 1.5: - predvecs = predvecs.reshape((1, -1)) - predvars = predvars.reshape((1, -1)) - try: - if ( - x is None - or np.all(np.equal(x, fitinfo["x"])) - or np.allclose(x, fitinfo["x"]) - ): - xind = np.arange(0, x.shape[0]) - xnewind = np.arange(0, x.shape[0]) - else: - raise - except Exception: - matchingmatrix = np.ones((x.shape[0], fitinfo["x"].shape[0])) - for k in range(0, x[0].shape[0]): - try: - matchingmatrix *= np.isclose(x[:, k][:, None], fitinfo["x"][:, k]) - except Exception: - matchingmatrix *= np.equal(x[:, k][:, None], fitinfo["x"][:, k]) - xind = np.argwhere(matchingmatrix > 0.5)[:, 1] - xnewind = np.argwhere(matchingmatrix > 0.5)[:, 0] - - rsave = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - if return_grad: - rsave[k], drsave[k] = __covmat( - theta, fitinfo["theta"], infos[k]["hypcov"], return_gradx1=True - ) - else: - rsave[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - # adjusted covariance matrix - r = (1 - infos[k]["nug"]) * np.squeeze(rsave[infos[k]["hypind"]]) - - try: - rVh = r @ infos[k]["Vh"] - rVh2 = rVh @ (infos[k]["Vh"]).T - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh.ndim < 1.5: - rVh = rVh.reshape((1, -1)) - if rVh2.ndim < 1.5: - rVh2 = np.reshape(rVh2, (1, -1)) - predvecs[:, k] = r @ infos[k]["pw"] - if return_grad: - drsave_hypind = np.squeeze(drsave[infos[k]["hypind"]]) - if drsave_hypind.ndim < 2.5 and theta.shape[1] < 1.5: - drsave_hypind = np.reshape(drsave_hypind, (*drsave_hypind.shape, 1)) - elif drsave_hypind.ndim < 2.5 and theta.shape[1] > 1.5: - drsave_hypind = np.reshape(drsave_hypind, (1, *drsave_hypind.shape)) - - dr = (1 - infos[k]["nug"]) * drsave_hypind - if dr.ndim == 2: - drVh = dr.T @ infos[k]["Vh"] - predvecs_gradtheta[:, k, :] = dr.T @ infos[k]["pw"] - predvars_gradtheta[:, k, :] = ( - -infos[k]["sig2"] * 2 * np.sum(rVh * drVh, 1) - ) - else: - drpw = np.squeeze(dr.transpose(0, 2, 1) @ infos[k]["pw"]) - if drpw.ndim < 1.5 and theta.shape[1] < 1.5: - drpw = np.reshape(drpw, (-1, 1)) - elif drpw.ndim < 1.5 and theta.shape[1] > 1.5: - drpw = np.reshape(drpw, (1, -1)) - - predvecs_gradtheta[:, k, :] = (1 - infos[k]["nug"]) * drpw - predvars_gradtheta[:, k, :] = -(infos[k]["sig2"] * 2) * np.einsum( - "ij,ijk->ik", rVh2, dr - ) - predvars[:, k] = infos[k]["sig2"] * np.abs(1 - np.sum(rVh**2, 1)) - - # calculate predictive mean and variance - predinfo["mean"] = np.full((x.shape[0], theta.shape[0]), np.nan) - predinfo["var"] = np.full((x.shape[0], theta.shape[0]), np.nan) - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - # pctscale = (fitinfo['pct'].T * fitinfo['standardpcinfo']['scale']).T - predinfo["mean"][xnewind, :] = ( - (predvecs @ pctscale[xind, :].T) + fitinfo["standardpcinfo"]["offset"][xind] - ).T - predinfo["var"][xnewind, :] = ( - ( - fitinfo["standardpcinfo"]["extravar"][xind] - + predvars @ (pctscale[xind, :] ** 2).T - ) - ).T - - predinfo["extravar"] = 1 * fitinfo["standardpcinfo"]["extravar"][xind] - predinfo["predvars"] = 1 * predvars - predinfo["predvecs"] = 1 * predvecs - predinfo["phi"] = 1 * pctscale[xind, :] - - if return_covx: - CH = np.sqrt(predvars)[:, :, None] * (pctscale[xind, :].T)[None, :, :] - predinfo["covxhalf"] = np.full( - (theta.shape[0], CH.shape[1], x.shape[0]), np.nan - ) - predinfo["covxhalf"][:, :, xnewind] = CH - predinfo["covxhalf"] = predinfo["covxhalf"].transpose((2, 0, 1)) - - if return_grad: - predinfo["mean_gradtheta"] = np.full( - (x.shape[0], theta.shape[0], theta.shape[1]), np.nan - ) - predinfo["mean_gradtheta"][xnewind, :, :] = ( - (predvecs_gradtheta.transpose(0, 2, 1) @ pctscale[xind, :].T) - ).transpose((2, 0, 1)) - predinfo["predvars_gradtheta"] = 1 * predvars_gradtheta - predinfo["predvecs_gradtheta"] = 1 * predvecs_gradtheta - - if return_covx: - - dsqrtpredvars = 0.5 * ( - predvars_gradtheta.transpose(2, 0, 1) / np.sqrt(predvars) - ).transpose(1, 2, 0) - - if np.allclose(xnewind, xind): - predinfo["covxhalf_gradtheta"] = ( - dsqrtpredvars.transpose(2, 0, 1)[:, :, :, None] - * (pctscale[xind, :].T)[None, :, :] - ).transpose(3, 1, 2, 0) - else: - predinfo["covxhalf_gradtheta"] = np.full( - (x.shape[0], theta.shape[0], CH.shape[1], theta.shape[1]), np.nan - ) - predinfo["covxhalf_gradtheta"][xnewind] = ( - dsqrtpredvars.transpose(2, 0, 1)[:, :, :, None] - * (pctscale[xind, :].T)[None, :, :] - ).transpose(3, 1, 2, 0) - return - - -def acquisition(fitinfo, x, theta1, theta2, **kwargs): - - infos = fitinfo["emulist"] - predcov = np.zeros((theta1.shape[0], theta2.shape[0], len(infos))) - tau = np.zeros((theta1.shape[0], theta2.shape[0], len(infos))) - predvars_2 = np.zeros((theta2.shape[0], len(infos))) - try: - if ( - x is None - or np.all(np.equal(x, fitinfo["x"])) - or np.allclose(x, fitinfo["x"]) - ): - xind = np.arange(0, x.shape[0]) - xnewind = np.arange(0, x.shape[0]) - else: - raise - except Exception: - matchingmatrix = np.ones((x.shape[0], fitinfo["x"].shape[0])) - for k in range(0, x[0].shape[0]): - try: - matchingmatrix *= np.isclose(x[:, k][:, None], fitinfo["x"][:, k]) - except Exception: - matchingmatrix *= np.equal(x[:, k][:, None], fitinfo["x"][:, k]) - xind = np.argwhere(matchingmatrix > 0.5)[:, 1] - xnewind = np.argwhere(matchingmatrix > 0.5)[:, 0] - - rsave_1 = np.array(np.ones(len(infos)), dtype=object) - rsave_2 = np.array(np.ones(len(infos)), dtype=object) - rsave_3 = np.array(np.ones(len(infos)), dtype=object) - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - rsave_1[k] = __covmat(theta1, fitinfo["theta"], infos[k]["hypcov"]) - rsave_2[k] = __covmat(theta2, fitinfo["theta"], infos[k]["hypcov"]) - rsave_3[k] = __covmat(theta1, theta2, infos[k]["hypcov"]) - - # adjusted covariance matrix - r_1 = (1 - infos[k]["nug"]) * np.squeeze(rsave_1[infos[k]["hypind"]]) - r_2 = (1 - infos[k]["nug"]) * np.squeeze(rsave_2[infos[k]["hypind"]]) - r_3 = (1 - infos[k]["nug"]) * np.squeeze(rsave_3[infos[k]["hypind"]]) - - # print(theta1.shape) - # print(theta2.shape) - - # print('r1:', r_1.shape) - # print('r2:', r_2.shape) - # print('r3:', r_3.shape) - try: - rVh_1 = r_1 @ infos[k]["Vh"] - rVh_2 = r_2 @ infos[k]["Vh"] - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh_1.ndim < 1.5: - rVh_1 = rVh_1.reshape((1, -1)) - if rVh_2.ndim < 1.5: - rVh_2 = rVh_2.reshape((1, -1)) - - # print('rVh1:', rVh_1.shape) - # print('rVh2:', rVh_2.shape) - - # predvars_1[:, k] = infos[k]['sig2'] * np.abs(1 - np.sum(rVh_1 ** 2, 1)) - - # predcov[:, :, k] = infos[k]['sig2'] * (r_3.reshape((theta1.shape[0], 1)) - rVh_1 @ rVh_2.T) - predcov[:, :, k] = infos[k]["sig2"] * ( - r_3.reshape((theta1.shape[0], theta2.shape[0])) - rVh_1 @ rVh_2.T - ) - # print('predcov:', predcov.shape) - - predvars_2[:, k] = infos[k]["sig2"] * np.abs(1 - np.sum(rVh_2**2, 1)) - predvars_2[:, k] += infos[k]["nug"] - - # print('predvars_2:', predvars_2.shape) - - tau[:, :, k] = (predcov[:, :, k] ** 2) / predvars_2[:, k] - # print('tau:', tau.shape) - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - - # print('here1') - - BtauT = np.sqrt(tau)[:, :, :, None] * (pctscale.T) - # print(BtauT.shape) - Btau = BtauT.transpose(0, 1, 3, 2) - Btau_torch = torch.from_numpy(Btau) - Btau_torchT = torch.from_numpy(BtauT) - phi_mat_torch = torch.matmul(Btau_torch, Btau_torchT) - phi_mat_np = phi_mat_torch.numpy() - phi_mat_np = phi_mat_np.transpose(0, 3, 2, 1) - - # print('here2') - # print(phi_mat_np[1, :, :, 0]) - # phi_matrix2 = np.zeros((theta1.shape[0], len(x), len(x), theta2.shape[0])) - # for i in range(theta1.shape[0]): - # for j in range(theta2.shape[0]): - # phi_matrix2[i, :, :, j] = pctscale@np.diag(tau[i, j, :])@pctscale.T - # phi_matrix[i, :, :, j] = Btau[i, j, :, :]@Btau[i, j, :, :].T - - # print(phi_matrix[1, :, :, 0]) - # print(phi_matrix2[1, :, :, 0]) - return phi_mat_np # phi_matrix - - -def predictlpdf(predinfo, f, return_grad=False, addvar=0, **kwargs): - totvar = addvar + predinfo["extravar"] - rf = ((f.T - predinfo["mean"].T) * (1 / np.sqrt(totvar))).T - Gf = predinfo["phi"].T * (1 / np.sqrt(totvar)) - Gfrf = Gf @ rf - Gf2 = Gf @ Gf.T - likv = np.sum(rf**2, 0) - if return_grad: - rf2 = -( - predinfo["mean_gradtheta"].transpose(2, 1, 0) * (1 / np.sqrt(totvar)) - ).transpose(2, 1, 0) - Gfrf2 = (Gf @ rf2.transpose(1, 0, 2)).transpose(1, 0, 2) - dlikv = 2 * np.sum(rf2.transpose(2, 1, 0) * rf.transpose(1, 0), 2).T - for c in range(0, predinfo["predvars"].shape[0]): - w, v = np.linalg.eig(np.diag(1 / (predinfo["predvars"][c, :])) + Gf2) - term1 = (v * (1 / w)) @ (v.T @ Gfrf[:, c]) - - likv[c] -= Gfrf[:, c].T @ term1 - likv[c] += np.sum(np.log(predinfo["predvars"][c, :])) - likv[c] += np.sum(np.log(w)) - if return_grad: - Si = (v * (1 / w)) @ v.T - grt = ( - predinfo["predvars_gradtheta"][c, :, :].T / predinfo["predvars"][c, :] - ).T - dlikv[c, :] += np.sum(grt, 0) - grt = (-grt.T / predinfo["predvars"][c, :]).T - dlikv[c, :] += np.diag(Si) @ grt - term2 = (term1 / predinfo["predvars"][c, :]) ** 2 - dlikv[c, :] -= 2 * Gfrf2[:, c, :].T @ term1 - dlikv[c, :] -= term2 @ (predinfo["predvars_gradtheta"][c, :, :]) - if return_grad: - return (-likv / 2).reshape(-1, 1), (-dlikv / 2) - else: - return (-likv / 2).reshape(-1, 1) - - -def __standardizef(fitinfo, offset=None, scale=None): - r"""Standardizes f by creating offset, scale and fs.""" - # Extracting from input dictionary - f = fitinfo["f"] - epsilonPC = fitinfo["epsilonPC"] - - if (offset is not None) and (scale is not None): - if offset.shape[0] == f.shape[1] and scale.shape[0] == f.shape[1]: - if np.any(np.nanmean(np.abs(f - offset) / scale, 1) > 4): - offset = None - scale = None - else: - offset = None - scale = None - if offset is None or scale is None: - offset = np.zeros(f.shape[1]) - scale = np.zeros(f.shape[1]) - for k in range(0, f.shape[1]): - offset[k] = np.nanmean(f[:, k]) - scale[k] = np.nanstd(f[:, k]) / np.sqrt(1 - np.isnan(f[:, k]).mean()) - if scale[k] == 0: - raise ValueError("You have a row that is non-varying.") - - fs = np.zeros(f.shape) - fs = (f - offset) / scale - - # Assigning new values to the dictionary - U, S, _ = np.linalg.svd(fs.T, full_matrices=False) - Sp = S**2 - epsilonPC - Up = U[:, Sp > 0] - - extravar = np.nanmean((fs - fs @ Up @ Up.T) ** 2, 0) * (scale**2) - - standardpcinfo = { - "offset": offset, - "scale": scale, - "fs": fs, - "U": U, - "S": S, - "extravar": extravar, - } - - fitinfo["standardpcinfo"] = standardpcinfo - return - - -def __PCs(fitinfo): - "Apply PCA to reduce the dimension of `f`." - # Extracting from input dictionary - f = fitinfo["f"] - epsilonPC = fitinfo["epsilonPC"] - fs = fitinfo["standardpcinfo"]["fs"] - - if "U" in fitinfo["standardpcinfo"]: - U = fitinfo["standardpcinfo"]["U"] - S = fitinfo["standardpcinfo"]["S"] - else: - U, S, _ = np.linalg.svd(fs.T, full_matrices=False) - Sp = S**2 - epsilonPC - pct = U[:, Sp > 0] - pcw = np.sqrt(Sp[Sp > 0]) - - pcstdvar = np.zeros((f.shape[0], pct.shape[1])) - fitinfo["pcw"] = pcw - fitinfo["pcto"] = 1 * pct - effn = np.sum(np.clip(1 - pcstdvar, 0, 1)) - fitinfo["pct"] = pct * pcw / np.sqrt(effn) - fitinfo["pcti"] = pct * (np.sqrt(effn) / pcw) - # fitinfo['pc'] = pc * (np.sqrt(effn) / pcw) - fitinfo["pc"] = fs @ fitinfo["pct"] - fitinfo["unscaled_pcstdvar"] = pcstdvar - return - - -def __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, varconstant): - """Fit emulators for all principle components.""" - if "emulist" in fitinfo.keys(): - hypstarts = np.zeros((numpcs, fitinfo["emulist"][0]["hyp"].shape[0])) - hypinds = -1 * np.ones(numpcs) - for pcanum in range(0, min(numpcs, len(fitinfo["emulist"]))): - hypstarts[pcanum, :] = fitinfo["emulist"][pcanum]["hyp"] - hypinds[pcanum] = fitinfo["emulist"][pcanum]["hypind"] - else: - hypstarts = None - hypinds = -1 * np.ones(numpcs) - - emulist = [dict() for x in range(0, numpcs)] - for iters in range(0, 3): - for pcanum in range(0, numpcs): - if np.sum(hypinds == np.array(range(0, numpcs))) > 0.5: - hypwhere = np.where(hypinds == np.array(range(0, numpcs)))[0] - emulist[pcanum] = __fitGP1d( - theta=theta, - g=fitinfo["pc"][:, pcanum], - hyp1=hyp1, - hyp2=hyp2, - hypvarconst=varconstant, - gvar=fitinfo["unscaled_pcstdvar"][:, pcanum], - dampalpha=fitinfo["dampalpha"], - eta=fitinfo["eta"], - hypstarts=hypstarts[hypwhere, :], - hypinds=hypwhere, - sig2ofconst=0.01, - ) - else: - emulist[pcanum] = __fitGP1d( - theta=theta, - g=fitinfo["pc"][:, pcanum], - hyp1=hyp1, - hyp2=hyp2, - hypvarconst=varconstant, - gvar=fitinfo["unscaled_pcstdvar"][:, pcanum], - dampalpha=fitinfo["dampalpha"], - eta=fitinfo["eta"], - sig2ofconst=0.01, - ) - hypstarts = np.zeros((numpcs, emulist[pcanum]["hyp"].shape[0])) - emulist[pcanum]["hypind"] = min(pcanum, emulist[pcanum]["hypind"]) - hypstarts[pcanum, :] = emulist[pcanum]["hyp"] - if emulist[pcanum]["hypind"] < -0.5: - emulist[pcanum]["hypind"] = 1 * pcanum - hypinds[pcanum] = 1 * emulist[pcanum]["hypind"] - return emulist - - -def __fitGP1d( - theta, - g, - hyp1, - hyp2, - hypvarconst, - gvar=None, - dampalpha=None, - eta=None, - hypstarts=None, - hypinds=None, - sig2ofconst=None, -): - """Return a fitted model from the emulator model using smart method.""" - hypvarconstmean = 4 if hypvarconst is None else hypvarconst - hypvarconstLB = -8 if hypvarconst is None else hypvarconst - 0.5 - hypvarconstUB = 8 if hypvarconst is None else hypvarconst + 0.5 - - subinfo = {} - subinfo["hypregmean"] = np.append( - 0 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (0, hypvarconstmean, hyp1), - ) - subinfo["hypregLB"] = np.append( - -4 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (-12, hypvarconstLB, hyp2), - ) - - subinfo["hypregUB"] = np.append( - 4 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (2, hypvarconstUB, -8), - ) - subinfo["hypregstd"] = (subinfo["hypregUB"] - subinfo["hypregLB"]) / 8 - subinfo["hypregstd"][-3] = 2 - subinfo["hypregstd"][-1] = 4 - subinfo["hyp"] = 1 * subinfo["hypregmean"] - nhyptrain = np.max(np.min((20 * theta.shape[1], theta.shape[0]))) - if theta.shape[0] > nhyptrain: - thetac = np.random.choice(theta.shape[0], nhyptrain, replace=False) - else: - thetac = range(0, theta.shape[0]) - subinfo["theta"] = theta[thetac, :] - subinfo["g"] = g[thetac] - - # maxgvar = np.max(gvar) - # gvar = gvar / ((np.abs(maxgvar*1.001 - gvar)) ** dampalpha) - - gvar = np.minimum(eta, gvar / ((1 - gvar) ** dampalpha)) - - # print(gvar) - subinfo["sig2ofconst"] = sig2ofconst - subinfo["gvar"] = gvar[thetac] - hypind0 = -1 - - L0 = __negloglik(subinfo["hyp"], subinfo) - if hypstarts is not None: - L0 = __negloglik(subinfo["hyp"], subinfo) - for k in range(0, hypstarts.shape[0]): - L1 = __negloglik(hypstarts[k, :], subinfo) - if L1 < L0: - subinfo["hyp"] = hypstarts[k, :] - L0 = 1 * L1 - hypind0 = hypinds[k] - - if hypind0 > -0.5 and hypstarts.ndim > 1: - dL = __negloglikgrad(subinfo["hyp"], subinfo) - scalL = np.std(hypstarts, 0) * hypstarts.shape[0] / (1 + hypstarts.shape[0]) + ( - 1 / (1 + hypstarts.shape[0]) * subinfo["hypregstd"] - ) - if np.sum((dL * scalL) ** 2) < 1.25 * ( - subinfo["hyp"].shape[0] + 5 * np.sqrt(subinfo["hyp"].shape[0]) - ): - skipop = True - else: - skipop = False - else: - skipop = False - - if not skipop: - - def scaledlik(hypv): - hyprs = subinfo["hypregmean"] + hypv * subinfo["hypregstd"] - return __negloglik(hyprs, subinfo) - - def scaledlikgrad(hypv): - hyprs = subinfo["hypregmean"] + hypv * subinfo["hypregstd"] - return __negloglikgrad(hyprs, subinfo) * subinfo["hypregstd"] - - newLB = (subinfo["hypregLB"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - newUB = (subinfo["hypregUB"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - - newhyp0 = (subinfo["hyp"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - - opval = spo.minimize( - scaledlik, - newhyp0, - method="L-BFGS-B", - options={"gtol": 0.1}, - jac=scaledlikgrad, - bounds=spo.Bounds(newLB, newUB), - ) - - hypn = subinfo["hypregmean"] + opval.x * subinfo["hypregstd"] - likdiff = L0 - __negloglik(hypn, subinfo) - else: - likdiff = 0 - if hypind0 > -0.5 and (2 * likdiff) < 1.25 * ( - subinfo["hyp"].shape[0] + 5 * np.sqrt(subinfo["hyp"].shape[0]) - ): - subinfo["hypcov"] = subinfo["hyp"][:-2] - subinfo["hypvarconst"] = subinfo["hyp"][-2] - subinfo["hypind"] = hypind0 - subinfo["nug"] = np.exp(subinfo["hyp"][-1]) / (1 + np.exp(subinfo["hyp"][-1])) - - R = __covmat(theta, theta, subinfo["hypcov"]) - - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - if gvar is not None: - subinfo["R"] += np.exp(subinfo["hypvarconst"]) * np.diag(gvar) - - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ g - subinfo["Vh"] = Vh - n = subinfo["R"].shape[0] - subinfo["sig2"] = (np.mean(fcenter**2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = V @ np.diag(1 / W) @ V.T - else: - subinfo["hyp"] = hypn - subinfo["hypind"] = -1 - subinfo["hypcov"] = subinfo["hyp"][:-2] - subinfo["hypvarconst"] = subinfo["hyp"][-2] - subinfo["nug"] = np.exp(subinfo["hyp"][-1]) / (1 + np.exp(subinfo["hyp"][-1])) - - R = __covmat(theta, theta, subinfo["hypcov"]) - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - if gvar is not None: - subinfo["R"] += np.exp(subinfo["hypvarconst"]) * np.diag(gvar) - n = subinfo["R"].shape[0] - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ g - subinfo["sig2"] = (np.mean(fcenter**2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = Vh @ Vh.T - subinfo["Vh"] = Vh - subinfo["pw"] = subinfo["Rinv"] @ g - return subinfo - - -def __negloglik(hyp, info): - """Return penalized log likelihood of single demensional GP model.""" - R0 = __covmat(info["theta"], info["theta"], hyp[:-2]) - nug = np.exp(hyp[-1]) / (1 + np.exp(hyp[-1])) - R = (1 - nug) * R0 + nug * np.eye(info["theta"].shape[0]) - - if info["gvar"] is not None: - R += np.exp(hyp[-2]) * np.diag(info["gvar"]) - W, V = np.linalg.eigh(R) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ info["g"] - n = info["g"].shape[0] - - sig2ofconst = info["sig2ofconst"] - sig2hat = (n * np.mean(fcenter**2) + sig2ofconst) / (n + sig2ofconst) - negloglik = 1 / 2 * np.sum(np.log(np.abs(W))) + 1 / 2 * n * np.log(sig2hat) - negloglik += 0.5 * np.sum( - ((10 ** (-8) + hyp - info["hypregmean"]) / (info["hypregstd"])) ** 2 - ) - return negloglik - - -def __negloglikgrad(hyp, info): - """Return gradient of the penalized log likelihood of single demensional - GP model.""" - R0, dR = __covmat(info["theta"], info["theta"], hyp[:-2], True) - nug = np.exp(hyp[-1]) / (1 + np.exp(hyp[-1])) - R = (1 - nug) * R0 + nug * np.eye(info["theta"].shape[0]) - dR = (1 - nug) * dR - dRappend2 = nug / (1 + np.exp(hyp[-1])) * (-R0 + np.eye(info["theta"].shape[0])) - - if info["gvar"] is not None: - R += np.exp(hyp[-2]) * np.diag(info["gvar"]) - dRappend1 = np.exp(hyp[-2]) * np.diag(info["gvar"]) - else: - dRappend1 = 0 * np.eye(info["theta"].shape[0]) - - dR = np.append(dR, dRappend1[:, :, None], axis=2) - dR = np.append(dR, dRappend2[:, :, None], axis=2) - W, V = np.linalg.eigh(R) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ info["g"] - n = info["g"].shape[0] - - sig2ofconst = info["sig2ofconst"] - sig2hat = (n * np.mean(fcenter**2) + sig2ofconst) / (n + sig2ofconst) - dnegloglik = np.zeros(dR.shape[2]) - Rinv = Vh @ Vh.T - - for k in range(0, dR.shape[2]): - dsig2hat = -np.sum( - (Vh @ np.multiply.outer(fcenter, fcenter) @ Vh.T) * dR[:, :, k] - ) / (n + sig2ofconst) - dnegloglik[k] += 0.5 * n * dsig2hat / sig2hat - dnegloglik[k] += 0.5 * np.sum(Rinv * dR[:, :, k]) - - dnegloglik += (10 ** (-8) + hyp - info["hypregmean"]) / ((info["hypregstd"]) ** 2) - return dnegloglik diff --git a/PUQ/surrogatemethods/PCGPexp.py b/PUQ/surrogatemethods/PCGPexp.py deleted file mode 100644 index c28da48..0000000 --- a/PUQ/surrogatemethods/PCGPexp.py +++ /dev/null @@ -1,1059 +0,0 @@ -"""PCGP (Higdon et al., 2008). """ - -import numpy as np -import scipy.optimize as spo -import scipy.linalg as spla -import copy -from PUQ.surrogatesupport.matern_covmat import covmat as __covmat -import torch -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - multiple_pdfs, - multiple_determinants, -) - - -def fit( - fitinfo, - x, - theta, - f, - epsilonPC=0.001, - lognugmean=-10, - lognugLB=-20, - varconstant=None, - dampalpha=0.3, - eta=10, - standardpcinfo=None, - verbose=0, - **kwargs -): - """ - The purpose of fit is to take information and plug all of our fit - information into fitinfo, which is a python dictionary. - - Parameters - ---------- - fitinfo : dict - A dictionary including the emulation fitting information once - complete. - The dictionary is passed by reference, so it returns None. - x : numpy.ndarray - An array of inputs. Each row should correspond to a row in f. - theta : numpy.ndarray - An array of parameters. Each row should correspond to a column in f. - f : numpy.ndarray - An array of responses. Each column in f should correspond to a row in - theta. Each row in f should correspond to a row in x. - epsilonPC : scalar - A parameter to control the number of PCs used. The suggested range for - epsilonPC is (0.001, 0.1). The larger epsilonPC is, the fewer PCs will be - used. Note that epsilonPC here is *not* the unexplained variance in - typical principal component analysis. - lognugmean : scalar - A parameter to control the log of the nugget used in fitting the GPs. - The suggested range for lognugmean is (-12, -4). The nugget is estimated, - and this parameter is used to guide the estimation. - lognugLB : scalar - A parameter to control the lower bound of the log of the nugget. The - suggested range for lognugLB is (-24, -12). - varconstant : scalar - A multiplying constant to control the inflation (deflation) of additional - variances if missing values are present. Default is None, the parameter will - be optimized in such case. A general working range is (np.exp(-4), np.exp(4)). - dampalpha : scalar - A parameter to control the rate of increase of variance as amount of missing - values increases. Default is 0.3, otherwise an appropriate range is (0, 0.5). - Values larger than 0.5 are permitted but it leads to poor empirical performance. - eta : scalar - A parameter as an upper bound for the additional variance term. Default is 10. - standardpcinfo : dict - A dictionary user supplies that contains information for standardization of `f`, - in the following format, such that fs = (f - offset) / scale, U are the - orthogonal basis vectors, and S are the singular values from SVD of `fs`. - The entry extravar contains the average squared residual for each column (x). - {'offset': offset, - 'scale': scale, - 'fs': fs, - 'extravar': extravar, - 'U': U, # optional - 'S': S # optional - } - - verbose : scalar - A parameter to suppress in-method console output. Use 0 to suppress output, - use 1 to show output. - - - kwargs : dict, optional - A dictionary containing options. The default is None. - - Returns - ------- - None. - - """ - # print('Fitting...') - f = f.T - fitinfo["epsilonPC"] = epsilonPC - hyp1 = lognugmean - hyp2 = lognugLB - hypvarconst = np.log(varconstant) if varconstant is not None else None - - fitinfo["dampalpha"] = dampalpha - fitinfo["eta"] = eta - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["x"] = x - - # Standardize the function evaluations f - if standardpcinfo is None: - __standardizef(fitinfo) - else: - fitinfo["standardpcinfo"] = standardpcinfo - - # Construct principal components - __PCs(fitinfo) - numpcs = fitinfo["pc"].shape[1] - - if verbose > 0: - print(fitinfo["method"], "considering ", numpcs, "PCs") - - # Fit emulators for all PCs - emulist = __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, hypvarconst) - fitinfo["varc_status"] = "fixed" if varconstant is not None else "optimized" - fitinfo["logvarc"] = np.array([emulist[i]["hypvarconst"] for i in range(numpcs)]) - fitinfo["pcstdvar"] = np.exp(fitinfo["logvarc"]) * fitinfo["unscaled_pcstdvar"] - fitinfo["emulist"] = emulist - - return - - -def update(fitinfo, x, theta, f, **kwargs): - print("Updating...") - f = f.T - # print(fitinfo['f'].shape) - # print(f.shape) - # print(fitinfo['pc'].shape) - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["x"] = x - - standardpcinfo = fitinfo["standardpcinfo"] - offset = standardpcinfo["offset"] - scale = standardpcinfo["scale"] - fs = (f - offset) / scale - standardpcinfo["fs"] = fs - fitinfo["pc"] = fs @ fitinfo["pct"] - emulist = fitinfo["emulist"] - numpcs = fitinfo["pc"].shape[1] - - for pcanum in range(0, numpcs): - subinfo = emulist[pcanum] - R = __covmat(theta, theta, subinfo["hypcov"]) - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - # sig2ofconst = subinfo['sig2ofconst'] - g = fitinfo["pc"][:, pcanum] - # fcenter = Vh.T @ g - subinfo["Vh"] = Vh - # n = subinfo['R'].shape[0] - # subinfo['sig2'] = (np.mean(fcenter ** 2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = V @ np.diag(1 / W) @ V.T - subinfo["pw"] = subinfo["Rinv"] @ g - - -def predict(predinfo, fitinfo, x, theta, **kwargs): - r""" - Finds prediction at theta and x given the dictionary fitinfo. - This [emulationpredictdocstring] automatically filled by docinfo.py when - running updatedocs.py - - Parameters - ---------- - predinfo : dict - An arbitary dictionary where you should place all of your prediction - information once complete. This dictionary is pass by reference, so - there is no reason to return anything. Keep only stuff that will be - used by predict. Key elements are - - - `predinfo['mean']` : `predinfo['mean'][k]` is mean of the prediction - at all x at `theta[k]`. - - `predinfo['var']` : `predinfo['var'][k]` is variance of the - prediction at all x at `theta[k]`. - - `predinfo['cov']` : `predinfo['cov'][k]` is covariance matrix of the prediction - at all x at `theta[k]`. - - `predinfo['covhalf']` : if `A = predinfo['covhalf'][k]` then - `A.T @ A = predinfo['cov'][k]`. - - fitinfo : dict - An arbitary dictionary where you placed all your important fitting - information from the fit function above. - - x : array of objects - An matrix (vector) of inputs for prediction. - - theta : array of objects - An matrix (vector) of parameters to prediction. - - kwargs : dict - A dictionary containing additional options - """ - return_grad = False - if ( - (kwargs is not None) - and ("return_grad" in kwargs.keys()) - and (kwargs["return_grad"] is True) - ): - return_grad = True - return_covx = True - if ( - (kwargs is not None) - and ("return_covx" in kwargs.keys()) - and (kwargs["return_covx"] is False) - ): - return_covx = False - infos = fitinfo["emulist"] - predvecs = np.zeros((theta.shape[0], len(infos))) - predvars = np.zeros((theta.shape[0], len(infos))) - predcovs = np.zeros((theta.shape[0], theta.shape[0], len(infos))) - - if predvecs.ndim < 1.5: - predvecs = predvecs.reshape((1, -1)) - predvars = predvars.reshape((1, -1)) - try: - if ( - x is None - or np.all(np.equal(x, fitinfo["x"])) - or np.allclose(x, fitinfo["x"]) - ): - xind = np.arange(0, x.shape[0]) - xnewind = np.arange(0, x.shape[0]) - else: - raise - except Exception: - matchingmatrix = np.ones((x.shape[0], fitinfo["x"].shape[0])) - for k in range(0, x[0].shape[0]): - try: - matchingmatrix *= np.isclose(x[:, k][:, None], fitinfo["x"][:, k]) - except Exception: - matchingmatrix *= np.equal(x[:, k][:, None], fitinfo["x"][:, k]) - xind = np.argwhere(matchingmatrix > 0.5)[:, 1] - xnewind = np.argwhere(matchingmatrix > 0.5)[:, 0] - - rsave = np.array(np.ones(len(infos)), dtype=object) - rsave_3 = np.array(np.ones(len(infos)), dtype=object) - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - rsave[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - - rsave_3[k] = __covmat(theta, theta, infos[k]["hypcov"]) - # adjusted covariance matrix - r = (1 - infos[k]["nug"]) * np.squeeze(rsave[infos[k]["hypind"]]) - r3 = (1 - infos[k]["nug"]) * np.squeeze(rsave_3[infos[k]["hypind"]]) - try: - rVh = r @ infos[k]["Vh"] - rVh2 = rVh @ (infos[k]["Vh"]).T - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh.ndim < 1.5: - rVh = rVh.reshape((1, -1)) - if rVh2.ndim < 1.5: - rVh2 = np.reshape(rVh2, (1, -1)) - predvecs[:, k] = r @ infos[k]["pw"] - predvars[:, k] = infos[k]["sig2"] * np.abs(1 - np.sum(rVh**2, 1)) - predcovs[:, :, k] = infos[k]["sig2"] * ( - r3 - rVh @ rVh.T - ) # np.abs(1 - np.sum(rVh ** 2, 1)) - - # calculate predictive mean and variance - predinfo["mean"] = np.full((x.shape[0], theta.shape[0]), np.nan) - predinfo["var"] = np.full((x.shape[0], theta.shape[0]), np.nan) - predinfo["covx"] = np.full((theta.shape[0], theta.shape[0]), np.nan) - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - # pctscale = (fitinfo['pct'].T * fitinfo['standardpcinfo']['scale']).T - predinfo["mean"][xnewind, :] = ( - (predvecs @ pctscale[xind, :].T) + fitinfo["standardpcinfo"]["offset"][xind] - ).T - predinfo["var"][xnewind, :] = ( - ( - fitinfo["standardpcinfo"]["extravar"][xind] - + predvars @ (pctscale[xind, :] ** 2).T - ) - ).T - - predinfo["covx"] = predcovs[:, :, 0] * (pctscale[:, :] ** 2) - - # print(predinfo['var']) - # print(predinfo['cov']) - - predinfo["extravar"] = 1 * fitinfo["standardpcinfo"]["extravar"][xind] - predinfo["predvars"] = 1 * predvars - predinfo["predvecs"] = 1 * predvecs - predinfo["phi"] = 1 * pctscale[xind, :] - - return - - -def predictlpdf(predinfo, f, return_grad=False, addvar=0, **kwargs): - totvar = addvar + predinfo["extravar"] - rf = ((f.T - predinfo["mean"].T) * (1 / np.sqrt(totvar))).T - Gf = predinfo["phi"].T * (1 / np.sqrt(totvar)) - Gfrf = Gf @ rf - Gf2 = Gf @ Gf.T - likv = np.sum(rf**2, 0) - if return_grad: - rf2 = -( - predinfo["mean_gradtheta"].transpose(2, 1, 0) * (1 / np.sqrt(totvar)) - ).transpose(2, 1, 0) - Gfrf2 = (Gf @ rf2.transpose(1, 0, 2)).transpose(1, 0, 2) - dlikv = 2 * np.sum(rf2.transpose(2, 1, 0) * rf.transpose(1, 0), 2).T - for c in range(0, predinfo["predvars"].shape[0]): - w, v = np.linalg.eig(np.diag(1 / (predinfo["predvars"][c, :])) + Gf2) - term1 = (v * (1 / w)) @ (v.T @ Gfrf[:, c]) - - likv[c] -= Gfrf[:, c].T @ term1 - likv[c] += np.sum(np.log(predinfo["predvars"][c, :])) - likv[c] += np.sum(np.log(w)) - if return_grad: - Si = (v * (1 / w)) @ v.T - grt = ( - predinfo["predvars_gradtheta"][c, :, :].T / predinfo["predvars"][c, :] - ).T - dlikv[c, :] += np.sum(grt, 0) - grt = (-grt.T / predinfo["predvars"][c, :]).T - dlikv[c, :] += np.diag(Si) @ grt - term2 = (term1 / predinfo["predvars"][c, :]) ** 2 - dlikv[c, :] -= 2 * Gfrf2[:, c, :].T @ term1 - dlikv[c, :] -= term2 @ (predinfo["predvars_gradtheta"][c, :, :]) - if return_grad: - return (-likv / 2).reshape(-1, 1), (-dlikv / 2) - else: - return (-likv / 2).reshape(-1, 1) - - -def __standardizef(fitinfo, offset=None, scale=None): - r"""Standardizes f by creating offset, scale and fs.""" - # Extracting from input dictionary - f = fitinfo["f"] - epsilonPC = fitinfo["epsilonPC"] - - if (offset is not None) and (scale is not None): - if offset.shape[0] == f.shape[1] and scale.shape[0] == f.shape[1]: - if np.any(np.nanmean(np.abs(f - offset) / scale, 1) > 4): - offset = None - scale = None - else: - offset = None - scale = None - if offset is None or scale is None: - offset = np.zeros(f.shape[1]) - scale = np.zeros(f.shape[1]) - for k in range(0, f.shape[1]): - offset[k] = np.nanmean(f[:, k]) - scale[k] = np.nanstd(f[:, k]) / np.sqrt(1 - np.isnan(f[:, k]).mean()) - if scale[k] == 0: - print(f) - raise ValueError("You have a row that is non-varying.") - - fs = np.zeros(f.shape) - fs = (f - offset) / scale - - # Assigning new values to the dictionary - U, S, _ = np.linalg.svd(fs.T, full_matrices=False) - Sp = S**2 - epsilonPC - Up = U[:, Sp > 0] - - extravar = np.nanmean((fs - fs @ Up @ Up.T) ** 2, 0) * (scale**2) - - standardpcinfo = { - "offset": offset, - "scale": scale, - "fs": fs, - "U": U, - "S": S, - "extravar": extravar, - } - - fitinfo["standardpcinfo"] = standardpcinfo - return - - -def __PCs(fitinfo): - "Apply PCA to reduce the dimension of `f`." - # Extracting from input dictionary - f = fitinfo["f"] - epsilonPC = fitinfo["epsilonPC"] - fs = fitinfo["standardpcinfo"]["fs"] - - if "U" in fitinfo["standardpcinfo"]: - U = fitinfo["standardpcinfo"]["U"] - S = fitinfo["standardpcinfo"]["S"] - else: - U, S, _ = np.linalg.svd(fs.T, full_matrices=False) - Sp = S**2 - epsilonPC - pct = U[:, Sp > 0] - pcw = np.sqrt(Sp[Sp > 0]) - - pcstdvar = np.zeros((f.shape[0], pct.shape[1])) - fitinfo["pcw"] = pcw - fitinfo["pcto"] = 1 * pct - effn = np.sum(np.clip(1 - pcstdvar, 0, 1)) - fitinfo["pct"] = pct * pcw / np.sqrt(effn) - fitinfo["pcti"] = pct * (np.sqrt(effn) / pcw) - # fitinfo['pc'] = pc * (np.sqrt(effn) / pcw) - fitinfo["pc"] = fs @ fitinfo["pct"] - fitinfo["unscaled_pcstdvar"] = pcstdvar - return - - -def __fitGPs(fitinfo, theta, numpcs, hyp1, hyp2, varconstant): - """Fit emulators for all principle components.""" - if "emulist" in fitinfo.keys(): - hypstarts = np.zeros((numpcs, fitinfo["emulist"][0]["hyp"].shape[0])) - hypinds = -1 * np.ones(numpcs) - for pcanum in range(0, min(numpcs, len(fitinfo["emulist"]))): - hypstarts[pcanum, :] = fitinfo["emulist"][pcanum]["hyp"] - hypinds[pcanum] = fitinfo["emulist"][pcanum]["hypind"] - else: - hypstarts = None - hypinds = -1 * np.ones(numpcs) - - emulist = [dict() for x in range(0, numpcs)] - for iters in range(0, 3): - for pcanum in range(0, numpcs): - if np.sum(hypinds == np.array(range(0, numpcs))) > 0.5: - hypwhere = np.where(hypinds == np.array(range(0, numpcs)))[0] - emulist[pcanum] = __fitGP1d( - theta=theta, - g=fitinfo["pc"][:, pcanum], - hyp1=hyp1, - hyp2=hyp2, - hypvarconst=varconstant, - gvar=fitinfo["unscaled_pcstdvar"][:, pcanum], - dampalpha=fitinfo["dampalpha"], - eta=fitinfo["eta"], - hypstarts=hypstarts[hypwhere, :], - hypinds=hypwhere, - sig2ofconst=0.01, - ) - else: - emulist[pcanum] = __fitGP1d( - theta=theta, - g=fitinfo["pc"][:, pcanum], - hyp1=hyp1, - hyp2=hyp2, - hypvarconst=varconstant, - gvar=fitinfo["unscaled_pcstdvar"][:, pcanum], - dampalpha=fitinfo["dampalpha"], - eta=fitinfo["eta"], - sig2ofconst=0.01, - ) - hypstarts = np.zeros((numpcs, emulist[pcanum]["hyp"].shape[0])) - emulist[pcanum]["hypind"] = min(pcanum, emulist[pcanum]["hypind"]) - hypstarts[pcanum, :] = emulist[pcanum]["hyp"] - if emulist[pcanum]["hypind"] < -0.5: - emulist[pcanum]["hypind"] = 1 * pcanum - hypinds[pcanum] = 1 * emulist[pcanum]["hypind"] - return emulist - - -def __fitGP1d( - theta, - g, - hyp1, - hyp2, - hypvarconst, - gvar=None, - dampalpha=None, - eta=None, - hypstarts=None, - hypinds=None, - sig2ofconst=None, -): - """Return a fitted model from the emulator model using smart method.""" - hypvarconstmean = 4 if hypvarconst is None else hypvarconst - hypvarconstLB = -8 if hypvarconst is None else hypvarconst - 0.5 - hypvarconstUB = 8 if hypvarconst is None else hypvarconst + 0.5 - - subinfo = {} - subinfo["hypregmean"] = np.append( - 0 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (0, hypvarconstmean, hyp1), - ) - subinfo["hypregLB"] = np.append( - -4 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (-12, hypvarconstLB, hyp2), - ) - - subinfo["hypregUB"] = np.append( - 4 + 0.5 * np.log(theta.shape[1]) + np.log(np.std(theta, 0)), - (2, hypvarconstUB, -8), - ) - subinfo["hypregstd"] = (subinfo["hypregUB"] - subinfo["hypregLB"]) / 8 - subinfo["hypregstd"][-3] = 2 - subinfo["hypregstd"][-1] = 4 - subinfo["hyp"] = 1 * subinfo["hypregmean"] - nhyptrain = np.max(np.min((20 * theta.shape[1], theta.shape[0]))) - if theta.shape[0] > nhyptrain: - thetac = np.random.choice(theta.shape[0], nhyptrain, replace=False) - else: - thetac = range(0, theta.shape[0]) - subinfo["theta"] = theta[thetac, :] - subinfo["g"] = g[thetac] - - # maxgvar = np.max(gvar) - # gvar = gvar / ((np.abs(maxgvar*1.001 - gvar)) ** dampalpha) - - gvar = np.minimum(eta, gvar / ((1 - gvar) ** dampalpha)) - - # print(gvar) - subinfo["sig2ofconst"] = sig2ofconst - subinfo["gvar"] = gvar[thetac] - hypind0 = -1 - - L0 = __negloglik(subinfo["hyp"], subinfo) - if hypstarts is not None: - L0 = __negloglik(subinfo["hyp"], subinfo) - for k in range(0, hypstarts.shape[0]): - L1 = __negloglik(hypstarts[k, :], subinfo) - if L1 < L0: - subinfo["hyp"] = hypstarts[k, :] - L0 = 1 * L1 - hypind0 = hypinds[k] - - if hypind0 > -0.5 and hypstarts.ndim > 1: - dL = __negloglikgrad(subinfo["hyp"], subinfo) - scalL = np.std(hypstarts, 0) * hypstarts.shape[0] / (1 + hypstarts.shape[0]) + ( - 1 / (1 + hypstarts.shape[0]) * subinfo["hypregstd"] - ) - if np.sum((dL * scalL) ** 2) < 1.25 * ( - subinfo["hyp"].shape[0] + 5 * np.sqrt(subinfo["hyp"].shape[0]) - ): - skipop = True - else: - skipop = False - else: - skipop = False - - if not skipop: - - def scaledlik(hypv): - hyprs = subinfo["hypregmean"] + hypv * subinfo["hypregstd"] - return __negloglik(hyprs, subinfo) - - def scaledlikgrad(hypv): - hyprs = subinfo["hypregmean"] + hypv * subinfo["hypregstd"] - return __negloglikgrad(hyprs, subinfo) * subinfo["hypregstd"] - - newLB = (subinfo["hypregLB"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - newUB = (subinfo["hypregUB"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - - newhyp0 = (subinfo["hyp"] - subinfo["hypregmean"]) / subinfo["hypregstd"] - - opval = spo.minimize( - scaledlik, - newhyp0, - method="L-BFGS-B", - options={"gtol": 0.1}, - jac=scaledlikgrad, - bounds=spo.Bounds(newLB, newUB), - ) - - hypn = subinfo["hypregmean"] + opval.x * subinfo["hypregstd"] - likdiff = L0 - __negloglik(hypn, subinfo) - else: - likdiff = 0 - if hypind0 > -0.5 and (2 * likdiff) < 1.25 * ( - subinfo["hyp"].shape[0] + 5 * np.sqrt(subinfo["hyp"].shape[0]) - ): - subinfo["hypcov"] = subinfo["hyp"][:-2] - subinfo["hypvarconst"] = subinfo["hyp"][-2] - subinfo["hypind"] = hypind0 - subinfo["nug"] = np.exp(subinfo["hyp"][-1]) / (1 + np.exp(subinfo["hyp"][-1])) - - R = __covmat(theta, theta, subinfo["hypcov"]) - - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - if gvar is not None: - subinfo["R"] += np.exp(subinfo["hypvarconst"]) * np.diag(gvar) - - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ g - subinfo["Vh"] = Vh - n = subinfo["R"].shape[0] - subinfo["sig2"] = (np.mean(fcenter**2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = V @ np.diag(1 / W) @ V.T - else: - subinfo["hyp"] = hypn - subinfo["hypind"] = -1 - subinfo["hypcov"] = subinfo["hyp"][:-2] - subinfo["hypvarconst"] = subinfo["hyp"][-2] - subinfo["nug"] = np.exp(subinfo["hyp"][-1]) / (1 + np.exp(subinfo["hyp"][-1])) - - R = __covmat(theta, theta, subinfo["hypcov"]) - subinfo["R"] = (1 - subinfo["nug"]) * R + subinfo["nug"] * np.eye(R.shape[0]) - if gvar is not None: - subinfo["R"] += np.exp(subinfo["hypvarconst"]) * np.diag(gvar) - n = subinfo["R"].shape[0] - W, V = np.linalg.eigh(subinfo["R"]) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ g - subinfo["sig2"] = (np.mean(fcenter**2) * n + sig2ofconst) / (n + sig2ofconst) - subinfo["Rinv"] = Vh @ Vh.T - subinfo["Vh"] = Vh - subinfo["pw"] = subinfo["Rinv"] @ g - return subinfo - - -def __negloglik(hyp, info): - """Return penalized log likelihood of single demensional GP model.""" - R0 = __covmat(info["theta"], info["theta"], hyp[:-2]) - nug = np.exp(hyp[-1]) / (1 + np.exp(hyp[-1])) - R = (1 - nug) * R0 + nug * np.eye(info["theta"].shape[0]) - - if info["gvar"] is not None: - R += np.exp(hyp[-2]) * np.diag(info["gvar"]) - W, V = np.linalg.eigh(R) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ info["g"] - n = info["g"].shape[0] - - sig2ofconst = info["sig2ofconst"] - sig2hat = (n * np.mean(fcenter**2) + sig2ofconst) / (n + sig2ofconst) - negloglik = 1 / 2 * np.sum(np.log(np.abs(W))) + 1 / 2 * n * np.log(sig2hat) - negloglik += 0.5 * np.sum( - ((10 ** (-8) + hyp - info["hypregmean"]) / (info["hypregstd"])) ** 2 - ) - return negloglik - - -def __negloglikgrad(hyp, info): - """Return gradient of the penalized log likelihood of single demensional - GP model.""" - R0, dR = __covmat(info["theta"], info["theta"], hyp[:-2], True) - nug = np.exp(hyp[-1]) / (1 + np.exp(hyp[-1])) - R = (1 - nug) * R0 + nug * np.eye(info["theta"].shape[0]) - dR = (1 - nug) * dR - dRappend2 = nug / (1 + np.exp(hyp[-1])) * (-R0 + np.eye(info["theta"].shape[0])) - - if info["gvar"] is not None: - R += np.exp(hyp[-2]) * np.diag(info["gvar"]) - dRappend1 = np.exp(hyp[-2]) * np.diag(info["gvar"]) - else: - dRappend1 = 0 * np.eye(info["theta"].shape[0]) - - dR = np.append(dR, dRappend1[:, :, None], axis=2) - dR = np.append(dR, dRappend2[:, :, None], axis=2) - W, V = np.linalg.eigh(R) - Vh = V / np.sqrt(np.abs(W)) - fcenter = Vh.T @ info["g"] - n = info["g"].shape[0] - - sig2ofconst = info["sig2ofconst"] - sig2hat = (n * np.mean(fcenter**2) + sig2ofconst) / (n + sig2ofconst) - dnegloglik = np.zeros(dR.shape[2]) - Rinv = Vh @ Vh.T - - for k in range(0, dR.shape[2]): - dsig2hat = -np.sum( - (Vh @ np.multiply.outer(fcenter, fcenter) @ Vh.T) * dR[:, :, k] - ) / (n + sig2ofconst) - dnegloglik[k] += 0.5 * n * dsig2hat / sig2hat - dnegloglik[k] += 0.5 * np.sum(Rinv * dR[:, :, k]) - - dnegloglik += (10 ** (-8) + hyp - info["hypregmean"]) / ((info["hypregstd"]) ** 2) - return dnegloglik - - -def postphimat( - fitinfo, n_x, theta, obs, obsvar, theta_cand, covmat_ref, rVh_1_3D, pred_mean -): - - # n_x = len(x) - n_tot_ref = theta.shape[0] - n_ref = int(n_tot_ref / n_x) - n_t = fitinfo["theta"].shape[0] - - infos = fitinfo["emulist"] - predvars_cand = np.zeros((theta_cand.shape[0], len(infos))) - - # n_ref x n_cand - rsave_2 = np.array(np.ones(len(infos)), dtype=object) - # n_cand x n_t - rsave_4 = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - - if infos[k]["hypind"] == k: - rsave_2[k] = __covmat(theta, theta_cand, infos[k]["hypcov"]) - - rsave_4[k] = __covmat(theta_cand, fitinfo["theta"], infos[k]["hypcov"]) - - # adjusted covariance matrix - # n_tot_ref - r_2 = (1 - infos[k]["nug"]) * np.squeeze(rsave_2[infos[k]["hypind"]]) - # n_acquired - r_4 = (1 - infos[k]["nug"]) * np.squeeze(rsave_4[infos[k]["hypind"]]) - - try: - rVh_4 = r_4.reshape(1, len(fitinfo["theta"])) @ infos[k]["Vh"] - - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - # print(np.round(r_2[0:50], 4)) - r_2_3D = r_2.reshape(n_ref, n_x, 1) - # print(np.round(r_2_3D[0:5, 0:n_x, 0], 4)) - rVh_4_3D = rVh_4.reshape(1, n_t, 1) - - # rVh_1_3D: n_ref x n_x x n_acquired - - # n_ref x n_x x 1 - cov3D = np.matmul(rVh_1_3D, rVh_4_3D) - - # n_ref x n_x x 1 - cov_cand_3D = infos[k]["sig2"] * (r_2_3D - cov3D) - - predvars_cand[:, k] = infos[k]["sig2"] * np.abs(1 - np.sum(rVh_4**2, 1)) - predvars_cand[:, k] += infos[k]["nug"] - - # rVh_4: used to compute candidate variance - - # calculate candidate variance - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - - # n_ref x n_x x 1 - cov_cand_3D = cov_cand_3D * (pctscale[:, :] ** 2) - # n_ref x 1 x n_x - cov_cand_3DT = np.transpose(cov_cand_3D, (0, 2, 1)) - - # (1 x 1) - var_cand = ( - ( - fitinfo["standardpcinfo"]["extravar"][:] - + predvars_cand @ (pctscale[:, :] ** 2).T - ) - ).T - - # (n_ref x n_x x n_x) - Phi3D = (cov_cand_3D * cov_cand_3DT) / var_cand - cov1 = 0.5 * (covmat_ref + Phi3D) - cov2 = covmat_ref - Phi3D - p1 = multiple_pdfs(obs, pred_mean, cov1) - - det1 = multiple_determinants(cov2) - eivar = np.sum((1 / ((2**n_x) * (np.sqrt(np.pi) ** n_x) * np.sqrt(det1))) * p1) - return eivar - - -def temp_postphimat(fitinfo, n_x, theta, obs, obsvar): - - n_tot_ref = theta.shape[0] - n_ref = int(n_tot_ref / n_x) - n_t = fitinfo["theta"].shape[0] - - infos = fitinfo["emulist"] - predmean_ref = np.zeros((theta.shape[0], len(infos))) - predvars_ref = np.zeros((theta.shape[0], len(infos))) - - if predmean_ref.ndim < 1.5: - predmean_ref = predmean_ref.reshape((1, -1)) - predvars_ref = predvars_ref.reshape((1, -1)) - - # n_ref x n_t - rsave_1 = np.array(np.ones(len(infos)), dtype=object) - # n_ref x n_ref - rsave_3 = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - rsave_1[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - - rsave_3[k] = __covmat(theta, theta, infos[k]["hypcov"]) - - # adjusted covariance matrix - r_1 = (1 - infos[k]["nug"]) * np.squeeze(rsave_1[infos[k]["hypind"]]) - r_3 = (1 - infos[k]["nug"]) * np.squeeze(rsave_3[infos[k]["hypind"]]) - - try: - rVh_1 = r_1 @ infos[k]["Vh"] - - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh_1.ndim < 1.5: - rVh_1 = rVh_1.reshape((1, -1)) - - id_row = np.arange(0, n_tot_ref) - id_col = np.arange(0, n_tot_ref).reshape(n_ref, n_x) - id_col = np.repeat(id_col, repeats=n_x, axis=0) - - # r_3 : n_tot_ref x n_tot_ref - # r_3_3D : n_ref x n_x x n_x - r_3_3D = r_3[id_row[:, None], id_col].reshape(n_ref, n_x, n_x) - - # rVh_1 : n_tot_ref x n_t - # rVh_1_3d : n_ref x n_x x n_t - rVh_1_3d = rVh_1.reshape(n_ref, n_x, n_t) - # rVh_1_3dT : n_ref x n_t x n_x - rVh_1_3dT = np.transpose(rVh_1_3d, (0, 2, 1)) - cov3D = np.matmul(rVh_1_3d, rVh_1_3dT) - - # cov3D : n_ref x n_x x n_x - cov_ref_3D = infos[k]["sig2"] * (r_3_3D - cov3D) - predmean_ref[:, k] = r_1 @ infos[k]["pw"] - - # print(np.round(obsvar, 2)) - # calculate predictive mean and variance - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - Smat3D = cov_ref_3D * (pctscale[:, :] ** 2) - pred_mean = ((predmean_ref @ pctscale.T) + fitinfo["standardpcinfo"]["offset"]).T - pred_mean = pred_mean.reshape(n_ref, n_x) - covmat_ref = Smat3D + obsvar - return covmat_ref, rVh_1_3d, pred_mean - - -def postpred(fitinfo, x, theta, obs, obsvar): - - n_x = len(x) - n_tot_ref = theta.shape[0] - n_ref = int(n_tot_ref / n_x) - n_t = fitinfo["theta"].shape[0] - - predinfo = {} - infos = fitinfo["emulist"] - predmean_ref = np.zeros((theta.shape[0], len(infos))) - predvars_ref = np.zeros((theta.shape[0], len(infos))) - - if predmean_ref.ndim < 1.5: - predmean_ref = predmean_ref.reshape((1, -1)) - predvars_ref = predvars_ref.reshape((1, -1)) - - # n_ref x n_t - rsave_1 = np.array(np.ones(len(infos)), dtype=object) - # n_ref x n_ref - rsave_3 = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - rsave_1[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - - rsave_3[k] = __covmat(theta, theta, infos[k]["hypcov"]) - - # adjusted covariance matrix - r_1 = (1 - infos[k]["nug"]) * np.squeeze(rsave_1[infos[k]["hypind"]]) - r_3 = (1 - infos[k]["nug"]) * np.squeeze(rsave_3[infos[k]["hypind"]]) - - try: - rVh_1 = r_1 @ infos[k]["Vh"] - - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh_1.ndim < 1.5: - rVh_1 = rVh_1.reshape((1, -1)) - - id_row = np.arange(0, n_tot_ref) - id_col = np.arange(0, n_tot_ref).reshape(n_ref, n_x) - id_col = np.repeat(id_col, repeats=n_x, axis=0) - - r_3_3D = r_3[id_row[:, None], id_col].reshape(n_ref, n_x, n_x) - - rVh_1_3d = rVh_1.reshape(n_ref, n_x, n_t) - rVh_1_3dT = np.transpose(rVh_1_3d, (0, 2, 1)) - cov3D = np.matmul(rVh_1_3d, rVh_1_3dT) - cov_ref_3D = infos[k]["sig2"] * (r_3_3D - cov3D) - - predmean_ref[:, k] = r_1 @ infos[k]["pw"] - - # calculate predictive mean and variance - predinfo["mean"] = np.full((x.shape[0], int(theta.shape[0] / x.shape[0])), np.nan) - - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - Smat3D = cov_ref_3D * (pctscale[:, :] ** 2) - - predinfo["mean"] = ( - (predmean_ref @ pctscale.T) + fitinfo["standardpcinfo"]["offset"] - ).T - predinfo["mean"] = predinfo["mean"].reshape(n_ref, n_x) - - d = x.shape[0] - - obsvar3D = obsvar.reshape(1, n_x, n_x) - cov1 = 0.5 * obsvar3D + Smat3D - cov2 = Smat3D + obsvar3D - # cov2 = obsvar3D - p1 = multiple_pdfs(obs, predinfo["mean"], cov1) - postmean = multiple_pdfs(obs, predinfo["mean"], cov2) - - det1 = multiple_determinants(obsvar3D) - postvar = (1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det1))) * p1 - postmean**2 - - return postmean, postvar - - -def postpredbias(fitinfo, x, theta, obs, obsvar, biasmean): - - n_x = len(x) - n_tot_ref = theta.shape[0] - n_ref = int(n_tot_ref / n_x) - n_t = fitinfo["theta"].shape[0] - - predinfo = {} - infos = fitinfo["emulist"] - predmean_ref = np.zeros((theta.shape[0], len(infos))) - predvars_ref = np.zeros((theta.shape[0], len(infos))) - - if predmean_ref.ndim < 1.5: - predmean_ref = predmean_ref.reshape((1, -1)) - predvars_ref = predvars_ref.reshape((1, -1)) - - # n_ref x n_t - rsave_1 = np.array(np.ones(len(infos)), dtype=object) - # n_ref x n_ref - rsave_3 = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - if infos[k]["hypind"] == k: - # covariance matrix between new theta and thetas from fit. - rsave_1[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - - rsave_3[k] = __covmat(theta, theta, infos[k]["hypcov"]) - - # adjusted covariance matrix - r_1 = (1 - infos[k]["nug"]) * np.squeeze(rsave_1[infos[k]["hypind"]]) - r_3 = (1 - infos[k]["nug"]) * np.squeeze(rsave_3[infos[k]["hypind"]]) - - try: - rVh_1 = r_1 @ infos[k]["Vh"] - - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - if rVh_1.ndim < 1.5: - rVh_1 = rVh_1.reshape((1, -1)) - - id_row = np.arange(0, n_tot_ref) - id_col = np.arange(0, n_tot_ref).reshape(n_ref, n_x) - id_col = np.repeat(id_col, repeats=n_x, axis=0) - # print(r_3) - # r_3 = np.array(r_3).reshape(1,1) - r_3_3D = r_3[id_row[:, None], id_col].reshape(n_ref, n_x, n_x) - - rVh_1_3d = rVh_1.reshape(n_ref, n_x, n_t) - rVh_1_3dT = np.transpose(rVh_1_3d, (0, 2, 1)) - cov3D = np.matmul(rVh_1_3d, rVh_1_3dT) - cov_ref_3D = infos[k]["sig2"] * (r_3_3D - cov3D) - - predmean_ref[:, k] = r_1 @ infos[k]["pw"] - - # calculate predictive mean and variance - predinfo["mean"] = np.full((x.shape[0], int(theta.shape[0] / x.shape[0])), np.nan) - - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - Smat3D = cov_ref_3D * (pctscale[:, :] ** 2) - - predinfo["mean"] = ( - (predmean_ref @ pctscale.T) + fitinfo["standardpcinfo"]["offset"] - ).T - predinfo["mean"] = predinfo["mean"].reshape(n_ref, n_x) - - d = x.shape[0] - - obsvar3D = obsvar.reshape(1, n_x, n_x) - cov1 = 0.5 * obsvar3D + Smat3D - cov2 = Smat3D + obsvar3D - # cov2 = obsvar3D - - p1 = multiple_pdfs(obs, predinfo["mean"] + biasmean, cov1) - postmean = multiple_pdfs(obs, predinfo["mean"] + biasmean, cov2) - - det1 = multiple_determinants(obsvar3D) - postvar = (1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det1))) * p1 - postmean**2 - - return postmean, postvar - - -def imspe_acq(fitinfo, theta, theta_cand): - - infos = fitinfo["emulist"] - predvars_cand = np.zeros((theta_cand.shape[0], len(infos))) - - # n_ref x n_t - rsave_1 = np.array(np.ones(len(infos)), dtype=object) - # n_ref x n_cand - rsave_2 = np.array(np.ones(len(infos)), dtype=object) - # n_cand x n_t - rsave_4 = np.array(np.ones(len(infos)), dtype=object) - - # loop over principal components - for k in range(0, len(infos)): - - if infos[k]["hypind"] == k: - rsave_1[k] = __covmat(theta, fitinfo["theta"], infos[k]["hypcov"]) - - rsave_2[k] = __covmat(theta, theta_cand, infos[k]["hypcov"]) - - rsave_4[k] = __covmat(theta_cand, fitinfo["theta"], infos[k]["hypcov"]) - - # nref x ntrain - r_1 = (1 - infos[k]["nug"]) * np.squeeze(rsave_1[infos[k]["hypind"]]) - # nref - r_2 = (1 - infos[k]["nug"]) * np.squeeze(rsave_2[infos[k]["hypind"]]) - # ntrain - r_4 = (1 - infos[k]["nug"]) * np.squeeze(rsave_4[infos[k]["hypind"]]) - - try: - # nref x 1 - r_2 = r_2[:, None] - # 1 x ntrain - rVh_4 = r_4.reshape(1, len(fitinfo["theta"])) @ infos[k]["Vh"] - # nref x ntrain - rVh_1 = r_1 @ infos[k]["Vh"] - - except Exception: - for i in range(0, len(infos)): - print((i, infos[i]["hypind"])) - raise ValueError("Something went wrong with fitted components") - - # nref x 1 - cov3D = rVh_1 @ rVh_4.T - cov3D = infos[k]["sig2"] * (r_2 - cov3D) - predvars_cand[:, k] = infos[k]["sig2"] * np.abs(1 - np.sum(rVh_4**2, 1)) - predvars_cand[:, k] += infos[k]["nug"] - - # calculate candidate variance - pctscale = (fitinfo["pcti"].T * fitinfo["standardpcinfo"]["scale"]).T - cov3D = cov3D * (pctscale[:, :] ** 2) - var_cand = ( - ( - fitinfo["standardpcinfo"]["extravar"][:] - + predvars_cand @ (pctscale[:, :] ** 2).T - ) - ).T - - Phi3D = (cov3D * cov3D) / var_cand - return np.sum(Phi3D) diff --git a/PUQ/surrogatemethods/highdimHetGP.py b/PUQ/surrogatemethods/highdimHetGP.py deleted file mode 100644 index 2b783ce..0000000 --- a/PUQ/surrogatemethods/highdimHetGP.py +++ /dev/null @@ -1,214 +0,0 @@ -from PUQ.surrogate import emulator -from PUQ.surrogatemethods.hetGP import predict as predict_hetGP -import numpy as np -from PUQ.surrogatemethods.covariances import cov_gen -from scipy.linalg import cholesky, inv -from hetgpy import hetGP - -def fit( - fitinfo, - x, - theta, - f, - lower=None, - upper=None, - noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, - init={}, - known={}, - settings={ - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - covtype="Gaussian", - pc_settings={"standardize": True, "latent": False}, - **kwargs -): - - numGPs = f.shape[0] - h = np.zeros((numGPs, 1)) - s = np.ones((numGPs)) - fs = np.zeros(f.shape) - - if pc_settings["standardize"]: - # print("Standardizing") - fitinfo["standardize"] = True - for k in range(0, numGPs): - h[k, 0] = np.mean(f[k, :]) - s[k] = np.std(f[k, :]) - - G = np.diag(s) - Gi = np.diag(1 / s) - fs = Gi @ (f - h) - B = np.eye(numGPs) - - emulist = [dict() for x in range(0, numGPs)] - for i in range(0, numGPs): - print("On site surrogate") - fi = fs[i, :][None, :] - # hetGP - model = hetGP() - print(theta.shape) - print(fi.shape) - model.mle(X=theta, - Z=fi.flatten(), - covtype="Gaussian", - known={"beta0":np.mean(fi)}) - - # emu = emulator( - # x=np.array([[i]]), - # theta=theta, - # f=fi, - # method="hetGP", - # args={ - # "noiseControl": noiseControl, - # "lower": lower, - # "upper": upper, - # "settings": settings, - # "init": init, - # "known": known, - # "covtype": covtype, - # }, - # ) - - emulist[i] = model.copy()#emu._info - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["emulist"] = emulist - fitinfo["numGPs"] = numGPs - fitinfo["fs"] = fs - fitinfo["h"] = h - fitinfo["G"] = G - fitinfo["Gi"] = Gi - fitinfo["B"] = B - - return - - -def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): - - numGPs = fitinfo["numGPs"] - d = fitinfo["fs"].shape[0] - n = theta.shape[0] - - # calculate predictive mean and variance - mean_pc = np.full((numGPs, n), np.nan) - var_pc = np.full((numGPs, n), np.nan) - nugs_pc = np.full((numGPs, n), np.nan) - - if thetaprime is not None: - npr = thetaprime.shape[0] - covmat_pc = np.full((numGPs, n, npr), np.nan) - for i in range(0, numGPs): - # predinfo_hetGP = {} - info = fitinfo["emulist"][i] - # predict_hetGP( - # predinfo=predinfo_hetGP, - # fitinfo=info, - # x=np.array([[i]]), - # theta=theta, - # thetaprime=thetaprime, - # ) - pr = info.predict(x=theta, xprime=thetaprime) - - mean_pc[i, :] = pr["mean"] # predinfo_hetGP["mean"] - var_pc[i, :] = pr["sd2"] # predinfo_hetGP["var"] - nugs_pc[i, :] = pr["nugs"] # predinfo_hetGP["nugs"] - - if thetaprime is not None: - covmat_pc[i, :, :] = pr["cov"] # predinfo_hetGP["covmat"] - - # calculate predictive mean and variance - predinfo["mean_o"] = 1 * mean_pc - predinfo["var_o"] = 1 * var_pc - predinfo["nugs_o"] = 1 * nugs_pc - if thetaprime is not None: - predinfo["cov_o"] = 1 * covmat_pc - - predinfo["S"] = np.full((d, d, n), np.nan) - predinfo["R"] = np.full((d, d, n), np.nan) - predinfo["var"] = np.full((d, n), np.nan) - predinfo["nugs"] = np.full((d, n), np.nan) - predinfo["var_noisy"] = np.full((d, n), np.nan) - if thetaprime is not None: - predinfo["covmat"] = np.full((d, n, npr), np.nan) - predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) - - for i in range(0, theta.shape[0]): - C = np.diag(var_pc[:, i]) - R = np.diag(nugs_pc[:, i]) - predinfo["S"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["R"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) - predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) - - predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] - - return predinfo - - -def update(fitinfo, x, X0new=None, mult=None): - numGPs = fitinfo["numGPs"] - - Gi = fitinfo["Gi"] - h = fitinfo["h"] - pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) - mu_ct = pred_ct["mean"] - mu_ct = Gi @ (mu_ct - h) - Z0new = mu_ct.T - # print(mu_ct.shape) - - for i in range(0, numGPs): - info = fitinfo["emulist"][i] - - if info["is_homGP"] == True: - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T - else: - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - - kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) - M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) - - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) - # Updated - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) - Lambda = info["nmean"] + M - if info["logN"]: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = info["eps"] - - info["Lambda"] = Lambda - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T diff --git a/PUQ/surrogatemethods/pcHetGP.py b/PUQ/surrogatemethods/pcHetGP.py deleted file mode 100644 index 8ad8fa0..0000000 --- a/PUQ/surrogatemethods/pcHetGP.py +++ /dev/null @@ -1,222 +0,0 @@ -from PUQ.surrogate import emulator -from PUQ.surrogatemethods.hetGP import predict as predict_hetGP -import numpy as np -from PUQ.surrogatemethods.covariances import cov_gen -from scipy.linalg import cholesky, inv - - -def fit( - fitinfo, - x, - theta, - f, - lower=None, - upper=None, - noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, - init={}, - known={}, - settings={ - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - covtype="Gaussian", - pc_settings={"standardize": True, "latent": False}, - **kwargs -): - - numGPs = f.shape[0] - h = np.zeros((numGPs, 1)) - s = np.ones((numGPs)) - fs = np.zeros(f.shape) - - if pc_settings["standardize"]: - # print("Standardizing") - fitinfo["standardize"] = True - for k in range(0, numGPs): - h[k, 0] = np.mean(f[k, :]) - s[k] = np.std(f[k, :]) - - G = np.diag(s) - Gi = np.diag(1 / s) - fs = Gi @ (f - h) - B = np.eye(numGPs) - - if pc_settings["latent"]: - # print("Dimension reduction") - fitinfo["latent"] = True - - U, S, _ = np.linalg.svd(fs, full_matrices=False) - exp_var = np.cumsum(S**2) / np.cumsum(S**2)[-1] - numGPs = int(np.argwhere(np.array(exp_var) > 0.99)[0]) + 1 - - # d x q orthogonal matrix - B = U[:, 0:numGPs] - - # trasform q x n - W = B.T @ fs - - emulist = [dict() for x in range(0, numGPs)] - for i in range(0, numGPs): - if pc_settings["latent"]: - print("Latent surrogate") - fi = W[i, :][None, :] - else: - # print("On site surrogate") - fi = fs[i, :][None, :] - emu = emulator( - x=theta, - theta=np.array([[i]]), - f=fi, - method="hetGP", - args={ - "noiseControl": noiseControl, - "lower": lower, - "upper": upper, - "settings": settings, - "init": init, - "known": known, - "covtype": covtype, - }, - ) - - emulist[i] = emu._info - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["emulist"] = emulist - fitinfo["numGPs"] = numGPs - fitinfo["fs"] = fs - fitinfo["h"] = h - fitinfo["G"] = G - fitinfo["Gi"] = Gi - fitinfo["B"] = B - - return - - -def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): - - numGPs = fitinfo["numGPs"] - d = fitinfo["fs"].shape[0] - n = theta.shape[0] - - # calculate predictive mean and variance - mean_pc = np.full((numGPs, n), np.nan) - var_pc = np.full((numGPs, n), np.nan) - nugs_pc = np.full((numGPs, n), np.nan) - - if thetaprime is not None: - npr = thetaprime.shape[0] - covmat_pc = np.full((numGPs, n, npr), np.nan) - for i in range(0, numGPs): - predinfo_hetGP = {} - info = fitinfo["emulist"][i] - predict_hetGP( - predinfo=predinfo_hetGP, - fitinfo=info, - x=theta, - theta=np.array([[i]]), - thetaprime=thetaprime, - ) - - mean_pc[i, :] = predinfo_hetGP["mean"] - var_pc[i, :] = predinfo_hetGP["var"] - nugs_pc[i, :] = predinfo_hetGP["nugs"] - - if thetaprime is not None: - covmat_pc[i, :, :] = predinfo_hetGP["covmat"] - - # calculate predictive mean and variance - predinfo["mean_o"] = 1 * mean_pc - predinfo["var_o"] = 1 * var_pc - predinfo["nugs_o"] = 1 * nugs_pc - if thetaprime is not None: - predinfo["cov_o"] = 1 * covmat_pc - - predinfo["S"] = np.full((d, d, n), np.nan) - predinfo["R"] = np.full((d, d, n), np.nan) - predinfo["var"] = np.full((d, n), np.nan) - predinfo["nugs"] = np.full((d, n), np.nan) - predinfo["var_noisy"] = np.full((d, n), np.nan) - if thetaprime is not None: - predinfo["covmat"] = np.full((d, n, npr), np.nan) - predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) - - for i in range(0, theta.shape[0]): - C = np.diag(var_pc[:, i]) - R = np.diag(nugs_pc[:, i]) - predinfo["S"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["R"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) - predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) - - predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] - - return predinfo - - -def update(fitinfo, x, X0new=None, mult=None): - numGPs = fitinfo["numGPs"] - - Gi = fitinfo["Gi"] - h = fitinfo["h"] - pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) - mu_ct = pred_ct["mean"] - mu_ct = Gi @ (mu_ct - h) - Z0new = mu_ct.T - # print(mu_ct.shape) - - for i in range(0, numGPs): - info = fitinfo["emulist"][i] - - if info["is_homGP"] == True: - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T - else: - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - - kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) - M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) - - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) - # Updated - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) - Lambda = info["nmean"] + M - if info["logN"]: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = info["eps"] - - info["Lambda"] = Lambda - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T diff --git a/PUQ/surrogatemethods/pcHomGP.py b/PUQ/surrogatemethods/pcHomGP.py deleted file mode 100644 index acd4b74..0000000 --- a/PUQ/surrogatemethods/pcHomGP.py +++ /dev/null @@ -1,224 +0,0 @@ -from PUQ.surrogate import emulator -from PUQ.surrogatemethods.homGP import predict as predict_homGP -import numpy as np -from PUQ.surrogatemethods.covariances import cov_gen -from scipy.linalg import cholesky, inv - - -def fit( - fitinfo, - x, - theta, - f, - lower=None, - upper=None, - noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, - init={}, - known={}, - settings={ - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - covtype="Gaussian", - pc_settings={"standardize": True, "latent": False}, - **kwargs -): - - numGPs = f.shape[0] - h = np.zeros((numGPs, 1)) - s = np.ones((numGPs)) - fs = np.zeros(f.shape) - - if pc_settings["standardize"]: - # print("Standardizing") - fitinfo["standardize"] = True - for k in range(0, numGPs): - h[k, 0] = np.mean(f[k, :]) - s[k] = np.std(f[k, :]) - - G = np.diag(s) - Gi = np.diag(1 / s) - fs = Gi @ (f - h) - B = np.eye(numGPs) - - if pc_settings["latent"]: - # print("Dimension reduction") - fitinfo["latent"] = True - - U, S, _ = np.linalg.svd(fs, full_matrices=False) - exp_var = np.cumsum(S**2) / np.cumsum(S**2)[-1] - numGPs = int(np.argwhere(np.array(exp_var) > 0.99)[0]) + 1 - - # d x q orthogonal matrix - B = U[:, 0:numGPs] - - # trasform q x n - W = B.T @ fs - - emulist = [dict() for x in range(0, numGPs)] - for i in range(0, numGPs): - if pc_settings["latent"]: - print("Latent surrogate") - fi = W[i, :][None, :] - else: - # print("On site surrogate") - fi = fs[i, :][None, :] - emu = emulator( - x=theta, - theta=np.array([[i]]), - f=fi, - method="homGP", - args={ - "noiseControl": noiseControl, - "lower": lower, - "upper": upper, - "settings": settings, - "init": init, - "known": known, - "covtype": covtype, - }, - ) - - emulist[i] = emu._info - - fitinfo["theta"] = theta - fitinfo["f"] = f - fitinfo["emulist"] = emulist - fitinfo["numGPs"] = numGPs - fitinfo["fs"] = fs - fitinfo["h"] = h - fitinfo["G"] = G - fitinfo["Gi"] = Gi - fitinfo["B"] = B - - return - - -def predict(predinfo, fitinfo, x, theta, thetaprime, **kwargs): - - numGPs = fitinfo["numGPs"] - d = fitinfo["fs"].shape[0] - n = theta.shape[0] - - # calculate predictive mean and variance - mean_pc = np.full((numGPs, n), np.nan) - var_pc = np.full((numGPs, n), np.nan) - nugs_pc = np.full((numGPs, n), np.nan) - - if thetaprime is not None: - npr = thetaprime.shape[0] - covmat_pc = np.full((numGPs, n, npr), np.nan) - - for i in range(0, numGPs): - predinfo_hetGP = {} - info = fitinfo["emulist"][i] - predict_homGP( - predinfo=predinfo_hetGP, - fitinfo=info, - x=theta, - theta=np.array([[i]]), - thetaprime=thetaprime, - ) - - mean_pc[i, :] = predinfo_hetGP["mean"] - var_pc[i, :] = predinfo_hetGP["var"] - nugs_pc[i, :] = predinfo_hetGP["nugs"] - - if thetaprime is not None: - covmat_pc[i, :, :] = predinfo_hetGP["covmat"] - - # calculate predictive mean and variance - predinfo["mean_o"] = 1 * mean_pc - predinfo["var_o"] = 1 * var_pc - predinfo["nugs_o"] = 1 * nugs_pc - if thetaprime is not None: - predinfo["cov_o"] = 1 * covmat_pc - - - predinfo["S"] = np.full((d, d, n), np.nan) - predinfo["R"] = np.full((d, d, n), np.nan) - predinfo["var"] = np.full((d, n), np.nan) - predinfo["nugs"] = np.full((d, n), np.nan) - predinfo["var_noisy"] = np.full((d, n), np.nan) - if thetaprime is not None: - predinfo["covmat"] = np.full((d, n, npr), np.nan) - predinfo["mean"] = fitinfo["h"] + fitinfo["G"] @ (fitinfo["B"] @ mean_pc) - - for i in range(0, theta.shape[0]): - C = np.diag(var_pc[:, i]) - R = np.diag(nugs_pc[:, i]) - predinfo["S"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ C @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["R"][:, :, i] = ( - fitinfo["G"] @ fitinfo["B"] @ R @ fitinfo["B"].T @ fitinfo["G"] - ) - predinfo["var"][:, i] = np.diag(predinfo["S"][:, :, i]) - predinfo["nugs"][:, i] = np.diag(predinfo["R"][:, :, i]) - - predinfo["var_noisy"] = predinfo["var"] + predinfo["nugs"] - - return predinfo - - -def update(fitinfo, x, X0new=None, mult=None): - numGPs = fitinfo["numGPs"] - - Gi = fitinfo["Gi"] - h = fitinfo["h"] - pred_ct = predict(predinfo={}, fitinfo=fitinfo, x=x, theta=X0new, thetaprime=None) - mu_ct = pred_ct["mean"] - mu_ct = Gi @ (mu_ct - h) - Z0new = mu_ct.T - # print(mu_ct.shape) - - for i in range(0, numGPs): - info = fitinfo["emulist"][i] - - if info["is_homGP"] == True: - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["eps"] + info["g"] / info["mult"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T - else: - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - - kg = cov_gen(X1=X0new, X2=info["X0"], theta=info["theta_g"]) - M = np.dot(kg, np.dot(Kgi, info["Delta"] - info["nmean"])) - - info["X0"] = np.concatenate((info["X0"], X0new), axis=0) - info["Z0"] = np.concatenate((info["Z0"], np.array([Z0new[0, i]]))) - info["mult"] = np.concatenate((info["mult"], np.array([mult]))) - info["Delta"] = np.concatenate((info["Delta"], M + info["nmean"])) - # Updated - Cg = cov_gen(X1=info["X0"], theta=info["theta_g"]) - Kg_c = cholesky(Cg + np.diag(info["eps"] + info["g"] / info["mult"])) - Kgi = np.linalg.inv((Kg_c)) - Kgi = Kgi @ Kgi.T - M = np.dot(Cg, np.dot(Kgi, info["Delta"] - info["nmean"])) - Lambda = info["nmean"] + M - if info["logN"]: - Lambda = np.exp(Lambda) - else: - Lambda[Lambda <= 0] = info["eps"] - - info["Lambda"] = Lambda - - C = cov_gen(X1=info["X0"], theta=info["theta"]) - Ki = cholesky(C + np.diag(info["Lambda"] / info["mult"] + info["eps"])) - Ki = np.linalg.inv((Ki)) - info["Ki"] = Ki @ Ki.T diff --git a/PUQ/surrogatesupport/__init__.py b/PUQ/surrogatesupport/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/PUQ/surrogatesupport/matern_covmat.pyx b/PUQ/surrogatesupport/matern_covmat.pyx deleted file mode 100644 index 4c82212..0000000 --- a/PUQ/surrogatesupport/matern_covmat.pyx +++ /dev/null @@ -1,60 +0,0 @@ - -import numpy as np -cimport numpy as np -from cpython cimport array -from libc.stdio cimport printf -from libc.math cimport fabs -cimport cython - -@cython.boundscheck(False) -@cython.wraparound(False) -def map4(double[:] x1, double[:] x2, double[:,:] s, double[:,:] r, double g): - m = s.shape[0] - n = s.shape[1] - with cython.nogil: - for i in range(m): - for j in range(n): - s[i,j] = x1[i] - x2[j] - r[i,j] = -g * s[i,j] - s[i,j] = fabs(s[i,j]) - r[i,j] /= (1 + s[i, j]) - - -def covmat(x1, x2, gammav, return_gradhyp=False, return_gradx1=False): - """Return the covariance between x1 and x2 given parameter gammav.""" - x1 = x1.reshape(1, gammav.shape[0]-1)/np.exp(gammav[:-1]) \ - if x1.ndim < 1.5 else x1/np.exp(gammav[:-1]) - x2 = x2.reshape(1, gammav.shape[0]-1)/np.exp(gammav[:-1]) \ - if x2.ndim < 1.5 else x2/np.exp(gammav[:-1]) - - V = np.zeros([x1.shape[0], x2.shape[0]]) - R = np.full((x1.shape[0], x2.shape[0]), 1/(1+np.exp(gammav[-1]))) - S = np.zeros([x1.shape[0], x2.shape[0]]) - - if return_gradhyp: - dR = np.zeros([gammav.shape[0], x1.shape[0], x2.shape[0]]) - elif return_gradx1: - dR = np.zeros([ x1.shape[1], x1.shape[0], x2.shape[0]]) - for k in range(0, gammav.shape[0]-1): - if return_gradx1: - map4(x1[:, k],x2[:, k], S, dR[k], np.exp(-gammav[k])) - else: - S = np.abs(np.subtract.outer(x1[:, k], x2[:, k])) - R *= (1 + S) - V -= S - if return_gradhyp: - dR[k] = (S ** 2) / (1 + S) - if return_gradhyp or return_gradx1: - dR = dR.transpose(1,2,0) - R *= np.exp(V) - if return_gradhyp: - dR *= R[:, :, None] - dR[:, :, -1] = np.exp(gammav[-1]) / ((1 + np.exp(gammav[-1]))) *\ - (1 / (1 + np.exp(gammav[-1])) - R) - elif return_gradx1: - dR *= R[:, :, None] - R += np.exp(gammav[-1])/(1+np.exp(gammav[-1])) - if return_gradhyp or return_gradx1: - return R, dR - else: - return R \ No newline at end of file From ec07e0513106e41e47a7c0fe21a32cb47ac046e1 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Tue, 30 Sep 2025 19:20:40 -0400 Subject: [PATCH 060/103] Removing old hetgp dependency --- .../gen_funcs/batch_allocate_reps.py | 7 +- PUQ/surrogatemethods/covariances.py | 57 ------- PUQ/surrogatemethods/distance.py | 141 ------------------ PUQ/surrogatemethods/helpers.py | 108 -------------- 4 files changed, 4 insertions(+), 309 deletions(-) delete mode 100644 PUQ/surrogatemethods/covariances.py delete mode 100644 PUQ/surrogatemethods/distance.py delete mode 100644 PUQ/surrogatemethods/helpers.py diff --git a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py index 6f24039..1e60c51 100644 --- a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py +++ b/PUQ/designmethods/gen_funcs/batch_allocate_reps.py @@ -1,7 +1,7 @@ import numpy as np import scipy from numpy.linalg import cholesky, inv, det -from PUQ.surrogatemethods.covariances import cov_gen +from hetgpy.covariance_functions import cov_gen from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( multiple_pdfs, build_emulator, @@ -186,6 +186,7 @@ def compute_ivar_weights(self): coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) q = len(self.emu._info['emulist']) + COVTYPE = self.emu._info['emulist'][0]._info["covtype"] Mb = np.zeros((n, n_integ, q, q)) for i in range(0, n): @@ -194,12 +195,12 @@ def compute_ivar_weights(self): for j in range(0, q): # emuinfo = self.emu._info['emulist'][j] emuinfo = self.emu._info['emulist'][j]._info - K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta']) + K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta'], type=COVTYPE) if self.use_Ki: Ki = emuinfo['Ki'] else: - K = cov_gen(X1=self.theta, theta=emuinfo['theta']) + K = cov_gen(X1=self.theta, theta=emuinfo['theta'], type=COVTYPE) Ki = scipy.linalg.pinv(K, rcond=self.eps) A = Ki@J@Ki diff --git a/PUQ/surrogatemethods/covariances.py b/PUQ/surrogatemethods/covariances.py deleted file mode 100644 index 2b6f4fd..0000000 --- a/PUQ/surrogatemethods/covariances.py +++ /dev/null @@ -1,57 +0,0 @@ -#from distance import distance_cpp -import numpy as np - -### A) Gaussian covariance - -## Gaussian covariance function : K = exp(-(X1 - X2)^2/theta) -## @param X1 matrix of design locations -## @param X2 matrix of design locations if covariance is calculated between X1 and X2 -## @param theta vector of lengthscale parameters (either of size one if isotropic or of size d if anisotropic) - -def distance_cpp(X1, X2=None): - if X2 is None: - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X2**2, axis=1) - 2 * np.dot(X1, X2.T) - -def cov_gen(X1, X2=None, theta=None): - - if len(theta) == 1: - return np.exp(-distance_cpp(X1, X2) / theta) - - if X2 is None: - #np.exp(-distance_cpp(X1 * np.tile(1 / np.sqrt(theta), (X1.shape[0], len(theta))))) - return np.exp(-distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))))) - - # np.exp(-distance_cpp(X1 * np.repeat(1/np.sqrt(theta), np.repeat(X1.shape[0], len(theta))), - # X2 * np.repeat(1/np.sqrt(theta), np.repeat(X2.shape[0], len(theta))))) - - return np.exp(-distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))), - X2 * (1/np.sqrt(np.repeat(theta[None, :], X2.shape[0], axis=0))))) - - - -def partial_cov_gen(X1, theta, type="Gaussian", arg=None, X2=None, **kwargs): - - if X2 is None: - if type == "Gaussian": - if arg == "theta_k": - return partial_d_C_Gaussian_dtheta_k(X1=X1, theta=theta, **kwargs) - if arg == "k_theta_g": - return partial_d_Cg_Gaussian_d_k_theta_g(X1=X1, theta=theta, **kwargs) - if arg == "X_i_j": - return partial_d_C_Gaussian_dX_i_j(X1=X1, theta=theta, **kwargs) - - -def partial_d_C_Gaussian_dtheta_k(X1, theta): - return distance_cpp(X1) / theta**2 - -def partial_d_k_Gaussian_dtheta_k(X1, X2, theta): - return distance_cpp(X1, X2) / theta**2 - -def partial_d_Cg_Gaussian_d_k_theta_g(X1, theta, k_theta_g): - # 1-dimensional/isotropic case - if len(theta) == 1: - return distance_cpp(X1) / (theta * k_theta_g**2) - - return distance_cpp(X1 * (1/np.sqrt(np.repeat(theta[None, :], X1.shape[0], axis=0))))/k_theta_g**2 #distance_cpp(X1 * np.tile(1 / np.sqrt(theta), (X1.shape[0], len(theta)))) / k_theta_g**2 - diff --git a/PUQ/surrogatemethods/distance.py b/PUQ/surrogatemethods/distance.py deleted file mode 100644 index cff7b95..0000000 --- a/PUQ/surrogatemethods/distance.py +++ /dev/null @@ -1,141 +0,0 @@ -import numpy as np - -# def distcpp(X1): -# nr = X1.shape[0] -# nc = X1.shape[1] -# s = np.zeros((nr, nr)) -# for i in range(1, nr): -# ptrs = s[:, i] -# ptrs2 = s[i, :] # symmetric -# for j in range(i): -# ptrX1 = X1[i, :] -# ptrX2 = X1[j, :] -# tmp = np.abs(ptrX1 - ptrX2) -# ptrs += tmp * tmp -# ptrs2[:] = ptrs -# ptrs2 += nr - -# print(np.round(s)) -# return s - -# def distcpp(X1): -# nr, nc = X1.shape -# s = np.zeros((nr, nr)) - -# for i in range(1, nr): -# ptrs = s[0:i, i] -# ptrs2 = s[i, 0:i] - -# for j in range(i): -# ptrX1 = X1[i, :] -# ptrX2 = X1[j, :] -# tmp = np.sum((ptrX1 - ptrX2) ** 2) -# ptrs += tmp -# ptrs2[j] = tmp - -# return s - -import numpy as np - -# def distcpp(X1): -# nr, nc = X1.shape -# s = np.zeros((nr, nr)) - -# for i in range(1, nr): -# ptrs = s[0:i, i] -# ptrs2 = s[i, 0:i] - -# for j in range(i): -# ptrX1 = X1[i, :] -# ptrX2 = X1[j, :] -# tmp = np.sum((ptrX1 - ptrX2) ** 2) -# ptrs[j] = tmp -# ptrs2[j] = tmp - -# # Make the matrix symmetric -# s = s + s.T - np.diag(s.diagonal()) - -# return s - -def distcpp(X1, X2=None): - if X2 is None: - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) - else: - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) - -def distcpp_2(X1, X2): - nr1 = X1.shape[0] - nr2 = X2.shape[0] - dim = X1.shape[1] - s = np.zeros((nr1, nr2)) - ptrs = s.flatten() - ptrX2 = X2.flatten() - ptrX1 = X1.flatten() - for i in range(nr2): - for j in range(nr1): - tmp = np.abs(ptrX1 - ptrX2) - ptrs += tmp * tmp - ptrX2 -= nr2 * dim - ptrX1 -= nr1 * dim - 1 - - ptrX2 += 1 - ptrX1 -= nr1 - - return s - -def distcppMaha(X1, m): - nr = X1.shape[0] - nc = X1.shape[1] - s = np.zeros((nr, nr)) - for i in range(1, nr): - ptrs = s[:, i] - ptrs2 = s[i, :] # symmetric - ptrX1 = X1[i, :] - ptrm = m - for j in range(i): - ptrX2 = X1[j, :] - tmp = np.abs(ptrX1 - ptrX2) - ptrs += tmp * tmp / ptrm - ptrX1 += nr - ptrX2 += nr - ptrm += 1 - - ptrs2[:] = ptrs - ptrs2 += nr - - return s - -def distcppMaha_2(X1, X2, m): - nr1 = X1.shape[0] - nr2 = X2.shape[0] - dim = X1.shape[1] - s = np.zeros((nr1, nr2)) - ptrs = s.flatten() - ptrX2 = X2.flatten() - ptrX1 = X1.flatten() - ptrm = m - for i in range(nr2): - for j in range(nr1): - tmp = np.abs(ptrX1 - ptrX2) - ptrs += tmp * tmp / ptrm - ptrX2 -= nr2 * dim - ptrX1 -= nr1 * dim - 1 - ptrm -= dim - - ptrX2 += 1 - ptrX1 -= nr1 - ptrm += dim - - return s - -def distance_cpp(X1, X2=None, m=None): - if X2 is not None: - if m is not None: - return distcppMaha_2(X1, X2, m) - else: - return distcpp_2(X1, X2) - else: - if m is not None: - return distcppMaha(X1, m) - else: - return distcpp(X1) diff --git a/PUQ/surrogatemethods/helpers.py b/PUQ/surrogatemethods/helpers.py deleted file mode 100644 index 27c3580..0000000 --- a/PUQ/surrogatemethods/helpers.py +++ /dev/null @@ -1,108 +0,0 @@ -import numpy as np -from scipy.optimize import minimize_scalar -#from distance import distance_cpp - -def distance_cpp(X1, X2=None): - if X2 is None: - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X1**2, axis=1) - 2 * np.dot(X1, X1.T) - return np.sum(X1**2, axis=1).reshape(-1, 1) + np.sum(X2**2, axis=1) - 2 * np.dot(X1, X2.T) - -def find_corres(X0, X): - # X0: matrix of unique designs - # X: matrix of all designs - # Return: vector associating rows of X with those of X0 - - corres = np.zeros(X.shape[0], dtype=int) - - for i in range(X.shape[0]): - for j in range(X0.shape[0]): - if np.array_equal(X[i, :], X0[j, :]): - corres[i] = j + 1 - break - - return corres - -def find_reps(X, Z, return_Zlist=True, rescale=False, normalize=False, input_bounds=None): - - if np.ndim(X) == 1: - X = np.expand_dims(X, axis=1) - - if X.shape[0] == 1: - if return_Zlist: - return {'X0': X, 'Z0': Z, 'mult': np.array([1]), 'Z': Z, 'Zlist': [Z]} - return {'X0': X, 'Z0': Z, 'mult': np.array([1]), 'Z': Z} - - if rescale: - if input_bounds is None: - input_bounds = np.array([np.min(X, axis=0), np.max(X, axis=0)]) - X = (X - input_bounds[0, :]) @ np.diag(1 / (input_bounds[1, :] - input_bounds[0, :])) - - output_stats = None - if normalize: - output_stats = [np.mean(Z), np.var(Z)] - Z = (Z - output_stats[0]) / np.sqrt(output_stats[1]) - - X0, indices, inverse_indices = np.unique(X, axis=0, return_index=True, return_inverse=True) - X0 = X0[np.argsort(indices)] - corresp = find_corres(X0, X) - 1 - - Z0 = [np.mean(Z[corresp == i]) for i in range(len(X0))] - mult = np.array([np.sum(corresp == i) for i in range(len(X0))]) - Zlist = [Z[corresp == i] for i in range(len(X0))] - - flattened_list = [item for sublist in Zlist for item in sublist] - - if return_Zlist: - return {'X0': X0, 'Z0': np.array(Z0), 'mult': mult, 'Z': np.array(flattened_list), - 'Zlist': [Z[corres == i] for i in range(len(X0))], 'input_bounds': input_bounds, - 'output_stats': output_stats} - return {'X0': X0, 'Z0': np.array(Z0), 'mult': mult, 'Z': np.array(flattened_list), - 'input_bounds': input_bounds, 'output_stats': output_stats} - - - -def auto_bounds(X, min_cor=0.01, max_cor=0.5, covtype="Gaussian", p=0.05): - Xsc = find_reps(X, np.ones(X.shape[0]), rescale=True, return_Zlist=False) # rescaled distances - dists = distance_cpp(Xsc['X0']) # find 2 closest points - - repr_low_dist = np.quantile(dists[np.tril_indices(len(dists), k=-1)], p) - repr_lar_dist = np.quantile(dists[np.tril_indices(len(dists), k=-1)], 1 - p) - - if covtype == "Gaussian": - theta_min = -repr_low_dist / np.log(min_cor) - theta_max = -repr_lar_dist / np.log(max_cor) - return {'lower': theta_min * (Xsc['input_bounds'][1, :] - Xsc['input_bounds'][0, :]) ** 2, - 'upper': theta_max * (Xsc['input_bounds'][1, :] - Xsc['input_bounds'][0, :]) ** 2} - # else: - # def tmpfun(theta, repr_dist, covtype, value): - # cov_gen = cov_gen_matrix(np.sqrt(repr_dist / X.shape[1]), np.zeros((X.shape[1], X.shape[1])), covtype, theta) - # return cov_gen - value - - # theta_min = minimize_scalar(tmpfun, bounds=(np.sqrt(np.finfo(float).eps), 100), - # args=(repr_low_dist, covtype, min_cor), - # method='bounded', tol=np.sqrt(np.finfo(float).eps)).x - # if theta_min < np.sqrt(np.finfo(float).eps): - # warnings.warn("The automatic selection of lengthscales bounds was not successful. Perhaps provide lower and upper values.") - # theta_min = 1e-2 - - # theta_max = minimize_scalar(tmpfun, bounds=(np.sqrt(np.finfo(float).eps), 100), - # args=(repr_lar_dist, covtype, max_cor), - # method='bounded', tol=np.sqrt(np.finfo(float).eps)).x - # if theta_max < np.sqrt(np.finfo(float).eps): - # theta_max = 5 - - # return {'lower': theta_min * (Xsc['inputBounds'][1, :] - Xsc['inputBounds'][0, :]), - # 'upper': max(1, theta_max) * (Xsc['inputBounds'][1, :] - Xsc['inputBounds'][0, :])} - - - - -def trace_sym(A, B): - n = A.shape[0] - trace = 0.0 - - for i in range(n): - for j in range(i + 1): - trace += A[i, j] * B[i, j] - - return trace From f2d76209d627b61de8fe51e400593cc09b7f86bc Mon Sep 17 00:00:00 2001 From: David O'Gara Date: Wed, 1 Oct 2025 11:29:28 -0500 Subject: [PATCH 061/103] fix GPWrapper to conditionally wrap either homGP or hetGP depending on underlying model --- PUQ/surrogatemethods/hetGP.py | 25 +++++++++---------------- PUQ/surrogatemethods/homGP.py | 28 +++++++++++++++++++--------- tests/unit_tests/test_hetGP.py | 15 ++++++++++++++- 3 files changed, 42 insertions(+), 26 deletions(-) diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 961ab18..4f023db 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -1,5 +1,6 @@ import numpy as np -from hetgpy import hetGP +from hetgpy import hetGP, homGP +from PUQ.surrogatemethods.homGP import GPWrapper ############################################################################### ## Heterogeneous GP with all options for the fit @@ -112,18 +113,10 @@ def fit(fitinfo, x, theta, f, ) for key in model.__dict__.keys(): fitinfo[key] = model.get(key) - fitinfo['is_homGP'] = False + fitinfo['is_homGP'] = isinstance(model,homGP) return -class hetGPWrapper(hetGP): - ''' - A class that converts the information in fitinfo (from the fit and predict methods) to a class so - it can be used to make predictions with hetgpy.hetGP - - ''' - def __init__(self,fitinfo): - for key in fitinfo.keys(): - setattr(self,key,fitinfo[key]) + def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): r''' @@ -132,14 +125,14 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): GP = fitinfo.get('model') if GP is None: # use wrapper class to instantiate trained GP - GP = hetGPWrapper(fitinfo=fitinfo) + GP = GPWrapper(fitinfo=fitinfo) # handle kws kws = {} eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] for key in eligible_keys: if key in kwargs.keys(): - kws[key] = kwargs.get('nugs_only') + kws[key] = kwargs.get(key) preds = GP.predict(x=x,xprime=thetaprime,**kws) @@ -151,11 +144,11 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): return def update(fitinfo, x,Y = None,**kwargs): r''' - Update function for homGP + Update function for hetGP Parameters ---------- - fitinfo: dictionary that contains the fit information for a hetgpy.homGP object + fitinfo: dictionary that contains the fit information for a hetgpy.hetGP object x: array of new design locations Y: new response. If None, then kwargs: key-value pairs that get passed to hetgpy.hetGP.update. @@ -167,7 +160,7 @@ def update(fitinfo, x,Y = None,**kwargs): for kw in kwargs.keys(): if kw not in valid_kws: raise ValueError(f"{kw} not found, must be one of {valid_kws}") - GP = hetGPWrapper(fitinfo) + GP = GPWrapper(fitinfo) if Y is None: maxit = 0 # impute mean response and do not update hyperparams Y = GP.predict(x)['mean'] diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index 64e0048..aeeacf7 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -1,5 +1,5 @@ import numpy as np -from hetgpy import homGP +from hetgpy import homGP, hetGP from hetgpy.auto_bounds import auto_bounds from hetgpy.find_reps import find_reps @@ -87,15 +87,25 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, class homGPWrapper(homGP): - ''' - A class that converts the information in fitinfo (from the fit and predict methods) to a class so - it can be used to make predictions with hetgpy.homGP - - ''' def __init__(self,fitinfo): - # model hyperparameters for key in fitinfo.keys(): setattr(self,key,fitinfo[key]) +class hetGPWrapper(hetGP): + def __init__(self,fitinfo): + for key in fitinfo.keys(): + setattr(self,key,fitinfo[key]) + +def GPWrapper(fitinfo): + ''' + A method that converts the information in fitinfo (from the fit and predict methods) to a class so + it can be used to make predictions with hetgpy.homGP or hetgpy.hetGP + + ''' + if fitinfo['is_homGP']: + return homGPWrapper(fitinfo) + else: + return hetGPWrapper(fitinfo) + def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): @@ -124,7 +134,7 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): GP = fitinfo.get('model') if GP is None: # use wrapper class to instantiate trained GP - GP = homGPWrapper(fitinfo=fitinfo) + GP = GPWrapper(fitinfo=fitinfo) # handle kws kws = {} eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] @@ -163,7 +173,7 @@ def update(fitinfo, x,Y = None,**kwargs): for kw in kwargs.keys(): if kw not in valid_kws: raise ValueError(f"{kw} not found, must be one of {valid_kws}") - GP = homGPWrapper(fitinfo) + GP = GPWrapper(fitinfo) if Y is None: maxit = 0 # impute mean response and do not update hyperparams Y = GP.predict(x)['mean'] diff --git a/tests/unit_tests/test_hetGP.py b/tests/unit_tests/test_hetGP.py index 052b98b..221925b 100644 --- a/tests/unit_tests/test_hetGP.py +++ b/tests/unit_tests/test_hetGP.py @@ -131,5 +131,18 @@ def test_hetGP_update(): assert test_model._info['g'] == reference_model['g'] assert test_model._info['beta0'] == reference_model['beta0'] + +def test_conversion_to_homGP(): + # should return homoskedastic GP + X = np.linspace(0,1,20).reshape(-1,1) + Y = X + model = emulator(x=X,theta=np.array([0]),f=Y, + method='hetGP') + Xp = np.linspace(X.min(),X.max(),100).reshape(-1,1) + preds = model.predict(Xp) + assert model._info['is_homGP'] + assert model._info.get('Delta') is None + assert np.unique(preds._info['nugs']).shape[0] == 1 + if __name__ == "__main__": - test_hetGP_update_kriging_believer() + test_conversion_to_homGP() From 94ac953cb67a949f0b61d6b6b7376bfb9d8ab35c Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 16:13:19 -0400 Subject: [PATCH 062/103] Including some toy examples under Example4 --- PUQ/design.py | 15 -- PUQ/designmethods/batch_sequential.py | 21 +-- PUQ/designmethods/batch_support.py | 71 -------- PUQ/designmethods/utils.py | 107 ------------ examples/Example4/emulator_check.py | 53 ------ examples/Example4/example.py | 44 +---- examples/Example4/toy_example_exploit.py | 99 +++++++++++ examples/Example4/toy_example_explore.py | 206 +++++++++++++++++++++++ examples/Example4/utilities.py | 82 +++++---- 9 files changed, 369 insertions(+), 329 deletions(-) delete mode 100644 PUQ/design.py delete mode 100644 PUQ/designmethods/batch_support.py delete mode 100644 PUQ/designmethods/utils.py delete mode 100644 examples/Example4/emulator_check.py create mode 100644 examples/Example4/toy_example_exploit.py create mode 100644 examples/Example4/toy_example_explore.py diff --git a/PUQ/design.py b/PUQ/design.py deleted file mode 100644 index ce42661..0000000 --- a/PUQ/design.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -This module contains a class that implements the main design method. -""" - -import importlib - -class designer(object): - def __init__(self, data_cls=None, acquisition=None, method="SEQCAL", args={}): - self.method = importlib.import_module("PUQ.designmethods." + method) - self._info = {"method": method} - - self.fit(data_cls, acquisition, **args) - - def fit(self, data_cls, acquisition, **args): - self.method.fit(self._info, data_cls, acquisition, **args) diff --git a/PUQ/designmethods/batch_sequential.py b/PUQ/designmethods/batch_sequential.py index 9e8f19c..17ad293 100644 --- a/PUQ/designmethods/batch_sequential.py +++ b/PUQ/designmethods/batch_sequential.py @@ -99,16 +99,11 @@ def build_design(self, t0, f0, af, args={}): fevals = Parallel(n_jobs=b)( delayed(self.cls_func.sim_f)(theta[i], persis_info[i]) for i in range(b) ) - print(fevals) + fevals = np.array(fevals).T - print("here 2") f_c = np.concatenate((f_c, fevals), axis=1) t_c = np.concatenate((t_c, theta), axis=0) - - print(f_c[:, -4:]) - print(t_c.shape) - unique_rows, counts = np.unique(t_c, axis=0, return_counts=True) self.f = f_c @@ -135,10 +130,8 @@ def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): # ntest x d # pred = emu.predict(x=x, theta=thetatest) - print("ttte") - print(thetatest.shape) pred = emu.predict(x=thetatest) - print("dblkdb") + mu = pred.mean().T S = pred._info['S'] St = np.transpose(S, (2, 0, 1)) @@ -193,12 +186,12 @@ def onebatch(self, r_exploit = allocate_obj.reps theta_exploit = allocate_obj.theta - import matplotlib.pyplot as plt - plt.scatter(theta_exploit[:,0], theta_exploit[:,1]) - for (x, y, label) in zip(theta_exploit[:, 0], theta_exploit[:, 1], r_exploit): - plt.annotate(str(label), (x, y), xytext=(5, 5), textcoords="offset points") + # import matplotlib.pyplot as plt + # plt.scatter(theta_exploit[:,0], theta_exploit[:,1]) + # for (x, y, label) in zip(theta_exploit[:, 0], theta_exploit[:, 1], r_exploit): + # plt.annotate(str(label), (x, y), xytext=(5, 5), textcoords="offset points") - plt.show() + # plt.show() emu._info = emu_original_info diff --git a/PUQ/designmethods/batch_support.py b/PUQ/designmethods/batch_support.py deleted file mode 100644 index 3194f18..0000000 --- a/PUQ/designmethods/batch_support.py +++ /dev/null @@ -1,71 +0,0 @@ -import numpy as np - - -def rebuild_condition(complete, prev_complete, n_theta=2): - - if (np.sum(complete) - np.sum(prev_complete) < n_theta): - nflag = False - else: - nflag = True - return nflag - -def create_arrays(n_x, n_thetas): - """Create 2D (point * rows) arrays fevals, pending and complete""" - - fevals = np.full((n_x, n_thetas), np.nan) - pending = np.full((1, n_thetas), True) - prev_pending = pending.copy() - complete = np.full((1, n_thetas), False) - prev_complete = np.full((1, n_thetas), False) - - return fevals, pending, prev_pending, complete, prev_complete - -def pad_arrays(n_x, thetanew, theta, fevals, pending, prev_pending, complete, prev_complete): - """Extend arrays to appropriate sizes.""" - n_thetanew = len(thetanew) - theta = np.vstack((theta, thetanew)) - fevals = np.hstack((fevals, np.full((n_x, n_thetanew), np.nan))) - pending = np.hstack((pending, np.full((1, n_thetanew), True))) - prev_pending = np.hstack((prev_pending, np.full((1, n_thetanew), True))) - complete = np.hstack((complete, np.full((1, n_thetanew), False))) - prev_complete = np.hstack((prev_complete, np.full((1, n_thetanew), False))) - - return theta, fevals, pending, prev_pending, complete, prev_complete - - -def update_arrays(n_x, fevals, pending, complete, calc_in, obs_offset, theta_offset): - """Unpack from calc_in into 2D (point * rows) fevals""" - - sim_id = calc_in['sim_id'] - r = np.repeat(0, len(sim_id - obs_offset)) - c = sim_id - obs_offset - - if n_x < 2: - fevals[r, c+theta_offset] = calc_in['f'] - else: - rc = [i for j in range(len(sim_id - obs_offset)) for i in range(n_x)] - cc = np.repeat(c, n_x) - fevals[rc, cc] = calc_in['f'].flatten() - - pending[r, c+theta_offset] = False - complete[r, c+theta_offset] = True - return - - -def load_H(H, thetas, mse, offset=0): - """Fill inputs into H0. - There will be num_points x num_thetas entries - """ - n_thetas = len(thetas) - start = offset*n_thetas - - if thetas.shape[1] < 2: - print(H['thetas'].shape) - print(thetas.flatten().shape) - H['thetas'][start:start+n_thetas] = thetas#.flatten() - else: - H['thetas'][start:start+n_thetas] = thetas - - H['TV'][start:start+n_thetas] = np.repeat(mse, n_thetas) - - return H \ No newline at end of file diff --git a/PUQ/designmethods/utils.py b/PUQ/designmethods/utils.py deleted file mode 100644 index caee712..0000000 --- a/PUQ/designmethods/utils.py +++ /dev/null @@ -1,107 +0,0 @@ -import argparse -import dill as pickle -import os - - -def parse_arguments(): - parser = argparse.ArgumentParser("Parameters for calibration") - parser.add_argument( - "-nworkers", metavar="N2", default=2, type=int, help="Number of workers." - ) - parser.add_argument( - "-minibatch", metavar="N2", default=1, type=int, help="Minibatch size." - ) - parser.add_argument( - "-max_eval", - metavar="N2", - default=100, - type=int, - help="Number of parameters to acquire.", - ) - parser.add_argument( - "-n_init_thetas", - metavar="N2", - default=8, - type=int, - help="Number of parameters from LHS.", - ) - parser.add_argument("-seed_n0", metavar="N2", default=1, type=int, help="Seed No.") - parser.add_argument( - "-init_seeds", metavar="N2", default=1, type=int, help="Init Seed No." - ) - parser.add_argument( - "-final_seeds", metavar="N2", default=2, type=int, help="Final Seed No." - ) - parser.add_argument( - "-al_func", - metavar="N2", - default="eivar", - type=str, - help="Acquisition function.", - ) - parser.add_argument( - "-funcname", - metavar="N2", - default="unimodal", - type=str, - help="Name of the function.", - ) - parser.add_argument( - "-candsize", metavar="N2", default=100, type=int, help="Candidate size." - ) - parser.add_argument( - "-refsize", metavar="N2", default=100, type=int, help="Reference list size." - ) - parser.add_argument( - "-believer", metavar="N2", default=0, type=int, help="Kriging believer type." - ) - args = parser.parse_args() - return args - - -def save_output(desing_obj, name, al_func, nworker, minibatch, seedno): - outputname = ( - "output_" + name + "_" + al_func + "_w_" + str(nworker) + "_b_" + str(minibatch) - ) - if not os.path.isdir(outputname): - os.mkdir(outputname) - - design_path = ( - outputname - + "/" - + name - + "_" - + al_func - + "_w_" - + str(nworker) - + "_b_" - + str(minibatch) - + "_seed_" - + str(seedno) - + ".pkl" - ) - with open(design_path, "wb") as file: - pickle.dump(desing_obj, file) - - -def read_output(path1, name, al_func, nworker, minibatch, seedno): - outputname = "output" # + name + '_' + al_func + '_w_' + str(nworker) + '_b_' + str(minibatch) - design_path = ( - path1 - + outputname - + "/" - + name - + "_" - + al_func - + "_w_" - + str(nworker) - + "_b_" - + str(minibatch) - + "_seed_" - + str(seedno) - + ".pkl" - ) - with open(design_path, "rb") as file: - design_obj = pickle.load(file) - - return design_obj diff --git a/examples/Example4/emulator_check.py b/examples/Example4/emulator_check.py deleted file mode 100644 index c9b2dd0..0000000 --- a/examples/Example4/emulator_check.py +++ /dev/null @@ -1,53 +0,0 @@ -import numpy as np -from PUQ.prior import prior_dist -from test_funcs import bimodal, banana, unimodal -from smt.sampling_methods import LHS -from PUQ.surrogate import emulator - -n0 = 15 -rep0 = 2 - -if __name__ == "__main__": - - for s in np.arange(0, 1): - - cls_func = eval("banana")() - cls_func.realdata(seed=s) - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - # Initial sample - sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) - theta0 = sampling(n0) - theta0 = np.repeat(theta0, rep0, axis=0) - f0 = np.zeros((cls_func.d, n0 * rep0)) - for i in range(0, n0 * rep0): - f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) - - - d = f0.shape[0] - md = np.arange(d).reshape(d, 1) - emu = emulator(x=theta0, - theta=md, - f=f0, - method="multihetGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - - nmesh = 50 - xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh) - ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], nmesh) - Xpl, Ypl = np.meshgrid(xpl, ypl) - theta_test = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - pre_obj = emu.predict(x=theta_test) \ No newline at end of file diff --git a/examples/Example4/example.py b/examples/Example4/example.py index 1e89c6c..7ad2f95 100644 --- a/examples/Example4/example.py +++ b/examples/Example4/example.py @@ -1,52 +1,28 @@ import numpy as np from PUQ.prior import prior_dist -# from PUQ.utils import parse_arguments, save_output -from PUQ.design import designer from test_funcs import bimodal, banana, unimodal from utilities import test_data_gen, twoD, heatmap -import pandas as pd from smt.sampling_methods import LHS import time from PUQ.designmethods.batch_sequential import batch_sequential_design -# args = parse_arguments() - -class Args: - pass - -args = Args() - - -def create_entry(desobj, fname, method, s, b, w): - return [ - { - "MAD": MAD, - "t": t, - "rep": s, - "batch": b, - "worker": w, - "method": method, - "example": fname, - } - for t, MAD in enumerate(desobj._info["TViter"]) - ] # # # # # -args.minibatch = 8 -args.funcname = "bimodal" -args.seedmin = 0 -args.seedmax = 1 +batch = 8 +workers = batch + 1 +funcname = "banana" +smin = 0 +smax = 1 # # # # # -workers = args.minibatch + 1 + n0 = 15 rep0 = 2 nmesh = 50 rho = 1 / 2 -batch = args.minibatch maxiter = 20 -dfl, dfr = [], [] + # Inputs to designer pcset = {"standardize": True, "latent": False} @@ -54,9 +30,9 @@ def create_entry(desobj, fname, method, s, b, w): if __name__ == "__main__": design_start = time.time() - for s in np.arange(args.seedmin, args.seedmax): + for s in np.arange(smin, smax): - cls_func = eval(args.funcname)() + cls_func = eval(funcname)() cls_func.realdata(seed=s) theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) @@ -146,5 +122,3 @@ def create_entry(desobj, fname, method, s, b, w): ax.tick_params(axis="both", labelsize=16) plt.show() - # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) - # twoD(des_obj, Xpl, Ypl, p_test, nmesh) diff --git a/examples/Example4/toy_example_exploit.py b/examples/Example4/toy_example_exploit.py new file mode 100644 index 0000000..1f34cae --- /dev/null +++ b/examples/Example4/toy_example_exploit.py @@ -0,0 +1,99 @@ +import numpy as np +from PUQ.prior import prior_dist +from test_funcs import sinf +from utilities import test_data_gen_1d, Figure1, Figure2 +import matplotlib.pyplot as plt +from PUQ.designmethods.batch_sequential import batch_sequential_design + +# # # # # +batch = 100 +example = "sinf" +smin = 13 +smax = 14 +# # # # # + + +n0 = 20 +rep0 = 5 +maxiter = 1 + +rho = 1 / 2 + +# Inputs to designer +pcset = {"standardize": True, "latent": False} +desset = {"is_exploit": True, "is_explore": False, "nL": 200, "impute_str": "update"} +if __name__ == "__main__": + for s in np.arange(smin, smax): + + cls_func = eval(example)() + cls_func.realdata(s) + + theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + theta = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0)[ + :, None + ] + theta = np.repeat(theta, rep0, axis=0) + f = np.zeros((cls_func.d, rep0 * n0)) + for i in range(0, rep0 * n0): + f[:, i] = cls_func.sim_f(theta[i, :], persis_info=persis_info) + + theta_test, nugs, phat = Figure1( + f, + theta, + cls_func.x, + cls_func.obsvar, + cls_func.real_data, + theta_test, + f_test, + p_test, + cls_func, + ) + + base_args = { + "prior": prior_func, + "data_test": test_data, + "max_iter": maxiter, + "nworkers": batch+1, + "batch_size": batch, + "des_init": {"seed": s, "theta": theta, "f": f}, + "alloc_settings": { + "use_Ki": True, + "rho": rho, + "theta": None, + "a0": None, + "gen": False, + }, + "pc_settings": pcset, + "des_settings": desset, + } + + methods = ["ivar"] + + args_list = [] + for method in methods: + args_ = base_args.copy() + args_["alloc_settings"] = args_["alloc_settings"].copy() + args_["alloc_settings"]["method"] = method + args_list.append(args_) + + des_obj = batch_sequential_design(cls_func) + des_obj.build_design(t0=theta, + f0=f, + af="seivar", + args=args_list[0]) + + + fig, ax = plt.subplots(1, 1, figsize=(7, 3.5)) + fig.subplots_adjust(wspace=0.6) + + Figure2(des_obj, theta_test, nugs, phat, "ivar", ax) diff --git a/examples/Example4/toy_example_explore.py b/examples/Example4/toy_example_explore.py new file mode 100644 index 0000000..2e55362 --- /dev/null +++ b/examples/Example4/toy_example_explore.py @@ -0,0 +1,206 @@ +import numpy as np +from PUQ.prior import prior_dist +from test_funcs import sinf +from utilities import test_data_gen_1d +import matplotlib.pyplot as plt +import scipy.stats as sps +from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( + build_emulator, + multiple_determinants, + multiple_pdfs, +) +from PUQ.designmethods.gen_funcs.acquire_new import get_pred + + + +# # # # # +funcname = "sinf" +smin = 3 # 13 +smax = 4 # 14 +# # # # # + +n0 = 6 +rep0 = 5 +nmesh = 50 +batch = 15 +reps = 5 +bnew = int(batch / reps) + +if __name__ == "__main__": + for s in np.arange(smin, smax): + cls_func = eval(funcname)() + cls_func.realdata(seed=s) + + theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) + test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] + ) + persis_info = {"rand_stream": np.random.default_rng(12345)} + + # Initial sample + theta0u = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0 + )[:, None] + f0mean = np.zeros(len(theta0u)) + p0 = np.zeros(len(theta0u)) + theta0 = np.repeat(theta0u, rep0, axis=0) + f0 = np.zeros(len(theta0)) + for tid, t in enumerate(theta0): + f0[tid] = cls_func.sim_f(t, persis_info=persis_info)[0] + + for tid, t in enumerate(theta0u): + f0mean[tid] = cls_func.function(t)[0] + rnd = sps.norm(loc=f0mean[tid], scale=np.sqrt(cls_func.obsvar)) + p0[tid] = rnd.pdf(cls_func.real_data)[0, 0] + + n_x, p, x = cls_func.x.shape[0], theta_test.shape[1], cls_func.x + obs, obsvar = cls_func.real_data, cls_func.obsvar + theta_acq, n_acq = None, None + obsvar3d = cls_func.obsvar.reshape(1, n_x, n_x) + is_cov = False + + # Create a candidate list + nL = 500 + nm = theta_test.shape[0] + pc_settings = {"standardize": True, "latent": False} + # plt.scatter(theta0, f0) + # plt.show() + + #fig, ax = plt.subplots(2, 3, figsize=(15, 7), constrained_layout=True) + + emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=pc_settings) + for i in range(1): + print(i) + ft = 18 + cL = np.linspace( + cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nL + )[:, None] + + mu, S, cov, cvar = get_pred( + cL=cL, emu=emu, x=x, ttest=theta_test, reps=reps + ) + + plt.plot(theta_test, mu) + plt.show() + + V1 = S + obsvar3d + V2 = S + 0.5 * obsvar3d + + # G = emu._info["G"] + # B = emu._info["B"] + # GB = G @ B + # BTG = B.T @ G + + # d = G.shape[0] + # q = B.shape[1] + d, q = 1, 1 + # tau = np.zeros((nm, q, q, nL)) + phi = np.zeros((nm, d, d, nL)) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) + + for j in range(0, q): + phi[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] + + # for k in range(0, nL): + # phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG + + vals = [] + for k in range(0, nL): + # C1: nm x d x d + # C2: nm x d x d + phic = phi[:, x, x.T, k] + C1 = (V1 + phic) * 0.5 + C2 = V1 - phic + + rpdf = multiple_pdfs(obs, mu, C1) + dets = multiple_determinants(C2) + part2 = rpdf / np.sqrt(dets) + + rpdf2 = multiple_pdfs(obs, mu, V2) + denum2 = obsvar + + cval = coef * (np.sum(rpdf2 / np.sqrt(denum2)) - np.sum(part2)) + + vals.append(cval) + + idc = np.argmin(vals) + minacq = np.min(vals) + cu = cL[idc, :].reshape((1, p)) + + # ax[0, i].plot(cL, vals, color="blue") + # ax[0, i].set_xlabel(r"$\theta$", fontsize=ft) + # ax[0, i].set_ylabel("IVAR", fontsize=ft) + # ax[0, i].tick_params(axis="both", labelsize=ft) + # ax[0, i].scatter(cu, minacq, color="green", marker="*", s=200) + + ctheta = np.repeat(cu, reps, axis=0) + cpred = emu.predict(x=x, theta=ctheta) + + cmean = cpred.mean() + cnoise = cpred._info["var"] + cpred._info["nugs"] + fnoise = persis_info["rand_stream"].normal( + loc=cmean[0], scale=np.sqrt(cnoise[0]), size=reps + ) + + theta0 = np.concatenate([theta0, ctheta], axis=0) + f0 = np.concatenate([f0, fnoise.flatten()]) + + fc = np.zeros(len(cu)) + pc = np.zeros(len(cu)) + for tid, t in enumerate(cu): + fc[tid] = cls_func.function(t) + rnd = sps.norm(loc=fc[tid], scale=np.sqrt(cls_func.obsvar)) + pc[tid] = rnd.pdf(cls_func.real_data) + + phat = np.zeros(theta_test.shape[0]) + phatvar = np.zeros(theta_test.shape[0]) + pvar1 = np.zeros(theta_test.shape[0]) + for tid in range(0, len(theta_test)): + rnd = sps.norm(loc=mu[tid, 0], scale=np.sqrt(obsvar + S[tid, 0, 0])) + phat[tid] = rnd.pdf(obs) + rnd = sps.norm( + loc=mu[tid, 0], scale=np.sqrt(0.5 * obsvar + S[tid, 0, 0]) + ) + pvar1[tid] = rnd.pdf(obs) + phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[ + tid + ] - phat[tid] ** 2 + + # ax[1, i].plot(theta_test, p_test, color="red") + # ax[1, i].plot( + # theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5 + # ) + # ax[1, i].fill_between( + # theta_test.flatten(), + # (phat - np.sqrt(phatvar)).flatten(), + # (phat + np.sqrt(phatvar)).flatten(), + # color="blue", + # alpha=0.1, + # ) + # ax[1, i].set_xlabel(r"$\theta$", fontsize=ft) + # ax[1, i].set_ylabel(r"$p(y|\theta)$", fontsize=ft) + # ax[1, i].tick_params(axis="both", labelsize=ft) + # ax[1, i].scatter(theta0u, p0, color="black", s=100) + # ax[1, i].scatter(cu, pc, color="green", marker="*", s=200) + + theta0u = np.concatenate([theta0u, cu], axis=0) + p0 = np.concatenate([p0, pc.flatten()]) + + print("Adding point:", np.round(ctheta, 2)) + print(emu._info["emulist"][0]._info["X0"].shape) + print(emu._info["emulist"][0]._info["mult"]) + # from PUQ.surrogatemethods.pcHetGP import update + X0new = np.repeat(cu, rep0)[:, None] + pm = emu.predict(x=X0new).mean() + emu.update(x=X0new) + + # update(emu._info, x=x, X0new=cu, mult=reps) + + print("hey") + + plt.savefig("Figure3.png", bbox_inches="tight") + plt.show() diff --git a/examples/Example4/utilities.py b/examples/Example4/utilities.py index bfe0388..e0d5000 100644 --- a/examples/Example4/utilities.py +++ b/examples/Example4/utilities.py @@ -635,41 +635,55 @@ def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func # theta = seqobject._info['theta'][0:ninit,:] # f = seqobject._info['f'][:, 0:ninit] - emu = emulator( - x=x, - theta=theta, - f=f, - method="pcHetGP", - args={ - "lower": None, - "upper": None, - "noiseControl": { - "k_theta_g_bounds": (1, 100), - "g_max": 1e2, - "g_bounds": (1e-6, 1), - }, - "init": {}, - "known": {}, - "settings": { - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - "pc_settings": pc_settings, - }, - ) - - emupred = emu.predict(x=x, theta=theta_test) + # emu = emulator( + # x=x, + # theta=theta, + # f=f, + # method="pcHetGP", + # args={ + # "lower": None, + # "upper": None, + # "noiseControl": { + # "k_theta_g_bounds": (1, 100), + # "g_max": 1e2, + # "g_bounds": (1e-6, 1), + # }, + # "init": {}, + # "known": {}, + # "settings": { + # "linkThetas": "joint", + # "logN": True, + # "initStrategy": "residuals", + # "checkHom": True, + # "penalty": True, + # "trace": 0, + # "return.matrices": True, + # "return.hom": False, + # "factr": 1e9, + # }, + # "pc_settings": pc_settings, + # }, + # ) + + d = f.shape[0] + md = np.arange(d).reshape(d, 1) + emu = emulator(x=theta, + theta=md, + f=f, + method="multihetGP", + args={'lower':None, 'upper':None, + 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + 'init':{}, + 'known':{}, + 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + "return.hom": False, "factr": 1e9}}) + + emupred = emu.predict(x=theta_test) mean = emupred.mean() var = emupred.var() - var_noisy = emupred._info["var_noisy"] + var_noisy = emupred._info["var"] + emupred._info["nugs"] # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) quantile = 0.025 @@ -755,8 +769,8 @@ def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func def Figure2(desobject, theta_test, nugs, phat, method, axs): - theta0 = desobject._info["theta0"] - reps0 = desobject._info["reps0"] + theta0 = desobject.theta0 + reps0 = desobject.rep0 ft = 15 From 859d2f68d0c00e8a7c4b679e70ffc6b08ec52423 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 16:59:08 -0400 Subject: [PATCH 063/103] Updatin Example4 --- ...uential.py => sequential_md_stochastic.py} | 2 +- examples/Example4/README.rst | 36 ++++++++ examples/Example4/example.py | 4 +- examples/Example4/toy1.png | Bin 0 -> 1739010 bytes examples/Example4/toy2.png | Bin 0 -> 929316 bytes examples/Example4/toy3.png | Bin 0 -> 3371221 bytes examples/Example4/toy_example_exploit.py | 7 +- examples/Example4/toy_example_explore.py | 78 +++++++----------- examples/Example4/utilities.py | 36 +------- 9 files changed, 73 insertions(+), 90 deletions(-) rename PUQ/designmethods/{batch_sequential.py => sequential_md_stochastic.py} (99%) create mode 100644 examples/Example4/README.rst create mode 100644 examples/Example4/toy1.png create mode 100644 examples/Example4/toy2.png create mode 100644 examples/Example4/toy3.png diff --git a/PUQ/designmethods/batch_sequential.py b/PUQ/designmethods/sequential_md_stochastic.py similarity index 99% rename from PUQ/designmethods/batch_sequential.py rename to PUQ/designmethods/sequential_md_stochastic.py index 17ad293..60518e4 100644 --- a/PUQ/designmethods/batch_sequential.py +++ b/PUQ/designmethods/sequential_md_stochastic.py @@ -8,7 +8,7 @@ from joblib import Parallel, delayed import copy -class batch_sequential_design: +class sequential_design: def __init__(self, cls_func, trace=True): self.cls_func = cls_func self.trace = trace diff --git a/examples/Example4/README.rst b/examples/Example4/README.rst new file mode 100644 index 0000000..60319f4 --- /dev/null +++ b/examples/Example4/README.rst @@ -0,0 +1,36 @@ +Examples +~~~~~~~~ + +This example demonstrates how to use the performance model from Sürer (2025), +Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models. + + +**Instructions for running the illustrative examples with the active learning procedurel** + +To replicate the figures below, respectively: + +1) Go to the ``examples/Example4`` directory. + +2) Execute any of the following from the command line: + +.. code-block:: python + + python toy_example_exploit.py + python toy_example_explore.py + +Running this script should not take more than 5 min. See the figures (jpeg files) saved under ``examples/`` directory. + +.. image:: toy1.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 + +.. image:: toy2.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 + +.. image:: toy3.png + :alt: Illustration of PUQ with the example + :align: center + :width: 600 \ No newline at end of file diff --git a/examples/Example4/example.py b/examples/Example4/example.py index 7ad2f95..4dd93ba 100644 --- a/examples/Example4/example.py +++ b/examples/Example4/example.py @@ -4,7 +4,7 @@ from utilities import test_data_gen, twoD, heatmap from smt.sampling_methods import LHS import time -from PUQ.designmethods.batch_sequential import batch_sequential_design +from PUQ.designmethods.sequential_md_stochastic import sequential_design # # # # # @@ -83,7 +83,7 @@ args_["alloc_settings"]["method"] = method args_list.append(args_) - des_obj = batch_sequential_design(cls_func) + des_obj = 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a/examples/Example1/example.py +++ b/examples/Example1/example.py @@ -1,50 +1,55 @@ import numpy as np import matplotlib.pyplot as plt import scipy.stats as sps +from scipy.stats import qmc from PUQ.prior import prior_dist from PUQ.designmethods.sequential_md_deterministic import sequential_design -from smt.sampling_methods import LHS from test_func import unimodal if __name__ == "__main__": - cls_unimodal = unimodal() + cex = unimodal() # # # Create a mesh for test set # # # xpl = np.linspace( - cls_unimodal.thetalimits[0][0], cls_unimodal.thetalimits[0][1], 50 + cex.thetalimits[0][0], cex.thetalimits[0][1], 50 ) ypl = np.linspace( - cls_unimodal.thetalimits[1][0], cls_unimodal.thetalimits[1][1], 50 + cex.thetalimits[1][0], cex.thetalimits[1][1], 50 ) Xpl, Ypl = np.meshgrid(xpl, ypl) thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T ftest = np.zeros((thetatest.shape[0], 1)) for i in range(thetatest.shape[0]): - ftest[i, 0] = cls_unimodal.function(thetatest[i,0], thetatest[i,1]) + ftest[i, 0] = cex.function(thetatest[i,0], thetatest[i,1]) ptest = sps.norm.pdf( - cls_unimodal.real_data - ftest, 0, np.sqrt(cls_unimodal.obsvar) + cex.real_data - ftest, 0, np.sqrt(cex.obsvar) ) test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} # # # # # # # # # # # # # # # # # # # # # prior_func = prior_dist(dist="uniform")( - a=cls_unimodal.thetalimits[:, 0], b=cls_unimodal.thetalimits[:, 1] + a=cex.thetalimits[:, 0], b=cex.thetalimits[:, 1] ) # Initial sample - n0 = 10 - s = 1 - sampling = LHS(xlimits=cls_unimodal.thetalimits, random_state=int(s)) - t0 = sampling(n0) + n0, s = 10, 1 + + ndim = cex.thetalimits.shape[0] + sampler = qmc.LatinHypercube(d=ndim, seed=s) + # Generate samples in [0,1]^d + unit_sample = sampler.random(n=n0) + # Scale using limits + t0 = qmc.scale(unit_sample, cex.thetalimits[:, 0], cex.thetalimits[:, 1]) + f0 = np.zeros((t0.shape[0], 1)) for i in range(t0.shape[0]): - f0[i, 0] = cls_unimodal.function(t0[i,0], t0[i,1]) + f0[i, 0] = cex.function(t0[i,0], t0[i,1]) fig, ax = plt.subplots(1, 3, figsize=(12, 4)) for i, af in enumerate(["rnd", "var", "ivar"]): - des_obj = sequential_design(cls_unimodal) + des_obj = sequential_design(cex) des_obj.build_design(z0=t0, f0=f0, T=50, diff --git a/examples/Example1/example_2d.py b/examples/Example1/example_2d.py new file mode 100644 index 0000000..5aa6478 --- /dev/null +++ b/examples/Example1/example_2d.py @@ -0,0 +1,95 @@ +import numpy as np +import scipy.stats as sps +from scipy.stats import qmc +from PUQ.designmethods.sequential_md_deterministic import sequential_design +from PUQ.prior import prior_dist +from test_func import unimodal, banana, bimodal, unidentifiable +import matplotlib.pyplot as plt + +FUNCNAME = "banana" + +if __name__ == "__main__": + + print("Running function: " + FUNCNAME) + + cex = eval(FUNCNAME)() + + # Create a mesh for test set + xpl = np.linspace(cex.thetalimits[0][0], cex.thetalimits[0][1], 50) + ypl = np.linspace(cex.thetalimits[1][0], cex.thetalimits[1][1], 50) + Xpl, Ypl = np.meshgrid(xpl, ypl) + thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + ftest = np.zeros((thetatest.shape[0], cex.d)) + for i in range(thetatest.shape[0]): + ftest[i, :] = cex.function(thetatest[i,0], thetatest[i,1]) + + ptest = np.zeros(thetatest.shape[0]) + if cex.data_name == "unimodal": + ptest = sps.norm.pdf(cex.real_data - ftest, 0, np.sqrt(cex.obsvar)) + else: + for i in range(ftest.shape[0]): + mean = ftest[i, :] + rnd = sps.multivariate_normal(mean=mean, cov=cex.obsvar) + ptest[i] = rnd.pdf(cex.real_data) + test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} + + # Set a uniform prior + prior_func = prior_dist(dist="uniform")( + a=cex.thetalimits[:, 0], b=cex.thetalimits[:, 1] + ) + + # Define acquisition functions + # acq_funcs = ["eivar", "rnd", "maxvar", "maxexp"] + acq_funcs = ["ivar", "rnd", "var"] + datalist = [] + rep_no, n0 = 2, 10 + + # Run over 50 replications + for seed_id in range(1, rep_no + 1): + # Initial sample + ndim = cex.thetalimits.shape[0] + sampler = qmc.LatinHypercube(d=ndim, seed=seed_id) + # Generate samples in [0,1]^d + unit_sample = sampler.random(n=n0) + # Scale using limits + t0 = qmc.scale(unit_sample, cex.thetalimits[:, 0], cex.thetalimits[:, 1]) + f0 = np.zeros((t0.shape[0], cex.d)) + for i in range(t0.shape[0]): + f0[i, :] = cex.function(t0[i,0], t0[i,1]) + + for func in acq_funcs: + print("Running " + func + " with seed " + str(seed_id)) + des_obj = sequential_design(cex) + des_obj.build_design(z0=t0, + f0=f0, + T=75, + test=test_data, + af=func, + args={ + "mini_batch": 1, + "n_init_thetas": 10, + "nworkers": 2, + "prior": prior_func, + "data_test": test_data, + "seed": seed_id, + "integral": "LHS" + }) + + + theta_al = des_obj.zs + + fig, ax = plt.subplots() + cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") + ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) + ax.scatter( + theta_al[0:n0, 0], + theta_al[0:n0, 1], + zorder=2, + marker="o", + facecolors="none", + edgecolors="blue", + ) + ax.set_xlabel(r"$\theta_1$", fontsize=16) + ax.set_ylabel(r"$\theta_2$", fontsize=16) + ax.tick_params(axis="both", labelsize=16) + plt.show() diff --git a/examples/Example1/toy_example.py b/examples/Example1/toy_example.py new file mode 100644 index 0000000..2f32c88 --- /dev/null +++ b/examples/Example1/toy_example.py @@ -0,0 +1,160 @@ +import scipy.stats as sps +import numpy as np +import matplotlib.pyplot as plt +from PUQ.surrogate import emulator +from PUQ.posterior import multiple_pdfs, compute_postvar + + +class sinlinear: + def __init__(self): + self.data_name = "sinlinear" + self.thetalimits = np.array([[-10, 10]]) + self.obsvar = np.array([[1]], dtype="float64") + self.real_data = np.array([[0]], dtype="float64") + self.out = [("f", float)] + self.p = 1 + self.d = 1 + self.x = np.arange(0, self.d)[:, None] + self.real_x = np.arange(0, self.d)[:, None] + + def function(self, theta): + f = np.sin(theta) + 0.1 * theta + return f + + def sim(self, H, persis_info, sim_specs, libE_info): + """ + Wraps the sin() function + """ + function = sim_specs["user"]["function"] + H_o = np.zeros(1, dtype=sim_specs["out"]) + theta = H["thetas"][0] + H_o["f"] = function(theta) + + return H_o, persis_info + + +if __name__ == "__main__": + cls_sinlin = sinlinear() + + # Generate training data + theta = np.array([-10, -8.7, -7.5, -5, -2.5, -1, 1, 2.5, 5, 7.5, 8.7, 10])[:, None] + f = cls_sinlin.function(theta) + ptr = sps.norm.pdf(cls_sinlin.real_data, f, np.sqrt(cls_sinlin.obsvar)) + + # Fit an emulator + # emu = emulator(cls_sinlin.x, theta, f, method="PCGP") + + emu = emulator(x=theta, + theta=cls_sinlin.x, + f=f, + method="multihomGP") + + # Generate test data + thetatest = np.arange(-10, 10, 0.0025)[:, None] + ftest = cls_sinlin.function(thetatest) + ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) + + # predict at mesh + nm, d = thetatest.shape[0], cls_sinlin.d + pr_test = emu.predict(x=thetatest, thetaprime=thetatest) + mu, Sn = pr_test._info["mean"], pr_test._info["S"] + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) + Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) + N = S + Sigma3d + M = S + 0.5*Sigma3d + posttesthat = multiple_pdfs(cls_sinlin.real_data, muT, N) + + diags = np.diag(cls_sinlin.obsvar[cls_sinlin.x, cls_sinlin.x.T]) + coef = (2**cls_sinlin.d) * (np.sqrt(np.pi) ** cls_sinlin.d) * np.sqrt(np.prod(diags)) + posttestvar = compute_postvar(cls_sinlin.real_data, + muT, + N, + M, + coef) + + + # predict at mesh + nm, d = theta.shape[0], cls_sinlin.d + pr_test = emu.predict(x=theta, thetaprime=theta) + mu, Sn = pr_test._info["mean"], pr_test._info["S"] + muT = mu.reshape(nm, d) + S = Sn.transpose(2, 0, 1) + Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) + N = S + Sigma3d + posttrhat = multiple_pdfs(cls_sinlin.real_data, muT, N) + + # Predict via the emulator + emupred_test = emu.predict(x=thetatest) + emupred_tr = emu.predict(x=theta) + + # Figure 2 (a) + ft = 20 + fig, ax = plt.subplots() + ax.plot(thetatest, ftest, color="black") + ax.plot( + thetatest.flatten(), + emupred_test.mean().flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + thetatest.flatten(), + (emupred_test.mean() - np.sqrt(emupred_test.var())).flatten(), + (emupred_test.mean() + np.sqrt(emupred_test.var())).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, emupred_tr.mean(), color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$\eta(\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2a.png", bbox_inches="tight") + + # Figure 2 (b) + fig, ax = plt.subplots() + ax.plot(thetatest, ptest, color="black") + ax.plot(thetatest, posttesthat, color="blue", linestyle="dashed", linewidth=2.5) + plt.fill_between( + thetatest.flatten(), + (posttesthat - np.sqrt(posttestvar)).flatten(), + (posttesthat + np.sqrt(posttestvar)).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, posttrhat, color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2b.png", bbox_inches="tight") + + # Figure 2 (c) + # Fit an emulator for posterior + emu = emulator(theta=cls_sinlin.x, + x=theta, + f=ptr, + method="multihomGP") + + emupred_test = emu.predict(x=thetatest) + emumean_test = emupred_test.mean() + emumean_var = emupred_test.var() + + fig, ax = plt.subplots() + ax.plot(thetatest, ptest, color="black") + ax.plot( + thetatest.flatten(), + emumean_test.flatten(), + linestyle="dashed", + color="blue", + linewidth=2.5, + ) + plt.fill_between( + thetatest.flatten(), + (emumean_test - np.sqrt(emumean_var)).flatten(), + (emumean_test + np.sqrt(emumean_var)).flatten(), + alpha=0.3, + ) + ax.scatter(theta.T, ptr, color="red", s=60) + ax.set_xlabel(r"$\theta$", fontsize=ft) + ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) + ax.tick_params(axis="both", labelsize=ft) + plt.savefig("Figure2c.png", bbox_inches="tight") From 06cc2bc07f20e474893b16f5f852d7fdd6c40c50 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 20:01:08 -0400 Subject: [PATCH 065/103] Updaet example 2 --- .../sequential_1d_deterministic.py | 10 ++++------ examples/Example2/README.rst | 8 +++++++- examples/Example2/ex2.png | Bin 1539794 -> 1530619 bytes examples/Example2/example.py | 7 ++++--- 4 files changed, 15 insertions(+), 10 deletions(-) diff --git a/PUQ/designmethods/sequential_1d_deterministic.py b/PUQ/designmethods/sequential_1d_deterministic.py index 0594d62..78ecafc 100644 --- a/PUQ/designmethods/sequential_1d_deterministic.py +++ b/PUQ/designmethods/sequential_1d_deterministic.py @@ -6,7 +6,7 @@ from PUQ.surrogate import emulator class sequential_design: - def __init__(self, cls_func, trace=True): + def __init__(self, cls_func, trace=False): self.cls_func = cls_func self.trace = trace self.y = self.cls_func.real_data @@ -34,10 +34,8 @@ def build_design(self, z0, f0, T, af, test=None, args={}): timel = [] metric_sum = {} for t in range(0, T): - # print(f"t: {t}") if self.trace else None - print(t) - print(z0.shape) - print(f0.shape) + print(f"t: {t}") if self.trace else None + tic = time.time() model = emulator(x=z0, theta=np.array([0]), @@ -49,7 +47,7 @@ def build_design(self, z0, f0, T, af, test=None, args={}): toc = time.time() timel.append(toc - tic) - print(toc - tic) + args["seed"] += 1 # if test is not None: diff --git a/examples/Example2/README.rst b/examples/Example2/README.rst index 1a1fc46..117d69d 100644 --- a/examples/Example2/README.rst +++ b/examples/Example2/README.rst @@ -23,4 +23,10 @@ Running this script should not take more than 60 sec. See the figures (png files .. image:: ex2.png :alt: Illustration of PUQ with the example :align: center - :width: 600 \ No newline at end of file + :width: 600 + + +The left panel shows simulation model outputs across design inputs (x) at four +different parameter values. Black dots represent field data observed at five +equally spaced design inputs. 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a/examples/Example2/example.py b/examples/Example2/example.py index 6059699..48e42fd 100644 --- a/examples/Example2/example.py +++ b/examples/Example2/example.py @@ -1,7 +1,7 @@ import numpy as np from ptest_funcs import sinfunc import matplotlib.pyplot as plt -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.sequential_1d_deterministic import sequential_design @@ -43,8 +43,9 @@ # Generate initial sample n0, nmax = 10, 30 - sampling = LHS(xlimits=cex.zlim, random_state=int(s)) - z0 = sampling(n0) + ndim = cex.zlim.shape[0] + sampler = qmc.LatinHypercube(d=ndim, seed=s) + z0 = sampler.random(n=n0) f0 = np.array([cex.function(z0[i, 0], z0[i, 1]) for i in range(n0)]) # Generate design From 21edb280674186d6bc2066746ac918736110efff Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 21:20:24 -0400 Subject: [PATCH 066/103] More cleaning --- .../gen_funcs/acquisition_1d_stochastic.py | 3 +- .../gen_funcs/acquisition_funcs.py | 349 ------------------ ...re_new.py => acquisition_md_stochastic.py} | 0 PUQ/designmethods/gen_funcs/allocate_reps.py | 187 ---------- ...n_allocate_reps.py => allocate_reps_1d.py} | 0 ...h_allocate_reps.py => allocate_reps_md.py} | 0 PUQ/designmethods/sequential_1d_stochastic.py | 19 +- PUQ/designmethods/sequential_md_stochastic.py | 24 +- examples/Example4/README.rst | 26 +- examples/Example4/example.py | 55 +-- examples/Example4/toy_example_exploit.py | 18 +- examples/Example4/toy_example_explore.py | 24 +- 12 files changed, 45 insertions(+), 660 deletions(-) delete mode 100644 PUQ/designmethods/gen_funcs/acquisition_funcs.py rename PUQ/designmethods/gen_funcs/{acquire_new.py => acquisition_md_stochastic.py} (100%) delete mode 100644 PUQ/designmethods/gen_funcs/allocate_reps.py rename PUQ/designmethods/gen_funcs/{acquisition_allocate_reps.py => allocate_reps_1d.py} (100%) rename PUQ/designmethods/gen_funcs/{batch_allocate_reps.py => allocate_reps_md.py} (100%) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py index 452e709..c83a389 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -4,8 +4,7 @@ from hetgpy.covariance_functions import cov_gen from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult import emcee -from PUQ.designmethods.gen_funcs.acquisition_allocate_reps import allocate -from copy import deepcopy +from PUQ.designmethods.gen_funcs.allocate_reps_1d import allocate import copy def generate_neighborhood(acq): diff --git a/PUQ/designmethods/gen_funcs/acquisition_funcs.py b/PUQ/designmethods/gen_funcs/acquisition_funcs.py deleted file mode 100644 index 3096f43..0000000 --- a/PUQ/designmethods/gen_funcs/acquisition_funcs.py +++ /dev/null @@ -1,349 +0,0 @@ -"""Contains acquisition functions.""" - -import numpy as np -from sklearn.metrics import pairwise_distances -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - compute_postvar, - compute_eivar, - multiple_pdfs, - get_emuvar, -) -import scipy - - -def rnd( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - theta_acq = prior_func.rnd(n, None) - return theta_acq - - -def maxvar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq, f_acq = [], [] - d, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, d, d) - diags = np.diag(obsvar[real_x, real_x.T]) - d_real = real_x.shape[0] - coef = (2**d_real) * (np.sqrt(np.pi) ** d_real) * np.sqrt(np.prod(diags)) - - # Create a candidate list. - n_clist = 100 * n - clist = prior_func.rnd(n_clist, None) - - # Acquire n thetas. - for i in range(n): - emupredict = emu.predict(x, clist) - emumean = emupredict.mean() - emuvar, is_cov = get_emuvar(emupredict) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - var_obsvar2 = emuvarT + 0.5 * obsvar3d - postmean = multiple_pdfs( - obs, emumeanT[:, real_x.flatten()], var_obsvar1[:, real_x, real_x.T] - ) - postvar = compute_postvar( - obs, - emumeanT[:, real_x.flatten()], - var_obsvar1[:, real_x, real_x.T], - var_obsvar2[:, real_x, real_x.T], - coef, - ) - - acq_func = postvar.copy() - idc = np.argmax(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - f_acq.append(postmean[idc]) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq - - -def eivar( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq = [] - n_x, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, n_x, n_x) - - # Create a candidate list - if n == 1: - n_clist = 100 * n - else: - n_clist = 100 * int(n) # 100*int(np.ceil(np.log(n))) - - clist = prior_func.rnd(n_clist, None) - - for i in range(n): - emupredict = emu.predict(x, thetatest) - emumean = emupredict.mean() - emumeanT = emumean.T - emuvar, is_cov = get_emuvar(emupredict) - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - - # Get the n_ref x d x d x n_cand phi matrix - emuphi4d = emu.acquisition(x=x, theta1=thetatest, theta2=clist) - acq_func = [] - - # Pass over all the candidates - for c_id in range(len(clist)): - posteivar = compute_eivar( - var_obsvar1[:, real_x, real_x.T], - emuphi4d[:, real_x, real_x.T, c_id], - emumeanT[:, real_x.flatten()], - emuvar[real_x, :, real_x.T], - obs, - is_cov, - posttest, - ) - acq_func.append(posteivar) - - idc = np.argmin(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq - - -def maxexp( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq, f_acq = [], [] - d, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, d, d) - - # Create a candidate list. - n_clist = 100 * n - clist = prior_func.rnd(n_clist, None) - - theta_sd = (theta - thetalimits[:, 0]) / (thetalimits[:, 1] - thetalimits[:, 0]) - theta_cand_sd = (clist - thetalimits[:, 0]) / ( - thetalimits[:, 1] - thetalimits[:, 0] - ) - - for j in range(n): - emupredict = emu.predict(x, clist) - emumean = emupredict.mean() - emuvar, is_cov = get_emuvar(emupredict) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - postmean = multiple_pdfs( - obs, emumeanT[:, real_x.flatten()], var_obsvar1[:, real_x, real_x.T] - ) - logpostmean = np.log(postmean) - - # Diversity term - d = pairwise_distances(theta_sd, theta_cand_sd, metric="euclidean") - min_dist = np.min(d, axis=0) - - # Acquisition function - acq_func = logpostmean + np.log(min_dist) - - # New theta - idc = np.argmax(acq_func) - theta_sd = np.concatenate((theta_sd, theta_cand_sd[idc][None, :]), axis=0) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - f_acq.append(postmean[idc]) - clist = np.delete(clist, idc, 0) - theta_cand_sd = np.delete(theta_cand_sd, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - return theta_acq - - -def imse( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - # Update emulator for uncompleted jobs. - idnan = np.isnan(fevals).any(axis=0).flatten() - theta_uc = theta[idnan, :] - if sum(idnan) > 0: - fevalshat_uc = emu.predict(x=x, theta=theta_uc) - emu.update(theta=theta_uc, f=fevalshat_uc.mean()) - - theta_acq = [] - n_x, p = x.shape[0], theta.shape[1] - obsvar3d = obsvar.reshape(1, n_x, n_x) - - # Create a candidate list - if n == 1: - n_clist = 100 * n - else: - n_clist = 100 * int(n) # 100*int(np.ceil(np.log(n))) - - clist = prior_func.rnd(n_clist, None) - - for i in range(n): - # Get the n_ref x d x d x n_cand phi matrix - emuphi4d = emu.acquisition(x=x, theta1=thetatest, theta2=clist) - acq_func = [] - - # print(emuphi4d.shape) - # Pass over all the candidates - for c_id in range(len(clist)): - # print(emuphi4d[c_id, :, :, 0]) - posteivar = np.sum(emuphi4d[:, :, :, c_id]) - acq_func.append(posteivar) - - idc = np.argmax(acq_func) - ctheta = clist[idc, :].reshape((1, p)) - theta_acq.append(ctheta) - clist = np.delete(clist, idc, 0) - - # Kriging believer strategy - fevalsnew = emu.predict(x=x, theta=ctheta) - emu.update(theta=ctheta, f=fevalsnew.mean()) - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq - - -def ei( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - thetatest=None, - posttest=None, - type_init=None, -): - n_x, p = x.shape[0], theta.shape[1] - # Create a candidate list - if n == 1: - n_clist = 100 * n - else: - n_clist = 100 * int(n) # 100*int(np.ceil(np.log(n))) - - clist = prior_func.rnd(n_clist, None) - maxpost = np.max(fevals) - - for i in range(n): - emupredict = emu.predict(np.arange(0, 1)[:, None], clist) - emumean = emupredict.mean() - emuvar = emupredict.var() - - zscore = (emumean - maxpost) / np.sqrt(emuvar) - - cdf_cand = scipy.stats.norm.cdf(zscore) - pdf_cand = scipy.stats.norm.pdf(zscore) - ei_cand = (emumean - maxpost) * cdf_cand + np.sqrt(emuvar) * pdf_cand - acqfun = ei_cand - - # print(acqfun) - # print(acqfun) - theta_acq = clist[np.argmax(acqfun), :] - - theta_acq = np.array(theta_acq).reshape((n, p)) - - return theta_acq diff --git a/PUQ/designmethods/gen_funcs/acquire_new.py b/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py similarity index 100% rename from PUQ/designmethods/gen_funcs/acquire_new.py rename to PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py diff --git a/PUQ/designmethods/gen_funcs/allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps.py deleted file mode 100644 index 251b1bc..0000000 --- a/PUQ/designmethods/gen_funcs/allocate_reps.py +++ /dev/null @@ -1,187 +0,0 @@ -import numpy as np -import scipy -from numpy.linalg import cholesky, inv, det -from hetgpy.covariance_functions import cov_gen -from PUQ.designmethods.support import multiple_pdfs - -class allocate: - def __init__(self, - budget, - model, - func_cls, - mesh, - trace=1, - alloc_settings={}, - rand_stream=None): - - self.budget = budget - self.model = model - self.x = func_cls.x - self.theta_mesh = mesh["theta"] - self.w_mesh = mesh["weight"] - self.obs = func_cls.real_data - self.obsvar = func_cls.obsvar - self.use_Ki = alloc_settings.get('use_Ki', True) - self.eps = np.sqrt(np.finfo(float).eps) - self.trace = trace - self.func_cls = func_cls - self.rand_stream = rand_stream - self.z = self.model['X0'] - self.a0 = self.model['mult'] - - - - def allocatereps(self): - - Cinfo = self.compute_ivar_weights() - C = Cinfo.flatten() - - idneg = C < 0 - weight = np.sqrt(np.abs(C[idneg])) - a_frac = np.zeros(len(C)) - a_frac_ub = np.zeros(len(C)) - total_a = np.sum(self.a0) + self.budget - - # Find an upper bound - a_frac_ub[idneg] = total_a*weight/sum(weight) - a_ub = self.make_int(total_a, a_frac_ub) - a_ub = np.maximum(0, a_ub - self.a0) - - a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) - a_final = self.make_int(self.budget, a_frac) - a_final = np.array([int(a) for a in a_final]) - if np.sum(a_final) != self.budget: - raise ValueError("Budget constraint is not satisfied.") - - self.reps = a_final - return - - - def compute_ivar_weights(self): - - d = len(self.x) - n_integ = self.theta_mesh.shape[0] - n = self.z.shape[0] - - #pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) - #pred_mesh = self.model.predict(x=self.theta_mesh) - - # obsvar3d = self.obsvar.reshape(1, d, d) - # # ntest x d - # mu = pred_mesh._info['mean'].T - # S = pred_mesh._info['S'] - # St = np.transpose(S, (2, 0, 1)) - - obsvar3d = self.obsvar.reshape(1, d, d) - - x = self.x - ntot = n_integ*d - t = self.theta_mesh - x_tiled = np.tile(x, (t.shape[0], 1)) - t_repeated = np.repeat(t, x.shape[0], axis=0) - z = np.hstack([x_tiled, t_repeated]) - - # to construct S matrix - id_row = np.arange(0, ntot) - id_col = np.arange(0, ntot).reshape(n_integ, d) - id_col = np.repeat(id_col, repeats=d, axis=0) - - # predict at mesh - meshPr = self.model.predict(x=z, xprime=z) - - # ntot, ntot x ntot, ntot - mu, Sn = meshPr["mean"], meshPr["cov"] - - muT = mu.reshape(n_integ, d) - S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) - - # ntest x d x d - Nb = S + 0.5*obsvar3d - N = S + obsvar3d - - f = multiple_pdfs(self.obs, muT, Nb) - g = multiple_pdfs(self.obs, muT, N) - - # ntest x 1 x d - h = (self.obs - muT).reshape(n_integ, 1, d) - - # ntest x d x d - Nbinv = inv(Nb) - - # ntest x d x d - Ninv = inv(N) - - # ntest x 1 x d - hNb = np.matmul(h, Nbinv) - hN = np.matmul(h, Ninv) - - coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) - - Mb = np.zeros((n, n_integ, d, d), dtype=np.float64) - - pred_nugs = self.model.predict(x=self.z, args=dict(nugs_only=True)) #nugs_only=True) - # n x n - if self.use_Ki: - Ki = self.model['Ki'] - else: - K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) - Ki = scipy.linalg.pinv(K, rcond=self.eps) - - ids = np.arange(0, len(z), d) - for i in range(0, n): - J = np.zeros((n, n)) - J[i, i] = 1 - - # n x n - A = Ki@J@Ki - - for j in range(0, d): - # n_integ x 1 - zo = z[ids + j] - - # n x n_integ - kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) - - for jp in range(0, d): - # n_integ x 1 - zop = z[ids + jp] - - # n x n_integ - kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) - Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) - - dlogfdai = np.zeros((n, n_integ)) - dloggdai = np.zeros((n, n_integ)) - C = np.zeros((n, 1)) - for i in range(0, n): - - M = Mb[i, :, :, :] - - part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) - part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) - part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) - part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) - - dlogfdai = part1f + part2f.flatten() - dloggdai = part1g + part2g.flatten() - subC = self.w_mesh*(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) - - C[i] = np.sum(subC) - - return C - - - def make_int(self, bdg, n_frac): - # Make integer - n_floor = np.floor(n_frac) - remain = n_frac - n_floor - tot_remain = bdg - np.sum(n_floor) - sorted_indices = np.array(np.argsort(remain)[::-1]) - if tot_remain > 0: - idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] - n_floor[idx] = n_floor[idx] + 1 - - return n_floor - - - \ No newline at end of file diff --git a/PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps_1d.py similarity index 100% rename from PUQ/designmethods/gen_funcs/acquisition_allocate_reps.py rename to PUQ/designmethods/gen_funcs/allocate_reps_1d.py diff --git a/PUQ/designmethods/gen_funcs/batch_allocate_reps.py b/PUQ/designmethods/gen_funcs/allocate_reps_md.py similarity index 100% rename from PUQ/designmethods/gen_funcs/batch_allocate_reps.py rename to PUQ/designmethods/gen_funcs/allocate_reps_md.py diff --git a/PUQ/designmethods/sequential_1d_stochastic.py b/PUQ/designmethods/sequential_1d_stochastic.py index 56e8764..438c4d9 100644 --- a/PUQ/designmethods/sequential_1d_stochastic.py +++ b/PUQ/designmethods/sequential_1d_stochastic.py @@ -1,9 +1,7 @@ -from hetgpy import hetGP import numpy as np import matplotlib.pyplot as plt from PUQ.designmethods.support import multiple_pdfs, multiple_determinants from PUQ.designmethods.gen_funcs.acquisition_1d_stochastic import var, ivar, imse, lookahead -import time from PUQ.surrogate import emulator class sequential_design: @@ -35,17 +33,8 @@ def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): timel = [] metric_sum = {} for t in range(0, T): - # print(f"t: {t}") if self.trace else None - print(t) - tic = time.time() - - # hetGP - # model = hetGP() - # model.mle(X=z0, - # Z=f0, - # covtype="Gaussian", - # known={"beta0":np.mean(f0)}) - + print(f"t: {t}") if self.trace else None + model = emulator(x=z0, theta=np.array([0]), f=f0, @@ -53,10 +42,6 @@ def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): args={"known": {"beta0":np.mean(f0)}}) model.fit() - - toc = time.time() - timel.append(toc - tic) - print(toc - tic) args["seed"] += 1 if test is not None: diff --git a/PUQ/designmethods/sequential_md_stochastic.py b/PUQ/designmethods/sequential_md_stochastic.py index 60518e4..e698eeb 100644 --- a/PUQ/designmethods/sequential_md_stochastic.py +++ b/PUQ/designmethods/sequential_md_stochastic.py @@ -1,6 +1,6 @@ import numpy as np -from PUQ.designmethods.gen_funcs.batch_allocate_reps import allocate -from PUQ.designmethods.gen_funcs.acquire_new import acquire +from PUQ.designmethods.gen_funcs.allocate_reps_md import allocate +from PUQ.designmethods.gen_funcs.acquisition_md_stochastic import acquire from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( multiple_pdfs, build_emulator, @@ -33,17 +33,12 @@ def get(self, key): def build_design(self, t0, f0, af, args={}): # data params - # d = data_cls.d - # x = data_cls.x - # y = data_cls.real_data - # Sigma = data_cls.obsvar - b = args["batch_size"] max_iter = args["max_iter"] alloc_settings = args["alloc_settings"] - des_settings = args["des_settings"] - pc_settings = args["pc_settings"] + des_settings = args.get("des_settings", None) + pc_settings = args.get("pc_settings", None) test_data = args["data_test"] prior_func = args["prior"] # explore params @@ -64,15 +59,9 @@ def build_design(self, t0, f0, af, args={}): f_c, t_c = f0, t0 - print("here") - print(f_c.shape) - print(t_c.shape) - iter_explore, iter_exploit = 0, 0 is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') - print("max_iter") - print(max_iter) for iteration in range(max_iter): emu, TV = self.newiteration(t_c, f_c, self.x, pc_settings, test_data, self.Sigma, self.y) @@ -186,12 +175,7 @@ def onebatch(self, r_exploit = allocate_obj.reps theta_exploit = allocate_obj.theta - # import matplotlib.pyplot as plt - # plt.scatter(theta_exploit[:,0], theta_exploit[:,1]) - # for (x, y, label) in zip(theta_exploit[:, 0], theta_exploit[:, 1], r_exploit): - # plt.annotate(str(label), (x, y), xytext=(5, 5), textcoords="offset points") - # plt.show() emu._info = emu_original_info diff --git a/examples/Example4/README.rst b/examples/Example4/README.rst index 60319f4..f93f038 100644 --- a/examples/Example4/README.rst +++ b/examples/Example4/README.rst @@ -1,8 +1,9 @@ Examples ~~~~~~~~ -This example demonstrates how to use the performance model from Sürer (2025), -Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models. +This example demonstrates how to use the active learning procedure from Sürer (2025), +Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models, +for the stochastic models with high-dimensional outputs. **Instructions for running the illustrative examples with the active learning procedurel** @@ -20,17 +21,34 @@ To replicate the figures below, respectively: Running this script should not take more than 5 min. See the figures (jpeg files) saved under ``examples/`` directory. + .. image:: toy1.png :alt: Illustration of PUQ with the example :align: center :width: 600 +Illustration with a simulation model. The red line shows the expected value of the simulation model (left +panel) or the likelihood (middle panel). The blue dashed line shows the prediction mean and the +shaded area illustrates one predictive standard deviation from the mean. Green dots indicate the +simulation data including five replicates of 20 uniformly spaced parameter values used to build +the emulator. The right panel demonstrates the true (black line) and estimated (blue dashed line) +intrinsic variance. + .. image:: toy2.png :alt: Illustration of PUQ with the example :align: center :width: 600 - + +Allocation of new 100 replicates guided by the proposed acquisition function using the example above. +Star markers indicate the number of replicates on the existing 20 design points. The estimated intrinsic variance (blue +dashed line) and likelihood (red dotted line) are depicted for reference. + .. image:: toy3.png :alt: Illustration of PUQ with the example :align: center - :width: 600 \ No newline at end of file + :width: 600 + +Allocation of b = 15 simulation evaluations guided by the IVAR criterion shown in +the first row. The emulator is constructed using simulation data including n0 = 6 unique parameters (black dots), +each replicated five times. The green star represents the acquired point. The second row shows the +true (red line) and estimated likelihood (blue dashed line). \ No newline at end of file diff --git a/examples/Example4/example.py b/examples/Example4/example.py index 4dd93ba..8dc4d4f 100644 --- a/examples/Example4/example.py +++ b/examples/Example4/example.py @@ -1,35 +1,32 @@ import numpy as np from PUQ.prior import prior_dist from test_funcs import bimodal, banana, unimodal -from utilities import test_data_gen, twoD, heatmap +from utilities import test_data_gen, twodpaper from smt.sampling_methods import LHS -import time from PUQ.designmethods.sequential_md_stochastic import sequential_design # # # # # batch = 8 workers = batch + 1 -funcname = "banana" +funcname = "unimodal" smin = 0 smax = 1 # # # # # -n0 = 15 -rep0 = 2 +n0, rep0, rho = 15, 2, 1 / 2 + nmesh = 50 -rho = 1 / 2 -maxiter = 20 +maxiter = 10 # Inputs to designer -pcset = {"standardize": True, "latent": False} desset = {"is_exploit": True, "is_explore": True, "nL": 200, "impute_str": "update"} if __name__ == "__main__": - design_start = time.time() + for s in np.arange(smin, smax): cls_func = eval(funcname)() @@ -60,9 +57,7 @@ "prior": prior_func, "data_test": test_data, "max_iter": maxiter, - "nworkers": workers, "batch_size": batch, - "des_init": {"seed": s, "theta": theta0, "f": f0}, "alloc_settings": { "use_Ki": True, "rho": rho, @@ -70,7 +65,6 @@ "a0": None, "gen": False, }, - "pc_settings": pcset, "des_settings": desset, } @@ -86,39 +80,8 @@ des_obj = sequential_design(cls_func) des_obj.build_design(t0=theta0, f0=f0, af="seivar", args=args_list[0]) - - theta = des_obj["theta0"] reps = des_obj["rep0"] - import matplotlib.pyplot as plt - - fig, ax = plt.subplots(figsize=(5, 5)) - cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="RdGy") - for label, x_count, y_count in zip(reps, theta[:, 0], theta[:, 1]): - row = np.array([x_count, y_count]) - exists = np.any(np.all(theta0 == row, axis=1)) - if exists: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="cyan", - weight="bold", - ) - else: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="blue", - weight="bold", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - + + twodpaper(cls_func, Xpl, Ypl, p_test, theta, reps, thetainit=theta0, name="fig1") + diff --git a/examples/Example4/toy_example_exploit.py b/examples/Example4/toy_example_exploit.py index 5d2b515..6371ea1 100644 --- a/examples/Example4/toy_example_exploit.py +++ b/examples/Example4/toy_example_exploit.py @@ -5,22 +5,11 @@ import matplotlib.pyplot as plt from PUQ.designmethods.sequential_md_stochastic import sequential_design -# # # # # -batch = 100 -example = "sinf" -smin = 13 -smax = 14 -# # # # # - -n0 = 20 -rep0 = 5 -maxiter = 1 - -rho = 1 / 2 +batch, example, smin, smax = 100, "sinf", 13, 14 +n0, rep0, maxiter, rho = 20, 5, 1, 1/2 # Inputs to designer -pcset = {"standardize": True, "latent": False} desset = {"is_exploit": True, "is_explore": False, "nL": 200, "impute_str": "update"} if __name__ == "__main__": for s in np.arange(smin, smax): @@ -63,9 +52,7 @@ "prior": prior_func, "data_test": test_data, "max_iter": maxiter, - "nworkers": batch+1, "batch_size": batch, - "des_init": {"seed": s, "theta": theta, "f": f}, "alloc_settings": { "use_Ki": True, "rho": rho, @@ -73,7 +60,6 @@ "a0": None, "gen": False, }, - "pc_settings": pcset, "des_settings": desset, } diff --git a/examples/Example4/toy_example_explore.py b/examples/Example4/toy_example_explore.py index 533f1c9..7d71b6a 100644 --- a/examples/Example4/toy_example_explore.py +++ b/examples/Example4/toy_example_explore.py @@ -9,21 +9,10 @@ multiple_determinants, multiple_pdfs, ) -from PUQ.designmethods.gen_funcs.acquire_new import get_pred +from PUQ.designmethods.gen_funcs.acquisition_md_stochastic import get_pred - - -# # # # # -funcname = "sinf" -smin = 3 # 13 -smax = 4 # 14 -# # # # # - -n0 = 6 -rep0 = 10 -nmesh = 50 -batch = 15 -reps = 5 +funcname, smin, smax = "sinf", 3, 4 +n0, rep0, nmesh, batch, reps = 6, 10, 50, 15, 5 bnew = int(batch / reps) if __name__ == "__main__": @@ -65,13 +54,10 @@ # Create a candidate list nL = 500 nm = theta_test.shape[0] - pc_settings = {"standardize": True, "latent": False} - # plt.scatter(theta0, f0) - # plt.show() - + fig, ax = plt.subplots(2, 3, figsize=(15, 7), constrained_layout=True) - emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=pc_settings) + emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=None) for i in range(3): print(i) ft = 18 From a985f6b809cf24b5253c55357b1f4bc8074955d1 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Wed, 1 Oct 2025 21:39:41 -0400 Subject: [PATCH 067/103] Update puq-ci.yml --- .github/workflows/puq-ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/puq-ci.yml b/.github/workflows/puq-ci.yml index 2bf8145..9b2111d 100644 --- a/.github/workflows/puq-ci.yml +++ b/.github/workflows/puq-ci.yml @@ -5,9 +5,9 @@ name: PUQ CI on: push: - branches: ["dev/PUQ_with_hetGPy"] + branches: ["main", "develop"] pull_request: - branches: ["dev/PUQ_with_hetGPy"] + branches: ["main", "develop"] jobs: run-full-tests: From 4af60ee714b717dae62a48d786da6935109bbe52 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 21:51:23 -0400 Subject: [PATCH 068/103] black --- .../gen_funcs/acquisition_1d_deterministic.py | 44 ++-- .../gen_funcs/acquisition_1d_stochastic.py | 114 ++++---- .../gen_funcs/acquisition_md_deterministic.py | 29 +- .../gen_funcs/acquisition_md_stochastic.py | 9 +- .../gen_funcs/allocate_reps_1d.py | 136 +++++----- .../gen_funcs/allocate_reps_md.py | 247 ++++++++++-------- .../batch_acquisition_funcs_support.py | 116 ++++---- .../sequential_1d_deterministic.py | 58 ++-- PUQ/designmethods/sequential_1d_stochastic.py | 62 +++-- .../sequential_md_deterministic.py | 70 +++-- PUQ/designmethods/sequential_md_stochastic.py | 205 ++++++++------- PUQ/designmethods/support.py | 3 +- PUQ/prior.py | 59 +++-- PUQ/surrogate.py | 8 +- PUQ/surrogatemethods/hetGP.py | 144 +++++----- PUQ/surrogatemethods/homGP.py | 145 +++++----- PUQ/surrogatemethods/multihetGP.py | 158 ++++++----- PUQ/surrogatemethods/multihomGP.py | 142 +++++----- 18 files changed, 971 insertions(+), 778 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py index d8e0a93..12a8dc7 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py @@ -5,11 +5,12 @@ import emcee from copy import deepcopy + def generate_neighborhood(acq): - + rand = np.random.default_rng(acq.seed) neigh_type = acq.args.get("neighbor", None) - + if neigh_type == "LHS": N = int(acq.nL) sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) @@ -19,38 +20,38 @@ def generate_neighborhood(acq): sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) L_explore = sampling(N) - + sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) Lt = sampling(N) num_options = len(acq.x) - + # Distribute the rows as evenly as possible counts = np.full(num_options, N // num_options) # Base count for each row type counts[ rand.choice(num_options, N % num_options, replace=False) ] += 1 # Assign extra rows randomly - + # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) Lx = np.vstack( [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] ) - + # Shuffle the rows randomly rand.shuffle(Lx) - + L_exploit = np.concatenate((Lx, Lt), axis=1) - + L = np.concatenate((L_explore, L_exploit)) - + return L class acquisition_function: def __init__(self, model, cls_func, args): - self.model = model#deepcopy(model) # model.copy() + self.model = model # deepcopy(model) # model.copy() self.cls_func = cls_func - #self.persis_info = persis_info + # self.persis_info = persis_info self.args = args self.x = self.cls_func.x self.d = self.cls_func.d @@ -71,9 +72,7 @@ def __init__(self, model, cls_func, args): def acquire_new(self): - return eval("self.evaluate")( - self.args.get("new", True), return_pseudo=False - ) + return eval("self.evaluate")(self.args.get("new", True), return_pseudo=False) def gen_pred(self, model, x, t, return_id=False, return_flat=False): nm, d = t.shape[0], x.shape[0] @@ -98,7 +97,7 @@ def gen_pred(self, model, x, t, return_id=False, return_flat=False): # ntot, ntot x ntot, ntot # mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["covmat"], meshPr._info["var"] - + muT = mu.reshape(nm, d) S = Sn[id_row[:, None], id_col].reshape(nm, d, d) @@ -136,7 +135,7 @@ def __init__(self, model, cls_func, args): self.seed = args.get("seed", None) self.explore = args.get("explore", True) self.nL = args.get("nL", 100) - + if args.get("integral") == "importance": # print("Importance sampling") self.epsilon = args.get("epsilon", 10 ** (-10)) @@ -153,7 +152,7 @@ def __init__(self, model, cls_func, args): else: self.t_ref = args.get("t_grid") self.weights = 1 - + def reference_set(self): def log_probability(ctheta): if np.any((ctheta < 0) | (ctheta > 1)): @@ -206,7 +205,6 @@ def evaluate_explore(self, L): return new_input - class imse(acquisition_function): def __init__(self, model, cls_func, args): super().__init__(model, cls_func, args) @@ -305,7 +303,6 @@ def evaluate(self, new, return_pseudo): def evaluate_explore(self, L): - nm = self.t_ref.shape[0] vals = np.zeros(self.nL) @@ -313,13 +310,17 @@ def evaluate_explore(self, L): mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) V1 = S + self.Sigma3d - + # predict at candidates candPr = self.model.predict(x=L, thetaprime=z) # nL, nL, nL x ntot # candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] - candvar, candnugs, candcov = candPr._info["var"], candPr._info["nugs"], candPr._info["covmat"] + candvar, candnugs, candcov = ( + candPr._info["var"], + candPr._info["nugs"], + candPr._info["covmat"], + ) candtotvat = candvar + candnugs for k in range(0, self.nL): @@ -346,4 +347,3 @@ def evaluate_explore(self, L): new_input = L[np.argmax(vals), :].reshape(1, self.p) return new_input - diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py index c83a389..42ed540 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -7,11 +7,12 @@ from PUQ.designmethods.gen_funcs.allocate_reps_1d import allocate import copy + def generate_neighborhood(acq): - + rand = np.random.default_rng(acq.seed) neigh_type = acq.args.get("neighbor", None) - + if neigh_type == "LHS": N = int(acq.nL) sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) @@ -21,38 +22,38 @@ def generate_neighborhood(acq): sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) L_explore = sampling(N) - + sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) Lt = sampling(N) num_options = len(acq.x) - + # Distribute the rows as evenly as possible counts = np.full(num_options, N // num_options) # Base count for each row type counts[ rand.choice(num_options, N % num_options, replace=False) ] += 1 # Assign extra rows randomly - + # Create the array by stacking the chosen rows (Fixed: Explicitly convert to a list) Lx = np.vstack( [row for i in range(num_options) for row in [acq.x[i]] * counts[i]] ) - + # Shuffle the rows randomly rand.shuffle(Lx) - + L_exploit = np.concatenate((Lx, Lt), axis=1) - + L = np.concatenate((L_explore, L_exploit)) - + return L class acquisition_function: def __init__(self, model, cls_func, args): - self.model = copy.deepcopy(model)#deepcopy(model) # model.copy() + self.model = copy.deepcopy(model) # deepcopy(model) # model.copy() self.cls_func = cls_func - #self.persis_info = persis_info + # self.persis_info = persis_info self.args = args self.x = self.cls_func.x self.d = self.cls_func.d @@ -104,7 +105,7 @@ def gen_pred(self, model, x, t, return_id=False, return_flat=False): # ntot, ntot x ntot, ntot # mu, Sn, sd2 = meshPr["mean"], meshPr["cov"], meshPr["sd2"] mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["covmat"], meshPr._info["var"] - + muT = mu.reshape(nm, d) S = Sn[id_row[:, None], id_col].reshape(nm, d, d) @@ -142,7 +143,7 @@ def __init__(self, model, cls_func, args): self.seed = args.get("seed", None) self.explore = args.get("explore", True) self.nL = args.get("nL", 100) - + if args.get("integral") == "importance": # print("Importance sampling") self.epsilon = args.get("epsilon", 10 ** (-10)) @@ -159,7 +160,7 @@ def __init__(self, model, cls_func, args): else: self.t_ref = args.get("t_grid") self.weights = 1 - + def reference_set(self): def log_probability(ctheta): if np.any((ctheta < 0) | (ctheta > 1)): @@ -340,7 +341,7 @@ def evaluate(self, new, return_pseudo): L = generate_neighborhood(self) new_input = self.evaluate_explore(L) else: - L = self.model._info["X0"] # self.model["X0"] + L = self.model._info["X0"] # self.model["X0"] new_input = self.evaluate_exploit(L) self.znew, self.new = new_input, new @@ -355,7 +356,6 @@ def evaluate(self, new, return_pseudo): def evaluate_explore(self, L): - nm = self.t_ref.shape[0] vals = np.zeros(self.nL) @@ -363,16 +363,20 @@ def evaluate_explore(self, L): mu, S, z = self.gen_pred(self.model, self.x, self.t_ref) V1 = S + self.Sigma3d - + # for i in np.arange(S.shape[0]): # print(np.diag(S[i, :, :])) - + # predict at candidates candPr = self.model.predict(x=L, thetaprime=z) # nL, nL, nL x ntot # candvar, candnugs, candcov = candPr["sd2"], candPr["nugs"], candPr["cov"] - candvar, candnugs, candcov = candPr._info["var"], candPr._info["nugs"], candPr._info["covmat"] + candvar, candnugs, candcov = ( + candPr._info["var"], + candPr._info["nugs"], + candPr._info["covmat"], + ) candtotvat = candvar + candnugs for k in range(0, self.nL): @@ -417,10 +421,16 @@ def evaluate_exploit(self, L): # Ki, mult = self.model.Ki, self.model.mult Ki, mult = self.model._info["Ki"], self.model._info["mult"] - candPr = self.model.predict(x=self.model._info["X0"]) # self.model.predict(x=self.model["X0"]) + candPr = self.model.predict( + x=self.model._info["X0"] + ) # self.model.predict(x=self.model["X0"]) # candmean, candvar, candnugs = candPr["mean"], candPr["sd2"], candPr["nugs"] - candmean, candvar, candnugs = candPr._info["mean"], candPr._info["var"], candPr._info["nugs"] - smean = self.model._info["Z0"] # self.model["Z0"] + candmean, candvar, candnugs = ( + candPr._info["mean"], + candPr._info["var"], + candPr._info["nugs"], + ) + smean = self.model._info["Z0"] # self.model["Z0"] # compute B # if self.model.get("Lambda") is None: @@ -437,7 +447,10 @@ def evaluate_exploit(self, L): # ntot x ntr kx = cov_gen( - X1=z, X2=self.model._info["X0"], theta=self.model._info["theta"], type=self.model._info["covtype"] + X1=z, + X2=self.model._info["X0"], + theta=self.model._info["theta"], + type=self.model._info["covtype"], ) # coefficients to be used @@ -480,7 +493,7 @@ def evaluate_exploit(self, L): part2 = c2 * (1 / np.sqrt(dets1)) * part2 vals[k] = np.sum(self.weights * (part1 - part2)) - + # print(np.argmin(vals)) new_input = L[np.argmin(vals), :].reshape(1, self.p) @@ -499,14 +512,15 @@ def __init__(self, model, cls_func, args): self.method = args.get("method", "ivar") self.nL = args.get("nL", 100) self.t_grid = args.get("t_grid", None) - def determine_h(self): horizon = self.args.get("horizon", None) # print(horizon) if horizon.get("method") == "target": if horizon.get("previous_ratio") is None: - horizon["previous_ratio"] = len(self.model._info["Z0"]) / len(self.model._info["Z"]) + horizon["previous_ratio"] = len(self.model._info["Z0"]) / len( + self.model._info["Z"] + ) self.h = horizon.get("h0") else: target = horizon.get("target_ratio") @@ -525,7 +539,9 @@ def determine_h(self): else: self.h = current_horizon - horizon["previous_ratio"] = len(self.model._info["Z0"]) / len(self.model._info["Z"]) + horizon["previous_ratio"] = len(self.model._info["Z0"]) / len( + self.model._info["Z"] + ) horizon["h0"] = self.h elif horizon.get("method") == "adaptive": @@ -534,21 +550,21 @@ def determine_h(self): if self.method == "imse": mult_star = allocate_mult(model=self.model, N=budget).astype(int) - - else: - alloc_obj = allocate(budget, - self.model, - self.cls_func, - {"theta":self.t_ref, "weight":self.weights}) + alloc_obj = allocate( + budget, + self.model, + self.cls_func, + {"theta": self.t_ref, "weight": self.weights}, + ) alloc_obj.allocatereps() mult_star = alloc_obj.reps - + # tab_input = mult_star - self.model.mult tab_input = mult_star - self.model._info["mult"] tab_input[tab_input < 0] = 0 - + # import matplotlib.pyplot as plt # for label, x_count, y_count in zip(tab_input, self.model.X0[:, 1], self.model.X0[:, 2]): # col = "blue" @@ -561,7 +577,7 @@ def determine_h(self): # color=col # ) # plt.show() - + rand = np.random.default_rng(self.seed) u, counts = np.unique(tab_input, return_counts=True) self.h = rand.choice(u, p=counts / counts.sum()) @@ -596,7 +612,7 @@ def evaluate_vl(self): self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( acq_obj, "weights", None ) - + # define horizon self.determine_h() @@ -679,14 +695,14 @@ def evaluate_vq(self): cls_func=self.cls_func, args=self.args, ) - + self.t_ref, self.weights = getattr(acq_obj, "t_ref", None), getattr( acq_obj, "weights", None ) - + # define horizon self.determine_h() - + acq_obj.evaluate(new=True, return_pseudo=True) z_A, f_A = acq_obj.znew, acq_obj.fnew path_A = [{"par": z_A, "new": True}] @@ -697,12 +713,12 @@ def evaluate_vq(self): else: # acq_obj.model.update(Xnew=z_A, Znew=f_A, maxit=0) acq_obj.model.update(x=z_A, Y=f_A, maxit=0) - model_A = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() + model_A = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() IVAR_A1 = self.crit_eval(model_A) if self.h > 0: - acq_obj.model = copy.deepcopy(model_A) # model_A.copy() + acq_obj.model = copy.deepcopy(model_A) # model_A.copy() for i in range(0, self.h): acq_obj.evaluate(new=False, return_pseudo=True) path_A.append({"par": acq_obj.znew, "new": False}) @@ -713,11 +729,11 @@ def evaluate_vq(self): designs.append({"input": z_A, "path": path_A, "value": IVAR_A}) if self.h == 0: - acq_obj.model = copy.deepcopy(self.model) # self.model.copy() + acq_obj.model = copy.deepcopy(self.model) # self.model.copy() acq_obj.evaluate(new=False, return_pseudo=True) z_B = acq_obj.znew acq_obj.model.update(x=z_B, Y=acq_obj.fnew, maxit=0) - model_B = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() + model_B = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() IVAR_B1 = self.crit_eval(model_B) if IVAR_A1 < IVAR_B1: @@ -728,10 +744,10 @@ def evaluate_vq(self): return else: - newmodelB = copy.deepcopy(self.model) # self.model.copy() + newmodelB = copy.deepcopy(self.model) # self.model.copy() for i in range(self.h): # Choose a new replicate - acq_obj.model = copy.deepcopy(newmodelB) # newmodelB.copy() + acq_obj.model = copy.deepcopy(newmodelB) # newmodelB.copy() acq_obj.evaluate(new=False, return_pseudo=True) acq_obj.model.update(x=acq_obj.znew, Y=acq_obj.fnew, maxit=0) @@ -740,7 +756,7 @@ def evaluate_vq(self): path_B = [] path_B.append({"par": acq_obj.znew, "new": False}) - newmodelB = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() + newmodelB = copy.deepcopy(acq_obj.model) # acq_obj.model.copy() # Choose a new design acq_obj.evaluate(new=True, return_pseudo=True) @@ -750,9 +766,7 @@ def evaluate_vq(self): for j in range(i, self.h - 1): # Remaining replicates acq_obj.evaluate(new=False, return_pseudo=True) - acq_obj.model.update( - x=acq_obj.znew, Y=acq_obj.fnew, maxit=0 - ) + acq_obj.model.update(x=acq_obj.znew, Y=acq_obj.fnew, maxit=0) path_C.append({"par": acq_obj.znew, "new": False}) IVAR_C = self.crit_eval(acq_obj.model) diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py index c02ec3d..769f4b3 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py @@ -4,12 +4,14 @@ from hetgpy.IMSE import crit_IMSPE import emcee + def generate_neighborhood(acq): N = int(acq.nL) sampling = LHS(xlimits=acq.tlim, random_state=acq.seed) L = sampling(N) return L + def get_pred(cL, emu, x, ttest, reps): cP = emu.predict(x=cL) @@ -24,9 +26,9 @@ def get_pred(cL, emu, x, ttest, reps): class acquisition_function: def __init__(self, model, cls_func, args): - self.model = model #deepcopy(model) + self.model = model # deepcopy(model) self.cls_func = cls_func - #self.persis_info = persis_info + # self.persis_info = persis_info self.args = args self.x = self.cls_func.x self.d = self.cls_func.d @@ -45,15 +47,13 @@ def __init__(self, model, cls_func, args): ) def acquire_new(self): - return eval("self.evaluate")( - self.args.get("new", True), return_pseudo=False - ) + return eval("self.evaluate")(self.args.get("new", True), return_pseudo=False) def gen_pred(self, model, x, t, return_id=False, return_flat=False): # predict at mesh meshPr = model.predict(x=t, thetaprime=t) - + # ntot, ntot x ntot, ntot mu, Sn, sd2 = meshPr._info["mean"], meshPr._info["S"], meshPr._info["var"] muT = mu.T @@ -71,7 +71,7 @@ def crit_pvar(self, model, x, t): f = multiple_pdfs(self.y, mu, M) g = multiple_pdfs(self.y, mu, N) - + vals = (1 / self.twopiddet) * f - g**2 return vals @@ -91,8 +91,7 @@ def __init__(self, model, cls_func, args): def evaluate(self, new, return_pseudo): - new_input = self.args["prior"].rnd(1, - self.args["rand_stream"]) + new_input = self.args["prior"].rnd(1, self.args["rand_stream"]) self.znew, self.new = new_input, new @@ -102,7 +101,7 @@ def evaluate(self, new, return_pseudo): self.fnew = fnew return self - + class var(acquisition_function): def __init__(self, model, cls_func, args): @@ -129,6 +128,7 @@ def evaluate_explore(self, L): new_input = L[np.argmax(vals), :].reshape(1, self.p) return new_input + class imse(acquisition_function): # ASK THIS FUNCTION TO DAVID def __init__(self, model, cls_func, args): @@ -158,6 +158,7 @@ def evaluate_explore(self, L): new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) return new_input + class exp(acquisition_function): # ASK THIS FUNCTION TO DAVID def __init__(self, model, cls_func, args): @@ -186,7 +187,7 @@ def evaluate_explore(self, L): IMSPE_grid = np.array([crit_IMSPE(x, model=self.model) for x in L]) new_input = L[np.argmin(IMSPE_grid), :].reshape(1, self.cls_func.p) return new_input - + class ivar(acquisition_function): def __init__(self, model, cls_func, args): @@ -264,10 +265,10 @@ def evaluate_explore(self, L): mu, S, cov, cvar = get_pred( cL=L, emu=self.model, x=self.x, ttest=self.t_ref, reps=1 ) - + V1 = S + self.Sigma3d - q, d = self.model._info['numGPs'], mu.shape[1] + q, d = self.model._info["numGPs"], mu.shape[1] phi = np.zeros((nm, d, d, nL)) @@ -289,8 +290,6 @@ def evaluate_explore(self, L): vals[k] = np.sum(self.weights * part2) - new_input = L[np.argmax(vals), :].reshape(1, self.p) return new_input - diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py index 96af85b..56f47b6 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py @@ -9,6 +9,7 @@ multiple_determinants, multiple_pdfs, ) + # from PUQ.surrogatemethods.pcHetGP import update @@ -23,7 +24,7 @@ def get_pred(cL, emu, x, ttest, reps): testP = emu.predict(x=ttest, thetaprime=cL) # mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["cov_o"] mu, S, cov = testP._info["mean"], testP._info["S"], testP._info["covmat"] - + mut = mu.T St = np.transpose(S, (2, 0, 1)) @@ -146,8 +147,8 @@ def seivar(self): liar = np.mean(fE, axis=1) tnew = [] - - q, d = self.emu._info['numGPs'], mu.shape[1] + + q, d = self.emu._info["numGPs"], mu.shape[1] for i in range(self.bnew): # G = emu._info["G"] # B = emu._info["B"] @@ -164,7 +165,7 @@ def seivar(self): for j in range(0, q): phi[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] - + # for j in range(0, q): # tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] diff --git a/PUQ/designmethods/gen_funcs/allocate_reps_1d.py b/PUQ/designmethods/gen_funcs/allocate_reps_1d.py index ee272cc..16ad40f 100644 --- a/PUQ/designmethods/gen_funcs/allocate_reps_1d.py +++ b/PUQ/designmethods/gen_funcs/allocate_reps_1d.py @@ -4,16 +4,19 @@ from hetgpy.covariance_functions import cov_gen from PUQ.designmethods.support import multiple_pdfs + class allocate: - def __init__(self, - budget, - model, - func_cls, - mesh, - trace=1, - alloc_settings={}, - rand_stream=None): - + def __init__( + self, + budget, + model, + func_cls, + mesh, + trace=1, + alloc_settings={}, + rand_stream=None, + ): + self.budget = budget self.model = model self.x = func_cls.x @@ -21,61 +24,58 @@ def __init__(self, self.w_mesh = mesh["weight"] self.obs = func_cls.real_data self.obsvar = func_cls.obsvar - self.use_Ki = alloc_settings.get('use_Ki', True) + self.use_Ki = alloc_settings.get("use_Ki", True) self.eps = np.sqrt(np.finfo(float).eps) self.trace = trace self.func_cls = func_cls self.rand_stream = rand_stream - self.z = self.model._info['X0'] # self.model['X0'] - self.a0 = self.model._info['mult'] # self.model['mult'] - - + self.z = self.model._info["X0"] # self.model['X0'] + self.a0 = self.model._info["mult"] # self.model['mult'] def allocatereps(self): - + Cinfo = self.compute_ivar_weights() - C = Cinfo.flatten() + C = Cinfo.flatten() idneg = C < 0 weight = np.sqrt(np.abs(C[idneg])) - a_frac = np.zeros(len(C)) + a_frac = np.zeros(len(C)) a_frac_ub = np.zeros(len(C)) - total_a = np.sum(self.a0) + self.budget - + total_a = np.sum(self.a0) + self.budget + # Find an upper bound - a_frac_ub[idneg] = total_a*weight/sum(weight) + a_frac_ub[idneg] = total_a * weight / sum(weight) a_ub = self.make_int(total_a, a_frac_ub) a_ub = np.maximum(0, a_ub - self.a0) - a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) + a_frac[idneg] = self.budget * a_ub[idneg] / sum(a_ub) a_final = self.make_int(self.budget, a_frac) a_final = np.array([int(a) for a in a_final]) if np.sum(a_final) != self.budget: raise ValueError("Budget constraint is not satisfied.") self.reps = a_final - return + return - def compute_ivar_weights(self): - + d = len(self.x) n_integ = self.theta_mesh.shape[0] n = self.z.shape[0] - #pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) - #pred_mesh = self.model.predict(x=self.theta_mesh) - - # obsvar3d = self.obsvar.reshape(1, d, d) + # pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) + # pred_mesh = self.model.predict(x=self.theta_mesh) + + # obsvar3d = self.obsvar.reshape(1, d, d) # # ntest x d - # mu = pred_mesh._info['mean'].T - # S = pred_mesh._info['S'] + # mu = pred_mesh._info['mean'].T + # S = pred_mesh._info['S'] # St = np.transpose(S, (2, 0, 1)) - + obsvar3d = self.obsvar.reshape(1, d, d) - + x = self.x - ntot = n_integ*d + ntot = n_integ * d t = self.theta_mesh x_tiled = np.tile(x, (t.shape[0], 1)) t_repeated = np.repeat(t, x.shape[0], axis=0) @@ -86,27 +86,27 @@ def compute_ivar_weights(self): id_col = np.arange(0, ntot).reshape(n_integ, d) id_col = np.repeat(id_col, repeats=d, axis=0) - # predict at mesh + # predict at mesh # meshPr = self.model.predict(x=z, xprime=z) meshPr = self.model.predict(x=z, thetaprime=z) - + # ntot, ntot x ntot, ntot # mu, Sn = meshPr["mean"], meshPr["cov"] mu, Sn = meshPr._info["mean"], meshPr._info["covmat"] muT = mu.reshape(n_integ, d) S = Sn[id_row[:, None], id_col].reshape(n_integ, d, d) - + # ntest x d x d - Nb = S + 0.5*obsvar3d + Nb = S + 0.5 * obsvar3d N = S + obsvar3d f = multiple_pdfs(self.obs, muT, Nb) g = multiple_pdfs(self.obs, muT, N) - + # ntest x 1 x d h = (self.obs - muT).reshape(n_integ, 1, d) - + # ntest x d x d Nbinv = inv(Nb) @@ -117,45 +117,63 @@ def compute_ivar_weights(self): hNb = np.matmul(h, Nbinv) hN = np.matmul(h, Ninv) - coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) - + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det(self.obsvar))) + Mb = np.zeros((n, n_integ, d, d), dtype=np.float64) - pred_nugs = self.model.predict(x=self.z, args=dict(nugs_only=True)) #nugs_only=True) + pred_nugs = self.model.predict( + x=self.z, args=dict(nugs_only=True) + ) # nugs_only=True) # n x n if self.use_Ki: # Ki = self.model['Ki'] - Ki = self.model._info['Ki'] + Ki = self.model._info["Ki"] else: # K = cov_gen(X1=self.z, theta=self.model['theta'], type = self.model['covtype']) - K = cov_gen(X1=self.z, theta=self.model._info['theta'], type = self.model._info['covtype']) + K = cov_gen( + X1=self.z, + theta=self.model._info["theta"], + type=self.model._info["covtype"], + ) Ki = scipy.linalg.pinv(K, rcond=self.eps) - + ids = np.arange(0, len(z), d) for i in range(0, n): J = np.zeros((n, n)) J[i, i] = 1 # n x n - A = Ki@J@Ki + A = Ki @ J @ Ki for j in range(0, d): # n_integ x 1 zo = z[ids + j] - + # n x n_integ # kv = cov_gen(X1=self.z, X2=zo, theta=self.model['theta'], type = self.model['covtype']) - kv = cov_gen(X1=self.z, X2=zo, theta=self.model._info['theta'], type = self.model._info['covtype']) + kv = cov_gen( + X1=self.z, + X2=zo, + theta=self.model._info["theta"], + type=self.model._info["covtype"], + ) for jp in range(0, d): # n_integ x 1 zop = z[ids + jp] - + # n x n_integ # kvp = cov_gen(X1=self.z, X2=zop, theta=self.model['theta'], type = self.model['covtype']) - kvp = cov_gen(X1=self.z, X2=zop, theta=self.model._info['theta'], type = self.model._info['covtype']) - # Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) - Mb[i, :, j, jp] = -pred_nugs._info['nugs'][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + kvp = cov_gen( + X1=self.z, + X2=zop, + theta=self.model._info["theta"], + type=self.model._info["covtype"], + ) + # Mb[i, :, j, jp] = -pred_nugs["nugs"][i]*np.einsum('ji,jk,ki->i', kv, A, kvp) + Mb[i, :, j, jp] = -pred_nugs._info["nugs"][i] * np.einsum( + "ji,jk,ki->i", kv, A, kvp + ) dlogfdai = np.zeros((n, n_integ)) dloggdai = np.zeros((n, n_integ)) @@ -163,21 +181,20 @@ def compute_ivar_weights(self): for i in range(0, n): M = Mb[i, :, :, :] - + part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) - + dlogfdai = part1f + part2f.flatten() dloggdai = part1g + part2g.flatten() - subC = self.w_mesh*(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) - + subC = self.w_mesh * (coef * (f * dlogfdai) - 2 * (g**2) * dloggdai) + C[i] = np.sum(subC) return C - def make_int(self, bdg, n_frac): # Make integer n_floor = np.floor(n_frac) @@ -187,8 +204,5 @@ def make_int(self, bdg, n_frac): if tot_remain > 0: idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] n_floor[idx] = n_floor[idx] + 1 - - return n_floor - - \ No newline at end of file + return n_floor diff --git a/PUQ/designmethods/gen_funcs/allocate_reps_md.py b/PUQ/designmethods/gen_funcs/allocate_reps_md.py index 1e60c51..44fd145 100644 --- a/PUQ/designmethods/gen_funcs/allocate_reps_md.py +++ b/PUQ/designmethods/gen_funcs/allocate_reps_md.py @@ -6,21 +6,24 @@ multiple_pdfs, build_emulator, impute, - compute_ivar + compute_ivar, ) + class allocate: - def __init__(self, - budget, - emu_info, - func_cls, - theta_mesh, - prior, - method='imse', - trace=1, - alloc_settings={}, - rand_stream=None): - + def __init__( + self, + budget, + emu_info, + func_cls, + theta_mesh, + prior, + method="imse", + trace=1, + alloc_settings={}, + rand_stream=None, + ): + self.budget = budget self.emu = emu_info self.x = func_cls.x @@ -28,143 +31,150 @@ def __init__(self, self.obs = func_cls.real_data self.obsvar = func_cls.obsvar self.method = method - self.use_Ki = alloc_settings.get('use_Ki') + self.use_Ki = alloc_settings.get("use_Ki") self.eps = np.sqrt(np.finfo(float).eps) self.trace = trace self.func_cls = func_cls self.rand_stream = rand_stream - - if alloc_settings.get('theta') is None: - if alloc_settings.get('gen') is False: + + if alloc_settings.get("theta") is None: + if alloc_settings.get("gen") is False: # self.theta = self.emu._info['emulist'][0]['X0'] - self.theta = self.emu._info['emulist'][0]._info["X0"] + self.theta = self.emu._info["emulist"][0]._info["X0"] else: self.theta = prior.rnd(100, None) - self.theta = np.concatenate((self.emu._info['emulist'][0]['X0'], self.theta), axis=0) + self.theta = np.concatenate( + (self.emu._info["emulist"][0]["X0"], self.theta), axis=0 + ) else: - self.theta = alloc_settings['theta'] + self.theta = alloc_settings["theta"] - if alloc_settings.get('a0') is None: - if alloc_settings.get('gen') is False: + if alloc_settings.get("a0") is None: + if alloc_settings.get("gen") is False: # self.a0 = self.emu._info['emulist'][0]['mult'] - self.a0 = self.emu._info['emulist'][0]._info['mult'] + self.a0 = self.emu._info["emulist"][0]._info["mult"] else: - self.a0 = np.concatenate((self.emu['emulist']._info[0]['mult'], np.repeat(0, 100))) + self.a0 = np.concatenate( + (self.emu["emulist"]._info[0]["mult"], np.repeat(0, 100)) + ) else: - self.a0 = alloc_settings['a0'] - + self.a0 = alloc_settings["a0"] def allocatereps(self): - + if self.trace == 1: - print("Allocation rule: ", self.method, " with ", self.emu._info['method']) - - if self.method == 'imse': + print("Allocation rule: ", self.method, " with ", self.emu._info["method"]) + + if self.method == "imse": Cinfo = self.compute_imse_weights() C = Cinfo.flatten() - elif self.method == 'ivar': + elif self.method == "ivar": Cinfo = self.compute_ivar_weights() - C = Cinfo.flatten() - elif self.method == 'var': + C = Cinfo.flatten() + elif self.method == "var": Cinfo = self.compute_var_weights() - C = Cinfo.flatten() + C = Cinfo.flatten() idneg = C < 0 weight = np.sqrt(np.abs(C[idneg])) - a_frac = np.zeros(len(C)) + a_frac = np.zeros(len(C)) a_frac_ub = np.zeros(len(C)) - total_a = np.sum(self.a0) + self.budget - + total_a = np.sum(self.a0) + self.budget + # Find an upper bound - a_frac_ub[idneg] = total_a*weight/sum(weight) + a_frac_ub[idneg] = total_a * weight / sum(weight) a_ub = self.make_int(total_a, a_frac_ub) a_ub = np.maximum(0, a_ub - self.a0) - a_frac[idneg] = self.budget*a_ub[idneg]/sum(a_ub) + a_frac[idneg] = self.budget * a_ub[idneg] / sum(a_ub) a_final = self.make_int(self.budget, a_frac) a_final = np.array([int(a) for a in a_final]) if np.sum(a_final) != self.budget: raise ValueError("Budget constraint is not satisfied.") self.reps = a_final - return + return def compute_imse_weights(self): - + d = len(self.x) n_integ = self.theta_mesh.shape[0] n = self.theta.shape[0] - + pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) - + # d x d - G = self.emu._info['G'] + G = self.emu._info["G"] # d x q - B = self.emu._info['B'] + B = self.emu._info["B"] # d x q GB = G @ B BTG = B.T @ G - - q = len(self.emu._info['emulist']) + + q = len(self.emu._info["emulist"]) Mb = np.zeros((n, n_integ, q, q)) for i in range(0, n): J = np.zeros((n, n)) J[i, i] = 1 for j in range(0, q): - emuinfo = self.emu._info['emulist'][j] - K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta']) - + emuinfo = self.emu._info["emulist"][j] + K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo["theta"]) + if self.use_Ki: - Ki = emuinfo['Ki'] + Ki = emuinfo["Ki"] else: - K = cov_gen(X1=self.theta, theta=emuinfo['theta']) + K = cov_gen(X1=self.theta, theta=emuinfo["theta"]) Ki = scipy.linalg.pinv(K, rcond=self.eps) # Ki = scipy.linalg.pinv2(K, rcond=self.eps) - - A = Ki@J@Ki - if emuinfo['is_homGP']: - Mb[i, :, j, j] = -emuinfo['nu_hat']*emuinfo['g']*np.einsum('ji,jk,ki->i', K_s, A, K_s) + + A = Ki @ J @ Ki + if emuinfo["is_homGP"]: + Mb[i, :, j, j] = ( + -emuinfo["nu_hat"] + * emuinfo["g"] + * np.einsum("ji,jk,ki->i", K_s, A, K_s) + ) else: - Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) - + Mb[i, :, j, j] = -(pred_nugs._info["nugs_o"][j, i]) * np.einsum( + "ji,jk,ki->i", K_s, A, K_s + ) + C = np.zeros((n, 1)) for i in range(0, n): for l in range(0, n_integ): par = GB @ Mb[i, l, :, :] @ BTG - C[i] += np.sum(np.diag(par)) + C[i] += np.sum(np.diag(par)) return C - - def compute_ivar_weights(self): - + d = len(self.x) n_integ = self.theta_mesh.shape[0] n = self.theta.shape[0] # pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) # pred_mesh = self.emu.predict(x=self.x, theta=self.theta_mesh, thetaprime=None) - + pred_nugs = self.emu.predict(x=self.theta, thetaprime=None) pred_mesh = self.emu.predict(x=self.theta_mesh, thetaprime=None) - - obsvar3d = self.obsvar.reshape(1, d, d) + + obsvar3d = self.obsvar.reshape(1, d, d) # ntest x d - mu = pred_mesh._info['mean'].T - S = pred_mesh._info['S'] + mu = pred_mesh._info["mean"].T + S = pred_mesh._info["S"] St = np.transpose(S, (2, 0, 1)) - + # ntest x d x d - Nb = St + 0.5*obsvar3d + Nb = St + 0.5 * obsvar3d N = St + obsvar3d f = multiple_pdfs(self.obs, mu, Nb) g = multiple_pdfs(self.obs, mu, N) - + # ntest x 1 x d h = (self.obs - mu).reshape(n_integ, 1, d) - + # ntest x d x d Nbinv = inv(Nb) @@ -183,10 +193,10 @@ def compute_ivar_weights(self): # GB = G @ B # BTG = B.T @ G - coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) - - q = len(self.emu._info['emulist']) - COVTYPE = self.emu._info['emulist'][0]._info["covtype"] + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det(self.obsvar))) + + q = len(self.emu._info["emulist"]) + COVTYPE = self.emu._info["emulist"][0]._info["covtype"] Mb = np.zeros((n, n_integ, q, q)) for i in range(0, n): @@ -194,66 +204,75 @@ def compute_ivar_weights(self): J[i, i] = 1 for j in range(0, q): # emuinfo = self.emu._info['emulist'][j] - emuinfo = self.emu._info['emulist'][j]._info - K_s = cov_gen(X1=self.theta, X2=self.theta_mesh, theta=emuinfo['theta'], type=COVTYPE) - + emuinfo = self.emu._info["emulist"][j]._info + K_s = cov_gen( + X1=self.theta, + X2=self.theta_mesh, + theta=emuinfo["theta"], + type=COVTYPE, + ) + if self.use_Ki: - Ki = emuinfo['Ki'] + Ki = emuinfo["Ki"] else: - K = cov_gen(X1=self.theta, theta=emuinfo['theta'], type=COVTYPE) + K = cov_gen(X1=self.theta, theta=emuinfo["theta"], type=COVTYPE) Ki = scipy.linalg.pinv(K, rcond=self.eps) - - A = Ki@J@Ki - if emuinfo['is_homGP']: - Mb[i, :, j, j] = -(emuinfo['g']*emuinfo['nu_hat'])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + + A = Ki @ J @ Ki + if emuinfo["is_homGP"]: + Mb[i, :, j, j] = -(emuinfo["g"] * emuinfo["nu_hat"]) * np.einsum( + "ji,jk,ki->i", K_s, A, K_s + ) else: - # Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) - Mb[i, :, j, j] = -(pred_nugs._info['nugs'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + # Mb[i, :, j, j] = -(pred_nugs._info['nugs_o'][j, i])*np.einsum('ji,jk,ki->i', K_s, A, K_s) + Mb[i, :, j, j] = -(pred_nugs._info["nugs"][j, i]) * np.einsum( + "ji,jk,ki->i", K_s, A, K_s + ) dlogfdai = np.zeros((n, n_integ)) dloggdai = np.zeros((n, n_integ)) C = np.zeros((n, 1)) for i in range(0, n): - #M = GB @ Mb[i, :, :, :] @ BTG + # M = GB @ Mb[i, :, :, :] @ BTG M = Mb[i, :, :, :] part1f = -0.5 * np.trace(np.matmul(Nbinv, M), axis1=1, axis2=2) part2f = 0.5 * (np.matmul(np.matmul(hNb, M), np.transpose(hNb, (0, 2, 1)))) part1g = -0.5 * np.trace(np.matmul(Ninv, M), axis1=1, axis2=2) part2g = 0.5 * (np.matmul(np.matmul(hN, M), np.transpose(hN, (0, 2, 1)))) - + dlogfdai = part1f + part2f.flatten() dloggdai = part1g + part2g.flatten() - C[i] = np.sum(coef*(f*dlogfdai) - 2*(g**2)*dloggdai) + C[i] = np.sum(coef * (f * dlogfdai) - 2 * (g**2) * dloggdai) return C def compute_var_weights(self): - + d = len(self.x) pred_nugs = self.emu.predict(x=self.x, theta=self.theta, thetaprime=None) - + obsvar3d = self.obsvar.reshape(1, d, d) - + # ntest x d - mu = pred_nugs._info['mean'].T - S = pred_nugs._info['S'] + mu = pred_nugs._info["mean"].T + S = pred_nugs._info["S"] St = np.transpose(S, (2, 0, 1)) - + # ntest x d x d - Nb = St + 0.5*obsvar3d + Nb = St + 0.5 * obsvar3d N = St + obsvar3d f = multiple_pdfs(self.obs, mu, Nb) - g = multiple_pdfs(self.obs, mu, N) - - coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(self.obsvar)))) - - var = coef*f - g**2 - C = -1*var + g = multiple_pdfs(self.obs, mu, N) + + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det(self.obsvar))) + + var = coef * f - g**2 + C = -1 * var return C - + def make_int(self, bdg, n_frac): # Make integer n_floor = np.floor(n_frac) @@ -263,9 +282,9 @@ def make_int(self, bdg, n_frac): if tot_remain > 0: idx = sorted_indices[np.arange(0, tot_remain, dtype=int)] n_floor[idx] = n_floor[idx] + 1 - + return n_floor - + def ivar_exploit(self, emu, pc_settings): texploit = self.theta rep = self.reps @@ -274,15 +293,21 @@ def ivar_exploit(self, emu, pc_settings): obs = self.obs obsvar = self.obsvar - fE = emu._info['f'] - tE = emu._info['theta'] + fE = emu._info["f"] + tE = emu._info["theta"] for cid, ct in enumerate(texploit): if rep[cid] > 0: - fE, tE = impute(ct=ct[None, :], x=x, fE=fE, tE=tE, reps=rep[cid], emu=emu, rnd_str=self.rand_stream) + fE, tE = impute( + ct=ct[None, :], + x=x, + fE=fE, + tE=tE, + reps=rep[cid], + emu=emu, + rnd_str=self.rand_stream, + ) emu = build_emulator(x=x, theta=tE, f=fE, pcset=pc_settings) - ivar = compute_ivar(emu=emu, ttest=tmesh, x=x, obs=obs, obsvar=obsvar) - + ivar = compute_ivar(emu=emu, ttest=tmesh, x=x, obs=obs, obsvar=obsvar) + return ivar - - \ No newline at end of file diff --git a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py index d7b5307..ed387b9 100644 --- a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py +++ b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py @@ -4,18 +4,19 @@ from PUQ.surrogate import emulator from numpy.linalg import inv, det + def impute(ct, x, fE, tE, reps, emu, rnd_str): - + # NEW PART if fE.shape[0] > 1: # cpred = emu.predict(x=x, theta=ct) cpred = emu.predict(x=ct) cm = cpred.mean() - cS = cpred._info['S'] - cR = cpred._info['R'] - fnoise = rnd_str.multivariate_normal(mean=cm.flatten(), - cov=cS[:, :, 0] + cR[:, :, 0], - size=reps) + cS = cpred._info["S"] + cR = cpred._info["R"] + fnoise = rnd_str.multivariate_normal( + mean=cm.flatten(), cov=cS[:, :, 0] + cR[:, :, 0], size=reps + ) tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) fE = np.concatenate([fE, fnoise.T], axis=1) @@ -24,84 +25,104 @@ def impute(ct, x, fE, tE, reps, emu, rnd_str): cpred = emu.predict(x=ct) cm = cpred.mean() # cv = cpred._info['var_noisy'] - cv = cpred._info['var'] + cpred._info['nugs'] - fnoise = rnd_str.normal(loc=cm.flatten(), - scale=np.sqrt(cv.flatten()), - size=reps) + cv = cpred._info["var"] + cpred._info["nugs"] + fnoise = rnd_str.normal( + loc=cm.flatten(), scale=np.sqrt(cv.flatten()), size=reps + ) tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) fE = np.concatenate([fE, fnoise.reshape(1, reps)], axis=1) - + return fE, tE + def impute_CL(ct, x, fE, tE, reps, liar): - #print(fE) + # print(fE) d = fE.shape[0] - + tE = np.concatenate([tE, np.repeat(ct, reps, axis=0)]) fE = np.concatenate([fE, np.repeat(liar, reps).reshape(d, reps)], axis=1) - - #print(fE) - + + # print(fE) + return fE, tE + def build_emulator(x, theta, f, pcset): - + print(theta.shape) - # emu = emulator(x=x, - # theta=theta, - # f=f, + # emu = emulator(x=x, + # theta=theta, + # f=f, # method="pcHetGP", # args={'lower':None, 'upper':None, - # 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - # 'init':{}, - # 'known':{}, - # 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - # "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, + # 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, + # 'init':{}, + # 'known':{}, + # 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', + # "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, # "return.hom": False, "factr": 1e9}, # 'pc_settings':pcset}) print(f.shape) d = f.shape[0] md = np.arange(d).reshape(d, 1) - emu = emulator(x=theta, - theta=md, - f=f, - method="multihetGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}, - 'pc_settings':pcset}) - + emu = emulator( + x=theta, + theta=md, + f=f, + method="multihetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + "pc_settings": pcset, + }, + ) + return emu + def compute_ivar(emu, ttest, x, obs, obsvar): - + # testP = emu.predict(x=x, theta=ttest) testP = emu.predict(x=ttest) d = len(x) - obsvar3d = obsvar.reshape(1, d, d) - + obsvar3d = obsvar.reshape(1, d, d) + # ntest x d - mu = testP._info['mean'].T - S = testP._info['S'] + mu = testP._info["mean"].T + S = testP._info["S"] St = np.transpose(S, (2, 0, 1)) # ntest x d x d - M = St + 0.5*obsvar3d + M = St + 0.5 * obsvar3d N = St + obsvar3d f = multiple_pdfs(obs, mu, M) g = multiple_pdfs(obs, mu, N) - coef = (1/((2**d)*(np.sqrt(np.pi)**d)*np.sqrt(det(obsvar)))) - + coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d) * np.sqrt(det(obsvar))) + # ivar compute - ivar = np.sum(coef*f - g**2) + ivar = np.sum(coef * f - g**2) return ivar @@ -139,4 +160,3 @@ def multiple_determinants(covs): # dets = np.prod(vals, axis=1) logdets = np.sum(np.log(vals), axis=1) return np.exp(logdets) # dets#np.exp(logdets) - diff --git a/PUQ/designmethods/sequential_1d_deterministic.py b/PUQ/designmethods/sequential_1d_deterministic.py index 78ecafc..4abceb1 100644 --- a/PUQ/designmethods/sequential_1d_deterministic.py +++ b/PUQ/designmethods/sequential_1d_deterministic.py @@ -5,6 +5,7 @@ import time from PUQ.surrogate import emulator + class sequential_design: def __init__(self, cls_func, trace=False): self.cls_func = cls_func @@ -26,7 +27,7 @@ def get(self, key): return self.__dict__.get(key) def build_design(self, z0, f0, T, af, test=None, args={}): - + # if test is not None: # self.test_data_gen(test) @@ -37,19 +38,20 @@ def build_design(self, z0, f0, T, af, test=None, args={}): print(f"t: {t}") if self.trace else None tic = time.time() - model = emulator(x=z0, - theta=np.array([0]), - f=f0, - method='homGP', - args={"known": {"beta0":np.mean(f0)}}) + model = emulator( + x=z0, + theta=np.array([0]), + f=f0, + method="homGP", + args={"known": {"beta0": np.mean(f0)}}, + ) model.fit() - toc = time.time() timel.append(toc - tic) args["seed"] += 1 - + # if test is not None: # metric_sum = self.eval_perf(model, args.get("extra_metric", True)) @@ -83,7 +85,7 @@ def build_design(self, z0, f0, T, af, test=None, args={}): ) unique_rows, counts = np.unique(z0, axis=0, return_counts=True) - + self.xt = unique_rows self.reps = counts self.fs = f0 @@ -134,7 +136,7 @@ def eval_perf(self, model, extra_metric=False): # ntot, ntot x ntot, ntot # mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] mu, Sn, nugs = pr._info["mean"], pr._info["covmat"], pr._info["nugs"] - + muT = mu.reshape(nm, d) S = Sn[self.idr, self.idc].reshape(nm, d, d) @@ -149,35 +151,41 @@ def eval_perf(self, model, extra_metric=False): twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) - + if extra_metric: diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) - - - MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) - MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) - - MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) - MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) - + + MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) + MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) + + MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) + MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) + # Weighted absolute differences f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) - + # Unweighted absolute differences f_wh = np.mean(np.abs(diff_y), axis=0) n_wh = np.mean(np.abs(diff_n), axis=0) summary = {} for di in range(d): summary[f"f{di+1}w"] = f_w[di] - summary[f"f{di+1}"] = f_wh[di] + summary[f"f{di+1}"] = f_wh[di] summary[f"n{di+1}w"] = n_w[di] - summary[f"n{di+1}"] = n_wh[di] + summary[f"n{di+1}"] = n_wh[di] else: MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 - metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, - "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, - "summary": summary} + metric_sum = { + "MSE": MSE, + "MAD": MAD, + "VAR": VAR, + "MSEy": MSEy, + "MADy": MADy, + "MSEn": MSEn, + "MADn": MADn, + "summary": summary, + } return metric_sum diff --git a/PUQ/designmethods/sequential_1d_stochastic.py b/PUQ/designmethods/sequential_1d_stochastic.py index 438c4d9..5a31bd3 100644 --- a/PUQ/designmethods/sequential_1d_stochastic.py +++ b/PUQ/designmethods/sequential_1d_stochastic.py @@ -1,9 +1,15 @@ import numpy as np import matplotlib.pyplot as plt from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from PUQ.designmethods.gen_funcs.acquisition_1d_stochastic import var, ivar, imse, lookahead +from PUQ.designmethods.gen_funcs.acquisition_1d_stochastic import ( + var, + ivar, + imse, + lookahead, +) from PUQ.surrogate import emulator + class sequential_design: def __init__(self, cls_func, trace=True): self.cls_func = cls_func @@ -35,15 +41,17 @@ def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): for t in range(0, T): print(f"t: {t}") if self.trace else None - model = emulator(x=z0, - theta=np.array([0]), - f=f0, - method='hetGP', - args={"known": {"beta0":np.mean(f0)}}) + model = emulator( + x=z0, + theta=np.array([0]), + f=f0, + method="hetGP", + args={"known": {"beta0": np.mean(f0)}}, + ) model.fit() args["seed"] += 1 - + if test is not None: metric_sum = self.eval_perf(model, args.get("extra_metric", True)) @@ -77,7 +85,7 @@ def build_design(self, z0, f0, T, persis_info, af, test=None, args={}): ) unique_rows, counts = np.unique(z0, axis=0, return_counts=True) - + self.xt = unique_rows self.reps = counts self.fs = f0 @@ -128,7 +136,7 @@ def eval_perf(self, model, extra_metric=False): # ntot, ntot x ntot, ntot # mu, Sn, nugs = pr["mean"], pr["cov"], pr["nugs"] mu, Sn, nugs = pr._info["mean"], pr._info["covmat"], pr._info["nugs"] - + muT = mu.reshape(nm, d) S = Sn[self.idr, self.idc].reshape(nm, d, d) @@ -143,35 +151,41 @@ def eval_perf(self, model, extra_metric=False): twopiddet = (2**self.d) * (np.sqrt(np.pi) ** self.d) * np.sqrt(self.detSigma) VAR = np.mean(((1 / twopiddet) * f - g**2) * self.wtest) - + if extra_metric: diff_y = (mu.flatten() - self.ftest.flatten()).reshape(nm, d) diff_n = (nugs.flatten() - self.ntest.flatten()).reshape(nm, d) - - - MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) - MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) - - MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) - MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) - + + MSEy = np.mean(np.mean(diff_y**2, axis=1).flatten() * self.wtest) + MADy = np.mean(np.mean(np.abs(diff_y), axis=1).flatten() * self.wtest) + + MSEn = np.mean(np.mean(diff_n**2, axis=1).flatten() * self.wtest) + MADn = np.mean(np.mean(np.abs(diff_n), axis=1).flatten() * self.wtest) + # Weighted absolute differences f_w = np.mean(np.abs(diff_y) * self.wtest[:, None], axis=0) n_w = np.mean(np.abs(diff_n) * self.wtest[:, None], axis=0) - + # Unweighted absolute differences f_wh = np.mean(np.abs(diff_y), axis=0) n_wh = np.mean(np.abs(diff_n), axis=0) summary = {} for di in range(d): summary[f"f{di+1}w"] = f_w[di] - summary[f"f{di+1}"] = f_wh[di] + summary[f"f{di+1}"] = f_wh[di] summary[f"n{di+1}w"] = n_w[di] - summary[f"n{di+1}"] = n_wh[di] + summary[f"n{di+1}"] = n_wh[di] else: MSEy, MADy, MSEn, MADn, summary = 0, 0, 0, 0, 0 - metric_sum = {"MSE": MSE, "MAD": MAD, "VAR": VAR, - "MSEy": MSEy, "MADy": MADy, "MSEn": MSEn, "MADn": MADn, - "summary": summary} + metric_sum = { + "MSE": MSE, + "MAD": MAD, + "VAR": VAR, + "MSEy": MSEy, + "MADy": MADy, + "MSEn": MSEn, + "MADn": MADn, + "summary": summary, + } return metric_sum diff --git a/PUQ/designmethods/sequential_md_deterministic.py b/PUQ/designmethods/sequential_md_deterministic.py index e7022d3..c2e1a64 100644 --- a/PUQ/designmethods/sequential_md_deterministic.py +++ b/PUQ/designmethods/sequential_md_deterministic.py @@ -1,9 +1,16 @@ import numpy as np from PUQ.designmethods.support import multiple_pdfs, multiple_determinants -from PUQ.designmethods.gen_funcs.acquisition_md_deterministic import ivar, var, imse, rnd, exp +from PUQ.designmethods.gen_funcs.acquisition_md_deterministic import ( + ivar, + var, + imse, + rnd, + exp, +) import time from PUQ.surrogate import emulator + class sequential_design: def __init__(self, cls_func, trace=True): self.cls_func = cls_func @@ -31,31 +38,47 @@ def build_design(self, z0, f0, T, af, test=None, args={}): H = [] timel = [] metric_sum = {} - + md = np.arange(f0.shape[1]).reshape(f0.shape[1], 1) for t in range(0, T): # print(f"t: {t}") if self.trace else None print(t) tic = time.time() - model = emulator(x=z0, - theta=md, - f=f0, - method="multihomGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - + model = emulator( + x=z0, + theta=md, + f=f0, + method="multihomGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + }, + ) toc = time.time() timel.append(toc - tic) args["seed"] += 1 - + if test is not None: metric_sum = self.eval_perf(model) @@ -71,17 +94,10 @@ def build_design(self, z0, f0, T, af, test=None, args={}): f0 = np.concatenate((f0, fnew), axis=0) z0 = np.concatenate((z0, tnew), axis=0) - H.append( - { - "t": t, - "f": fnew, - "z": tnew, - "MAD": metric_sum.get("MAD", None) - } - ) + H.append({"t": t, "f": fnew, "z": tnew, "MAD": metric_sum.get("MAD", None)}) unique_rows, counts = np.unique(z0, axis=0, return_counts=True) - + self.t = unique_rows self.reps = counts self.fs = f0 @@ -91,24 +107,22 @@ def build_design(self, z0, f0, T, af, test=None, args={}): return self - def eval_perf(self, model): nm, d = self.test["theta"].shape[0], self.cls_func.d # predict at mesh pr = model.predict(x=self.test["theta"], thetaprime=self.test["theta"]) - + # ntot, ntot x ntot, ntot mu, Sn = pr._info["mean"], pr._info["S"] muT = mu.reshape(nm, d) - S = Sn.transpose(2, 0, 1) # Sn[self.idr, self.idc].reshape(nm, d, d) + S = Sn.transpose(2, 0, 1) # Sn[self.idr, self.idc].reshape(nm, d, d) N = S + self.Sigma3d g = multiple_pdfs(self.y, muT, N) - MSE = np.mean(((g.flatten() - self.test["p"].flatten()) ** 2)) MAD = np.mean(np.abs(g.flatten() - self.test["p"].flatten())) diff --git a/PUQ/designmethods/sequential_md_stochastic.py b/PUQ/designmethods/sequential_md_stochastic.py index e698eeb..7f0fe6c 100644 --- a/PUQ/designmethods/sequential_md_stochastic.py +++ b/PUQ/designmethods/sequential_md_stochastic.py @@ -8,6 +8,7 @@ from joblib import Parallel, delayed import copy + class sequential_design: def __init__(self, cls_func, trace=True): self.cls_func = cls_func @@ -29,8 +30,6 @@ def __setitem__(self, item, value): def get(self, key): return self.__dict__.get(key) - - def build_design(self, t0, f0, af, args={}): # data params @@ -42,53 +41,60 @@ def build_design(self, t0, f0, af, args={}): test_data = args["data_test"] prior_func = args["prior"] # explore params - rho = alloc_settings.get('rho') + rho = alloc_settings.get("rho") if rho is not None: - b_new = int(b*rho) - r_new = int(b/b_new) - + b_new = int(b * rho) + r_new = int(b / b_new) + seed = 1 persis_info = {"rand_stream": np.random.default_rng(1)} - - persis_info = {i: {} for i in range(b)} # add_unique_random_streams({}, b) + + persis_info = {i: {} for i in range(b)} # add_unique_random_streams({}, b) for perid, per in enumerate(persis_info): - persis_info[perid]['rand_stream'] = np.random.default_rng(seed*b + perid) - - rand_stream = np.random.default_rng(seed*(b+1)) #np.random.Generator(np.random.PCG64()) + persis_info[perid]["rand_stream"] = np.random.default_rng(seed * b + perid) + + rand_stream = np.random.default_rng( + seed * (b + 1) + ) # np.random.Generator(np.random.PCG64()) f_c, t_c = f0, t0 - + iter_explore, iter_exploit = 0, 0 - is_explore, is_exploit = des_settings.get('is_explore'), des_settings.get('is_exploit') - + is_explore, is_exploit = des_settings.get("is_explore"), des_settings.get( + "is_exploit" + ) + for iteration in range(max_iter): - - emu, TV = self.newiteration(t_c, f_c, self.x, pc_settings, test_data, self.Sigma, self.y) - - theta, iter_exploit, iter_explore = self.onebatch(b, - b_new, - r_new, - emu, - prior_func, - af, - self.cls_func, - test_data, - alloc_settings, - pc_settings, - des_settings, - iter_exploit, - iter_explore, - is_exploit, - is_explore, - rand_stream, - self.trace) - - + + emu, TV = self.newiteration( + t_c, f_c, self.x, pc_settings, test_data, self.Sigma, self.y + ) + + theta, iter_exploit, iter_explore = self.onebatch( + b, + b_new, + r_new, + emu, + prior_func, + af, + self.cls_func, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + self.trace, + ) + fevals = Parallel(n_jobs=b)( delayed(self.cls_func.sim_f)(theta[i], persis_info[i]) for i in range(b) ) - + fevals = np.array(fevals).T f_c = np.concatenate((f_c, fevals), axis=1) @@ -100,7 +106,6 @@ def build_design(self, t0, f0, af, args={}): self.theta0 = unique_rows self.rep0 = counts - def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): thetatest, ptest, ftest, priortest = None, None, None, None @@ -111,100 +116,104 @@ def newiteration(self, theta, fevals, x, pc_settings, test_data, obsvar, obs): test_data["f"], test_data["p_prior"], ) - - emu = build_emulator(x, theta, fevals, pc_settings) - + + emu = build_emulator(x, theta, fevals, pc_settings) + d = len(x) - obsvar3d = obsvar.reshape(1, d, d) - + obsvar3d = obsvar.reshape(1, d, d) + # ntest x d # pred = emu.predict(x=x, theta=thetatest) pred = emu.predict(x=thetatest) - mu = pred.mean().T - S = pred._info['S'] + mu = pred.mean().T + S = pred._info["S"] St = np.transpose(S, (2, 0, 1)) N = St + obsvar3d phat = multiple_pdfs(obs, mu, N) - + # Obtain the accuracy on the test set if ptest is not None: TV = np.mean(np.abs(ptest - phat)) - + return emu, TV - def onebatch(self, - b, - b_new, - r_new, - emu, - prior_func, - acqfunc, - data_cls, - test_data, - alloc_settings, - pc_settings, - des_settings, - iter_exploit, - iter_explore, - is_exploit, - is_explore, - rand_stream, - trace): - + def onebatch( + self, + b, + b_new, + r_new, + emu, + prior_func, + acqfunc, + data_cls, + test_data, + alloc_settings, + pc_settings, + des_settings, + iter_exploit, + iter_explore, + is_exploit, + is_explore, + rand_stream, + trace, + ): + ivar_exploit, ivar_explore = np.inf, np.inf - + emu_original_info = copy.deepcopy(emu._info) - + if is_exploit: # Allocate existing ones - allocate_obj = allocate(budget=b, - emu_info=emu, - prior=prior_func, - func_cls=data_cls, - theta_mesh=test_data["theta"], - method=alloc_settings.get('method'), - alloc_settings=alloc_settings, - rand_stream=rand_stream, - trace=trace) + allocate_obj = allocate( + budget=b, + emu_info=emu, + prior=prior_func, + func_cls=data_cls, + theta_mesh=test_data["theta"], + method=alloc_settings.get("method"), + alloc_settings=alloc_settings, + rand_stream=rand_stream, + trace=trace, + ) allocate_obj.allocatereps() if not is_explore: ivar_exploit = 0 else: ivar_exploit = allocate_obj.ivar_exploit(emu, pc_settings) - + r_exploit = allocate_obj.reps theta_exploit = allocate_obj.theta - - emu._info = emu_original_info - + emu._info = emu_original_info + if is_explore: # Find new ones - acquire_obj = acquire(bnew=b_new, - rep=r_new, - emu=emu, - func_cls=data_cls, - theta_mesh=test_data["theta"], - prior=prior_func, - method=acqfunc, - nL=des_settings.get('nL'), - pc_settings=pc_settings, - rand_stream=rand_stream, - impute_str=des_settings.get('impute_str'), - skip=not is_exploit) - + acquire_obj = acquire( + bnew=b_new, + rep=r_new, + emu=emu, + func_cls=data_cls, + theta_mesh=test_data["theta"], + prior=prior_func, + method=acqfunc, + nL=des_settings.get("nL"), + pc_settings=pc_settings, + rand_stream=rand_stream, + impute_str=des_settings.get("impute_str"), + skip=not is_exploit, + ) + acquire_obj.acquire_new() - + ivar_explore = acquire_obj.ivar theta_explore = acquire_obj.tnew - + if ivar_exploit <= ivar_explore: new_theta = np.repeat(theta_exploit, r_exploit, axis=0) iter_exploit += 1 else: new_theta = np.repeat(theta_explore, r_new, axis=0) iter_explore += 1 - + return new_theta, iter_exploit, iter_explore - \ No newline at end of file diff --git a/PUQ/designmethods/support.py b/PUQ/designmethods/support.py index d128472..6bd6964 100644 --- a/PUQ/designmethods/support.py +++ b/PUQ/designmethods/support.py @@ -1,6 +1,7 @@ import numpy as np from numpy.linalg import inv, det + def multiple_pdfs(x, means, covs): # Cite: http://gregorygundersen.com/blog/2020/12/12/group-multivariate-normal-pdf/ @@ -34,4 +35,4 @@ def multiple_determinants(covs): # Compute the log determinants across the second axis. # dets = np.prod(vals, axis=1) logdets = np.sum(np.log(vals), axis=1) - return np.exp(logdets) # dets#np.exp(logdets) \ No newline at end of file + return np.exp(logdets) # dets#np.exp(logdets) diff --git a/PUQ/prior.py b/PUQ/prior.py index 2bcee66..657e64e 100644 --- a/PUQ/prior.py +++ b/PUQ/prior.py @@ -16,9 +16,10 @@ def rnd(self, n, seed=None): thlist = [] for i in range(self.p): thlist.append(seed.uniform(self.a[i], self.b[i], size=n)) - #thlist.append(sps.uniform.rvs(self.a[i], self.b[i] - self.a[i], size=n)) + # thlist.append(sps.uniform.rvs(self.a[i], self.b[i] - self.a[i], size=n)) return np.array(thlist).T + # class prior_uniform: # def __init__(self, a, b): # self.a = a @@ -82,34 +83,34 @@ def rnd(self, n, seed=None): # ) # return np.array(thlist).T - # if seed == None: - # pass - # else: - # np.random.seed(seed) - # sps.truncnorm.rvs(a=-2.5, b=1.5, loc=3, scale=2, size=1000) - # 1.5=(6-3)/2, -2.5=(-2-3)/2 - # 3=(6-3)/1, -5=(-2-3)/1 - # 6=(6-3)/0.5, -10=(-2-3)/0.5 - # class prior_norm: - # def rnd(n): - # thlist = [] - # for i in range(thetalimits.shape[0]): - # thlist.append(sps.truncnorm.rvs(a=-10, b=2, loc=3, scale=1, size=n)) - # return np.array(thlist).T - - # def pdf(thetacnd): - # ncnd = thetacnd.shape[0] - # thlist = np.ones(ncnd) - # for i in range(thetalimits.shape[0]): - # thlist *= sps.truncnorm.pdf(thetacnd[:, i], a=-10, b=2, loc=3, scale=1) - # return np.array(thlist).T - - # if rnd == True: - # thetas = prior_norm.rnd(n) - # return thetas - # else: - # thetapdf = prior_norm.pdf(thetacnd) - # return thetapdf +# if seed == None: +# pass +# else: +# np.random.seed(seed) +# sps.truncnorm.rvs(a=-2.5, b=1.5, loc=3, scale=2, size=1000) +# 1.5=(6-3)/2, -2.5=(-2-3)/2 +# 3=(6-3)/1, -5=(-2-3)/1 +# 6=(6-3)/0.5, -10=(-2-3)/0.5 +# class prior_norm: +# def rnd(n): +# thlist = [] +# for i in range(thetalimits.shape[0]): +# thlist.append(sps.truncnorm.rvs(a=-10, b=2, loc=3, scale=1, size=n)) +# return np.array(thlist).T + +# def pdf(thetacnd): +# ncnd = thetacnd.shape[0] +# thlist = np.ones(ncnd) +# for i in range(thetalimits.shape[0]): +# thlist *= sps.truncnorm.pdf(thetacnd[:, i], a=-10, b=2, loc=3, scale=1) +# return np.array(thlist).T + +# if rnd == True: +# thetas = prior_norm.rnd(n) +# return thetas +# else: +# thetapdf = prior_norm.pdf(thetacnd) +# return thetapdf def prior_dist(dist="uniform"): diff --git a/PUQ/surrogate.py b/PUQ/surrogate.py index 50d0a59..e131010 100644 --- a/PUQ/surrogate.py +++ b/PUQ/surrogate.py @@ -1,11 +1,13 @@ """ This module contains a class that implements the main emulation method. """ + import numpy as np import importlib import warnings import copy, types + class emulator(object): def __init__( self, @@ -338,9 +340,10 @@ def predict(self, x=None, theta=None, thetaprime=None, args={}): info = {} self.method.predict(info, self._info, x, theta, thetaprime, **argstemp) return prediction(info, self) - def update(self,x=None,Y=None,**kwargs): - self.method.update(self._info,x=x,Y=Y,**kwargs) + def update(self, x=None, Y=None, **kwargs): + + self.method.update(self._info, x=x, Y=Y, **kwargs) def acquisition(self, x=None, theta1=None, theta2=None): return self.method.acquisition(self._info, x, theta1, theta2) @@ -348,7 +351,6 @@ def acquisition(self, x=None, theta1=None, theta2=None): def computeC(self, x=None, theta1=None, realdata=None, realvar=None): return self.method.computeC(self._info, x, theta1, realdata, realvar) - def __deepcopy__(self, memo): # create a blank instance without calling __init__ diff --git a/PUQ/surrogatemethods/hetGP.py b/PUQ/surrogatemethods/hetGP.py index 4f023db..ad1ef0d 100644 --- a/PUQ/surrogatemethods/hetGP.py +++ b/PUQ/surrogatemethods/hetGP.py @@ -28,19 +28,34 @@ ## ' @param hom_ll reference homoskedastic likelihood ## ' @export -def fit(fitinfo, x, theta, f, - lower=None, - upper=None, - maxit=100, - noiseControl={'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - init={}, - known={}, - eps=np.sqrt(np.finfo(float).eps), - settings={"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}, - covtype = 'Gaussian', **kwargs): - r''' + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + maxit=100, + noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, + init={}, + known={}, + eps=np.sqrt(np.finfo(float).eps), + settings={ + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + covtype="Gaussian", + **kwargs, +): + r""" Wrapper function for hetgpy.hetGP.mleHetGP Arguments @@ -54,18 +69,18 @@ def fit(fitinfo, x, theta, f, theta: not used f : ndarray_like Z vector of all observations. If using a list with ``X``, ``Z`` has to be ordered with respect to ``X0``, and of length ``sum(mult)`` - lower,upper : ndarray_like + lower,upper : ndarray_like optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) noiseControl : dict dict with elements related to optimization of the noise process parameters: - ``g_min``, ``g_max`` minimal and maximal noise to signal ratio (of the mean process) - - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) + - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) - ``lowerpX``, ``upperpX`` optional vectors of bounds of the input domain if `pX` is used. - ``lowerTheta_g``, ``upperTheta_g`` optional vectors of bounds for the lengthscales of the noise process if ``linkThetas == 'none'``. Same as for ``theta`` if not provided. - ``k_theta_g_bounds`` if ``linkThetas == 'joint'``, vector with minimal and maximal values for ``k_theta_g`` (default to ``(1, 100)``). See Notes. - ``g_bounds`` vector for minimal and maximal noise to signal ratios for the noise of the noise process, i.e., the smoothing parameter for the noise process. (default to ``(1e-6, 1)``). - settings : dict + settings : dict dict for options about the general modeling procedure, with elements: - ``linkThetas`` defines the relation between lengthscales of the mean and noise processes. Either ``'none'``, ``'joint'``(default) or ``'constr'``, see Notes. - ``logN``, when ``True`` (default), the log-noise process is modeled. @@ -76,12 +91,12 @@ def fit(fitinfo, x, theta, f, - ``trace`` optional scalar (default to ``0``). If negative, fit silently. If ``0``, only high level information is given. If ``1``, information is given about the result of the heterogeneous model optimization. Level ``2`` gives more details. Level ``3`` additionaly displays all details about initialization of hyperparameters. - ``return_matrices`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). - ``return_hom`` boolean to include homoskedastic GP models used for initialization (i.e., ``modHom`` and ``modNugs``). - - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. eps : float jitter used in the inversion of the covariance matrix for numerical stability init,known : dict optional lists of starting values for mle optimization or that should not be optimized over, respectively. - Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. + Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. One can set one or several of the following: - ``theta`` lengthscale parameter(s) for the mean process either one value (isotropic) or a vector (anistropic) - ``Delta`` vector of nuggets corresponding to each design in ``X0``, that are smoothed to give ``Lambda`` (as the global covariance matrix depends on ``Delta`` and ``nu_hat``, it is recommended to also pass values for ``theta``) @@ -91,84 +106,93 @@ def fit(fitinfo, x, theta, f, - ``g`` scalar nugget of the noise process - ``g_H`` scalar homoskedastic nugget for the initialisation with a :func: homGP.mleHomGP. See Notes. - ``pX`` matrix of fixed pseudo inputs locations of the noise process corresponding to Delta - covtype : str + covtype : str covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` maxit : int maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization - ''' + """ f = f.flatten() model = hetGP() - model.mle(X = x, - Z = f, - known=known, - noiseControl=noiseControl, - lower=lower, - upper=upper, - maxit=maxit, - settings=settings, - init=init, - eps=eps, - covtype=covtype + model.mle( + X=x, + Z=f, + known=known, + noiseControl=noiseControl, + lower=lower, + upper=upper, + maxit=maxit, + settings=settings, + init=init, + eps=eps, + covtype=covtype, ) for key in model.__dict__.keys(): - fitinfo[key] = model.get(key) - fitinfo['is_homGP'] = isinstance(model,homGP) + fitinfo[key] = model.get(key) + fitinfo["is_homGP"] = isinstance(model, homGP) return - - -def predict(predinfo, fitinfo, x, theta, thetaprime=None,rep_no=None, **kwargs): - r''' + +def predict(predinfo, fitinfo, x, theta, thetaprime=None, rep_no=None, **kwargs): + r""" Wrapper method for hetgpy.hetGP.predict - ''' - GP = fitinfo.get('model') + """ + GP = fitinfo.get("model") if GP is None: # use wrapper class to instantiate trained GP GP = GPWrapper(fitinfo=fitinfo) - + # handle kws kws = {} - eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] + eligible_keys = ["nugs_only", "interval", "interval_lower", "interval_upper"] for key in eligible_keys: if key in kwargs.keys(): kws[key] = kwargs.get(key) - - preds = GP.predict(x=x,xprime=thetaprime,**kws) + preds = GP.predict(x=x, xprime=thetaprime, **kws) # ensure naming consistency - predinfo['mean'] = preds.get('mean') - predinfo['var'] = preds.get('sd2') - predinfo['nugs'] = preds.get('nugs') - predinfo['covmat'] = preds.get('cov') + predinfo["mean"] = preds.get("mean") + predinfo["var"] = preds.get("sd2") + predinfo["nugs"] = preds.get("nugs") + predinfo["covmat"] = preds.get("cov") return -def update(fitinfo, x,Y = None,**kwargs): - r''' + + +def update(fitinfo, x, Y=None, **kwargs): + r""" Update function for hetGP Parameters ---------- fitinfo: dictionary that contains the fit information for a hetgpy.hetGP object x: array of new design locations - Y: new response. If None, then - kwargs: key-value pairs that get passed to hetgpy.hetGP.update. + Y: new response. If None, then + kwargs: key-value pairs that get passed to hetgpy.hetGP.update. Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit, method - ''' + """ # validate kwargs - valid_kws = ('ginit','lower','upper', - 'noiseControl','settings','known','maxit','method') + valid_kws = ( + "ginit", + "lower", + "upper", + "noiseControl", + "settings", + "known", + "maxit", + "method", + ) for kw in kwargs.keys(): if kw not in valid_kws: raise ValueError(f"{kw} not found, must be one of {valid_kws}") GP = GPWrapper(fitinfo) if Y is None: - maxit = 0 # impute mean response and do not update hyperparams - Y = GP.predict(x)['mean'] + maxit = 0 # impute mean response and do not update hyperparams + Y = GP.predict(x)["mean"] else: - maxit = kwargs.get('maxit',100) - kwargs['maxit'] = maxit - GP.update(Xnew=x,Znew=Y,**kwargs) + maxit = kwargs.get("maxit", 100) + kwargs["maxit"] = maxit + GP.update(Xnew=x, Znew=Y, **kwargs) for key in GP.__dict__.keys(): fitinfo[key] = GP.get(key) del GP - return \ No newline at end of file + return diff --git a/PUQ/surrogatemethods/homGP.py b/PUQ/surrogatemethods/homGP.py index aeeacf7..89c02b6 100644 --- a/PUQ/surrogatemethods/homGP.py +++ b/PUQ/surrogatemethods/homGP.py @@ -17,16 +17,24 @@ # g noise variance for the process # beta0 trend -def fit(fitinfo, x, theta, f, lower=None, upper=None, - known={}, - noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, - init={}, - covtype='Gaussian', - maxit=100, - eps=np.sqrt(np.finfo(float).eps), - settings={"return.Ki": True, "factr": 1e7}, - **kwargs): - r''' + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + known={}, + noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, + init={}, + covtype="Gaussian", + maxit=100, + eps=np.sqrt(np.finfo(float).eps), + settings={"return.Ki": True, "factr": 1e7}, + **kwargs, +): + r""" Wrapper function for hetgpy.homGP.mleHomGP Arguments @@ -35,16 +43,16 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, x: nxd design matrix (must have at least one column) f: output array for training theta: not used - lower,upper : ndarray_like + lower,upper : ndarray_like optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) noiseControl : dict dict with element: - ``g_bounds`` vector providing minimal and maximal noise to signal ratio (default to ``(sqrt(MACHINE_DOUBLE_EPS), 100)``). - settings : dict + settings : dict dict for options about the general modeling procedure, with elements: - ``return_Ki`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). - - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. eps : float jitter used in the inversion of the covariance matrix for numerical stability known : dict @@ -53,63 +61,67 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, optional lists of starting values for mle optimization: - ``theta_init`` initial value of the theta parameters to be optimized over (default to 10% of the range determined with ``lower`` and ``upper``) - ``g_init`` vector of nugget parameter to be optimized over - covtype : str + covtype : str covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` maxit : int maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization - + Returns ------- None, but fitinfo is updated with maximum likelihood estimates - - ''' + + """ f = f.flatten() model = homGP() - model.mleHomGP(X = x, - Z = f, - lower=lower, - upper=upper, - known=known, - noiseControl=noiseControl, - init=init, - covtype=covtype, - maxit=maxit, - eps=eps, - settings=settings) - + model.mleHomGP( + X=x, + Z=f, + lower=lower, + upper=upper, + known=known, + noiseControl=noiseControl, + init=init, + covtype=covtype, + maxit=maxit, + eps=eps, + settings=settings, + ) + for key in model.__dict__.keys(): - fitinfo[key] = model.get(key) - fitinfo['is_homGP'] = True + fitinfo[key] = model.get(key) + fitinfo["is_homGP"] = True del model return class homGPWrapper(homGP): - def __init__(self,fitinfo): + def __init__(self, fitinfo): for key in fitinfo.keys(): - setattr(self,key,fitinfo[key]) + setattr(self, key, fitinfo[key]) + + class hetGPWrapper(hetGP): - def __init__(self,fitinfo): + def __init__(self, fitinfo): for key in fitinfo.keys(): - setattr(self,key,fitinfo[key]) + setattr(self, key, fitinfo[key]) + def GPWrapper(fitinfo): - ''' + """ A method that converts the information in fitinfo (from the fit and predict methods) to a class so it can be used to make predictions with hetgpy.homGP or hetgpy.hetGP - ''' - if fitinfo['is_homGP']: + """ + if fitinfo["is_homGP"]: return homGPWrapper(fitinfo) else: return hetGPWrapper(fitinfo) - def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): - r''' + r""" Wrapper method for hetgpy.homGP.predict Parameters @@ -126,33 +138,33 @@ def predict(predinfo, fitinfo, x, theta, thetaprime=None, **kwargs): nxd numpy array for calculating covariance matrix kwargs: dict additional keyword arguments passed to hetgpy.homGP.predict - + Returns ------- None, but predinfo is populated with `mean`, `variance`, and `covmat` fields - ''' - GP = fitinfo.get('model') + """ + GP = fitinfo.get("model") if GP is None: # use wrapper class to instantiate trained GP GP = GPWrapper(fitinfo=fitinfo) # handle kws kws = {} - eligible_keys = ['nugs_only','interval','interval_lower','interval_upper'] + eligible_keys = ["nugs_only", "interval", "interval_lower", "interval_upper"] for key in eligible_keys: if key in kwargs.keys(): - kws[key] = kwargs.get('nugs_only') + kws[key] = kwargs.get("nugs_only") - - preds = GP.predict(x=x,xprime=thetaprime,**kws) + preds = GP.predict(x=x, xprime=thetaprime, **kws) # ensure naming consistency - predinfo['mean'] = preds.get('mean') - predinfo['var'] = preds.get('sd2') - predinfo['nugs'] = preds.get('nugs') - predinfo['covmat'] = preds.get('cov') + predinfo["mean"] = preds.get("mean") + predinfo["var"] = preds.get("sd2") + predinfo["nugs"] = preds.get("nugs") + predinfo["covmat"] = preds.get("cov") del GP -def update(fitinfo, x,Y = None,**kwargs): - r''' + +def update(fitinfo, x, Y=None, **kwargs): + r""" Update function for homGP Parameters @@ -160,27 +172,34 @@ def update(fitinfo, x,Y = None,**kwargs): fitinfo: dictionary that contains the fit information for a hetgpy.homGP object x: array of new design locations Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location - kwargs: key-value pairs that get passed to hetgpy.homGP.update. + kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit - + Returns ------- None, but fitinfo is updated in place - ''' + """ # validate kwargs - valid_kws = ('ginit','lower','upper', - 'noiseControl','settings','known','maxit') + valid_kws = ( + "ginit", + "lower", + "upper", + "noiseControl", + "settings", + "known", + "maxit", + ) for kw in kwargs.keys(): if kw not in valid_kws: raise ValueError(f"{kw} not found, must be one of {valid_kws}") GP = GPWrapper(fitinfo) if Y is None: - maxit = 0 # impute mean response and do not update hyperparams - Y = GP.predict(x)['mean'] + maxit = 0 # impute mean response and do not update hyperparams + Y = GP.predict(x)["mean"] else: - maxit = kwargs.get('maxit',100) - kwargs['maxit'] = maxit - GP.update(Xnew=x,Znew=Y,**kwargs) + maxit = kwargs.get("maxit", 100) + kwargs["maxit"] = maxit + GP.update(Xnew=x, Znew=Y, **kwargs) for key in GP.__dict__.keys(): fitinfo[key] = GP.get(key) del GP diff --git a/PUQ/surrogatemethods/multihetGP.py b/PUQ/surrogatemethods/multihetGP.py index 8c552a1..508d291 100644 --- a/PUQ/surrogatemethods/multihetGP.py +++ b/PUQ/surrogatemethods/multihetGP.py @@ -1,22 +1,38 @@ -''' +""" Set of functions to fit a heteroskedastic GP to each dimension of output data -''' +""" + import numpy as np from PUQ.surrogate import emulator -def fit(fitinfo, x, theta, f, - lower=None, - upper=None, - maxit=100, - noiseControl={'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - init={}, - known={}, - eps=np.sqrt(np.finfo(float).eps), - settings={"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}, - covtype = 'Gaussian', **kwargs): - r''' + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + maxit=100, + noiseControl={"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)}, + init={}, + known={}, + eps=np.sqrt(np.finfo(float).eps), + settings={ + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + covtype="Gaussian", + **kwargs +): + r""" Wrapper function for hetgpy.hetGP.mleHetGP Arguments @@ -30,18 +46,18 @@ def fit(fitinfo, x, theta, f, theta: not used f : ndarray_like output array for training. One GP is trained for each output column. - lower,upper : ndarray_like + lower,upper : ndarray_like optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) noiseControl : dict dict with elements related to optimization of the noise process parameters: - ``g_min``, ``g_max`` minimal and maximal noise to signal ratio (of the mean process) - - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) + - ``lowerDelta``, ``upperDelta`` optional vectors (or scalars) of bounds on ``Delta``, of length ``len(X0)`` (default to ``np.repeat(eps, X0.shape[0])`` and ``np.repeat(noiseControl["g_max"], X0.shape[0])`` resp., or their ``log``) - ``lowerpX``, ``upperpX`` optional vectors of bounds of the input domain if `pX` is used. - ``lowerTheta_g``, ``upperTheta_g`` optional vectors of bounds for the lengthscales of the noise process if ``linkThetas == 'none'``. Same as for ``theta`` if not provided. - ``k_theta_g_bounds`` if ``linkThetas == 'joint'``, vector with minimal and maximal values for ``k_theta_g`` (default to ``(1, 100)``). See Notes. - ``g_bounds`` vector for minimal and maximal noise to signal ratios for the noise of the noise process, i.e., the smoothing parameter for the noise process. (default to ``(1e-6, 1)``). - settings : dict + settings : dict dict for options about the general modeling procedure, with elements: - ``linkThetas`` defines the relation between lengthscales of the mean and noise processes. Either ``'none'``, ``'joint'``(default) or ``'constr'``, see Notes. - ``logN``, when ``True`` (default), the log-noise process is modeled. @@ -52,12 +68,12 @@ def fit(fitinfo, x, theta, f, - ``trace`` optional scalar (default to ``0``). If negative, fit silently. If ``0``, only high level information is given. If ``1``, information is given about the result of the heterogeneous model optimization. Level ``2`` gives more details. Level ``3`` additionaly displays all details about initialization of hyperparameters. - ``return_matrices`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). - ``return_hom`` boolean to include homoskedastic GP models used for initialization (i.e., ``modHom`` and ``modNugs``). - - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + - ``factr`` (default to 1e9) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. eps : float jitter used in the inversion of the covariance matrix for numerical stability init,known : dict optional lists of starting values for mle optimization or that should not be optimized over, respectively. - Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. + Values in ``known`` are not modified, while it can happen to these of ``init``, see Notes. One can set one or several of the following: - ``theta`` lengthscale parameter(s) for the mean process either one value (isotropic) or a vector (anistropic) - ``Delta`` vector of nuggets corresponding to each design in ``X0``, that are smoothed to give ``Lambda`` (as the global covariance matrix depends on ``Delta`` and ``nu_hat``, it is recommended to also pass values for ``theta``) @@ -67,39 +83,41 @@ def fit(fitinfo, x, theta, f, - ``g`` scalar nugget of the noise process - ``g_H`` scalar homoskedastic nugget for the initialisation with a :func: homGP.mleHomGP. See Notes. - ``pX`` matrix of fixed pseudo inputs locations of the noise process corresponding to Delta - covtype : str + covtype : str covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` maxit : int maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization - ''' + """ numGPs = f.shape[1] emulist = [dict() for x in range(0, numGPs)] for i in range(numGPs): emu = emulator( - x=x, - theta=np.array([[0]]), - f=f[:,i:i+1], - method="hetGP", - args={ - "noiseControl": noiseControl, - "lower": lower, - "upper": upper, - "settings": settings, - "init": init, - "known": known, - "covtype": covtype, - "maxit": maxit, - "eps": eps - } - ) + x=x, + theta=np.array([[0]]), + f=f[:, i : i + 1], + method="hetGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + "maxit": maxit, + "eps": eps, + }, + ) emulist[i] = emu fitinfo["f"] = f.T fitinfo["theta"] = x fitinfo["emulist"] = emulist fitinfo["numGPs"] = numGPs return -def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): - r''' + + +def predict(predinfo, fitinfo, x, theta, thetaprime, **kws): + r""" Wrapper method for hetGP.predict Parameters @@ -116,46 +134,46 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): nxd numpy array for calculating covariance matrix kwargs: dict additional keyword arguments passed to hetgpy.hetGP.predict - + Returns ------- - None, but predinfo is populated with `mean`, `variance`,`nugs`, and `covmat` fields. - `mean`, `variance`, and `nugs` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. + None, but predinfo is populated with `mean`, `variance`,`nugs`, and `covmat` fields. + `mean`, `variance`, and `nugs` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. `covmat` is stored as a list of matrices with the ith element corresponding to the ith output column - ''' - numGPs = fitinfo['numGPs'] + """ + numGPs = fitinfo["numGPs"] emulist = [dict() for x in range(0, numGPs)] # instantiate outputs - nr, nc = (x.shape[0],numGPs) - for key in ('mean','var','nugs'): - predinfo[key] = np.zeros(shape=(nc,nr),dtype=float) + nr, nc = (x.shape[0], numGPs) + for key in ("mean", "var", "nugs"): + predinfo[key] = np.zeros(shape=(nc, nr), dtype=float) if thetaprime is None: - predinfo['covmat'] = np.zeros(shape=(nc,nr,nr),dtype=float) + predinfo["covmat"] = np.zeros(shape=(nc, nr, nr), dtype=float) else: - predinfo['covmat'] = np.zeros(shape=(nc,nr,thetaprime.shape[0]),dtype=float) - - for i in range(numGPs): - preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) - predinfo['mean'][i,:] = preds._info['mean'] - predinfo['var'][i,:] = preds._info['var'] - predinfo['nugs'][i,:] = preds._info['nugs'] - predinfo['covmat'][i,:,:] = preds._info['covmat'] + predinfo["covmat"] = np.zeros(shape=(nc, nr, thetaprime.shape[0]), dtype=float) + for i in range(numGPs): + preds = fitinfo["emulist"][i].predict(x=x, thetaprime=thetaprime) + predinfo["mean"][i, :] = preds._info["mean"] + predinfo["var"][i, :] = preds._info["var"] + predinfo["nugs"][i, :] = preds._info["nugs"] + predinfo["covmat"][i, :, :] = preds._info["covmat"] predinfo["S"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) predinfo["R"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) for i in range(0, x.shape[0]): - C = np.diag(predinfo['var'][:,i]) - R = np.diag(predinfo['nugs'][:,i]) + C = np.diag(predinfo["var"][:, i]) + R = np.diag(predinfo["nugs"][:, i]) predinfo["S"][:, :, i] = C predinfo["R"][:, :, i] = R - + return -def update(fitinfo, x,Y = None,**kwargs): - r''' + +def update(fitinfo, x, Y=None, **kwargs): + r""" Update function for hetGP Parameters @@ -163,19 +181,19 @@ def update(fitinfo, x,Y = None,**kwargs): fitinfo: dictionary that contains the fit information for a hetgpy.hetGP object x: array of new design locations Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location - kwargs: key-value pairs that get passed to hetgpy.hetGP.update. + kwargs: key-value pairs that get passed to hetgpy.hetGP.update. Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit - + Returns ------- None, but fitinfo is updated in place and individual emulator are accessed via fitinfo["emulist"] - ''' - numGPs = fitinfo['numGPs'] + """ + numGPs = fitinfo["numGPs"] for i in range(numGPs): - emu = fitinfo['emulist'][i] + emu = fitinfo["emulist"][i] if Y is not None: - Yi = Y[:,i] + Yi = Y[:, i] else: Yi = None - emu.update(x=x,Y=Yi,**kwargs) - return \ No newline at end of file + emu.update(x=x, Y=Yi, **kwargs) + return diff --git a/PUQ/surrogatemethods/multihomGP.py b/PUQ/surrogatemethods/multihomGP.py index f62197a..ed00e8d 100644 --- a/PUQ/surrogatemethods/multihomGP.py +++ b/PUQ/surrogatemethods/multihomGP.py @@ -1,19 +1,28 @@ -''' +""" Set of functions to fit a homoskedastic GP to each dimension of output data -''' +""" + import numpy as np from PUQ.surrogate import emulator -def fit(fitinfo, x, theta, f, lower=None, upper=None, - known={}, - noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, - init={}, - covtype='Gaussian', - maxit=100, - eps=np.sqrt(np.finfo(float).eps), - settings={"return.Ki": True, "factr": 1e7}, - **kwargs): - r''' + +def fit( + fitinfo, + x, + theta, + f, + lower=None, + upper=None, + known={}, + noiseControl={"g_bounds": [np.sqrt(np.finfo(float).eps), 100]}, + init={}, + covtype="Gaussian", + maxit=100, + eps=np.sqrt(np.finfo(float).eps), + settings={"return.Ki": True, "factr": 1e7}, + **kwargs +): + r""" Fit a homoskedastic GP to each dimension of output data. Parameters @@ -24,16 +33,16 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, f: ndarray output array for training. One GP is trained for each output column. theta: not used - lower,upper : ndarray_like + lower,upper : ndarray_like optional bounds for the ``theta`` parameter (see :func: covariance_functions.cov_gen for the exact parameterization). In the multivariate case, it is possible to give vectors for bounds (resp. scalars) for anisotropy (resp. isotropy) noiseControl : dict dict with element: - ``g_bounds`` vector providing minimal and maximal noise to signal ratio (default to ``(sqrt(MACHINE_DOUBLE_EPS), 100)``). - settings : dict + settings : dict dict for options about the general modeling procedure, with elements: - ``return_Ki`` boolean to include the inverse covariance matrix in the object for further use (e.g., prediction). - - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. + - ``factr`` (default to 1e7) and ``pgtol`` are available to be passed to `options` for L-BFGS-B in :func: ``scipy.optimize.minimize``. eps : float jitter used in the inversion of the covariance matrix for numerical stability known : dict @@ -42,45 +51,46 @@ def fit(fitinfo, x, theta, f, lower=None, upper=None, optional lists of starting values for mle optimization: - ``theta_init`` initial value of the theta parameters to be optimized over (default to 10% of the range determined with ``lower`` and ``upper``) - ``g_init`` vector of nugget parameter to be optimized over - covtype : str + covtype : str covariance kernel type, either ``'Gaussian'``, ``'Matern5_2'`` or ``'Matern3_2'``, see :func: ``~covariance_functions.cov_gen`` maxit : int maximum number of iterations for `L-BFGS-B` of :func: ``scipy.optimize.minimize`` dedicated to maximum likelihood optimization - + Returns ------- None, but fitinfo is updated with maximum likelihood estimates. Individual emulators are accessed via fitinfo["emulist"] - - ''' - + + """ + numGPs = f.shape[1] emulist = [dict() for x in range(0, numGPs)] for i in range(numGPs): emu = emulator( - x=x, - theta=np.array([[i]]), - f=f[:,i:i+1], - method="homGP", - args={ - "noiseControl": noiseControl, - "lower": lower, - "upper": upper, - "settings": settings, - "init": init, - "known": known, - "covtype": covtype, - "maxit": maxit, - "eps": eps - } - ) + x=x, + theta=np.array([[i]]), + f=f[:, i : i + 1], + method="homGP", + args={ + "noiseControl": noiseControl, + "lower": lower, + "upper": upper, + "settings": settings, + "init": init, + "known": known, + "covtype": covtype, + "maxit": maxit, + "eps": eps, + }, + ) emulist[i] = emu fitinfo["f"] = f fitinfo["emulist"] = emulist fitinfo["numGPs"] = numGPs return -def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): - r''' + +def predict(predinfo, fitinfo, x, theta, thetaprime, **kws): + r""" Wrapper method for homGP.predict Parameters @@ -97,43 +107,43 @@ def predict(predinfo,fitinfo,x,theta,thetaprime,**kws): nxd numpy array for calculating covariance matrix kwargs: dict additional keyword arguments passed to hetgpy.homGP.predict - + Returns ------- - None, but predinfo is populated with `mean`, `variance`, and `covmat` fields. - `mean` and `variance` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. + None, but predinfo is populated with `mean`, `variance`, and `covmat` fields. + `mean` and `variance` are stored as ndarrays, with each column corresponding to the prediction for the ith output column. `covmat` is stored as a list of matrices with the ith element corresponding to the ith output column - ''' - numGPs = fitinfo['numGPs'] + """ + numGPs = fitinfo["numGPs"] emulist = [dict() for x in range(0, numGPs)] # instantiate outputs - nr, nc = (x.shape[0],numGPs) - for key in ('mean','var','nugs'): - predinfo[key] = np.zeros(shape=(nc,nr),dtype=float) + nr, nc = (x.shape[0], numGPs) + for key in ("mean", "var", "nugs"): + predinfo[key] = np.zeros(shape=(nc, nr), dtype=float) if thetaprime is None: - predinfo['covmat'] = np.zeros(shape=(nc,nr,nr),dtype=float) + predinfo["covmat"] = np.zeros(shape=(nc, nr, nr), dtype=float) else: - predinfo['covmat'] = np.zeros(shape=(nc,nr,thetaprime.shape[0]),dtype=float) - - for i in range(numGPs): - preds = fitinfo['emulist'][i].predict(x=x,thetaprime=thetaprime) - predinfo['mean'][i,:] = preds._info['mean'] - predinfo['var'][i,:] = preds._info['var'] - predinfo['nugs'][i,:] = preds._info['nugs'] - predinfo['covmat'][i,:,:] = preds._info['covmat'] + predinfo["covmat"] = np.zeros(shape=(nc, nr, thetaprime.shape[0]), dtype=float) + for i in range(numGPs): + preds = fitinfo["emulist"][i].predict(x=x, thetaprime=thetaprime) + predinfo["mean"][i, :] = preds._info["mean"] + predinfo["var"][i, :] = preds._info["var"] + predinfo["nugs"][i, :] = preds._info["nugs"] + predinfo["covmat"][i, :, :] = preds._info["covmat"] predinfo["S"] = np.full((numGPs, numGPs, x.shape[0]), np.nan) for i in range(0, x.shape[0]): - C = np.diag(predinfo['var'][:,i]) + C = np.diag(predinfo["var"][:, i]) predinfo["S"][:, :, i] = C return -def update(fitinfo, x,Y = None,**kwargs): - r''' + +def update(fitinfo, x, Y=None, **kwargs): + r""" Update function for homGP Parameters @@ -141,19 +151,19 @@ def update(fitinfo, x,Y = None,**kwargs): fitinfo: dictionary that contains the fit information for a hetgpy.homGP object x: array of new design locations Y: new response. If None, then a kriging believer approach is used to impute the predicted mean at the design location - kwargs: key-value pairs that get passed to hetgpy.homGP.update. + kwargs: key-value pairs that get passed to hetgpy.homGP.update. Must be one of: ginit, lower, upper, noiseControl, settings, known, maxit - + Returns ------- None, but fitinfo is updated in place and individual emulator are accessed via fitinfo["emulist"] - ''' - numGPs = fitinfo['numGPs'] + """ + numGPs = fitinfo["numGPs"] for i in range(numGPs): - emu = fitinfo['emulist'][i] + emu = fitinfo["emulist"][i] if Y is not None: - Yi = Y[:,i] + Yi = Y[:, i] else: Yi = None - emu.update(x=x,Y=Yi,**kwargs) - return \ No newline at end of file + emu.update(x=x, Y=Yi, **kwargs) + return From 1772f26b9dab7f6168fd0efb7a0a2354650062ad Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 22:28:22 -0400 Subject: [PATCH 069/103] Example5 --- examples/2025+/toy_example.py | 101 +++++++++++++++++++++++++++++++ examples/Example5/README.rst | 10 ++- examples/Example5/toy_example.py | 101 +++++++++++++++++++++++++++++++ 3 files changed, 210 insertions(+), 2 deletions(-) create mode 100644 examples/2025+/toy_example.py create mode 100644 examples/Example5/toy_example.py diff --git a/examples/2025+/toy_example.py b/examples/2025+/toy_example.py new file mode 100644 index 0000000..79a63a2 --- /dev/null +++ b/examples/2025+/toy_example.py @@ -0,0 +1,101 @@ +import numpy as np +from test_functions import sinfunc +import scipy.stats as sps +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential import sequential_design +from utils import toy_example, visual_xt + +maxiter = 50 +new = True +seedmin, seedmax = 2, 3 +#seedmin, seedmax = 1, 2 +dfl = [] + +if __name__ == "__main__": + + for s in np.arange(seedmin, seedmax): + + cls_data = sinfunc() + dt = len(cls_data.theta_true) + cls_data.realdata(x=np.array([0.22222222, 0.88888889])[:, None], seed=None) + + # test data + nmesh, nt = 50, 100 + xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + zg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + tg = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nt)[:, None] + feval = np.array( + [[cls_data.function(x, t) for x in cls_data.x] for t in tg] + ).squeeze() + pg = np.array( + [ + sps.multivariate_normal(mean=f, cov=cls_data.obsvar).pdf( + cls_data.real_data + ) + for f in feval + ] + )[:, None] + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Visualize + toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) + + if new: + # Initial sample + n0, rep0 = 20, 5 + sampling = LHS(xlimits=cls_data.zlim, random_state=int(s)) + z0u = sampling(n0) + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + else: + # Initial sample + grid_size = 10 + rep0 = 5 + z1 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + z2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + zm1, zm2 = np.meshgrid(z1, z2) + z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + + + test = { + "f": fg, + "theta": tg, + "xt": zg, + "p": pg, + "noise": ng, + "p_prior": 1, + "w": 1 + } + + # Methods to iterate over + methods = ["ivar", "imse"] + for mid, method in enumerate(methods): + des_obj = sequential_design(cls_data) + des_obj.build_design( + z0=z0, + f0=f0, + T=maxiter, + persis_info=persis_info, + test=test, + af=method, + args={ + "new": new, + "nL": 200, + "seed": s, + "method": method, + "t_grid": tg, + "neighbor": "LHS", + "extra_metric": False, + }, + ) + + visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new) diff --git a/examples/Example5/README.rst b/examples/Example5/README.rst index 731759a..fa0f1c1 100644 --- a/examples/Example5/README.rst +++ b/examples/Example5/README.rst @@ -1,7 +1,7 @@ Examples ~~~~~~~~ -This example demonstrates how to use the performance model from Sürer (2025), +This example demonstrates how to use the active learning procedure from Sürer (2025), Active Learning for Data-Efficient Calibration of Stochastic Simulation Models. @@ -22,4 +22,10 @@ Running this script should not take more than 5 min. See the figures (jpeg files .. image:: ex5.png :alt: Illustration of PUQ with the example :align: center - :width: 600 \ No newline at end of file + :width: 600 + +The proposed procedure is illustrated with examples featuring different numbers +of posterior modes. Contour lines represent the posterior distribution of the +parameters, while the background color indicates the intrinsic noise. +Cyan markers indicate the points in the initial design, +while blue markers denote the acquired points, with numbers showing the replications. \ No newline at end of file diff --git a/examples/Example5/toy_example.py b/examples/Example5/toy_example.py new file mode 100644 index 0000000..1d44f79 --- /dev/null +++ b/examples/Example5/toy_example.py @@ -0,0 +1,101 @@ +import numpy as np +from test_functions import sinfunc +import scipy.stats as sps +from smt.sampling_methods import LHS +from PUQ.designmethods.sequential_1d_stochastic import sequential_design +from utils import toy_example, visual_xt + +maxiter = 50 +new = False +seedmin, seedmax = 2, 3 +#seedmin, seedmax = 1, 2 +dfl = [] + +if __name__ == "__main__": + + for s in np.arange(seedmin, seedmax): + + cls_data = sinfunc() + dt = len(cls_data.theta_true) + cls_data.realdata(x=np.array([0.22222222, 0.88888889])[:, None], seed=None) + + # test data + nmesh, nt = 50, 100 + xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) + ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) + Xpl, Ypl = np.meshgrid(xpl, ypl) + zg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T + fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] + ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] + + tg = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nt)[:, None] + feval = np.array( + [[cls_data.function(x, t) for x in cls_data.x] for t in tg] + ).squeeze() + pg = np.array( + [ + sps.multivariate_normal(mean=f, cov=cls_data.obsvar).pdf( + cls_data.real_data + ) + for f in feval + ] + )[:, None] + + # Set random stream for initial design + persis_info = {"rand_stream": np.random.default_rng(s)} + + # Visualize + toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) + + if new: + # Initial sample + n0, rep0 = 20, 5 + sampling = LHS(xlimits=cls_data.zlim, random_state=int(s)) + z0u = sampling(n0) + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + else: + # Initial sample + grid_size = 10 + rep0 = 5 + z1 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + z2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) + zm1, zm2 = np.meshgrid(z1, z2) + z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T + z0 = np.repeat(z0u, rep0, axis=0) + f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) + + + test = { + "f": fg, + "theta": tg, + "xt": zg, + "p": pg, + "noise": ng, + "p_prior": 1, + "w": 1 + } + + # Methods to iterate over + methods = ["ivar"] + for mid, method in enumerate(methods): + des_obj = sequential_design(cls_data) + des_obj.build_design( + z0=z0, + f0=f0, + T=maxiter, + persis_info=persis_info, + test=test, + af=method, + args={ + "new": new, + "nL": 200, + "seed": s, + "method": method, + "t_grid": tg, + "neighbor": "LHS", + "extra_metric": False, + }, + ) + + visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new) From 476d558d9a346f6c00691f9fc87c4616930de3a0 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 22:30:59 -0400 Subject: [PATCH 070/103] delte examples --- examples/2025+/1_example_3d_solo.py | 93 --- examples/2025+/Figure1_2.py | 101 --- examples/2025+/paper_figs.py | 731 ----------------- examples/2025+/test_functions.py | 249 ------ examples/2025+/toy_example.py | 101 --- examples/2025+/utils.py | 344 -------- examples/2025+/utils_sample.py | 143 ---- examples/JQT2024/Figure1.py | 194 ----- examples/JQT2024/Figure2.py | 147 ---- examples/JQT2024/Figure2support.py | 325 -------- examples/JQT2024/Figure3.py | 85 -- examples/JQT2024/Figure4.py | 79 -- examples/JQT2024/Figure5ab_sinf.py | 185 ----- examples/JQT2024/Figure5c_sinf.py | 184 ----- examples/JQT2024/Figure6ab_pritam.py | 182 ----- examples/JQT2024/Figure6c_pritam.py | 185 ----- examples/JQT2024/README.rst | 35 - examples/JQT2024/highdim_ex.py | 200 ----- examples/JQT2024/plot_reps.py | 115 --- examples/JQT2024/plots_design.py | 366 --------- examples/JQT2024/ptest_funcs.py | 309 ------- examples/NeurIPS2024/Figure2.py | 92 --- examples/NeurIPS2024/Figure3.py | 137 ---- examples/NeurIPS2024/README.rst | 21 - examples/NeurIPS2024/WS0.py | 113 --- examples/NeurIPS2024/WS0_run.py | 101 --- examples/NeurIPS2024/WS1_real.py | 99 --- examples/NeurIPS2024/WS2_real.py | 135 --- examples/NeurIPS2024/plotutils.py | 51 -- examples/NeurIPS2024/test_funcs.py | 361 -------- examples/README.rst | 10 +- examples/Technometrics2024/README.rst | 52 -- examples/Technometrics2024/figure1b.py | 114 --- examples/Technometrics2024/figure1b_new.py | 83 -- examples/Technometrics2024/figure2.py | 127 --- examples/Technometrics2024/figure2_new.py | 180 ---- examples/Technometrics2024/figure3.py | 140 ---- examples/Technometrics2024/figure6.py | 107 --- examples/Technometrics2024/figure6_new.py | 135 --- examples/Technometrics2024/plots.py | 50 -- examples/Technometrics2024/test_funcs.py | 133 --- examples/Technometrics2024/test_funcs_new.py | 109 --- examples/Technometrics2025/Figure1_2.py | 125 --- examples/Technometrics2025/Figure3.py | 201 ----- examples/Technometrics2025/bseq2d_exploit.py | 163 ---- examples/Technometrics2025/bseq2d_explore.py | 182 ----- examples/Technometrics2025/test_funcs.py | 562 ------------- examples/Technometrics2025/utilities.py | 818 ------------------- 48 files changed, 5 insertions(+), 8749 deletions(-) delete mode 100644 examples/2025+/1_example_3d_solo.py delete mode 100644 examples/2025+/Figure1_2.py delete mode 100644 examples/2025+/paper_figs.py delete mode 100644 examples/2025+/test_functions.py delete mode 100644 examples/2025+/toy_example.py delete mode 100644 examples/2025+/utils.py delete mode 100644 examples/2025+/utils_sample.py delete mode 100644 examples/JQT2024/Figure1.py delete mode 100644 examples/JQT2024/Figure2.py delete mode 100644 examples/JQT2024/Figure2support.py delete mode 100644 examples/JQT2024/Figure3.py delete mode 100644 examples/JQT2024/Figure4.py delete mode 100644 examples/JQT2024/Figure5ab_sinf.py delete mode 100644 examples/JQT2024/Figure5c_sinf.py delete mode 100644 examples/JQT2024/Figure6ab_pritam.py delete mode 100644 examples/JQT2024/Figure6c_pritam.py delete mode 100644 examples/JQT2024/README.rst delete mode 100644 examples/JQT2024/highdim_ex.py delete mode 100644 examples/JQT2024/plot_reps.py delete mode 100644 examples/JQT2024/plots_design.py delete mode 100644 examples/JQT2024/ptest_funcs.py delete mode 100644 examples/NeurIPS2024/Figure2.py delete mode 100644 examples/NeurIPS2024/Figure3.py delete mode 100644 examples/NeurIPS2024/README.rst delete mode 100644 examples/NeurIPS2024/WS0.py delete mode 100644 examples/NeurIPS2024/WS0_run.py delete mode 100644 examples/NeurIPS2024/WS1_real.py delete mode 100644 examples/NeurIPS2024/WS2_real.py delete mode 100644 examples/NeurIPS2024/plotutils.py delete mode 100644 examples/NeurIPS2024/test_funcs.py delete mode 100644 examples/Technometrics2024/README.rst delete mode 100644 examples/Technometrics2024/figure1b.py delete mode 100644 examples/Technometrics2024/figure1b_new.py delete mode 100644 examples/Technometrics2024/figure2.py delete mode 100644 examples/Technometrics2024/figure2_new.py delete mode 100644 examples/Technometrics2024/figure3.py delete mode 100644 examples/Technometrics2024/figure6.py delete mode 100644 examples/Technometrics2024/figure6_new.py delete mode 100644 examples/Technometrics2024/plots.py delete mode 100644 examples/Technometrics2024/test_funcs.py delete mode 100644 examples/Technometrics2024/test_funcs_new.py delete mode 100644 examples/Technometrics2025/Figure1_2.py delete mode 100644 examples/Technometrics2025/Figure3.py delete mode 100644 examples/Technometrics2025/bseq2d_exploit.py delete mode 100644 examples/Technometrics2025/bseq2d_explore.py delete mode 100644 examples/Technometrics2025/test_funcs.py delete mode 100644 examples/Technometrics2025/utilities.py diff --git a/examples/2025+/1_example_3d_solo.py b/examples/2025+/1_example_3d_solo.py deleted file mode 100644 index 698f6e8..0000000 --- a/examples/2025+/1_example_3d_solo.py +++ /dev/null @@ -1,93 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from test_functions import unimodalx, bimodalx, braninx -from smt.sampling_methods import LHS -from PUQ.designmethods.sequential import sequential_design -from utils import fig6, create_entry, save_output -from paper_figs import lineplot, boxplot -from utils_sample import test_data_gen -import pandas as pd - - -explore = True -maxiter = 50 -seedmin, seedmax = 0, 1 - -dfl = [] - -example = "braninx" - -if __name__ == "__main__": - - cex = eval(example)() - cex.realdata(x=np.array([0.5])[:, None], seed=None) - - tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl = test_data_gen(cex, sample=True) - - for s in np.arange(seedmin, seedmax): - - # Set random stream for initial design - persis_info = {'rand_stream': np.random.default_rng(s)} - - if explore: - # Initial sample - n0, rep0 = 30, 5 - sampling = LHS(xlimits=cex.zlim, random_state=int(s)) - z0u = sampling(n0) - z0 = np.repeat(z0u, rep0, axis=0) - f0 = np.array([cex.sim_f(z0[i, :], persis_info=persis_info) for i in range(n0 * rep0)]) - else: - # Initial sample - grid_size, rep0 = 10, 2 - z1 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], grid_size) - z2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], grid_size) - zm1, zm2 = np.meshgrid(z1, z2) - z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T - z0 = np.repeat(z0u, rep0, axis=0) - x_tiled = np.tile(np.array([[0.40], [0.50]]), (z0.shape[0], 1)) - t_repeated = np.repeat(z0, np.array([[0.40], [0.50]]).shape[0], axis=0) - z0 = np.hstack([x_tiled, t_repeated]) - f0 = np.array([cex.sim_f(z, persis_info=persis_info) for z in z0]) - - test = {'f': f_s, - 'theta': t_s, - 'xt': None, - 'p': p_s, - 'noise': n_s, - "w": w_s, - "p_prior": 1} - - - # Methods to iterate over - methods = ["ivar", "imse", "var"] - - labs = ["ivar", "imse", "var"] - for mid, method in enumerate(methods): - - des_obj = sequential_design(cex) - des_obj.build_design(z0=z0, - f0=f0, - T=maxiter, - persis_info=persis_info, - test=test, - af=method, - args={"new":explore, - "nL":200, - "seed":s, - "t_grid":None, - "integral":"importance"}) - - dfl.extend(create_entry(des_obj, cex.data_name, method, s, labs[mid])) - - fig, ax = plt.subplots(1, 1, figsize=(4, 3), constrained_layout=True) - fig6(des_obj, z0[:, 1:3], rep0, Xpl, Ypl, pg, ng, ax, {}, fig, exp=explore) - plt.show() - - df = pd.DataFrame(dfl) - df2 = df.groupby(["s", "h", "example", "method"])["new"].mean().mul(100).reset_index() - df2.rename(columns={"new": "percent"}, inplace=True) - - fig, ax = plt.subplots(1, 2, figsize=(12, 4), constrained_layout=True) - lineplot(df, [example], metric="MAD", hue="method", ax=ax[0]) - boxplot(df=df2, ax=ax[1], x="h", hue="method") - plt.show() \ No newline at end of file diff --git a/examples/2025+/Figure1_2.py b/examples/2025+/Figure1_2.py deleted file mode 100644 index 79a63a2..0000000 --- a/examples/2025+/Figure1_2.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -from test_functions import sinfunc -import scipy.stats as sps -from smt.sampling_methods import LHS -from PUQ.designmethods.sequential import sequential_design -from utils import toy_example, visual_xt - -maxiter = 50 -new = True -seedmin, seedmax = 2, 3 -#seedmin, seedmax = 1, 2 -dfl = [] - -if __name__ == "__main__": - - for s in np.arange(seedmin, seedmax): - - cls_data = sinfunc() - dt = len(cls_data.theta_true) - cls_data.realdata(x=np.array([0.22222222, 0.88888889])[:, None], seed=None) - - # test data - nmesh, nt = 50, 100 - xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) - ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) - Xpl, Ypl = np.meshgrid(xpl, ypl) - zg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] - ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] - - tg = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nt)[:, None] - feval = np.array( - [[cls_data.function(x, t) for x in cls_data.x] for t in tg] - ).squeeze() - pg = np.array( - [ - sps.multivariate_normal(mean=f, cov=cls_data.obsvar).pdf( - cls_data.real_data - ) - for f in feval - ] - )[:, None] - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - # Visualize - toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) - - if new: - # Initial sample - n0, rep0 = 20, 5 - sampling = LHS(xlimits=cls_data.zlim, random_state=int(s)) - z0u = sampling(n0) - z0 = np.repeat(z0u, rep0, axis=0) - f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) - else: - # Initial sample - grid_size = 10 - rep0 = 5 - z1 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) - z2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) - zm1, zm2 = np.meshgrid(z1, z2) - z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T - z0 = np.repeat(z0u, rep0, axis=0) - f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) - - - test = { - "f": fg, - "theta": tg, - "xt": zg, - "p": pg, - "noise": ng, - "p_prior": 1, - "w": 1 - } - - # Methods to iterate over - methods = ["ivar", "imse"] - for mid, method in enumerate(methods): - des_obj = sequential_design(cls_data) - des_obj.build_design( - z0=z0, - f0=f0, - T=maxiter, - persis_info=persis_info, - test=test, - af=method, - args={ - "new": new, - "nL": 200, - "seed": s, - "method": method, - "t_grid": tg, - "neighbor": "LHS", - "extra_metric": False, - }, - ) - - visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new) diff --git a/examples/2025+/paper_figs.py b/examples/2025+/paper_figs.py deleted file mode 100644 index 3d527a7..0000000 --- a/examples/2025+/paper_figs.py +++ /dev/null @@ -1,731 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Tue May 20 14:45:08 2025 - -@author: surero -""" -import seaborn as sns -import matplotlib.pyplot as plt -from utils import read_data, read_output, read_summary_metric -import matplotlib as mpl -import pandas as pd -import numpy as np -from matplotlib.colors import ListedColormap - -# Custom yellow-orange colormap -yellow_colors = [ - (1, 1, 1), - (1, 1, 0.8), - (1, 1, 0.6), - (1, 1, 0.4), - (1, 1, 0.2), - (1, 1, 0), - (1, 0.9, 0), - (1, 0.8, 0), - (1, 0.6, 0), - (1, 0.4, 0), - (1, 0.2, 0), -] -yellow_cmap = ListedColormap(yellow_colors, name="yellow") - - -custom_palette = {"ivar": "red", "imse": "blue", "var": "green", "varx": "magenta"} -hue_order = ["ivar", "imse", "var"] - -hatch_color_map = { - (0.875, 0.125, 0.125, 1): "//", - (0.125, 0.125, 0.875, 1): "\\", - (0.06274509803921569, 0.4392156862745098, 0.06274509803921569, 1): "xx", -} - - - - -def lineplot( - df, - examples, - metric="TV", - ci=None, - label=None, - custom_labels=None, - ax=None, - hue=None, - figset={}, - scaled=True, -): - ifLeg = figset.get("ifLeg", True) - sety = figset.get("sety", True) - ft = figset.get("ft", 12) - lw = figset.get("lw", 3) - - for i, example in enumerate(examples): - df1 = df.loc[df["example"] == example] - - if scaled: - group_cols = ["t", hue, "h"] - mean_df = df1.groupby(group_cols)[metric].mean().reset_index() - max_val = mean_df[metric].max() - mean_df[metric] = mean_df[metric] / max_val - data = mean_df.copy() - else: - data = df1.copy() - - sns.lineplot( - data=data, - x="t", - y=metric, - hue=hue, - style="h", - palette=custom_palette, - errorbar=ci, - linewidth=lw, - ax=ax, - ) - - lgd = ax.legend( - loc="upper center", - bbox_to_anchor=(1.3, 1), - fancybox=True, - shadow=True, - ncol=1, - fontsize=ft, - ) - - ax.set_yscale("log") - ax.set_xlabel("t", fontsize=ft) - if sety == False: - ax.set_ylabel("") - else: - ax.set_ylabel(metric, fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - if ifLeg == False: - ax.legend().remove() - - -def boxplot(df, ax, x, hue, figset={}): - ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) - ft = figset.get("ft", 12) - - sns.boxplot( - x=x, - y="percent", - hue="method", - data=df, - ax=ax, - showfliers=False, - palette=custom_palette, - hue_order=hue_order, - ) - - # select the correct patches - patches = [patch for patch in ax.patches if type(patch) == mpl.patches.PathPatch] - - # iterate through the patches for each subplot - for patch in patches: - fc = patch.get_facecolor() - patch.set_edgecolor(fc) - patch.set_facecolor("none") - patch.set_hatch(hatch_color_map[fc]) - - # # # Fix legend to match hatches and colors - handles, labels = ax.get_legend_handles_labels() - legend = ax.legend( - handles, - labels, - title="method", - loc="center left", - bbox_to_anchor=(1.02, 0.5), - borderaxespad=0.0, - ) - - for patch in legend.get_patches(): - fc = patch.get_facecolor() - patch.set_edgecolor(fc) - patch.set_facecolor("none") - patch.set_hatch(hatch_color_map[fc]) - - if sety == False: - ax.set_ylabel("") - else: - ax.set_ylabel("Percent of exploration", fontsize=ft) - - plt.xticks(fontsize=ft) - - if ifLeg == False: - ax.legend().remove() - - -def plot_experiment_figure( - examples, - path, - ms, - ls, - lg, - ly, - reps=(1, 31), - metric="MAD", - layout="2xN", - figsize=(12, 6), - ft=10, - r0=5, - n0=150, - title=None, -): - - single_example = len(examples) == 1 - if layout == "1x2" and single_example: - fig, ax = plt.subplots(1, 2, figsize=figsize) - elif layout == "2xN": - fig, ax = plt.subplots(2, len(examples), figsize=figsize) - else: - raise ValueError("Unsupported layout or mismatched number of examples.") - - for i, ex in enumerate(examples): - print(ex) - figset = {"ifLeg": lg[i], "sety": ly[i], "ft": ft, "r0": r0, "n0": n0} - df1 = read_data( - rep0=reps[0], - repf=reps[1], - methods=ms, - examples=[ex], - folderpath=path, - label=ls, - ) - print(df1["s"]) - df2 = ( - df1.groupby(["s", "h", "example", "method"])["new"] - .mean() - .mul(100) - .reset_index() - ) - df2.rename(columns={"new": "percent"}, inplace=True) - - if layout == "1x2": - lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0], figset=figset) - boxplot_alternative(path, ms, ls, ex, ax=ax[1], figset=figset) - #boxplot(df=df2, ax=ax[1], x="h", hue="method", figset=figset) - else: # layout == "2xN" - lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0, i], figset=figset) - boxplot_alternative(path, ms, ls, ex, ax=ax[1, i], figset=figset) - #boxplot(df=df2, ax=ax[1, i], x="h", hue="method", figset=figset) - - plt.tight_layout(pad=0.2) - if title is not None: - plt.savefig(title, dpi=300, bbox_inches="tight") - plt.show() - - - - -def plot_SIR_experiment_figure( - path, ms, ls, reps=(1, 31), metric="MAD", figsize=(12, 6), ft=10, figset={}, title=None, -): - usual_boxplot = figset.get("usual_boxplot", True) - shift, scaled = figset.get("shift_x", 0), figset.get("scaled", True) - - fig, ax = plt.subplots( - 1, 4, figsize=figsize, gridspec_kw={"wspace": 0.25} - ) - #, "width_ratios": [1.25, 1.25, 1, 1] - lw = 3 - - df1 = read_data(rep0=reps[0], repf=reps[1], methods=ms, examples=["SIR"], folderpath=path, label=ls) - print(df1["s"]) - df3 = df1.loc[df1["t"] == 199] - - - if scaled: - group_cols = ["t", "method", "h"] - mean_df = df1.groupby(group_cols)[metric].mean().reset_index() - max_val = mean_df[metric].max() - mean_df[metric] = mean_df[metric] / max_val - data = mean_df.copy() - else: - data = df1.copy() - - # Lineplot - sns.lineplot( - data=data, - x="t", - y=metric, - hue="method", - style="h", - palette=custom_palette, - errorbar=None, - linewidth=lw, - ax=ax[0], - ) - ax[0].legend( - loc="upper center", - bbox_to_anchor=(0.5, -0.2), - fancybox=True, - shadow=True, - ncol=2, - fontsize=ft, - ) - ax[0].set_yscale("log") - - if usual_boxplot: - df2 = ( - df1.groupby(["s", "h", "example", "method"])["new"] - .mean() - .mul(100) - .reset_index() - ) - df2.rename(columns={"new": "percent"}, inplace=True) - box_data = [ - (df2, "percent", ax[1], "percent"), - (df3, "MADy", ax[2], r"$MAD^y$"), - (df3, "MADn", ax[3], r"$MAD^n$"), - ] - else: - figset["legend_right"] = False - boxplot_alternative(path, ms, ls, "SIR", ax=ax[1], figset=figset)#{"legend_right":False}) - box_data = [ - (df3, "MADy", ax[2], r"$MAD^y$"), - (df3, "MADn", ax[3], r"$MAD^n$"), - ] - - for boxid, box in enumerate(box_data): - sns.boxplot( - x=box[1], - y="h", - hue="method", - data=box[0], - showfliers=False, - palette=custom_palette, - hue_order=hue_order, - ax=box[2], - ) - - # select the correct patches - patches = [ - patch for patch in box[2].patches if type(patch) == mpl.patches.PathPatch - ] - - # iterate through the patches for each subplot - for patch in patches: - fc = patch.get_facecolor() - patch.set_edgecolor(fc) - patch.set_facecolor("none") - patch.set_hatch(hatch_color_map[fc]) - - box[2].legend_.remove() - - # # # Fix legend to match hatches and colors - if boxid == 1: - handles, labels = box[2].get_legend_handles_labels() - legend = box[2].legend( - handles, - labels, - title="method", - loc="lower center", - bbox_to_anchor=(0.5+shift, -0.5), - ncol=3, - ) - box[2].set_yticks([]) - - for patch in legend.get_patches(): - fc = patch.get_facecolor() - patch.set_edgecolor(fc) - patch.set_facecolor("none") - patch.set_hatch(hatch_color_map[fc]) - - for label in box[2].get_yticklabels(): - label.set_rotation(90) - - box[2].set_ylabel("") - box[2].set_xlabel(box[3], fontsize=ft) - - if title is not None: - plt.savefig(title, dpi=300, bbox_inches="tight") - plt.show() - - - - -def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1,31)): - - df = read_summary_metric(path, examples, methods, ls, rep) - - for metric in metrics: - fig, ax = plt.subplots(1, 1, figsize=(6, 4), constrained_layout=True) - sns.lineplot( - data=df, - x="t", - y=metric, - hue="method", - style="h", - ax=ax, - ) - ax.set_yscale("log") - plt.show() - - - -def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, repid=(1, 2), title=None): - - # Prepare data - from utils_SIR import test_gen_SIR - from SIR_funcs import SIRx - - cls_func = SIRx() - cls_func.realdata(x=np.array([[0.25, 0.25], [0.75, 0.75]]), seed=None) - p_grid, t_grid, noise_grid, Xpl, Ypl = test_gen_SIR(cls_func, return_XY=False) - - - for r in np.arange(repid[0], repid[1]): - # Plotting - fig, ax = plt.subplots(1, len(methods), figsize=(11, 3), constrained_layout=True) - - for mid, method in enumerate(methods): - desobj = read_output(path, "SIR", method, r, label=label) - - # Contour plots - ax[mid].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm", zorder=2) - cs = ax[mid].contourf( - Xpl, - Ypl, - np.sum(noise_grid, axis=1).reshape(50, 50), - cmap=yellow_cmap, - alpha=0.75, - zorder=1, - ) - - # Add colorbar only to the last axis - if mid == len(methods) - 1: - fig.colorbar(cs, ax=ax[mid], pad=0.1) - - # Annotate sample locations and frequency - unique_rows, counts = np.unique( - desobj.zs[200:, 2:4], axis=0, return_counts=True - ) - print(counts) - for lbl, x, y in zip(counts, unique_rows[:, 0], unique_rows[:, 1]): - ax[mid].annotate( - lbl, - xy=(x, y), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color="blue", - zorder=3, - ) - - # Axis formatting - ax[mid].set_xticks([0, 0.5, 1]) - ax[mid].set_xlabel(r"$\theta_1$", fontsize=ft + 2) - ax[mid].tick_params(axis="both", labelsize=ft + 2) - if mid == 0: - ax[mid].set_yticks([0, 0.5, 1]) - ax[mid].set_ylabel(r"$\theta_2$", fontsize=ft + 2) - else: - ax[mid].set_yticks([]) - - if title is not None: - plt.savefig(title, dpi=300, bbox_inches="tight") - plt.show() - -def plot_pritam(path, repid=1, label="target", title=None): - from test_functions import pritam - from utils_sample import test_data_gen_pri - - # Plot settings - ft = 12 - ms = ["ivar", "imse", "var"] - n0 = 150 - - - # Define test points - x = np.array([0.2, 0.8]) - y = np.array([0.2, 0.8]) - xr = np.array([[xx, yy] for xx in x for yy in y]) - - # Instantiate test function object and generate real data - cex = pritam() - cex.realdata(x=xr, seed=None) - - # Generate test grid - nmesh = 50 - x1 = np.linspace(cex.zlim[0][0], cex.zlim[0][1], nmesh) - x2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], nmesh) - X1, X2 = np.meshgrid(x1, x2) - Xg = np.vstack([X1.ravel(), X2.ravel()]).T - - # Evaluate noise and function values on the grid - ng = np.array([cex.noise(x[0], x[1], cex.theta_true[0]) for x in Xg]) - fg = np.array([cex.function(x[0], x[1], cex.theta_true[0]) for x in Xg]) - - # Create subplots - fig, ax = plt.subplots(2, 3, figsize=(9, 5), constrained_layout=True) - - for mid, m in enumerate(ms): - desobj = read_output(path, "pritam", m, repid, label="target") - - # Top row: histogram of theta samples - ax[0, mid].hist( - desobj.zs[n0:, 2], - bins=30, - edgecolor="black", - alpha=0.75, - color="blue" - ) - ax[0, mid].set_xlim(0, 1) - ax[0, mid].set_xlabel(r"$\vartheta$", fontsize=ft) - if mid == 0: - ax[0, mid].set_ylabel("Frequency", fontsize=ft) - - # Bottom row: scatter plot of x-samples with contour background - ax[1, mid].scatter( - desobj.zs[n0:, 0], - desobj.zs[n0:, 1], - marker="o", - facecolors="none", - edgecolors="blue", - zorder=2 - ) - - # Overlay test design points - for xpt in xr: - ax[1, mid].scatter(xpt[0], xpt[1], marker="x", color="black", s=100, zorder=3) - - # Contour of noise - cs = ax[1, mid].contourf( - X1, X2, ng.reshape(nmesh, nmesh), - cmap=yellow_cmap, - alpha=0.75, - zorder=1 - ) - - ax[1, mid].set_xlabel(r"$x_1$", fontsize=ft) - ax[1, mid].set_ylabel(r"$x_2$", fontsize=ft) - ax[1, mid].set_xticks([0, 0.5, 1]) - ax[1, mid].set_yticks([0, 0.5, 1]) - - if title is not None: - plt.savefig(title, dpi=300, bbox_inches="tight") - plt.show() - - -def plot_horizon_progress( - path, - example="SIR", - method="ivar", - single_seed=1, - labels=("target", "adapt"), - reps=(1, 31), - figsize=(8, 2), - colors=("gray", "orange"), - title=None, -): - - fig, ax = plt.subplots(1, 2, figsize=figsize) - - for mid, label in enumerate(labels): - # Read and filter data - df = read_data( - rep0=reps[0], - repf=reps[1], - methods=[method], - examples=[example], - folderpath=path, - label=[label], - ) - - df_label = df.loc[df["h"] == label].copy() - df_label["new_cumsum"] = df_label.groupby("s")["new"].cumsum() - - # Plot for a single seed - df_seed1 = df_label[df_label["s"] == single_seed] - ax[0].plot(df_seed1["t"], df_seed1["horizon"], color=colors[mid], linestyle=':') - - # Median horizon across reps - df_median = df_label.groupby("t", as_index=False)["horizon"].mean() - ax[0].plot(df_median["t"], df_median["horizon"], color=colors[mid], label=label, linewidth=3) - - # Mean cumulative new per t - df_cumsum_avg = df_label.groupby("t", as_index=False)["new_cumsum"].mean() - ax[1].plot( - df_cumsum_avg["t"], - df_cumsum_avg["new_cumsum"] / np.arange(1, len(df_cumsum_avg) + 1), - color=colors[mid], - linewidth=3, - label=label, - ) - - # Axis labels and legends - ax[0].set_xlabel("t") - ax[0].set_ylabel("Horizon") - ax[0].legend() - - ax[1].set_xlabel("t") - ax[1].set_ylabel(r"$n_t / \sum_{i=1}^{n_t} a_i$") - ax[1].legend() - plt.tight_layout() - - if title is not None: - plt.savefig(title, dpi=300, bbox_inches="tight") - - plt.show() - - -def plot_pairwise_from_output(path, example, ms, n0=90, rep0=5, d=2, label="h=-1"): - - for m in ms: - desobj = read_output(path, example, m, 1, label=label) - df = pd.DataFrame(desobj.zs[(n0 * rep0):, d:]) - sns.pairplot(df, diag_kind="kde") - plt.suptitle(f"m = {m}", y=1.02) - plt.tight_layout() - plt.show() - - -def boxplot_alternative(path, methods, horizons, example, ax, figset={}): - ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) - legend_right = figset.get("legend_right", True) - ft = figset.get("ft", 10) - r0, n0 = figset.get("r0", 5), figset.get("n0", 50) - - lst = [] - for m in methods: - for hor in horizons: - for i in np.arange(1, 31): - desobj = read_output(path, example, m, i, label=hor) - - # Eliminate the initial sample - initial_des = desobj.zs[0:n0, :] - unique_init, counts_init = np.unique(initial_des, axis=0, return_counts=True) - - # Convert unique_init to a set of tuple rows for fast lookup - unique_init_set = set(map(tuple, unique_init)) - - count_reps = [] - for xtid, xt in enumerate(desobj.xt): - xt_tuple = tuple(xt) - if (xt_tuple in unique_init_set) and (desobj.reps[xtid] == r0): - continue # skip this one - count_reps.append(desobj.reps[xtid]) - - count_reps = np.array(count_reps) - total = len(count_reps) - # Count percentages - percent_1 = np.sum(count_reps == 1) / total * 100 - percent_2 = np.sum(count_reps == 2) / total * 100 - percent_3 = np.sum(count_reps == 3) / total * 100 - percent_4 = np.sum(count_reps == 4) / total * 100 - percent_5 = np.sum(count_reps >= 5) / total * 100 - - lst.append({ - "1": percent_1, - "2": percent_2, - "3": percent_3, - "4": percent_4, - "5": percent_5, - "r": i, - "method": m, - "h": hor - }) - - df = pd.DataFrame(lst) - - # Average over r before plotting - components = ['1', '2', '3', '4', '5'] - - df_avg = df.groupby(['h', 'method'])[components].mean().reset_index() - - # Prepare for plotting - h_values = sorted(df_avg['h'].unique()) - methods_u = df_avg['method'].unique() - num_methods = len(methods_u) - - bar_width = 0.1 - group_spacing = 0.1 - hatches = ['/', '\\', 'x', 'o', '.'] - colors = ['blue', 'green', 'orange', 'red', 'purple'] - - # Create x positions - x_positions = [] - current_x = 0 - grouped = df_avg.sort_values(['h', 'method']).reset_index(drop=True) - - for i, h in enumerate(h_values): - methods_in_group = grouped[grouped['h'] == h] - for _ in range(len(methods_in_group)): - x_positions.append(current_x) - current_x += bar_width - current_x += group_spacing - - # Plot - #fig, ax = plt.subplots(figsize=(6, 6)) - bottoms = np.zeros(len(grouped)) - - # First pass: fill with white so we can hatch over it - for i, comp in enumerate(components): - ax.bar( - x_positions, - grouped[comp], - bottom=bottoms, - width=bar_width, - color='white', - edgecolor='black' - ) - bottoms += grouped[comp].values - - # Second pass: hatch overlay with colored edges - bottoms = np.zeros(len(grouped)) - for i, comp in enumerate(components): - ax.bar( - x_positions, - grouped[comp], - bottom=bottoms, - width=bar_width, - fill=False, - hatch=hatches[i % len(hatches)], - edgecolor=colors[i % len(colors)], - linewidth=1.5, - label=f'{comp}' - ) - bottoms += grouped[comp].values - - # Method labels on x-axis - ax.set_xticks(x_positions) - ax.set_xticklabels(grouped['method'].values, rotation=45, ha='right') - - # h group labels below the x-axis - for i, h in enumerate(h_values): - group_start = i * (num_methods * bar_width + group_spacing) - group_mid = group_start + (num_methods - 1) * bar_width / 2 - ax.text( - group_mid, -0.002 * ax.get_ylim()[1], - f'h = {h}', - ha='center', - va='top', - fontsize=ft, - transform=ax.get_xaxis_transform() - ) - - if legend_right: - ax.legend( - loc="upper center", - bbox_to_anchor=(1.3, 1), - fancybox=True, - shadow=True, - ncol=1, - fontsize=ft) - else: - ax.legend( - loc="lower center", - bbox_to_anchor=(0.5, -0.6), - fancybox=True, - shadow=True, - ncol=3, - fontsize=ft) - - if sety == False: - ax.set_ylabel("") - else: - ax.set_ylabel("Percentage (%)", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - if ifLeg == False: - ax.legend().remove() diff --git a/examples/2025+/test_functions.py b/examples/2025+/test_functions.py deleted file mode 100644 index 1566078..0000000 --- a/examples/2025+/test_functions.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -Created on Wed Feb 12 13:13:23 2025 - -@author: surero -""" -import numpy as np -import scipy - -class sinfunc: - def __init__(self): - self.data_name = "sinfunc" - self.zlim = np.array([[0, 1], [0, 1]]) - self.theta_true = np.array([0.5]) # np.array([np.pi / 5]) - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.dx = 1 - self.dt = 1 - self.x = None - self.sigma2 = 0.1 - - def function(self, x, theta): - f = np.sin(10 * x - 5 * theta) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - var_noise = self.noise(thetas[0], thetas[1]) - noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise), 1) - f += noise - return f - - def realdata(self, x, seed): - self.x = x - self.d = len(x) - self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - - M = np.array([self.function(x, self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) - if seed is not None: - rand_stream = np.random.default_rng(seed) - R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) - self.real_data = M + R - else: - self.real_data = M - - def noise(self, x, theta): - alpha = 10 - min_value = 0.01#0.05 - max_value = 0.1#0.3 - - weight = 1 / (1 + np.exp(-alpha * (x - 0.5))) # Sigmoid transition - value = min_value + (max_value - min_value) * weight # Scale between 0.1 and 10 - return value - - -class unimodalx: - def __init__(self): - self.data_name = "unimodalx" - self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 3 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.5, 0.5]) - self.sigma2 = 0.1 - self.dx = 1 - self.dt = 2 - - def function(self, x, t1, t2): - t1 = -10 + t1 * 20 - t2 = -10 + t2 * 20 - f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 + (2 * x - 1) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1], thetas[2]) - V = self.noise(thetas[0], thetas[1], thetas[2]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) - f += R - return f - - def noise(self, x, t1, t2): - V = 0.01 + 1.2 * (t1**2 + t2**2) * 2 - V = V - return V - - def realdata(self, x, seed): - self.x = x - self.d = len(x) - self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) - if seed is not None: - rand_stream = np.random.default_rng(seed) - R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) - self.real_data = M + R - else: - self.real_data = M - - -class bimodalx: - def __init__(self): - self.data_name = "bimodalx" - self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) - self.theta_true = np.array([0.35, 0.35]) - self.real_data = None - self.out = [("f", float)] - self.p = 3 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.sigma2 = 0.05 - self.dx = 1 - self.dt = 2 - - def function(self, x, t1, t2): - mu1 = (0.35, 0.35) - mu2 = (0.65, 0.65) - sigma = 0.15 - term1 = np.exp(-((t1 - mu1[0]) ** 2 + (t2 - mu1[1]) ** 2) / sigma**2) - term2 = np.exp(-((t1 - mu2[0]) ** 2 + (t2 - mu2[1]) ** 2) / sigma**2) - f = (term1 + term2) + (2 * x - 1) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1], thetas[2]) - V = self.noise(thetas[0], thetas[1], thetas[2]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) - f += R - return f - - def noise(self, x, t1, t2): - cov = np.array([[0.05, 0], [0, 0.05]]) - var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) - return 0.1*var.pdf(np.array([t1, t2])) - - def realdata(self, x, seed): - self.x = x - self.d = len(x) - self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) - if seed is not None: - rand_stream = np.random.default_rng(seed) - R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) - self.real_data = M + R - else: - self.real_data = M - -class braninx: - def __init__(self): - self.data_name = "braninx" - self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 3 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) - self.sigma2 = 5 - self.dx = 1 - self.dt = 2 - - def function(self, x, t1, t2): - t1 = -5 + 15 * t1 - t2 = 15 * t2 - f = ( - (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 - + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) - + 10 - + (2 * x - 1) - ) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1], thetas[2]) - V = self.noise(thetas[0], thetas[1], thetas[2]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) - f += R - return f - - def noise(self, x, t1, t2): - alpha = 10 - min_value = 0.1 - max_value = 15 - - weight = 1 / (1 + np.exp(-alpha * (t2 - 0.5))) - value = min_value + (max_value - min_value) * weight - - return value - - def realdata(self, x, seed): - self.x = x - self.d = len(x) - self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x, *self.theta_true) for x in self.x], dtype=float) - if seed is not None: - rand_stream = np.random.default_rng(seed) - R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) - self.real_data = M + R - else: - self.real_data = M - - -class pritam: - def __init__(self): - self.data_name = "pritam" - self.zlim = np.array([[0, 1], [0, 1], [0, 1]]) - self.theta_true = np.array([0.5]) - self.out = [("f", float)] - self.d = 1 - self.p = 3 - self.real_data = None - self.dx = 2 - self.sigma2 = 10 #0.5**2 - self.dx = 2 - self.dt = 1 - - def function(self, x1, x2, theta1): - f = (30 + 5 * x1 * np.sin(5 * x1)) * (6 * theta1 + 1 + np.exp(-5 * x2)) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1], thetas[2]) - V = self.noise(thetas[0], thetas[1], thetas[2]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V), 1) - f += R - return f - - def realdata(self, x, seed): - self.x = x - self.d = len(x) - self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x[0], x[1], self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) - if seed is not None: - rand_stream = np.random.default_rng(seed) - R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) - self.real_data = M + R - else: - self.real_data = M - - def noise(self, x1, x2, t1): - # return 5 - - cov = np.array([[0.1, 0], [0, 0.1]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) - return (15*t1)*var.pdf(np.array([x1, x2])) - diff --git a/examples/2025+/toy_example.py b/examples/2025+/toy_example.py deleted file mode 100644 index 79a63a2..0000000 --- a/examples/2025+/toy_example.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -from test_functions import sinfunc -import scipy.stats as sps -from smt.sampling_methods import LHS -from PUQ.designmethods.sequential import sequential_design -from utils import toy_example, visual_xt - -maxiter = 50 -new = True -seedmin, seedmax = 2, 3 -#seedmin, seedmax = 1, 2 -dfl = [] - -if __name__ == "__main__": - - for s in np.arange(seedmin, seedmax): - - cls_data = sinfunc() - dt = len(cls_data.theta_true) - cls_data.realdata(x=np.array([0.22222222, 0.88888889])[:, None], seed=None) - - # test data - nmesh, nt = 50, 100 - xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) - ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) - Xpl, Ypl = np.meshgrid(xpl, ypl) - zg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] - ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] - - tg = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nt)[:, None] - feval = np.array( - [[cls_data.function(x, t) for x in cls_data.x] for t in tg] - ).squeeze() - pg = np.array( - [ - sps.multivariate_normal(mean=f, cov=cls_data.obsvar).pdf( - cls_data.real_data - ) - for f in feval - ] - )[:, None] - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - # Visualize - toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) - - if new: - # Initial sample - n0, rep0 = 20, 5 - sampling = LHS(xlimits=cls_data.zlim, random_state=int(s)) - z0u = sampling(n0) - z0 = np.repeat(z0u, rep0, axis=0) - f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) - else: - # Initial sample - grid_size = 10 - rep0 = 5 - z1 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) - z2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], grid_size) - zm1, zm2 = np.meshgrid(z1, z2) - z0u = np.vstack([zm1.ravel(), zm2.ravel()]).T - z0 = np.repeat(z0u, rep0, axis=0) - f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) - - - test = { - "f": fg, - "theta": tg, - "xt": zg, - "p": pg, - "noise": ng, - "p_prior": 1, - "w": 1 - } - - # Methods to iterate over - methods = ["ivar", "imse"] - for mid, method in enumerate(methods): - des_obj = sequential_design(cls_data) - des_obj.build_design( - z0=z0, - f0=f0, - T=maxiter, - persis_info=persis_info, - test=test, - af=method, - args={ - "new": new, - "nL": 200, - "seed": s, - "method": method, - "t_grid": tg, - "neighbor": "LHS", - "extra_metric": False, - }, - ) - - visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new) diff --git a/examples/2025+/utils.py b/examples/2025+/utils.py deleted file mode 100644 index a296d44..0000000 --- a/examples/2025+/utils.py +++ /dev/null @@ -1,344 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.colors import ListedColormap -import pandas as pd -import matplotlib as mpl -import matplotlib.patches as mpatches - -yellow_colors = [ - (1, 1, 1), - (1, 1, 0.8), # light yellow - (1, 1, 0.6), - (1, 1, 0.4), - (1, 1, 0.2), - (1, 1, 0), # yellow - (1, 0.8, 0), # yellow-orange - (1, 0.6, 0), # orange - (1, 0.4, 0), # dark orange - (1, 0.2, 0), # very dark orange - (1, 0, 0), # very dark orange -] -yellow_cmap = ListedColormap(yellow_colors, name="yellow") - -def heatmap(Xpl, Ypl, noise_grid, f_grid, p_grid, t_sample=None): - - - fig, ax = plt.subplots(1, 2, figsize=(12, 4)) - cs = ax[0].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - CS = ax[0].contour(Xpl, Ypl, f_grid.reshape(50, 50), colors="black") - ax[0].clabel(CS, inline=True, fontsize=10) - ax[0].set_xlabel(r"$\theta_1$", fontsize=16) - ax[0].set_ylabel(r"$\theta_2$", fontsize=16) - ax[0].tick_params(axis="both", labelsize=16) - - cs = ax[1].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - CS = ax[1].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm") - if t_sample is not None: - ax[1].scatter(t_sample[:, 0], t_sample[:, 1]) - ax[1].clabel(CS, inline=True, fontsize=10) - ax[1].set_xlabel(r"$\theta_1$", fontsize=16) - ax[1].set_ylabel(r"$\theta_2$", fontsize=16) - ax[1].tick_params(axis="both", labelsize=16) - plt.show() - -def heatmap_pritam(cls_data): - # test data - nmesh = 50 - x1 = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) - x2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) - X1, X2 = np.meshgrid(x1, x2) - Xg = np.vstack([X1.ravel(), X2.ravel()]).T - #cls_data.theta_true[0] - ng = np.array([cls_data.noise(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) - fg = np.array([cls_data.function(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) - - fig, ax = plt.subplots(1, 2, figsize=(12, 4)) - cs = ax[0].contourf(X1, X2, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - #ax[1].clabel(CS, inline=True, fontsize=10) - for x in cls_data.x: - ax[0].scatter(x[0], x[1], marker="*", color="black") - ax[0].set_xlabel(r"$x_1$", fontsize=16) - ax[0].set_ylabel(r"$x_2$", fontsize=16) - ax[0].tick_params(axis="both", labelsize=16) - - cs = ax[1].contourf(X1, X2, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - #ax[0].clabel(CS, inline=True, fontsize=10) - for x in cls_data.x: - ax[1].scatter(x[0], x[1], marker="*", color="black") - ax[1].set_xlabel(r"$x_1$", fontsize=16) - ax[1].set_ylabel(r"$x_2$", fontsize=16) - ax[1].tick_params(axis="both", labelsize=16) - plt.show() - - - - - - - -def toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info): - - from mpl_toolkits.axes_grid1 import make_axes_locatable - - ft = 12 - fig, axs = plt.subplots(1, 3, figsize=(14, 3.5), constrained_layout=False) # Disable constrained_layout - fig.subplots_adjust(wspace=0.4, left=0.1, right=0.9) # Adjust space between subplots - - # Contour plot for axs[1] - divider = make_axes_locatable(axs[0]) - cax1 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar - cs1 = axs[0].contourf(Xpl, Ypl, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - fig.colorbar(cs1, cax=cax1) - - xt_joint = np.column_stack((cls_data.x, np.repeat(cls_data.theta_true, len(cls_data.x)))) - axs[0].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") - axs[0].set_xlabel(r"$x$", fontsize=ft) - axs[0].set_ylabel(r"$\vartheta$", fontsize=ft) - axs[0].tick_params(axis="both", labelsize=ft) - - # Contour plot for axs[2] - divider = make_axes_locatable(axs[1]) - cax2 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar - cs2 = axs[1].contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - fig.colorbar(cs2, cax=cax2) - - axs[1].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") - axs[1].set_xlabel(r"$x$", fontsize=ft) - axs[1].set_ylabel(r"$\vartheta$", fontsize=ft) - axs[1].tick_params(axis="both", labelsize=ft) - - # Scatter plot for axs[0] - xs = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], 100) - for k in range(10): - axs[2].scatter(xs, - np.array([cls_data.sim_f(np.array([x, cls_data.theta_true[0]]), persis_info) for x in xs]), - facecolors="none", edgecolors="green", s=80, linewidth=2) - axs[2].plot(xs, - np.array([cls_data.function(x, cls_data.theta_true[0]) for x in xs]), - linestyle="dotted", linewidth=3, color="black") - axs[2].set_xlabel(r"$x$", fontsize=ft) - #axs[2].set_ylabel(r"$\theta$", fontsize=ft) - axs[2].tick_params(axis="both", labelsize=ft) - - plt.show() - - - -def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): - - z, reps = des_obj.xt, des_obj.reps - - ft = 14 - fig, ax = plt.subplots(figsize=(5, 4)) - - for xitem in cls_data.x: - ax.vlines( - xitem, 0, 1, linestyles="dotted", colors="green", linewidth=3, zorder=5 - ) - for label, x_count, y_count in zip(reps, z[:, 0], z[:, 1]): - if new: - if np.any(np.all(z0u == np.array([x_count, y_count]), axis=1)): - col = "cyan" - else: - col = "blue" - else: - if label > rep0: - col = "blue" - else: - col = "cyan" - - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft-2, - color=col, - weight="bold", - zorder=6, - ) - - ax.contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.set_xlabel(r"$x$", fontsize=ft) - ax.set_ylabel(r"$\theta$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - ax2 = ax.twiny() - ax2.plot(pg, tg, color='black', label="Y-axis scatter") - ax2.set_xlabel(r"$p(y|\theta)$", fontsize=ft) - ax2.tick_params(axis="both", labelsize=ft) - plt.show() - - -def fig6(desobj, theta0, rep0, Xpl, Ypl, pg, ng, ax, figset={}, fig={}, exp=True): - - th, reps = desobj.xt[:, 1:3], desobj.reps - - xtick, ytick, cbarf = figset.get("xtick", True), figset.get("ytick", True), figset.get("cbar", True) - - ft = 12 - nm = Xpl.shape[0] - cs = ax.contourf(Xpl, Ypl, ng.reshape(nm, nm), cmap=yellow_cmap, alpha=0.75) - cp = ax.contour(Xpl, Ypl, pg.reshape(nm, nm), cmap="coolwarm") - - - if cbarf: - cbar = fig.colorbar(cs, ax=ax, pad=0.1) - - for label, x_count, y_count in zip(reps, th[:, 0], th[:, 1]): - - if exp: - if np.any(np.all(theta0 == np.array([x_count, y_count]), axis=1)): - col = "cyan" - else: - col = "blue" - else: - if label > rep0: - col = "blue" - else: - col = "cyan" - - ax.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color=col - ) - - if xtick: - ax.set_xticks([0, 0.5, 1]) - ax.set_xlabel(r"$\vartheta_1$", fontsize=ft) - else: - ax.set_xticks([]) - - if ytick: - ax.set_yticks([0, 0.5, 1]) - ax.set_ylabel(r"$\vartheta_2$", fontsize=ft) - else: - ax.set_yticks([]) - - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - ax.tick_params(axis="both", labelsize=ft) - - - -def visual_pritam(des_obj, z0u): - ft = 16 - xtu, repu = des_obj.xt, des_obj.reps - for xtid, xt in enumerate(xtu): - # print(xt) - if xt in z0u: - col = "cyan" - else: - col = "blue" - - plt.annotate( - repu[xtid], - xy=(xt[0], xt[1]), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color=col, - weight="bold", - ) - - plt.xlabel(r"$x_1$") - plt.ylabel(r"$x_2$") - plt.show() - - ut = [] - for xtid, xt in enumerate(xtu): - if xt not in z0u: - ut.append(xt[2]) - plt.hist(ut) - plt.show() - -import os -import dill as pickle -def save_output(desing_obj, name, al_func, seedno, path=None, label=None): - - if path is None: - if not os.path.isdir('output'): - os.mkdir('output') - - design_path = 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' - else: - design_path = path + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' - - with open(design_path, 'wb') as file: - pickle.dump(desing_obj, file) - -def read_output(path1, name, al_func, seedno, label=None): - - design_path = path1 + 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' - with open(design_path, 'rb') as file: - design_obj = pickle.load(file) - - return design_obj - - - -def create_entry(desobj, fname, method, s, h): - return [ - {"MSE": he["MSE"], - "MAD": he["MAD"], - "VAR": he["VAR"], - "MSEy": he["MSEy"], - "MADy": he["MADy"], - "MSEn": he["MSEn"], - "MADn": he["MADn"], - "t": he["t"], - "new": he["new"], - "method": method, - "example": fname, - "s": s, - "h": h, - "horizon": he["h"]} - for he in desobj["H"] - ] - -def read_data(rep0=0, - repf=10, - methods=["ivar", "imse", "unif"], - examples=["unimodal", "banana", "bimodal"], - folderpath=None, - label=None, - ): - - datalist = [] - for eid, example in enumerate(examples): - for mid, m in enumerate(methods): - for r in range(rep0, repf): - - desobj = read_output(folderpath, example, m, r, label=label[mid]) - entry = create_entry(desobj, example, m, r, label[mid]) - datalist.extend(entry) - - df = pd.DataFrame(datalist) - return df - -def read_summary_metric(folderpath, examples, methods, ls, rep=(1,31)): - dfl = [] - for eid, example in enumerate(examples): - for mid, m in enumerate(methods): - for r in range(rep[0], rep[1]): - - desobj = read_output(folderpath, example, m, r, label=ls[mid]) - for heid, he in enumerate(desobj.H): - sm = he["summary_metric"] - sm["r"] = r - sm["method"] = m - sm["h"] = ls[mid] - sm["t"] = heid - dfl.append(sm) - - df = pd.DataFrame(dfl) - return df diff --git a/examples/2025+/utils_sample.py b/examples/2025+/utils_sample.py deleted file mode 100644 index 35ae0b4..0000000 --- a/examples/2025+/utils_sample.py +++ /dev/null @@ -1,143 +0,0 @@ -import numpy as np -import scipy.stats as sps -from utils import heatmap, heatmap_pritam -import matplotlib.pyplot as plt -import sys - -def sample_from_posterior(cls_data, seed): - import emcee - import scipy.stats as sps - from smt.sampling_methods import LHS - - discard = 250 - nsteps = 1000 - thin = 30 - nwalkers = 40 - seed = int(seed) - - def log_probability(ctheta): - if np.any((ctheta < 0) | (ctheta > 1)): - return -np.inf - else: - if cls_data.dx == 1: - feval = np.array([cls_data.function(x, *ctheta) for x in cls_data.x]).squeeze() - elif cls_data.dx == 2: - feval = np.array([cls_data.function(x[0], x[1], *ctheta) for x in cls_data.x]).squeeze() - - - rnd = sps.multivariate_normal(mean=feval, cov=cls_data.obsvar) - pvar = rnd.pdf(cls_data.real_data) + sys.float_info.epsilon - return np.log(pvar) - - def sample(ndim, nwalkers, seed): - np.random.seed(seed) - sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - sampling = LHS( - xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed - ) - loc0 = sampling(nwalkers) - sampler.run_mcmc(initial_state=loc0, nsteps=nsteps, progress=False) - samples = sampler.get_chain(discard=discard, thin=thin, flat=True) - return samples - - return sample(cls_data.dt, nwalkers, seed) - -def test_data_gen(cls_data, sample=False): - - # test data - nmesh = 50 - xpl = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], nmesh) - ypl = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) - Xpl, Ypl = np.meshgrid(xpl, ypl) - tg = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - pg = np.array( - [ - sps.multivariate_normal( - mean=np.array([cls_data.function(x, *t) for x in cls_data.x]), - cov=cls_data.obsvar, - ).pdf(cls_data.real_data) - for t in tg - ] - )[:, None] - - zg = np.column_stack((np.tile(cls_data.x, (tg.shape[0], 1)), tg)) - fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] - ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] - - if sample: - t_s = sample_from_posterior(cls_data, 1234) - p_s = np.zeros((t_s.shape[0], 1)) - f_s = np.zeros((t_s.shape[0], 1)) - n_s = np.zeros((t_s.shape[0], 1)) - for t_id, t in enumerate(t_s): - f_s[t_id, 0] = np.array([cls_data.function(x, t[0], t[1]) for x in cls_data.x]) - n_s[t_id, 0] = np.array([cls_data.noise(x, t[0], t[1]) for x in cls_data.x]) - rnd = sps.multivariate_normal(mean=f_s[t_id, 0], cov=cls_data.obsvar) - p_s[t_id, 0] = rnd.pdf(cls_data.real_data) - - p_se = p_s + sys.float_info.epsilon - w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() - - - heatmap(Xpl, Ypl, ng, fg, pg, t_s) - - return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl - else: - return tg, fg, pg, zg, ng, Xpl, Ypl - - -def test_data_gen_pri(cls_data, sample=False): - - nt = 100 - tg = np.linspace(cls_data.zlim[2][0], cls_data.zlim[2][1], nt)[:, None] - pg = np.array( - [ - sps.multivariate_normal( - mean=np.array( - [cls_data.function(x[0], x[1], t) for x in cls_data.x] - ).squeeze(), - cov=cls_data.obsvar, - ).pdf(cls_data.real_data) - for t in tg - ] - ) - - - - heatmap_pritam(cls_data) - - # (ntot, d) - x_tiled = np.tile(cls_data.x, (tg.shape[0], 1)) - # (ntot, p-d) - t_repeated = np.repeat(tg, cls_data.x.shape[0], axis=0) - # (ntot, p) - zg = np.hstack([x_tiled, t_repeated]) - - fg = np.array([cls_data.function(*xt) for xt in zg])[:, None] - ng = np.array([cls_data.noise(*xt) for xt in zg])[:, None] - - if sample: - t_s = sample_from_posterior(cls_data, 1234) - #t_s = np.linspace(0, 1, 900)[:, None] - p_s = np.zeros((t_s.shape[0], 1)) - f_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) - n_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) - - for t_id, t in enumerate(t_s): - feval = np.array([cls_data.function(x[0], x[1], t) for x in cls_data.x]) - f_s[t_id*4 : (t_id + 1)*4, 0] = feval.flatten() - n_s[t_id*4 : (t_id + 1)*4, 0] = np.array([cls_data.noise(x[0], x[1], t) for x in cls_data.x]).flatten() - - rnd = sps.multivariate_normal(mean=feval.flatten(), cov=cls_data.obsvar) - p_s[t_id, 0] = rnd.pdf(cls_data.real_data) - - p_se = p_s + sys.float_info.epsilon - w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() - - plt.scatter(tg, pg, alpha=0.5, marker="+", color="blue", s=50) - plt.scatter(t_s, p_s, alpha=0.1, marker="*", color="red", s=10) - plt.show() - - return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s - else: - return tg, fg, pg, zg, ng diff --git a/examples/JQT2024/Figure1.py b/examples/JQT2024/Figure1.py deleted file mode 100644 index 92691b8..0000000 --- a/examples/JQT2024/Figure1.py +++ /dev/null @@ -1,194 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -from PUQ.prior import prior_dist -from plots_design import create_test, samplingdata -from PUQ.surrogate import emulator -from PUQ.surrogatemethods.PCGPexp import postpred -from ptest_funcs import sinfunc -import matplotlib.pyplot as plt - -args = parse_arguments() - -s = 1 -ninit = 10 -nmax = 30 -result = [] - -cls_data = sinfunc() -dt = len(cls_data.true_theta) -cls_data.realdata( - x=np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], seed=s -) - -prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] -) -prior_x = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[0][0]]), b=np.array([cls_data.thetalimits[0][1]]) -) -prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[1][0]]), b=np.array([cls_data.thetalimits[1][1]]) -) - -priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - -# # # Create a mesh for test set # # # -xt_test, ftest, ptest, thetamesh, xmesh = create_test(cls_data) -nmesh = len(xmesh) -cls_data_y = sinfunc() -cls_data_y.realdata(x=xmesh, seed=s) -ytest = cls_data_y.real_data - -test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, -} - -if __name__ == "__main__": - # # # # # # # # # # # # # # # # # # # # # - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarx", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "theta_torun": None, - "max_evals": nmax, - "is_thetamle": False, - }, - ) - - xt_eivarx = al_ceivarx._info["theta"] - f_eivarx = al_ceivarx._info["f"] - thetamle_eivarx = al_ceivarx._info["thetamle"][-1] - - al_ceivar = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivar", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "theta_torun": None, - "max_evals": nmax, - "is_thetamle": False, - }, - ) - - xt_eivar = al_ceivar._info["theta"] - f_eivar = al_ceivar._info["f"] - thetamle_eivar = al_ceivar._info["thetamle"][-1] - - # LHS - xt_LHS = samplingdata("LHS", nmax - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "bias": False, - "is_thetamle": False, - }, - ) - xt_LHS = al_LHS._info["theta"] - f_LHS = al_LHS._info["f"] - thetamle_LHS = al_LHS._info["thetamle"][-1] - - dataset = [] - - dataset.append({"mle": thetamle_LHS, "f": f_LHS, "xt": xt_LHS, "method": "Alhs"}) - dataset.append( - {"mle": thetamle_eivar, "f": f_eivar, "xt": xt_eivar, "method": "Ap"} - ) - dataset.append( - {"mle": thetamle_eivarx, "f": f_eivarx, "xt": xt_eivarx, "method": "Ay"} - ) - - xt_true = [ - np.concatenate([xc.reshape(1, 1), cls_data.true_theta.reshape(1, 1)], axis=1) - for xc in xmesh - ] - xt_true = np.array([m for mesh in xt_true for m in mesh]) - true_model = cls_data.function(xt_true[:, 0], xt_true[:, 1]) - - x_emu = np.arange(0, 1)[:, None] - - fig, axs = plt.subplots(2, 3, figsize=(15, 7)) - for pid, point in enumerate(dataset): - theta_mle = point["mle"] - f = point["f"] - xt = point["xt"] - - xt_ref = [np.concatenate([xc.reshape(1, 1), theta_mle], axis=1) for xc in xmesh] - xt_ref = np.array([m for mesh in xt_ref for m in mesh]) - - emu = emulator(x_emu, xt, f[None, :], method="PCGPexp") - - # Predictions - emupred = emu.predict(x=x_emu, theta=xt_ref) - predmean = emupred.mean().flatten() - predsd = np.sqrt(emupred.var().flatten()) - - # optional for presentation - ft = 16 - axs[0, pid].plot( - xmesh.flatten(), predmean, color="blue", linestyle="dashed", linewidth=3 - ) - axs[0, pid].fill_between( - xmesh.flatten(), - predmean - predsd, - predmean + predsd, - color="blue", - alpha=0.1, - ) - axs[0, pid].plot( - xmesh.flatten(), true_model.flatten(), color="red", linewidth=3 - ) - axs[0, pid].scatter( - cls_data.x.flatten(), cls_data.real_data, color="black", s=50 - ) - axs[0, pid].set_xlabel(r"$x$", fontsize=ft) - axs[0, pid].set_ylabel(r"$\eta(x, \theta=\pi/5)$", fontsize=ft) - axs[0, pid].tick_params(labelsize=ft) - - # Posterior - pmeanhat, pvarhat = postpred( - emu._info, cls_data.x, xt_test, cls_data.real_data, cls_data.obsvar - ) - axs[1, pid].plot( - thetamesh.flatten(), pmeanhat, color="blue", linestyle="dashed", linewidth=3 - ) - pl = pmeanhat + np.sqrt(pvarhat) - mn = pmeanhat - np.sqrt(pvarhat) - axs[1, pid].fill_between(thetamesh.flatten(), mn, pl, color="blue", alpha=0.2) - axs[1, pid].plot(thetamesh.flatten(), ptest.flatten(), color="red", linewidth=3) - axs[1, pid].set_ylabel(r"$\tilde{p}(\theta|y)$", fontsize=ft) - axs[1, pid].set_xlabel(r"$\theta$", fontsize=ft) - axs[1, pid].tick_params(labelsize=ft) - - -plt.savefig("Figure1.jpg", format="jpeg", bbox_inches="tight", dpi=1000) -plt.show() diff --git a/examples/JQT2024/Figure2.py b/examples/JQT2024/Figure2.py deleted file mode 100644 index 0c11d76..0000000 --- a/examples/JQT2024/Figure2.py +++ /dev/null @@ -1,147 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.prior import prior_dist -from plots_design import create_test -from ptest_funcs import sinfunc -import matplotlib.pyplot as plt -from PUQ.surrogate import emulator -from Figure2support import ceivarfig, ceivarxfig -from PUQ.designmethods.SEQDESsupport import find_mle - - -options = [0, 1] -fig, axs = plt.subplots(2, 3, figsize=(15, 7)) -for o in options: - s = 1 - - cls_data = sinfunc() - dt = len(cls_data.true_theta) - cls_data.realdata( - x=np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], seed=s - ) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[0][0]]), - b=np.array([cls_data.thetalimits[0][1]]), - ) - prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[1][0]]), - b=np.array([cls_data.thetalimits[1][1]]), - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - # # # Create a mesh for test set # # # - xt_test, ftest, ptest, thetamesh, xmesh = create_test(cls_data) - nmesh = len(xmesh) - cls_data_y = sinfunc() - cls_data_y.realdata(x=xmesh, seed=s) - ytest = cls_data_y.real_data - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - # # # # # # # # # # # # # # # # # # # # # - x_emu = np.arange(0, 1)[:, None] - sinit = 5 - ninit = 10 - nmax = 30 - - # Create initial data - xt = prior_xt.rnd(ninit, sinit) - f = cls_data.function(xt[:, 0], xt[:, 1]) - - if o == 0: - # Acquire new points - for i in range(nmax - ninit): - emu = emulator(x_emu, xt, f[None, :], method="PCGPexp") - - theta_mle = find_mle( - emu, - cls_data.x, - x_emu, - cls_data.real_data, - cls_data.obsvar, - 1, - 1, - cls_data.thetalimits, - is_bias=False, - ) - - xnew = ceivarfig( - 1, - cls_data.x, - cls_data.x, - emu, - xt, - f[None, :], - cls_data.real_data, - cls_data.obsvar, - cls_data.thetalimits, - prior_xt, - prior_t, - thetatest=None, - x_mesh=xmesh, - thetamesh=thetamesh, - posttest=ptest, - type_init=None, - synth_info=cls_data, - theta_mle=theta_mle, - axis=axs, - ) - - xt = np.concatenate((xt, xnew), axis=0) - f = cls_data.function(xt[:, 0], xt[:, 1]) - else: - # Acquire new points - for i in range(nmax - ninit): - emu = emulator(x_emu, xt, f[None, :], method="PCGPexp") - - theta_mle = find_mle( - emu, - cls_data.x, - x_emu, - cls_data.real_data, - cls_data.obsvar, - 1, - 1, - cls_data.thetalimits, - is_bias=False, - ) - - xnew = ceivarxfig( - 1, - cls_data.x, - cls_data.x, - emu, - xt, - f[None, :], - cls_data.real_data, - cls_data.obsvar, - cls_data.thetalimits, - prior_xt, - prior_t, - thetatest=None, - x_mesh=xmesh, - thetamesh=thetamesh, - posttest=ptest, - type_init=None, - synth_info=cls_data, - theta_mle=theta_mle, - axis=axs, - ) - - xt = np.concatenate((xt, xnew), axis=0) - f = cls_data.function(xt[:, 0], xt[:, 1]) - -plt.savefig("Figure2.jpg", format="jpeg", bbox_inches="tight", dpi=1000) -plt.show() diff --git a/examples/JQT2024/Figure2support.py b/examples/JQT2024/Figure2support.py deleted file mode 100644 index e5962c8..0000000 --- a/examples/JQT2024/Figure2support.py +++ /dev/null @@ -1,325 +0,0 @@ -import numpy as np -from PUQ.surrogatemethods.PCGPexp import temp_postphimat, postphimat -from smt.sampling_methods import LHS -from numpy.random import rand -import scipy.stats as sps -import matplotlib.pyplot as plt - - -def ceivarxfig( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - x_mesh=None, - thetamesh=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, - axis=None, -): - - ft = 16 - - p = theta.shape[1] - dt = thetamesh.shape[1] - dx = x.shape[1] - type_init = "CMB" - x_emu = np.arange(0, 1)[:, None] - xuniq = np.unique(x, axis=0) - - nx_ref = x_mesh.shape[0] - dx = x_mesh.shape[1] - nt_ref = thetamesh.shape[0] - dt = thetamesh.shape[1] - nf = x.shape[0] - - x_ref = 1 * x_mesh - # nt_ref x d_t - theta_ref = 1 * thetamesh - - # Get estimate for real data at theta_mle for reference x - # nx_ref x (d_x + d_t) - xt_ref = np.concatenate( - (x_ref, np.repeat(theta_mle, nx_ref).reshape(nx_ref, dt)), axis=1 - ) - xt_ref = [np.concatenate([xc.reshape(1, dx), theta_mle], axis=1) for xc in x_ref] - xt_ref = np.array([m for mesh in xt_ref for m in mesh]) - - # 1 x nx_ref - y_ref = emu.predict(x=x_emu, theta=xt_ref).mean() - # y_ref_var = emu.predict(x=x_emu, theta=xt_ref).var() - - # nx_ref x nf - f_temp_rep = np.repeat(obs, nx_ref, axis=0) - # nx_ref x (nf + 1) - f_field_rep = np.concatenate((f_temp_rep, y_ref.T), axis=1) - - xs = [np.concatenate([x, xc.reshape(1, dx)], axis=0) for xc in x_ref] - ts = [np.repeat(theta_mle.reshape(1, dt), nf + 1, axis=0)] - mesh_grid = [np.concatenate([xc, th], axis=1).tolist() for xc in xs for th in ts] - mesh_grid = np.array([m for mesh in mesh_grid for m in mesh]) - - n_x = nf + 1 - - # Construct obsvar - obsvar3D = np.zeros(shape=(nx_ref, n_x, n_x)) - for i in range(nx_ref): - obsvar3D[i, :, :] = np.diag(np.repeat(synth_info.sigma2, n_x)) - - Smat3D, rVh_1_3d, pred_mean = temp_postphimat( - emu._info, n_x, mesh_grid, f_field_rep, obsvar3D - ) - - nmesh = 50 - a = np.arange(nmesh) / nmesh - b = np.arange(nmesh) / nmesh - X, Y = np.meshgrid(a, b) - Z = np.zeros((nmesh, nmesh)) - for i in range(nmesh): - for j in range(nmesh): - xt_cand = np.array([X[i, j], Y[i, j]]).reshape(1, dx + dt) - Z[i, j] = postphimat( - emu._info, - n_x, - mesh_grid, - f_field_rep, - obsvar3D, - xt_cand, - Smat3D, - rVh_1_3d, - pred_mean, - ) - - ids = np.where(Z == Z.max()) - xnew = np.array( - [X[ids[0].flatten(), ids[1].flatten()], Y[ids[0].flatten(), ids[1].flatten()]] - ).reshape(1, dx + dt) - - if theta.shape[0] == 18: - axis[1, 0].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[1, 0].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[1, 0].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - axis[1, 0].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[1, 0].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[1, 0].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[1, 0].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[1, 0].set_xlabel(r"$x$", fontsize=ft) - axis[1, 0].set_ylabel(r"$\theta$", fontsize=ft) - axis[1, 0].tick_params(labelsize=ft) - elif theta.shape[0] == 21: - axis[1, 1].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[1, 1].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[1, 1].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - axis[1, 1].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[1, 1].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[1, 1].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[1, 1].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[1, 1].set_xlabel(r"$x$", fontsize=ft) - axis[1, 1].set_ylabel(r"$\theta$", fontsize=ft) - axis[1, 1].tick_params(labelsize=ft) - elif theta.shape[0] == 29: - axis[1, 2].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[1, 2].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[1, 2].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - axis[1, 2].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[1, 2].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[1, 2].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[1, 2].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[1, 2].set_xlabel(r"$x$", fontsize=ft) - axis[1, 2].set_ylabel(r"$\theta$", fontsize=ft) - axis[1, 2].tick_params(labelsize=ft) - - return xnew - - -def ceivarfig( - n, - x, - real_x, - emu, - theta, - fevals, - obs, - obsvar, - thetalimits, - prior_func, - prior_func_t, - thetatest=None, - x_mesh=None, - thetamesh=None, - posttest=None, - type_init=None, - synth_info=None, - theta_mle=None, - axis=None, -): - - ft = 16 - - p = theta.shape[1] - dt = thetamesh.shape[1] - dx = x.shape[1] - n_x = x.shape[0] - - xuniq = np.unique(x, axis=0) - - xt_ref = np.array([np.concatenate([xc, th]) for th in thetamesh for xc in x]) - - Smat3D, rVh_1_3d, pred_mean = temp_postphimat(emu._info, n_x, xt_ref, obs, obsvar) - - nmesh = 50 - a = np.arange(nmesh) / nmesh - b = np.arange(nmesh) / nmesh - X, Y = np.meshgrid(a, b) - Z = np.zeros((nmesh, nmesh)) - for i in range(nmesh): - for j in range(nmesh): - xt_cand = np.array([X[i, j], Y[i, j]]).reshape(1, dx + dt) - Z[i, j] = postphimat( - emu._info, - n_x, - xt_ref, - obs, - obsvar, - xt_cand, - Smat3D, - rVh_1_3d, - pred_mean, - ) - - ids = np.where(Z == Z.max()) - xnew = np.array( - [X[ids[0].flatten(), ids[1].flatten()], Y[ids[0].flatten(), ids[1].flatten()]] - ).reshape(1, dx + dt) - if theta.shape[0] == 11: - axis[0, 0].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[0, 0].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[0, 0].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - - axis[0, 0].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[0, 0].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[0, 0].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[0, 0].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[0, 0].set_xlabel(r"$x$", fontsize=ft) - axis[0, 0].set_ylabel(r"$\theta$", fontsize=ft) - axis[0, 0].tick_params(labelsize=ft) - elif theta.shape[0] == 16: - axis[0, 1].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[0, 1].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[0, 1].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - - axis[0, 1].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[0, 1].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[0, 1].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[0, 1].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[0, 1].set_xlabel(r"$x$", fontsize=ft) - axis[0, 1].set_ylabel(r"$\theta$", fontsize=ft) - axis[0, 1].tick_params(labelsize=ft) - elif theta.shape[0] == 29: - axis[0, 2].contourf(X, Y, Z, cmap="Purples", alpha=1) - axis[0, 2].hlines( - synth_info.true_theta, - 0, - 1, - linestyles="dotted", - linewidth=3, - colors="orange", - ) - for xitem in x: - axis[0, 2].vlines( - xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 - ) - - axis[0, 2].scatter( - xnew[0, 0], xnew[0, 1], marker="x", c="cyan", s=200, zorder=2, linewidth=3 - ) - axis[0, 2].scatter(theta[0:10, 0], theta[0:10, 1], marker="*", c="blue", s=50) - axis[0, 2].scatter( - theta[10:, 0], theta[10:, 1], marker="+", c="red", s=200, linewidth=3 - ) - axis[0, 2].text(0.8, 0.05, r"$n_t=$" + str(theta.shape[0]), fontsize=ft) - axis[0, 2].set_xlabel(r"$x$", fontsize=ft) - axis[0, 2].set_ylabel(r"$\theta$", fontsize=ft) - axis[0, 2].tick_params(labelsize=ft) - - return xnew diff --git a/examples/JQT2024/Figure3.py b/examples/JQT2024/Figure3.py deleted file mode 100644 index 852c115..0000000 --- a/examples/JQT2024/Figure3.py +++ /dev/null @@ -1,85 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from ptest_funcs import sinfunc - -# optional for presentation -# fig, axs = plt.subplots(1, 2, figsize=(24, 6)) -fig, axs = plt.subplots(1, 2, figsize=(12, 4)) -s = 1 -cls_data = sinfunc() -dt = len(cls_data.true_theta) -cls_data.realdata( - x=np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], seed=s -) - -th_vec = [np.pi / 7, np.pi / 6, np.pi / 5, np.pi / 4] -thlabel = [7, 6, 5, 4] -x_vec = (np.arange(0, 100, 1) / 100)[:, None] -fvec = np.zeros((len(th_vec), len(x_vec))) -colors = ["blue", "orange", "red", "green", "purple"] -for t_id, t in enumerate(th_vec): - for x_id, x in enumerate(x_vec): - fvec[t_id, x_id] = cls_data.function(x, t)[0] - axs[0].plot( - x_vec, - fvec[t_id, :], - label=r"$\theta=\pi/$" + str(thlabel[t_id]), - color=colors[t_id], - linewidth=3, - ) - -for d_id in range(len(cls_data.x)): - axs[0].scatter( - cls_data.x[d_id, 0], cls_data.real_data[0, d_id], color="black", s=50 - ) -# ft = 16 -ft = 16 -axs[0].set_xlabel(r"$x$", fontsize=ft) -axs[0].set_ylabel(r"$\eta(x, \theta)$", fontsize=ft) -axs[0].set_xticks([0.1, 0.3, 0.5, 0.7, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]) -axs[0].tick_params(labelsize=ft - 2) -axs[0].legend(bbox_to_anchor=(1.1, -0.2), fontsize=ft - 2, ncol=4) - -s = 1 -cls_data = sinfunc() -dt = len(cls_data.true_theta) -cls_data.realdata( - x=np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], - seed=s, - isbias=True, -) - -th_vec = [np.pi / 5] -thlabel = [5] -x_vec = (np.arange(0, 100, 1) / 100)[:, None] -fvec = np.zeros((len(th_vec), len(x_vec))) -colors = ["blue", "orange", "red", "green", "purple"] -biasvec = cls_data.bias(x_vec) -for t_id, t in enumerate(th_vec): - for x_id, x in enumerate(x_vec): - fvec[t_id, x_id] = cls_data.function(x, t)[0] - - axs[1].plot( - x_vec, fvec[t_id, :], label=r"$\eta(x, \theta=\pi/5)$", color="red", linewidth=3 - ) - axs[1].plot( - x_vec, - fvec[t_id, :] + biasvec.flatten(), - label=r"$\mathbb{E}[y(x)]$", - color="purple", - linestyle="dashed", - linewidth=3, - ) - -for d_id in range(len(cls_data.x)): - axs[1].scatter( - cls_data.x[d_id, 0], cls_data.real_data[0, d_id], color="black", s=50 - ) -ft = 16 -axs[1].set_xlabel(r"$x$", fontsize=ft) -axs[1].set_xticks([0.1, 0.3, 0.5, 0.7, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]) -axs[1].tick_params(labelsize=ft - 2) -axs[1].legend(bbox_to_anchor=(0.9, -0.2), fontsize=ft - 2, ncol=2) - -plt.savefig("Figure3.jpg", format="jpeg", bbox_inches="tight", dpi=1000) -plt.show() diff --git a/examples/JQT2024/Figure4.py b/examples/JQT2024/Figure4.py deleted file mode 100644 index 4a5200f..0000000 --- a/examples/JQT2024/Figure4.py +++ /dev/null @@ -1,79 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from ptest_funcs import pritam - -# fig, axs = plt.subplots(1, 2, figsize=(15, 6)) -fig, axs = plt.subplots(1, 2, figsize=(12, 4)) -s = 1 -x = np.linspace(0, 1, 3) -y = np.linspace(0, 1, 3) -xr = np.array([[xx, yy] for xx in x for yy in y]) -xr = np.concatenate((xr, xr)) - -# Data -cls_data = pritam() -cls_data.realdata(xr, seed=s) - -# Model -nmesh = 50 -a = np.arange(nmesh + 1) / nmesh -b = np.arange(nmesh + 1) / nmesh -X, Y = np.meshgrid(a, b) -Z = np.zeros((nmesh + 1, nmesh + 1)) -B = np.zeros((nmesh + 1, nmesh + 1)) -for i in range(nmesh + 1): - for j in range(nmesh + 1): - xt_cand = np.array([X[i, j], Y[i, j]]).reshape(1, 2) - Z[i, j] = cls_data.function(X[i, j], Y[i, j], 0.5) - B[i, j] = cls_data.bias(X[i, j], Y[i, j]) -# fig, ax = plt.subplots() -CS = axs[0].contourf(X, Y, Z, cmap="Purples", alpha=0.75) -fig.colorbar(CS) -for xid1 in range(len(x)): - for xid2 in range(len(y)): - axs[0].scatter( - x[xid1], x[xid2], marker="x", c="black", linewidth=3, s=100, zorder=2 - ) -ft = 16 -axs[0].set_xlim(-0.05, 1.05) -axs[0].set_ylim(-0.05, 1.05) -axs[0].set_xlabel(r"$x_1$", fontsize=ft) -axs[0].set_ylabel(r"$x_2$", fontsize=ft) -axs[0].set_xticks([0, 0.5, 1], [0, 0.5, 1], fontsize=ft - 2) -axs[0].set_yticks([0, 0.5, 1], [0, 0.5, 1], fontsize=ft - 2) -# plt.savefig("Figure4a.png", bbox_inches="tight") -# plt.show() - -# Bias -# CS = plt.contour(X, Y, B, cmap='Purples', alpha=0.75) -# plt.clabel(CS, inline=1, fontsize=14) -# for xid1 in range(len(x)): -# for xid2 in range(len(y)): -# plt.scatter(x[xid1], x[xid2], marker='x', c='black', linewidth=3, s=100, zorder=2) - -# plt.xlim(-0.02, 1.02) -# plt.ylim(-0.02, 1.02) -# plt.xlabel(r'$x_1$', fontsize=20) -# plt.ylabel(r'$x_2$', fontsize=20) -# plt.xticks([0, 0.5, 1], [0, 0.5, 1], fontsize=15) -# plt.yticks([0, 0.5, 1], [0, 0.5, 1], fontsize=15) -# plt.show() - -# Model + Bias -# fig, ax = plt.subplots() -CS = axs[1].contourf(X, Y, Z + B, cmap="Purples", alpha=0.75) -fig.colorbar(CS) -for xid1 in range(len(x)): - for xid2 in range(len(y)): - axs[1].scatter( - x[xid1], x[xid2], marker="x", c="black", linewidth=3, s=100, zorder=2 - ) - -axs[1].set_xlim(-0.05, 1.05) -axs[1].set_ylim(-0.05, 1.05) -axs[1].set_xlabel(r"$x_1$", fontsize=ft) -axs[1].set_ylabel(r"$x_2$", fontsize=ft) -axs[1].set_xticks([0, 0.5, 1], [0, 0.5, 1], fontsize=ft - 2) -axs[1].set_yticks([0, 0.5, 1], [0, 0.5, 1], fontsize=ft - 2) -plt.savefig("Figure4.jpg", format="jpeg", bbox_inches="tight", dpi=1000) -plt.show() diff --git a/examples/JQT2024/Figure5ab_sinf.py b/examples/JQT2024/Figure5ab_sinf.py deleted file mode 100644 index 78fde48..0000000 --- a/examples/JQT2024/Figure5ab_sinf.py +++ /dev/null @@ -1,185 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments, save_output -from PUQ.prior import prior_dist -from plots_design import create_test, add_result, samplingdata, observe_results -from ptest_funcs import sinfunc - -args = parse_arguments() - -ninit = 10 -nmax = 100 -result = [] - -args.seedmin = 0 -args.seedmax = 30 -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - print("Start replication=" + str(s)) - s = int(s) - cls_data = sinfunc() - dt = len(cls_data.true_theta) - cls_data.realdata( - x=np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], - seed=s, - ) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[0][0]]), - b=np.array([cls_data.thetalimits[0][1]]), - ) - prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[1][0]]), - b=np.array([cls_data.thetalimits[1][1]]), - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - # # # Create a mesh for test set # # # - xt_test, ftest, ptest, thetamesh, xmesh = create_test(cls_data) - nmesh = len(xmesh) - ytest = cls_data.function(xmesh, cls_data.true_theta).T - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - # # # # # # # # # # # # # # # # # # # # # - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarx", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - xt_eivarx = al_ceivarx._info["theta"] - f_eivarx = al_ceivarx._info["f"] - thetamle_eivarx = al_ceivarx._info["thetamle"][-1] - - save_output(al_ceivarx, cls_data.data_name, "ceivarx", 2, 1, s) - - res = { - "method": "eivarx", - "repno": s, - "Prediction Error": al_ceivarx._info["TV"], - "Posterior Error": al_ceivarx._info["HD"], - } - result.append(res) - # # # # # # # # # # # # # # # # # # # # # - al_ceivar = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivar", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - xt_eivar = al_ceivar._info["theta"] - f_eivar = al_ceivar._info["f"] - thetamle_eivar = al_ceivar._info["thetamle"][-1] - - save_output(al_ceivar, cls_data.data_name, "ceivar", 2, 1, s) - - res = { - "method": "eivar", - "repno": s, - "Prediction Error": al_ceivar._info["TV"], - "Posterior Error": al_ceivar._info["HD"], - } - result.append(res) - - # LHS - xt_LHS = samplingdata("LHS", nmax - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "is_thetamle": False, - }, - ) - xt_LHS = al_LHS._info["theta"] - f_LHS = al_LHS._info["f"] - thetamle_LHS = al_LHS._info["thetamle"][-1] - - save_output(al_LHS, cls_data.data_name, "lhs", 2, 1, s) - - res = { - "method": "lhs", - "repno": s, - "Prediction Error": al_LHS._info["TV"], - "Posterior Error": al_LHS._info["HD"], - } - result.append(res) - - # rnd - xt_RND = samplingdata("Random", nmax - ninit, cls_data, s, prior_xt) - al_RND = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_RND, - "is_thetamle": False, - }, - ) - xt_RND = al_RND._info["theta"] - f_RND = al_RND._info["f"] - thetamle_RND = al_RND._info["thetamle"][-1] - - save_output(al_RND, cls_data.data_name, "rnd", 2, 1, s) - - res = { - "method": "rnd", - "repno": s, - "Prediction Error": al_RND._info["TV"], - "Posterior Error": al_RND._info["HD"], - } - result.append(res) - - print("End replication=" + str(s)) - - method = ["eivarx", "eivar", "lhs", "rnd"] - observe_results(result, method, args.seedmax - args.seedmin, ninit, nmax) diff --git a/examples/JQT2024/Figure5c_sinf.py b/examples/JQT2024/Figure5c_sinf.py deleted file mode 100644 index 815c0e8..0000000 --- a/examples/JQT2024/Figure5c_sinf.py +++ /dev/null @@ -1,184 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.utils import parse_arguments, save_output -from PUQ.prior import prior_dist -from plots_design import create_test, add_result, samplingdata, observe_results -from ptest_funcs import sinfunc - -args = parse_arguments() - - -ninit = 10 -nmax = 100 -result = [] -args.seedmin = 1 -args.seedmax = 2 - -if __name__ == "__main__": - - for s in np.arange(args.seedmin, args.seedmax): - - s = int(s) - bias = True - - cls_data = sinfunc() - dt = len(cls_data.true_theta) - cls_data.realdata( - np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None], - seed=s, - isbias=bias, - ) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[0][0]]), - b=np.array([cls_data.thetalimits[0][1]]), - ) - prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[1][0]]), - b=np.array([cls_data.thetalimits[1][1]]), - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - # # # Create a mesh for test set # # # - xt_test, ftest, ptest, thetamesh, xmesh = create_test(cls_data, isbias=bias) - nmesh = len(xmesh) - ytest = (cls_data.function(xmesh, cls_data.true_theta) + cls_data.bias(xmesh)).T - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - # # # # # # # # # # # # # # # # # # # # # - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarxbias", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "unknowncov": True, - }, - ) - - xt_eivarx = al_ceivarx._info["theta"] - f_eivarx = al_ceivarx._info["f"] - - save_output(al_ceivarx, cls_data.data_name, "ceivarx", 2, 1, s) - - res = { - "method": "ceivarxbias", - "repno": s, - "Prediction Error": al_ceivarx._info["TV"], - "Posterior Error": al_ceivarx._info["HD"], - } - result.append(res) - # # # # # # # # # # # # # # # # # # # # # - al_ceivar = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarbias", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "unknowncov": True, - }, - ) - - xt_eivar = al_ceivar._info["theta"] - f_eivar = al_ceivar._info["f"] - - save_output(al_ceivar, cls_data.data_name, "ceivar", 2, 1, s) - - res = { - "method": "ceivarbias", - "repno": s, - "Prediction Error": al_ceivar._info["TV"], - "Posterior Error": al_ceivar._info["HD"], - } - result.append(res) - - # LHS - xt_LHS = samplingdata("LHS", nmax - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "unknowncov": True, - }, - ) - xt_LHS = al_LHS._info["theta"] - f_LHS = al_LHS._info["f"] - - save_output(al_LHS, cls_data.data_name, "lhs", 2, 1, s) - - res = { - "method": "lhs", - "repno": s, - "Prediction Error": al_LHS._info["TV"], - "Posterior Error": al_LHS._info["HD"], - } - result.append(res) - - # rnd - xt_RND = samplingdata("Random", nmax - ninit, cls_data, s, prior_xt) - al_RND = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_RND, - "unknowncov": True, - }, - ) - xt_RND = al_RND._info["theta"] - f_RND = al_RND._info["f"] - - save_output(al_RND, cls_data.data_name, "rnd", 2, 1, s) - - res = { - "method": "rnd", - "repno": s, - "Prediction Error": al_RND._info["TV"], - "Posterior Error": al_RND._info["HD"], - } - result.append(res) - - method = ["ceivarxbias", "ceivarbias", "lhs", "rnd"] - observe_results(result, method, args.seedmax - args.seedmin, ninit, nmax) diff --git a/examples/JQT2024/Figure6ab_pritam.py b/examples/JQT2024/Figure6ab_pritam.py deleted file mode 100644 index af79ec6..0000000 --- a/examples/JQT2024/Figure6ab_pritam.py +++ /dev/null @@ -1,182 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.utils import parse_arguments, save_output -from PUQ.prior import prior_dist -from plots_design import create_test_non, add_result, samplingdata, observe_results -from ptest_funcs import pritam - -args = parse_arguments() - - -ninit = 30 -nmax = 180 -result = [] - -args.seedmin = 0 -args.seedmax = 1 - -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - - s = int(s) - - x = np.linspace(0, 1, 3) - y = np.linspace(0, 1, 3) - xr = np.array([[xx, yy] for xx in x for yy in y]) - xr = np.concatenate((xr, xr)) - cls_data = pritam() - cls_data.realdata(xr, seed=s) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=cls_data.thetalimits[0:2, 0], b=cls_data.thetalimits[0:2, 1] - ) - prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[2][0]]), - b=np.array([cls_data.thetalimits[2][1]]), - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - xt_test, ftest, ptest, thetamesh, xmesh = create_test_non(cls_data) - ytest = cls_data.function( - xmesh[:, 0], xmesh[:, 1], cls_data.true_theta - ).reshape(1, len(xmesh)) - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarx", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - xt_eivarx = al_ceivarx._info["theta"] - f_eivarx = al_ceivarx._info["f"] - - save_output(al_ceivarx, cls_data.data_name, "ceivarx", 2, 1, s) - - res = { - "method": "eivarx", - "repno": s, - "Prediction Error": al_ceivarx._info["TV"], - "Posterior Error": al_ceivarx._info["HD"], - } - result.append(res) - - # # # # # # # # # # # # # # # # # # # # # - al_ceivar = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivar", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - xt_eivar = al_ceivar._info["theta"] - f_eivar = al_ceivar._info["f"] - - save_output(al_ceivar, cls_data.data_name, "ceivar", 2, 1, s) - - res = { - "method": "eivar", - "repno": s, - "Prediction Error": al_ceivar._info["TV"], - "Posterior Error": al_ceivar._info["HD"], - } - result.append(res) - - # LHS - xt_LHS = samplingdata("LHS", nmax - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "is_thetamle": False, - }, - ) - xt_LHS = al_LHS._info["theta"] - f_LHS = al_LHS._info["f"] - - save_output(al_LHS, cls_data.data_name, "lhs", 2, 1, s) - - res = { - "method": "lhs", - "repno": s, - "Prediction Error": al_LHS._info["TV"], - "Posterior Error": al_LHS._info["HD"], - } - result.append(res) - - # rnd - xt_RND = samplingdata("Random", nmax - ninit, cls_data, s, prior_xt) - al_RND = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_RND, - "is_thetamle": False, - }, - ) - xt_RND = al_RND._info["theta"] - f_RND = al_RND._info["f"] - - save_output(al_RND, cls_data.data_name, "rnd", 2, 1, s) - - res = { - "method": "rnd", - "repno": s, - "Prediction Error": al_RND._info["TV"], - "Posterior Error": al_RND._info["HD"], - } - result.append(res) - - method = ["eivarx", "eivar", "lhs", "rnd"] - observe_results(result, method, args.seedmax - args.seedmin, ninit, nmax) diff --git a/examples/JQT2024/Figure6c_pritam.py b/examples/JQT2024/Figure6c_pritam.py deleted file mode 100644 index 8f1e817..0000000 --- a/examples/JQT2024/Figure6c_pritam.py +++ /dev/null @@ -1,185 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.utils import parse_arguments, save_output -from PUQ.prior import prior_dist -from plots_design import create_test_non, add_result, samplingdata, observe_results -from ptest_funcs import pritam - -args = parse_arguments() - - -ninit = 30 -nmax = 80 -result = [] -args.seedmin = 0 -args.seedmax = 1 -for s in np.arange(args.seedmin, args.seedmax): - - s = int(s) - bias = True - - x = np.linspace(0, 1, 3) - y = np.linspace(0, 1, 3) - xr = np.array([[xx, yy] for xx in x for yy in y]) - xr = np.concatenate((xr, xr)) - - cls_data = pritam() - cls_data.realdata(xr, seed=s, isbias=bias) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=cls_data.thetalimits[0:2, 0], b=cls_data.thetalimits[0:2, 1] - ) - prior_t = prior_dist(dist="uniform")( - a=np.array([cls_data.thetalimits[2][0]]), - b=np.array([cls_data.thetalimits[2][1]]), - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - xt_test, ftest, ptest, thetamesh, xmesh = create_test_non(cls_data, is_bias=bias) - ytest = cls_data.function(xmesh[:, 0], xmesh[:, 1], cls_data.true_theta).reshape( - 1, len(xmesh) - ) + cls_data.bias(xmesh[:, 0], xmesh[:, 1]).reshape(1, len(xmesh)) - - # plt.plot(cls_data.function(cls_data.x[:, 0], cls_data.x[:, 1], cls_data.true_theta) + cls_data.bias(cls_data.x[:, 0], cls_data.x[:, 1])) - # plt.plot(cls_data.real_data.flatten()) - # plt.show() - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarxbias", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "unknowncov": True, - }, - ) - - xt_eivarx = al_ceivarx._info["theta"] - f_eivarx = al_ceivarx._info["f"] - - save_output(al_ceivarx, cls_data.data_name, "ceivarxbias", 2, 1, s) - - res = { - "method": "ceivarxbias", - "repno": s, - "Prediction Error": al_ceivarx._info["TV"], - "Posterior Error": al_ceivarx._info["HD"], - } - result.append(res) - - # # # # # # # # # # # # # # # # # # # # # - al_ceivar = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarbias", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "unknowncov": True, - }, - ) - - xt_eivar = al_ceivar._info["theta"] - f_eivar = al_ceivar._info["f"] - - save_output(al_ceivar, cls_data.data_name, "ceivarbias", 2, 1, s) - - res = { - "method": "ceivarbias", - "repno": s, - "Prediction Error": al_ceivar._info["TV"], - "Posterior Error": al_ceivar._info["HD"], - } - result.append(res) - - # LHS - xt_LHS = samplingdata("LHS", 180 - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "unknowncov": True, - }, - ) - xt_LHS = al_LHS._info["theta"] - f_LHS = al_LHS._info["f"] - - save_output(al_LHS, cls_data.data_name, "lhs", 2, 1, s) - - res = { - "method": "lhs", - "repno": s, - "Prediction Error": al_LHS._info["TV"], - "Posterior Error": al_LHS._info["HD"], - } - result.append(res) - - # rnd - xt_RND = samplingdata("Random", 180 - ninit, cls_data, s, prior_xt) - al_RND = designer( - data_cls=cls_data, - method="SEQDESBIAS", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_RND, - "unknowncov": True, - }, - ) - xt_RND = al_RND._info["theta"] - f_RND = al_RND._info["f"] - - save_output(al_RND, cls_data.data_name, "rnd", 2, 1, s) - - res = { - "method": "rnd", - "repno": s, - "Prediction Error": al_RND._info["TV"], - "Posterior Error": al_RND._info["HD"], - } - result.append(res) - -method = ["ceivarxbias", "ceivarbias", "lhs", "rnd"] -observe_results(result, method, args.seedmax - args.seedmin, ninit, nmax) diff --git a/examples/JQT2024/README.rst b/examples/JQT2024/README.rst deleted file mode 100644 index 60d13a6..0000000 --- a/examples/JQT2024/README.rst +++ /dev/null @@ -1,35 +0,0 @@ - -Examples -~~~~~~~~ - -These examples replicate the results from Sürer’s (2024) paper 'Simulation Experiment Design for Calibration via Active Learning'. - - -Instructions for running the illustrative examples -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -To replicate Figures~1--4, respectively: - -1) Go to the ``examples/JQT2024`` directory. - -2) Execute the followings from the command line:: - - python3 Figure1.py - python3 Figure2.py - python3 Figure3.py - python3 Figure4.py - -Running each script should not take more than 60 sec. See the figures (png files) saved under the directory. - -Instructions for running one of the prominent empirical results -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Instructions are provided to replicate the first two panels in Figure~6. - -To replicate, execute the following from the command line:: - - python3 Figure5ab_sinf.py - -Running this script takes about 3hrs on a personal Mac laptop. -Once completed, ``Figure5a.png`` and ``Figure5b.png`` are saved under the directory. - diff --git a/examples/JQT2024/highdim_ex.py b/examples/JQT2024/highdim_ex.py deleted file mode 100644 index 0d449b5..0000000 --- a/examples/JQT2024/highdim_ex.py +++ /dev/null @@ -1,200 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.utils import parse_arguments, save_output -from PUQ.prior import prior_dist -from plots_design import create_test_highdim, add_result, samplingdata -from ptest_funcs import highdim2 - -args = parse_arguments() - -ninit = 50 -nmax = 200 -result = [] - -size_x = 2 - -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - s = int(s) - xr = np.concatenate( - ( - np.repeat(0.5, size_x)[None, :], - np.repeat(0.5, size_x)[None, :], - np.repeat(0.5, size_x)[None, :], - np.repeat(0.5, size_x)[None, :], - ), - axis=0, - ) - # xr = np.concatenate((np.repeat(0.5, size_x)[None, :], np.repeat(0.5, size_x)[None, :], np.repeat(0.5, size_x)[None, :]), axis=0) - # xr = np.concatenate((np.repeat(0.5, size_x)[None, :], np.repeat(0.5, size_x)[None, :]), axis=0) - - cls_data = highdim2() - cls_data.realdata(xr, seed=s) - print(cls_data.sigma2) - - prior_xt = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] - ) - prior_x = prior_dist(dist="uniform")( - a=cls_data.thetalimits[0:size_x, 0], b=cls_data.thetalimits[0:size_x, 1] - ) - prior_t = prior_dist(dist="uniform")( - a=cls_data.thetalimits[size_x:, 0], b=cls_data.thetalimits[size_x:, 1] - ) - - priors = {"prior": prior_xt, "priorx": prior_x, "priort": prior_t} - - xt_test, ftest, ptest, thetamesh, xmesh = create_test_highdim(cls_data) - - if size_x == 2: - ytest = cls_data.function( - xmesh[:, 0], - xmesh[:, 1], - cls_data.true_theta[0], - cls_data.true_theta[1], - cls_data.true_theta[2], - cls_data.true_theta[3], - cls_data.true_theta[4], - cls_data.true_theta[5], - cls_data.true_theta[6], - cls_data.true_theta[7], - cls_data.true_theta[8], - cls_data.true_theta[9], - ).reshape(1, len(xmesh)) - elif size_x == 6: - ytest = cls_data.function( - xmesh[:, 0], - xmesh[:, 1], - xmesh[:, 2], - xmesh[:, 3], - xmesh[:, 4], - xmesh[:, 5], - cls_data.true_theta[0], - cls_data.true_theta[1], - cls_data.true_theta[2], - cls_data.true_theta[3], - cls_data.true_theta[4], - cls_data.true_theta[5], - ).reshape(1, len(xmesh)) - elif size_x == 10: - ytest = cls_data.function( - xmesh[:, 0], - xmesh[:, 1], - xmesh[:, 2], - xmesh[:, 3], - xmesh[:, 4], - xmesh[:, 5], - xmesh[:, 6], - xmesh[:, 7], - xmesh[:, 8], - xmesh[:, 9], - cls_data.true_theta[0], - cls_data.true_theta[1], - ).reshape(1, len(xmesh)) - - test_data = { - "theta": xt_test, - "f": ftest, - "p": ptest, - "y": ytest, - "th": thetamesh, - "xmesh": xmesh, - "p_prior": 1, - } - - al_imspe = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "imspe", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - save_output(al_imspe, cls_data.data_name, "imspe", 2, 1, s) - - al_ceivar = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivar", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - save_output(al_ceivar, cls_data.data_name, "ceivar", 2, 1, s) - - al_ceivarx = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "ceivarx", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - save_output(al_ceivarx, cls_data.data_name, "ceivarx", 2, 1, s) - - # LHS - xt_LHS = samplingdata("LHS", nmax - ninit, cls_data, s, prior_xt) - al_LHS = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": None, - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": xt_LHS, - "is_thetamle": False, - }, - ) - - save_output(al_LHS, cls_data.data_name, "lhs", 2, 1, s) - - al_maxvar = designer( - data_cls=cls_data, - method="SEQDES", - args={ - "mini_batch": 1, - "n_init_thetas": ninit, - "nworkers": 2, - "AL": "maxvar", - "seed_n0": s, - "prior": priors, - "data_test": test_data, - "max_evals": nmax, - "theta_torun": None, - "is_thetamle": False, - }, - ) - - save_output(al_maxvar, cls_data.data_name, "maxvar", 2, 1, s) diff --git a/examples/JQT2024/plot_reps.py b/examples/JQT2024/plot_reps.py deleted file mode 100644 index 22111ac..0000000 --- a/examples/JQT2024/plot_reps.py +++ /dev/null @@ -1,115 +0,0 @@ -from PUQ.utils import parse_arguments, save_output, read_output -import matplotlib.pyplot as plt -import matplotlib -import numpy as np - - -def plotresult(path, out, ex_name, w, b, rep, method, n0, nf): - - HDlist = [] - TVlist = [] - timelist = [] - for i in range(0, rep): - design_saved = read_output("", ex_name, method, w, b, i) - - TV = design_saved._info["TV"] - HD = design_saved._info["HD"] - - TVlist.append(TV[n0:nf]) - HDlist.append(HD[n0:nf]) - - avgTV = np.mean(np.array(TVlist), 0) - sdTV = np.std(np.array(TVlist), 0) - avgHD = np.mean(np.array(HDlist), 0) - sdHD = np.std(np.array(HDlist), 0) - - return avgHD, sdHD, avgTV, sdTV - - -def plot_aggregated(example_name="sinfunc", is_bias=False, rep=30): - - # choose either 'pritam' or 'sinfunc' - clist = ["b", "r", "g", "m", "y", "c", "pink", "purple"] - mlist = ["P", "p", "*", "o", "s", "h"] - linelist = ["-", "--", "-.", ":", "-.", ":"] - labelsb = [ - r"$\mathcal{A}^y$", - r"$\mathcal{A}^p$", - r"$\mathcal{A}^{lhs}$", - r"$\mathcal{A}^{rnd}$", - ] - batch = 1 - worker = 2 - fonts = 18 - # path = '/Users/ozgesurer/Desktop/des_examples/newPUQ/examples/' - # example_name = 'pritam' - # is_bias = True - if example_name == "pritam": - n0, nf = 30, 180 - if is_bias: - outs = "pritam_bias" - method = ["ceivarxbias", "ceivarbias", "lhs", "rnd"] - else: - outs = "pritam" - method = ["ceivarx", "ceivar", "lhs", "rnd"] - - elif example_name == "sinfunc": - n0, nf = 10, 100 - if is_bias: - outs = "sinf_bias" - method = ["ceivarx", "ceivar", "lhs", "rnd"] - else: - outs = "sinf" - method = ["ceivarx", "ceivar", "lhs", "rnd"] - - for metric in ["TV", "HD"]: - fig, axes = plt.subplots(1, 1, figsize=(6, 5)) - plt.rcParams["figure.autolayout"] = True - for mid, m in enumerate(method): - avgPOST, sdPOST, avgPRED, sdPRED = plotresult( - None, outs, example_name, worker, batch, rep, m, n0=n0, nf=nf - ) - if metric == "TV": - axes.plot( - np.arange(len(avgPRED)), - avgPRED, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=4, - ) - plt.fill_between( - np.arange(len(avgPRED)), - avgPRED - 1.96 * sdPRED / rep, - avgPRED + 1.96 * sdPRED / rep, - color=clist[mid], - alpha=0.1, - ) - elif metric == "HD": - axes.plot( - np.arange(len(avgPOST)), - avgPOST, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=4, - ) - plt.fill_between( - np.arange(len(avgPOST)), - avgPOST - 1.96 * sdPOST / rep, - avgPOST + 1.96 * sdPOST / rep, - color=clist[mid], - alpha=0.1, - ) - axes.set_yscale("log") - # axes.set_xscale('log') - axes.set_xlabel("# of parameters", fontsize=fonts) - - if metric == "TV": - axes.set_ylabel(r"${\rm MAD}^y$", fontsize=fonts) - elif metric == "HD": - axes.set_ylabel(r"${\rm MAD}^p$", fontsize=fonts) - axes.tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes.legend(bbox_to_anchor=(1, -0.2), ncol=4, fontsize=fonts, handletextpad=0.1) - plt.show() diff --git a/examples/JQT2024/plots_design.py b/examples/JQT2024/plots_design.py deleted file mode 100644 index 39ba87c..0000000 --- a/examples/JQT2024/plots_design.py +++ /dev/null @@ -1,366 +0,0 @@ -import matplotlib.pyplot as plt -import numpy as np -import scipy.stats as sps -from smt.sampling_methods import LHS - - -def plot_EIVAR(xt, cls_data, ninit, xlim1=0, xlim2=1): - - plt.scatter( - cls_data.x, - np.repeat(cls_data.true_theta, len(cls_data.x)), - marker="o", - color="black", - ) - plt.scatter(xt[0:ninit, 0], xt[0:ninit, 1], marker="*", color="blue") - plt.scatter(xt[:, 0][ninit:], xt[:, 1][ninit:], marker="+", color="red") - plt.axhline(y=cls_data.true_theta, color="green") - plt.xlabel("x") - plt.ylabel(r"$\theta$") - plt.show() - - plt.hist(xt[:, 1][ninit:]) - plt.axvline(x=cls_data.true_theta, color="r") - # plt.ylim(0, 1) - plt.xlim(0, 1) - plt.xlabel(r"$\theta$") - plt.show() - - plt.hist(xt[:, 0][ninit:]) - plt.xlabel(r"x") - plt.xlim(xlim1, xlim2) - plt.show() - - -def plot_des(des, xt, n0, cls_data): - xdes = np.array([e["x"] for e in des]) - fdes = np.array([e["feval"][0] for e in des]).T - xu_des, xcount = np.unique(xdes, return_counts=True) - repeatth = np.repeat(cls_data.true_theta, len(xu_des)) - for label, x_count, y_count in zip(xcount, xu_des, repeatth): - plt.annotate(label, xy=(x_count, y_count), xytext=(x_count, y_count)) - plt.scatter(xt[0:n0, 0], xt[0:n0, 1], marker="*", color="blue") - plt.scatter(xt[:, 0][n0:], xt[:, 1][n0:], marker="+", color="red") - plt.axhline(y=cls_data.true_theta, color="black") - plt.xlabel("x") - plt.ylabel(r"$\theta$") - plt.show() - - -def plot_des_pri(xt, cls_data, ninit, nmax): - xacq = xt[ninit:nmax, 0:2] - tacq = xt[ninit:nmax, 2] - - plt.hist(tacq) - plt.axvline(x=cls_data.true_theta, color="r") - plt.xlabel(r"$\theta$") - plt.xlim(0, 1) - plt.show() - - unq, cnt = np.unique(xacq, return_counts=True, axis=0) - plt.scatter(unq[:, 0], unq[:, 1]) - for label, x_count, y_count in zip(cnt, unq[:, 0], unq[:, 1]): - plt.annotate( - label, xy=(x_count, y_count), xytext=(5, -5), textcoords="offset points" - ) - plt.show() - - -def plot_LHS(xt, cls_data): - plt.scatter( - cls_data.x, - np.repeat(cls_data.true_theta, len(cls_data.x)), - marker="o", - color="black", - ) - plt.scatter(xt[:, 0], xt[:, 1], marker="*", color="blue") - plt.axhline(y=cls_data.true_theta, color="green") - plt.xlabel("x") - plt.ylabel(r"$\theta$") - plt.show() - - -def plot_post(theta, phat, ptest, phatvar): - if theta.shape[1] == 1: - plt.plot(theta.flatten(), phat, c="blue", linestyle="dashed") - plt.plot(theta.flatten(), ptest, c="black") - plt.fill_between( - theta.flatten(), phat - np.sqrt(phatvar), phat + np.sqrt(phatvar), alpha=0.2 - ) - plt.show() - else: - plt.scatter(theta[:, 0], theta[:, 1], c=phat) - plt.show() - - -def obsdata(cls_data, is_bias): - th_vec = [0.3, 0.4, 0.5, 0.6, 0.7] - x_vec = (np.arange(0, 100, 1) / 100)[:, None] - fvec = np.zeros((len(th_vec), len(x_vec))) - colors = ["blue", "orange", "green", "red", "purple"] - for t_id, t in enumerate(th_vec): - for x_id, x in enumerate(x_vec): - fvec[t_id, x_id] = cls_data.function(x, t) - plt.plot(x_vec, fvec[t_id, :], label=r"$\theta=$" + str(t), color=colors[t_id]) - - fvec = np.zeros(len(x_vec)) - for x_id, x in enumerate(x_vec): - fvec[x_id] = cls_data.function(x[0], cls_data.true_theta[0]) - - if is_bias: - fvec += cls_data.bias(x_vec).flatten() - plt.plot(x_vec, fvec) - - for d_id in range(cls_data.real_data.shape[1]): - plt.scatter(cls_data.x[d_id, 0], cls_data.real_data[0, d_id], color="black") - plt.xlabel("x") - plt.legend() - plt.show() - - -def create_test(cls_data, isbias=False): - thetamesh = np.linspace( - cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 100 - )[:, None] - xmesh = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 100)[ - :, None - ] - - n_t = thetamesh.shape[0] - n_x = xmesh.shape[0] - d = len(cls_data.x) - if cls_data.nodata: - thetatest, ftest, ptest = None, None, None - else: - # xdesign_vec = np.tile(cls_data.x.flatten(), n_t) - # thetatest = np.concatenate((xdesign_vec[:, None], np.repeat(thetamesh, d)[:, None]), axis=1) - xt_test = np.array( - [np.concatenate([xc, th]) for th in thetamesh for xc in cls_data.x] - ) - ftest = np.zeros((n_t, d)) - for t_id in range(n_t): - for x_id in range(d): - ftest[t_id, x_id] = cls_data.function( - cls_data.x[x_id, 0], thetamesh[t_id, 0] - ) - - ptest = np.zeros(n_t) - for i in range(n_t): - meanval = ftest[i, :] - if isbias: - meanval += cls_data.bias(cls_data.x).flatten() - rnd = sps.multivariate_normal(mean=meanval, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - - return xt_test, ftest, ptest, thetamesh, xmesh - - -def create_test_non(cls_data, is_bias=False): - n_t = 100 - n_x = cls_data.x.shape[0] - - thetamesh = np.linspace( - cls_data.thetalimits[2][0], cls_data.thetalimits[2][1], n_t - )[:, None] - - xt_test = np.array( - [np.concatenate([xc, th]) for th in thetamesh for xc in cls_data.x] - ) - ftest = np.zeros((n_t, n_x)) - for j in range(n_t): - for i in range(n_x): - ftest[j, i] = cls_data.function( - cls_data.x[i, 0], cls_data.x[i, 1], thetamesh[j, 0] - ) - - if is_bias: - biastrue = cls_data.bias(cls_data.x[:, 0], cls_data.x[:, 1]) - ptest = np.zeros(n_t) - for j in range(n_t): - rnd = sps.multivariate_normal( - mean=ftest[j, :] + biastrue, cov=cls_data.obsvar - ) - ptest[j] = rnd.pdf(cls_data.real_data) - else: - ptest = np.zeros(n_t) - for j in range(n_t): - rnd = sps.multivariate_normal(mean=ftest[j, :], cov=cls_data.obsvar) - ptest[j] = rnd.pdf(cls_data.real_data) - - x1 = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 20) - x2 = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 20) - X1, X2 = np.meshgrid(x1, x2) - xmesh = np.vstack([X1.ravel(), X2.ravel()]).T - - return xt_test, ftest, ptest, thetamesh, xmesh - - -def add_result(method_name, phat, ptest, yhat, ytest, s): - rep = {} - rep["method"] = method_name - rep["Posterior Error"] = np.mean(np.abs(phat - ptest)) - rep["Prediction Error"] = np.mean(np.abs(yhat - ytest)) - rep["repno"] = s - return rep - - -def samplingdata(typesampling, nmax, cls_data, seed, prior_xt): - - if typesampling == "LHS": - sampling = LHS(xlimits=cls_data.thetalimits, random_state=seed) - xt = sampling(nmax) - elif typesampling == "Random": - xt = prior_xt.rnd(nmax, seed=seed) - - return xt - - -def observe_results(result, method, rep, ninit, nmax): - - clist = ["b", "r", "g", "m"] - mlist = ["P", "p", "*", "o"] - linelist = ["-", "--", "-.", ":"] - labelsb = [ - r"$\mathcal{A}^y$", - r"$\mathcal{A}^p$", - r"$\mathcal{A}^{lhs}$", - r"$\mathcal{A}^{rnd}$", - ] - - fonts = 18 - for metric in ["TV", "HD"]: - fig, axes = plt.subplots(1, 1, figsize=(6, 5)) - plt.rcParams["figure.autolayout"] = True - for mid, m in enumerate(method): - if metric == "TV": - p = np.array( - [ - r["Prediction Error"][ninit:nmax] - for r in result - if r["method"] == m - ] - ) - meanerror = np.mean(p, axis=0) - sderror = np.std(p, axis=0) - axes.plot( - np.arange(len(meanerror)), - meanerror, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=4, - ) - plt.fill_between( - np.arange(len(meanerror)), - meanerror - 1.96 * sderror / rep, - meanerror + 1.96 * sderror / rep, - color=clist[mid], - alpha=0.1, - ) - elif metric == "HD": - p = np.array( - [ - r["Posterior Error"][ninit:nmax] - for r in result - if r["method"] == m - ] - ) - meanerror = np.mean(p, axis=0) - sderror = np.std(p, axis=0) - axes.plot( - np.arange(len(meanerror)), - meanerror, - label=labelsb[mid], - color=clist[mid], - linestyle=linelist[mid], - linewidth=4, - ) - plt.fill_between( - np.arange(len(meanerror)), - meanerror - 1.96 * sderror / rep, - meanerror + 1.96 * sderror / rep, - color=clist[mid], - alpha=0.1, - ) - axes.set_yscale("log") - axes.set_xlabel("# of simulation evals", fontsize=fonts) - - if metric == "TV": - axes.set_ylabel(r"${\rm MAD}^y$", fontsize=fonts) - elif metric == "HD": - axes.set_ylabel(r"${\rm MAD}^p$", fontsize=fonts) - axes.tick_params(axis="both", which="major", labelsize=fonts - 5) - - axes.legend( - bbox_to_anchor=(1.1, -0.2), ncol=4, fontsize=fonts, handletextpad=0.1 - ) - plt.show() - - -def create_test_highdim(cls_data, is_bias=False): - n_t = 1500 - n_x = cls_data.x.shape[0] - d_x = cls_data.x.shape[1] - sampling = LHS(xlimits=cls_data.thetalimits[d_x:, :], random_state=0) - thetamesh = sampling(n_t) - - xt_test = np.array( - [np.concatenate([xc, th]) for th in thetamesh for xc in cls_data.x] - ) - ftest = np.zeros((n_t, n_x)) - for j in range(n_t): - for i in range(n_x): - if d_x == 2: - ftest[j, i] = cls_data.function( - cls_data.x[i, 0], - cls_data.x[i, 1], - thetamesh[j, 0], - thetamesh[j, 1], - thetamesh[j, 2], - thetamesh[j, 3], - thetamesh[j, 4], - thetamesh[j, 5], - thetamesh[j, 6], - thetamesh[j, 7], - thetamesh[j, 8], - thetamesh[j, 9], - ) - elif d_x == 6: - ftest[j, i] = cls_data.function( - cls_data.x[i, 0], - cls_data.x[i, 1], - cls_data.x[i, 2], - cls_data.x[i, 3], - cls_data.x[i, 4], - cls_data.x[i, 5], - thetamesh[j, 0], - thetamesh[j, 1], - thetamesh[j, 2], - thetamesh[j, 3], - thetamesh[j, 4], - thetamesh[j, 5], - ) - elif d_x == 10: - ftest[j, i] = cls_data.function( - cls_data.x[i, 0], - cls_data.x[i, 1], - cls_data.x[i, 2], - cls_data.x[i, 3], - cls_data.x[i, 4], - cls_data.x[i, 5], - cls_data.x[i, 6], - cls_data.x[i, 7], - cls_data.x[i, 8], - cls_data.x[i, 9], - thetamesh[j, 0], - thetamesh[j, 1], - ) - - ptest = np.zeros(n_t) - for j in range(n_t): - rnd = sps.multivariate_normal(mean=ftest[j, :], cov=cls_data.obsvar) - ptest[j] = rnd.pdf(cls_data.real_data) - sampling = LHS(xlimits=cls_data.thetalimits[0:d_x, :], random_state=0) - xmesh = sampling(1500) - - return xt_test, ftest, ptest, thetamesh, xmesh diff --git a/examples/JQT2024/ptest_funcs.py b/examples/JQT2024/ptest_funcs.py deleted file mode 100644 index 6bcf41f..0000000 --- a/examples/JQT2024/ptest_funcs.py +++ /dev/null @@ -1,309 +0,0 @@ -import numpy as np - - -class sinfunc: - def __init__(self): - self.data_name = "sinfunc" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.true_theta = np.array([np.pi / 5]) - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.dx = 1 - self.x = None - self.real_data = None - self.sigma2 = 0.2**2 - self.nodata = True - - def function(self, x, theta): - f = np.sin(10 * x - 5 * theta) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - return H_o, persis_info - - def realdata(self, x, seed, isbias=False): - self.x = x - self.nodata = False - self.obsvar = np.diag(np.repeat(self.sigma2, len(self.x))) - - np.random.seed(seed) - fevals = np.zeros(len(x)) - for xid, x in enumerate(self.x): - fevals[xid] = self.genobsdata(x, isbias) - - self.real_data = np.array([fevals], dtype="float64") - - def genobsdata(self, x, isbias=False): - if isbias: - return ( - self.function(x[0], self.true_theta[0]) - + self.bias(x[0]) - + np.random.normal(0, np.sqrt(self.sigma2), 1) - ) - else: - return self.function(x[0], self.true_theta[0]) + np.random.normal( - 0, np.sqrt(self.sigma2), 1 - ) - - def bias(self, x): - return 1 - (1 / 3) * x - (2 / 3) * (x**2) - - -class pritam: - def __init__(self): - self.data_name = "pritam" - self.thetalimits = np.array([[0, 1], [0, 1], [0, 1]]) - self.true_theta = np.array([0.5]) - self.out = [("f", float)] - self.d = 1 - self.p = 3 - self.x = None - self.real_data = None - self.dx = 2 - self.sigma2 = 0.5**2 - self.nodata = True - - def function(self, x1, x2, theta1): - f = (30 + 5 * x1 * np.sin(5 * x1)) * (6 * theta1 + 1 + np.exp(-5 * x2)) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1], H["thetas"][0][2]) - return H_o, persis_info - - def realdata(self, x, seed, isbias=False): - self.x = x - self.nodata = False - self.obsvar = np.diag(np.repeat(self.sigma2, len(self.x))) - - np.random.seed(seed) - fevals = np.zeros(len(x)) - for xid, x in enumerate(self.x): - fevals[xid] = self.genobsdata(x, isbias) - - self.real_data = np.array([fevals], dtype="float64") - - def genobsdata(self, x, isbias=False): - if isbias: - return ( - self.function(x[0], x[1], self.true_theta[0]) - + self.bias(x[0], x[1]) - + np.random.normal(0, np.sqrt(self.sigma2), 1) - ) - else: - return self.function(x[0], x[1], self.true_theta[0]) + np.random.normal( - 0, np.sqrt(self.sigma2), 1 - ) - - def bias(self, x1, x2): - return -50 * (np.exp(-0.2 * x1 - 0.1 * x2)) - - -class highdim2: - def __init__(self): - self.data_name = "highdim" - self.thetalimits = np.array( - [ - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - [0, 1], - ] - ) - self.true_theta = None - self.out = [("f", float)] - self.d = 1 - self.p = 12 - self.x = None - self.real_data = None - self.dx = None - self.sigma2 = None # 1#5#10 - self.nodata = True - - def function( - self, - theta1, - theta2, - theta3, - theta4, - theta5, - theta6, - theta7, - theta8, - theta9, - theta10, - theta11, - theta12, - ): - - a = -5 - b = 10 - if self.dx == 2: - # print(self.dx) - theta3 = a + b * theta3 - theta4 = a + b * theta4 - theta5 = a + b * theta5 - theta6 = a + b * theta6 - theta7 = a + b * theta7 - theta8 = a + b * theta8 - theta9 = a + b * theta9 - theta10 = a + b * theta10 - theta11 = a + b * theta11 - theta12 = a + b * theta12 - elif self.dx == 6: - # print(self.dx) - theta7 = a + b * theta7 - theta8 = a + b * theta8 - theta9 = a + b * theta9 - theta10 = a + b * theta10 - theta11 = a + b * theta11 - theta12 = a + b * theta12 - elif self.dx == 10: - # print(self.dx) - theta11 = a + b * theta11 - theta12 = a + b * theta12 - - if self.x.shape[1] == 2: - f = np.sqrt(np.abs(theta1 + theta2)) * np.power( - theta3 - + theta4 - + theta5 - + theta6 - + theta7 - + theta8 - + theta9 - + theta10 - + theta11 - + theta12, - 2, - ) - # f = np.sqrt(np.abs(theta1 + theta2))*((theta3 + theta4)**2 + (theta5 + theta6)**2 + (theta7 + theta8)**2 + (theta9 + theta10)**2 + (theta11 + theta12)**2) - elif self.x.shape[1] == 6: - f = np.sqrt( - np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6) - ) * np.power(theta7 + theta8 + theta9 + theta10 + theta11 + theta12, 2) - # f = np.sqrt(np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6))*((theta7 + theta8)**2 + (theta9 + theta10)**2 + (theta11 + theta12)**2) - elif self.x.shape[1] == 10: - f = np.sqrt( - np.abs( - theta1 - + theta2 - + theta3 - + theta4 - + theta5 - + theta6 - + theta7 - + theta8 - + theta9 - + theta10 - ) - ) * np.power(theta11 + theta12, 2) - # f = np.sqrt(np.abs(theta1 + theta2 + theta3 + theta4 + theta5 + theta6 + theta7 + theta8 + theta9 + theta10))*((theta11 + theta12)**2) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function( - H["thetas"][0][0], - H["thetas"][0][1], - H["thetas"][0][2], - H["thetas"][0][3], - H["thetas"][0][4], - H["thetas"][0][5], - H["thetas"][0][6], - H["thetas"][0][7], - H["thetas"][0][8], - H["thetas"][0][9], - H["thetas"][0][10], - H["thetas"][0][11], - ) - return H_o, persis_info - - def realdata(self, x, seed, isbias=False): - self.x = x - self.dx = x.shape[1] - - if self.dx == 2: - self.sigma2 = 25 - elif self.dx == 6: - self.sigma2 = 5 - elif self.dx == 10: - self.sigma2 = 1 - - self.true_theta = np.repeat(0.5, self.p - self.dx) - self.nodata = False - self.obsvar = np.diag(np.repeat(self.sigma2, x.shape[0])) - - np.random.seed(seed) - fevals = np.zeros(len(x)) - for xid, x in enumerate(self.x): - fevals[xid] = self.genobsdata(x, isbias) - - self.real_data = np.array([fevals], dtype="float64") - - def genobsdata(self, x, isbias=False): - - if len(x) == 2: - print("Obs:", len(x)) - return self.function( - x[0], - x[1], - self.true_theta[0], - self.true_theta[1], - self.true_theta[2], - self.true_theta[3], - self.true_theta[4], - self.true_theta[5], - self.true_theta[6], - self.true_theta[7], - self.true_theta[8], - self.true_theta[9], - ) + np.random.normal(0, np.sqrt(self.sigma2), 1) - elif len(x) == 6: - print("Obs:", len(x)) - return self.function( - x[0], - x[1], - x[2], - x[3], - x[4], - x[5], - self.true_theta[0], - self.true_theta[1], - self.true_theta[2], - self.true_theta[3], - self.true_theta[4], - self.true_theta[5], - ) + np.random.normal(0, np.sqrt(self.sigma2), 1) - - elif len(x) == 10: - print("Obs:", len(x)) - return self.function( - x[0], - x[1], - x[2], - x[3], - x[4], - x[5], - x[6], - x[7], - x[8], - x[9], - self.true_theta[0], - self.true_theta[1], - ) + np.random.normal(0, np.sqrt(self.sigma2), 1) diff --git a/examples/NeurIPS2024/Figure2.py b/examples/NeurIPS2024/Figure2.py deleted file mode 100644 index 5b7e8a3..0000000 --- a/examples/NeurIPS2024/Figure2.py +++ /dev/null @@ -1,92 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from mpl_toolkits.axes_grid1 import make_axes_locatable -from matplotlib.ticker import FixedFormatter - - -repno = 1 -n = 2560 -varlist = [0.1, 10] -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2, 2**6] -a_mean = [2 ** (-2), 2 ** (-1), 2**0, 2**1, 2**2, 2**3, 2**4, 2**5] -acq_tick = [ - r"$2^{-2}$", - r"$2^{-1}$", - r"$2^0$", - r"$2^1$", - r"$2^2$", - r"$2^3$", - r"$2^4$", - r"$2^5$", -] -accparams = [ - [-1, 0.2], - [-1, 0.22], - [-1, 0.24], - [-1, 0.26], - [-1, 0.28], - [-1, 0.3], - [-1, 0.32], - [-1, 0.34], - [-1, 0.36], -] -acclevel = 0.2 - - -fig, ax = plt.subplots(1, 2, figsize=(12, 5)) -for sid, sm in enumerate(s_mean): - result = [] - for aid, am in enumerate(a_mean): - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 0.001, typeGen="linear") - PM.gen_simtime(sm, sm * 1, 0.01, typeSim="normal", seed=r) - - PM.gen_curve(-1, accparams[id_b][1], typeAcc="exponential") - - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "am": am, - "var": 1, - "sm": sm, - "res": PM, - } - ) - - timemat = np.zeros((len(a_mean), len(batches))) - bmat = np.zeros((len(a_mean), len(batches))) - for aid, am in enumerate(a_mean): - for bid, b in enumerate(batches): - res_c = [res for res in result if ((res["am"] == am) & (res["b"] == b))] - timemat[aid, bid] = res_c[0]["res"].complete_time - - ft = 20 - bo = np.argsort(np.argsort(timemat, axis=1), axis=1) - im = ax[sid].imshow(bo, aspect="auto", cmap="YlOrRd") - cbar = fig.colorbar( - im, - ticks=np.array([0.0, 0.5, 1.0]) * bo.max(), - format=FixedFormatter(["low", "mid", "high"]), - ) - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.set_ylabel("Wall-Clock Time", rotation=-90, va="bottom", fontsize=ft - 5) - # # Show all ticks and label them with the respective list entries - ax[sid].set_xticks(np.arange(len(batches)), labels=batches) - ax[sid].set_yticks(np.arange(len(a_mean)), labels=acq_tick) - ax[sid].set_xlabel("Batch Size", fontsize=ft) - ax[sid].set_ylabel("Acquisition Time", fontsize=ft) - ax[sid].tick_params(axis="both", which="major", labelsize=ft - 5) -fig.suptitle("Simulation Time Increases \u2192", fontsize=ft) -fig.tight_layout() -plt.savefig("Figure_WS_1.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/Figure3.py b/examples/NeurIPS2024/Figure3.py deleted file mode 100644 index 2b40347..0000000 --- a/examples/NeurIPS2024/Figure3.py +++ /dev/null @@ -1,137 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from mpl_toolkits.axes_grid1 import make_axes_locatable - -repno = 1 -n = 2560 -varlist = [0.01, 1] -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2**0, 2**1, 2**2, 2**3, 2**4, 2**5] -a_mean = [2 ** (-5), 2 ** (-3), 2 ** (-1), 2**1, 2**3, 2**5] -sim_tick = [r"$2^0$", r"$2^1$", r"$2^2$", r"$2^3$", r"$2^4$", r"$2^5$"] -acq_tick = [r"$2^{-5}$", r"$2^{-3}$", r"$2^{-1}$", r"$2$", r"$2^3$", r"$2^5$"] - -accparams = [ - [-1, 0.2], - [-1, 0.22], - [-1, 0.24], - [-1, 0.26], - [-1, 0.28], - [-1, 0.3], - [-1, 0.32], - [-1, 0.34], - [-1, 0.36], -] -acclevel = 0.2 - - -fig, ax = plt.subplots(1, 2, figsize=(12, 6)) -for vid, var in enumerate(varlist): - result = [] - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - res = [] - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 2 ** (-5), typeGen="linear") - PM.gen_simtime(sm, sm * var, 0.01, typeSim="normal", seed=r) - PM.gen_curve(-1, accparams[id_b][1], typeAcc="exponential") - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "am": am, - "var": var, - "sm": sm, - "res": PM, - } - ) - - timemat = np.zeros((len(a_mean), len(s_mean))) - bmat = np.zeros((len(a_mean), len(s_mean))) - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - res_c = [res for res in result if ((res["am"] == am) & (res["sm"] == sm))] - - rmin = np.inf - bmin = np.inf - for bid, b in enumerate(batches): - cs = [rs["res"].complete_time for rs in res_c if rs["b"] == b] - if np.mean(cs) < rmin: - rmin = np.mean(cs) - bmin = b - - timemat[aid, sid] = rmin - bmat[aid, sid] = bmin - import matplotlib.colors as mcolors - - bounds = [ - 0.5, - 1.5, - 3.5, - 7.5, - 15.5, - 31.5, - 63.5, - 127.5, - 255.5, - 512, - ] # bounds to differentiate colors - cmap = plt.get_cmap("YlOrRd", len(batches)) - norm = mcolors.BoundaryNorm(bounds, cmap.N) # Normalize colors based on bounds - - # Heatmap - ft = 20 - divider = make_axes_locatable(ax[vid]) - cax = divider.append_axes("right", size="5%", pad=0.05) - im = ax[vid].imshow(bmat, cmap=cmap, norm=norm) - - # Show all ticks and label them with the respective list entries - ax[vid].set_xticks(np.arange(len(s_mean)), labels=sim_tick) - ax[vid].set_yticks(np.arange(len(a_mean)), labels=acq_tick) - ax[vid].tick_params(axis="both", which="major", labelsize=ft - 5) - # Rotate the tick labels and set their alignment. - plt.setp(ax[vid].get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") - - # Loop over data dimensions and create text annotations. - for i in range(len(a_mean)): - for j in range(len(s_mean)): - if bmat[i, j] >= 64: - text = ax[vid].text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="white", - fontsize=ft - 5, - ) - else: - text = ax[vid].text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="black", - fontsize=ft - 5, - ) - - ax[vid].grid(which="minor", color="w", linestyle="-", linewidth=3) - ax[vid].set_xlabel("Simulation Time", fontsize=ft) - ax[vid].set_ylabel("Acquisition Time", fontsize=ft) - cbar = fig.colorbar(im, cax=cax, orientation="vertical") - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.set_ylabel("Batch Size", rotation=-90, va="bottom", fontsize=ft - 5) - cbar.set_ticks(batches) -fig.suptitle("Variability in Simulation Time Increases \u2192", fontsize=ft) -fig.tight_layout() -plt.savefig("Figure_WS_2.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/README.rst b/examples/NeurIPS2024/README.rst deleted file mode 100644 index c3d2132..0000000 --- a/examples/NeurIPS2024/README.rst +++ /dev/null @@ -1,21 +0,0 @@ -Examples -~~~~~~~~ - -These examples replicate the results presented in the paper titled 'An Active Learning Performance Model -for Parallel Bayesian Calibration of Expensive Simulations ' -by Sürer and Wild (2024). - -**Instructions for running the illustrative examples with performance model** - -To replicate Figures~2 and 3: - -1) Go to the ``examples/`` directory. - -2) Execute any of the following from the command line: - -.. code-block:: python - - python Figure2.py - python Figure3.py - -Running each script should not take more than 90 sec. See the figures (jpeg files) saved under ``examples/`` directory. diff --git a/examples/NeurIPS2024/WS0.py b/examples/NeurIPS2024/WS0.py deleted file mode 100644 index 3d4dd6b..0000000 --- a/examples/NeurIPS2024/WS0.py +++ /dev/null @@ -1,113 +0,0 @@ -from plotutils import plotresult, plotparams -import matplotlib.pyplot as plt -import numpy as np - -# Generates Fig 3, 4, 11, 12 -clist = ["b", "r", "g", "m", "y", "c"] -mlist = ["P", "o", "*", "s", "p", "h"] -linelist = ["-", "--", "-.", ":", "-.", ":"] - -lw = 5 -ms = 15 -me = 200 - -labelsb = ["EIVAR"] - -batch = 1 -worker = 2 -rep = 0 -fonts = 25 - -path = "/Users/ozgesurer/Desktop/WS_data/batch_newjobs/" -ex = "himmelblau" -m = "eivar" -markers = ["o", "+", "*", "P", "^", "D", "s", "p", "h"] -colors = [ - "red", - "blue", - "orange", - "magenta", - "cyan", - "green", - "purple", - "yellow", - "pink", -] - -n = 2560 -ft = 20 -fig, axes = plt.subplots(1, 3, figsize=(20, 5)) -for id_b, b in enumerate([1, 2, 4, 8, 16, 32, 64, 128, 256]): - - out = "b" + str(b) - avgAE, avgtime, avgTV = plotresult(path, out, ex, b + 1, b, rep, m, n0=0, nf=n + 1) - - # if b == 256: - # print(avgTV[-1]) - - print(np.where(avgTV <= 1.7536062885144855e-05)[0][0]) - - axes[0].plot(np.arange(0, n + 1), avgTV, c=colors[id_b]) - - axes[0].plot( - np.arange(0, n + 1), - avgTV, - markers[id_b], - markevery=256, - markersize=ms, - label=str(b), - c=colors[id_b], - ) - - axes[1].plot( - np.arange(0, len(np.unique(avgTV))), - sorted(np.unique(avgTV), reverse=True), - c=colors[id_b], - ) - - axes[1].plot( - np.arange(0, len(np.unique(avgTV))), - sorted(np.unique(avgTV), reverse=True), - markers[id_b], - markersize=ms, - label=str(b), - c=colors[id_b], - ) - - print(sum(avgtime)) - - axes[2].scatter( - np.arange(0, len(avgtime[0:][avgtime[0:] > 0])), - avgtime[0:][avgtime[0:] > 0], - marker=markers[id_b], - s=ms * 2, - label=str(b), - c=colors[id_b], - ) - - -axes[0].set_xlabel("# of sim evals", fontsize=ft) -axes[0].set_ylabel("MAD", fontsize=ft) -axes[1].set_xlabel("# of stages", fontsize=ft) -axes[1].set_ylabel("MAD", fontsize=ft) -axes[2].set_xlabel("# of stages", fontsize=ft) -axes[2].set_ylabel("Acquisition time", fontsize=ft) - -axes[1].set_xscale("log") -axes[0].set_yscale("log") -axes[1].set_yscale("log") -axes[2].set_yscale("log") -axes[2].set_xscale("log") - -axes[2].tick_params(axis="both", which="major", labelsize=ft - 10) -axes[0].tick_params(axis="both", which="major", labelsize=ft - 10) -axes[1].tick_params(axis="x", which="major", labelsize=ft - 10) -axes[1].tick_params(axis="y", which="major", length=0) - - -axes[0].legend( - loc="upper center", bbox_to_anchor=(1.6, -0.25), ncol=9, fontsize=fonts - 10 -) -# fig.tight_layout() -plt.savefig("Figure_WS_0.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/WS0_run.py b/examples/NeurIPS2024/WS0_run.py deleted file mode 100644 index 045bf7d..0000000 --- a/examples/NeurIPS2024/WS0_run.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments, save_output -import scipy.stats as sps -from PUQ.prior import prior_dist -from test_funcs import holder, ackley, easom, sphere, matyas, himmelblau -import matplotlib.pyplot as plt -import time - -start = time.time() - -args = parse_arguments() - -cls_data = eval(args.funcname)() - -# # # Create a mesh for test set # # # -xpl = np.linspace(cls_data.thetalimits[0][0], cls_data.thetalimits[0][1], 50) -ypl = np.linspace(cls_data.thetalimits[1][0], cls_data.thetalimits[1][1], 50) -Xpl, Ypl = np.meshgrid(xpl, ypl) -th = np.vstack([Xpl.ravel(), Ypl.ravel()]) -setattr(cls_data, "theta", th.T) - -ftest = np.zeros(2500) -for tid, t in enumerate(th.T): - ftest[tid] = cls_data.function(t[0], t[1]) -thetatest = th.T -ptest = np.zeros(thetatest.shape[0]) -for i in range(ftest.shape[0]): - mean = ftest[i] - rnd = sps.multivariate_normal(mean=mean, cov=cls_data.obsvar) - ptest[i] = rnd.pdf(cls_data.real_data) - -test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - -# # # # # # # # # # # # # # # # # # # # # -prior_func = prior_dist(dist="uniform")( - a=cls_data.thetalimits[:, 0], b=cls_data.thetalimits[:, 1] -) -# # # # # # # # # # # # # # # # # # # # # - -init_seeds = 0 # args.init_seeds -final_seeds = 1 # args.final_seeds -args.max_eval = 512 -n_init = 10 -s = 0 - -for b in [32]: - - args.nworkers = 257 - args.minibatch = b - - thetainit = prior_func.rnd(n_init, s) - finit = np.zeros(n_init) - for tid, t in enumerate(thetainit): - finit[tid] = cls_data.function(t[0], t[1]) - test_data["thetainit"] = thetainit - test_data["finit"] = finit[None, :] - - al_data = designer( - data_cls=cls_data, - method="SEQCALOPT", - args={ - "mini_batch": args.minibatch, - "nworkers": args.minibatch + 1, - "AL": "eivar", - "seed_n0": int(s), - "prior": prior_func, - "data_test": test_data, - "max_evals": args.max_eval, - "candsize": args.candsize, - "refsize": args.refsize, - "believer": 2, - }, - ) - save_output( - al_data, cls_data.data_name, args.al_func, args.nworkers, args.minibatch, int(s) - ) - - plt.plot(al_data._info["TV"]) - # theta_al = al_data._info["theta"] - # ft = 20 - # ms = 50 - # fig, ax = plt.subplots(1, 1, figsize=(8, 6)) - # cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - # ax.scatter(theta_al[:, 0], theta_al[:, 1], c="black", marker="+", s=ms, zorder=2) - # ax.scatter( - # thetainit[:, 0], - # thetainit[:, 1], - # zorder=2, - # marker="o", - # facecolors="none", - # edgecolors="blue", - # ) - # ax.set_xlabel(r"$\theta_1$", fontsize=ft) - # ax.set_ylabel(r"$\theta_2$", fontsize=ft) - # ax.tick_params(axis="both", labelsize=ft) - # plt.show() -plt.yscale("log") -plt.show() -end = time.time() -print("Elapsed time =", round(end - start, 3)) diff --git a/examples/NeurIPS2024/WS1_real.py b/examples/NeurIPS2024/WS1_real.py deleted file mode 100644 index 3b52907..0000000 --- a/examples/NeurIPS2024/WS1_real.py +++ /dev/null @@ -1,99 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from matplotlib.ticker import FixedFormatter -from plotutils import plotresult - - -repno = 5 -n = 2560 -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2, 2**6] -a_mean = [ - 2 ** (-2), - 2 ** (-1), - 2 ** (0), - 2 ** (1), - 2 ** (2), - 2 ** (3), - 2 ** (4), - 2 ** (5), -] -acq_tick = [ - r"$2^{-2}$", - r"$2^{-1}$", - r"$2^{0}$", - r"$2^1$", - r"$2^2$", - r"$2^3$", - r"$2^4$", - r"$2^5$", -] - -acclevel = 1.7536062885144855e-05 -rep = 0 -ex = "himmelblau" -m = "eivar" -path = "/Users/ozgesurer/Desktop/WS_data/batch_newjobs/" - -fig, ax = plt.subplots(1, 2, figsize=(12, 5)) -for sid, sm in enumerate(s_mean): - result = [] - for aid, am in enumerate(a_mean): - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 0, typeGen="linear") - PM.gen_simtime(sm, sm, 0.01, typeSim="normal", seed=r) - - out = "b" + str(b) - avgAE, avgtime, avgTV = plotresult( - path, out, ex, b + 1, b, rep, m, n0=0, nf=n + 1 - ) - PM.acc = avgTV - - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "am": am, - "var": 1, - "sm": sm, - "res": PM, - } - ) - - timemat = np.zeros((len(a_mean), len(batches))) - bmat = np.zeros((len(a_mean), len(batches))) - for aid, am in enumerate(a_mean): - for bid, b in enumerate(batches): - res_c = [res for res in result if ((res["am"] == am) & (res["b"] == b))] - timemat[aid, bid] = np.mean( - [r["res"].complete_time for r in res_c] - ) # res_c[0]["res"].complete_time - - ft = 20 - bo = np.argsort(np.argsort(timemat, axis=1), axis=1) - im = ax[sid].imshow(bo, aspect="auto", cmap="YlOrRd") - cbar = fig.colorbar( - im, - ticks=np.array([0.0, 0.5, 1.0]) * bo.max(), - format=FixedFormatter(["low", "mid", "high"]), - ) - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.set_ylabel("Wall-Clock Time", rotation=-90, va="bottom", fontsize=ft - 5) - # # Show all ticks and label them with the respective list entries - ax[sid].set_xticks(np.arange(len(batches)), labels=batches) - ax[sid].set_yticks(np.arange(len(a_mean)), labels=acq_tick) - ax[sid].set_xlabel("Batch Size", fontsize=ft) - ax[sid].set_ylabel("Acquisition Time", fontsize=ft) - ax[sid].tick_params(axis="both", which="major", labelsize=ft - 5) -fig.suptitle("Simulation Time Increases \u2192", fontsize=ft) -fig.tight_layout() -plt.savefig("Figure_WS_1.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/WS2_real.py b/examples/NeurIPS2024/WS2_real.py deleted file mode 100644 index 7705805..0000000 --- a/examples/NeurIPS2024/WS2_real.py +++ /dev/null @@ -1,135 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -from PUQ.performance import performanceModel -from mpl_toolkits.axes_grid1 import make_axes_locatable -from plotutils import plotresult - -repno = 5 -n = 2560 -varlist = [0.01, 1] -worker = 256 -batches = [1, 2, 4, 8, 16, 32, 64, 128, 256] -s_mean = [2**0, 2**1, 2**2, 2**3, 2**4, 2**5] -a_mean = [2 ** (-5), 2 ** (-3), 2 ** (-1), 2, 2**3, 2**5] -sim_tick = [r"$2^0$", r"$2^1$", r"$2^2$", r"$2^3$", r"$2^4$", r"$2^5$"] -acq_tick = [r"$2^{-5}$", r"$2^{-3}$", r"$2^{-1}$", r"$2^{1}$", r"$2^3$", r"$2^5$"] -acclevel = 1.7536062885144855e-05 -rep = 0 -ex = "himmelblau" -m = "eivar" -path = "/Users/ozgesurer/Desktop/WS_data/batch_newjobs/" - -fig, ax = plt.subplots(1, 2, figsize=(12, 6)) -for vid, var in enumerate(varlist): - result = [] - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - res = [] - for r in range(repno): - for id_b, b in enumerate(batches): - PM = performanceModel(worker=worker, batch=b, n=n, n0=worker) - PM.gen_acqtime(am, am, 0, typeGen="linear") - PM.gen_simtime(sm, sm * var, 0.01, typeSim="normal", seed=r) - - out = "b" + str(b) - avgAE, avgtime, avgTV = plotresult( - path, out, ex, b + 1, b, rep, m, n0=0, nf=n + 1 - ) - PM.acc = avgTV - - PM.simulate() - PM.summarize() - PM.complete(acclevel) - - result.append( - { - "r": r, - "b": b, - "am": am, - "var": var, - "sm": sm, - "res": PM, - } - ) - - timemat = np.zeros((len(a_mean), len(s_mean))) - bmat = np.zeros((len(a_mean), len(s_mean))) - for aid, am in enumerate(a_mean): - for sid, sm in enumerate(s_mean): - res_c = [res for res in result if ((res["am"] == am) & (res["sm"] == sm))] - - rmin = np.inf - bmin = np.inf - for bid, b in enumerate(batches): - cs = [rs["res"].complete_time for rs in res_c if rs["b"] == b] - if np.mean(cs) < rmin: - rmin = np.mean(cs) - bmin = b - - timemat[aid, sid] = rmin - bmat[aid, sid] = bmin - import matplotlib.colors as mcolors - - bounds = [ - 0.5, - 1.5, - 3.5, - 7.5, - 15.5, - 31.5, - 63.5, - 127.5, - 255.5, - 512, - ] # bounds to differentiate colors - cmap = plt.get_cmap("YlOrRd", len(batches)) - norm = mcolors.BoundaryNorm(bounds, cmap.N) # Normalize colors based on bounds - - # Heatmap - ft = 20 - divider = make_axes_locatable(ax[vid]) - cax = divider.append_axes("right", size="5%", pad=0.05) - im = ax[vid].imshow(bmat, cmap=cmap, norm=norm) - - # Show all ticks and label them with the respective list entries - ax[vid].set_xticks(np.arange(len(s_mean)), labels=sim_tick) - ax[vid].set_yticks(np.arange(len(a_mean)), labels=acq_tick) - ax[vid].tick_params(axis="both", which="major", labelsize=ft - 5) - # Rotate the tick labels and set their alignment. - plt.setp(ax[vid].get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor") - - # Loop over data dimensions and create text annotations. - for i in range(len(a_mean)): - for j in range(len(s_mean)): - if bmat[i, j] >= 64: - text = ax[vid].text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="white", - fontsize=ft - 5, - ) - else: - text = ax[vid].text( - j, - i, - int(timemat[i, j]), - ha="center", - va="center", - color="black", - fontsize=ft - 5, - ) - - ax[vid].grid(which="minor", color="w", linestyle="-", linewidth=3) - ax[vid].set_xlabel("Simulation Time", fontsize=ft) - ax[vid].set_ylabel("Acquisition Time", fontsize=ft) - cbar = fig.colorbar(im, cax=cax, orientation="vertical") - cbar.ax.tick_params(labelsize=ft - 5) - cbar.ax.set_ylabel("Batch Size", rotation=-90, va="bottom", fontsize=ft - 5) - cbar.set_ticks(batches) -fig.suptitle("Variability in Simulation Time Increases \u2192", fontsize=ft) -fig.tight_layout() -plt.savefig("Figure_WS_2.jpg", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() diff --git a/examples/NeurIPS2024/plotutils.py b/examples/NeurIPS2024/plotutils.py deleted file mode 100644 index df209cf..0000000 --- a/examples/NeurIPS2024/plotutils.py +++ /dev/null @@ -1,51 +0,0 @@ -from PUQ.designmethods.utils import parse_arguments, save_output, read_output -import matplotlib.pyplot as plt -import matplotlib -import numpy as np - - -def plotresult(path, out, ex_name, w, b, rep, method, n0, nf): - - AElist = [] - TVlist = [] - timelist = [] - for i in range(1, 1 + rep): - design_saved = read_output(path + out + "/", ex_name, method, w, b, i) - - TV = design_saved._info["TV"] - AE = design_saved._info["AE"] - time = design_saved._info["time"] - - if method == "rnd": - time = np.repeat(0.1, len(time)) - - bestTV = np.zeros(TV.shape) - for i in range(len(TV)): - bestTV[i] = np.min(TV[0 : (i + 1)]) - - AElist.append(AE[n0:nf]) - timelist.append(time[n0:nf]) - TVlist.append(bestTV[n0:nf]) - - avgtime = np.mean(np.array(timelist), 0) - avgAE = np.mean(np.array(AElist), 0) - avgTV = np.mean(np.array(TVlist), 0) - - return avgAE, avgtime, avgTV - - -def plotparams(path, out, ex_name, w, b, rep, method, n0, nf, thetalim): - - lst = [] - for i in range(1, 1 + rep): - design_saved = read_output(path + out + "/", ex_name, method, w, b, i) - lst.append(design_saved._info["AE"][nf]) - # print(design_saved._info['AE'][-1]) - theta = design_saved._info["theta"] - plt.scatter(theta[:, 0], theta[:, 1]) - plt.xlim(thetalim[0][0], thetalim[0][1]) - plt.ylim(thetalim[1][0], thetalim[1][1]) - plt.show() - - print(len(design_saved._info["AE"])) - print(np.mean(lst)) diff --git a/examples/NeurIPS2024/test_funcs.py b/examples/NeurIPS2024/test_funcs.py deleted file mode 100644 index e23f30a..0000000 --- a/examples/NeurIPS2024/test_funcs.py +++ /dev/null @@ -1,361 +0,0 @@ -import numpy as np -from threading import Event - - -def artificial_time(persis_info, sim_specs): - rand_stream = persis_info["rand_stream"] - run_time = rand_stream.normal(0.1, 0.1, 1) - if run_time[0] < 0.01: - r = 0.01 - else: - r = run_time[0] - Event().wait(r) - - -class banana: - def __init__(self): - self.data_name = "banana" - self.thetalimits = np.array([[-20, 20], [-10, 5]]) - self.obsvar = np.array([[10**2, 0], [0, 1]]) - self.real_data = np.array([[1, 3]], dtype="float64") - self.out = [("f", float, (2,))] - self.p = 2 - self.d = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta1, theta2 + 0.03 * theta1**2]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the banana function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class bimodal: - def __init__(self): - - self.data_name = "bimodal" - self.thetalimits = np.array([[-6, 6], [-4, 8]]) - self.obsvar = np.array([[1 / np.sqrt(0.2), 0], [0, 1 / np.sqrt(0.75)]]) - self.real_data = np.array([[0, 2]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta2 - theta1**2, theta2 - theta1]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the bimodal function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[-4, 4], [-4, 4]]) - self.obsvar = np.array([[4]], dtype="float64") - self.real_data = np.array([[-6]], dtype="float64") - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - """ - Wraps the unimodal function - """ - thetas = np.array([theta1, theta2]).reshape((1, 2)) - S = np.array([[1, 0.5], [0.5, 1]]) - f = (thetas @ S) @ thetas.T - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the simulator - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class unidentifiable: - def __init__(self): - - self.data_name = "unidentifiable" - self.thetalimits = np.array([[-8, 8], [-8, 8]]) - self.obsvar = np.array([[1 / 0.01, 0], [0, 1]]) - self.real_data = np.array([[0, 0]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta1, theta2]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the unidentifiable function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class himmelblau: - def __init__(self): - - self.data_name = "himmelblau" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = np.array([[1]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = (theta1**2 + theta2 - 11) ** 2 + (theta1 + theta2**2 - 7) ** 2 - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the himmelblau function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class holder: - def __init__(self): - - self.data_name = "holder" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-10, 10], [-10, 10]]) - self.obsvar = np.array([[50]], dtype="float64") - self.real_data = np.array([[-19.208502567767606]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = -np.abs( - np.sin(theta1) - * np.cos(theta2) - * np.exp(np.abs(1 - (np.sqrt(theta1**2 + theta2**2) / np.pi))) - ) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the holder function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class ackley: - def __init__(self): - - self.data_name = "ackley" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = ( - -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (theta1**2 + theta2**2))) - - np.exp(0.5 * (np.cos(2 * np.pi * theta1) + np.cos(2 * np.pi * theta2))) - + np.e - + 20 - ) - - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the ackley function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class easom: - def __init__(self): - - self.data_name = "easom" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-10, 10], [-10, 10]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = np.array([[-1]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = ( - -np.cos(theta1) - * np.cos(theta2) - * np.exp(-((theta1 - np.pi) ** 2 + (theta2 - np.pi) ** 2)) - ) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the easom function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class sphere: - def __init__(self): - - self.data_name = "sphere" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = theta1**2 + theta2**2 - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the sphere function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class matyas: - def __init__(self): - - self.data_name = "matyas" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-10, 10], [-10, 10]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = 0.26 * (theta1**2 + theta2**2) - 0.48 * theta1 * theta2 - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the matyas function - """ - artificial_time(persis_info, sim_specs) - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info diff --git a/examples/README.rst b/examples/README.rst index 410a5c1..5f777db 100644 --- a/examples/README.rst +++ b/examples/README.rst @@ -6,14 +6,14 @@ We provide a collection of examples in this directory to demonstrate the usage o The available examples include: -* `Technometrics2024 `_ : These examples replicate the results from the paper `Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models `_ by Sürer, Plumlee, and Wild (2024). +* `Example1 `_ : These examples replicate the results from the paper `Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models `_ by Sürer, Plumlee, and Wild (2024). -* `JQT2024 `_ : These examples reproduce the results from Sürer’s (2024) paper `Simulation Experiment Design for Calibration via Active Learning `_ . +* `Example2 `_ : These examples reproduce the results from Sürer’s (2024) paper `Simulation Experiment Design for Calibration via Active Learning `_ . -* `NeurIPS2024 `_ : These examples replicate results from the paper `An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations `_ by Sürer and Wild (2024). +* `Example3 `_ : These examples replicate results from the paper `An Active Learning Performance Model for Parallel Bayesian Calibration of Expensive Simulations `_ by Sürer and Wild (2024). -* `Technometrics2025 `_ : These examples replicate the results from the paper `Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models `_ by Sürer (2025). +* `Example4 `_ : These examples replicate the results from the paper `Batch Sequential Experimental Design for Calibration of Stochastic Simulation Models `_ by Sürer (2025). -* `2025+ `_ : These examples replicate the results from the paper `Active Learning for Data-Efficient Calibration of Stochastic Simulation Models` by Sürer (2025). +* `Example5 `_ : These examples replicate the results from the paper `Active Learning for Data-Efficient Calibration of Stochastic Simulation Models` by Sürer (2025). * `fresco_example `_ : This example showcases the application of PUQ with the physics reaction code `fresco`. diff --git a/examples/Technometrics2024/README.rst b/examples/Technometrics2024/README.rst deleted file mode 100644 index dca9da5..0000000 --- a/examples/Technometrics2024/README.rst +++ /dev/null @@ -1,52 +0,0 @@ - -Examples -~~~~~~~~ - -These examples replicate the results presented in the paper titled 'Sequential Bayesian -Experimental Design for Calibration of Expensive Simulation Models' by Sürer, Plumlee, and Wild (2024). - -**Instructions for running the illustrative examples** - -To replicate Figures~1--3, respectively: - -1) Go to the ``examples/Technometrics2024`` directory. - -2) Execute the followings from the command line:: - - python3 figure1b.py - python3 figure2.py - python3 figure3.py - -Running each script should not take more than 60 sec. See the figures (png files) saved under the directory. - -**Instructions for running the prominent empirical results** - -Instructions are provided to replicate each panel in Figure~6. - -To replicate the upper-left panel (banana function), execute the following from the command line:: - - python3 figure6.py -funcname banana - -Running this script takes about 2.5hrs on a personal Mac laptop. -Once completed, ``Figure6_banana.png`` is saved under the directory. - -To replicate the upper-right panel (bimodal function), execute the following from the command line:: - - python3 figure6.py -funcname bimodal - -Running this script takes about 2.5hrs on a personal Mac laptop. -Once completed, ``Figure6_bimodal.png`` is saved under the directory. - -To replicate the lower-left panel (unimodal function), execute the following from the command line:: - - python3 figure6.py -funcname unimodal - -Running this script takes about 2hr on a personal Mac laptop. -Once completed, ``Figure6_unimodal.png`` is saved under the directory. - -To replicate the lower-right panel (unidentifiable function), execute the following from the command line:: - - python3 figure6.py -funcname unidentifiable - -Running this script takes about 2.5hrs on a personal Mac laptop. -Once completed, ``Figure6_unidentifiable.png`` is saved under the directory. diff --git a/examples/Technometrics2024/figure1b.py b/examples/Technometrics2024/figure1b.py deleted file mode 100644 index 017c22b..0000000 --- a/examples/Technometrics2024/figure1b.py +++ /dev/null @@ -1,114 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import scipy.stats as sps -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -from PUQ.prior import prior_dist - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[-4, 4], [-4, 4]]) - self.obsvar = np.array([[4]], dtype="float64") - self.real_data = np.array([[-6]], dtype="float64") - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - """ - Wraps the unimodal function - """ - thetas = np.array([theta1, theta2]).reshape((1, 2)) - S = np.array([[1, 0.5], [0.5, 1]]) - f = (thetas @ S) @ thetas.T - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the simulator - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -if __name__ == "__main__": - args = parse_arguments() - cls_unimodal = unimodal() - - # # # Create a mesh for test set # # # - xpl = np.linspace( - cls_unimodal.thetalimits[0][0], cls_unimodal.thetalimits[0][1], 50 - ) - ypl = np.linspace( - cls_unimodal.thetalimits[1][0], cls_unimodal.thetalimits[1][1], 50 - ) - Xpl, Ypl = np.meshgrid(xpl, ypl) - th = np.vstack([Xpl.ravel(), Ypl.ravel()]) - setattr(cls_unimodal, "theta", th.T) - - al_unimodal_test = designer( - data_cls=cls_unimodal, - method="SEQUNIFORM", - args={ - "mini_batch": 4, - "n_init_thetas": 10, - "nworkers": 5, - "max_evals": th.shape[1], - }, - ) - - ftest = al_unimodal_test._info["f"] - thetatest = al_unimodal_test._info["theta"] - ptest = sps.norm.pdf( - cls_unimodal.real_data - ftest, 0, np.sqrt(cls_unimodal.obsvar) - ) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - # # # # # # # # # # # # # # # # # # # # # - prior_func = prior_dist(dist="uniform")( - a=cls_unimodal.thetalimits[:, 0], b=cls_unimodal.thetalimits[:, 1] - ) - - al_unimodal = designer( - data_cls=cls_unimodal, - method="SEQCAL", - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "AL": "eivar", - "seed_n0": 1, - "prior": prior_func, - "data_test": test_data, - "max_evals": 60, - "type_init": None, - }, - ) - - theta_al = al_unimodal._info["theta"] - TV = al_unimodal._info["TV"] - HD = al_unimodal._info["HD"] - - print(theta_al) - fig, ax = plt.subplots() - cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax.scatter(theta_al[10:, 0], theta_al[10:, 1], c="black", marker="+", zorder=2) - ax.scatter( - theta_al[0:10, 0], - theta_al[0:10, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.savefig("Figure1b.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure1b_new.py b/examples/Technometrics2024/figure1b_new.py deleted file mode 100644 index a505a09..0000000 --- a/examples/Technometrics2024/figure1b_new.py +++ /dev/null @@ -1,83 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import scipy.stats as sps -from PUQ.designmethods.utils import parse_arguments -from PUQ.prior import prior_dist -from PUQ.designmethods.sequential_md_deterministic import sequential_design -from smt.sampling_methods import LHS -from test_funcs_new import unimodal - -if __name__ == "__main__": - args = parse_arguments() - cls_unimodal = unimodal() - - # # # Create a mesh for test set # # # - xpl = np.linspace( - cls_unimodal.thetalimits[0][0], cls_unimodal.thetalimits[0][1], 50 - ) - ypl = np.linspace( - cls_unimodal.thetalimits[1][0], cls_unimodal.thetalimits[1][1], 50 - ) - Xpl, Ypl = np.meshgrid(xpl, ypl) - thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - ftest = np.zeros((thetatest.shape[0], 1)) - for i in range(thetatest.shape[0]): - ftest[i, 0] = cls_unimodal.function(thetatest[i,0], thetatest[i,1]) - - ptest = sps.norm.pdf( - cls_unimodal.real_data - ftest, 0, np.sqrt(cls_unimodal.obsvar) - ) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - # # # # # # # # # # # # # # # # # # # # # - prior_func = prior_dist(dist="uniform")( - a=cls_unimodal.thetalimits[:, 0], b=cls_unimodal.thetalimits[:, 1] - ) - - # Initial sample - n0 = 10 - s = 1 - sampling = LHS(xlimits=cls_unimodal.thetalimits, random_state=int(s)) - t0 = sampling(n0) - f0 = np.zeros((t0.shape[0], 1)) - for i in range(t0.shape[0]): - f0[i, 0] = cls_unimodal.function(t0[i,0], t0[i,1]) - - for af in ["rnd", "var", "ivar"]: - des_obj = sequential_design(cls_unimodal) - des_obj.build_design(z0=t0, - f0=f0, - T=50, - test=test_data, - af=af, - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "seed_n0": 1, - "prior": prior_func, - "data_test": test_data, - "max_evals": 60, - "type_init": None, - "seed": 0, - "integral": "LHS" - }) - - - theta_al = des_obj.zs - - fig, ax = plt.subplots() - cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) - ax.scatter( - theta_al[0:n0, 0], - theta_al[0:n0, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.savefig("Figure1b.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure2.py b/examples/Technometrics2024/figure2.py deleted file mode 100644 index 7029f5a..0000000 --- a/examples/Technometrics2024/figure2.py +++ /dev/null @@ -1,127 +0,0 @@ -import scipy.stats as sps -import numpy as np -import matplotlib.pyplot as plt -from PUQ.surrogate import emulator -from PUQ.posterior import posterior - - -class sinlinear: - def __init__(self): - self.data_name = "sinlinear" - self.thetalimits = np.array([[-10, 10]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 1 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta): - f = np.sin(theta) + 0.1 * theta - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the sin() function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - theta = H["thetas"][0] - H_o["f"] = function(theta) - - return H_o, persis_info - - -if __name__ == "__main__": - cls_sinlin = sinlinear() - - # Generate training data - theta = np.array([-10, -8.7, -7.5, -5, -2.5, -1, 1, 2.5, 5, 7.5, 8.7, 10])[:, None] - f = cls_sinlin.function(theta) - ptr = sps.norm.pdf(cls_sinlin.real_data, f, np.sqrt(cls_sinlin.obsvar)) - - # Fit an emulator - emu = emulator(cls_sinlin.x, theta, f, method="PCGP") - - post = posterior(data_cls=cls_sinlin, emulator=emu) - - # Generate test data - thetatest = np.arange(-10, 10, 0.0025)[:, None] - ftest = cls_sinlin.function(thetatest) - ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) - - # Predict via the emulator - emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) - emupred_tr = emu.predict(x=cls_sinlin.x, theta=theta) - - posttesthat, posttestvar = post.predict(thetatest) - posttrhat, posttrvar = post.predict(theta) - - # Figure 2 (a) - ft = 20 - fig, ax = plt.subplots() - ax.plot(thetatest, ftest, color="black") - ax.plot( - thetatest.flatten(), - emupred_test.mean().flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - plt.fill_between( - thetatest.flatten(), - (emupred_test.mean() - np.sqrt(emupred_test.var())).flatten(), - (emupred_test.mean() + np.sqrt(emupred_test.var())).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, emupred_tr.mean(), color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$\eta(\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2a.png", bbox_inches="tight") - - # Figure 2 (b) - fig, ax = plt.subplots() - ax.plot(thetatest, ptest, color="black") - ax.plot(thetatest, posttesthat, color="blue", linestyle="dashed", linewidth=2.5) - plt.fill_between( - thetatest.flatten(), - (posttesthat - np.sqrt(posttestvar)).flatten(), - (posttesthat + np.sqrt(posttestvar)).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, posttrhat, color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2b.png", bbox_inches="tight") - - # Figure 2 (c) - # Fit an emulator for posterior - emu = emulator(cls_sinlin.x, theta, ptr, method="PCGP") - - emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) - emumean_test = emupred_test.mean() - emumean_var = emupred_test.var() - - fig, ax = plt.subplots() - ax.plot(thetatest, ptest, color="black") - ax.plot( - thetatest.flatten(), - emumean_test.flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - plt.fill_between( - thetatest.flatten(), - (emumean_test - np.sqrt(emumean_var)).flatten(), - (emumean_test + np.sqrt(emumean_var)).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, ptr, color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2c.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure2_new.py b/examples/Technometrics2024/figure2_new.py deleted file mode 100644 index e98baf6..0000000 --- a/examples/Technometrics2024/figure2_new.py +++ /dev/null @@ -1,180 +0,0 @@ -import scipy.stats as sps -import numpy as np -import matplotlib.pyplot as plt -from PUQ.surrogate import emulator -from PUQ.posterior import multiple_pdfs, compute_postvar - - -class sinlinear: - def __init__(self): - self.data_name = "sinlinear" - self.thetalimits = np.array([[-10, 10]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 1 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta): - f = np.sin(theta) + 0.1 * theta - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the sin() function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - theta = H["thetas"][0] - H_o["f"] = function(theta) - - return H_o, persis_info - - -if __name__ == "__main__": - cls_sinlin = sinlinear() - - # Generate training data - theta = np.array([-10, -8.7, -7.5, -5, -2.5, -1, 1, 2.5, 5, 7.5, 8.7, 10])[:, None] - f = cls_sinlin.function(theta) - ptr = sps.norm.pdf(cls_sinlin.real_data, f, np.sqrt(cls_sinlin.obsvar)) - - # Fit an emulator - # emu = emulator(cls_sinlin.x, theta, f, method="PCGP") - - emu = emulator(x=cls_sinlin.x, - theta=theta, - f=f, - method="pcHomGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - - # Generate test data - thetatest = np.arange(-10, 10, 0.0025)[:, None] - ftest = cls_sinlin.function(thetatest) - ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) - - # predict at mesh - nm, d = thetatest.shape[0], cls_sinlin.d - pr_test = emu.predict(theta=thetatest, thetaprime=thetatest) - mu, Sn = pr_test._info["mean"], pr_test._info["S"] - muT = mu.reshape(nm, d) - S = Sn.transpose(2, 0, 1) - Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) - N = S + Sigma3d - M = S + 0.5*Sigma3d - posttesthat = multiple_pdfs(cls_sinlin.real_data, muT, N) - - diags = np.diag(cls_sinlin.obsvar[cls_sinlin.x, cls_sinlin.x.T]) - coef = (2**cls_sinlin.d) * (np.sqrt(np.pi) ** cls_sinlin.d) * np.sqrt(np.prod(diags)) - posttestvar = compute_postvar(cls_sinlin.real_data, - muT, - N, - M, - coef) - - - # predict at mesh - nm, d = theta.shape[0], cls_sinlin.d - pr_test = emu.predict(theta=theta, thetaprime=theta) - mu, Sn = pr_test._info["mean"], pr_test._info["S"] - muT = mu.reshape(nm, d) - S = Sn.transpose(2, 0, 1) - Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) - N = S + Sigma3d - posttrhat = multiple_pdfs(cls_sinlin.real_data, muT, N) - - # post = posterior(data_cls=cls_sinlin, emulator=emu) - - # Predict via the emulator - emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) - emupred_tr = emu.predict(x=cls_sinlin.x, theta=theta) - - # posttesthat, posttestvar = post.predict(thetatest) - # posttrhat, posttrvar = post.predict(theta) - - # Figure 2 (a) - ft = 20 - fig, ax = plt.subplots() - ax.plot(thetatest, ftest, color="black") - ax.plot( - thetatest.flatten(), - emupred_test.mean().flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - plt.fill_between( - thetatest.flatten(), - (emupred_test.mean() - np.sqrt(emupred_test.var())).flatten(), - (emupred_test.mean() + np.sqrt(emupred_test.var())).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, emupred_tr.mean(), color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$\eta(\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2a.png", bbox_inches="tight") - - # Figure 2 (b) - fig, ax = plt.subplots() - ax.plot(thetatest, ptest, color="black") - ax.plot(thetatest, posttesthat, color="blue", linestyle="dashed", linewidth=2.5) - plt.fill_between( - thetatest.flatten(), - (posttesthat - np.sqrt(posttestvar)).flatten(), - (posttesthat + np.sqrt(posttestvar)).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, posttrhat, color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2b.png", bbox_inches="tight") - - # Figure 2 (c) - # Fit an emulator for posterior - # emu = emulator(cls_sinlin.x, theta, ptr, method="PCGP") - emu = emulator(x=cls_sinlin.x, - theta=theta, - f=ptr, - method="pcHomGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) - - emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) - emumean_test = emupred_test.mean() - emumean_var = emupred_test.var() - - fig, ax = plt.subplots() - ax.plot(thetatest, ptest, color="black") - ax.plot( - thetatest.flatten(), - emumean_test.flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - plt.fill_between( - thetatest.flatten(), - (emumean_test - np.sqrt(emumean_var)).flatten(), - (emumean_test + np.sqrt(emumean_var)).flatten(), - alpha=0.3, - ) - ax.scatter(theta.T, ptr, color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure2c.png", bbox_inches="tight") diff --git a/examples/Technometrics2024/figure3.py b/examples/Technometrics2024/figure3.py deleted file mode 100644 index eb10fdd..0000000 --- a/examples/Technometrics2024/figure3.py +++ /dev/null @@ -1,140 +0,0 @@ -import scipy.stats as sps -import numpy as np -import matplotlib.pyplot as plt -from PUQ.surrogate import emulator -from PUQ.posterior import posterior -from PUQ.designmethods.gen_funcs.acquisition_funcs_support import ( - get_emuvar, - compute_eivar_fig, -) - - -class sinlinear: - def __init__(self): - self.data_name = "sinlinear" - self.thetalimits = np.array([[-10, 10]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = np.array([[0]], dtype="float64") - self.out = [("f", float)] - self.p = 1 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta): - f = np.sin(theta) + 0.1 * theta - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the sin() function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - theta = H["thetas"][0] - H_o["f"] = function(theta) - - return H_o, persis_info - - -if __name__ == "__main__": - cls_sinlin = sinlinear() - - # Generate training data - theta = np.array([-10, -8.7, -7.5, -5, -2.5, -1, 1, 2.5, 5, 7.5, 8.7, 10])[:, None] - f = cls_sinlin.function(theta) - - for i in range(3): - # Fit an emulator - emu = emulator(cls_sinlin.x, theta, f, method="PCGP") - - post = posterior(data_cls=cls_sinlin, emulator=emu) - - # Generate test data - thetatest = np.arange(-10, 10, 0.0025)[:, None] - ftest = cls_sinlin.function(thetatest) - ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) - - # Predict via the emulator - emupred_test = emu.predict(x=cls_sinlin.x, theta=thetatest) - emupred_tr = emu.predict(x=cls_sinlin.x, theta=theta) - - posttesthat, posttestvar = post.predict(thetatest) - posttrhat, posttrvar = post.predict(theta) - - clist = np.arange(-10, 10.01, 0.1)[:, None] - - def compute_eivar_full(clist, theta, theta_test, emu): - real_x = cls_sinlin.real_x - obs = cls_sinlin.real_data - obsvar = cls_sinlin.obsvar - obsvar3d = obsvar.reshape(1, 1, 1) - d_real = real_x.shape[0] - x = cls_sinlin.x - emupred_test = emu.predict(x=cls_sinlin.x, theta=theta_test) - emumean = emupred_test.mean() - emuvar, is_cov = get_emuvar(emupred_test) - emumeanT = emumean.T - emuvarT = emuvar.transpose(1, 0, 2) - var_obsvar1 = emuvarT + obsvar3d - var_obsvar2 = emuvarT + 0.5 * obsvar3d - diags = np.diag(obsvar[real_x, real_x.T]) - coef = (2**d_real) * (np.sqrt(np.pi) ** d_real) * np.sqrt(np.prod(diags)) - - # Get the n_ref x d x d x n_cand phi matrix - emuphi4d = emu.acquisition(x=x, theta1=theta_test, theta2=clist) - acq_func = [] - - # Pass over all the candidates - for c_id in range(len(clist)): - posteivar = compute_eivar_fig( - obsvar, - var_obsvar2[:, real_x, real_x.T], - var_obsvar1[:, real_x, real_x.T], - emuphi4d[:, real_x, real_x.T, c_id], - emumeanT[:, real_x.flatten()], - emuvar[real_x, :, real_x.T], - obs, - is_cov, - ) - acq_func.append(posteivar) - - return acq_func - - acq = compute_eivar_full(clist, theta, thetatest, emu) - acqtr = compute_eivar_full(theta, theta, thetatest, emu) - - ft = 20 - # Figure 3 (a)/ 2nd row - cand_theta = clist[np.argmin(acq)] - fig, ax = plt.subplots() - ax.plot(clist, acq, color="blue") - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel("EIVAR", fontsize=16) - ax.tick_params(axis="both", labelsize=ft) - plt.scatter(cand_theta, np.min(acq), color="green", marker="*", s=200) - plt.scatter(theta, acqtr, color="red", marker="o", s=60) - plt.savefig("Figure3_1_" + str(i) + ".png", bbox_inches="tight") - # plt.show() - - fig, ax = plt.subplots() - ax.plot(thetatest, ptest, color="black") - ax.plot(thetatest, posttesthat, color="blue", linestyle="dashed", linewidth=2.5) - plt.fill_between( - thetatest.flatten(), - (posttesthat - np.sqrt(posttestvar)).flatten(), - (posttesthat + np.sqrt(posttestvar)).flatten(), - alpha=0.3, - ) - - postcand, postcandvar = post.predict(cand_theta.reshape(1, 1)) - plt.scatter(cand_theta, postcand, color="green", marker="*", s=200) - ax.scatter(theta.T, posttrhat, color="red", s=60) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure3_2_" + str(i) + ".png", bbox_inches="tight") - # plt.show() - - theta = np.concatenate((theta, cand_theta.reshape(1, 1))) - f = np.concatenate((f, cls_sinlin.function(cand_theta.reshape(1, 1)))) diff --git a/examples/Technometrics2024/figure6.py b/examples/Technometrics2024/figure6.py deleted file mode 100644 index 19106d1..0000000 --- a/examples/Technometrics2024/figure6.py +++ /dev/null @@ -1,107 +0,0 @@ -import pandas as pd -import numpy as np -from PUQ.design import designer -from PUQ.designmethods.utils import parse_arguments -from PUQ.prior import prior_dist -from plots import plotline -from test_funcs import unimodal, banana, bimodal, unidentifiable, create_test_data -import time - -if __name__ == "__main__": - design_start = time.time() - args = parse_arguments() - print("Running function: " + args.funcname) - - # Choose the test function - if args.funcname == "unimodal": - cls_func = unimodal() - elif args.funcname == "banana": - cls_func = banana() - elif args.funcname == "bimodal": - cls_func = bimodal() - elif args.funcname == "unidentifiable": - cls_func = unidentifiable() - - # Create a mesh for test set - xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], 50) - ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], 50) - Xpl, Ypl = np.meshgrid(xpl, ypl) - th = np.vstack([Xpl.ravel(), Ypl.ravel()]) - setattr(cls_func, "theta", th.T) - al_test = designer( - data_cls=cls_func, - method="SEQUNIFORM", - args={ - "mini_batch": 4, - "n_init_thetas": 10, - "nworkers": 5, - "max_evals": th.shape[1], - }, - ) - - test_data = create_test_data(al_test, cls_func) - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Define acquisition functions - acq_funcs = ["eivar", "rnd", "maxvar", "maxexp"] - datalist = [] - rep_no = 50 - n0 = 10 - # Run over 50 replications - for seed_id in range(1, rep_no + 1): - for func in acq_funcs: - print("Running " + func + " with seed " + str(seed_id)) - al_unimodal = designer( - data_cls=cls_func, - method="SEQCAL", - args={ - "mini_batch": 1, - "n_init_thetas": n0, - "nworkers": 2, - "AL": func, - "seed_n0": seed_id, - "prior": prior_func, - "data_test": test_data, - "max_evals": 210, - "type_init": None, - }, - ) - - TV = al_unimodal._info["TV"] - - for ms_id, ms in enumerate(TV): - if ms_id >= n0: - d = { - "TV": ms, - "TV_id": ms_id, - "rep": seed_id, - "batch": 1, - "methods": func, - "worker": 2, - "synth": args.funcname, - "theta_id": ms_id - n0, - } - - datalist.append(d) - - datalist.append(d) - df = pd.DataFrame(datalist) - - design_end = time.time() - print("Elapsed time: " + str(round(design_end - design_start, 2))) - - # Create the plot and save it - plotline( - df, - acq_funcs, - rep_no, - w=2, - b=1, - s=args.funcname, - ylim=[0.000005, 0.00001], - idstart=0, - ) diff --git a/examples/Technometrics2024/figure6_new.py b/examples/Technometrics2024/figure6_new.py deleted file mode 100644 index b508da1..0000000 --- a/examples/Technometrics2024/figure6_new.py +++ /dev/null @@ -1,135 +0,0 @@ -import pandas as pd -import numpy as np -import scipy.stats as sps -from PUQ.designmethods.sequential_md_deterministic import sequential_design -from PUQ.designmethods.utils import parse_arguments -from PUQ.prior import prior_dist -from plots import plotline -from test_funcs_new import unimodal, banana, bimodal, unidentifiable -import time -from smt.sampling_methods import LHS -import matplotlib.pyplot as plt - -if __name__ == "__main__": - design_start = time.time() - args = parse_arguments() - print("Running function: " + args.funcname) - args.funcname = "bimodal" - # Choose the test function - if args.funcname == "unimodal": - cls_func = unimodal() - elif args.funcname == "banana": - cls_func = banana() - elif args.funcname == "bimodal": - cls_func = bimodal() - elif args.funcname == "unidentifiable": - cls_func = unidentifiable() - - # Create a mesh for test set - xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], 50) - ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], 50) - Xpl, Ypl = np.meshgrid(xpl, ypl) - thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - ftest = np.zeros((thetatest.shape[0], cls_func.d)) - for i in range(thetatest.shape[0]): - ftest[i, :] = cls_func.function(thetatest[i,0], thetatest[i,1]) - - ptest = np.zeros(thetatest.shape[0]) - if cls_func.data_name == "unimodal": - ptest = sps.norm.pdf(cls_func.real_data - ftest, 0, np.sqrt(cls_func.obsvar)) - else: - for i in range(ftest.shape[0]): - mean = ftest[i, :] - rnd = sps.multivariate_normal(mean=mean, cov=cls_func.obsvar) - ptest[i] = rnd.pdf(cls_func.real_data) - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Define acquisition functions - # acq_funcs = ["eivar", "rnd", "maxvar", "maxexp"] - acq_funcs = ["ivar", "rnd", "var"] - datalist = [] - rep_no = 2 - n0 = 10 - # Run over 50 replications - for seed_id in range(1, rep_no + 1): - # Initial sample - n0 = 10 - sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(seed_id)) - t0 = sampling(n0) - f0 = np.zeros((t0.shape[0], cls_func.d)) - for i in range(t0.shape[0]): - f0[i, :] = cls_func.function(t0[i,0], t0[i,1]) - - for func in acq_funcs: - print("Running " + func + " with seed " + str(seed_id)) - des_obj = sequential_design(cls_func) - des_obj.build_design(z0=t0, - f0=f0, - T=75, - test=test_data, - af=func, - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "prior": prior_func, - "data_test": test_data, - "seed": seed_id, - "integral": "LHS" - }) - - - theta_al = des_obj.zs - - fig, ax = plt.subplots() - cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) - ax.scatter( - theta_al[0:n0, 0], - theta_al[0:n0, 1], - zorder=2, - marker="o", - facecolors="none", - edgecolors="blue", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - - # TV = al_unimodal._info["TV"] - - for ms_id, ms in enumerate(des_obj.H): - d = { - "TV": ms["MAD"], - "TV_id": ms_id, - "rep": seed_id, - "batch": 1, - "methods": func, - "worker": 2, - "synth": args.funcname, - "theta_id": ms_id, - } - - datalist.append(d) - - datalist.append(d) - df = pd.DataFrame(datalist) - - - # Create the plot and save it - plotline( - df, - acq_funcs, - rep_no, - w=2, - b=1, - s=args.funcname, - ylim=[0.000005, 0.00001], - idstart=0, - ) diff --git a/examples/Technometrics2024/plots.py b/examples/Technometrics2024/plots.py deleted file mode 100644 index e81120e..0000000 --- a/examples/Technometrics2024/plots.py +++ /dev/null @@ -1,50 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt - - -def plotline(df, methods, rep_no, w=2, b=1, s="banana", ylim=[0.000001, 1], idstart=0): - colors = ["red", "green", "blue", "cyan", "magenta"] - markers = ["o", "+", "*", "D", "v"] - linestyles = [(0, ()), (0, (1, 1)), (0, (5, 1)), (0, (3, 5, 1, 5)), (0, (5, 10))] - fig, ax = plt.subplots() - - idmse = 210 - maxval = 0 - minval = 1 - ft = 20 - for m_id, m in enumerate(methods): - dfnnew = df[df["methods"] == m] - dfnnnew = dfnnew[dfnnew["theta_id"] <= idmse] - - mslist = [] - sdlist = [] - for i in range(idmse + 1): - mn = dfnnnew[dfnnnew["theta_id"] == i]["TV"].mean() - sd = dfnnnew[dfnnnew["theta_id"] == i]["TV"].std() - mslist.append(mn) - sdlist.append(1.96 * sd / np.sqrt(rep_no)) - - ax.plot( - range(0, len(mslist)), - mslist, - marker=markers[m_id], - color=colors[m_id], - ls=linestyles[m_id], - markersize=10, - markevery=40, - linewidth=2, - label=m, - ) - - maxval = max(maxval, max(mslist)) - minval = min(minval, min(mslist)) - - ax.set_ylim((minval, maxval)) - ax.legend( - loc="lower center", bbox_to_anchor=(0.5, -0.55), ncol=2, prop={"size": 16} - ) # 16for small - ax.set_yscale("log") - ax.set_xlabel("Stage", fontsize=ft) - ax.set_ylabel("MAD", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.savefig("Figure6_" + s + ".png", bbox_inches="tight") diff --git a/examples/Technometrics2024/test_funcs.py b/examples/Technometrics2024/test_funcs.py deleted file mode 100644 index 15bdb6f..0000000 --- a/examples/Technometrics2024/test_funcs.py +++ /dev/null @@ -1,133 +0,0 @@ -import numpy as np -import scipy.stats as sps - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[-4, 4], [-4, 4]]) - self.obsvar = np.array([[4]], dtype="float64") - self.real_data = np.array([[-6]], dtype="float64") - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - """ - Wraps the unimodal function - """ - thetas = np.array([theta1, theta2]).reshape((1, 2)) - S = np.array([[1, 0.5], [0.5, 1]]) - f = (thetas @ S) @ thetas.T - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the simulator - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class banana: - def __init__(self): - self.data_name = "banana" - self.thetalimits = np.array([[-20, 20], [-10, 5]]) - self.obsvar = np.array([[10**2, 0], [0, 1]]) - self.real_data = np.array([[1, 3]], dtype="float64") - self.out = [("f", float, (2,))] - self.p = 2 - self.d = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta1, theta2 + 0.03 * theta1**2]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the banana function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class unidentifiable: - def __init__(self): - self.data_name = "unidentifiable" - self.thetalimits = np.array([[-8, 8], [-8, 8]]) - self.obsvar = np.array([[1 / 0.01, 0], [0, 1]]) - self.real_data = np.array([[0, 0]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta1, theta2]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the unidentifiable function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -class bimodal: - def __init__(self): - self.data_name = "bimodal" - self.thetalimits = np.array([[-6, 6], [-4, 8]]) - self.obsvar = np.array([[1 / np.sqrt(0.2), 0], [0, 1 / np.sqrt(0.75)]]) - self.real_data = np.array([[0, 2]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - - def function(self, theta1, theta2): - f = np.array([theta2 - theta1**2, theta2 - theta1]) - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - """ - Wraps the bimodal function - """ - function = sim_specs["user"]["function"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0][0], H["thetas"][0][1]) - - return H_o, persis_info - - -def create_test_data(al_test, cls_func): - ftest = al_test._info["f"] - thetatest = al_test._info["theta"] - ptest = np.zeros(thetatest.shape[0]) - - if cls_func.data_name == "unimodal": - ptest = sps.norm.pdf(cls_func.real_data - ftest, 0, np.sqrt(cls_func.obsvar)) - else: - for i in range(ftest.shape[0]): - mean = ftest[i, :] - rnd = sps.multivariate_normal(mean=mean, cov=cls_func.obsvar) - ptest[i] = rnd.pdf(cls_func.real_data) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - return test_data diff --git a/examples/Technometrics2024/test_funcs_new.py b/examples/Technometrics2024/test_funcs_new.py deleted file mode 100644 index 8b2c038..0000000 --- a/examples/Technometrics2024/test_funcs_new.py +++ /dev/null @@ -1,109 +0,0 @@ -import numpy as np -import scipy.stats as sps - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[-4, 4], [-4, 4]]) - self.obsvar = np.array([[4]], dtype="float64") - self.real_data = np.array([[-6]], dtype="float64") - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - #### - self.dx = 1 - self.dt = 1 - - def function(self, theta1, theta2): - """ - Wraps the unimodal function - """ - thetas = np.array([theta1, theta2]).reshape((1, 2)) - S = np.array([[1, 0.5], [0.5, 1]]) - f = (thetas @ S) @ thetas.T - return f - - - -class banana: - def __init__(self): - self.data_name = "banana" - self.thetalimits = np.array([[-20, 20], [-10, 5]]) - self.obsvar = np.array([[10**2, 0], [0, 1]]) - self.real_data = np.array([[1, 3]], dtype="float64") - self.out = [("f", float, (2,))] - self.p = 2 - self.d = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - #### - self.dx = 1 - self.dt = 1 - - def function(self, theta1, theta2): - f = np.array([theta1, theta2 + 0.03 * theta1**2]) - return f - - - -class unidentifiable: - def __init__(self): - self.data_name = "unidentifiable" - self.thetalimits = np.array([[-8, 8], [-8, 8]]) - self.obsvar = np.array([[1 / 0.01, 0], [0, 1]]) - self.real_data = np.array([[0, 0]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - #### - self.dx = 1 - self.dt = 1 - - def function(self, theta1, theta2): - f = np.array([theta1, theta2]) - return f - - - -class bimodal: - def __init__(self): - self.data_name = "bimodal" - self.thetalimits = np.array([[-6, 6], [-4, 8]]) - self.obsvar = np.array([[1 / np.sqrt(0.2), 0], [0, 1 / np.sqrt(0.75)]]) - self.real_data = np.array([[0, 2]], dtype="float64") - self.out = [("f", float, (2,))] - self.d = 2 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.real_x = np.arange(0, self.d)[:, None] - #### - self.dx = 1 - self.dt = 1 - - def function(self, theta1, theta2): - f = np.array([theta2 - theta1**2, theta2 - theta1]) - return f - - - -def create_test_data(al_test, cls_func): - ftest = al_test._info["f"] - thetatest = al_test._info["theta"] - ptest = np.zeros(thetatest.shape[0]) - - if cls_func.data_name == "unimodal": - ptest = sps.norm.pdf(cls_func.real_data - ftest, 0, np.sqrt(cls_func.obsvar)) - else: - for i in range(ftest.shape[0]): - mean = ftest[i, :] - rnd = sps.multivariate_normal(mean=mean, cov=cls_func.obsvar) - ptest[i] = rnd.pdf(cls_func.real_data) - - test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - - return test_data diff --git a/examples/Technometrics2025/Figure1_2.py b/examples/Technometrics2025/Figure1_2.py deleted file mode 100644 index 6fea602..0000000 --- a/examples/Technometrics2025/Figure1_2.py +++ /dev/null @@ -1,125 +0,0 @@ -import numpy as np -from PUQ.prior import prior_dist -#from PUQ.utils import parse_arguments -from PUQ.design import designer -from test_funcs import sinf -from utilities import test_data_gen_1d, Figure1, Figure2 -import matplotlib.pyplot as plt - -#args = parse_arguments() - -class Args: - pass - -args = Args() - -# # # # # -args.minibatch = 100 -example = "sinf" -args.seedmin = 13 -args.seedmax = 14 -# # # # # - -datalist = [] -n0 = 20 -rep0 = 5 -batch = args.minibatch -maxiter = 100 -worker = args.minibatch + 1 -rho = 1 / 2 -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - - cls_func = eval(example)() - cls_func.realdata(s) - - theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) - test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - theta = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0)[ - :, None - ] - theta = np.repeat(theta, rep0, axis=0) - f = np.zeros((cls_func.d, rep0 * n0)) - for i in range(0, rep0 * n0): - f[:, i] = cls_func.sim_f(theta[i, :], persis_info=persis_info) - - theta_test, nugs, phat = Figure1( - f, - theta, - cls_func.x, - cls_func.obsvar, - cls_func.real_data, - theta_test, - f_test, - p_test, - cls_func, - ) - - fig, ax = plt.subplots(1, 2, figsize=(15, 3.5)) - fig.subplots_adjust(wspace=0.6) - - al_ivar = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="seivar", - args={ - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": worker, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta, "f": f}, - "alloc_settings": { - "method": "ivar", - "use_Ki": True, - "theta": None, - "a0": None, - "gen": False, - "rho": rho, - }, - "des_settings": {"is_explore": False, "is_exploit": True}, - "pc_settings": {"standardize": True, "latent": False}, - "trace": 0, - }, - ) - - Figure2(al_ivar, theta_test, nugs, phat, "ivar", ax[0]) - - al_imse = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="simse", - args={ - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": worker, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta, "f": f}, - "alloc_settings": { - "method": "imse", - "use_Ki": True, - "theta": None, - "a0": None, - "gen": False, - "rho": rho, - }, - "des_settings": {"is_explore": False, "is_exploit": True}, - "pc_settings": {"standardize": True, "latent": False}, - "trace": 0, - }, - ) - - Figure2(al_imse, theta_test, nugs, phat, "imse", ax[1]) - - plt.savefig("Figure2.png", bbox_inches="tight") - plt.show() diff --git a/examples/Technometrics2025/Figure3.py b/examples/Technometrics2025/Figure3.py deleted file mode 100644 index a92dbde..0000000 --- a/examples/Technometrics2025/Figure3.py +++ /dev/null @@ -1,201 +0,0 @@ -import numpy as np -from PUQ.prior import prior_dist -#from PUQ.utils import parse_arguments -from test_funcs import sinf -from utilities import test_data_gen_1d -import matplotlib.pyplot as plt -import scipy.stats as sps -from PUQ.designmethods.gen_funcs.batch_acquisition_funcs_support import ( - build_emulator, - multiple_determinants, - multiple_pdfs, -) -from PUQ.designmethods.gen_funcs.acquire_new import get_pred - -#args = parse_arguments() - -class Args: - pass - -args = Args() - -# # # # # -args.funcname = "sinf" -args.seedmin = 3 # 13 -args.seedmax = 4 # 14 -# # # # # - -n0 = 6 -rep0 = 10 -nmesh = 50 -batch = 15 -reps = 5 -bnew = int(batch / reps) - -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - cls_func = eval(args.funcname)() - cls_func.realdata(seed=s) - - theta_test, p_test, f_test = test_data_gen_1d(cls_func, 100) - test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - persis_info = {"rand_stream": np.random.default_rng(12345)} - - # Initial sample - theta0u = np.linspace( - cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], n0 - )[:, None] - f0mean = np.zeros(len(theta0u)) - p0 = np.zeros(len(theta0u)) - theta0 = np.repeat(theta0u, rep0, axis=0) - f0 = np.zeros(len(theta0)) - for tid, t in enumerate(theta0): - f0[tid] = cls_func.sim_f(t, persis_info=persis_info)[0] - - for tid, t in enumerate(theta0u): - f0mean[tid] = cls_func.function(t)[0] - rnd = sps.norm(loc=f0mean[tid], scale=np.sqrt(cls_func.obsvar)) - p0[tid] = rnd.pdf(cls_func.real_data)[0, 0] - - n_x, p, x = cls_func.x.shape[0], theta_test.shape[1], cls_func.x - obs, obsvar = cls_func.real_data, cls_func.obsvar - theta_acq, n_acq = None, None - obsvar3d = cls_func.obsvar.reshape(1, n_x, n_x) - is_cov = False - - # Create a candidate list - nL = 500 - nm = theta_test.shape[0] - pc_settings = {"standardize": True, "latent": False} - # plt.scatter(theta0, f0) - # plt.show() - - fig, ax = plt.subplots(2, 3, figsize=(15, 7), constrained_layout=True) - - emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=pc_settings) - for i in range(bnew): - ft = 18 - cL = np.linspace( - cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nL - )[:, None] - - mu, S, cov, cvar = get_pred( - cL=cL, emu=emu, x=x, ttest=theta_test, reps=reps - ) - V1 = S + obsvar3d - V2 = S + 0.5 * obsvar3d - - G = emu._info["G"] - B = emu._info["B"] - GB = G @ B - BTG = B.T @ G - - d = G.shape[0] - q = B.shape[1] - - tau = np.zeros((nm, q, q, nL)) - phi = np.zeros((nm, d, d, nL)) - - coef = 1 / ((2**d) * (np.sqrt(np.pi) ** d)) - - for j in range(0, q): - tau[:, j, j, :] = cov[j, :, :] ** 2 / cvar[j, :] - - for k in range(0, nL): - phi[:, :, :, k] = GB @ tau[:, :, :, k] @ BTG - - vals = [] - for k in range(0, nL): - # C1: nm x d x d - # C2: nm x d x d - phic = phi[:, x, x.T, k] - C1 = (V1 + phic) * 0.5 - C2 = V1 - phic - - rpdf = multiple_pdfs(obs, mu, C1) - dets = multiple_determinants(C2) - part2 = rpdf / np.sqrt(dets) - - rpdf2 = multiple_pdfs(obs, mu, V2) - denum2 = obsvar - - cval = coef * (np.sum(rpdf2 / np.sqrt(denum2)) - np.sum(part2)) - - vals.append(cval) - - idc = np.argmin(vals) - minacq = np.min(vals) - cu = cL[idc, :].reshape((1, p)) - - ax[0, i].plot(cL, vals, color="blue") - ax[0, i].set_xlabel(r"$\theta$", fontsize=ft) - ax[0, i].set_ylabel("IVAR", fontsize=ft) - ax[0, i].tick_params(axis="both", labelsize=ft) - ax[0, i].scatter(cu, minacq, color="green", marker="*", s=200) - - ctheta = np.repeat(cu, reps, axis=0) - cpred = emu.predict(x=x, theta=ctheta) - - cmean = cpred.mean() - cnoise = cpred._info["var_noisy"] - fnoise = persis_info["rand_stream"].normal( - loc=cmean[0], scale=np.sqrt(cnoise[0]), size=reps - ) - - theta0 = np.concatenate([theta0, ctheta], axis=0) - f0 = np.concatenate([f0, fnoise.flatten()]) - - fc = np.zeros(len(cu)) - pc = np.zeros(len(cu)) - for tid, t in enumerate(cu): - fc[tid] = cls_func.function(t) - rnd = sps.norm(loc=fc[tid], scale=np.sqrt(cls_func.obsvar)) - pc[tid] = rnd.pdf(cls_func.real_data) - - phat = np.zeros(theta_test.shape[0]) - phatvar = np.zeros(theta_test.shape[0]) - pvar1 = np.zeros(theta_test.shape[0]) - for tid in range(0, len(theta_test)): - rnd = sps.norm(loc=mu[tid, 0], scale=np.sqrt(obsvar + S[tid, 0, 0])) - phat[tid] = rnd.pdf(obs) - rnd = sps.norm( - loc=mu[tid, 0], scale=np.sqrt(0.5 * obsvar + S[tid, 0, 0]) - ) - pvar1[tid] = rnd.pdf(obs) - phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[ - tid - ] - phat[tid] ** 2 - - ax[1, i].plot(theta_test, p_test, color="red") - ax[1, i].plot( - theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5 - ) - ax[1, i].fill_between( - theta_test.flatten(), - (phat - np.sqrt(phatvar)).flatten(), - (phat + np.sqrt(phatvar)).flatten(), - color="blue", - alpha=0.1, - ) - ax[1, i].set_xlabel(r"$\theta$", fontsize=ft) - ax[1, i].set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax[1, i].tick_params(axis="both", labelsize=ft) - ax[1, i].scatter(theta0u, p0, color="black", s=100) - ax[1, i].scatter(cu, pc, color="green", marker="*", s=200) - - theta0u = np.concatenate([theta0u, cu], axis=0) - p0 = np.concatenate([p0, pc.flatten()]) - - print("Adding point:", np.round(ctheta, 2)) - - from PUQ.surrogatemethods.pcHetGP import update - - update(emu._info, x=x, X0new=cu, mult=reps) - - plt.savefig("Figure3.png", bbox_inches="tight") - plt.show() diff --git a/examples/Technometrics2025/bseq2d_exploit.py b/examples/Technometrics2025/bseq2d_exploit.py deleted file mode 100644 index c6b9c62..0000000 --- a/examples/Technometrics2025/bseq2d_exploit.py +++ /dev/null @@ -1,163 +0,0 @@ -import numpy as np -from PUQ.prior import prior_dist -from PUQ.design import designer -from test_funcs import bimodal, banana, unimodal -from utilities import test_data_gen, twoD, heatmap -import pandas as pd - - -# args = parse_arguments() - -class Args: - pass - -args = Args() - -def create_entry(desobj, fname, method, s, b, w): - return [ - { - "MAD": MAD, - "t": t, - "rep": s, - "batch": b, - "worker": w, - "method": method, - "example": fname, - } - for t, MAD in enumerate(desobj._info["TViter"]) - ] - - -# # # # # -args.minibatch = 64 -# -args.funcname = "unimodal" - -args.seedmin = 0 -args.seedmax = 1 -# # # # # - -workers = args.minibatch + 1 -grid = 10 -rep0 = 2 -nmesh = 50 -rho = 1 / 2 -batch = args.minibatch -maxiter = 192 -dfl = [] -if __name__ == "__main__": - for s in np.arange(args.seedmin, args.seedmax): - - cls_func = eval(args.funcname)() - cls_func.realdata(seed=s) - - theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) - test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} - - heatmap(cls_func) - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - # Initial sample - theta1 = np.linspace( - cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], grid - ) - theta2 = np.linspace( - cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], grid - ) - th1, th2 = np.meshgrid(theta1, theta2) - theta0u = np.vstack([th1.ravel(), th2.ravel()]).T - theta0 = np.repeat(theta0u, rep0, axis=0) - f0 = np.zeros((cls_func.d, rep0 * (grid**2))) - for i in range(0, rep0 * (grid**2)): - f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) - - base_args = { - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": workers, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta0, "f": f0}, - "alloc_settings": { - "use_Ki": True, - "rho": rho, - "theta": None, - "a0": None, - "gen": False, - }, - "pc_settings": {"standardize": True, "latent": False}, - "des_settings": {"is_exploit": True, "is_explore": False}, - } - - methods = ["ivar", "imse"] - - args_list = [] - for method in methods: - args_ = base_args.copy() - args_["alloc_settings"] = args_["alloc_settings"].copy() - args_["alloc_settings"]["method"] = method - args_list.append(args_) - - al_ivar = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="seivar", - args=args_list[0], - ) - - # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) - twoD(al_ivar, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_ivar, args.funcname, "ivar", s, batch, workers)) - - al_imse = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="simse", - args=args_list[1], - ) - - # save_output(al_imse, cls_func.data_name, "imse", workers, batch, s) - twoD(al_imse, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_imse, args.funcname, "imse", s, batch, workers)) - - theta0r = theta0u[persis_info["rand_stream"].permutation(theta0u.shape[0]), :] - - al_unif = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition=None, - args={ - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": workers, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta0, "f": f0}, - "des_add": {"theta": theta0r}, - "alloc_settings": { - "use_Ki": True, - "rho": rho, - "theta": None, - "a0": None, - "gen": False, - }, - "pc_settings": {"standardize": True, "latent": False}, - "des_settings": {"is_exploit": True, "is_explore": True}, - }, - ) - - # save_output(al_unif, cls_func.data_name, "unif", workers, batch, s) - twoD(al_unif, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_unif, args.funcname, "unif", s, batch, workers)) - -from summary import lineplot - -df = pd.DataFrame(dfl) -lineplot(df, examples=[args.funcname], batches=[batch]) diff --git a/examples/Technometrics2025/bseq2d_explore.py b/examples/Technometrics2025/bseq2d_explore.py deleted file mode 100644 index c36c74a..0000000 --- a/examples/Technometrics2025/bseq2d_explore.py +++ /dev/null @@ -1,182 +0,0 @@ -import numpy as np -from PUQ.prior import prior_dist -# from PUQ.utils import parse_arguments, save_output -from PUQ.design import designer -from test_funcs import bimodal, banana, unimodal -from utilities import test_data_gen, twoD, heatmap -import pandas as pd -from smt.sampling_methods import LHS -import time - -# args = parse_arguments() - -class Args: - pass - -args = Args() - - -def create_entry(desobj, fname, method, s, b, w): - return [ - { - "MAD": MAD, - "t": t, - "rep": s, - "batch": b, - "worker": w, - "method": method, - "example": fname, - } - for t, MAD in enumerate(desobj._info["TViter"]) - ] - - -# # # # # -args.minibatch = 8 -args.funcname = "unimodal" -args.seedmin = 0 -args.seedmax = 1 - -# # # # # - -workers = args.minibatch + 1 -n0 = 15 -rep0 = 2 -nmesh = 50 -rho = 1 / 2 -batch = args.minibatch -maxiter = 256 -dfl, dfr = [], [] - -# Inputs to designer -pcset = {"standardize": True, "latent": False} -desset = {"is_exploit": True, "is_explore": True, "nL": 200, "impute_str": "update"} - -if __name__ == "__main__": - design_start = time.time() - for s in np.arange(args.seedmin, args.seedmax): - - cls_func = eval(args.funcname)() - cls_func.realdata(seed=s) - - theta_test, p_test, f_test, Xpl, Ypl = test_data_gen(cls_func, nmesh) - test_data = {"theta": theta_test, "f": f_test, "p": p_test, "p_prior": 1} - - # heatmap(cls_func) - - # Set a uniform prior - prior_func = prior_dist(dist="uniform")( - a=cls_func.thetalimits[:, 0], b=cls_func.thetalimits[:, 1] - ) - - # Set random stream for initial design - persis_info = {"rand_stream": np.random.default_rng(s)} - - # Initial sample - sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) - theta0 = sampling(n0) - theta0 = np.repeat(theta0, rep0, axis=0) - f0 = np.zeros((cls_func.d, n0 * rep0)) - for i in range(0, n0 * rep0): - f0[:, i] = cls_func.sim_f(theta0[i, :], persis_info=persis_info) - - base_args = { - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": workers, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta0, "f": f0}, - "alloc_settings": { - "use_Ki": True, - "rho": rho, - "theta": None, - "a0": None, - "gen": False, - }, - "pc_settings": pcset, - "des_settings": desset, - } - - methods = ["ivar", "imse"] - - args_list = [] - for method in methods: - args_ = base_args.copy() - args_["alloc_settings"] = args_["alloc_settings"].copy() - args_["alloc_settings"]["method"] = method - args_list.append(args_) - - al_ivar = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="seivar", - args=args_list[0], - ) - - # save_output(al_ivar, cls_func.data_name, "ivar", workers, batch, s) - twoD(al_ivar, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_ivar, args.funcname, "ivar", s, batch, workers)) - - al_imse = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="simse", - args=args_list[1], - ) - - # save_output(al_imse, cls_func.data_name, "imse", workers, batch, s) - twoD(al_imse, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_imse, args.funcname, "imse", s, batch, workers)) - - rholhs = 1 / 4 - sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) - theta_lhs = sampling(int(maxiter * rholhs)) - - al_unif = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition=None, - args={ - "prior": prior_func, - "data_test": test_data, - "max_iter": maxiter, - "nworkers": workers, - "batch_size": batch, - "des_init": {"seed": s, "theta": theta0, "f": f0}, - "des_add": {"theta": theta_lhs}, - "alloc_settings": { - "use_Ki": True, - "rho": rholhs, - "theta": None, - "a0": None, - "gen": False, - }, - "pc_settings": pcset, - "des_settings": {"is_exploit": True, "is_explore": True}, - }, - ) - - # save_output(al_unif, cls_func.data_name, "unif", workers, batch, s) - twoD(al_unif, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_unif, args.funcname, "unif", s, batch, workers)) - - al_var = designer( - data_cls=cls_func, - method="p_sto_bseq", - acquisition="var", - args=args_list[0], - ) - - # save_output(al_var, cls_func.data_name, "var", workers, batch, s) - twoD(al_var, Xpl, Ypl, p_test, nmesh) - dfl.extend(create_entry(al_var, args.funcname, "var", s, batch, workers)) - - design_end = time.time() - print("Elapsed time: " + str(round(design_end - design_start, 2))) - - -# from summary import lineplot - -# df = pd.DataFrame(dfl) -# lineplot(df, examples=[args.funcname], batches=[batch]) diff --git a/examples/Technometrics2025/test_funcs.py b/examples/Technometrics2025/test_funcs.py deleted file mode 100644 index ad0d341..0000000 --- a/examples/Technometrics2025/test_funcs.py +++ /dev/null @@ -1,562 +0,0 @@ -import numpy as np -import scipy - - -class unimodal: - def __init__(self): - self.data_name = "unimodal" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.obsvar = np.array([[0.1]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.5, 0.5]) - - def function(self, t1, t2): - t1 = -10 + t1 * 20 - t2 = -10 + t2 * 20 - f = 0.26 * (t1**2 + t2**2) - 0.48 * t1 * t2 - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.05, 0], [0, 0.05]]) - var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) - return 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class bimodal: - def __init__(self): - self.data_name = "bimodal" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.obsvar = np.array([[0.5, 0], [0, 0.5]]) - self.theta_true = np.array([8 / 12, 8 / 12]) - self.real_data = None - self.out = [("f", float, (2,))] - self.p = 2 - self.d = 2 - self.x = np.arange(0, self.d)[:, None] - - def function(self, t1, t2): - t1 = 12 * t1 - 6 - t2 = 12 * t2 - 4 - f = np.array([np.sqrt(0.2) * (t2 - t1**2), np.sqrt(0.75) * (t2 - t1)]) - return f - - def sim_f(self, thetas, persis_info): - - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].multivariate_normal( - np.array([0, 0]), np.diag(V.flatten()), 1 - ) - f += R.flatten() - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - V = np.repeat(0.5 + (theta[:, 0] ** 2 + theta[:, 1] ** 2) * 2, self.d) - V = V.reshape(self.d, theta.shape[0]) - return V - - def realdata(self, seed): - - M = np.array( - [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].multivariate_normal( - mean=[0, 0], cov=self.obsvar, size=1 - ) - self.real_data = M + R - - -class banana: - def __init__(self): - self.data_name = "banana" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.obsvar = np.array([[0.03, 0], [0, 0.5]]) - self.theta_true = np.array([0.5, 0.75]) - self.real_data = None - self.out = [("f", float, (2,))] - self.p = 2 - self.d = 2 - self.x = np.arange(0, self.d)[:, None] - - def function(self, t1, t2): - t1 = 40 * t1 - 20 - t2 = 15 * t2 - 15 - f = np.array([0.03 * t1, t2 + 0.06 * t1**2]) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].multivariate_normal( - np.array([0, 0]), np.diag(V.flatten()), 1 - ) - f += R.flatten() - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - - f = self.function(theta[0, 0], theta[0, 1]) - if theta[0, 0] < 0.5: - noise1 = 0.01 * np.abs(f[0]) - noise2 = 0.01 * np.abs(f[1]) - else: - noise1 = 0.1 * np.abs(f[0]) - noise2 = 0.2 * np.abs(f[1]) - return np.array([noise1, noise2]).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - [self.function(self.theta_true[0], self.theta_true[1])], dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].multivariate_normal( - mean=[0, 0], cov=self.obsvar, size=1 - ) - self.real_data = M + R - - -class sinf: - def __init__(self): - self.data_name = "sinf" - self.thetalimits = np.array([[0, 1]]) - self.obsvar = np.array([[0.05]], dtype="float64") - self.theta_true = 0.5 - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 1 - self.x = np.arange(0, self.d)[:, None] - - def function(self, theta1): - return np.sin(10 * theta1) - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0]) - var_noise = self.noise(np.array([thetas[0]])[None, :]) - noise = persis_info["rand_stream"].normal(0, np.sqrt(var_noise[0]), 1) - f += noise - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"], persis_info) - return H_o, persis_info - - def realdata(self, seed): - - mean = np.array([[self.function(self.theta_true)]], dtype="float64") - if seed is None: - self.real_data = mean - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - noise = persis_info["rand_stream"].normal( - 0, np.sqrt(self.obsvar[0]), size=1 - ) - self.real_data = mean + noise - - def noise(self, theta): - rx = (1.1 + np.sin(2 * np.pi * theta)) * 0.05 - return rx.reshape(self.d, theta.shape[0]) - - -class branin: - def __init__(self): - self.data_name = "branin" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.d = 1 - self.p = 2 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.9613333333333334, 0.16466666666666668]) - - def function(self, t1, t2): - t1 = -5 + 15 * t1 - t2 = 15 * t2 - f = ( - (t2 - (5.1 / (4 * np.pi**2)) * (t1**2) + (5 / np.pi) * t1 - 6) ** 2 - + 10 * (1 - 1 / (8 * np.pi)) * np.cos(t1) - + 10 - ) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - return np.repeat(1, self.d * theta.shape[0]).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class himmelblau: - def __init__(self): - - self.data_name = "himmelblau" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[100]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.11085, 0.16]) - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = (theta1**2 + theta2 - 11) ** 2 + (theta1 + theta2**2 - 7) ** 2 - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.01, 0], [0, 0.01]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) - return 10 + 5 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class holder: - def __init__(self): - - self.data_name = "holder" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-10, 10], [-10, 10]]) - self.obsvar = np.array([[50]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.1, 0.01]) - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = -np.abs( - np.sin(theta1) - * np.cos(theta2) - * np.exp(np.abs(1 - (np.sqrt(theta1**2 + theta2**2) / np.pi))) - ) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.1, 0], [0, 0.1]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) - return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class easom: - def __init__(self): - - self.data_name = "easom" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-10, 10], [-10, 10]]) - self.obsvar = np.array([[0.001]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.75, 0.75]) - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = ( - -np.cos(theta1) - * np.cos(theta2) - * np.exp(-((theta1 - np.pi) ** 2 + (theta2 - np.pi) ** 2)) - ) - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.1, 0], [0, 0.1]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.75], cov=cov) - return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class ackley: - def __init__(self): - - self.data_name = "ackley" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[10]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.5, 0.5]) - - def function(self, theta1, theta2): - - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = ( - -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (theta1**2 + theta2**2))) - - np.exp(0.5 * (np.cos(2 * np.pi * theta1) + np.cos(2 * np.pi * theta2))) - + np.e - + 20 - ) - - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.1, 0], [0, 0.1]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.85], cov=cov) - return 1 + 2 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R - - -class sphere: - def __init__(self): - - self.data_name = "sphere" - self.thetalimits = np.array([[0, 1], [0, 1]]) - self.truelimits = np.array([[-5, 5], [-5, 5]]) - self.obsvar = np.array([[1]], dtype="float64") - self.real_data = None - self.out = [("f", float)] - self.p = 2 - self.d = 1 - self.x = np.arange(0, self.d)[:, None] - self.theta_true = np.array([0.5, 0.5]) - - def function(self, theta1, theta2): - theta1 = self.truelimits[0][0] + theta1 * ( - self.truelimits[0][1] - self.truelimits[0][0] - ) - theta2 = self.truelimits[1][0] + theta2 * ( - self.truelimits[1][1] - self.truelimits[1][0] - ) - f = theta1**2 + theta2**2 - return f - - def sim_f(self, thetas, persis_info): - f = self.function(thetas[0], thetas[1]) - V = self.noise(np.array([thetas[0], thetas[1]])[None, :]) - R = persis_info["rand_stream"].normal(0, np.sqrt(V[0]), 1) - f += R - return f - - def sim(self, H, persis_info, sim_specs, libE_info): - function = sim_specs["user"]["sim_f"] - H_o = np.zeros(1, dtype=sim_specs["out"]) - H_o["f"] = function(H["thetas"][0], persis_info) - - return H_o, persis_info - - def noise(self, theta): - cov = np.array([[0.1, 0], [0, 0.1]]) - var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) - return 1 + 1 * var.pdf(theta).reshape(self.d, theta.shape[0]) - - def realdata(self, seed): - - M = np.array( - self.function(self.theta_true[0], self.theta_true[1]), dtype="float64" - ) - if seed is None: - self.real_data = M - else: - persis_info = {"rand_stream": np.random.default_rng(seed)} - R = persis_info["rand_stream"].normal(0, np.sqrt(self.obsvar[0]), size=1) - self.real_data = M + R diff --git a/examples/Technometrics2025/utilities.py b/examples/Technometrics2025/utilities.py deleted file mode 100644 index bfe0388..0000000 --- a/examples/Technometrics2025/utilities.py +++ /dev/null @@ -1,818 +0,0 @@ -import numpy as np -import matplotlib.pyplot as plt -import scipy.stats as sps -from smt.sampling_methods import LHS - - -def twoD(designobj, Xpl, Ypl, p_test, nmesh): - theta0 = designobj._info["theta0"] - reps0 = designobj._info["reps0"] - - fig, ax = plt.subplots(figsize=(5, 5)) - cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="RdGy") - for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): - if label == 2: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="cyan", - weight="bold", - ) - else: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="blue", - weight="bold", - ) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - - -def test_data_gen(cls_func, nmesh): - xpl = np.linspace(cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh) - ypl = np.linspace(cls_func.thetalimits[1][0], cls_func.thetalimits[1][1], nmesh) - Xpl, Ypl = np.meshgrid(xpl, ypl) - theta_test = np.vstack([Xpl.ravel(), Ypl.ravel()]).T - if cls_func.data_name in [ - "unimodal", - "branin", - "himmelblau", - "holder", - "easom", - "ackley", - "sphere", - ]: - f_test = np.zeros(theta_test.shape[0]) - else: - f_test = np.zeros((theta_test.shape[0], 2)) - p_test = np.zeros(theta_test.shape[0]) - for tid in range(0, len(theta_test)): - if cls_func.data_name in [ - "unimodal", - "branin", - "himmelblau", - "holder", - "easom", - "ackley", - "sphere", - ]: - f_test[tid] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) - rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) - else: - f_test[tid, :] = cls_func.function(theta_test[tid, 0], theta_test[tid, 1]) - rnd = sps.multivariate_normal(mean=f_test[tid, :], cov=(cls_func.obsvar)) - p_test[tid] = rnd.pdf(cls_func.real_data) - - return theta_test, p_test, f_test, Xpl, Ypl - - -def test_data_gen_1d(cls_func, nmesh): - - # Create test data - theta_test = np.linspace( - cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nmesh - )[:, None] - f_test, p_test = np.zeros(theta_test.shape[0]), np.zeros(theta_test.shape[0]) - for tid in range(0, len(theta_test)): - f_test[tid] = cls_func.function(theta_test[tid, 0]) - rnd = sps.norm(loc=f_test[tid], scale=np.sqrt(cls_func.obsvar)) - p_test[tid] = rnd.pdf(cls_func.real_data) - - return theta_test, p_test, f_test - - -def oned(seqobject, x, obsvar, real_data, theta_test, f_test, p_test, cls_func, ninit): - from PUQ.surrogate import emulator - from scipy.stats import norm - import matplotlib.pyplot as plt - - pc_settings = {"standardize": True, "latent": True} - - theta = seqobject._info["theta"][0:ninit, :] - f = seqobject._info["f"][:, 0:ninit] - - theta_all = seqobject._info["theta"] - f_all = seqobject._info["f"] - - theta0 = seqobject._info["theta0"] - reps0 = seqobject._info["reps0"] - - emu = emulator( - x=x, - theta=theta, - f=f, - method="pcHetGP", - args={ - "lower": None, - "upper": None, - "noiseControl": { - "k_theta_g_bounds": (1, 100), - "g_max": 1e2, - "g_bounds": (1e-6, 1), - }, - "init": {}, - "known": {}, - "settings": { - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - "pc_settings": pc_settings, - }, - ) - - emupred = emu.predict(x=x, theta=theta_test) - - mean = emupred.mean() - var = emupred.var() - var_noisy = emupred._info["var_noisy"] - - # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) - quantile = 0.025 - lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) - quantile = 0.975 - upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) - # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) - quantile = 0.025 - lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) - quantile = 0.975 - upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) - - ft = 20 - fig, ax = plt.subplots() - ax.plot(theta_test, f_test, color="red") - ax.plot( - theta_test.flatten(), - mean.flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - plt.fill_between( - theta_test.flatten(), - mean.flatten() - np.sqrt(var.flatten()), - mean.flatten() + np.sqrt(var.flatten()), - # lower_bound.flatten(), - # upper_bound.flatten(), - color="blue", - alpha=0.3, - linestyle="dotted", - ) - - # plt.fill_between( - # theta_test.flatten(), - # lower_bound_nug.flatten(), - # upper_bound_nug.flatten(), - # color='blue', - # alpha=0.1, - # linestyle="dotted", - # ) - ax.scatter( - theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" - ) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$M(\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.show() - - phat = np.zeros(theta_test.shape[0]) - phatvar = np.zeros(theta_test.shape[0]) - pvar1 = np.zeros(theta_test.shape[0]) - for tid in range(0, len(theta_test)): - rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) - phat[tid] = rnd.pdf(real_data) - rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) - pvar1[tid] = rnd.pdf(real_data) - phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ - tid - ] ** 2 - - fig, ax = plt.subplots() - ax.plot(theta_test, p_test, color="red") - ax.plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) - plt.fill_between( - theta_test.flatten(), - (phat - np.sqrt(phatvar)).flatten(), - (phat + np.sqrt(phatvar)).flatten(), - color="blue", - alpha=0.1, - ) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.show() - - p_alloc = np.zeros(theta0.shape[0]) - f_alloc = np.zeros((1, theta0.shape[0])) - for tid in range(0, len(theta0)): - f_alloc[0, tid] = cls_func.function(theta0[tid]) - rnd = sps.norm(loc=f_alloc[0, tid], scale=np.sqrt(cls_func.obsvar)) - p_alloc[tid] = rnd.pdf(cls_func.real_data) - - fig, ax = plt.subplots(figsize=(10, 6)) - ax.plot(theta_test, p_test, color="red", linewidth=2.5) - for label, x_count, pval in zip(reps0, theta0, p_alloc): - plt.annotate( - label, - xy=(x_count, pval), - xytext=(0, 0), - textcoords="offset points", - color="blue", - size=20, - ) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.show() - - fig, ax = plt.subplots() - ax.scatter(theta0.flatten(), reps0.flatten(), color="blue", marker="*", s=55) - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"a", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.show() - - fig, ax = plt.subplots() - ax.plot( - theta_test.flatten(), - emupred._info["nugs"].flatten(), - color="blue", - linestyle="dashed", - linewidth=2.5, - ) - ax.plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") - ax.set_xlabel(r"$\theta$", fontsize=ft) - ax.set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) - ax.tick_params(axis="both", labelsize=ft) - plt.show() - - ft = 15 - - def make_patch_spines_invisible(ax): - ax.set_frame_on(True) - ax.patch.set_visible(False) - for sp in ax.spines.values(): - sp.set_visible(False) - - fig, host = plt.subplots(figsize=(8, 4)) - fig.subplots_adjust(right=0.75) - - par1 = host.twinx() - par2 = host.twinx() - - # Offset the right spine of par2. The ticks and label have already been - # placed on the right by twinx above. - par2.spines["right"].set_position(("axes", 1.2)) - # Having been created by twinx, par2 has its frame off, so the line of its - # detached spine is invisible. First, activate the frame but make the patch - # and spines invisible. - make_patch_spines_invisible(par2) - # Second, show the right spine. - par2.spines["right"].set_visible(True) - - p1 = host.scatter( - theta0.flatten(), - reps0.flatten(), - color="black", - marker="*", - s=55, - label=r"$a_i$", - ) - (p2,) = par1.plot( - theta_test.flatten(), - emupred._info["nugs"].flatten(), - "b", - linestyle="dashed", - label="Variance", - linewidth=2.5, - ) - (p3,) = par2.plot( - theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 - ) - - host.set_xlim(-0.1, 1.1) - host.set_ylim(0.9 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) - par1.set_ylim( - 0.9 * np.min(emupred._info["nugs"].flatten()), - 1.1 * np.max(emupred._info["nugs"].flatten()), - ) - par2.set_ylim(-0.01, 1.1 * np.max(phat)) - - host.set_xlabel(r"$\theta$", fontsize=ft) - host.set_ylabel(r"$a_i$", fontsize=ft) - par1.set_ylabel("Variance", fontsize=ft) - par2.set_ylabel("Likelihood", fontsize=ft) - host.tick_params(axis="both", labelsize=ft) - par1.tick_params(axis="both", labelsize=ft) - par2.tick_params(axis="both", labelsize=ft) - lines = [p1, p2, p3] - - host.legend( - lines, - [l.get_label() for l in lines], - loc="center", - bbox_to_anchor=(0.5, -0.3), - ncol=3, - prop={"size": ft}, - ) - - plt.show() - - -def twodpaper(cls_func, Xpl, Ypl, p_test, theta0, reps0, thetainit=None, name=None): - from matplotlib.ticker import MaxNLocator - from matplotlib.colors import ListedColormap - - yellow_colors = [ - (1, 1, 1), - (1, 1, 0.8), # light yellow - (1, 1, 0.6), - (1, 1, 0.4), - (1, 1, 0.2), - (1, 1, 0), # yellow - (1, 0.9, 0), # dark yellow - (1, 0.8, 0), # yellow-orange - (1, 0.6, 0), # orange - (1, 0.4, 0), # dark orange - (1, 0.2, 0), # very dark orange - ] - yellow_cmap = ListedColormap(yellow_colors, name="yellow") - - if cls_func.data_name in ["unimodal", "branin"]: - nmesh = len(Xpl) - P = np.zeros((nmesh, nmesh)) - for i in range(nmesh): - for j in range(nmesh): - P[i, j] = cls_func.noise( - np.array([Xpl[i, j], Ypl[i, j]])[None, :] - ).flatten() - - Pvar = P - else: - nmesh = len(Xpl) - P = np.zeros((nmesh, nmesh, 2)) - for i in range(nmesh): - for j in range(nmesh): - P[i, j, :] = cls_func.noise( - np.array([Xpl[i, j], Ypl[i, j]])[None, :] - ).flatten() - Pvar = np.sum(P, axis=2) - - fig, ax = plt.subplots() - cs = ax.contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) - # cbar = fig.colorbar(cs, pad=0.1) - cp = ax.contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") - - if thetainit is None: - for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): - if label <= 2: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="cyan", - ) - else: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="black", - ) - else: - for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): - if np.array([x_count, y_count]) in thetainit: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="cyan", - ) - else: - plt.annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=12, - color="black", - ) - - ax.set_xticks([0, 0.5, 1]) # Custom tick locations for x-axis - ax.set_yticks([0, 0.5, 1]) # Custom tick locations for y-axis - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.savefig(name, bbox_inches="tight") - plt.show() - - -def twodpaperrev(cls_func, Xpl, Ypl, p_test, dictl, thetainit=None, name=None): - from matplotlib.ticker import MaxNLocator - from matplotlib.colors import ListedColormap - - yellow_colors = [ - (1, 1, 1), - (1, 1, 0.8), # light yellow - (1, 1, 0.6), - (1, 1, 0.4), - (1, 1, 0.2), - (1, 1, 0), # yellow - (1, 0.9, 0), # dark yellow - (1, 0.8, 0), # yellow-orange - (1, 0.6, 0), # orange - (1, 0.4, 0), # dark orange - (1, 0.2, 0), # very dark orange - ] - yellow_cmap = ListedColormap(yellow_colors, name="yellow") - - if cls_func.data_name in ["unimodal", "branin"]: - nmesh = len(Xpl) - P = np.zeros((nmesh, nmesh)) - for i in range(nmesh): - for j in range(nmesh): - P[i, j] = cls_func.noise( - np.array([Xpl[i, j], Ypl[i, j]])[None, :] - ).flatten() - - Pvar = P - else: - nmesh = len(Xpl) - P = np.zeros((nmesh, nmesh, 2)) - for i in range(nmesh): - for j in range(nmesh): - P[i, j, :] = cls_func.noise( - np.array([Xpl[i, j], Ypl[i, j]])[None, :] - ).flatten() - Pvar = np.sum(P, axis=2) - - fig, ax = plt.subplots(1, 3, figsize=(15, 4.5), constrained_layout=True) - - for i in range(0, 3): - if i == 0: - for el in dictl: - if el["method"] == "ivar": - reps0 = el["reps0"] - theta0 = el["theta0"] - elif i == 1: - for el in dictl: - if el["method"] == "var": - reps0 = el["reps0"] - theta0 = el["theta0"] - elif i == 2: - for el in dictl: - if el["method"] == "imse": - reps0 = el["reps0"] - theta0 = el["theta0"] - - cs = ax[i].contourf(Xpl, Ypl, Pvar, cmap=yellow_cmap, alpha=0.75) - if i == 2: - cbar = fig.colorbar(cs, ax=ax[i], pad=0.1) - cp = ax[i].contour(Xpl, Ypl, p_test.reshape(nmesh, nmesh), 20, cmap="coolwarm") - ft = 14 - if thetainit is None: - for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): - if label <= 2: - ax[i].annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color="cyan", - ) - else: - ax[i].annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color="black", - ) - else: - for label, x_count, y_count in zip(reps0, theta0[:, 0], theta0[:, 1]): - if np.array([x_count, y_count]) in thetainit: - ax[i].annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color="cyan", - ) - else: - ax[i].annotate( - label, - xy=(x_count, y_count), - xytext=(0, 0), - textcoords="offset points", - fontsize=ft, - color="black", - ) - - if i == 0: - ax[i].set_yticks([0, 0.5, 1]) # Custom tick locations for x-axis - ax[i].set_ylabel(r"$\theta_2$", fontsize=16) - else: - ax[i].set_yticks([]) - ax[i].set_xticks([0, 0.5, 1]) - - ax[i].set_xlabel(r"$\theta_1$", fontsize=16) - - ax[i].tick_params(axis="both", labelsize=16) - - plt.savefig(name, bbox_inches="tight") - plt.show() - - -def heatmap(cls_func): - - from matplotlib.colors import ListedColormap - - yellow_colors = [ - (1, 1, 1), - (1, 1, 0.8), # light yellow - (1, 1, 0.6), - (1, 1, 0.4), - (1, 1, 0.2), - (1, 1, 0), # yellow - (1, 0.9, 0), # dark yellow - (1, 0.8, 0), # yellow-orange - (1, 0.6, 0), # orange - (1, 0.4, 0), # dark orange - (1, 0.2, 0), # very dark orange - ] - yellow_cmap = ListedColormap(yellow_colors, name="yellow") - - if cls_func.data_name in ["unimodal", "branin"]: - nmesh = 50 - a = np.arange(nmesh + 1) / nmesh - b = np.arange(nmesh + 1) / nmesh - X, Y = np.meshgrid(a, b) - Z = np.zeros((nmesh + 1, nmesh + 1)) - P = np.zeros((nmesh + 1, nmesh + 1)) - for i in range(nmesh + 1): - for j in range(nmesh + 1): - Z[i, j] = cls_func.function(X[i, j], Y[i, j]) - P[i, j] = cls_func.noise( - np.array([X[i, j], Y[i, j]])[None, :] - ).flatten() - - fig, ax = plt.subplots() - cs = ax.contourf(X, Y, P, cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - CS = ax.contour(X, Y, Z, colors="black") - ax.clabel(CS, inline=True, fontsize=10) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - else: - nmesh = 50 - a = np.arange(nmesh + 1) / nmesh - b = np.arange(nmesh + 1) / nmesh - X, Y = np.meshgrid(a, b) - Z = np.zeros((nmesh + 1, nmesh + 1, 2)) - P = np.zeros((nmesh + 1, nmesh + 1, 2)) - - for i in range(nmesh + 1): - for j in range(nmesh + 1): - Z[i, j, :] = cls_func.function(X[i, j], Y[i, j]) - P[i, j, :] = cls_func.noise( - np.array([X[i, j], Y[i, j]])[None, :] - ).flatten() - - fig, ax = plt.subplots() - cs = ax.contourf(X, Y, P[:, :, 0], cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - CS = ax.contour(X, Y, Z[:, :, 0], colors="black") - ax.clabel(CS, inline=True, fontsize=10) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - - fig, ax = plt.subplots() - cs = ax.contourf(X, Y, P[:, :, 1], cmap=yellow_cmap, alpha=0.75) - cbar = fig.colorbar(cs) - CS = ax.contour(X, Y, Z[:, :, 1], colors="black") - ax.clabel(CS, inline=True, fontsize=10) - ax.set_xlabel(r"$\theta_1$", fontsize=16) - ax.set_ylabel(r"$\theta_2$", fontsize=16) - ax.tick_params(axis="both", labelsize=16) - plt.show() - - -def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func): - from PUQ.surrogate import emulator - from scipy.stats import norm - import matplotlib.pyplot as plt - - pc_settings = {"standardize": True, "latent": False} - - # theta = seqobject._info['theta'][0:ninit,:] - # f = seqobject._info['f'][:, 0:ninit] - - emu = emulator( - x=x, - theta=theta, - f=f, - method="pcHetGP", - args={ - "lower": None, - "upper": None, - "noiseControl": { - "k_theta_g_bounds": (1, 100), - "g_max": 1e2, - "g_bounds": (1e-6, 1), - }, - "init": {}, - "known": {}, - "settings": { - "linkThetas": "joint", - "logN": True, - "initStrategy": "residuals", - "checkHom": True, - "penalty": True, - "trace": 0, - "return.matrices": True, - "return.hom": False, - "factr": 1e9, - }, - "pc_settings": pc_settings, - }, - ) - - emupred = emu.predict(x=x, theta=theta_test) - - mean = emupred.mean() - var = emupred.var() - var_noisy = emupred._info["var_noisy"] - - # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) - quantile = 0.025 - lower_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) - quantile = 0.975 - upper_bound = norm.ppf(quantile, loc=mean, scale=np.sqrt(var)) - # Probability corresponding to the quantile (e.g., 0.025 for the lower bound) - quantile = 0.025 - lower_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) - quantile = 0.975 - upper_bound_nug = norm.ppf(quantile, loc=mean, scale=np.sqrt(var_noisy)) - - fig, ax = plt.subplots(1, 3, figsize=(15, 3.5), constrained_layout=True) - ft = 18 - ax[0].plot(theta_test, f_test, color="red") - ax[0].plot( - theta_test.flatten(), - mean.flatten(), - linestyle="dashed", - color="blue", - linewidth=2.5, - ) - ax[0].fill_between( - theta_test.flatten(), - mean.flatten() - np.sqrt(var.flatten()), - mean.flatten() + np.sqrt(var.flatten()), - # lower_bound.flatten(), - # upper_bound.flatten(), - color="blue", - alpha=0.3, - linestyle="dotted", - ) - - ax[0].scatter( - theta.flatten(), f.flatten(), s=60, facecolors="none", edgecolors="green" - ) - ax[0].set_xlabel(r"$\theta$", fontsize=ft) - ax[0].set_ylabel(r"$\zeta(\theta)$", fontsize=ft) - ax[0].tick_params(axis="both", labelsize=ft) - - - phat = np.zeros(theta_test.shape[0]) - phatvar = np.zeros(theta_test.shape[0]) - pvar1 = np.zeros(theta_test.shape[0]) - for tid in range(0, len(theta_test)): - rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(obsvar + var[0, tid])) - phat[tid] = rnd.pdf(real_data) - rnd = sps.norm(loc=mean[0, tid], scale=np.sqrt(0.5 * obsvar + var[0, tid])) - pvar1[tid] = rnd.pdf(real_data) - phatvar[tid] = (1 / (2 * np.sqrt(np.pi) * np.sqrt(obsvar))) * pvar1[tid] - phat[ - tid - ] ** 2 - - ax[1].plot(theta_test, p_test, color="red") - ax[1].plot(theta_test, phat, color="blue", linestyle="dashed", linewidth=2.5) - ax[1].fill_between( - theta_test.flatten(), - (phat - np.sqrt(phatvar)).flatten(), - (phat + np.sqrt(phatvar)).flatten(), - color="blue", - alpha=0.1, - ) - ax[1].set_xlabel(r"$\theta$", fontsize=ft) - ax[1].set_ylabel(r"$p(y|\theta)$", fontsize=ft) - ax[1].tick_params(axis="both", labelsize=ft) - - ax[2].plot( - theta_test.flatten(), - emupred._info["nugs"].flatten(), - color="blue", - linestyle="dashed", - linewidth=2.5, - ) - ax[2].plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") - ax[2].set_xlabel(r"$\theta$", fontsize=ft) - ax[2].set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) - ax[2].tick_params(axis="both", labelsize=ft) - plt.savefig("Figure1.png", bbox_inches="tight") - plt.show() - - return theta_test, emupred._info["nugs"].flatten(), phat - - -def Figure2(desobject, theta_test, nugs, phat, method, axs): - - theta0 = desobject._info["theta0"] - reps0 = desobject._info["reps0"] - - ft = 15 - - def make_patch_spines_invisible(ax): - ax.set_frame_on(True) - ax.patch.set_visible(False) - for sp in ax.spines.values(): - sp.set_visible(False) - - par1 = axs.twinx() - par2 = axs.twinx() - - par2.spines["right"].set_position(("axes", 1.25)) - make_patch_spines_invisible(par2) - par2.spines["right"].set_visible(True) - - p1 = axs.scatter( - theta0.flatten(), - reps0.flatten(), - color="black", - marker="*", - s=55, - label=r"$a_i$", - ) - (p2,) = par1.plot( - theta_test.flatten(), - nugs, - "b", - linestyle="dashed", - label="Variance", - linewidth=2.5, - ) - (p3,) = par2.plot( - theta_test, phat, "r", linestyle="dotted", label="Likelihood", linewidth=2.5 - ) - - axs.set_xlim(-0.1, 1.1) - axs.set_ylim(0.8 * np.min(reps0.flatten()), 1.1 * np.max(reps0.flatten())) - par1.set_ylim(0.8 * np.min(nugs), 1.1 * np.max(nugs)) - par2.set_ylim(-0.1, 1.1 * np.max(phat)) - - axs.set_xlabel(r"$\theta$", fontsize=ft) - axs.set_ylabel(r"$a_i$", fontsize=ft) - par1.set_ylabel("Variance", fontsize=ft) - par2.set_ylabel("Likelihood", fontsize=ft) - axs.tick_params(axis="both", labelsize=ft) - par1.tick_params(axis="both", labelsize=ft) - par2.tick_params(axis="both", labelsize=ft) - lines = [p1, p2, p3] - - axs.legend( - lines, - [l.get_label() for l in lines], - loc="center", - bbox_to_anchor=(0.5, -0.3), - ncol=3, - prop={"size": ft}, - ) - From ce6f78270cd997130c68764e1783d557f463ec83 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 22:32:04 -0400 Subject: [PATCH 071/103] black test --- examples/Example1/example.py | 65 +++--- examples/Example1/example_2d.py | 51 ++--- examples/Example1/test_func.py | 4 - examples/Example1/toy_example.py | 33 ++- examples/Example2/example.py | 43 ++-- examples/Example4/example.py | 7 +- examples/Example4/toy_example_exploit.py | 12 +- examples/Example4/toy_example_explore.py | 6 +- examples/Example4/utilities.py | 45 ++-- examples/Example5/example.py | 34 +-- examples/Example5/paper_figs.py | 251 +++++++++++---------- examples/Example5/test_functions.py | 25 ++- examples/Example5/toy_example.py | 9 +- examples/Example5/utils.py | 264 ++++++++++++++--------- examples/Example5/utils_sample.py | 32 +-- 15 files changed, 493 insertions(+), 388 deletions(-) diff --git a/examples/Example1/example.py b/examples/Example1/example.py index 28dc70d..823ea71 100644 --- a/examples/Example1/example.py +++ b/examples/Example1/example.py @@ -11,28 +11,22 @@ cex = unimodal() # # # Create a mesh for test set # # # - xpl = np.linspace( - cex.thetalimits[0][0], cex.thetalimits[0][1], 50 - ) - ypl = np.linspace( - cex.thetalimits[1][0], cex.thetalimits[1][1], 50 - ) + xpl = np.linspace(cex.thetalimits[0][0], cex.thetalimits[0][1], 50) + ypl = np.linspace(cex.thetalimits[1][0], cex.thetalimits[1][1], 50) Xpl, Ypl = np.meshgrid(xpl, ypl) thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T ftest = np.zeros((thetatest.shape[0], 1)) for i in range(thetatest.shape[0]): - ftest[i, 0] = cex.function(thetatest[i,0], thetatest[i,1]) + ftest[i, 0] = cex.function(thetatest[i, 0], thetatest[i, 1]) - ptest = sps.norm.pdf( - cex.real_data - ftest, 0, np.sqrt(cex.obsvar) - ) + ptest = sps.norm.pdf(cex.real_data - ftest, 0, np.sqrt(cex.obsvar)) test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} # # # # # # # # # # # # # # # # # # # # # prior_func = prior_dist(dist="uniform")( a=cex.thetalimits[:, 0], b=cex.thetalimits[:, 1] ) - + # Initial sample n0, s = 10, 1 @@ -45,34 +39,37 @@ f0 = np.zeros((t0.shape[0], 1)) for i in range(t0.shape[0]): - f0[i, 0] = cex.function(t0[i,0], t0[i,1]) + f0[i, 0] = cex.function(t0[i, 0], t0[i, 1]) fig, ax = plt.subplots(1, 3, figsize=(12, 4)) for i, af in enumerate(["rnd", "var", "ivar"]): des_obj = sequential_design(cex) - des_obj.build_design(z0=t0, - f0=f0, - T=50, - test=test_data, - af=af, - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "seed_n0": 1, - "prior": prior_func, - "data_test": None, - "max_evals": 60, - "type_init": None, - "seed": 0, - "integral": "LHS" - }) - - + des_obj.build_design( + z0=t0, + f0=f0, + T=50, + test=test_data, + af=af, + args={ + "mini_batch": 1, + "n_init_thetas": 10, + "nworkers": 2, + "seed_n0": 1, + "prior": prior_func, + "data_test": None, + "max_evals": 60, + "type_init": None, + "seed": 0, + "integral": "LHS", + }, + ) + theta_al = des_obj.zs - + cp = ax[i].contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax[i].scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) + ax[i].scatter( + theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2 + ) ax[i].scatter( theta_al[0:n0, 0], theta_al[0:n0, 1], @@ -84,7 +81,7 @@ ax[i].set_xlabel(r"$\theta_1$", fontsize=16) if i == 0: ax[i].set_ylabel(r"$\theta_2$", fontsize=16) - + ax[i].tick_params(axis="both", labelsize=16) plt.savefig("ex1.png", format="jpeg", bbox_inches="tight", dpi=1000) plt.show() diff --git a/examples/Example1/example_2d.py b/examples/Example1/example_2d.py index 5aa6478..77fbeba 100644 --- a/examples/Example1/example_2d.py +++ b/examples/Example1/example_2d.py @@ -11,7 +11,7 @@ if __name__ == "__main__": print("Running function: " + FUNCNAME) - + cex = eval(FUNCNAME)() # Create a mesh for test set @@ -21,8 +21,8 @@ thetatest = np.vstack([Xpl.ravel(), Ypl.ravel()]).T ftest = np.zeros((thetatest.shape[0], cex.d)) for i in range(thetatest.shape[0]): - ftest[i, :] = cex.function(thetatest[i,0], thetatest[i,1]) - + ftest[i, :] = cex.function(thetatest[i, 0], thetatest[i, 1]) + ptest = np.zeros(thetatest.shape[0]) if cex.data_name == "unimodal": ptest = sps.norm.pdf(cex.real_data - ftest, 0, np.sqrt(cex.obsvar)) @@ -32,7 +32,7 @@ rnd = sps.multivariate_normal(mean=mean, cov=cex.obsvar) ptest[i] = rnd.pdf(cex.real_data) test_data = {"theta": thetatest, "f": ftest, "p": ptest, "p_prior": 1} - + # Set a uniform prior prior_func = prior_dist(dist="uniform")( a=cex.thetalimits[:, 0], b=cex.thetalimits[:, 1] @@ -55,32 +55,35 @@ t0 = qmc.scale(unit_sample, cex.thetalimits[:, 0], cex.thetalimits[:, 1]) f0 = np.zeros((t0.shape[0], cex.d)) for i in range(t0.shape[0]): - f0[i, :] = cex.function(t0[i,0], t0[i,1]) - + f0[i, :] = cex.function(t0[i, 0], t0[i, 1]) + for func in acq_funcs: print("Running " + func + " with seed " + str(seed_id)) des_obj = sequential_design(cex) - des_obj.build_design(z0=t0, - f0=f0, - T=75, - test=test_data, - af=func, - args={ - "mini_batch": 1, - "n_init_thetas": 10, - "nworkers": 2, - "prior": prior_func, - "data_test": test_data, - "seed": seed_id, - "integral": "LHS" - }) - - + des_obj.build_design( + z0=t0, + f0=f0, + T=75, + test=test_data, + af=func, + args={ + "mini_batch": 1, + "n_init_thetas": 10, + "nworkers": 2, + "prior": prior_func, + "data_test": test_data, + "seed": seed_id, + "integral": "LHS", + }, + ) + theta_al = des_obj.zs - + fig, ax = plt.subplots() cp = ax.contour(Xpl, Ypl, ptest.reshape(50, 50), 20, cmap="RdGy") - ax.scatter(theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2) + ax.scatter( + theta_al[n0:, 0], theta_al[n0:, 1], c="black", marker="+", zorder=2 + ) ax.scatter( theta_al[0:n0, 0], theta_al[0:n0, 1], diff --git a/examples/Example1/test_func.py b/examples/Example1/test_func.py index 8b2c038..3b27778 100644 --- a/examples/Example1/test_func.py +++ b/examples/Example1/test_func.py @@ -27,7 +27,6 @@ def function(self, theta1, theta2): return f - class banana: def __init__(self): self.data_name = "banana" @@ -48,7 +47,6 @@ def function(self, theta1, theta2): return f - class unidentifiable: def __init__(self): self.data_name = "unidentifiable" @@ -69,7 +67,6 @@ def function(self, theta1, theta2): return f - class bimodal: def __init__(self): self.data_name = "bimodal" @@ -90,7 +87,6 @@ def function(self, theta1, theta2): return f - def create_test_data(al_test, cls_func): ftest = al_test._info["f"] thetatest = al_test._info["theta"] diff --git a/examples/Example1/toy_example.py b/examples/Example1/toy_example.py index 2f32c88..a8fb412 100644 --- a/examples/Example1/toy_example.py +++ b/examples/Example1/toy_example.py @@ -43,17 +43,14 @@ def sim(self, H, persis_info, sim_specs, libE_info): # Fit an emulator # emu = emulator(cls_sinlin.x, theta, f, method="PCGP") - - emu = emulator(x=theta, - theta=cls_sinlin.x, - f=f, - method="multihomGP") + + emu = emulator(x=theta, theta=cls_sinlin.x, f=f, method="multihomGP") # Generate test data thetatest = np.arange(-10, 10, 0.0025)[:, None] ftest = cls_sinlin.function(thetatest) ptest = sps.norm.pdf(cls_sinlin.real_data, ftest, np.sqrt(cls_sinlin.obsvar)) - + # predict at mesh nm, d = thetatest.shape[0], cls_sinlin.d pr_test = emu.predict(x=thetatest, thetaprime=thetatest) @@ -62,17 +59,14 @@ def sim(self, H, persis_info, sim_specs, libE_info): S = Sn.transpose(2, 0, 1) Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) N = S + Sigma3d - M = S + 0.5*Sigma3d + M = S + 0.5 * Sigma3d posttesthat = multiple_pdfs(cls_sinlin.real_data, muT, N) - - diags = np.diag(cls_sinlin.obsvar[cls_sinlin.x, cls_sinlin.x.T]) - coef = (2**cls_sinlin.d) * (np.sqrt(np.pi) ** cls_sinlin.d) * np.sqrt(np.prod(diags)) - posttestvar = compute_postvar(cls_sinlin.real_data, - muT, - N, - M, - coef) + diags = np.diag(cls_sinlin.obsvar[cls_sinlin.x, cls_sinlin.x.T]) + coef = ( + (2**cls_sinlin.d) * (np.sqrt(np.pi) ** cls_sinlin.d) * np.sqrt(np.prod(diags)) + ) + posttestvar = compute_postvar(cls_sinlin.real_data, muT, N, M, coef) # predict at mesh nm, d = theta.shape[0], cls_sinlin.d @@ -83,7 +77,7 @@ def sim(self, H, persis_info, sim_specs, libE_info): Sigma3d = cls_sinlin.obsvar.reshape(1, cls_sinlin.d, cls_sinlin.d) N = S + Sigma3d posttrhat = multiple_pdfs(cls_sinlin.real_data, muT, N) - + # Predict via the emulator emupred_test = emu.predict(x=thetatest) emupred_tr = emu.predict(x=theta) @@ -129,11 +123,8 @@ def sim(self, H, persis_info, sim_specs, libE_info): # Figure 2 (c) # Fit an emulator for posterior - emu = emulator(theta=cls_sinlin.x, - x=theta, - f=ptr, - method="multihomGP") - + emu = emulator(theta=cls_sinlin.x, x=theta, f=ptr, method="multihomGP") + emupred_test = emu.predict(x=thetatest) emumean_test = emupred_test.mean() emumean_var = emupred_test.var() diff --git a/examples/Example2/example.py b/examples/Example2/example.py index 48e42fd..609dc34 100644 --- a/examples/Example2/example.py +++ b/examples/Example2/example.py @@ -11,8 +11,7 @@ dt = len(cex.true_theta) x_obs = np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None] cex.realdata(x=x_obs, seed=s) - - + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) s = 1 th_vec = [np.pi / 7, np.pi / 6, np.pi / 5, np.pi / 4] @@ -31,16 +30,14 @@ linewidth=3, ) for d_id in range(len(cex.x)): - ax[0].scatter( - cex.x[d_id, 0], cex.real_data[0, d_id], color="black", s=50 - ) + ax[0].scatter(cex.x[d_id, 0], cex.real_data[0, d_id], color="black", s=50) ft = 16 ax[0].set_xlabel(r"$x$", fontsize=ft) ax[0].set_ylabel(r"$\eta(x, \theta)$", fontsize=ft) ax[0].set_xticks([0.1, 0.3, 0.5, 0.7, 0.9], [0.1, 0.3, 0.5, 0.7, 0.9]) ax[0].tick_params(labelsize=ft - 2) ax[0].legend(bbox_to_anchor=(1.1, -0.2), fontsize=ft - 2, ncol=4) - + # Generate initial sample n0, nmax = 10, 30 ndim = cex.zlim.shape[0] @@ -50,23 +47,33 @@ # Generate design des_obj = sequential_design(cex) - des_obj.build_design(z0=z0, - f0=f0[:, None], - T=nmax, - af="ivar", - args={"nL":200, - "seed":s, - "integral":"importance"}) + des_obj.build_design( + z0=z0, + f0=f0[:, None], + T=nmax, + af="ivar", + args={"nL": 200, "seed": s, "integral": "importance"}, + ) - - ax[1].scatter(des_obj.zs[n0:, 0], des_obj.zs[n0:, 1], marker="+", c="red", s=200, linewidth=3) - ax[1].hlines(cex.true_theta, 0, 1, linestyles="dotted", linewidth=3, colors="orange",) + ax[1].scatter( + des_obj.zs[n0:, 0], des_obj.zs[n0:, 1], marker="+", c="red", s=200, linewidth=3 + ) + ax[1].hlines( + cex.true_theta, + 0, + 1, + linestyles="dotted", + linewidth=3, + colors="orange", + ) for xitem in x_obs: - ax[1].vlines(xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1) + ax[1].vlines( + xitem, 0, 1, linestyles="dotted", colors="orange", linewidth=3, zorder=1 + ) ax[1].set_xlabel(r"$x$", fontsize=ft) ax[1].set_ylabel(r"$\theta$", fontsize=ft) ax[1].tick_params(labelsize=ft) ax[1].set_xlim(0, 1) ax[1].set_ylim(0, 1) plt.savefig("ex2.png", format="jpeg", bbox_inches="tight", dpi=1000) - plt.show() \ No newline at end of file + plt.show() diff --git a/examples/Example4/example.py b/examples/Example4/example.py index 8dc4d4f..82482c2 100644 --- a/examples/Example4/example.py +++ b/examples/Example4/example.py @@ -79,9 +79,10 @@ des_obj = sequential_design(cls_func) des_obj.build_design(t0=theta0, f0=f0, af="seivar", args=args_list[0]) - + theta = des_obj["theta0"] reps = des_obj["rep0"] - twodpaper(cls_func, Xpl, Ypl, p_test, theta, reps, thetainit=theta0, name="fig1") - + twodpaper( + cls_func, Xpl, Ypl, p_test, theta, reps, thetainit=theta0, name="fig1" + ) diff --git a/examples/Example4/toy_example_exploit.py b/examples/Example4/toy_example_exploit.py index 6371ea1..fbb057e 100644 --- a/examples/Example4/toy_example_exploit.py +++ b/examples/Example4/toy_example_exploit.py @@ -7,7 +7,7 @@ batch, example, smin, smax = 100, "sinf", 13, 14 -n0, rep0, maxiter, rho = 20, 5, 1, 1/2 +n0, rep0, maxiter, rho = 20, 5, 1, 1 / 2 # Inputs to designer desset = {"is_exploit": True, "is_explore": False, "nL": 200, "impute_str": "update"} @@ -71,14 +71,10 @@ args_["alloc_settings"] = args_["alloc_settings"].copy() args_["alloc_settings"]["method"] = method args_list.append(args_) - + des_obj = sequential_design(cls_func) - des_obj.build_design(t0=theta, - f0=f, - af="seivar", - args=args_list[0]) - - + des_obj.build_design(t0=theta, f0=f, af="seivar", args=args_list[0]) + fig, ax = plt.subplots(1, 1, figsize=(7, 3.5)) fig.subplots_adjust(wspace=0.6) Figure2(des_obj, theta_test, nugs, phat, "ivar", ax) diff --git a/examples/Example4/toy_example_explore.py b/examples/Example4/toy_example_explore.py index 7d71b6a..96b780f 100644 --- a/examples/Example4/toy_example_explore.py +++ b/examples/Example4/toy_example_explore.py @@ -54,7 +54,7 @@ # Create a candidate list nL = 500 nm = theta_test.shape[0] - + fig, ax = plt.subplots(2, 3, figsize=(15, 7), constrained_layout=True) emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=None) @@ -68,10 +68,10 @@ mu, S, cov, cvar = get_pred( cL=cL, emu=emu, x=x, ttest=theta_test, reps=reps ) - + # plt.plot(theta_test, mu) # plt.show() - + V1 = S + obsvar3d V2 = S + 0.5 * obsvar3d diff --git a/examples/Example4/utilities.py b/examples/Example4/utilities.py index 3b5a3ef..b449114 100644 --- a/examples/Example4/utilities.py +++ b/examples/Example4/utilities.py @@ -633,17 +633,34 @@ def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func pc_settings = {"standardize": True, "latent": False} d = f.shape[0] md = np.arange(d).reshape(d, 1) - emu = emulator(x=theta, - theta=md, - f=f, - method="multihetGP", - args={'lower':None, 'upper':None, - 'noiseControl':{'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)}, - 'init':{}, - 'known':{}, - 'settings':{"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9}}) + emu = emulator( + x=theta, + theta=md, + f=f, + method="multihetGP", + args={ + "lower": None, + "upper": None, + "noiseControl": { + "k_theta_g_bounds": (1, 100), + "g_max": 1e2, + "g_bounds": (1e-6, 1), + }, + "init": {}, + "known": {}, + "settings": { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, + }, + }, + ) emupred = emu.predict(x=theta_test) @@ -690,7 +707,6 @@ def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func ax[0].set_ylabel(r"$\zeta(\theta)$", fontsize=ft) ax[0].tick_params(axis="both", labelsize=ft) - phat = np.zeros(theta_test.shape[0]) phatvar = np.zeros(theta_test.shape[0]) pvar1 = np.zeros(theta_test.shape[0]) @@ -723,7 +739,9 @@ def Figure1(f, theta, x, obsvar, real_data, theta_test, f_test, p_test, cls_func linestyle="dashed", linewidth=2.5, ) - ax[2].plot(theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black") + ax[2].plot( + theta_test.flatten(), cls_func.noise(theta_test).flatten(), color="black" + ) ax[2].set_xlabel(r"$\theta$", fontsize=ft) ax[2].set_ylabel(r"$\mathbb{V}[\nu]$", fontsize=ft) ax[2].tick_params(axis="both", labelsize=ft) @@ -795,4 +813,3 @@ def make_patch_spines_invisible(ax): ncol=3, prop={"size": ft}, ) - diff --git a/examples/Example5/example.py b/examples/Example5/example.py index 0132c63..9b26786 100644 --- a/examples/Example5/example.py +++ b/examples/Example5/example.py @@ -5,7 +5,7 @@ from smt.sampling_methods import LHS from PUQ.designmethods.sequential_1d_stochastic import sequential_design from test_functions import unimodalx, bimodalx, braninx - + T, s = 100, 1 # Methods to iterate over dict_meth = { @@ -18,14 +18,16 @@ fig, ax = plt.subplots(1, 3, figsize=(12, 3), constrained_layout=True) if __name__ == "__main__": - + for eid, example in enumerate(["unimodalx", "bimodalx", "braninx"]): cex = eval(example)() cex.realdata(x=np.array([0.5])[:, None], seed=None) - - tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl = test_data_gen(cex, sample=True) - + + tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl = test_data_gen( + cex, sample=True + ) + tdat = { "f": f_s, "theta": t_s, @@ -35,13 +37,20 @@ "w": w_s, "p_prior": 1, } - - gdat = {"f": fg, "theta": tg, "xt": zg, "p": pg, "noise": ng, "X": Xpl, "Y": Ypl} - + gdat = { + "f": fg, + "theta": tg, + "xt": zg, + "p": pg, + "noise": ng, + "X": Xpl, + "Y": Ypl, + } + # Set random stream for initial design persis_info = {"rand_stream": np.random.default_rng(s)} - + # Initial sample n0, rep0 = 30, 5 sampling = LHS(xlimits=cex.zlim, random_state=int(s)) @@ -50,12 +59,12 @@ f0 = np.array( [cex.sim_f(z0[i, :], persis_info=persis_info) for i in range(n0 * rep0)] ) - + for mid, method in enumerate(dict_meth["method"]): print(method) # Set random stream for initial design persis_info = {"rand_stream": np.random.default_rng(s)} - + # Set random stream for initial design des_obj = sequential_design(cex) des_obj.build_design( @@ -75,7 +84,6 @@ "integral": "importance", }, ) - fig6( des_obj, @@ -91,4 +99,4 @@ ) plt.savefig("ex5.png", format="jpeg", bbox_inches="tight", dpi=500) -plt.show() \ No newline at end of file +plt.show() diff --git a/examples/Example5/paper_figs.py b/examples/Example5/paper_figs.py index 3d527a7..e9c2740 100644 --- a/examples/Example5/paper_figs.py +++ b/examples/Example5/paper_figs.py @@ -40,8 +40,6 @@ } - - def lineplot( df, examples, @@ -204,11 +202,11 @@ def plot_experiment_figure( if layout == "1x2": lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0], figset=figset) boxplot_alternative(path, ms, ls, ex, ax=ax[1], figset=figset) - #boxplot(df=df2, ax=ax[1], x="h", hue="method", figset=figset) + # boxplot(df=df2, ax=ax[1], x="h", hue="method", figset=figset) else: # layout == "2xN" lineplot(df1, [ex], metric=metric, hue="method", ax=ax[0, i], figset=figset) boxplot_alternative(path, ms, ls, ex, ax=ax[1, i], figset=figset) - #boxplot(df=df2, ax=ax[1, i], x="h", hue="method", figset=figset) + # boxplot(df=df2, ax=ax[1, i], x="h", hue="method", figset=figset) plt.tight_layout(pad=0.2) if title is not None: @@ -216,25 +214,35 @@ def plot_experiment_figure( plt.show() - - def plot_SIR_experiment_figure( - path, ms, ls, reps=(1, 31), metric="MAD", figsize=(12, 6), ft=10, figset={}, title=None, + path, + ms, + ls, + reps=(1, 31), + metric="MAD", + figsize=(12, 6), + ft=10, + figset={}, + title=None, ): usual_boxplot = figset.get("usual_boxplot", True) shift, scaled = figset.get("shift_x", 0), figset.get("scaled", True) - fig, ax = plt.subplots( - 1, 4, figsize=figsize, gridspec_kw={"wspace": 0.25} - ) - #, "width_ratios": [1.25, 1.25, 1, 1] + fig, ax = plt.subplots(1, 4, figsize=figsize, gridspec_kw={"wspace": 0.25}) + # , "width_ratios": [1.25, 1.25, 1, 1] lw = 3 - df1 = read_data(rep0=reps[0], repf=reps[1], methods=ms, examples=["SIR"], folderpath=path, label=ls) + df1 = read_data( + rep0=reps[0], + repf=reps[1], + methods=ms, + examples=["SIR"], + folderpath=path, + label=ls, + ) print(df1["s"]) df3 = df1.loc[df1["t"] == 199] - - + if scaled: group_cols = ["t", "method", "h"] mean_df = df1.groupby(group_cols)[metric].mean().reset_index() @@ -281,12 +289,14 @@ def plot_SIR_experiment_figure( ] else: figset["legend_right"] = False - boxplot_alternative(path, ms, ls, "SIR", ax=ax[1], figset=figset)#{"legend_right":False}) + boxplot_alternative( + path, ms, ls, "SIR", ax=ax[1], figset=figset + ) # {"legend_right":False}) box_data = [ (df3, "MADy", ax[2], r"$MAD^y$"), (df3, "MADn", ax[3], r"$MAD^n$"), ] - + for boxid, box in enumerate(box_data): sns.boxplot( x=box[1], @@ -321,7 +331,7 @@ def plot_SIR_experiment_figure( labels, title="method", loc="lower center", - bbox_to_anchor=(0.5+shift, -0.5), + bbox_to_anchor=(0.5 + shift, -0.5), ncol=3, ) box[2].set_yticks([]) @@ -337,15 +347,13 @@ def plot_SIR_experiment_figure( box[2].set_ylabel("") box[2].set_xlabel(box[3], fontsize=ft) - + if title is not None: plt.savefig(title, dpi=300, bbox_inches="tight") plt.show() - - -def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1,31)): +def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1, 31)): df = read_summary_metric(path, examples, methods, ls, rep) @@ -363,8 +371,14 @@ def plot_summary_metric(path, examples, methods, ls, metrics, rep=(1,31)): plt.show() - -def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, repid=(1, 2), title=None): +def plot_sir_figure( + path, + label="adapt", + methods=["ivar", "imse", "var"], + ft=12, + repid=(1, 2), + title=None, +): # Prepare data from utils_SIR import test_gen_SIR @@ -373,15 +387,16 @@ def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, cls_func = SIRx() cls_func.realdata(x=np.array([[0.25, 0.25], [0.75, 0.75]]), seed=None) p_grid, t_grid, noise_grid, Xpl, Ypl = test_gen_SIR(cls_func, return_XY=False) - - + for r in np.arange(repid[0], repid[1]): # Plotting - fig, ax = plt.subplots(1, len(methods), figsize=(11, 3), constrained_layout=True) - + fig, ax = plt.subplots( + 1, len(methods), figsize=(11, 3), constrained_layout=True + ) + for mid, method in enumerate(methods): desobj = read_output(path, "SIR", method, r, label=label) - + # Contour plots ax[mid].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm", zorder=2) cs = ax[mid].contourf( @@ -392,11 +407,11 @@ def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, alpha=0.75, zorder=1, ) - + # Add colorbar only to the last axis if mid == len(methods) - 1: fig.colorbar(cs, ax=ax[mid], pad=0.1) - + # Annotate sample locations and frequency unique_rows, counts = np.unique( desobj.zs[200:, 2:4], axis=0, return_counts=True @@ -412,7 +427,7 @@ def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, color="blue", zorder=3, ) - + # Axis formatting ax[mid].set_xticks([0, 0.5, 1]) ax[mid].set_xlabel(r"$\theta_1$", fontsize=ft + 2) @@ -422,60 +437,56 @@ def plot_sir_figure(path, label="adapt", methods=["ivar", "imse", "var"], ft=12, ax[mid].set_ylabel(r"$\theta_2$", fontsize=ft + 2) else: ax[mid].set_yticks([]) - + if title is not None: plt.savefig(title, dpi=300, bbox_inches="tight") plt.show() + def plot_pritam(path, repid=1, label="target", title=None): from test_functions import pritam from utils_sample import test_data_gen_pri - + # Plot settings ft = 12 ms = ["ivar", "imse", "var"] n0 = 150 - # Define test points x = np.array([0.2, 0.8]) y = np.array([0.2, 0.8]) xr = np.array([[xx, yy] for xx in x for yy in y]) - + # Instantiate test function object and generate real data cex = pritam() cex.realdata(x=xr, seed=None) - + # Generate test grid nmesh = 50 x1 = np.linspace(cex.zlim[0][0], cex.zlim[0][1], nmesh) x2 = np.linspace(cex.zlim[1][0], cex.zlim[1][1], nmesh) X1, X2 = np.meshgrid(x1, x2) Xg = np.vstack([X1.ravel(), X2.ravel()]).T - + # Evaluate noise and function values on the grid ng = np.array([cex.noise(x[0], x[1], cex.theta_true[0]) for x in Xg]) fg = np.array([cex.function(x[0], x[1], cex.theta_true[0]) for x in Xg]) - + # Create subplots fig, ax = plt.subplots(2, 3, figsize=(9, 5), constrained_layout=True) - + for mid, m in enumerate(ms): desobj = read_output(path, "pritam", m, repid, label="target") - + # Top row: histogram of theta samples ax[0, mid].hist( - desobj.zs[n0:, 2], - bins=30, - edgecolor="black", - alpha=0.75, - color="blue" + desobj.zs[n0:, 2], bins=30, edgecolor="black", alpha=0.75, color="blue" ) ax[0, mid].set_xlim(0, 1) ax[0, mid].set_xlabel(r"$\vartheta$", fontsize=ft) if mid == 0: ax[0, mid].set_ylabel("Frequency", fontsize=ft) - + # Bottom row: scatter plot of x-samples with contour background ax[1, mid].scatter( desobj.zs[n0:, 0], @@ -483,26 +494,25 @@ def plot_pritam(path, repid=1, label="target", title=None): marker="o", facecolors="none", edgecolors="blue", - zorder=2 + zorder=2, ) - + # Overlay test design points for xpt in xr: - ax[1, mid].scatter(xpt[0], xpt[1], marker="x", color="black", s=100, zorder=3) - + ax[1, mid].scatter( + xpt[0], xpt[1], marker="x", color="black", s=100, zorder=3 + ) + # Contour of noise cs = ax[1, mid].contourf( - X1, X2, ng.reshape(nmesh, nmesh), - cmap=yellow_cmap, - alpha=0.75, - zorder=1 + X1, X2, ng.reshape(nmesh, nmesh), cmap=yellow_cmap, alpha=0.75, zorder=1 ) - + ax[1, mid].set_xlabel(r"$x_1$", fontsize=ft) ax[1, mid].set_ylabel(r"$x_2$", fontsize=ft) ax[1, mid].set_xticks([0, 0.5, 1]) ax[1, mid].set_yticks([0, 0.5, 1]) - + if title is not None: plt.savefig(title, dpi=300, bbox_inches="tight") plt.show() @@ -519,7 +529,7 @@ def plot_horizon_progress( colors=("gray", "orange"), title=None, ): - + fig, ax = plt.subplots(1, 2, figsize=figsize) for mid, label in enumerate(labels): @@ -538,11 +548,17 @@ def plot_horizon_progress( # Plot for a single seed df_seed1 = df_label[df_label["s"] == single_seed] - ax[0].plot(df_seed1["t"], df_seed1["horizon"], color=colors[mid], linestyle=':') + ax[0].plot(df_seed1["t"], df_seed1["horizon"], color=colors[mid], linestyle=":") # Median horizon across reps df_median = df_label.groupby("t", as_index=False)["horizon"].mean() - ax[0].plot(df_median["t"], df_median["horizon"], color=colors[mid], label=label, linewidth=3) + ax[0].plot( + df_median["t"], + df_median["horizon"], + color=colors[mid], + label=label, + linewidth=3, + ) # Mean cumulative new per t df_cumsum_avg = df_label.groupby("t", as_index=False)["new_cumsum"].mean() @@ -563,18 +579,18 @@ def plot_horizon_progress( ax[1].set_ylabel(r"$n_t / \sum_{i=1}^{n_t} a_i$") ax[1].legend() plt.tight_layout() - + if title is not None: plt.savefig(title, dpi=300, bbox_inches="tight") - + plt.show() - + def plot_pairwise_from_output(path, example, ms, n0=90, rep0=5, d=2, label="h=-1"): - + for m in ms: desobj = read_output(path, example, m, 1, label=label) - df = pd.DataFrame(desobj.zs[(n0 * rep0):, d:]) + df = pd.DataFrame(desobj.zs[(n0 * rep0) :, d:]) sns.pairplot(df, diag_kind="kde") plt.suptitle(f"m = {m}", y=1.02) plt.tight_layout() @@ -583,7 +599,7 @@ def plot_pairwise_from_output(path, example, ms, n0=90, rep0=5, d=2, label="h=-1 def boxplot_alternative(path, methods, horizons, example, ax, figset={}): ifLeg, sety = figset.get("ifLeg", True), figset.get("sety", True) - legend_right = figset.get("legend_right", True) + legend_right = figset.get("legend_right", True) ft = figset.get("ft", 10) r0, n0 = figset.get("r0", 5), figset.get("n0", 50) @@ -592,21 +608,23 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): for hor in horizons: for i in np.arange(1, 31): desobj = read_output(path, example, m, i, label=hor) - + # Eliminate the initial sample initial_des = desobj.zs[0:n0, :] - unique_init, counts_init = np.unique(initial_des, axis=0, return_counts=True) - + unique_init, counts_init = np.unique( + initial_des, axis=0, return_counts=True + ) + # Convert unique_init to a set of tuple rows for fast lookup unique_init_set = set(map(tuple, unique_init)) - + count_reps = [] for xtid, xt in enumerate(desobj.xt): xt_tuple = tuple(xt) if (xt_tuple in unique_init_set) and (desobj.reps[xtid] == r0): continue # skip this one count_reps.append(desobj.reps[xtid]) - + count_reps = np.array(count_reps) total = len(count_reps) # Count percentages @@ -615,51 +633,53 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): percent_3 = np.sum(count_reps == 3) / total * 100 percent_4 = np.sum(count_reps == 4) / total * 100 percent_5 = np.sum(count_reps >= 5) / total * 100 - - lst.append({ - "1": percent_1, - "2": percent_2, - "3": percent_3, - "4": percent_4, - "5": percent_5, - "r": i, - "method": m, - "h": hor - }) - + + lst.append( + { + "1": percent_1, + "2": percent_2, + "3": percent_3, + "4": percent_4, + "5": percent_5, + "r": i, + "method": m, + "h": hor, + } + ) + df = pd.DataFrame(lst) - + # Average over r before plotting - components = ['1', '2', '3', '4', '5'] - - df_avg = df.groupby(['h', 'method'])[components].mean().reset_index() - + components = ["1", "2", "3", "4", "5"] + + df_avg = df.groupby(["h", "method"])[components].mean().reset_index() + # Prepare for plotting - h_values = sorted(df_avg['h'].unique()) - methods_u = df_avg['method'].unique() + h_values = sorted(df_avg["h"].unique()) + methods_u = df_avg["method"].unique() num_methods = len(methods_u) - + bar_width = 0.1 group_spacing = 0.1 - hatches = ['/', '\\', 'x', 'o', '.'] - colors = ['blue', 'green', 'orange', 'red', 'purple'] - + hatches = ["/", "\\", "x", "o", "."] + colors = ["blue", "green", "orange", "red", "purple"] + # Create x positions x_positions = [] current_x = 0 - grouped = df_avg.sort_values(['h', 'method']).reset_index(drop=True) - + grouped = df_avg.sort_values(["h", "method"]).reset_index(drop=True) + for i, h in enumerate(h_values): - methods_in_group = grouped[grouped['h'] == h] + methods_in_group = grouped[grouped["h"] == h] for _ in range(len(methods_in_group)): x_positions.append(current_x) current_x += bar_width current_x += group_spacing - + # Plot - #fig, ax = plt.subplots(figsize=(6, 6)) + # fig, ax = plt.subplots(figsize=(6, 6)) bottoms = np.zeros(len(grouped)) - + # First pass: fill with white so we can hatch over it for i, comp in enumerate(components): ax.bar( @@ -667,11 +687,11 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): grouped[comp], bottom=bottoms, width=bar_width, - color='white', - edgecolor='black' + color="white", + edgecolor="black", ) bottoms += grouped[comp].values - + # Second pass: hatch overlay with colored edges bottoms = np.zeros(len(grouped)) for i, comp in enumerate(components): @@ -684,27 +704,28 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): hatch=hatches[i % len(hatches)], edgecolor=colors[i % len(colors)], linewidth=1.5, - label=f'{comp}' + label=f"{comp}", ) bottoms += grouped[comp].values - + # Method labels on x-axis ax.set_xticks(x_positions) - ax.set_xticklabels(grouped['method'].values, rotation=45, ha='right') - + ax.set_xticklabels(grouped["method"].values, rotation=45, ha="right") + # h group labels below the x-axis for i, h in enumerate(h_values): group_start = i * (num_methods * bar_width + group_spacing) group_mid = group_start + (num_methods - 1) * bar_width / 2 ax.text( - group_mid, -0.002 * ax.get_ylim()[1], - f'h = {h}', - ha='center', - va='top', + group_mid, + -0.002 * ax.get_ylim()[1], + f"h = {h}", + ha="center", + va="top", fontsize=ft, - transform=ax.get_xaxis_transform() + transform=ax.get_xaxis_transform(), ) - + if legend_right: ax.legend( loc="upper center", @@ -712,7 +733,8 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): fancybox=True, shadow=True, ncol=1, - fontsize=ft) + fontsize=ft, + ) else: ax.legend( loc="lower center", @@ -720,7 +742,8 @@ def boxplot_alternative(path, methods, horizons, example, ax, figset={}): fancybox=True, shadow=True, ncol=3, - fontsize=ft) + fontsize=ft, + ) if sety == False: ax.set_ylabel("") diff --git a/examples/Example5/test_functions.py b/examples/Example5/test_functions.py index 1566078..50232c7 100644 --- a/examples/Example5/test_functions.py +++ b/examples/Example5/test_functions.py @@ -8,11 +8,12 @@ import numpy as np import scipy + class sinfunc: def __init__(self): self.data_name = "sinfunc" self.zlim = np.array([[0, 1], [0, 1]]) - self.theta_true = np.array([0.5]) # np.array([np.pi / 5]) + self.theta_true = np.array([0.5]) # np.array([np.pi / 5]) self.real_data = None self.out = [("f", float)] self.d = 1 @@ -38,7 +39,9 @@ def realdata(self, x, seed): self.d = len(x) self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x, self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + M = np.array( + [self.function(x, self.theta_true) for x in self.x], dtype=float + ).reshape(1, self.d) if seed is not None: rand_stream = np.random.default_rng(seed) R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) @@ -48,8 +51,8 @@ def realdata(self, x, seed): def noise(self, x, theta): alpha = 10 - min_value = 0.01#0.05 - max_value = 0.1#0.3 + min_value = 0.01 # 0.05 + max_value = 0.1 # 0.3 weight = 1 / (1 + np.exp(-alpha * (x - 0.5))) # Sigmoid transition value = min_value + (max_value - min_value) * weight # Scale between 0.1 and 10 @@ -134,7 +137,7 @@ def sim_f(self, thetas, persis_info): def noise(self, x, t1, t2): cov = np.array([[0.05, 0], [0, 0.05]]) var = scipy.stats.multivariate_normal(mean=[0.85, 0.85], cov=cov) - return 0.1*var.pdf(np.array([t1, t2])) + return 0.1 * var.pdf(np.array([t1, t2])) def realdata(self, x, seed): self.x = x @@ -148,6 +151,7 @@ def realdata(self, x, seed): else: self.real_data = M + class braninx: def __init__(self): self.data_name = "braninx" @@ -213,7 +217,7 @@ def __init__(self): self.p = 3 self.real_data = None self.dx = 2 - self.sigma2 = 10 #0.5**2 + self.sigma2 = 10 # 0.5**2 self.dx = 2 self.dt = 1 @@ -232,18 +236,19 @@ def realdata(self, x, seed): self.x = x self.d = len(x) self.obsvar = np.diag(np.repeat(self.sigma2, self.d)) - M = np.array([self.function(x[0], x[1], self.theta_true) for x in self.x], dtype=float).reshape(1, self.d) + M = np.array( + [self.function(x[0], x[1], self.theta_true) for x in self.x], dtype=float + ).reshape(1, self.d) if seed is not None: rand_stream = np.random.default_rng(seed) R = rand_stream.normal(0, np.sqrt(self.sigma2), size=(1, self.d)) self.real_data = M + R else: self.real_data = M - + def noise(self, x1, x2, t1): # return 5 cov = np.array([[0.1, 0], [0, 0.1]]) var = scipy.stats.multivariate_normal(mean=[0.5, 0.5], cov=cov) - return (15*t1)*var.pdf(np.array([x1, x2])) - + return (15 * t1) * var.pdf(np.array([x1, x2])) diff --git a/examples/Example5/toy_example.py b/examples/Example5/toy_example.py index 1d44f79..ffe6468 100644 --- a/examples/Example5/toy_example.py +++ b/examples/Example5/toy_example.py @@ -8,7 +8,7 @@ maxiter = 50 new = False seedmin, seedmax = 2, 3 -#seedmin, seedmax = 1, 2 +# seedmin, seedmax = 1, 2 dfl = [] if __name__ == "__main__": @@ -40,10 +40,10 @@ for f in feval ] )[:, None] - + # Set random stream for initial design persis_info = {"rand_stream": np.random.default_rng(s)} - + # Visualize toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info) @@ -65,7 +65,6 @@ z0 = np.repeat(z0u, rep0, axis=0) f0 = np.array([cls_data.sim_f(z, persis_info=persis_info) for z in z0]) - test = { "f": fg, "theta": tg, @@ -73,7 +72,7 @@ "p": pg, "noise": ng, "p_prior": 1, - "w": 1 + "w": 1, } # Methods to iterate over diff --git a/examples/Example5/utils.py b/examples/Example5/utils.py index a296d44..76a5ec7 100644 --- a/examples/Example5/utils.py +++ b/examples/Example5/utils.py @@ -4,7 +4,7 @@ import pandas as pd import matplotlib as mpl import matplotlib.patches as mpatches - + yellow_colors = [ (1, 1, 1), (1, 1, 0.8), # light yellow @@ -19,20 +19,24 @@ (1, 0, 0), # very dark orange ] yellow_cmap = ListedColormap(yellow_colors, name="yellow") - + + def heatmap(Xpl, Ypl, noise_grid, f_grid, p_grid, t_sample=None): - fig, ax = plt.subplots(1, 2, figsize=(12, 4)) - cs = ax[0].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + cs = ax[0].contourf( + Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75 + ) cbar = fig.colorbar(cs) CS = ax[0].contour(Xpl, Ypl, f_grid.reshape(50, 50), colors="black") ax[0].clabel(CS, inline=True, fontsize=10) ax[0].set_xlabel(r"$\theta_1$", fontsize=16) ax[0].set_ylabel(r"$\theta_2$", fontsize=16) ax[0].tick_params(axis="both", labelsize=16) - - cs = ax[1].contourf(Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) + + cs = ax[1].contourf( + Xpl, Ypl, noise_grid.reshape(50, 50), cmap=yellow_cmap, alpha=0.75 + ) cbar = fig.colorbar(cs) CS = ax[1].contour(Xpl, Ypl, p_grid.reshape(50, 50), cmap="coolwarm") if t_sample is not None: @@ -42,7 +46,8 @@ def heatmap(Xpl, Ypl, noise_grid, f_grid, p_grid, t_sample=None): ax[1].set_ylabel(r"$\theta_2$", fontsize=16) ax[1].tick_params(axis="both", labelsize=16) plt.show() - + + def heatmap_pritam(cls_data): # test data nmesh = 50 @@ -50,95 +55,113 @@ def heatmap_pritam(cls_data): x2 = np.linspace(cls_data.zlim[1][0], cls_data.zlim[1][1], nmesh) X1, X2 = np.meshgrid(x1, x2) Xg = np.vstack([X1.ravel(), X2.ravel()]).T - #cls_data.theta_true[0] + # cls_data.theta_true[0] ng = np.array([cls_data.noise(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) fg = np.array([cls_data.function(x[0], x[1], cls_data.theta_true[0]) for x in Xg]) - + fig, ax = plt.subplots(1, 2, figsize=(12, 4)) cs = ax[0].contourf(X1, X2, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) cbar = fig.colorbar(cs) - #ax[1].clabel(CS, inline=True, fontsize=10) + # ax[1].clabel(CS, inline=True, fontsize=10) for x in cls_data.x: ax[0].scatter(x[0], x[1], marker="*", color="black") ax[0].set_xlabel(r"$x_1$", fontsize=16) ax[0].set_ylabel(r"$x_2$", fontsize=16) ax[0].tick_params(axis="both", labelsize=16) - + cs = ax[1].contourf(X1, X2, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) cbar = fig.colorbar(cs) - #ax[0].clabel(CS, inline=True, fontsize=10) + # ax[0].clabel(CS, inline=True, fontsize=10) for x in cls_data.x: ax[1].scatter(x[0], x[1], marker="*", color="black") ax[1].set_xlabel(r"$x_1$", fontsize=16) ax[1].set_ylabel(r"$x_2$", fontsize=16) ax[1].tick_params(axis="both", labelsize=16) plt.show() - - - - - def toy_example(cls_data, Xpl, Ypl, fg, ng, persis_info): from mpl_toolkits.axes_grid1 import make_axes_locatable - + ft = 12 - fig, axs = plt.subplots(1, 3, figsize=(14, 3.5), constrained_layout=False) # Disable constrained_layout - fig.subplots_adjust(wspace=0.4, left=0.1, right=0.9) # Adjust space between subplots - + fig, axs = plt.subplots( + 1, 3, figsize=(14, 3.5), constrained_layout=False + ) # Disable constrained_layout + fig.subplots_adjust( + wspace=0.4, left=0.1, right=0.9 + ) # Adjust space between subplots + # Contour plot for axs[1] divider = make_axes_locatable(axs[0]) - cax1 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cax1 = divider.append_axes( + "right", size="5%", pad=0.05 + ) # Adjust pad for the colorbar cs1 = axs[0].contourf(Xpl, Ypl, fg.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) fig.colorbar(cs1, cax=cax1) - - xt_joint = np.column_stack((cls_data.x, np.repeat(cls_data.theta_true, len(cls_data.x)))) + + xt_joint = np.column_stack( + (cls_data.x, np.repeat(cls_data.theta_true, len(cls_data.x))) + ) axs[0].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") axs[0].set_xlabel(r"$x$", fontsize=ft) axs[0].set_ylabel(r"$\vartheta$", fontsize=ft) axs[0].tick_params(axis="both", labelsize=ft) - + # Contour plot for axs[2] divider = make_axes_locatable(axs[1]) - cax2 = divider.append_axes("right", size="5%", pad=0.05) # Adjust pad for the colorbar + cax2 = divider.append_axes( + "right", size="5%", pad=0.05 + ) # Adjust pad for the colorbar cs2 = axs[1].contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) fig.colorbar(cs2, cax=cax2) - + axs[1].scatter(xt_joint[:, 0], xt_joint[:, 1], marker="x", c="black") axs[1].set_xlabel(r"$x$", fontsize=ft) axs[1].set_ylabel(r"$\vartheta$", fontsize=ft) axs[1].tick_params(axis="both", labelsize=ft) - + # Scatter plot for axs[0] xs = np.linspace(cls_data.zlim[0][0], cls_data.zlim[0][1], 100) for k in range(10): - axs[2].scatter(xs, - np.array([cls_data.sim_f(np.array([x, cls_data.theta_true[0]]), persis_info) for x in xs]), - facecolors="none", edgecolors="green", s=80, linewidth=2) - axs[2].plot(xs, - np.array([cls_data.function(x, cls_data.theta_true[0]) for x in xs]), - linestyle="dotted", linewidth=3, color="black") + axs[2].scatter( + xs, + np.array( + [ + cls_data.sim_f(np.array([x, cls_data.theta_true[0]]), persis_info) + for x in xs + ] + ), + facecolors="none", + edgecolors="green", + s=80, + linewidth=2, + ) + axs[2].plot( + xs, + np.array([cls_data.function(x, cls_data.theta_true[0]) for x in xs]), + linestyle="dotted", + linewidth=3, + color="black", + ) axs[2].set_xlabel(r"$x$", fontsize=ft) - #axs[2].set_ylabel(r"$\theta$", fontsize=ft) + # axs[2].set_ylabel(r"$\theta$", fontsize=ft) axs[2].tick_params(axis="both", labelsize=ft) - - plt.show() + plt.show() def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): - + z, reps = des_obj.xt, des_obj.reps - + ft = 14 fig, ax = plt.subplots(figsize=(5, 4)) for xitem in cls_data.x: ax.vlines( xitem, 0, 1, linestyles="dotted", colors="green", linewidth=3, zorder=5 - ) + ) for label, x_count, y_count in zip(reps, z[:, 0], z[:, 1]): if new: if np.any(np.all(z0u == np.array([x_count, y_count]), axis=1)): @@ -149,19 +172,19 @@ def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): if label > rep0: col = "blue" else: - col = "cyan" + col = "cyan" plt.annotate( label, xy=(x_count, y_count), xytext=(0, 0), textcoords="offset points", - fontsize=ft-2, + fontsize=ft - 2, color=col, weight="bold", zorder=6, ) - + ax.contourf(Xpl, Ypl, ng.reshape(50, 50), cmap=yellow_cmap, alpha=0.75) ax.set_xlim(0, 1) ax.set_ylim(0, 1) @@ -169,37 +192,40 @@ def visual_xt(cls_data, des_obj, z0u, rep0, tg, pg, ng, Xpl, Ypl, new=True): ax.set_ylabel(r"$\theta$", fontsize=ft) ax.tick_params(axis="both", labelsize=ft) ax2 = ax.twiny() - ax2.plot(pg, tg, color='black', label="Y-axis scatter") + ax2.plot(pg, tg, color="black", label="Y-axis scatter") ax2.set_xlabel(r"$p(y|\theta)$", fontsize=ft) ax2.tick_params(axis="both", labelsize=ft) plt.show() def fig6(desobj, theta0, rep0, Xpl, Ypl, pg, ng, ax, figset={}, fig={}, exp=True): - + th, reps = desobj.xt[:, 1:3], desobj.reps - - xtick, ytick, cbarf = figset.get("xtick", True), figset.get("ytick", True), figset.get("cbar", True) + + xtick, ytick, cbarf = ( + figset.get("xtick", True), + figset.get("ytick", True), + figset.get("cbar", True), + ) ft = 12 nm = Xpl.shape[0] - cs = ax.contourf(Xpl, Ypl, ng.reshape(nm, nm), cmap=yellow_cmap, alpha=0.75) - cp = ax.contour(Xpl, Ypl, pg.reshape(nm, nm), cmap="coolwarm") - - + cs = ax.contourf(Xpl, Ypl, ng.reshape(nm, nm), cmap=yellow_cmap, alpha=0.75) + cp = ax.contour(Xpl, Ypl, pg.reshape(nm, nm), cmap="coolwarm") + if cbarf: cbar = fig.colorbar(cs, ax=ax, pad=0.1) - + for label, x_count, y_count in zip(reps, th[:, 0], th[:, 1]): - + if exp: if np.any(np.all(theta0 == np.array([x_count, y_count]), axis=1)): col = "cyan" else: - col = "blue" + col = "blue" else: if label > rep0: - col = "blue" + col = "blue" else: col = "cyan" @@ -209,27 +235,26 @@ def fig6(desobj, theta0, rep0, Xpl, Ypl, pg, ng, ax, figset={}, fig={}, exp=True xytext=(0, 0), textcoords="offset points", fontsize=ft, - color=col + color=col, ) - + if xtick: ax.set_xticks([0, 0.5, 1]) ax.set_xlabel(r"$\vartheta_1$", fontsize=ft) else: ax.set_xticks([]) - + if ytick: - ax.set_yticks([0, 0.5, 1]) + ax.set_yticks([0, 0.5, 1]) ax.set_ylabel(r"$\vartheta_2$", fontsize=ft) else: - ax.set_yticks([]) - + ax.set_yticks([]) + ax.set_xlim(0, 1) ax.set_ylim(0, 1) ax.tick_params(axis="both", labelsize=ft) - def visual_pritam(des_obj, z0u): ft = 16 xtu, repu = des_obj.xt, des_obj.reps @@ -239,7 +264,7 @@ def visual_pritam(des_obj, z0u): col = "cyan" else: col = "blue" - + plt.annotate( repu[xtid], xy=(xt[0], xt[1]), @@ -249,69 +274,99 @@ def visual_pritam(des_obj, z0u): color=col, weight="bold", ) - + plt.xlabel(r"$x_1$") plt.ylabel(r"$x_2$") plt.show() - + ut = [] for xtid, xt in enumerate(xtu): if xt not in z0u: ut.append(xt[2]) plt.hist(ut) plt.show() - + + import os import dill as pickle + + def save_output(desing_obj, name, al_func, seedno, path=None, label=None): - + if path is None: - if not os.path.isdir('output'): - os.mkdir('output') - - design_path = 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' + if not os.path.isdir("output"): + os.mkdir("output") + + design_path = ( + "output/" + + name + + "_" + + al_func + + "_seed_" + + str(seedno) + + "_" + + label + + ".pkl" + ) else: - design_path = path + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' - - with open(design_path, 'wb') as file: + design_path = ( + path + name + "_" + al_func + "_seed_" + str(seedno) + "_" + label + ".pkl" + ) + + with open(design_path, "wb") as file: pickle.dump(desing_obj, file) - + + def read_output(path1, name, al_func, seedno, label=None): - - design_path = path1 + 'output/' + name + '_' + al_func + '_seed_' + str(seedno) + "_" + label + '.pkl' - with open(design_path, 'rb') as file: - design_obj = pickle.load(file) - return design_obj + design_path = ( + path1 + + "output/" + + name + + "_" + + al_func + + "_seed_" + + str(seedno) + + "_" + + label + + ".pkl" + ) + with open(design_path, "rb") as file: + design_obj = pickle.load(file) + return design_obj def create_entry(desobj, fname, method, s, h): return [ - {"MSE": he["MSE"], - "MAD": he["MAD"], - "VAR": he["VAR"], - "MSEy": he["MSEy"], - "MADy": he["MADy"], - "MSEn": he["MSEn"], - "MADn": he["MADn"], - "t": he["t"], - "new": he["new"], - "method": method, - "example": fname, - "s": s, - "h": h, - "horizon": he["h"]} + { + "MSE": he["MSE"], + "MAD": he["MAD"], + "VAR": he["VAR"], + "MSEy": he["MSEy"], + "MADy": he["MADy"], + "MSEn": he["MSEn"], + "MADn": he["MADn"], + "t": he["t"], + "new": he["new"], + "method": method, + "example": fname, + "s": s, + "h": h, + "horizon": he["h"], + } for he in desobj["H"] ] -def read_data(rep0=0, - repf=10, - methods=["ivar", "imse", "unif"], - examples=["unimodal", "banana", "bimodal"], - folderpath=None, - label=None, - ): + +def read_data( + rep0=0, + repf=10, + methods=["ivar", "imse", "unif"], + examples=["unimodal", "banana", "bimodal"], + folderpath=None, + label=None, +): datalist = [] for eid, example in enumerate(examples): @@ -325,12 +380,13 @@ def read_data(rep0=0, df = pd.DataFrame(datalist) return df -def read_summary_metric(folderpath, examples, methods, ls, rep=(1,31)): + +def read_summary_metric(folderpath, examples, methods, ls, rep=(1, 31)): dfl = [] for eid, example in enumerate(examples): for mid, m in enumerate(methods): for r in range(rep[0], rep[1]): - + desobj = read_output(folderpath, example, m, r, label=ls[mid]) for heid, he in enumerate(desobj.H): sm = he["summary_metric"] @@ -339,6 +395,6 @@ def read_summary_metric(folderpath, examples, methods, ls, rep=(1,31)): sm["h"] = ls[mid] sm["t"] = heid dfl.append(sm) - + df = pd.DataFrame(dfl) return df diff --git a/examples/Example5/utils_sample.py b/examples/Example5/utils_sample.py index 2f2c7ce..8251a6d 100644 --- a/examples/Example5/utils_sample.py +++ b/examples/Example5/utils_sample.py @@ -4,6 +4,7 @@ import matplotlib.pyplot as plt import sys + def sample_from_posterior(cls_data, seed): import emcee import scipy.stats as sps @@ -20,11 +21,14 @@ def log_probability(ctheta): return -np.inf else: if cls_data.dx == 1: - feval = np.array([cls_data.function(x, *ctheta) for x in cls_data.x]).squeeze() + feval = np.array( + [cls_data.function(x, *ctheta) for x in cls_data.x] + ).squeeze() elif cls_data.dx == 2: - feval = np.array([cls_data.function(x[0], x[1], *ctheta) for x in cls_data.x]).squeeze() - - + feval = np.array( + [cls_data.function(x[0], x[1], *ctheta) for x in cls_data.x] + ).squeeze() + rnd = sps.multivariate_normal(mean=feval, cov=cls_data.obsvar) pvar = rnd.pdf(cls_data.real_data) + sys.float_info.epsilon return np.log(pvar) @@ -42,6 +46,7 @@ def sample(ndim, nwalkers, seed): return sample(cls_data.dt, nwalkers, seed) + def test_data_gen(cls_data, sample=False): # test data @@ -70,15 +75,16 @@ def test_data_gen(cls_data, sample=False): f_s = np.zeros((t_s.shape[0], 1)) n_s = np.zeros((t_s.shape[0], 1)) for t_id, t in enumerate(t_s): - f_s[t_id, 0] = np.array([cls_data.function(x, t[0], t[1]) for x in cls_data.x]) + f_s[t_id, 0] = np.array( + [cls_data.function(x, t[0], t[1]) for x in cls_data.x] + ) n_s[t_id, 0] = np.array([cls_data.noise(x, t[0], t[1]) for x in cls_data.x]) rnd = sps.multivariate_normal(mean=f_s[t_id, 0], cov=cls_data.obsvar) p_s[t_id, 0] = rnd.pdf(cls_data.real_data) - + p_se = p_s + sys.float_info.epsilon w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() - # heatmap(Xpl, Ypl, ng, fg, pg, t_s) return tg, fg, pg, zg, ng, t_s, p_s, w_s, f_s, n_s, Xpl, Ypl @@ -102,8 +108,6 @@ def test_data_gen_pri(cls_data, sample=False): ] ) - - heatmap_pritam(cls_data) # (ntot, d) @@ -118,19 +122,21 @@ def test_data_gen_pri(cls_data, sample=False): if sample: t_s = sample_from_posterior(cls_data, 1234) - #t_s = np.linspace(0, 1, 900)[:, None] + # t_s = np.linspace(0, 1, 900)[:, None] p_s = np.zeros((t_s.shape[0], 1)) f_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) n_s = np.zeros((t_s.shape[0] * cls_data.x.shape[0], 1)) for t_id, t in enumerate(t_s): feval = np.array([cls_data.function(x[0], x[1], t) for x in cls_data.x]) - f_s[t_id*4 : (t_id + 1)*4, 0] = feval.flatten() - n_s[t_id*4 : (t_id + 1)*4, 0] = np.array([cls_data.noise(x[0], x[1], t) for x in cls_data.x]).flatten() + f_s[t_id * 4 : (t_id + 1) * 4, 0] = feval.flatten() + n_s[t_id * 4 : (t_id + 1) * 4, 0] = np.array( + [cls_data.noise(x[0], x[1], t) for x in cls_data.x] + ).flatten() rnd = sps.multivariate_normal(mean=feval.flatten(), cov=cls_data.obsvar) p_s[t_id, 0] = rnd.pdf(cls_data.real_data) - + p_se = p_s + sys.float_info.epsilon w_s = (((1 / p_se)) / np.sum((1 / p_se))).flatten() From d6b1936a9ed7b7e95344d53c0fd52f543e71ac91 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Wed, 1 Oct 2025 22:39:35 -0400 Subject: [PATCH 072/103] black tests --- tests/unit_tests/test_1d_deter.py | 27 +++-- tests/unit_tests/test_hetGP.py | 160 +++++++++++++------------ tests/unit_tests/test_homGP.py | 142 +++++++++++----------- tests/unit_tests/test_multihetGP.py | 177 ++++++++++++++++++---------- tests/unit_tests/test_multihomGP.py | 130 ++++++++++++-------- 5 files changed, 362 insertions(+), 274 deletions(-) diff --git a/tests/unit_tests/test_1d_deter.py b/tests/unit_tests/test_1d_deter.py index 75c38b3..0a746e4 100644 --- a/tests/unit_tests/test_1d_deter.py +++ b/tests/unit_tests/test_1d_deter.py @@ -1,7 +1,8 @@ -'''Tests whether the we obtain a design for a one-dimensional deterministic simulation model''' +"""Tests whether the we obtain a design for a one-dimensional deterministic simulation model""" import sys -sys.path.append('./') + +sys.path.append("./") import numpy as np from smt.sampling_methods import LHS from PUQ.designmethods.sequential_1d_deterministic import sequential_design @@ -43,36 +44,36 @@ def genobsdata(self, x, isbias=False): return self.function(x[0], self.true_theta[0]) + np.random.normal( 0, np.sqrt(self.sigma2), 1 ) - + + cex = sinfunc() dt = len(cex.true_theta) x_obs = np.array([0.1, 0.1, 0.3, 0.3, 0.5, 0.5, 0.7, 0.7, 0.9, 0.9])[:, None] cex.realdata(x=x_obs, seed=1) - + # Generate initial sample n0, nmax = 10, 30 sampling = LHS(xlimits=cex.zlim, random_state=1) z0 = sampling(n0) f0 = np.array([cex.function(z0[i, 0], z0[i, 1]) for i in range(n0)]) + def test_build_design(): # Generate design des_obj = sequential_design(cex) - des_obj.build_design(z0=z0, - f0=f0[:, None], - T=nmax, - af="ivar", - args={"nL":200, - "seed":1, - "integral":"importance"}) + des_obj.build_design( + z0=z0, + f0=f0[:, None], + T=nmax, + af="ivar", + args={"nL": 200, "seed": 1, "integral": "importance"}, + ) assert des_obj.zs.shape == (n0 + nmax, 2) assert des_obj.fs.shape == (n0 + nmax, 1) - if __name__ == "__main__": test_build_design() - diff --git a/tests/unit_tests/test_hetGP.py b/tests/unit_tests/test_hetGP.py index 221925b..427e714 100644 --- a/tests/unit_tests/test_hetGP.py +++ b/tests/unit_tests/test_hetGP.py @@ -1,39 +1,41 @@ -'''Tests whether the emulator with hetGP gives the same result as just using hetGP''' +"""Tests whether the emulator with hetGP gives the same result as just using hetGP""" import sys -sys.path.append('./') + +sys.path.append("./") import numpy as np from hetgpy import hetGP from PUQ.surrogate import emulator + # generate some test data rng = np.random.default_rng(123) -X = np.linspace(0,2*np.pi,20).reshape(-1,1) +X = np.linspace(0, 2 * np.pi, 20).reshape(-1, 1) # add some replicates -reps = rng.choice(len(X),size=100,replace=True) +reps = rng.choice(len(X), size=100, replace=True) X = X[reps,] Ytrue = np.sin(X) -noise = 0.5 * rng.normal(size = (len(Ytrue),1)) +noise = 0.5 * rng.normal(size=(len(Ytrue), 1)) Y = Ytrue + noise # prediction grid (interpolation) -Xgrid = np.linspace(X.min(),X.max(),100).reshape(-1,1) +Xgrid = np.linspace(X.min(), X.max(), 100).reshape(-1, 1) + def test_fit(): reference_model = hetGP() - reference_model.mle(X,Y.flatten()) + reference_model.mle(X, Y.flatten()) - - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") test_model.fit() - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] - assert np.allclose(test_model._info['Delta'],reference_model['Delta']) - assert np.allclose(test_model._info['Lambda'],reference_model['Lambda']) + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] + assert np.allclose(test_model._info["Delta"], reference_model["Delta"]) + assert np.allclose(test_model._info["Lambda"], reference_model["Lambda"]) def test_matern(): @@ -42,107 +44,111 @@ def test_matern(): MAXIT = 50 SETTINGS = {"return.Ki": False, "factr": 1e5} - reference_model = hetGP() - reference_model.mle(X,Y.flatten(),covtype=COVTYPE,maxit=MAXIT,settings=SETTINGS) - - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='hetGP', - args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + reference_model.mle(X, Y.flatten(), covtype=COVTYPE, maxit=MAXIT, settings=SETTINGS) + + test_model = emulator( + x=X, + theta=np.array([0]), + f=Y, + method="hetGP", + args={"covtype": COVTYPE, "maxit": MAXIT, "settings": SETTINGS}, + ) test_model.fit() - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] - assert np.allclose(test_model._info['Delta'],reference_model['Delta']) - assert np.allclose(test_model._info['Lambda'],reference_model['Lambda']) + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] + assert np.allclose(test_model._info["Delta"], reference_model["Delta"]) + assert np.allclose(test_model._info["Lambda"], reference_model["Lambda"]) + def test_predict(): # refits models and then tests predictions - - reference_model = hetGP() - reference_model.mle(X,Y.flatten()) + reference_model = hetGP() + reference_model.mle(X, Y.flatten()) - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") test_model.fit() # test predictions - preds = reference_model.predict(Xgrid,xprime=Xgrid) + preds = reference_model.predict(Xgrid, xprime=Xgrid) + + test_preds = test_model.predict(x=Xgrid, thetaprime=Xgrid) + assert np.allclose(test_preds._info["mean"], preds["mean"]) + assert np.allclose(test_preds._info["var"], preds["sd2"]) + assert np.allclose(test_preds._info["nugs"], preds["nugs"]) + assert np.allclose(test_preds._info["covmat"], preds["cov"]) - test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid) - assert np.allclose(test_preds._info['mean'],preds['mean']) - assert np.allclose(test_preds._info['var'],preds['sd2']) - assert np.allclose(test_preds._info['nugs'],preds['nugs']) - assert np.allclose(test_preds._info['covmat'],preds['cov']) def test_predict_nugs_only(): reference_model = hetGP() - reference_model.mle(X,Y.flatten()) - + reference_model.mle(X, Y.flatten()) - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='hetGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") test_model.fit() # test predictions - preds = reference_model.predict(Xgrid,xprime=Xgrid,nugs_only=True) + preds = reference_model.predict(Xgrid, xprime=Xgrid, nugs_only=True) - test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid,args=dict(nugs_only=True)) - for key in ['mean','var','covmat']: + test_preds = test_model.predict( + x=Xgrid, thetaprime=Xgrid, args=dict(nugs_only=True) + ) + for key in ["mean", "var", "covmat"]: assert test_preds._info.get(key) is None - assert np.allclose(test_preds._info['nugs'],preds['nugs']) + assert np.allclose(test_preds._info["nugs"], preds["nugs"]) + def test_hetGP_update_kriging_believer(): reference_model = hetGP() - reference_model.mle(X,Y.flatten()) - - Xnew = X.mean().reshape(-1,1) - Ypred = reference_model.predict(Xnew)['mean'] - reference_model.update(Xnew,Ypred,maxit=0) - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='hetGP') + reference_model.mle(X, Y.flatten()) + + Xnew = X.mean().reshape(-1, 1) + Ypred = reference_model.predict(Xnew)["mean"] + reference_model.update(Xnew, Ypred, maxit=0) + + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") test_model.fit() test_model.update(Xnew) - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] + def test_hetGP_update(): reference_model = hetGP() - reference_model.mle(X,Y.flatten()) - - Xnew = X.mean().reshape(-1,1) - Ypred = reference_model.predict(Xnew)['mean'] - reference_model.update(Xnew,Ypred) - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='hetGP') + reference_model.mle(X, Y.flatten()) + + Xnew = X.mean().reshape(-1, 1) + Ypred = reference_model.predict(Xnew)["mean"] + reference_model.update(Xnew, Ypred) + + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") test_model.fit() - test_model.update(x=Xnew,Y=Ypred) + test_model.update(x=Xnew, Y=Ypred) - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] def test_conversion_to_homGP(): # should return homoskedastic GP - X = np.linspace(0,1,20).reshape(-1,1) + X = np.linspace(0, 1, 20).reshape(-1, 1) Y = X - model = emulator(x=X,theta=np.array([0]),f=Y, - method='hetGP') - Xp = np.linspace(X.min(),X.max(),100).reshape(-1,1) + model = emulator(x=X, theta=np.array([0]), f=Y, method="hetGP") + Xp = np.linspace(X.min(), X.max(), 100).reshape(-1, 1) preds = model.predict(Xp) - assert model._info['is_homGP'] - assert model._info.get('Delta') is None - assert np.unique(preds._info['nugs']).shape[0] == 1 + assert model._info["is_homGP"] + assert model._info.get("Delta") is None + assert np.unique(preds._info["nugs"]).shape[0] == 1 + if __name__ == "__main__": test_conversion_to_homGP() diff --git a/tests/unit_tests/test_homGP.py b/tests/unit_tests/test_homGP.py index 23f2d15..f5ce2d1 100644 --- a/tests/unit_tests/test_homGP.py +++ b/tests/unit_tests/test_homGP.py @@ -1,135 +1,139 @@ -'''Tests whether the emulator with homGP gives the same result as just using homGP''' +"""Tests whether the emulator with homGP gives the same result as just using homGP""" import sys -sys.path.append('./') + +sys.path.append("./") import numpy as np from hetgpy import homGP from PUQ.surrogate import emulator + # generate some test data rng = np.random.default_rng(123) -X = np.linspace(0,2*np.pi,20).reshape(-1,1) +X = np.linspace(0, 2 * np.pi, 20).reshape(-1, 1) # add some replicates -reps = rng.choice(len(X),size=100,replace=True) +reps = rng.choice(len(X), size=100, replace=True) X = X[reps,] Ytrue = np.sin(X) -noise = 0.5 * rng.normal(size = (len(Ytrue),1)) +noise = 0.5 * rng.normal(size=(len(Ytrue), 1)) Y = Ytrue + noise # prediction grid (interpolation) -Xgrid = np.linspace(X.min(),X.max(),100).reshape(-1,1) +Xgrid = np.linspace(X.min(), X.max(), 100).reshape(-1, 1) + def test_fit(): reference_model = homGP() - reference_model.mle(X,Y.flatten()) + reference_model.mle(X, Y.flatten()) - - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="homGP") test_model.fit() - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] def test_predict(): # refits models and then tests predictions - - reference_model = homGP() - reference_model.mle(X,Y.flatten()) + reference_model = homGP() + reference_model.mle(X, Y.flatten()) - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="homGP") test_model.fit() # test predictions - preds = reference_model.predict(Xgrid,xprime=Xgrid) + preds = reference_model.predict(Xgrid, xprime=Xgrid) + + test_preds = test_model.predict(x=Xgrid, thetaprime=Xgrid) + assert np.allclose(test_preds._info["mean"], preds["mean"]) + assert np.allclose(test_preds._info["var"], preds["sd2"]) + assert np.allclose(test_preds._info["nugs"], preds["nugs"]) + assert np.allclose(test_preds._info["covmat"], preds["cov"]) - test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid) - assert np.allclose(test_preds._info['mean'],preds['mean']) - assert np.allclose(test_preds._info['var'],preds['sd2']) - assert np.allclose(test_preds._info['nugs'],preds['nugs']) - assert np.allclose(test_preds._info['covmat'],preds['cov']) def test_predict_nugs_only(): reference_model = homGP() - reference_model.mle(X,Y.flatten()) - + reference_model.mle(X, Y.flatten()) - test_model = emulator(x=X,theta=np.array([0]),f=Y,method='homGP') + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="homGP") test_model.fit() # test predictions - preds = reference_model.predict(Xgrid,xprime=Xgrid,nugs_only=True) + preds = reference_model.predict(Xgrid, xprime=Xgrid, nugs_only=True) - test_preds = test_model.predict(x=Xgrid,thetaprime=Xgrid,args=dict(nugs_only=True)) - for key in ['mean','var','covmat']: + test_preds = test_model.predict( + x=Xgrid, thetaprime=Xgrid, args=dict(nugs_only=True) + ) + for key in ["mean", "var", "covmat"]: assert test_preds._info.get(key) is None - assert np.allclose(test_preds._info['nugs'],preds['nugs']) - + assert np.allclose(test_preds._info["nugs"], preds["nugs"]) + + def test_matern(): # test a different covariance type and define some input args COVTYPE = "Matern5_2" MAXIT = 50 SETTINGS = {"return.Ki": False, "factr": 1e5} - reference_model = homGP() - reference_model.mle(X,Y.flatten(),covtype=COVTYPE,maxit=MAXIT,settings=SETTINGS) - - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='homGP', - args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + reference_model.mle(X, Y.flatten(), covtype=COVTYPE, maxit=MAXIT, settings=SETTINGS) + + test_model = emulator( + x=X, + theta=np.array([0]), + f=Y, + method="homGP", + args={"covtype": COVTYPE, "maxit": MAXIT, "settings": SETTINGS}, + ) test_model.fit() - - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] def test_homGP_update_kriging_believer(): reference_model = homGP() - reference_model.mle(X,Y.flatten()) - - Xnew = X.mean().reshape(-1,1) - Ypred = reference_model.predict(Xnew)['mean'] - reference_model.update(Xnew,Ypred,maxit=0) - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='homGP') + reference_model.mle(X, Y.flatten()) + + Xnew = X.mean().reshape(-1, 1) + Ypred = reference_model.predict(Xnew)["mean"] + reference_model.update(Xnew, Ypred, maxit=0) + + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="homGP") test_model.fit() test_model.update(Xnew) - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] + def test_homGP_update(): reference_model = homGP() - reference_model.mle(X,Y.flatten()) - - Xnew = X.mean().reshape(-1,1) - Ypred = reference_model.predict(Xnew)['mean'] - reference_model.update(Xnew,Ypred) - - test_model = emulator(x=X,theta=np.array([0]),f=Y, - method='homGP') + reference_model.mle(X, Y.flatten()) + + Xnew = X.mean().reshape(-1, 1) + Ypred = reference_model.predict(Xnew)["mean"] + reference_model.update(Xnew, Ypred) + + test_model = emulator(x=X, theta=np.array([0]), f=Y, method="homGP") test_model.fit() - test_model.update(x=Xnew,Y=Ypred) + test_model.update(x=Xnew, Y=Ypred) - assert test_model._info['ll'] == reference_model['ll'] - assert test_model._info['theta'] == reference_model['theta'] - assert test_model._info['g'] == reference_model['g'] - assert test_model._info['beta0'] == reference_model['beta0'] + assert test_model._info["ll"] == reference_model["ll"] + assert test_model._info["theta"] == reference_model["theta"] + assert test_model._info["g"] == reference_model["g"] + assert test_model._info["beta0"] == reference_model["beta0"] if __name__ == "__main__": test_homGP_update() - diff --git a/tests/unit_tests/test_multihetGP.py b/tests/unit_tests/test_multihetGP.py index 9b194d4..15ee070 100644 --- a/tests/unit_tests/test_multihetGP.py +++ b/tests/unit_tests/test_multihetGP.py @@ -1,136 +1,185 @@ import sys -sys.path.append('./') + +sys.path.append("./") import numpy as np from scipy.stats import qmc from PUQ.surrogate import emulator from hetgpy import hetGP from copy import deepcopy + # create some noise 2D data rng = np.random.default_rng(1) -lhs = qmc.LatinHypercube(d=2,rng=rng) +lhs = qmc.LatinHypercube(d=2, rng=rng) X = lhs.random(n=100) -reps = rng.choice(len(X),size=100,replace=True) +reps = rng.choice(len(X), size=100, replace=True) X = X[reps,] # prediction grid Xp = lhs.random(n=100) -Y = 0*X -Y[:,0] = np.sin(X[:,0]) -Y[:,1] = np.cos(X[:,1]) +Y = 0 * X +Y[:, 0] = np.sin(X[:, 0]) +Y[:, 1] = np.cos(X[:, 1]) # varying noise field -noise = rng.normal(size=Y.shape) * np.exp(-X**2) -noise *=2 +noise = rng.normal(size=Y.shape) * np.exp(-(X**2)) +noise *= 2 -Y+= noise +Y += noise COVTYPE = "Matern5_2" # module wide settings -SETTINGS = {"linkThetas": 'joint', "logN": True, "initStrategy": 'residuals', - "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, - "return.hom": False, "factr": 1e9} -NOISECONTROL = {'k_theta_g_bounds': (1, 100), 'g_max': 1e2, 'g_bounds': (1e-6, 1)} - +SETTINGS = { + "linkThetas": "joint", + "logN": True, + "initStrategy": "residuals", + "checkHom": True, + "penalty": True, + "trace": 0, + "return.matrices": True, + "return.hom": False, + "factr": 1e9, +} +NOISECONTROL = {"k_theta_g_bounds": (1, 100), "g_max": 1e2, "g_bounds": (1e-6, 1)} + MAXIT = 100 + + def test_fit(): GPlist = [ - hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, - settings=SETTINGS,noiseControl=NOISECONTROL) + hetGP().mle( + X=X, + Z=Y[:, i], + covtype=COVTYPE, + maxit=MAXIT, + settings=SETTINGS, + noiseControl=NOISECONTROL, + ) for i in range(Y.shape[1]) ] - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS,'noiseControl':NOISECONTROL}) - #multiGP.fit() - - emulator_keys = ['ll','theta','beta0','Delta'] + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihetGP", + args={ + "covtype": COVTYPE, + "maxit": MAXIT, + "settings": SETTINGS, + "noiseControl": NOISECONTROL, + }, + ) + # multiGP.fit() + + emulator_keys = ["ll", "theta", "beta0", "Delta"] for i in range(len(GPlist)): GP = GPlist[i] for key in emulator_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) - + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) + return + def test_predict(): - - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihetGP", + args={"covtype": COVTYPE, "maxit": MAXIT, "settings": SETTINGS}, + ) multiGP.fit() - preds = multiGP.predict(x=Xp,thetaprime=Xp) + preds = multiGP.predict(x=Xp, thetaprime=Xp) GPlist = [ - hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE, - maxit=MAXIT,settings=SETTINGS - ).predict(Xp,xprime=Xp) + hetGP() + .mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=MAXIT, settings=SETTINGS) + .predict(Xp, xprime=Xp) for i in range(Y.shape[1]) ] - em2GPkey = {'mean':'mean','var':'sd2','nugs':'nugs'} + em2GPkey = {"mean": "mean", "var": "sd2", "nugs": "nugs"} for i in range(len(GPlist)): GP = GPlist[i] for em_key, GP_key in em2GPkey.items(): - assert np.allclose(preds._info[em_key][i,:],GP[GP_key]) - + assert np.allclose(preds._info[em_key][i, :], GP[GP_key]) + # test cov and covmat for i in range(len(GPlist)): - assert np.allclose(preds._info['covmat'][i,:,:],GPlist[i]['cov']) + assert np.allclose(preds._info["covmat"][i, :, :], GPlist[i]["cov"]) return + def test_update_kriging_believer(): - - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihetGP", + args={"covtype": COVTYPE, "maxit": MAXIT, "settings": SETTINGS}, + ) multiGP.fit() - initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = [ + multiGP._info["emulist"][i]._info["ll"] for i in range(multiGP._info["numGPs"]) + ] initial_lls = deepcopy(initial_lls) GPlist = [ - hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, - settings=SETTINGS) + hetGP().mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=MAXIT, settings=SETTINGS) for i in range(Y.shape[1]) ] GPlist_initial = deepcopy(GPlist) - Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + Xnew = X.mean(axis=0).reshape(-1, X.shape[1]) multiGP.update(x=Xnew) - em_keys = ['ll','theta','beta0','Delta'] + em_keys = ["ll", "theta", "beta0", "Delta"] for GP in GPlist: - Ynew = GP.predict(Xnew)['mean'] - GP.update(Xnew,Ynew,maxit=0) - for i in range(multiGP._info['numGPs']): + Ynew = GP.predict(Xnew)["mean"] + GP.update(Xnew, Ynew, maxit=0) + for i in range(multiGP._info["numGPs"]): GP = GPlist[i] for key in em_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) + def test_update(): - '''Test update and that log likelihood changes''' - - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihetGP',args={'covtype':COVTYPE,'maxit':MAXIT,'settings':SETTINGS}) + """Test update and that log likelihood changes""" + + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihetGP", + args={"covtype": COVTYPE, "maxit": MAXIT, "settings": SETTINGS}, + ) multiGP.fit() - initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = [ + multiGP._info["emulist"][i]._info["ll"] for i in range(multiGP._info["numGPs"]) + ] initial_lls = deepcopy(initial_lls) - + GPlist = [ - hetGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=MAXIT, - settings=SETTINGS) + hetGP().mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=MAXIT, settings=SETTINGS) for i in range(Y.shape[1]) ] GPlist_initial = deepcopy(GPlist) - Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + Xnew = X.mean(axis=0).reshape(-1, X.shape[1]) mpreds = multiGP.predict(x=Xnew) - Ynew = mpreds._info['mean'].T - multiGP.update(x=Xnew,Y=Ynew) - em_keys = ['ll','theta','beta0','Delta'] + Ynew = mpreds._info["mean"].T + multiGP.update(x=Xnew, Y=Ynew) + em_keys = ["ll", "theta", "beta0", "Delta"] for GP in GPlist: - Ynew = GP.predict(Xnew)['mean'] - GP.update(Xnew,Ynew,maxit=MAXIT) - for i in range(multiGP._info['numGPs']): + Ynew = GP.predict(Xnew)["mean"] + GP.update(Xnew, Ynew, maxit=MAXIT) + for i in range(multiGP._info["numGPs"]): GP = GPlist[i] for key in em_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) # also check that likelihoods changed after update for i in range(len(initial_lls)): old_ll = initial_lls[i] - assert not np.allclose(old_ll,multiGP._info['emulist'][i]._info['ll']) + assert not np.allclose(old_ll, multiGP._info["emulist"][i]._info["ll"]) + if __name__ == "__main__": - test_update() \ No newline at end of file + test_update() diff --git a/tests/unit_tests/test_multihomGP.py b/tests/unit_tests/test_multihomGP.py index 2112d24..2b1aab2 100644 --- a/tests/unit_tests/test_multihomGP.py +++ b/tests/unit_tests/test_multihomGP.py @@ -1,125 +1,153 @@ import sys -sys.path.append('./') + +sys.path.append("./") import numpy as np from scipy.stats import qmc from PUQ.surrogate import emulator from hetgpy import homGP from copy import deepcopy + # create some noise 2D data rng = np.random.default_rng(1) -lhs = qmc.LatinHypercube(d=2,rng=rng) +lhs = qmc.LatinHypercube(d=2, rng=rng) X = lhs.random(n=50) # prediction grid Xp = lhs.random(n=100) -Y = 0*X -Y[:,0] = np.sin(X[:,0]) -Y[:,1] = np.cos(X[:,1]) +Y = 0 * X +Y[:, 0] = np.sin(X[:, 0]) +Y[:, 1] = np.cos(X[:, 1]) noise = rng.normal(size=Y.shape) -Y+= noise +Y += noise COVTYPE = "Matern5_2" + def test_fit(): SETTINGS = {"return.Ki": False, "factr": 1e9} - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihomGP", + args={"covtype": COVTYPE, "maxit": 50, "settings": SETTINGS}, + ) multiGP.fit() GPlist = [ - homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, - settings=SETTINGS) + homGP().mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=50, settings=SETTINGS) for i in range(Y.shape[1]) ] - emulator_keys = ['ll','theta','beta0'] + emulator_keys = ["ll", "theta", "beta0"] for i in range(len(GPlist)): GP = GPlist[i] for key in emulator_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) - + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) + return + def test_predict(): SETTINGS = {"return.Ki": False, "factr": 1e9} - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihomGP", + args={"covtype": COVTYPE, "maxit": 50, "settings": SETTINGS}, + ) multiGP.fit() - preds = multiGP.predict(x=Xp,thetaprime=Xp) + preds = multiGP.predict(x=Xp, thetaprime=Xp) GPlist = [ - homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE, - maxit=50,settings=SETTINGS - ).predict(Xp,xprime=Xp) + homGP() + .mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=50, settings=SETTINGS) + .predict(Xp, xprime=Xp) for i in range(Y.shape[1]) ] - em2GPkey = {'mean':'mean','var':'sd2'} + em2GPkey = {"mean": "mean", "var": "sd2"} for i in range(len(GPlist)): GP = GPlist[i] for em_key, GP_key in em2GPkey.items(): - assert np.allclose(preds._info[em_key][i,:],GP[GP_key]) - + assert np.allclose(preds._info[em_key][i, :], GP[GP_key]) + # test cov and covmat for i in range(len(GPlist)): - assert np.allclose(preds._info['covmat'][i,:,:],GPlist[i]['cov']) + assert np.allclose(preds._info["covmat"][i, :, :], GPlist[i]["cov"]) return + def test_update_kriging_believer(): SETTINGS = {"return.Ki": False, "factr": 1e9} - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihomGP", + args={"covtype": COVTYPE, "maxit": 50, "settings": SETTINGS}, + ) multiGP.fit() - initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = [ + multiGP._info["emulist"][i]._info["ll"] for i in range(multiGP._info["numGPs"]) + ] initial_lls = deepcopy(initial_lls) GPlist = [ - homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, - settings=SETTINGS) + homGP().mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=50, settings=SETTINGS) for i in range(Y.shape[1]) ] GPlist_initial = deepcopy(GPlist) - Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + Xnew = X.mean(axis=0).reshape(-1, X.shape[1]) multiGP.update(x=Xnew) - em_keys = ['ll','theta','beta0'] + em_keys = ["ll", "theta", "beta0"] for GP in GPlist: - Ynew = GP.predict(Xnew)['mean'] - GP.update(Xnew,Ynew,maxit=0) - for i in range(multiGP._info['numGPs']): + Ynew = GP.predict(Xnew)["mean"] + GP.update(Xnew, Ynew, maxit=0) + for i in range(multiGP._info["numGPs"]): GP = GPlist[i] for key in em_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) + def test_update(): - '''Test update and that log likelihood changes''' + """Test update and that log likelihood changes""" SETTINGS = {"return.Ki": False, "factr": 1e9} - multiGP = emulator(x=X,theta=np.array([0,1]).reshape(2,1),f=Y, - method='multihomGP',args={'covtype':COVTYPE,'maxit':50,'settings':SETTINGS}) + multiGP = emulator( + x=X, + theta=np.array([0, 1]).reshape(2, 1), + f=Y, + method="multihomGP", + args={"covtype": COVTYPE, "maxit": 50, "settings": SETTINGS}, + ) multiGP.fit() - initial_lls = [multiGP._info['emulist'][i]._info['ll'] for i in range(multiGP._info['numGPs'])] + initial_lls = [ + multiGP._info["emulist"][i]._info["ll"] for i in range(multiGP._info["numGPs"]) + ] initial_lls = deepcopy(initial_lls) - + GPlist = [ - homGP().mle(X=X,Z=Y[:,i],covtype=COVTYPE,maxit=50, - settings=SETTINGS) + homGP().mle(X=X, Z=Y[:, i], covtype=COVTYPE, maxit=50, settings=SETTINGS) for i in range(Y.shape[1]) ] GPlist_initial = deepcopy(GPlist) - Xnew = X.mean(axis=0).reshape(-1,X.shape[1]) + Xnew = X.mean(axis=0).reshape(-1, X.shape[1]) mpreds = multiGP.predict(x=Xnew) - Ynew = mpreds._info['mean'].T - multiGP.update(x=Xnew,Y=Ynew) - em_keys = ['ll','theta','beta0'] + Ynew = mpreds._info["mean"].T + multiGP.update(x=Xnew, Y=Ynew) + em_keys = ["ll", "theta", "beta0"] for GP in GPlist: - Ynew = GP.predict(Xnew)['mean'] - GP.update(Xnew,Ynew,maxit=50) - for i in range(multiGP._info['numGPs']): + Ynew = GP.predict(Xnew)["mean"] + GP.update(Xnew, Ynew, maxit=50) + for i in range(multiGP._info["numGPs"]): GP = GPlist[i] for key in em_keys: - assert np.allclose(multiGP._info['emulist'][i]._info[key],GP[key]) + assert np.allclose(multiGP._info["emulist"][i]._info[key], GP[key]) # also check that likelihoods changed after update for i in range(len(initial_lls)): old_ll = initial_lls[i] - assert not np.allclose(old_ll,multiGP._info['emulist'][i]._info['ll']) + assert not np.allclose(old_ll, multiGP._info["emulist"][i]._info["ll"]) + if __name__ == "__main__": - test_update_kriging_believer() \ No newline at end of file + test_update_kriging_believer() From 3ebafc64ed1f2dbc3dfdda05005fb7744acc0d87 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 11:46:15 -0400 Subject: [PATCH 073/103] remove lhs from example 1 --- .../gen_funcs/acquisition_md_deterministic.py | 24 +++++++++++------- .../sequential_md_deterministic.py | 6 ++--- examples/Example1/ex1.png | Bin 1322468 -> 1325978 bytes examples/Example1/example.py | 2 +- examples/Example1/test_func.py | 2 +- 5 files changed, 20 insertions(+), 14 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py index 769f4b3..afd252b 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py @@ -1,5 +1,5 @@ import numpy as np -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.support import multiple_pdfs, multiple_determinants from hetgpy.IMSE import crit_IMSPE import emcee @@ -7,8 +7,11 @@ def generate_neighborhood(acq): N = int(acq.nL) - sampling = LHS(xlimits=acq.tlim, random_state=acq.seed) - L = sampling(N) + ndim = acq.tlim.shape[0] + sampler = qmc.LatinHypercube(d=ndim, seed=acq.seed) + unit_sample = sampler.random(n=N) + L = qmc.scale(unit_sample, acq.tlim[:, 0], acq.tlim[:, 1]) + return L @@ -203,10 +206,12 @@ def __init__(self, model, cls_func, args): self.thin = args.get("thin", 20) self.nwalkers = args.get("nwalkers", 20) self.reference_set() + elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) + ndim = self.tlim.shape[0] + sampler = qmc.LatinHypercube(d=ndim, seed=int(self.seed)) + unit_sample = sampler.random(n=500) + self.t_ref = qmc.scale(unit_sample, self.tlim[:, 0], self.tlim[:, 1]) self.weights = 1 else: self.t_ref = args.get("t_grid") @@ -226,8 +231,9 @@ def sample(ndim, nwalkers): np.random.seed(int(self.seed)) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) + sampler_init = qmc.LatinHypercube(d=ndim, seed=int(self.seed)) + unit_sample = sampler_init.random(n=nwalkers) + loc0 = qmc.scale(unit_sample, self.tlim[:, 0], self.tlim[:, 1]) sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) @@ -240,7 +246,7 @@ def importance_weight(theta): unnorm_weight = 1 / pvar weight = unnorm_weight / np.sum(unnorm_weight) return weight - + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) self.weights = importance_weight(theta=self.t_ref) diff --git a/PUQ/designmethods/sequential_md_deterministic.py b/PUQ/designmethods/sequential_md_deterministic.py index c2e1a64..7908f11 100644 --- a/PUQ/designmethods/sequential_md_deterministic.py +++ b/PUQ/designmethods/sequential_md_deterministic.py @@ -12,7 +12,7 @@ class sequential_design: - def __init__(self, cls_func, trace=True): + def __init__(self, cls_func, trace=False): self.cls_func = cls_func self.trace = trace self.y = self.cls_func.real_data @@ -41,8 +41,8 @@ def build_design(self, z0, f0, T, af, test=None, args={}): md = np.arange(f0.shape[1]).reshape(f0.shape[1], 1) for t in range(0, T): - # print(f"t: {t}") if self.trace else None - print(t) + print(f"t: {t}") if self.trace else None + tic = time.time() model = emulator( diff --git a/examples/Example1/ex1.png b/examples/Example1/ex1.png index d64faf92951e8f17bb6aff87e29cf2a358d82f97..fb9ec6647db8c2b9ecb239f5d9a99d3bb0464270 100644 GIT binary patch delta 565746 zcma(22UHVnw+0MPKtyUlL5e_BkSZV|A~hfnBJd!BLX;LOQbd$q0s|_dQ~_ZWq=TrG 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z<`c57Y1Z~zS(&X}n8y;gIH>``pl?OGWd=vifk&!o2t&PkOA&8b#nH!dS1HjhMv(b` zt@qo>a~ziJ(--i4LJV!ytMNDK1CxRxa6%+D`sb|m@d+nCgg^YU$)x|`pUpx?{%(3a zss8O!^XX;pg`*I*YfoWZ_+kA&&+_*DuP@Xa$F>Gv+Ok_$*c*6`){C0i)+IMZs=IUL zW+r+5@r{A{U7GP9(56Cor-UvK#0XUxC$-H?&2{ZF{h1 Date: Mon, 6 Oct 2025 12:14:50 -0400 Subject: [PATCH 074/103] removing lhs from example 2 --- .../gen_funcs/acquisition_1d_deterministic.py | 28 ++++++++---------- .../sequential_1d_deterministic.py | 1 - examples/Example2/ex2.png | Bin 1530619 -> 1531475 bytes examples/Example2/example.py | 2 +- 4 files changed, 14 insertions(+), 17 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py index 12a8dc7..7dd5db1 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py @@ -1,5 +1,5 @@ import numpy as np -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.support import multiple_pdfs, multiple_determinants from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult import emcee @@ -13,17 +13,16 @@ def generate_neighborhood(acq): if neigh_type == "LHS": N = int(acq.nL) - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L = sampling(N) + sampling = qmc.LatinHypercube(d=acq.zlim.shape[0], seed=int(acq.seed)) + L = sampling.random(n=N) else: N = int(acq.nL * 0.5) - - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L_explore = sampling(N) - - sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) - Lt = sampling(N) - + sampling = qmc.LatinHypercube(d=acq.zlim.shape[0], seed=int(acq.seed)) + L_explore = sampling.random(n=N) + + sampling = qmc.LatinHypercube(d=acq.tlim.shape[0], seed=int(acq.seed)) + Lt = sampling.random(n=N) + num_options = len(acq.x) # Distribute the rows as evenly as possible @@ -248,9 +247,8 @@ def __init__(self, model, cls_func, args): self.nwalkers = args.get("nwalkers", 20) self.reference_set() elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) + sampling = qmc.LatinHypercube(d=self.tlim.shape[0], seed=int(self.seed)) + self.t_ref = sampling.random(n=500) self.weights = 1 else: self.t_ref = args.get("t_grid") @@ -270,8 +268,8 @@ def sample(ndim, nwalkers): np.random.seed(int(self.seed)) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) + sampling = qmc.LatinHypercube(d=self.tlim.shape[0], seed=int(self.seed)) + loc0 = sampling.random(n=nwalkers) sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) diff --git a/PUQ/designmethods/sequential_1d_deterministic.py b/PUQ/designmethods/sequential_1d_deterministic.py index 4abceb1..e836a3a 100644 --- a/PUQ/designmethods/sequential_1d_deterministic.py +++ b/PUQ/designmethods/sequential_1d_deterministic.py @@ -1,5 +1,4 @@ import numpy as np -import matplotlib.pyplot as plt from PUQ.designmethods.support import multiple_pdfs, multiple_determinants from PUQ.designmethods.gen_funcs.acquisition_1d_deterministic import var, ivar, imse import time diff --git a/examples/Example2/ex2.png b/examples/Example2/ex2.png index 1c972dc5ad54a36f8acad557618705829753cfca..945db56081ae6cd079acc6014b20772c85cc29aa 100644 GIT binary patch delta 587791 zcmb5WXINA1wgnoR*Z@(QAVj4~RXPa71_+2q4Lu?%O$7v`hJ_-cQ~@DM6%dgcsi6jx zs`M_s_Zku)2}!t%Z+~a+Z{Kt7kNaaOk1=b#YreCLIp%oTFSF^Zu?c!j_nQ7SLn-5J z^#_XD=9y0C)-{$|Z@;MRjw^b4_Sa{q$Qa)wK zxVL_4`fd1g*PGn{rnHVkF=#!2gg{3}5EPo|9PqP-9ELl93|bp(YlDBwVNmDQ7-hw7 zC|THBoZ8T$`w%tNms|Mh0CGMLMxII|AI&q}HJMvv5*-lW7?MmxrRuXZ-lne|pcW)_ zm9`gcc8)D31LPIV?k?&80((b-Zsh|!gyI9p%LB+-SsT3lvC4daw1|lvM{oJwO5BDY z(HfiLto!`gCZ+2-YX2(ZS?f^hqs9Y>5CFxi!it@|fl!h_Fts-80Ma!Rs7(>St>AJZ zCxPdg>D-g)YifIF)dB=7(CRlzV>*2INtQ;Qu(EXhCX3RloxrbN#lP=mUlJkQ*>ubZ 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zZ~e2cSLzRYWdX0g)V~2-s_}fi-X)t*vx$!@{`kvoDe{@FU;JdVz>;kh=i?tO;NPLK zelD=loyi-OJ!6p->jb`so}cIU%=)D_u&aSbS7VQc|7aQ*O#`E8U^ESkrh(BkFq#HN v)4*sN7)=ACX<#%BjHZFnG%%V5M$^D(8W>FjqiJ9?4UDFN5tjz+|K9`vXb(9I From 9a8a6702d51a227cc12f9f2eed48917cade6a3d5 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 19:27:27 -0400 Subject: [PATCH 094/103] Update README.rst --- README.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.rst b/README.rst index bc80abd..1a6eb17 100644 --- a/README.rst +++ b/README.rst @@ -68,7 +68,11 @@ To install the PUQ package: pip install -r requirements.txt -3) From the command line, use the following command to install PUQ:: +3) Install the latest development version of hetGPy:: + + python -m pip install git+https://github.com/davidogara/hetGPy.git + +4) From the command line, use the following command to install PUQ:: pip install -e . From c9d0e7bc36dfac9e4076465e8b134a5c3c73f0ee Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 19:28:59 -0400 Subject: [PATCH 095/103] Update requirements.txt --- requirements.txt | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index ea1ff1e..26dff51 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,9 +3,8 @@ sphinx>=7.2 sphinx-rtd-theme==1.3.0 docutils<0.19 dill -hetGPy emcee scipy matplotlib pandas -scikit-learn \ No newline at end of file +scikit-learn From ca9163d1447aea7d8a9f5a2331b1014b82b8c93f Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 19:33:43 -0400 Subject: [PATCH 096/103] Update puq-ci.yml --- .github/workflows/puq-ci.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/puq-ci.yml b/.github/workflows/puq-ci.yml index ddf3c47..481d6b0 100644 --- a/.github/workflows/puq-ci.yml +++ b/.github/workflows/puq-ci.yml @@ -30,6 +30,7 @@ jobs: python -m pip install --upgrade pip setuptools wheel pip install numpy pip install -r requirements.txt + python -m pip install git+https://github.com/davidogara/hetGPy.git python -m pip install flake8 pytest pytest-cov Cython pip install -e . - name: Test with pytest From 6ab862d91b7432536b703669d242609933959947 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 20:13:55 -0400 Subject: [PATCH 097/103] Update conf.py --- docs/conf.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/conf.py b/docs/conf.py index 832d7a1..870b02b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -21,11 +21,11 @@ # -- Project information ----------------------------------------------------- project = "PUQ" -copyright = "2024, Özge Sürer, Matthew Plumlee, Stefan M. Wild" -author = "Özge Sürer, Matthew Plumlee, Stefan M. Wild" +copyright = "2025, Özge Sürer, David O'Gara, Matthew Plumlee, Stefan M. Wild" +author = "Özge Sürer, David O'Gara, Matthew Plumlee, Stefan M. Wild" # The full version, including alpha/beta/rc tags -release = "0.1" +release = "0.1.1" needs_sphinx = "3.0" # -- General configuration --------------------------------------------------- From 554c176f2faf41ff13620f3a5036fb4592d53a52 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 20:21:56 -0400 Subject: [PATCH 098/103] Update README.rst --- examples/Example3/README.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/Example3/README.rst b/examples/Example3/README.rst index a313987..9ee4329 100644 --- a/examples/Example3/README.rst +++ b/examples/Example3/README.rst @@ -18,7 +18,7 @@ To replicate the figures below, respectively: python example_a.py python example_b.py -Running each script should not take more than 60 sec. See the figures (jpeg files) saved under ``examples/`` directory. +Running each script should not take more than 60 sec. See the figures (png files) saved under ``examples/Example3`` directory. .. image:: ex3_a.png :alt: Illustration of PUQ with the example From 3c1d90905d44fc0bee2f25ba08da2d94d109c35f Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 20:23:49 -0400 Subject: [PATCH 099/103] Update README.rst --- examples/Example4/README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/Example4/README.rst b/examples/Example4/README.rst index f93f038..6accbca 100644 --- a/examples/Example4/README.rst +++ b/examples/Example4/README.rst @@ -6,7 +6,7 @@ Batch Sequential Experimental Design for Calibration of Stochastic Simulation Mo for the stochastic models with high-dimensional outputs. -**Instructions for running the illustrative examples with the active learning procedurel** +**Instructions for running the illustrative examples with the active learning procedure** To replicate the figures below, respectively: @@ -19,7 +19,7 @@ To replicate the figures below, respectively: python toy_example_exploit.py python toy_example_explore.py -Running this script should not take more than 5 min. See the figures (jpeg files) saved under ``examples/`` directory. +Running this script should not take more than 5 min. See the figures (png files) saved under ``examples/Example4`` directory. .. image:: toy1.png @@ -51,4 +51,4 @@ dashed line) and likelihood (red dotted line) are depicted for reference. Allocation of b = 15 simulation evaluations guided by the IVAR criterion shown in the first row. The emulator is constructed using simulation data including n0 = 6 unique parameters (black dots), each replicated five times. The green star represents the acquired point. The second row shows the -true (red line) and estimated likelihood (blue dashed line). \ No newline at end of file +true (red line) and estimated likelihood (blue dashed line). 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examples/Example4/example.py | 19 +++++-------------- examples/Example4/toy_example_explore.py | 1 - 3 files changed, 7 insertions(+), 17 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py index ed387b9..25b3d6a 100644 --- a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py +++ b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py @@ -51,7 +51,7 @@ def impute_CL(ct, x, fE, tE, reps, liar): def build_emulator(x, theta, f, pcset): - print(theta.shape) + # emu = emulator(x=x, # theta=theta, # f=f, @@ -64,7 +64,7 @@ def build_emulator(x, theta, f, pcset): # "checkHom": True, "penalty": True, "trace": 0, "return.matrices": True, # "return.hom": False, "factr": 1e9}, # 'pc_settings':pcset}) - print(f.shape) + d = f.shape[0] md = np.arange(d).reshape(d, 1) emu = emulator( diff --git a/examples/Example4/example.py b/examples/Example4/example.py index 82482c2..aec23be 100644 --- a/examples/Example4/example.py +++ b/examples/Example4/example.py @@ -2,25 +2,16 @@ from PUQ.prior import prior_dist from test_funcs import bimodal, banana, unimodal from utilities import test_data_gen, twodpaper -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.sequential_md_stochastic import sequential_design # # # # # batch = 8 -workers = batch + 1 funcname = "unimodal" -smin = 0 -smax = 1 - -# # # # # - - +smin, smax = 0, 1 n0, rep0, rho = 15, 2, 1 / 2 - -nmesh = 50 -maxiter = 10 - +nmesh, maxiter = 50, 10 # Inputs to designer desset = {"is_exploit": True, "is_explore": True, "nL": 200, "impute_str": "update"} @@ -46,8 +37,8 @@ persis_info = {"rand_stream": np.random.default_rng(s)} # Initial sample - sampling = LHS(xlimits=cls_func.thetalimits, random_state=int(s)) - theta0 = sampling(n0) + sampling = qmc.LatinHypercube(d=cls_func.thetalimits.shape[0], seed=int(s)) + theta0 = sampling.random(n=n0) theta0 = np.repeat(theta0, rep0, axis=0) f0 = np.zeros((cls_func.d, n0 * rep0)) for i in range(0, n0 * rep0): diff --git a/examples/Example4/toy_example_explore.py b/examples/Example4/toy_example_explore.py index 96b780f..700f2e8 100644 --- a/examples/Example4/toy_example_explore.py +++ b/examples/Example4/toy_example_explore.py @@ -59,7 +59,6 @@ emu = build_emulator(x=x, theta=theta0, f=f0[None, :], pcset=None) for i in range(3): - print(i) ft = 18 cL = np.linspace( cls_func.thetalimits[0][0], cls_func.thetalimits[0][1], nL From 8b9c9f51a0506f7a2acb348318c5e2c16629dbfd Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 13:08:57 -0400 Subject: [PATCH 076/103] Example 5 --- .../gen_funcs/acquisition_md_stochastic.py | 1 - PUQ/designmethods/sequential_1d_stochastic.py | 2 +- examples/Example5/ex5.png | Bin 540504 -> 543679 bytes examples/Example5/example.py | 6 +++--- requirements.txt | 6 +++++- 5 files changed, 9 insertions(+), 6 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py index 56f47b6..aa20051 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_stochastic.py @@ -51,7 +51,6 @@ def impute_strategy(ct, x, fE, tE, emu, liar, des_obj): if impute_str == "update": # update(emu._info, x=x, X0new=ct, mult=rep) Xnew = np.repeat(ct, rep, axis=0) - print(Xnew) emu.update(x=Xnew) fE, tE = impute( ct=ct, x=x, fE=fE, tE=tE, reps=rep, emu=emu, rnd_str=rand_stream diff --git a/PUQ/designmethods/sequential_1d_stochastic.py b/PUQ/designmethods/sequential_1d_stochastic.py index 5a31bd3..c567002 100644 --- a/PUQ/designmethods/sequential_1d_stochastic.py +++ b/PUQ/designmethods/sequential_1d_stochastic.py @@ -11,7 +11,7 @@ class sequential_design: - def __init__(self, cls_func, trace=True): + def __init__(self, cls_func, trace=False): self.cls_func = cls_func self.trace = trace self.y = 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    Nqw)cxE|Wucp|UX)EU^6!XY8E#`wA-0$<6FMS=i0Xx+i z^;`{a+TEPjV-FuNWL){oW@?s$@FMw>#yo22SF`oB)&-OXeU?e3th4jkba@dN9@?J* z)?Nmr#yNl9ADt{W*9Z}-l&qyJ&u#wd;sRMrK&;nuXA}+TR48JsoH<%`xZ8tUcqdA4 z`rFiX2&SZf$`jx7l{$^p;}Pb$flpEnUvNa?)?1@FAuB{_y_uTWsFDN`tg}xkOGPlG zXzM$Yikp`m4}tzs^Giuw#RR%bUUVh4%x_Xr<>X3_mF1%3CBJ=wyXYJ88G88XPQ>%U zzMKn7zUtP9LQIU|OWeRH{O?p0kB;cfq(yBTmKF^ycA16-+J+)#3HuquJP^A;olJLat}LvJNZ5%|k1tdB!sR$oq*hH1jP%{WB(e|=)uUpfLN zY&=_A(uJ|=hqc^lcQ8M3`HA;$&`z3Vvh{07G2GlU@!2o-e{o_a6C%7ywweFEnzlGO zXZull|5`Bk6L&J}$lrinc@kfFuy25reL9J#xmA(9=XL4gS&($NtYm(CI$prP(Bh+D z0q|tGV$MHh2902bks_8U;%7qt-3uAYykyoA+-XS&EUmqpmsd9J#L8&xie-a52f!neocxE|8auZE`)j~%3Fm-t>^0H*WLP9R$YW2D@KI|2 z^}?U2l(%ncW~O$Hg-OGT3h<$-w}KM9b>NS`2dzX+_A@ov5(mmm=+BbKL#=sN{ZXdJ zP0TCLC#;SZYkh?<1??WUng_oGg2{8(O5y0)MbcG-Ng_jtKe-yud4E%S1kR7E81FvW z@hCj@Z~ng$-0WmOWS?g(*j;4QS?e$r$HRC3xv}oUG9_%+S)5vTMV$d_a{hab3eA;J zH>mBdNsFIqs`%2Avaa8@4pD3>&oi_;K@2B#OI0XS{S+l5%ptre$eB@3WJiH2F*Pn z*rpAnx+_ggR-BF)`U_uc&?K3nMkrqk8hsi2xsK`oF@^rOW%Qp Date: Mon, 6 Oct 2025 13:12:48 -0400 Subject: [PATCH 077/103] Update puq-ci.yml --- .github/workflows/puq-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/puq-ci.yml b/.github/workflows/puq-ci.yml index 9b2111d..81a34d5 100644 --- a/.github/workflows/puq-ci.yml +++ b/.github/workflows/puq-ci.yml @@ -28,7 +28,7 @@ jobs: run: | python -m ensurepip --upgrade python -m pip install --upgrade pip setuptools wheel - pip install numpy scipy matplotlib pandas scikit-learn smt + pip install numpy pip install -r requirements.txt python -m pip install flake8 pytest pytest-cov Cython pip install -e . From d83b177082a274ca6a4c61b32928ff1fd1da5a7a Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 13:18:05 -0400 Subject: [PATCH 078/103] Update README.rst --- README.rst | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.rst b/README.rst index 21db17e..54fccaa 100644 --- a/README.rst +++ b/README.rst @@ -28,9 +28,9 @@ Dependencies PUQ is a Python package that employs novel experimental design techniques with intelligent selection criteria, refining data collection to enhance the efficiency and effectiveness of uncertainty quantification. -This code is tested with Python 3.9, 3.10, 3.11, and 3.12 and requires pip. +This code is tested with Python 3.10, 3.11, and 3.12 and requires pip. -Python 3.12 users need to install setuptools manually via:: +Users need to install setuptools manually via:: python -m ensurepip --upgrade python -m pip install --upgrade setuptools @@ -105,12 +105,12 @@ The HTML files are then stored in ``docs/html`` .. code-block:: bibtex - @techreport{PUQ2022, - author = {Özge Sürer, Matthew Plumlee, Stefan M. Wild}, + @techreport{PUQ2025, + author = {Özge Sürer, David O'Gara, Matthew Plumlee, Stefan M. Wild}, title = {PUQ Users Manual}, institution = {}, - number = {Version 0.1.0}, - year = {2022}, + number = {Version 0.1.1}, + year = {2025}, url = {https://github.com/parallelUQ/PUQ} } From 46801908f86140a6d66963f6078edcd785fe9990 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 13:21:58 -0400 Subject: [PATCH 079/103] black --- PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py | 4 ++-- PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py | 4 ++-- .../gen_funcs/batch_acquisition_funcs_support.py | 1 - 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py index 7dd5db1..9b89895 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_deterministic.py @@ -19,10 +19,10 @@ def generate_neighborhood(acq): N = int(acq.nL * 0.5) sampling = qmc.LatinHypercube(d=acq.zlim.shape[0], seed=int(acq.seed)) L_explore = sampling.random(n=N) - + sampling = qmc.LatinHypercube(d=acq.tlim.shape[0], seed=int(acq.seed)) Lt = sampling.random(n=N) - + num_options = len(acq.x) # Distribute the rows as evenly as possible diff --git a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py index afd252b..b0006c1 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_md_deterministic.py @@ -11,7 +11,7 @@ def generate_neighborhood(acq): sampler = qmc.LatinHypercube(d=ndim, seed=acq.seed) unit_sample = sampler.random(n=N) L = qmc.scale(unit_sample, acq.tlim[:, 0], acq.tlim[:, 1]) - + return L @@ -246,7 +246,7 @@ def importance_weight(theta): unnorm_weight = 1 / pvar weight = unnorm_weight / np.sum(unnorm_weight) return weight - + self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) self.weights = importance_weight(theta=self.t_ref) diff --git a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py index 25b3d6a..622b2ee 100644 --- a/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py +++ b/PUQ/designmethods/gen_funcs/batch_acquisition_funcs_support.py @@ -51,7 +51,6 @@ def impute_CL(ct, x, fE, tE, reps, liar): def build_emulator(x, theta, f, pcset): - # emu = emulator(x=x, # theta=theta, # f=f, From ee7e3c06dfe1539dbabefb7ed40820b6eacd0a69 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 13:22:47 -0400 Subject: [PATCH 080/103] Update .wordlist.txt --- .wordlist.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/.wordlist.txt b/.wordlist.txt index 557638f..9581791 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -69,3 +69,4 @@ jpg matyas ensurepip setuptools +O'Gara From 5f04339e9d4a4fd082b0b91d6cb6f4d7189db1f4 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 13:51:24 -0400 Subject: [PATCH 081/103] Update CHANGELOG.rst --- CHANGELOG.rst | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index eb567e6..4e93dca 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -6,6 +6,19 @@ Below are the release notes for PUQ. May reference issues on: https://github.com/parallelUQ/PUQ/issues +Release 0.1.1 +------------- + +:Date: Oct 8, 2025 + +- :code:`surrogatemethods` have been updated to use the `hetGPy `_ package as their base. +- :code:`surrogatemethods` provide four emulators for deterministic, stochastic, and one- or multi-dimensional outputs. +- :code:`designmethods` provide four sequential design procedures. +- :code:`examples` have been revised to include illustrative cases from five different papers. +- :code:`tests` include new checks for both emulators and design methods. + + + Release 0.1.0 ------------- From 46aae3102ace69ca6b91a52a43ac7d97e0c0062e Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 13:53:49 -0400 Subject: [PATCH 082/103] Update .wordlist.txt --- .wordlist.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.wordlist.txt b/.wordlist.txt index 9581791..f8859aa 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -70,3 +70,7 @@ matyas ensurepip setuptools O'Gara +davidogara +designmethods +hetGPy +surrogatemethods From 5c3816e78687c4f25533ad72bdf22bc6f9bbf70f Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 15:48:21 -0400 Subject: [PATCH 083/103] Update tests --- tests/unit_tests/test_1d_deter.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/unit_tests/test_1d_deter.py b/tests/unit_tests/test_1d_deter.py index 0a746e4..50e599f 100644 --- a/tests/unit_tests/test_1d_deter.py +++ b/tests/unit_tests/test_1d_deter.py @@ -4,7 +4,7 @@ sys.path.append("./") import numpy as np -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.sequential_1d_deterministic import sequential_design @@ -54,8 +54,8 @@ def genobsdata(self, x, isbias=False): # Generate initial sample n0, nmax = 10, 30 -sampling = LHS(xlimits=cex.zlim, random_state=1) -z0 = sampling(n0) +sampling = qmc.LatinHypercube(d=cex.zlim.shape[0], seed=1) +z0 = sampling.random(n=n0) f0 = np.array([cex.function(z0[i, 0], z0[i, 1]) for i in range(n0)]) From 0c3cb574bf1c46f2dc291f87a6b7f4ec84a74c6a Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 15:57:32 -0400 Subject: [PATCH 084/103] Update puq-ci.yml --- .github/workflows/puq-ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/puq-ci.yml b/.github/workflows/puq-ci.yml index 81a34d5..ddf3c47 100644 --- a/.github/workflows/puq-ci.yml +++ b/.github/workflows/puq-ci.yml @@ -34,4 +34,4 @@ jobs: pip install -e . - name: Test with pytest run: | - ./tests/run-tests.sh + pytest tests/ From c37f64da9e97bb98b5a5e0d4d3270879146aa707 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 16:05:04 -0400 Subject: [PATCH 085/103] Update README.rst --- README.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 54fccaa..bc80abd 100644 --- a/README.rst +++ b/README.rst @@ -30,7 +30,7 @@ refining data collection to enhance the efficiency and effectiveness of uncertai This code is tested with Python 3.10, 3.11, and 3.12 and requires pip. -Users need to install setuptools manually via:: +Users may need to install setuptools manually via:: python -m ensurepip --upgrade python -m pip install --upgrade setuptools @@ -80,9 +80,9 @@ Testing The test suite requires the pytest_ and pytest-cov_ packages, which can be installed via ``pip install pytest pytest-cov``. -The test suite can be run from the ``tests/`` directory of the source distribution by running:: +The test suite can be run from the main directory of the source distribution by running:: -./run-tests.sh + pytest tests/ Documentation From a28401f9868427899df2e16fbc97eadb2ed2e656 Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 16:19:27 -0400 Subject: [PATCH 086/103] Update README.rst --- examples/Example2/README.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/Example2/README.rst b/examples/Example2/README.rst index 117d69d..6d88e29 100644 --- a/examples/Example2/README.rst +++ b/examples/Example2/README.rst @@ -15,7 +15,7 @@ To replicate the figures below, respectively: 2) Execute the followings from the command line:: - python3 example.py + python example.py Running this script should not take more than 60 sec. See the figures (png files) saved under the directory. @@ -29,4 +29,4 @@ Running this script should not take more than 60 sec. See the figures (png files The left panel shows simulation model outputs across design inputs (x) at four different parameter values. Black dots represent field data observed at five equally spaced design inputs. The right panel displays the points acquired -using the proposed acquisition function. \ No newline at end of file +using the proposed acquisition function. From a3dbc686020feddb8d25d6e5e44fd0e6d302c108 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 16:28:41 -0400 Subject: [PATCH 087/103] remove LHS --- examples/Example4/utilities.py | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/Example4/utilities.py b/examples/Example4/utilities.py index b449114..bb54107 100644 --- a/examples/Example4/utilities.py +++ b/examples/Example4/utilities.py @@ -1,7 +1,6 @@ import numpy as np import matplotlib.pyplot as plt import scipy.stats as sps -from smt.sampling_methods import LHS def twoD(designobj, Xpl, Ypl, p_test, nmesh): From 9e9d2a847ff6a7ea9fd69e61a54d05b8d716a967 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 16:53:06 -0400 Subject: [PATCH 088/103] LHS --- examples/Example5/utils_sample.py | 16 +++++++++++----- 1 file changed, 11 insertions(+), 5 deletions(-) diff --git a/examples/Example5/utils_sample.py b/examples/Example5/utils_sample.py index 8251a6d..8b5897e 100644 --- a/examples/Example5/utils_sample.py +++ b/examples/Example5/utils_sample.py @@ -8,7 +8,7 @@ def sample_from_posterior(cls_data, seed): import emcee import scipy.stats as sps - from smt.sampling_methods import LHS + from scipy.stats import qmc discard = 250 nsteps = 1000 @@ -36,10 +36,16 @@ def log_probability(ctheta): def sample(ndim, nwalkers, seed): np.random.seed(seed) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - sampling = LHS( - xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed - ) - loc0 = sampling(nwalkers) + + sampling = qmc.LatinHypercube(d=cls_data.p-cls_data.dx, seed=seed) + loc0 = sampling.random(n=nwalkers) + + # sampling = LHS( + # xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed + # ) + # loc0 = sampling(nwalkers) + + sampler.run_mcmc(initial_state=loc0, nsteps=nsteps, progress=False) samples = sampler.get_chain(discard=discard, thin=thin, flat=True) return samples From 45cfcf38cf493defb8cfbf6c99318cf2c00d0546 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 16:55:43 -0400 Subject: [PATCH 089/103] black --- examples/Example5/utils_sample.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/Example5/utils_sample.py b/examples/Example5/utils_sample.py index 8b5897e..c70b16b 100644 --- a/examples/Example5/utils_sample.py +++ b/examples/Example5/utils_sample.py @@ -36,16 +36,15 @@ def log_probability(ctheta): def sample(ndim, nwalkers, seed): np.random.seed(seed) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - - sampling = qmc.LatinHypercube(d=cls_data.p-cls_data.dx, seed=seed) + + sampling = qmc.LatinHypercube(d=cls_data.p - cls_data.dx, seed=seed) loc0 = sampling.random(n=nwalkers) - + # sampling = LHS( # xlimits=cls_data.zlim[cls_data.dx : cls_data.p, :], random_state=seed # ) # loc0 = sampling(nwalkers) - - + sampler.run_mcmc(initial_state=loc0, nsteps=nsteps, progress=False) samples = sampler.get_chain(discard=discard, thin=thin, flat=True) return samples From a45190dc5dcd20faa69a782140ee64ff8272768a Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 16:56:46 -0400 Subject: [PATCH 090/103] Update setup.py --- setup.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index 6aff491..f6eef09 100644 --- a/setup.py +++ b/setup.py @@ -5,8 +5,8 @@ setup( name="PUQ", - version="0.1.0", - author="Özge Sürer, Matthew Plumlee, Stefan M. Wild", + version="0.1.1", + author="Özge Sürer, David O'Gara, Matthew Plumlee, Stefan M. Wild", author_email="surero@miamioh.edu", description="Python package for generating experimental designs tailored for uncertainty quantification, featuring parallel implementations", url="https://github.com/parallelUQ/PUQ", @@ -16,6 +16,6 @@ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], - python_requires=">=3.6", + python_requires=">=3.9", include_dirs=[numpy.get_include()], ) From e9808a6710fe12f2d41a752cff8b85eb06c71c0a Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 16:58:00 -0400 Subject: [PATCH 091/103] Update LICENSE --- LICENSE | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/LICENSE b/LICENSE index e95030b..ec4bcdc 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2024 PUQ Development Team +Copyright (c) 2025 PUQ Development Team Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal From c51a5d5e9e253e8f0e3bc8cf3ef39f96cfbde5ae Mon Sep 17 00:00:00 2001 From: Ozge Surer <54813137+ozgesurer@users.noreply.github.com> Date: Mon, 6 Oct 2025 16:59:14 -0400 Subject: [PATCH 092/103] Update CHANGELOG.rst --- CHANGELOG.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 4e93dca..aaf156e 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -11,7 +11,7 @@ Release 0.1.1 :Date: Oct 8, 2025 -- :code:`surrogatemethods` have been updated to use the `hetGPy `_ package as their base. +- :code:`surrogatemethods` have been updated to use the `hetGPy `_ package as a base. - :code:`surrogatemethods` provide four emulators for deterministic, stochastic, and one- or multi-dimensional outputs. - :code:`designmethods` provide four sequential design procedures. - :code:`examples` have been revised to include illustrative cases from five different papers. From 0f8a2d0baa6506a58c14ee8088a8fbc47bfb8947 Mon Sep 17 00:00:00 2001 From: ozgesurer Date: Mon, 6 Oct 2025 17:40:08 -0400 Subject: [PATCH 093/103] Example5 --- .../gen_funcs/acquisition_1d_stochastic.py | 39 +++++------------- examples/Example5/README.rst | 5 ++- examples/Example5/ex5.png | Bin 543679 -> 542711 bytes 3 files changed, 14 insertions(+), 30 deletions(-) diff --git a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py index 42ed540..78606a2 100644 --- a/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py +++ b/PUQ/designmethods/gen_funcs/acquisition_1d_stochastic.py @@ -1,5 +1,5 @@ import numpy as np -from smt.sampling_methods import LHS +from scipy.stats import qmc from PUQ.designmethods.support import multiple_pdfs, multiple_determinants from hetgpy.covariance_functions import cov_gen from hetgpy.IMSE import crit_IMSPE, Wij, IMSPE, allocate_mult @@ -15,16 +15,16 @@ def generate_neighborhood(acq): if neigh_type == "LHS": N = int(acq.nL) - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L = sampling(N) + sampling = qmc.LatinHypercube(d=acq.zlim.shape[0], seed=int(acq.seed)) + L = sampling.random(n=N) else: N = int(acq.nL * 0.5) - sampling = LHS(xlimits=acq.zlim, random_state=int(acq.seed)) - L_explore = sampling(N) + sampling = qmc.LatinHypercube(d=acq.zlim.shape[0], seed=int(acq.seed)) + L_explore = sampling.random(n=N) - sampling = LHS(xlimits=acq.tlim, random_state=int(acq.seed)) - Lt = sampling(N) + sampling = qmc.LatinHypercube(d=acq.tlim.shape[0], seed=int(acq.seed)) + Lt = sampling.random(n=N) num_options = len(acq.x) @@ -279,9 +279,8 @@ def __init__(self, model, cls_func, args): self.nwalkers = args.get("nwalkers", 20) self.reference_set() elif args.get("integral") == "LHS": - print("LHS") - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - self.t_ref = sampling(500) + sampling = qmc.LatinHypercube(d=self.tlim.shape[0], seed=int(self.seed)) + self.t_ref = sampling.random(n=500) self.weights = 1 else: self.t_ref = args.get("t_grid") @@ -301,8 +300,8 @@ def sample(ndim, nwalkers): np.random.seed(int(self.seed)) sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability) - sampling = LHS(xlimits=self.tlim, random_state=int(self.seed)) - loc0 = sampling(nwalkers) + sampling = qmc.LatinHypercube(d=self.tlim.shape[0], seed=int(self.seed)) + loc0 = sampling.random(n=nwalkers) sampler.run_mcmc(initial_state=loc0, nsteps=self.nsteps, progress=False) @@ -317,22 +316,6 @@ def importance_weight(theta): return weight self.t_ref = sample(ndim=self.dt, nwalkers=self.nwalkers) - - # import pandas as pd - # import seaborn as sns - # import matplotlib.pyplot as plt - # df = pd.DataFrame(self.t_ref) - # sns.pairplot(df, diag_kind="kde") # Use KDE for diagonal histograms - # plt.show() - # print(self.t_ref.shape) - # import matplotlib.pyplot as plt - # if self.t_ref.shape[1] > 1: - # plt.scatter(self.t_ref[:, 0], self.t_ref[:, 1]) - # plt.show() - # else: - # plt.hist(self.t_ref) - # plt.show() - self.weights = importance_weight(theta=self.t_ref) def evaluate(self, new, return_pseudo): diff --git a/examples/Example5/README.rst b/examples/Example5/README.rst index fa0f1c1..c74ac4c 100644 --- a/examples/Example5/README.rst +++ b/examples/Example5/README.rst @@ -5,7 +5,7 @@ This example demonstrates how to use the active learning procedure from Sürer ( Active Learning for Data-Efficient Calibration of Stochastic Simulation Models. -**Instructions for running the illustrative examples with the active learning procedurel** +**Instructions for running the illustrative examples with the active learning procedure** To replicate the figures below, respectively: @@ -17,7 +17,8 @@ To replicate the figures below, respectively: python example.py -Running this script should not take more than 5 min. See the figures (jpeg files) saved under ``examples/`` directory. +Running this script should not take more than 5 min. 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z1(k-7pp7sjCo&~Cab#ST47cYIsW^9By!XbfUmM^u1Ku+9^_!b#y~=2sW;ch97#T`AoMAvad}X^_&X>?>9a?nGg? zJKP%y*UO19fxo)hfY4Acm|Zg1=!bhY;fHu>DRiKA%kisBuGrn`&zHr&EL!|+t0+5+ z0v Date: Wed, 1 Oct 2025 19:21:47 -0400 Subject: [PATCH 064/103] Updating Example1 with more examples --- PUQ/designmethods/gen_funcs/acquire_new.py | 4 +- examples/Example1/README.rst | 11 +- examples/Example1/ex1.png | Bin 1326237 -> 1322468 bytes examples/Example1/example.py | 31 ++-- examples/Example1/example_2d.py | 95 ++++++++++++ examples/Example1/toy_example.py | 160 +++++++++++++++++++++ 6 files changed, 284 insertions(+), 17 deletions(-) create mode 100644 examples/Example1/example_2d.py create mode 100644 examples/Example1/toy_example.py diff --git a/PUQ/designmethods/gen_funcs/acquire_new.py b/PUQ/designmethods/gen_funcs/acquire_new.py index 2196ca7..96af85b 100644 --- a/PUQ/designmethods/gen_funcs/acquire_new.py +++ b/PUQ/designmethods/gen_funcs/acquire_new.py @@ -49,7 +49,9 @@ def impute_strategy(ct, x, fE, tE, emu, liar, des_obj): if impute_str == "update": # update(emu._info, x=x, X0new=ct, mult=rep) - emu.update(x=ct) + Xnew = np.repeat(ct, rep, axis=0) + print(Xnew) + emu.update(x=Xnew) fE, tE = impute( ct=ct, x=x, fE=fE, tE=tE, reps=rep, emu=emu, rnd_str=rand_stream ) diff --git a/examples/Example1/README.rst b/examples/Example1/README.rst index 9fd8c54..805bfcf 100644 --- a/examples/Example1/README.rst +++ b/examples/Example1/README.rst @@ -3,7 +3,8 @@ Examples ~~~~~~~~ This example demonstrates how to apply the proposed method from Sürer, Plumlee, and Wild (2024), -Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models, to a deterministic simulation model with high-dimensional outputs. +Sequential Bayesian Experimental Design for Calibration of Expensive Simulation Models, +to a deterministic simulation model with high-dimensional outputs. **Instructions for running the illustrative examples** @@ -14,11 +15,15 @@ To replicate the figures below, respectively: 2) Execute the followings from the command line:: - python3 example.py + python example.py Running this script should not take more than 120 sec. See the figures (png files) saved under the directory. .. image:: ex1.png :alt: Illustration of PUQ with the example :align: center - :width: 600 \ No newline at end of file + :width: 600 + +Blue circles denote the initial design, and plus markers indicate the acquired +points obtained via random sampling from the prior (left), variance (middle), and proposed +integrated variance (IVAR) acquisition functions (right). \ No newline at end of file diff --git a/examples/Example1/ex1.png b/examples/Example1/ex1.png index 6f5e3820191cde7b07b2c547a7bf5465a92c8c9f..d64faf92951e8f17bb6aff87e29cf2a358d82f97 100644 GIT binary patch delta 561294 zcma%jc|25Y|No37r5MUm#8k3GQY2*?C3`|*vQCmL6GHaoXhCENX(IcYEJK#DHHb)A zvrNdo?=#k6w%>6--|z4H-1qbPEq|PsmpNn3nR8v&=kwk!DZ7T#!-m1-&^y9_37SID zm;IslbLoB2O6Cb)JLga5vZt4EOdY(FsprUBJh!(;PkcUR8&fgD#J`x|h`6KmtFGW4 zX)Z#3K#T#gsC|f7|DmwnOW(C*-@64rY>O^-diQ)|Y?T2~I{u^1bSTZ@j}xD17{9_h zA_GFuF#!^D)e9DI9F-iJzf3m)orIWq^kkgQ+4wlWcn!awFKnFAXgZuM2Haa$oy^3` z{h}Ha&qo*KE)w1~`ki%8sgKx1yu#`d39Tu&d*WbRL!!$n9L*3di5;mO3kF08tHl4+ zr$#R+@-5*H1M-t}L4I;a^?By!0K}xT$TvRt<6kmICoeU9oS0IL%!@s0-oL?nt}{7V ztdOATlCsS%k%_tV1R8bnh(Goa*=2iH??XLo0m*+2YIf@Y>1PpuIQNZjj56ouWA0hwycu4~gPuKpcA|() zubN*mC_Hz_5^`71)$yeBi@t{t^qqJk^^FtQ;2Z|ToB>%AplsurFf=p+Qs)3IH`2gl zJmfUD!mE&fEi)i|6&>xj2VNdYebIJXYFU!JF@>NeeDSA|>o;``+1RXNn(772e2$r8 zOw-iqy|wGqs^folZ=9q}s%o~0M3SDRnNNL8hXD*oXc7aGuf>2!qG+FDFjXg^aOl5& z05N1o?A}{o9lV{Pf(D6n&ZbF3bp6DApU^Q=dMAY~-l1|7nKnDexl*fyq>x*bkZ=ti z2(oIvzT9A2GbC>zjriKP-nKG&texIHT>l`XQ#mVgDi_`KpBGL598=oasAp7eBc?_7 zFE_X6eh?@`f$yw$h`)~F$8}4x$U*^Y=Sc=6vW7n_RZssa;DpL=+T8N@&|!N^37M4Y8-8& z0y%Co0=1Va(AU*o~8 zwBDY2&cF~all9+`DwfJ4iam$jf*vj87C(=WDAX)Ft@j>^LFgZf0yfrGF0Ray9TkK= zxZM6jN*mcgT}O5Sh8zPTceh|ZpXibb(3}G>)MMhWmSb?IYH&4H0~_BYwxXH<$t3)#2(!~~ba4Z`Ucx)W~-)DgI_)Y4kodh9~{clZq{0jiLxJViDc;UPwROtjF49 zLMB>3vv>hreDiW`?$x8KBg}B@U8>Ix)>zAo+v>RN#UG!OpQ~sb55UVXziRA?t9#6V zu+4X5iAozTN@P~EQ}4oM2r86JkKx)JZb55SRK*G!mi4_mpxt1$n2kcR6#z zrP%JCP0SOk&Q{37H*Dfxyuc!wjX%wRbbnz$;w!-sb}ymBfi_Lz813Z>1G0G^TRG1> z4I|${ae-CY8vN*Cae#i!D&}if$fzuFtl3J&q`w>}8&nh95T)Fb!9Bg572@s}JA{wM zaO*<_Fb!iIalnZnQGA*nvAFc31us8erLh6GO>DJc$@jJI*hItx;4?=LIu8(efT%#x zDc4=DIS_@ z?T`8%E+3ITnN3=YT&;7QJRx)Pv)N5ub2Z?ZiZ-3J2weDD?L<>J^L^rZ?x%j_M+O8Y zx>puagcJwtN=!x+wE8MO#E9G~sjkD+G@b|ubn6a852(Te_?0LI(i$py1_k_FK*g&$JnB72Pl)n?Wg>i1z+O&?^|{~h8;oRQwyl|XM)V@%Bp+U zM9XVK#;ZaibOa-LMN0EG1Fd(i_ZMxcsM_+jb5skKZn1c?JkjtQ#2aZ{%JIzJL-gzh z-8B9<-c=z{h&V`2B7DQ2tn4p(g1um)|64`%XH#=fi=Jj;N*47;;COjpfWqYtLTY8|k_b%O}}Sc>rD zv@zGtU{%`x00-s2PFxAz2S1I=OlId$SE%}Avnc~E>dpQND;Z6SMnaS31I&tH?y_ct zk3EY!%m}4pqy|5Q3F2Wnjp4Wgy^R@=f{Fu2eB!?jb-naFzAXO}@!3j;E77<^NA~3# zPQ%!KgPD6<%kXk<>RS~Ay%B5_^MmNtc}Vf>&+(lYdBMZ}!&ZmeaaG}u!s!IapGaBG z!_!Z+%qI#~5v&Zz_wb1|$^i>tJ|`W9K}_fbs52lLU{x1)WdppM_tAD1MfN!9R4?oaEPkRr;^Omf zlf2Vg5i4^`!?@x<6`yEtdB`^JCLe|#A2qX_Fybk@v6sD}Ig$h;&HC!C3vjJ!;_kc!{yuy%b>`L+yAcEDn6%t8W7M9bWyin)enHoo+iIW~g%e>l4+gVq^xksvwF4H0d*c z>(Mz|x#akfdwouGLA)+nNPD|S-lT)DHZ^Il3EQ*>)$ z=KP^8k>H^8J;F!ijL#p@TSC5u1vS)w&iOVSD^?yO%bYTj=36L6ErliejL%%F3^O^V zMb5!FePImES8(;i4yhIAb}MlE1SKyo$6p9jA_djQEXhM}ogrz3s|t||zA8TT-kVL> z)-x03DxhValbt?#fIECXZ@t($-pGen&dMN6^&}blEeX9SAeOLQaQE%kUxArtn}QD= zf5eu>K2xxW7$nejIta9v;A84>>t>7oYjKDL0vZr^#NkaV^`?5x@)Ri(97ScB33Ww! 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