Remove unused type error suppressions (landing group 94) (#5305)

ax/adapter/transforms/log.py

@@ -157,7 +157,6 @@ def untransform_observation_features(
                     if p_name in self.original_values:
                         val = assert_is_instance_of_tuple(
                             min(
-                                # pyrefly: ignore [unsupported-operation]
                                 self.original_values[p_name],
                                 # pyrefly: ignore [unsupported-operation]
                                 key=lambda x: abs(x - val),

ax/adapter/transforms/tests/test_cast_transform.py

@@ -490,7 +490,6 @@ def test_transform_experiment_data_cast(self) -> None:
             .astype({"x": float, "y": float})
         )
         expected_arm_data["z"] = None
-        # pyrefly: ignore [missing-attribute]
         expected_arm_data["z"] = expected_arm_data["z"].astype("Int64")
         expected_arm_data = expected_arm_data[["x", "y", "z", "metadata"]]
         assert_frame_equal(transformed.arm_data, expected_arm_data)

ax/analysis/utils.py

@@ -1051,7 +1051,6 @@ def _get_status_quo_df(
             col for col in df.columns if col.endswith("_mean") or col.endswith("_sem")
         ]
         status_quo_df = pd.DataFrame(status_quo_rows)[["trial_index", *all_metric_cols]]
-    # pyrefly: ignore [bad-return]
     return status_quo_df
 
 

ax/core/data.py

@@ -871,7 +871,6 @@ def _subsample_one_metric(
         else:
             filtered_df = df_g.iloc[:: int(derived_keep_every)]
         filtered_dfs.append(filtered_df)
-    # pyrefly: ignore [bad-return]
     return pd.concat(filtered_dfs)
 
 

ax/core/utils.py

@@ -735,13 +735,12 @@ def _sort_trials(
                     (
                         _time_trial_completed_safe(
                             trial=t  # pyrefly: ignore [bad-argument-type]
-                        ).timestamp()  # pyrefly: ignore [bad-argument-type]
+                        ).timestamp()
                         if not trials_are_running
                         else default_time_run_started.timestamp()
                     )
                     - _time_trial_started_safe(
                         trial=t,  # pyrefly: ignore [bad-argument-type]
-                        # pyrefly: ignore [bad-argument-type]
                         default_time_run_started=default_time_run_started,
                     ).timestamp()
                 )

ax/generators/discrete/ashr_utils.py

@@ -127,7 +127,6 @@ def fit(
         lambdas = lambdas.astype(np.float64)
 
         results = fit_ashr_em(
-            # pyrefly: ignore [bad-argument-type]
             ll=self.ll,
             # pyrefly: ignore [bad-argument-type]
             lambdas=lambdas,

ax/generators/tests/test_botorch_moo_utils.py

@@ -219,9 +219,7 @@ def test_get_weighted_mc_objective(self) -> None:
         weighted_obj = get_weighted_mc_objective(
             objective_weights=objective_weights,
         )
-        # pyrefly: ignore [bad-argument-type]
         self.assertTrue(torch.equal(weighted_obj.weights, torch.tensor([1.0, 1.0])))
-        # pyrefly: ignore [not-callable]
         self.assertEqual(weighted_obj.outcomes.tolist(), [1, 3])
 
     # test infer objective thresholds alone

ax/generators/torch/tests/test_generator.py

@@ -195,7 +195,6 @@ def setUp(self) -> None:
                 # pyrefly: ignore [bad-assignment]
                 Keys.OPTIMIZER_KWARGS: self.optimizer_options,
                 Keys.ACQF_KWARGS: {"eta": 3.0},
-                # pyrefly: ignore [bad-assignment]
                 Keys.AX_ACQUISITION_KWARGS: {Keys.SUBSET_MODEL: True},
             },
         )
@@ -1128,7 +1127,6 @@ def test_MOO(self) -> None:
         self.assertIsInstance(ckwargs["objective"], WeightedMCMultiOutputObjective)
         self.assertTrue(
             torch.equal(
-                # pyrefly: ignore [bad-argument-type]
                 ckwargs["objective"].weights,
                 extract_objectives(self.moo_objective_weights)[1],
             )

ax/generators/torch/tests/test_kernels.py

@@ -63,9 +63,9 @@ def test_scalematern_kernel_active_dims(self) -> None:
         base_kernel = assert_is_instance(covar.base_kernel, MaternKernel)
         # active_dims lands on the inner MaternKernel, and the ScaleKernel
         # inherits it (so subsetting happens exactly once at the wrapper level).
-        # pyrefly: ignore [missing-attribute, not-callable]
+        # pyrefly: ignore [missing-attribute]
         self.assertEqual(covar.active_dims.tolist(), active_dims)
-        # pyrefly: ignore [missing-attribute, not-callable]
+        # pyrefly: ignore [missing-attribute]
         self.assertEqual(base_kernel.active_dims.tolist(), active_dims)
         self.assertEqual(base_kernel.ard_num_dims, len(active_dims))
         # The kernel only consumes the active columns: perturbing an inactive
@@ -121,11 +121,11 @@ def test_scale_rbf_linear_kernel_active_dims(self) -> None:
         rbf = assert_is_instance(scale_rbf.base_kernel, RBFKernel)
         # active_dims lands on both leaves, and the ScaleKernel inherits it
         # from the wrapped RBF kernel (so subsetting happens exactly once).
-        # pyrefly: ignore [missing-attribute, not-callable]
+        # pyrefly: ignore [missing-attribute]
         self.assertEqual(scale_rbf.active_dims.tolist(), active_dims)
-        # pyrefly: ignore [missing-attribute, not-callable]
+        # pyrefly: ignore [missing-attribute]
         self.assertEqual(rbf.active_dims.tolist(), active_dims)
-        # pyrefly: ignore [missing-attribute, not-callable]
+        # pyrefly: ignore [missing-attribute]
         self.assertEqual(linear.active_dims.tolist(), active_dims)
         # ard_num_dims matches the number of active dims.
         self.assertEqual(rbf.ard_num_dims, len(active_dims))
@@ -283,9 +283,9 @@ def test_default_kernel(self) -> None:
                 self.assertIsNone(botorch_kernel.active_dims)
             else:
                 self.assertEqual(
-                    # pyrefly: ignore [missing-attribute, not-callable]
+                    # pyrefly: ignore [missing-attribute]
                     ax_kernel.active_dims.tolist(),
-                    # pyrefly: ignore [missing-attribute, not-callable]
+                    # pyrefly: ignore [missing-attribute]
                     botorch_kernel.active_dims.tolist(),
                 )
 
@@ -301,5 +301,5 @@ def test_default_mle(self) -> None:
             self.assertTrue((kernel.lengthscale == sqrt(2) / 10).all())
             self.assertEqual(kernel.lengthscale.shape, torch.Size([3, 1, 2]))
             self.assertFalse(hasattr(kernel, "lengthscale_prior"))
-            # pyrefly: ignore [missing-attribute, not-callable]
+            # pyrefly: ignore [missing-attribute]
             self.assertEqual(kernel.active_dims.tolist(), active_dims)

ax/generators/torch/tests/test_surrogate.py

@@ -280,7 +280,6 @@ def test__make_botorch_input_transform(self) -> None:
                 dataset=dataset,
             )
             transform = assert_is_instance(transform, Normalize)
-            # pyrefly: ignore [not-callable]
             self.assertEqual(transform.indices.tolist(), [0])
             self.assertEqual(transform.bounds.tolist(), [[1.0], [5.0]])
 

ax/generators/types.py

@@ -12,7 +12,6 @@
 from ax.generators.winsorization_config import WinsorizationConfig
 from botorch.acquisition import AcquisitionFunction
 
-# pyre-ignore[33]: `TConfig` cannot alias to a type containing `Any`.
 TConfig = dict[
     str,
     int

ax/generators/utils.py

@@ -385,7 +385,6 @@ def best_observed_point(
         fixed_features=fixed_features,
         options=options,
     )
-    # pyrefly: ignore [bad-return]
     return None if best_point_and_value is None else best_point_and_value[0]
 
 

ax/metrics/tests/test_tensorboard.py

@@ -51,7 +51,6 @@ def _get_fake_multiplexer(
     # pyre-ignore[8] Return fake tags when content is requested
     mul.PluginRunToTagToContent = lambda plugin: {".": {"loss": ""}}
 
-    # pyre-ignore[8] Return fake data when tensors requested
     mul.Tensors = lambda run, tag: [
         _TensorEvent(step=i, tensor_proto=_TensorProto(double_val=[dat]))
         for i, dat in enumerate(fake_data)

ax/plot/helper.py

@@ -252,9 +252,7 @@ def _get_in_sample_arms(
                         cov_dict=obs.data.covariance_matrix,
                         agg_metric_weight_dict=agg_metric["weight"],
                     )
-                    # pyrefly: ignore [unsupported-operation]
                     obs_y[agg_metric_name] = agg_mean
-                    # pyrefly: ignore [unsupported-operation]
                     obs_se[agg_metric_name] = np.sqrt(agg_var)
         if training_in_design[i]:
             # Update with the input fixed features

ax/service/tests/test_ax_client.py

@@ -156,7 +156,6 @@ def get_branin_currin_optimization_with_N_sobol_trials(
             "branin": ObjectiveProperties(
                 minimize=minimize,
                 threshold=(
-                    # pyrefly: ignore [bad-index]
                     float(branin_currin.ref_point[0])
                     if include_objective_thresholds
                     else None
@@ -165,7 +164,6 @@ def get_branin_currin_optimization_with_N_sobol_trials(
             "currin": ObjectiveProperties(
                 minimize=minimize,
                 threshold=(
-                    # pyrefly: ignore [bad-index]
                     float(branin_currin.ref_point[1])
                     if include_objective_thresholds
                     else None
@@ -3068,9 +3066,7 @@ def helper_test_get_pareto_optimal_points(
         for obs in observed_pareto.values():
             branin: float = obs[1][0]["branin"]
             currin: float = obs[1][0]["currin"]
-            # pyrefly: ignore [bad-index]
             self.assertGreater(branin, branin_currin.ref_point[0].item())
-            # pyrefly: ignore [bad-index]
             self.assertGreater(currin, branin_currin.ref_point[1].item())
             if outcome_constraints is not None:
                 self.assertEqual(branin + currin, obs[1][0]["c"])

ax/utils/stats/no_effects.py

@@ -173,7 +173,6 @@ def check_experiment_effects(
     effective = np.min(ps) < no_effect_alpha
     bounds_df = pd.DataFrame(fx_bounds, columns=["metric_name", "min", "max", "p"])
     bounds_df.sort_values(by="p", inplace=True)
-    # pyrefly: ignore [bad-return]
     return effective, ineffective_on_objectives, bounds_df