gsm-v2 squashed

Examples.py

@@ -1164,7 +1164,7 @@ def passive_vpa_learning_arithmetics():
 def passive_vpa_learning_on_all_benchmark_models():
     from aalpy.learning_algs import run_PAPNI
     from aalpy.utils.BenchmarkVpaModels import vpa_L1, vpa_L12, vpa_for_odd_parentheses
-    from aalpy.utils import generate_input_output_data_from_vpa, convert_i_o_traces_for_RPNI
+    from aalpy.utils import generate_input_output_data_from_vpa
 
     for gt in [vpa_L1(), vpa_L12(), vpa_for_odd_parentheses()]:
         vpa_alphabet = gt.input_alphabet
@@ -1200,7 +1200,7 @@ def gsm_edsm():
     from aalpy import load_automaton_from_file
     from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits
     from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM
-    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreCalculation
+    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import SimpleScoreCalculation
     from aalpy.learning_algs.general_passive.GsmNode import GsmNode
 
     automaton = load_automaton_from_file("DotModels/car_alarm.dot", "moore")
@@ -1212,7 +1212,7 @@ def EDSM_score(part: Dict[GsmNode, GsmNode]):
         nr_merged = len(part)
         return nr_merged - nr_partitions
 
-    score = ScoreCalculation(score_function=EDSM_score)
+    score = SimpleScoreCalculation(score_function=EDSM_score)
     learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="deterministic", score_calc=score)
     learned_model.visualize()
 
@@ -1221,7 +1221,7 @@ def gsm_likelihood_ratio():
     from typing import Dict
     from scipy.stats import chi2
     from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM
-    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreFunction, differential_info, ScoreCalculation
+    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import ScoreFunction, differential_info, SimpleScoreCalculation
     from aalpy.learning_algs.general_passive.GsmNode import GsmNode
     from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits
     from aalpy import load_automaton_from_file
@@ -1246,55 +1246,58 @@ def score_fun(part: Dict[GsmNode, GsmNode]):
 
         return score_fun
 
-    score = ScoreCalculation(score_function=likelihood_ratio_score())
+    score = SimpleScoreCalculation(score_function=likelihood_ratio_score())
     learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score)
     learned_model.visualize()
 
 
 def example_Alergia_extension():
+    from typing import Any
+    from aalpy.learning_algs.general_passive.DataHandler import CountOnPTADataHandler
     from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM
-    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, ScoreCalculation
     from aalpy.learning_algs.general_passive.GsmNode import GsmNode
+    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, SimpleFutureBasedCompatibility, SpecialScores
     from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits
     from aalpy import load_automaton_from_file
 
     automaton = load_automaton_from_file("DotModels/MDPs/faulty_car_alarm.dot", "mdp")
     input_traces = sample_with_length_limits(automaton.get_input_alphabet(), 2000, 20, 30)
     traces = get_io_traces(automaton, input_traces)
 
-    # NOTE THAT This example is equivalent to a call to a function run_Alergia_EDSM
+    # NOTE: a more general version of this is provided in aalpy.learning_algs.general_passive.ScoreFunctionsGSM
+    class ScoreIOAlergiaWithEDSM(SimpleFutureBasedCompatibility):
+        def __init__(self, eps: float):
+            self.compat = hoeffding_compatibility(eps)
+            SimpleFutureBasedCompatibility.__init__(self, compatibility_on_pta=True)
+            self.score = None
 
-    class IOAlergiaWithEDSM(ScoreCalculation):
-        def __init__(self, epsilon):
-            super().__init__()
-            self.ioa_compatibility = hoeffding_compatibility(epsilon)
-            self.evidence = 0
-
-        def reset(self):
-            self.evidence = 0
+        def early_score(self, red: GsmNode, blue: GsmNode) -> Any:
+            self.score = 0
+            verdict = super().early_score(red, blue)
+            if verdict is SpecialScores.ImmediateReject:
+                return verdict
+            return self.score
 
-        def local_compatibility(self, a: GsmNode, b: GsmNode):
-            self.evidence += 1
-            return self.ioa_compatibility(a, b)
-
-        def score_function(self, part):
-            return self.evidence
+        def local_compatibility(self, red: GsmNode, blue: GsmNode) -> float:
+            self.score += 1
+            return self.compat(red, blue)
 
     epsilon = 0.05
     scores = {
-        "IOA": ScoreCalculation(hoeffding_compatibility(epsilon)),
-        "IOA+EDSM": IOAlergiaWithEDSM(epsilon),
+        "IOA": SimpleFutureBasedCompatibility(local_compatibility=hoeffding_compatibility(epsilon, True), compatibility_on_pta=True),
+        "IOA+EDSM": ScoreIOAlergiaWithEDSM(epsilon),
     }
 
     for name, score in scores.items():
         learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score,
-                            compatibility_on_pta=True, compatibility_on_futures=True)
+                                data_handler=CountOnPTADataHandler())
         learned_model.visualize(name)
 
 
 def gsm_IOAlergia_domain_knowldege():
     from aalpy.learning_algs.general_passive.GeneralizedStateMerging import run_GSM
-    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, ScoreCalculation
+    from aalpy.learning_algs.general_passive.ScoreFunctionsGSM import hoeffding_compatibility, SimpleFutureBasedCompatibility
+    from aalpy.learning_algs.general_passive.DataHandler import CountOnPTADataHandler
     from aalpy.learning_algs.general_passive.GsmNode import GsmNode
     from aalpy.utils.Sampling import get_io_traces, sample_with_length_limits
     from aalpy import load_automaton_from_file
@@ -1320,12 +1323,12 @@ def ioa_compat_domain_knowledge(a: GsmNode, b: GsmNode):
         return parity and ioa
 
     scores = {
-        "IOA": ScoreCalculation(ioa_compat),
-        "IOA+DK": ScoreCalculation(ioa_compat_domain_knowledge),
+        "IOA": SimpleFutureBasedCompatibility(local_compatibility=ioa_compat, compatibility_on_pta=True),
+        "IOA+DK": SimpleFutureBasedCompatibility(local_compatibility=ioa_compat_domain_knowledge, compatibility_on_pta=True),
     }
     for name, score in scores.items():
         learned_model = run_GSM(traces, output_behavior="moore", transition_behavior="stochastic", score_calc=score,
-                            compatibility_on_pta=True, compatibility_on_futures=True)
+                                data_handler=CountOnPTADataHandler())
         learned_model.visualize(name)
 
 def k_tails_example():
@@ -1336,16 +1339,13 @@ def k_tails_example():
                                                    input_alphabet_size=3,
                                                    output_alphabet_size=3)
 
+    # data is a list of sequences in this format [(i1, o1), (i2, o1), (i1, o3)]
     data = generate_input_output_data_from_automata(model, num_sequences=2000,
                                                     min_seq_len=1, max_seq_len=12,
                                                     sequance_type='io_traces')
 
-    # k-trails works with prefix-closed input output traces, not labeled sequences like RPNI
-    # data is a list of sequences in this format [(i1, o1), (i2, o1), (i1, o3)]
-
     # run k_tails with two different k's
-    k_trails_1 = run_k_tails(data, k=3, automaton_type='moore', print_info=True)
-
+    k_tails_1 = run_k_tails(data, k=3, automaton_type='moore', print_info=True)
     k_tails_2 = run_k_tails(data, k=8, automaton_type='mealy', print_info=True)
 
 

aalpy/learning_algs/__init__.py

@@ -12,6 +12,6 @@
 from .deterministic_passive.PAPNI import run_PAPNI
 from .deterministic_passive.active_RPNI import run_active_RPNI
 from .general_passive.GeneralizedStateMerging import run_GSM
-from .general_passive.GsmAlgorithms import run_EDSM, run_Alergia_EDSM, run_k_tails
+from .general_passive.GsmAlgorithms import run_EDSM, run_Alergia_GSM, run_Alergia_EDSM, run_k_tails
 from .resetless.hW import run_hW
 from .resetless.resetless_oracles import hWOracle, RandomhWOracle, RandomWphWOracle

aalpy/learning_algs/general_passive/AssociatedData.py

@@ -0,0 +1,63 @@
+import math
+from abc import abstractmethod, ABC
+from collections import defaultdict
+from typing import Any
+
+
+ProbabilityDict = dict[Any, dict[Any, float]]
+
+class StochasticData(ABC):
+    """
+    Interface class for data used with `transition_behavior` set to "stochastic".
+    """
+    @abstractmethod
+    def get_probabilities(self) -> ProbabilityDict:
+        """
+        Method for extracting transition probabilities when converting to automaton models.
+
+        :return ProbabilityDict: Nested dictionary of transition probabilities.
+        """
+        pass
+
+CountDict = dict[Any, dict[Any, int]]
+
+def int_dict_increment(c_dict, out_sym, cnt):
+    c_dict[out_sym] = c_dict.get(out_sym, 0) + cnt
+
+class CountData(StochasticData):
+    def __init__(self):
+        # TODO get rid of this indirection
+        self.transition_count: CountDict = defaultdict(dict)
+
+    def local_log_likelihood_contribution(self):
+        llc = 0
+        for in_sym, trans in self.transition_count.items():
+            total_count = 0
+            for out_sym, count in trans.items():
+                total_count += count
+                llc += count * math.log(count)
+            if total_count != 0:
+                llc -= total_count * math.log(total_count)
+        return llc
+
+    def count(self):
+        return sum(sum(trans.values()) for trans in self.transition_count.values())
+
+    def get_probabilities(self) -> ProbabilityDict:
+        ret = dict()
+        for in_sym, trans in self.transition_count.items():
+            total_count = sum(trans.values())
+            ret[in_sym] = {out_sym: count / total_count for out_sym, count in trans.items()}
+        return ret
+
+
+ShadowPTA = dict[Any, dict[Any, 'GsmNode']]
+class ShadowPTAData:
+    def __init__(self):
+        self.shadow_pta: ShadowPTA = defaultdict(dict)
+
+class CountOnPTAData(ShadowPTAData, CountData):
+    def __init__(self):
+        ShadowPTAData.__init__(self)
+        CountData.__init__(self)
+        self.pta_count: CountDict = defaultdict(dict)