Fixed alpha_xx' bug

main_package/classes/estimators/fam_score_calculator.py

@@ -143,6 +143,7 @@ class FamScoreCalculator:
         Returns:
             the value of the marginal likelihood over q
         """
+
         return np.sum([self.variable_cim_xu_marginal_likelihood_q(cim, tau_xu, alpha_xu) for cim in cims])
 
     def variable_cim_xu_marginal_likelihood_q(self,
@@ -206,18 +207,27 @@ class FamScoreCalculator:
     def get_fam_score(self,
                 cims: np.array,
                 tau_xu: float=1,
-                alpha_xu: float=1,
-                alpha_xxu: float=1):
+                alpha_xu: float=1):
         """
         calculate the FamScore value of the node identified by the label node_id
         Parameters:
             cims: np.array with all the node's cims,
             tau_xu: hyperparameter over the CTBN’s q parameters
             alpha_xu: hyperparameter over the CTBN’s q parameters
-            alpha_xxu: hyperparameter over the CTBN’s theta parameters
         Returns:
             the FamScore value of the node
         """
+        #print("------")
+        #print(self.marginal_likelihood_q(cims,
+                                    # tau_xu,
+                                    # alpha_xu))
+
+        #print(self.marginal_likelihood_theta(cims, 
+                                        # alpha_xu,
+                                        # alpha_xxu))
+        'calculate alpha_xxu as a uniform distribution'                                
+        alpha_xxu = alpha_xu /(len(cims[0].state_residence_times) - 1)
+
         return self.marginal_likelihood_q(cims,
                                     tau_xu,
                                     alpha_xu) \

main_package/classes/estimators/structure_score_based_estimator.py

@@ -47,7 +47,7 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         super().__init__(sample_path)
 
 
-    
+    @timing
     def estimate_structure(self, max_parents:int = None, iterations_number:int= 40, patience:int = None ):
         """
         Compute the score-based algorithm to find the optimal structure
@@ -79,19 +79,22 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         'get the number of CPU'
         cpu_count = multiprocessing.cpu_count()
 
+        #cpu_count = 1
+
         'Estimate the best parents for each node'
         with multiprocessing.Pool(processes=cpu_count) as pool:
             list_edges_partial = pool.starmap(estimate_parents, zip(self.nodes,l_max_parents,l_iterations_number,l_patience))
             #list_edges_partial = [estimate_parents(n,max_parents,iterations_number,patience) for n in self.nodes]
             #list_edges_partial = p.map(estimate_parents, self.nodes)
+            #list_edges_partial= estimate_parents('Y',max_parents,iterations_number,patience) 
 
         'Concatenate all the edges list'
         set_list_edges =  set(itertools.chain.from_iterable(list_edges_partial))
 
-        print('-------------------------')
+        #print('-------------------------')
 
-        'TODO: Pensare a un modo migliore -- set difference sembra non funzionare '
 
+        'calculate precision and recall'
         n_missing_edges = 0
         n_added_fake_edges = 0
 
@@ -117,12 +120,14 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
         #         print(true_edge)
 
 
-        print(f"n archi reali non trovati: {n_missing_edges}")
-        print(f"n archi non reali aggiunti: {n_added_fake_edges}")
+        # 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} ")
+
+        return set_list_edges
     
 
     def estimate_parents(self,node_id:str, max_parents:int = None, iterations_number:int= 40, patience:int = 10 ):
@@ -163,9 +168,10 @@ class StructureScoreBasedEstimator(se.StructureEstimator):
                         graph.remove_edges([parent_removed])
                 graph.add_edges([current_edge])
                 added = True
-            
+            #print('**************************')
             current_score =  self.get_score_from_graph(graph,node_id)
-            
+
+
             if current_score > actual_best_score:
                 'update current best score' 
                 actual_best_score = current_score

main_package/tests/estimators/test_parameters_estimator.py

@@ -17,7 +17,7 @@ class TestParametersEstimatior(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         cls.read_files = glob.glob(os.path.join('../../data', "*.json"))
-        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_ternary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
@@ -45,6 +45,7 @@ class TestParametersEstimatior(unittest.TestCase):
             sofc1 = p1.compute_parameters_for_node(node)
             sampled_cims = self.aux_import_sampled_cims('dyn.cims')
             sc = list(sampled_cims.values())
+            print(sampled_cims.values())
             #print(sc[indx])
             self.equality_of_cims_of_node(sc[indx], sofc1.actual_cims)
 

main_package/tests/estimators/test_structure_score_based_estimator.py

@@ -9,6 +9,7 @@ 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
@@ -22,45 +23,29 @@ class TestStructureScoreBasedEstimator(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_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter("../../data/networks_and_trajectories_quaternary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name')
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
 
 
 
-    def test_esecuzione(self):
+    def test_structure(self):
+        true_edges = copy.deepcopy(self.s1.structure.edges)
+        true_edges = set(map(tuple, true_edges))
+
         se1 = se.StructureScoreBasedEstimator(self.s1)
-        se1.estimate_structure(
-                            max_parents = 6,
-                            iterations_number = 80,
+        edges = se1.estimate_structure(
+                            max_parents = None,
+                            iterations_number = 100,
                             patience = None 
                             )
+        
 
-if __name__ == '__main__':
-    unittest.main()
+        self.assertEqual(edges, true_edges)
+        
 
-    '''
 
-        def test_init(self):
-        exp_alfa = 0.1
-        chi_alfa = 0.1
-        se1 = se.StructureScoreBasedEstimator(self.s1)
-        self.assertEqual(self.s1, se1.sample_path)
-        self.assertTrue(np.array_equal(se1.nodes, np.array(self.s1.structure.nodes_labels)))
-        self.assertTrue(np.array_equal(se1.nodes_indxs, self.s1.structure.nodes_indexes))
-        self.assertTrue(np.array_equal(se1.nodes_vals, self.s1.structure.nodes_values))
-        self.assertIsInstance(se1.complete_graph, nx.DiGraph)
-        self.assertIsInstance(se1.cache, ch.Cache)
+if __name__ == '__main__':
+    unittest.main()
 
-    def test_build_complete_graph(self):
-        nodes_numb = len(self.s1.structure.nodes_labels)
-        se1 = se.StructureScoreBasedEstimator(self.s1)
-        cg = se1.build_complete_graph(self.s1.structure.nodes_labels)
-        self.assertEqual(len(cg.edges), nodes_numb*(nodes_numb - 1))
-        ''for node in self.s1.structure.nodes_labels:
-            no_self_loops = self.s1.structure.nodes_labels[:]
-            no_self_loops.remove(node)
-            for n2 in no_self_loops:
-                self.assertIn((node, n2), cg.edges)''
-    '''