Cleaned smells

PyCTBN/PyCTBN/estimators/fam_score_calculator.py

@@ -252,14 +252,7 @@ class FamScoreCalculator:
         :return: the FamScore value of the node
         :rtype: float
         """
-        #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)
 

PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py

@@ -141,8 +141,7 @@ class StructureConstraintBasedEstimator(StructureEstimator):
         r2s = M2.diagonal()
         C1 = cim1.cim
         C2 = cim2.cim
-        if child_states_numb > 2:
+        if child_states_numb > 2 and (np.sum(np.diagonal(M1)) / thumb_value) < self._thumb_threshold:
-            if (np.sum(np.diagonal(M1)) / thumb_value) < self._thumb_threshold:
                 self._removable_edges_matrix[parent_indx][child_indx] = False
                 return False
         F_stats = C2.diagonal() / C1.diagonal()
@@ -225,7 +224,6 @@ class StructureConstraintBasedEstimator(StructureEstimator):
                 list_edges_partial = executor.map(ctpc_algo,
                                                                  self._nodes,
                                                                  total_vars_numb_array)
-            #list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes]
             
         'Update the graph'
         edges = set(itertools.chain.from_iterable(list_edges_partial))

PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py

@@ -121,15 +121,9 @@ class StructureScoreBasedEstimator(StructureEstimator):
                                                             l_optimizer)
 
 
-
-            #list_edges_partial = p.map(estimate_parents, self._nodes)
-            #list_edges_partial= estimate_parents('Q',max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) 
-
         'Concatenate all the edges list'
         set_list_edges =  set(itertools.chain.from_iterable(list_edges_partial))
 
-        #print('-------------------------')
-
 
         'calculate precision and recall'
         n_missing_edges = 0
@@ -145,10 +139,7 @@ class StructureScoreBasedEstimator(StructureEstimator):
             precision = n_true_positive / (n_true_positive + n_added_fake_edges)
 
             recall = n_true_positive / (n_true_positive + n_missing_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} ")

PyCTBN/PyCTBN/optimizers/constraint_based_optimizer.py

@@ -60,9 +60,6 @@ class ConstraintBasedOptimizer(Optimizer):
 
         
         u = other_nodes
-        #tests_parents_numb = len(u)
-        #complete_frame = self.complete_graph_frame
-        #test_frame = complete_frame.loc[complete_frame['To'].isin([self.node_id])]
         child_states_numb = self.structure_estimator._sample_path.structure.get_states_number(self.node_id)
         b = 0
         while b < len(u):

PyCTBN/PyCTBN/optimizers/optimizer.py

@@ -36,4 +36,4 @@ class Optimizer(abc.ABC):
         :return: the estimated structure for the node
         :rtype: List
         """
-    pass
+        pass

PyCTBN/PyCTBN/optimizers/tabu_search.py

@@ -140,20 +140,9 @@ class TabuSearch(Optimizer):
                         graph.remove_edges([parent_removed])
                 graph.add_edges([current_edge])
                 added = True
-            #print('**************************')
+           
             current_score =  self.structure_estimator.get_score_from_graph(graph,self.node_id)
 
-
-            # print("-------------------------------------------")
-            # print(f"Current new parent: {current_new_parent}")
-            # print(f"Current score: {current_score}")
-            # print(f"Current best score: {actual_best_score}")
-            # print(f"tabu list : {str(tabu_set)} length: {len(tabu_set)}")
-            # print(f"tabu queue : {str(tabu_queue)} length: {tabu_queue.qsize()}")
-            # print(f"graph edges: {graph.edges}")
-
-            # print("-------------------------------------------")
-            # input()
             if current_score > actual_best_score:
                 'update current best score' 
                 actual_best_score = current_score

PyCTBN/PyCTBN/structure_graph/sample_path.py

@@ -8,6 +8,7 @@ from .trajectory import Trajectory
 from ..utility.abstract_importer import AbstractImporter
 
 
+MESSAGE_HAS_TO_CONTAIN_EXCEPTION = 'The importer object has to contain the all processed data!'
 
 class SamplePath(object):
     """Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
@@ -25,15 +26,15 @@ class SamplePath(object):
         """
         self._importer = importer
         if self._importer._df_variables is None or self._importer._concatenated_samples is None:
-            raise RuntimeError('The importer object has to contain the all processed data!')
+            raise RuntimeError()
         if self._importer._df_variables.empty:
-            raise RuntimeError('The importer object has to contain the all processed data!')
+            raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
-        if isinstance(self._importer._concatenated_samples, pd.DataFrame):
+        if isinstance(self._importer._concatenated_samples, pd.DataFrame) and\
-            if self._importer._concatenated_samples.empty:
+            self._importer._concatenated_samples.empty:
-                raise RuntimeError('The importer object has to contain the all processed data!')
+                raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
-        if isinstance(self._importer._concatenated_samples, np.ndarray):
+        if isinstance(self._importer._concatenated_samples, np.ndarray) and\
-            if self._importer._concatenated_samples.size == 0:
+            self._importer._concatenated_samples.size == 0:
-                raise RuntimeError('The importer object has to contain the all processed data!')
+                raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
         self._trajectories = None
         self._structure = None
         self._total_variables_count = None

PyCTBN/PyCTBN/utility/abstract_importer.py

@@ -7,7 +7,6 @@ import pandas as pd
 
 import copy
 
-#from sklearn.utils import resample
 
 
 class AbstractImporter(ABC):

PyCTBN/PyCTBN/utility/cache.py

@@ -30,10 +30,7 @@ class Cache:
         :rtype: SetOfCims
         """
         try:
-            #print("Cache State:", self.list_of_sets_of_indxs)
-            #print("Look For:", parents_comb)
             result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)]
-            #print("CACHE HIT!!!!", parents_comb)
             return result
         except ValueError:
             return None
@@ -47,7 +44,6 @@ class Cache:
         :param socim: the related SetOfCims object
         :type socim: SetOfCims
         """
-        #print("Putting in cache:", parents_comb)
         self._list_of_sets_of_parents.append(parents_comb)
         self._actual_cache.append(socim)