Add remove_node method; refactor complete_test method

PyCTBN/PyCTBN/network_graph.py

@@ -78,6 +78,24 @@ class NetworkGraph:
         """
         self._graph.add_edges_from(list_of_edges)
 
+    def remove_node(self, node_id: str) -> None:
+        """Remove the node ``node_id`` from all the class members.
+        Initialize all the filtering/indexing structures.
+        """
+        self._graph.remove_node(node_id)
+        self._graph_struct.remove_node(node_id)
+        self.clear_indexing_filtering_structures()
+
+    def clear_indexing_filtering_structures(self) -> None:
+        """Initialize all the filtering/indexing structures.
+        """
+        self._aggregated_info_about_nodes_parents = None
+        self._time_scalar_indexing_structure = None
+        self._transition_scalar_indexing_structure = None
+        self._time_filtering = None
+        self._transition_filtering = None
+        self._p_combs_structure = None
+
     def get_ordered_by_indx_set_of_parents(self, node: str) -> typing.Tuple:
         """Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
         (parents_labels, parents_indexes, parents_cardinalities).
@@ -234,63 +252,3 @@ class NetworkGraph:
     @property
     def p_combs(self) -> np.ndarray:
         return self._p_combs_structure
-
-    """
-    ##############These Methods are actually unused but could become useful in the near future################
-
-    def init_graph(self):
-        self.add_nodes(self._nodes_labels)
-        self.add_edges(self._graph_struct.edges)
-        self._aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
-        self._fancy_indexing = self.build_fancy_indexing_structure(0)
-        self.build_scalar_indexing_structures()
-        self.build_time_columns_filtering_structure()
-        self.build_transition_columns_filtering_structure()
-        self._p_combs_structure = self.build_p_combs_structure()
-
-    def build_time_columns_filtering_structure(self):
-        nodes_indxs = self._nodes_indexes
-        self._time_filtering = [np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
-            for node_indx, p_indxs in zip(nodes_indxs, self._fancy_indexing)]
-
-    def build_transition_columns_filtering_structure(self):
-        nodes_number = self._graph_struct.total_variables_number
-        nodes_indxs = self._nodes_indexes
-        self._transition_filtering = [np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
-                                      for node_indx, p_indxs in zip(nodes_indxs,
-                                                                    self._fancy_indexing)]
-
-    def build_scalar_indexing_structures(self):
-        parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
-        build_transition_scalar_indexing_structure_for_a_node = \
-            self.build_transition_scalar_indexing_structure_for_a_node
-        build_time_scalar_indexing_structure_for_a_node = self.build_time_scalar_indexing_structure_for_a_node
-        aggr = [(build_transition_scalar_indexing_structure_for_a_node(node_id, p_vals),
-                 build_time_scalar_indexing_structure_for_a_node(node_id, p_vals))
-                                                      for node_id, p_vals in
-                                                      zip(self._nodes_labels,
-                                                          parents_values_for_all_nodes)]
-        self._transition_scalar_indexing_structure = [i[0] for i in aggr]
-        self._time_scalar_indexing_structure = [i[1] for i in aggr]
-
-    def build_p_combs_structure(self):
-        parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
-        p_combs_struct = [self.build_p_comb_structure_for_a_node(p_vals) for p_vals in parents_values_for_all_nodes]
-        return p_combs_struct
-
-    def get_ord_set_of_par_of_all_nodes(self):
-        get_ordered_by_indx_set_of_parents = self.get_ordered_by_indx_set_of_parents
-        result = [get_ordered_by_indx_set_of_parents(node) for node in self._nodes_labels]
-        return result
-
-    def get_ordered_by_indx_parents_values_for_all_nodes(self):
-        pars_values = [i[2] for i in self._aggregated_info_about_nodes_parents]
-        return pars_values
-
-    def build_fancy_indexing_structure(self, start_indx):
-        if start_indx > 0:
-            pass
-        else:
-            fancy_indx = [i[1] for i in self._aggregated_info_about_nodes_parents]
-            return fancy_indx
-    """

PyCTBN/PyCTBN/parameters_estimator.py

@@ -45,20 +45,20 @@ class ParametersEstimator:
         node_indx = self._net_graph.get_node_indx(node_id)
         state_res_times = self._single_set_of_cims._state_residence_times
         transition_matrices = self._single_set_of_cims._transition_matrices
-        self.compute_state_res_time_for_node(node_indx, self._trajectories.times,
+        ParametersEstimator.compute_state_res_time_for_node(node_indx, self._trajectories.times,
                                              self._trajectories.trajectory,
                                              self._net_graph.time_filtering,
                                              self._net_graph.time_scalar_indexing_strucure,
                                              state_res_times)
-        self.compute_state_transitions_for_a_node(node_indx,
+        ParametersEstimator.compute_state_transitions_for_a_node(node_indx, self._trajectories.complete_trajectory,
-                                                  self._trajectories.complete_trajectory,
+                                                                 self._net_graph.transition_filtering,
-                                                  self._net_graph.transition_filtering,
+                                                                 self._net_graph.transition_scalar_indexing_structure,
-                                                  self._net_graph.transition_scalar_indexing_structure,
+                                                                 transition_matrices)
-                                                  transition_matrices)
         self._single_set_of_cims.build_cims(state_res_times, transition_matrices)
         return self._single_set_of_cims
 
-    def compute_state_res_time_for_node(self, node_indx: int, times: np.ndarray, trajectory: np.ndarray,
+    @staticmethod
+    def compute_state_res_time_for_node(node_indx: int, times: np.ndarray, trajectory: np.ndarray,
                                         cols_filter: np.ndarray, scalar_indexes_struct: np.ndarray,
                                         T: np.ndarray) -> None:
         """Compute the state residence times for a node and fill the matrix ``T`` with the results
@@ -81,8 +81,9 @@ class ParametersEstimator:
                            times,
                            minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
 
-    def compute_state_transitions_for_a_node(self, node_indx: int, trajectory: np.ndarray, cols_filter: np.ndarray,
+    @staticmethod
-                                             scalar_indexing: np.ndarray, M: np.ndarray):
+    def compute_state_transitions_for_a_node(node_indx: int, trajectory: np.ndarray, cols_filter: np.ndarray,
+                                             scalar_indexing: np.ndarray, M: np.ndarray) -> None:
         """Compute the state residence times for a node and fill the matrices ``M`` with the results.
 
         :param node_indx: the index of the node
@@ -105,30 +106,6 @@ class ParametersEstimator:
         M_raveled[diag_indices] = 0
         M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
 
-    """
-    ##############These Methods are actually unused but could become useful in the near future################
-
-    def init_sets_cims_container(self):
-        self.sets_of_cims_struct = acims.SetsOfCimsContainer(self._net_graph.nodes,
-                                                             self._net_graph.nodes_values,
-                                                             self._net_graph.
-                                                             get_ordered_by_indx_parents_values_for_all_nodes(),
-                                                             self._net_graph.p_combs)
-
-    def compute_parameters(self):
-        for indx, aggr in enumerate(zip(self._net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
-            self.compute_state_res_time_for_node(self._net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
-                                                 self.sample_path.trajectories.trajectory,
-                                                 self._net_graph.time_filtering[indx],
-                                                 self._net_graph.time_scalar_indexing_strucure[indx],
-                                                 aggr[1]._state_residence_times)
-            self.compute_state_transitions_for_a_node(self._net_graph.get_node_indx(aggr[0]),
-                                                      self.sample_path.trajectories.complete_trajectory,
-                                                      self._net_graph.transition_filtering[indx],
-                                                      self._net_graph.transition_scalar_indexing_structure[indx],
-                                                      aggr[1]._transition_matrices)
-            aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices)
-    """
 
 
 

PyCTBN/PyCTBN/structure.py

@@ -13,7 +13,7 @@ class Structure:
     :type nodes_vals_arr: numpy.ndArray
     :param edges_list: the edges of the network
     :type edges_list: List
-    :param total_variables_number: the total number of variables in the net
+    :param total_variables_number: the total number of variables in the dataset
     :type total_variables_number: int
     """
 
@@ -27,6 +27,17 @@ class Structure:
         self._edges_list = edges_list
         self._total_variables_number = total_variables_number
 
+    def remove_node(self, node_id: str) -> None:
+        """Remove the node ``node_id`` from all the class members.
+        The class member ``_total_variables_number`` since it refers to the total number of variables in the dataset.
+        """
+        node_positional_indx = self._nodes_labels_list.index(node_id)
+        del self._nodes_labels_list[node_positional_indx]
+        self._nodes_indexes_arr = np.delete(self._nodes_indexes_arr, node_positional_indx)
+        self._nodes_vals_arr = np.delete(self._nodes_vals_arr, node_positional_indx)
+        self._edges_list = [(from_node, to_node) for (from_node, to_node) in self._edges_list if (from_node != node_id
+                                                                                        and to_node != node_id)]
+
     @property
     def edges(self) -> ty.List:
         return self._edges_list

PyCTBN/PyCTBN/structure_estimator.py

@@ -85,25 +85,10 @@ class StructureEstimator:
         parents = np.append(parents, test_parent)
         sorted_parents = self._nodes[np.isin(self._nodes, parents)]
         cims_filter = sorted_parents != test_parent
-        sofc1 = self._cache.find(set(p_set))
 
-        if not sofc1:
-            bool_mask1 = np.isin(self._nodes, complete_info)
-            l1 = list(self._nodes[bool_mask1])
-            indxs1 = self._nodes_indxs[bool_mask1]
-            vals1 = self._nodes_vals[bool_mask1]
-            eds1 = list(itertools.product(parent_set,test_child))
-            s1 = Structure(l1, indxs1, vals1, eds1, tot_vars_count)
-            g1 = NetworkGraph(s1)
-            g1.fast_init(test_child)
-            p1 = ParametersEstimator(self._sample_path.trajectories, g1)
-            p1.fast_init(test_child)
-            sofc1 = p1.compute_parameters_for_node(test_child)
-            self._cache.put(set(p_set), sofc1)
-        sofc2 = None
         p_set.insert(0, test_parent)
-        if p_set:
+        sofc2 = self._cache.find(set(p_set))
-            sofc2 = self._cache.find(set(p_set))
+
         if not sofc2:
             complete_info.append(test_parent)
             bool_mask2 = np.isin(self._nodes, complete_info)
@@ -118,6 +103,17 @@ class StructureEstimator:
             p2.fast_init(test_child)
             sofc2 = p2.compute_parameters_for_node(test_child)
             self._cache.put(set(p_set), sofc2)
+
+        del p_set[0]
+        sofc1 = self._cache.find(set(p_set))
+        if not sofc1:
+            g2.remove_node(test_parent)
+            g2.fast_init(test_child)
+            p2 = ParametersEstimator(self._sample_path.trajectories, g2)
+            p2.fast_init(test_child)
+            sofc1 = p2.compute_parameters_for_node(test_child)
+            self._cache.put(set(p_set), sofc1)
+
         for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
             cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
             for cim2 in cond_cims:

PyCTBN/tests/performance_comparisons.py

@@ -59,11 +59,16 @@ class PerformanceComparisons(unittest.TestCase):
         self.original_algo.prepare_trajectories(self.original_algo.df_samples_list, self.original_algo.variables)
 
     def save_datas(self, original_list, opt_list):
+        if not os.path.exists('results'):
+            os.makedirs('results')
         df_results = pd.DataFrame({'orginal_execution_time': original_list, 'optimized_execution_time': opt_list})
         name = self.importer.file_path.rsplit('/', 1)[-1]
         name = name.split('.', 1)[0]
         name = 'execution_times_' + name + '.csv'
-        df_results.to_csv(name, index=False)
+        path = os.path.abspath('./results/')
+        print(path)
+        file_dest = path + '/' + name
+        df_results.to_csv(file_dest, index=False)
 
 
 if __name__ == '__main__':