Added parameters aplha_xu and tau_xu and ensambled model simulation

main_package/classes/estimators/fam_score_calculator.py

@@ -207,7 +207,7 @@ class FamScoreCalculator:
 
     def get_fam_score(self,
                 cims: np.array,
-                tau_xu: float=1,
+                tau_xu: float=0.1,
                 alpha_xu: float=1):
         """
         calculate the FamScore value of the node identified by the label node_id

main_package/classes/estimators/structure_constraint_based_estimator.py

@@ -211,7 +211,7 @@ class StructureConstraintBasedEstimator(se.StructureEstimator):
                                                             tot_vars_count = tot_vars_count)
         optimizer_obj.optimize_structure()
 
-    @timing_write
+    @timing
     def ctpc_algorithm(self):
         """
         Compute the CTPC algorithm.
@@ -224,4 +224,11 @@ class StructureConstraintBasedEstimator(se.StructureEstimator):
         total_vars_numb = self.sample_path.total_variables_count
         [ctpc_algo(n, total_vars_numb) for n in self.nodes]
 
+    @timing 
+    def estimate_structure(self):
+        self.ctpc_algorithm()
+        return set(self.complete_graph.edges)
+
+    
+
 

main_package/classes/estimators/structure_estimator.py

@@ -10,6 +10,8 @@ from networkx.readwrite import json_graph
 
 from abc import ABC
 
+import abc
+
 import utility.cache as ch
 import structure_graph.conditional_intensity_matrix as condim
 import structure_graph.network_graph as ng
@@ -94,3 +96,17 @@ class StructureEstimator(ABC):
         s0, s1 = matrix.strides
         return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
 
+
+    @abc.abstractmethod
+    def estimate_structure(self) -> typing.List:
+        """
+        Compute Optimization process for a structure_estimator
+
+        Parameters:
+
+        Returns:
+            the estimated structure for the node
+
+        """
+        pass
+

main_package/classes/estimators/structure_score_based_estimator.py

@@ -41,11 +41,13 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
 
     """
 
-    def __init__(self, sample_path: sp.SamplePath):
+    def __init__(self, sample_path: sp.SamplePath, tau_xu:int=0.1, alpha_xu:int = 1):
         super().__init__(sample_path)
+        self.tau_xu=tau_xu
+        self.alpha_xu=alpha_xu
 
 
-    @timing_write
+    @timing
     def estimate_structure(self, max_parents:int = None, iterations_number:int= 40,
                          patience:int = None, tabu_length:int = None, tabu_rules_duration:int = None,
                          optimizer: str = 'hill',disable_multiprocessing:bool= False ):
@@ -176,7 +178,8 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         return optimizer.optimize_structure()
 
     
-    def get_score_from_graph(self,graph: ng.NetworkGraph,node_id:str):
+    def get_score_from_graph(self,
+                            graph: ng.NetworkGraph,node_id:str):
         """
         Use the FamScore of a node in order to find the best parent nodes
         Parameters:
@@ -198,7 +201,7 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         'calculate the FamScore for the node'
         fam_score_obj = fam_score.FamScoreCalculator()
 
-        score = fam_score_obj.get_fam_score(SoCims.actual_cims)
+        score = fam_score_obj.get_fam_score(SoCims.actual_cims,tau_xu = self.tau_xu,alpha_xu=self.alpha_xu)
         
         #print(f" lo score per {node_id} risulta: {score} ")
         return score 

main_package/classes/optimizers/hill_climbing_search.py

@@ -34,6 +34,8 @@ class HillClimbing(Optimizer):
         Compute Optimization process for a structure_estimator
 
         Parameters:
+            node_id: the node label
+            structure_estimator: a structure estimator object with the information about the net
             max_parents: maximum number of parents for each variable. If None, disabled
             iterations_number: maximum number of optimization algorithm's iteration
             patience: number of iteration without any improvement before to stop the search.If None, disabled

main_package/classes/optimizers/tabu_search.py

@@ -38,6 +38,8 @@ class TabuSearch(Optimizer):
         Compute Optimization process for a structure_estimator
 
         Parameters:
+            node_id: the node label
+            structure_estimator: a structure estimator object with the information about the net
             max_parents: maximum number of parents for each variable. If None, disabled
             iterations_number: maximum number of optimization algorithm's iteration
             patience: number of iteration without any improvement before to stop the search.If None, disabled

main_package/tests/results/results.csv

@@ -1,4 +1,4 @@
-Time,Constraint,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
+Time,Type,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
 89.185,1,15,0.1,3,0,0.88,0.786,1.0
 96.358,1,15,0.1,3,1,0.939,0.885,1.0
 83.178,1,15,0.1,3,2,0.954,0.913,1.0
@@ -1151,3 +1151,199 @@ Time,Constraint,Variables,Density_Network,Cardinality,Index,F1,Precision,Recall
 112.711,0,15,0.4,4,0,0.311,1.0,0.184
 119.185,0,15,0.4,4,1,0.46,1.0,0.299
 119.897,0,15,0.4,4,2,0.383,1.0,0.237
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main_package/tests/results/results16-32RAM.csv

@@ -0,0 +1,20 @@
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main_package/tests/simulators/test_ensambled_model.py

@@ -0,0 +1,84 @@
+import sys
+sys.path.append("../../classes/")
+import glob
+import math
+import os
+import unittest
+
+import networkx as nx
+import numpy as np
+import psutil
+from line_profiler import LineProfiler
+import copy
+
+import utility.cache as ch
+import structure_graph.sample_path as sp
+import estimators.structure_score_based_estimator as se
+import estimators.structure_constraint_based_estimator as se_
+import utility.json_importer as ji
+
+
+
+class TestHybridMethod(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
+        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_binary_data_04_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.s1 = sp.SamplePath(cls.importer)
+        cls.s1.build_trajectories()
+        cls.s1.build_structure()
+
+
+
+    def test_structure(self):
+        true_edges = copy.deepcopy(self.s1.structure.edges)
+        true_edges = set(map(tuple, true_edges))
+
+        s2= copy.deepcopy(self.s1)
+
+        se1 = se.StructureScoreBasedEstimator(self.s1,1,1)
+        edges_score = se1.estimate_structure(
+                                max_parents = None,
+                                iterations_number = 100,
+                                patience = 50,
+                                tabu_length = 20,
+                                tabu_rules_duration = 20,
+                                optimizer = 'tabu'
+                            )
+
+        se2 = se_.StructureConstraintBasedEstimator(s2, 0.1, 0.1)
+        edges_constraint = se2.estimate_structure()
+        
+        set_list_edges = set.union(edges_constraint,edges_score)
+
+        
+        n_added_fake_edges = len(set_list_edges.difference(true_edges))
+
+        n_missing_edges = len(true_edges.difference(set_list_edges))
+
+        n_true_positive = len(true_edges) - n_missing_edges
+
+        precision = n_true_positive / (n_true_positive + n_added_fake_edges)
+
+        recall = n_true_positive / (n_true_positive + n_missing_edges)
+
+        f1_measure = round(2* (precision*recall) / (precision+recall),3)
+
+        # print(f"n archi reali non trovati: {n_missing_edges}")
+        # print(f"n archi non reali aggiunti: {n_added_fake_edges}")
+        print(true_edges)
+        print(set_list_edges)
+        print(f"precision: {precision} ")
+        print(f"recall: {recall} ")
+        print(f"F1: {f1_measure} ")
+
+
+
+        self.assertEqual(set_list_edges, true_edges)
+        
+
+
+if __name__ == '__main__':
+    unittest.main()
+

main_package/tests/simulators/test_ensambled_model_all.py

@@ -0,0 +1,134 @@
+import sys
+sys.path.append("../../classes/")
+import glob
+import math
+import os
+import unittest
+
+import networkx as nx
+import numpy as np
+import psutil
+from line_profiler import LineProfiler
+import copy
+
+import utility.cache as ch
+import structure_graph.sample_path as sp
+import estimators.structure_score_based_estimator as se
+import estimators.structure_constraint_based_estimator as se_
+import utility.json_importer as ji
+
+
+
+class TestTabuSearch(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        pass
+
+
+
+    def test_constr(self):
+
+        list_vals= [3,4,5,6,10,15,20]
+        
+        list_card=[[3,"ternary"],[4,"quaternary"]]
+
+        list_dens = [["0.1","_01"],["0.2","_02"], ["0.3",""], ["0.4","_04"] ]
+        
+        for card in list_card:
+            for dens in list_dens:
+
+                list_vals= [3,4,5,6,10,15,20]
+                
+                if card[0]==4:
+                    list_vals.remove(20)
+
+                for var_n in list_vals:
+
+                    patience = 20
+
+                    var_number= var_n
+
+                    if var_number > 11:
+                        patience = 25
+
+                    if var_number > 16:
+                        patience = 35
+
+
+                    cardinality = card[0]
+                    cardinality_string = card[1]
+
+                    density= dens[0]
+                    density_string = dens[1]
+
+                    constraint = 2
+
+                    index = 0 
+                    num_networks=10
+
+                    if var_number > 9:
+                        num_networks=3
+
+                    while index < num_networks:
+                        #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
+                        self.importer = ji.JsonImporter(f"../../data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}.json", 
+                                                    'samples', 'dyn.str', 'variables', 'Time', 'Name', index )
+                        self.s1 = sp.SamplePath(self.importer)
+                        self.s1.build_trajectories()
+                        self.s1.build_structure()
+
+
+                        true_edges = copy.deepcopy(self.s1.structure.edges)
+                        true_edges = set(map(tuple, true_edges))
+                        
+                        s2= copy.deepcopy(self.s1)
+
+                        se1 = se.StructureScoreBasedEstimator(self.s1,1,1)
+                        edges_score = se1.estimate_structure(
+                                                max_parents = None,
+                                                iterations_number = 100,
+                                                patience = patience,
+                                                tabu_length = var_number,
+                                                tabu_rules_duration = var_number,
+                                                optimizer = 'tabu'
+                                            )
+
+                        se2 = se_.StructureConstraintBasedEstimator(s2, 0.1, 0.1)
+                        edges_constraint = se2.estimate_structure()
+                                        
+                        set_list_edges = set.union(edges_constraint,edges_score)
+
+            
+                        n_added_fake_edges = len(set_list_edges.difference(true_edges))
+
+                        n_missing_edges = len(true_edges.difference(set_list_edges))
+
+                        n_true_positive = len(true_edges) - n_missing_edges
+
+                        precision = n_true_positive / (n_true_positive + n_added_fake_edges)
+
+                        recall = n_true_positive / (n_true_positive + n_missing_edges)
+
+                        f1_measure = round(2* (precision*recall) / (precision+recall),3)
+
+                        # print(f"n archi reali non trovati: {n_missing_edges}")
+                        # print(f"n archi non reali aggiunti: {n_added_fake_edges}")
+                        print(true_edges)
+                        print(set_list_edges)
+                        print(f"precision: {precision} ")
+                        print(f"recall: {recall} ")
+
+
+                        with open("../results/results.csv", 'a+') as fi:
+                            fi.write(f"\n2,{constraint},{var_number},{density},{cardinality},{index},{f1_measure},{round(precision,3)},{round(recall,3)}")
+
+                        index += 1
+
+        self.assertEqual(set_list_edges, true_edges)
+                
+
+
+if __name__ == '__main__':
+    unittest.main()
+

main_package/tests/simulators/test_simulation_score.py

@@ -61,6 +61,7 @@ class TestTabuSearch(unittest.TestCase):
                 if var_number > 9:
                     num_networks=3
 
+
                 while index < num_networks:
                     #cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
                     self.importer = ji.JsonImporter(f"../../data/networks_and_trajectories_{cardinality_string}_data{density_string}_{var_number}.json", 
@@ -80,7 +81,7 @@ class TestTabuSearch(unittest.TestCase):
                                         patience = patience,
                                         tabu_length = var_number,
                                         tabu_rules_duration = var_number,
-                                        optimizer = 'tabu'
+                                        optimizer = 'hill'
                                         )