Add other tests; optimized NetGraph Initialization

main_package/classes/cache.py

@@ -3,26 +3,60 @@ import set_of_cims as sofc
 
 
 class Cache:
+    """
+    This class has the role of a cache for SetOfCIMS of a test node that have been already computed during the ctpc algorithm.
+
+    :list_of_sets_of_parents: a list of Sets of the parents to which the cim in cache at SAME index is related
+    :actual_cache: a list of setOfCims objects
+    """
 
     def __init__(self):
-        self.list_of_sets_of_indxs = []
+        self.list_of_sets_of_parents = []
         self.actual_cache = []
 
     def find(self, parents_comb: typing.Union[typing.Set, str]):
-            try:
-                #print("Cache State:", self.list_of_sets_of_indxs)
-                #print("Look For:", parents_comb)
-                result = self.actual_cache[self.list_of_sets_of_indxs.index(parents_comb)]
-                print("CACHE HIT!!!!", parents_comb)
-                return result
-            except ValueError:
-                return None
+        """
+        Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims related to that parents_comb.
+        N.B. if the parents_comb is not a Set, than the index refers to the SetOfCims of the actual node with no parents.
+        Parameters:
+            parents_comb: the parents related to that SetOfCims
+        Returns:
+            A SetOfCims object if the parents_comb index is found in list_of_sets_of_parents.
+            None otherwise.
+
+        """
+        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
 
     def put(self, parents_comb: typing.Union[typing.Set, str], socim: sofc.SetOfCims):
+        """
+        Place in cache the SetOfCims object, and the related sybolyc index parents_comb in list_of_sets_of_parents
+
+        Parameters:
+            parents_comb: the symbolic set index
+            socim: the related SetOfCims object
+
+        Returns:
+            void
+        """
         #print("Putting in cache:", parents_comb)
-        self.list_of_sets_of_indxs.append(parents_comb)
+        self.list_of_sets_of_parents.append(parents_comb)
         self.actual_cache.append(socim)
 
     def clear(self):
-        del self.list_of_sets_of_indxs[:]
+        """
+        Clear the contents of both caches.
+
+        Parameters:
+            void
+        Returns:
+            void
+        """
+        del self.list_of_sets_of_parents[:]
         del self.actual_cache[:]

main_package/classes/conditional_intensity_matrix.py

@@ -2,13 +2,28 @@ import numpy as np
 
 
 class ConditionalIntensityMatrix:
-    def __init__(self, state_residence_times, state_transition_matrix):
+    """
+    Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector
+    and state transition matrix and the actual CIM matrix.
+
+    :_state_residence_times: state residence times vector
+    :_state_transition_matrix: the transitions count matrix
+    :_cim: the actual cim of the node
+    """
+    def __init__(self, state_residence_times: np.array, state_transition_matrix: np.array):
         self._state_residence_times = state_residence_times
         self._state_transition_matrix = state_transition_matrix
-        #self.cim = np.zeros(shape=(dimension, dimension), dtype=float)
         self._cim = self.state_transition_matrix.astype(np.float64)
 
     def compute_cim_coefficients(self):
+        """
+        Compute the coefficients of the matrix _cim by using the following equality q_xx' = M[x, x'] / T[x]
+
+        Parameters:
+            void
+        Returns:
+            void
+        """
         np.fill_diagonal(self._cim, self._cim.diagonal() * -1)
         self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
 

main_package/classes/json_importer.py

@@ -237,7 +237,7 @@ class JsonImporter(AbstractImporter):
         for indx in range(len(self.df_samples_list)):  # Le singole traj non servono più #TODO usare list comprens
             self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
 
-    def import_sampled_cims(self, raw_data: pd.DataFrame, indx: int, cims_key: str) -> typing.Dict:
+    def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
         cims_for_all_vars = {}
         for var in raw_data[indx][cims_key]:
             sampled_cims_list = []

main_package/classes/network_graph.py

@@ -1,17 +1,27 @@
 
 import networkx as nx
 import numpy as np
+import typing
 
 
 class NetworkGraph:
     """
-    Rappresenta il grafo che contiene i nodi e gli archi presenti nell'oggetto Structure graph_struct.
-    Ogni nodo contine la label node_id, al nodo è anche associato un id numerico progressivo indx che rappresenta la posizione
-    dei sui valori nella colonna indx della traj
-
-    :graph_struct: l'oggetto Structure da cui estrarre i dati per costruire il grafo graph
-    :graph: il grafo
-
+    Abstracts the infos contained in the Structure class in the form of a directed graph.
+    Has the task of creating all the necessary filtering structures for parameters estimation
+
+    :graph_struct: the Structure object from which infos about the net will be extracted
+    :graph: directed graph
+    :nodes_labels: the symbolic names of the variables
+    :nodes_indexes: the indexes of the nodes
+    :nodes_values: the cardinalites of the nodes
+    :aggregated_info_about_nodes_parents: a structure that contains all the necessary infos about every parents of every
+    node in the net
+    :_fancy_indexing: the indexes of every parent of every node in the net
+    :_time_scalar_indexing_structure: the indexing structure for state res time estimation
+    :_transition_scalar_indexing_structure: the indexing structure for transition computation
+    :_time_filtering: the columns filtering structure used in the computation of the state res times
+    :_transition_filtering: the columns filtering structure used in the computation of the transition from one state to another
+    :self._p_combs_structure: all the possibile parents states combination for every node in the net
     """
 
     def __init__(self, graph_struct):
@@ -37,71 +47,88 @@ class NetworkGraph:
         self.build_time_columns_filtering_structure()
         self.build_transition_columns_filtering_structure()
         self._p_combs_structure = self.build_p_combs_structure()
-    #ATTENZIONE LIST_OF_NODES DEVE ESSERE COERENTE CON L?ORDINAMENTO DEL DS
-    def add_nodes(self, list_of_nodes):
-        #self.graph.add_nodes_from(list_of_nodes)
+
+    def fast_init(self, node_id: str):
+        """
+        Initializes all the necessary structures for parameters estimation of the node identified by the label node_id
+        Parameters:
+            node_id: the label of the node
+        Returns:
+            void
+        """
+        self.add_nodes(self._nodes_labels)
+        self.add_edges(self.graph_struct.edges)
+        self.aggregated_info_about_nodes_parents = self.get_ordered_by_indx_set_of_parents(node_id)
+        self._fancy_indexing = self.aggregated_info_about_nodes_parents[1]
+        p_indxs = self._fancy_indexing
+        p_vals = self.aggregated_info_about_nodes_parents[2]
+        self._time_scalar_indexing_structure = self.build_time_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                    p_vals)
+        self._transition_scalar_indexing_structure = self.build_transition_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                                p_vals)
+        node_indx = self.get_node_indx(node_id)
+        self._time_filtering = self.build_time_columns_filtering_for_a_node(node_indx, p_indxs)
+        self._transition_filtering = self.build_transition_filtering_for_a_node(node_indx, p_indxs)
+        self._p_combs_structure = self.build_p_comb_structure_for_a_node(p_vals)
+
+    def add_nodes(self, list_of_nodes: typing.List):
+        """
+        Adds the nodes to the graph contained in the list of nodes list_of_nodes.
+        Sets all the properties that identify a nodes (index, positional index, cardinality)
+
+        Parameters:
+            list_of_nodes: the nodes to add to graph
+        Returns:
+            void
+        """
         nodes_indxs = self._nodes_indexes
         nodes_vals = self.graph_struct.nodes_values
         pos = 0
-        #print("LIST OF NODES", list_of_nodes)
         for id, node_indx, node_val in zip(list_of_nodes, nodes_indxs, nodes_vals):
             self.graph.add_node(id, indx=node_indx, val=node_val, pos_indx=pos)
             pos += 1
-            #set_node_attr(self.graph, {id:node_indx}, 'indx')
 
-    def add_edges(self, list_of_edges):
+    def add_edges(self, list_of_edges: typing.List):
+        """
+        Add the edges to the graph contained in the list list_of_edges.
+
+        Parameters:
+            list_of_edges
+        Returns:
+            void
+        """
         self.graph.add_edges_from(list_of_edges)
 
-    def get_ordered_by_indx_set_of_parents(self, node):
+    def get_ordered_by_indx_set_of_parents(self, node: str):
+        """
+        Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
+        (parents_labels, parents_indexes, parents_cardinalities).
+        N.B. The parent set is sorted using the list of sorted nodes nodes
+
+        Parameters:
+            node: the label of the node
+        Returns:
+            a tuple containing all the parent set infos
+
+        """
         parents = self.get_parents_by_id(node)
-        #print("PARENTS", parents)
-        nodes = self.get_nodes()
-        #print("NODES", nodes)
+        nodes = self._nodes_labels
         d = {v: i for i, v in enumerate(nodes)}
         sorted_parents = sorted(parents, key=lambda v: d[v])
-        #sorted_parents = [x for _, x in sorted(zip(nodes, parents))]
-        #print("SORTED PARENTS IN GRAPH",sorted_parents)
-        #p_indxes= []
-        #p_values = []
         get_node_indx = self.get_node_indx
         p_indxes = [get_node_indx(node) for node in sorted_parents]
-        #p_indxes.sort()
         p_values = [self.get_states_number(node) for node in sorted_parents]
-        #print("P INDXS", p_indxes)
-        #print("P VALS", p_values)
         return (sorted_parents, p_indxes, p_values)
 
     def get_ord_set_of_par_of_all_nodes(self):
-        #result = []
-        #for node in self._nodes_labels:
-            #result.append(self.get_ordered_by_indx_set_of_parents(node))
         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(self, node):
-        parents_values = []
-        parents = self.get_ordered_by_indx_set_of_parents(node)
-        for n in parents:
-            parents_values.append(self.graph_struct.get_states_number(n))
-        return parents_values"""
-
     def get_ordered_by_indx_parents_values_for_all_nodes(self):
-        """result = []
-        for node in self._nodes_labels:
-            result.append(self.get_ordered_by_indx_parents_values(node))
-        return result"""
         pars_values = [i[2] for i in self.aggregated_info_about_nodes_parents]
         return pars_values
 
-    def get_states_number_of_all_nodes_sorted(self):
-        #states_number_list = []
-        #for node in self._nodes_labels:
-            #states_number_list.append(self.get_states_number(node))
-        #get_states_number = self.get_states_number
-        #states_number_list = [get_states_number(node) for node in self._nodes_labels]
-        return self._nodes_values
-
     def build_fancy_indexing_structure(self, start_indx):
         if start_indx > 0:
             pass
@@ -109,42 +136,95 @@ class NetworkGraph:
             fancy_indx = [i[1] for i in self.aggregated_info_about_nodes_parents]
             return fancy_indx
 
+    def build_time_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List):
+        """
+        Builds an indexing structure for the computation of state residence times values.
+
+        Parameters:
+            node_id: the node label
+            parents_vals: the caridinalites of the node's parents
+        Returns:
+            a numpy array.
 
-    def build_time_scalar_indexing_structure_for_a_node(self, node_id, parents_indxs):
+        """
         T_vector = np.array([self.get_states_number(node_id)])
-        T_vector = np.append(T_vector, parents_indxs)
+        T_vector = np.append(T_vector, parents_vals)
         T_vector = T_vector.cumprod().astype(np.int)
-        # print(T_vector)
         return T_vector
 
 
-    def build_transition_scalar_indexing_structure_for_a_node(self, node_id, parents_indxs):
-        #M_vector = np.array([self.graph_struct.variables_frame.iloc[node_id, 1],
-                             #self.graph_struct.variables_frame.iloc[node_id, 1].astype(np.int)])
+    def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List):
+        """
+        Builds an indexing structure for the computation of state transitions values.
+
+        Parameters:
+            node_id: the node label
+            parents_vals: the caridinalites of the node's parents
+        Returns:
+            a numpy array.
+
+        """
         node_states_number = self.get_states_number(node_id)
-        #get_states_number_by_indx = self.graph_struct.get_states_number_by_indx
         M_vector = np.array([node_states_number,
                              node_states_number])
-        #M_vector = np.append(M_vector, [get_states_number_by_indx(x) for x in parents_indxs])
-        M_vector = np.append(M_vector, parents_indxs)
+        M_vector = np.append(M_vector, parents_vals)
         M_vector = M_vector.cumprod().astype(np.int)
         return M_vector
 
+    def build_time_columns_filtering_for_a_node(self, node_indx: int, p_indxs: typing.List):
+        """
+        Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset.
+        This structute will be used in the computation of the state res times.
+        Parameters:
+            node_indx: the index of the node
+            p_indxs: the indexes of the node's parents
+        Returns:
+            a numpy array
+        """
+        return np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
+
+    def build_transition_filtering_for_a_node(self, node_indx, p_indxs):
+        """
+        Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset.
+        This structute will be used in the computation of the state transitions values.
+        Parameters:
+            node_indx: the index of the node
+            p_indxs: the indexes of the node's parents
+        Returns:
+            a numpy array
+        """
+        nodes_number = self.graph_struct.total_variables_number
+        return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
+
+    def build_p_comb_structure_for_a_node(self, parents_values: typing.List):
+        """
+        Builds the combinatory structure that contains the combinations of all the values contained in parents_values.
+
+        Parameters:
+            parents_values: the cardinalities of the nodes
+        Returns:
+            a numpy matrix containinga grid of the combinations
+        """
+        tmp = []
+        for val in parents_values:
+            tmp.append([x for x in range(val)])
+        if len(parents_values) > 0:
+            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
+            if len(parents_values) > 1:
+                tmp_comb = parents_comb[:, 1].copy()
+                parents_comb[:, 1] = parents_comb[:, 0].copy()
+                parents_comb[:, 0] = tmp_comb
+        else:
+            parents_comb = np.array([[]], dtype=np.int)
+        return parents_comb
+
     def build_time_columns_filtering_structure(self):
-        #parents_indexes_list = self._fancy_indexing
-        """for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), self._fancy_indexing):
-                self._time_filtering.append(np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int))"""
         nodes_indxs = self._nodes_indexes
-        #print("FINDXING", self._fancy_indexing)
-        #print("Nodes Indxs", nodes_indxs)
         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):
-        #parents_indexes_list = self._fancy_indexing
         nodes_number = self.graph_struct.total_variables_number
-        """for node_indx, p_indxs in zip(self.graph_struct.list_of_nodes_indexes(), self._fancy_indexing):
-            self._transition_filtering.append(np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int))"""
         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,
@@ -154,9 +234,9 @@ class NetworkGraph:
         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_indx, p_indxs),
-                 build_time_scalar_indexing_structure_for_a_node(node_indx, p_indxs))
-                                                      for node_indx, p_indxs in
+        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]
@@ -167,52 +247,40 @@ class NetworkGraph:
         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 build_p_comb_structure_for_a_node(self, parents_values):
-        tmp = []
-        for val in parents_values:
-            tmp.append([x for x in range(val)])
-        #print("TIMP", tmp)
-        if len(parents_values) > 0:
-            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
-            #print("PArents COmb", parents_comb)
-            if len(parents_values) > 1:
-                tmp_comb = parents_comb[:, 1].copy()
-                #print(tmp_comb)
-                parents_comb[:, 1] = parents_comb[:, 0].copy()
-                parents_comb[:, 0] = tmp_comb
-        else:
-            parents_comb = np.array([[]], dtype=np.int)
-        return parents_comb
-
-    def get_nodes(self):
-        return list(self.graph.nodes)
-
-    def get_edges(self):
-        return list(self.graph.edges)
-
-    def get_nodes_sorted_by_indx(self):
-        return self.graph_struct.list_of_nodes_labels()
-
     def get_parents_by_id(self, node_id):
         return list(self.graph.predecessors(node_id))
 
     def get_states_number(self, node_id):
-        #return self.graph_struct.get_states_number(node_id)
         return self.graph.nodes[node_id]['val']
-
+    """
     def get_states_number_by_indx(self, node_indx):
         return self.graph_struct.get_states_number_by_indx(node_indx)
-
+    
     def get_node_by_index(self, node_indx):
-        return self.graph_struct.get_node_id(node_indx)
+        return self.graph_struct.get_node_id(node_indx)"""
 
     def get_node_indx(self, node_id):
         return nx.get_node_attributes(self.graph, 'indx')[node_id]
-        #return self.graph_struct.get_node_indx(node_id)
 
     def get_positional_node_indx(self, node_id):
         return self.graph.nodes[node_id]['pos_indx']
 
+    @property
+    def nodes(self):
+        return self._nodes_labels
+
+    @property
+    def edges(self):
+        return list(self.graph.edges)
+
+    @property
+    def nodes_indexes(self):
+        return self._nodes_indexes
+
+    @property
+    def nodes_values(self):
+        return self._nodes_values
+
     @property
     def time_scalar_indexing_strucure(self):
         return self._time_scalar_indexing_structure

main_package/classes/parameters_estimator.py

@@ -1,33 +1,52 @@
-import os
 
-from line_profiler import LineProfiler
-
-from numba.experimental import jitclass
 import numpy as np
-import network_graph as ng
-import sample_path as sp
 import sets_of_cims_container as acims
+import set_of_cims as sofc
+import sample_path as sp
+import network_graph as ng
 
 
 class ParametersEstimator:
+    """
+    Has the task of computing the cims of particular node given the trajectories in samplepath and the net structure
+    in the graph net_graph
 
-    def __init__(self, sample_path, net_graph):
+    :sample_path: the container of the trajectories
+    :net_graph: the net structure
+    :single_srt_of_cims: the set of cims object that will hold the cims of the node
+    """
+
+    def __init__(self, sample_path: sp.SamplePath, net_graph: ng.NetworkGraph):
         self.sample_path = sample_path
         self.net_graph = net_graph
         self.sets_of_cims_struct = None
+        self.single_set_of_cims = None
 
     def init_sets_cims_container(self):
-        self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.get_nodes(),
-                                                             self.net_graph.get_states_number_of_all_nodes_sorted(),
+        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 fast_init(self, node_id: str):
+        """
+        Initializes all the necessary structures for the parameters estimation.
+
+        Parameters:
+            node_id: the node label
+        Returns:
+            void
+        """
+        p_vals = self.net_graph.aggregated_info_about_nodes_parents[2]
+        node_states_number = self.net_graph.get_states_number(node_id)
+        self.single_set_of_cims = sofc.SetOfCims(node_id, p_vals, node_states_number, self.net_graph.p_combs)
+
 
     def compute_parameters(self):
         #print(self.net_graph.get_nodes())
         #print(self.amalgamated_cims_struct.sets_of_cims)
         #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims))
-        for indx, aggr in enumerate(zip(self.net_graph.get_nodes(), self.sets_of_cims_struct.sets_of_cims)):
+        for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
             #print(self.net_graph.time_filtering[indx])
             #print(self.net_graph.time_scalar_indexing_strucure[indx])
             self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
@@ -43,7 +62,7 @@ class ParametersEstimator:
                                                       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)
-#TODO togliere tutti sti self.
+    """
     def compute_parameters_for_node(self, node_id):
         pos_index = self.net_graph.get_positional_node_indx(node_id)
         node_indx = self.net_graph.get_node_indx(node_id)
@@ -64,57 +83,81 @@ class ParametersEstimator:
                                                   transition_matrices)
         self.sets_of_cims_struct.sets_of_cims[pos_index].build_cims(
             state_res_times,
-            transition_matrices)
-
-    def compute_state_res_time_for_node(self, node_indx, times, trajectory, cols_filter, scalar_indexes_struct, T):
-        #print(times.size)
-        #print(trajectory)
-        #print(cols_filter)
-        #print(scalar_indexes_struct)
-        #print(T)
+            transition_matrices) #TODO potrebbe restituire direttamente l'oggetto setof cims
+    """
+    def compute_parameters_for_node(self, node_id: str):
+        """
+        Compute the CIMS of the node identified by the label node_id
+
+        Parameters:
+            node_id: the node label
+        Returns:
+            A setOfCims object filled with the computed CIMS
+        """
+        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
+        trajectory = self.sample_path.trajectories.trajectory
+        self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories.times,
+                                             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,
+                                                  self.sample_path.trajectories.complete_trajectory,
+                                                  self.net_graph.transition_filtering,
+                                                  self.net_graph.transition_scalar_indexing_structure,
+                                                  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,
+                                        cols_filter: np.ndarray, scalar_indexes_struct: np.ndarray, T: np.ndarray):
+        """
+        Compute the state residence times for a node and fill the matrix T with the results
+
+        Parameters:
+            node_indx: the index of the node
+            times: the times deltas vector
+            trajectory: the trajectory
+            cols_filter: the columns filtering structure
+            scalar_indexes_struct: the indexing structure
+            T: the state residence times vectors
+        Returns:
+            void
+        """
         T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
                            .astype(np.int), \
                            times,
                            minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
-        #print("Done This NODE", T)
-
-    def compute_state_residence_time_for_all_nodes(self):
-        for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
-            self.compute_state_res_time_for_node(node_indx, self.sample_path.trajectories[0].get_times(),
-                self.sample_path.trajectories[0].get_trajectory(), self.columns_filtering_structure[node_indx],
-                    self.scalar_indexes_converter[node_indx], set_of_cims.state_residence_times)
-
 
     def compute_state_transitions_for_a_node(self, node_indx, trajectory, cols_filter, scalar_indexing, M):
-        #print(node_indx)
-        #print(trajectory)
-        #print(cols_filter)
-        #print(scalar_indexing)
-        #print(M)
+        """
+        Compute the state residence times for a node and fill the matrices M with the results
+
+        Parameters:
+            node_indx: the index of the node
+            times: the times deltas vector
+            trajectory: the trajectory
+            cols_filter: the columns filtering structure
+            scalar_indexes: the indexing structure
+            M: the state transition matrices
+        Returns:
+            void
+        """
         diag_indices = np.array([x * M.shape[1] + x % M.shape[1] for x in range(M.shape[0] * M.shape[1])],
                                 dtype=np.int64)
         trj_tmp = trajectory[trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0]
-        #print(trj_tmp)
-        #print("Summing", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int))
-        #print(M.shape[1])
-        #print(M.shape[2])
-
         M[:] = np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int),
                            minlength=scalar_indexing[-1]).reshape(-1, M.shape[1], M.shape[2])
-        #print(M)
         M_raveled = M.ravel()
         M_raveled[diag_indices] = 0
-        #print(M_raveled)
         M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
-        #print(M_raveled)
 
-        #print(M)
 
-    def compute_state_transitions_for_all_nodes(self):
-        for node_indx, set_of_cims in enumerate(self.amalgamated_cims_struct.sets_of_cims):
-            self.compute_state_transitions_for_a_node(node_indx, self.sample_path.trajectories[0].get_complete_trajectory(),
-                 self.transition_filtering[node_indx],
-                    self.transition_scalar_index_converter[node_indx], set_of_cims.transition_matrices)
+
+
+
 
 
 

main_package/classes/set_of_cims.py

@@ -1,18 +1,23 @@
 import numpy as np
+import typing
 import conditional_intensity_matrix as cim
 
 
 class SetOfCims:
     """
-    Rappresenta la struttura che aggrega tutte le CIM per la variabile di label node_id
+    Aggregates all the CIMS of the node identified by the label node_id.
 
-    :node_id: la label della varibile a cui fanno riferimento le CIM
-    :ordered_parent_set: il set dei parent della variabile node_id ordinata secondo la property indx
-    :value: il numero massimo di stati assumibili dalla variabile
-    :actual_cims: le CIM della varibile
+    :node_id: the node label
+    :parents_states_number: the cardinalities of the parents
+    :node_states_number: the caridinality of the node
+    :p_combs: the relative p_comb structure
+
+    :state_residence_time: matrix containing all the state residence time vectors for the node
+    :transition_matrices: matrix containing all the transition matrices for the node
+    :actaul_cims: the cims of the node
     """
 
-    def __init__(self, node_id, parents_states_number, node_states_number, p_combs):
+    def __init__(self, node_id: str, parents_states_number: typing.List, node_states_number: int, p_combs: np.ndarray):
         self.node_id = node_id
         self.parents_states_number = parents_states_number
         self.node_states_number = node_states_number
@@ -20,9 +25,17 @@ class SetOfCims:
         self.state_residence_times = None
         self.transition_matrices = None
         self.p_combs = p_combs
-        self.build_actual_cims_structure()
+        self.build_times_and_transitions_structures()
+
+    def build_times_and_transitions_structures(self):
+        """
+        Initializes at the correct dimensions the state residence times matrix and the state transition matrices
 
-    def build_actual_cims_structure(self):
+        Parameters:
+            void
+        Returns:
+            void
+        """
         if not self.parents_states_number:
             self.state_residence_times = np.zeros((1, self.node_states_number), dtype=np.float)
             self.transition_matrices = np.zeros((1,self.node_states_number, self.node_states_number), dtype=np.int)
@@ -32,49 +45,41 @@ class SetOfCims:
             self.transition_matrices = np.zeros([np.prod(self.parents_states_number), self.node_states_number,
                                                  self.node_states_number], dtype=np.int)
 
-
-    def get_cims_number(self):
-        return len(self.actual_cims)
-
-    def indexes_converter(self, indexes): # Si aspetta array del tipo [2,2] dove
-        assert len(indexes) == len(self.parents_states_number)
-        vector_index = 0
-        if not indexes:
-            return vector_index
-        else:
-            for indx, value in enumerate(indexes):
-                vector_index = vector_index*self.parents_states_number[indx] + indexes[indx]
-            return vector_index
-
-    def build_cims(self, state_res_times, transition_matrices):
+    def build_cims(self, state_res_times: typing.List, transition_matrices: typing.List):
         for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
-            #print(state_res_time_vector, transition_matrix)
             cim_to_add = cim.ConditionalIntensityMatrix(state_res_time_vector, transition_matrix)
             cim_to_add.compute_cim_coefficients()
-            #print(cim_to_add)
             self.actual_cims.append(cim_to_add)
         self.actual_cims = np.array(self.actual_cims)
         self.transition_matrices = None
         self.state_residence_times = None
 
+    def filter_cims_with_mask(self, mask_arr: np.ndarray, comb: typing.List):
+        if mask_arr.size <= 1:
+            return self.actual_cims
+        else:
+            tmp_parents_comb_from_ids = np.argwhere(np.all(self.p_combs[:, mask_arr] == comb, axis=1)).ravel()
+            return self.actual_cims[tmp_parents_comb_from_ids]
+
+    @property
     def get_cims(self):
         return self.actual_cims
 
+    def get_cims_number(self):
+        return len(self.actual_cims)
+"""
     def get_cim(self, index):
         flat_index = self.indexes_converter(index)
         return self.actual_cims[flat_index]
 
-    def filter_cims_with_mask(self, mask_arr, comb):
-        if mask_arr.size <= 1:
-            return self.actual_cims
+    def indexes_converter(self, indexes):
+        assert len(indexes) == len(self.parents_states_number)
+        vector_index = 0
+        if not indexes:
+            return vector_index
         else:
-            tmp_parents_comb_from_ids = np.argwhere(np.all(self.p_combs[:, mask_arr] == comb, axis=1)).ravel()
-            #print("CIMS INDEXES TO USE!",tmp_parents_comb_from_ids)
-            return self.actual_cims[tmp_parents_comb_from_ids]
+            for indx, value in enumerate(indexes):
+                vector_index = vector_index*self.parents_states_number[indx] + indexes[indx]
+            return vector_index"""
+
 
-"""sofc = SetOfCims('Z', [3, 3], 3)
-sofc.build_actual_cims_structure()
-print(sofc.actual_cims)
-print(sofc.actual_cims[0,0])
-print(sofc.actual_cims[1,2])
-#print(sofc.indexes_converter([]))"""

main_package/classes/sets_of_cims_container.py

@@ -1,7 +1,6 @@
 import set_of_cims as socim
 
 
-
 class SetsOfCimsContainer:
     """
     Aggrega un insieme di oggetti SetOfCims
@@ -24,13 +23,3 @@ class SetsOfCimsContainer:
     def get_cims_of_node(self, node_indx, cim_indx):
         return self.sets_of_cims[node_indx].get_cim(cim_indx)
 
-"""
-    def get_vars_order(self, node):
-        return self.actual_cims[node][1]
-
-    def update_state_transition_for_matrix(self, node, which_matrix, element_indx):
-        self.sets_of_cims[node].update_state_transition(which_matrix, element_indx)
-
-    def update_state_residence_time_for_matrix(self, which_node, which_matrix, which_element, time):
-        self.sets_of_cims[which_node].update_state_residence_time(which_matrix, which_element, time)
-"""

main_package/classes/structure.py

@@ -72,7 +72,7 @@ class Structure:
             return set(self._nodes_labels_list) == set(other._nodes_labels_list) and \
                    np.array_equal(self._nodes_vals_arr, other._nodes_vals_arr) and \
                    np.array_equal(self._nodes_indexes_arr, other._nodes_indexes_arr) and \
-                   set(self._edges_list) == set(other._edges_list)
+                   self._edges_list == other._edges_list
 
         return NotImplemented
 

main_package/classes/structure_estimator.py

@@ -1,10 +1,11 @@
-import pandas as pd
+
 import numpy as np
 import itertools
 import networkx as nx
 from scipy.stats import f as f_dist
 from scipy.stats import chi2 as chi2_dist
 
+import sample_path as sp
 import structure as st
 import network_graph as ng
 import parameters_estimator as pe
@@ -12,8 +13,21 @@ import cache as ch
 
 
 class StructureEstimator:
+    """
+    Has the task of estimating the network structure given the trajectories in samplepath.
+
+    :sample_path: the sample_path object containing the trajectories and the real structure
+    :exp_test_sign: the significance level for the exponential Hp test
+    :chi_test_alfa: the significance level for the chi Hp test
+    
+    :nodes: the nodes labels
+    :nodes_vals: the nodes cardinalities
+    :nodes_indxs: the nodes indexes
+    :complete_graph: the complete directed graph built using the nodes labels in nodes
+    :cache: the cache object
+    """
 
-    def __init__(self, sample_path, exp_test_alfa, chi_test_alfa):
+    def __init__(self, sample_path: sp.SamplePath, exp_test_alfa: float, chi_test_alfa: float):
         self.sample_path = sample_path
         self.nodes = np.array(self.sample_path.structure.nodes_labels)
         #print("NODES", self.nodes)
@@ -26,11 +40,6 @@ class StructureEstimator:
         self.chi_test_alfa = chi_test_alfa
         self.cache = ch.Cache()
 
-    def build_complete_graph_frame(self, node_ids):
-        complete_frame = pd.DataFrame(itertools.permutations(node_ids, 2))
-        complete_frame.columns = ['From', 'To']
-        return complete_frame
-
     def build_complete_graph(self, node_ids):
         complete_graph = nx.DiGraph()
         complete_graph.add_nodes_from(node_ids)
@@ -53,13 +62,13 @@ class StructureEstimator:
         cims_filter = sorted_parents != test_parent
         #print("PARENTS NO FROM MASK", cims_filter)
         if not p_set:
-            print("EMPTY PSET TRYING TO FIND", test_child)
+            #print("EMPTY PSET TRYING TO FIND", test_child)
             sofc1 = self.cache.find(test_child)
         else:
             sofc1 = self.cache.find(set(p_set))
 
         if not sofc1:
-            print("CACHE MISSS SOFC1")
+            #print("CACHE MISSS SOFC1")
             bool_mask1 = np.isin(self.nodes,complete_info)
             #print("Bool mask 1", bool_mask1)
             l1 = list(self.nodes[bool_mask1])
@@ -70,7 +79,8 @@ class StructureEstimator:
             eds1 = list(itertools.product(parent_set,test_child))
             s1 = st.Structure(l1, indxs1, vals1, eds1, tot_vars_count)
             g1 = ng.NetworkGraph(s1)
-            g1.init_graph()
+            #g1.init_graph()
+            g1.fast_init(test_child)
         #print("M Vector", g1.transition_scalar_indexing_structure)
         #print("Time Vecotr", g1.time_scalar_indexing_strucure)
         #print("Time Filter", g1.time_filtering)
@@ -78,10 +88,11 @@ class StructureEstimator:
         #print("G1 NODES", g1.get_nodes())
         #print("G1 Edges", g1.get_edges())
             p1 = pe.ParametersEstimator(self.sample_path, g1)
-            p1.init_sets_cims_container()
-            p1.compute_parameters_for_node(test_child)
+            #p1.init_sets_cims_container()
+            p1.fast_init(test_child)
+            sofc1 = p1.compute_parameters_for_node(test_child)
             
-            sofc1 = p1.sets_of_cims_struct.sets_of_cims[g1.get_positional_node_indx(test_child)]
+            #sofc1 = p1.sets_of_cims_struct.sets_of_cims[g1.get_positional_node_indx(test_child)]
             if not p_set:
                 self.cache.put(test_child, sofc1)
             else:
@@ -90,7 +101,7 @@ class StructureEstimator:
         #p_set.append(test_parent)
         p_set.insert(0, test_parent)
         if p_set:
-            print("FULL PSET TRYING TO FIND", p_set)
+            #print("FULL PSET TRYING TO FIND", p_set)
             #p_set.append(test_parent)
             #print("PSET ", p_set)
             #set_p_set = set(p_set)
@@ -105,7 +116,7 @@ class StructureEstimator:
         p2.compute_parameters_for_node(test_child)
         sofc2 = p2.sets_of_cims_struct.sets_of_cims[s2.get_positional_node_indx(test_child)]"""
         if not sofc2:
-            print("Cache MISSS SOFC2")
+            #print("Cache MISSS SOFC2")
             complete_info.append(test_parent)
             bool_mask2 = np.isin(self.nodes, complete_info)
             #print("BOOL MASK 2",bool_mask2)
@@ -117,7 +128,8 @@ class StructureEstimator:
             eds2 = list(itertools.product(p_set, test_child))
             s2 = st.Structure(l2, indxs2, vals2, eds2, tot_vars_count)
             g2 = ng.NetworkGraph(s2)
-            g2.init_graph()
+            #g2.init_graph()
+            g2.fast_init(test_child)
         #print("M Vector", g2.transition_scalar_indexing_structure)
         #print("Time Vecotr", g2.time_scalar_indexing_strucure)
         #print("Time Filter", g2.time_filtering)
@@ -125,9 +137,10 @@ class StructureEstimator:
         #print("G2 Nodes", g2.get_nodes())
         #print("G2 Edges", g2.get_edges())
             p2 = pe.ParametersEstimator(self.sample_path, g2)
-            p2.init_sets_cims_container()
-            p2.compute_parameters_for_node(test_child)
-            sofc2 = p2.sets_of_cims_struct.sets_of_cims[g2.get_positional_node_indx(test_child)]
+            #p2.init_sets_cims_container()
+            p2.fast_init(test_child)
+            sofc2 = p2.compute_parameters_for_node(test_child)
+            #sofc2 = p2.sets_of_cims_struct.sets_of_cims[g2.get_positional_node_indx(test_child)]
             #if p_set:
                 #set_p_set = set(p_set)
             self.cache.put(set(p_set), sofc2)
@@ -235,12 +248,7 @@ class StructureEstimator:
 
     def generate_possible_sub_sets_of_size(self, u, size, parent_indx):
         list_without_test_parent = u[:]
-        #del list_without_test_parent[parent_indx]
-        #print("U", u)
-        #print("Szie", size)
-        #print("parent indx", parent_indx)
         list_without_test_parent.remove(parent_indx)
-        #print(list(map(list, itertools.combinations(list_without_test_parent, size))))
         return map(list, itertools.combinations(list_without_test_parent, size))
 
     def remove_diagonal_elements(self, matrix):

main_package/tests/test_cache.py

@@ -0,0 +1,61 @@
+import unittest
+import numpy as np
+
+import cache as ch
+import set_of_cims as soci
+
+
+class TestCache(unittest.TestCase):
+
+    def test_init(self):
+        c1 = ch.Cache()
+        self.assertFalse(c1.list_of_sets_of_parents)
+        self.assertFalse(c1.actual_cache)
+
+    def test_put(self):
+        c1 = ch.Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = soci.SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1.actual_cache))
+        self.assertEqual(1, len(c1.list_of_sets_of_parents))
+        self.assertEqual(sofc1, c1.actual_cache[0])
+        pset2 = {'X'}
+        sofc2 = soci.SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset2, sofc2)
+        self.assertEqual(2, len(c1.actual_cache))
+        self.assertEqual(2, len(c1.list_of_sets_of_parents))
+        self.assertEqual(sofc2, c1.actual_cache[1])
+
+    def test_find(self):
+        c1 = ch.Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = soci.SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1.actual_cache))
+        self.assertEqual(1, len(c1.list_of_sets_of_parents))
+        self.assertIsInstance(c1.find(pset1), soci.SetOfCims)
+        self.assertEqual(sofc1, c1.find(pset1))
+        self.assertIsInstance(c1.find({'Y', 'X'}), soci.SetOfCims)
+        self.assertEqual(sofc1, c1.find({'Y', 'X'}))
+        self.assertIsNone(c1.find({'X'}))
+
+    def test_clear(self):
+        c1 = ch.Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = soci.SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1.actual_cache))
+        self.assertEqual(1, len(c1.list_of_sets_of_parents))
+        c1.clear()
+        self.assertFalse(c1.list_of_sets_of_parents)
+        self.assertFalse(c1.actual_cache)
+
+
+
+
+
+
+
+if __name__ == '__main__':
+    unittest.main()

main_package/tests/test_networkgraph.py

@@ -1,12 +1,14 @@
 import unittest
 import networkx as nx
 import numpy as np
+import itertools
 from line_profiler import LineProfiler
 
 import sample_path as sp
 import network_graph as ng
 
 
+
 class TestNetworkGraph(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
@@ -18,26 +20,26 @@ class TestNetworkGraph(unittest.TestCase):
         g1 = ng.NetworkGraph(self.s1.structure)
         self.assertEqual(self.s1.structure, g1.graph_struct)
         self.assertIsInstance(g1.graph, nx.DiGraph)
-        #TODO MANCANO TUTTI I TEST DI INIZIALIZZAZIONE DEI DATI PRIVATI della classe aggiungere le property necessarie
+        self.assertTrue(np.array_equal(g1._nodes_indexes, self.s1.structure.nodes_indexes))
+        self.assertListEqual(g1._nodes_labels, self.s1.structure.nodes_labels)
+        self.assertTrue(np.array_equal(g1._nodes_values, self.s1.structure.nodes_values))
+        self.assertIsNone(g1._fancy_indexing)
+        self.assertIsNone(g1.time_scalar_indexing_strucure)
+        self.assertIsNone(g1.transition_scalar_indexing_structure)
+        self.assertIsNone(g1.transition_filtering)
+        self.assertIsNone(g1.p_combs)
 
     def test_add_nodes(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        for n1, n2 in zip(g1.get_nodes(), self.s1.structure.list_of_nodes_labels()):
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        for n1, n2 in zip(g1.nodes, self.s1.structure.nodes_labels):
             self.assertEqual(n1, n2)
 
     def test_add_edges(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_edges(self.s1.structure.list_of_edges())
-        for e in self.s1.structure.list_of_edges():
-            self.assertIn(tuple(e), g1.get_edges())
-
-    """def test_get_ordered_by_indx_set_of_parents(self):
-        g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
-        sorted_par_list_aggregated_info = g1.get_ordered_by_indx_set_of_parents(g1.get_nodes()[2])
-        self.test_aggregated_par_list_data(g1, g1.get_nodes()[2], sorted_par_list_aggregated_info)"""
+        g1.add_edges(self.s1.structure.edges)
+        for e in self.s1.structure.edges:
+            self.assertIn(tuple(e), g1.edges)
 
     def aux_aggregated_par_list_data(self, graph, node_id, sorted_par_list_aggregated_info):
         for indx, element in enumerate(sorted_par_list_aggregated_info):
@@ -55,12 +57,12 @@ class TestNetworkGraph(unittest.TestCase):
 
     def test_get_ord_set_of_par_of_all_nodes(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
         sorted_list_of_par_lists = g1.get_ord_set_of_par_of_all_nodes()
-        for node, par_list in zip(g1.get_nodes_sorted_by_indx(), sorted_list_of_par_lists):
+        for node, par_list in zip(g1.nodes, sorted_list_of_par_lists):
             self.aux_aggregated_par_list_data(g1, node, par_list)
-
+    """
     def test_get_ordered_by_indx_parents_values_for_all_nodes(self):
         g1 = ng.NetworkGraph(self.s1.structure)
         g1.add_nodes(self.s1.structure.list_of_nodes_labels())
@@ -89,67 +91,67 @@ class TestNetworkGraph(unittest.TestCase):
             self.assertEqual(par_indxs, aggr[1])
 
     def test_build_fancy_indexing_structure_offset(self):
-        pass #TODO il codice di netgraph deve gestire questo caso
+        pass #TODO il codice di netgraph deve gestire questo caso"""
 
-    def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_indx, parents_indxs):
-        time_scalar_indexing = graph.build_time_scalar_indexing_structure_for_a_node(node_indx, parents_indxs)
+    def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels, parents_vals):
+        time_scalar_indexing = graph.build_time_scalar_indexing_structure_for_a_node(node_id, parents_vals)
         self.assertEqual(len(time_scalar_indexing), len(parents_indxs) + 1)
-        merged_list = parents_indxs[:]
-        merged_list.insert(0, node_indx)
-        #print(merged_list)
+        merged_list = parents_labels[:]
+        merged_list.insert(0, node_id)
         vals_list = []
         for node in merged_list:
-            vals_list.append(graph.get_states_number_by_indx(node))
+            vals_list.append(graph.get_states_number(node))
         t_vec = np.array(vals_list)
         t_vec = t_vec.cumprod()
-        #print(t_vec)
         self.assertTrue(np.array_equal(time_scalar_indexing, t_vec))
 
-    def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_indx, parents_indxs):
-        transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_indx,
-                                                                                                 parents_indxs)
-        print(transition_scalar_indexing)
+    def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels,
+                                                                  parents_values):
+        transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                 parents_values)
         self.assertEqual(len(transition_scalar_indexing), len(parents_indxs) + 2)
-        merged_list = parents_indxs[:]
-        merged_list.insert(0, node_indx)
-        merged_list.insert(0, node_indx)
+        merged_list = parents_labels[:]
+        merged_list.insert(0, node_id)
+        merged_list.insert(0, node_id)
         vals_list = []
-        for node in merged_list:
-            vals_list.append(graph.get_states_number_by_indx(node))
+        for node_id in merged_list:
+            vals_list.append(graph.get_states_number(node_id))
         m_vec = np.array([vals_list])
         m_vec = m_vec.cumprod()
         self.assertTrue(np.array_equal(transition_scalar_indexing, m_vec))
 
     def test_build_transition_scalar_indexing_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
         g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        p_labels = [i[0] for i in g1.aggregated_info_about_nodes_parents]
+        p_vals = g1.get_ordered_by_indx_parents_values_for_all_nodes()
         fancy_indx = g1.build_fancy_indexing_structure(0)
-        print(fancy_indx)
-        for node_id, p_indxs in zip(g1.graph_struct.list_of_nodes_indexes(), fancy_indx):
-            self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node_id, p_indxs)
+        for node_id, p_i ,p_l, p_v in zip(g1.graph_struct.nodes_labels, fancy_indx, p_labels, p_vals):
+            self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node_id, p_i ,p_l, p_v)
 
     def test_build_time_scalar_indexing_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
         g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
         fancy_indx = g1.build_fancy_indexing_structure(0)
+        p_labels = [i[0] for i in g1.aggregated_info_about_nodes_parents]
+        p_vals = g1.get_ordered_by_indx_parents_values_for_all_nodes()
         #print(fancy_indx)
-        for node_id, p_indxs in zip(g1.graph_struct.list_of_nodes_indexes(), fancy_indx):
-            self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node_id, p_indxs)
-
+        for node_id, p_indxs, p_labels, p_v in zip(g1.graph_struct.nodes_labels, fancy_indx, p_labels, p_vals):
+            self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node_id, p_indxs, p_labels, p_v)
+#TODO Sei arrivato QUI
     def test_build_time_columns_filtering_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
         g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
         g1._fancy_indexing = g1.build_fancy_indexing_structure(0)
         g1.build_time_columns_filtering_structure()
-        print(g1.time_filtering)
         t_filter = []
-        for node_id, p_indxs in zip(g1.get_nodes_sorted_by_indx(), g1._fancy_indexing):
+        for node_id, p_indxs in zip(g1.nodes, g1._fancy_indexing):
             single_filter = []
             single_filter.append(g1.get_node_indx(node_id))
             single_filter.extend(p_indxs)
@@ -160,47 +162,72 @@ class TestNetworkGraph(unittest.TestCase):
 
     def test_build_transition_columns_filtering_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        g1.add_nodes(self.s1.structure.list_of_nodes_labels())
-        g1.add_edges(self.s1.structure.list_of_edges())
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
         g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
         g1._fancy_indexing = g1.build_fancy_indexing_structure(0)
         g1.build_transition_columns_filtering_structure()
-        print(g1.transition_filtering)
         m_filter = []
-        for node_id, p_indxs in zip(g1.get_nodes_sorted_by_indx(), g1._fancy_indexing):
+        for node_id, p_indxs in zip(g1.nodes, g1._fancy_indexing):
             single_filter = []
             single_filter.append(g1.get_node_indx(node_id) + g1.graph_struct.total_variables_number)
             single_filter.append(g1.get_node_indx(node_id))
             single_filter.extend(p_indxs)
             m_filter.append(np.array(single_filter))
-        print(m_filter)
         for a1, a2 in zip(g1.transition_filtering, m_filter):
             self.assertTrue(np.array_equal(a1, a2))
 
-    def test_init_graph(self):
+    def test_build_p_combs_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        #g1.build_scalar_indexing_structures()
-        lp = LineProfiler()
-        #lp.add_function(g1.get_ordered_by_indx_set_of_parents)
-        #lp.add_function(g1.get_states_number)
-        lp_wrapper = lp(g1.init_graph)
-        print(g1.time_scalar_indexing_strucure)
-        print(g1.transition_scalar_indexing_structure)
-        """[array([3]), array([3, 9]), array([ 3,  9, 27])]
-[array([3, 9]), array([ 3,  9, 27]), array([ 3,  9, 27, 81])]"""
-        lp_wrapper()
-        lp.print_stats()
-
-    """def test_remove_node(self):
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
+        p_vals = g1.get_ordered_by_indx_parents_values_for_all_nodes()
+        p_combs = g1.build_p_combs_structure()
+
+        for matrix, p_v in zip(p_combs, p_vals):
+            p_possible_vals = []
+            for val in p_v:
+                vals = [v for v in range(val)]
+                p_possible_vals.extend(vals)
+                comb_struct = set(itertools.product(p_possible_vals,repeat=len(p_v)))
+                #print(comb_struct)
+                for comb in comb_struct:
+                    self.assertIn(np.array(comb), matrix)
+
+    def test_fast_init(self):
         g1 = ng.NetworkGraph(self.s1.structure)
+        g2 = ng.NetworkGraph(self.s1.structure)
         g1.init_graph()
-        g1.remove_node('Y')
-        print(g1.get_nodes())
-        print(g1.get_edges())"""
-
+        for indx, node in enumerate(g1.nodes):
+            g2.fast_init(node)
+            self.assertListEqual(g2._fancy_indexing, g1._fancy_indexing[indx])
+            self.assertTrue(np.array_equal(g2.time_scalar_indexing_strucure, g1.time_scalar_indexing_strucure[indx]))
+            self.assertTrue(np.array_equal(g2.transition_scalar_indexing_structure, g1.transition_scalar_indexing_structure[indx]))
+            self.assertTrue(np.array_equal(g2.time_filtering, g1.time_filtering[indx]))
+            self.assertTrue(np.array_equal(g2.transition_filtering, g1.transition_filtering[indx]))
+            self.assertTrue(np.array_equal(g2.p_combs, g1.p_combs[indx]))
+
+    def test_get_parents_by_id(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in g1.nodes:
+            self.assertListEqual(g1.get_parents_by_id(node), list(g1.graph.predecessors(node)))
 
+    def test_get_states_number(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node, val in zip(g1.nodes, g1.nodes_values):
+            self.assertEqual(val, g1.get_states_number(node))
 
+    def test_get_node_indx(self):
+        g1 = ng.NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node, indx in zip(g1.nodes, g1.nodes_indexes):
+            self.assertEqual(indx, g1.get_node_indx(node))
 
-#TODO mancano i test sulle property e sui getters_vari
 if __name__ == '__main__':
     unittest.main()

main_package/tests/test_parameters_estimator.py

@@ -4,6 +4,7 @@ from line_profiler import LineProfiler
 
 import network_graph as ng
 import sample_path as sp
+import set_of_cims as sofc
 import sets_of_cims_container as scc
 import parameters_estimator as pe
 import json_importer as ji
@@ -19,35 +20,33 @@ class TestParametersEstimatior(unittest.TestCase):
         cls.g1 = ng.NetworkGraph(cls.s1.structure)
         cls.g1.init_graph()
 
-    def test_init(self):
-        self.aux_test_init(self.s1, self.g1)
-
-    def test_init_sets_of_cims_container(self):
-        self.aux_test_init_sets_cims_container(self.s1, self.g1)
-
-    def aux_test_init(self, sample_p, graph):
-        pe1 = pe.ParametersEstimator(sample_p, graph)
-        self.assertEqual(sample_p, pe1.sample_path)
-        self.assertEqual(graph, pe1.net_graph)
-        self.assertIsNone(pe1.sets_of_cims_struct)
-
-    def aux_test_init_sets_cims_container(self, sample_p, graph):
-        pe1 = pe.ParametersEstimator(sample_p, graph)
-        pe1.init_sets_cims_container()
-        self.assertIsInstance(pe1.sets_of_cims_struct, scc.SetsOfCimsContainer)
-
-    def test_compute_parameters(self):
-        self.aux_test_compute_parameters(self.s1, self.g1)
-
-    def aux_test_compute_parameters(self, sample_p, graph):
-        pe1 = pe.ParametersEstimator(sample_p, graph)
-        pe1.init_sets_cims_container()
-        pe1.compute_parameters()
-        samples_cims = self.aux_import_sampled_cims('dyn.cims')
-        for indx, sc in enumerate(samples_cims.values()):
-            self.equality_of_cims_of_node(sc, pe1.sets_of_cims_struct.get_set_of_cims(indx).get_cims())
+    def test_fast_init(self):
+        for node in self.g1.nodes:
+            g = ng.NetworkGraph(self.s1.structure)
+            g.fast_init(node)
+            p1 = pe.ParametersEstimator(self.s1, g)
+            self.assertEqual(p1.sample_path, self.s1)
+            self.assertEqual(p1.net_graph, g)
+            self.assertIsNone(p1.single_set_of_cims)
+            p1.fast_init(node)
+            self.assertIsInstance(p1.single_set_of_cims, sofc.SetOfCims)
+
+    def test_compute_parameters_for_node(self):
+        for indx, node in enumerate(self.g1.nodes):
+            print(node)
+            g = ng.NetworkGraph(self.s1.structure)
+            g.fast_init(node)
+            p1 = pe.ParametersEstimator(self.s1, g)
+            p1.fast_init(node)
+            sofc1 = p1.compute_parameters_for_node(node)
+            sampled_cims = self.aux_import_sampled_cims('dyn.cims')
+            sc = list(sampled_cims.values())
+            print(sc[indx])
+            self.equality_of_cims_of_node(sc[indx], sofc1.actual_cims)
 
     def equality_of_cims_of_node(self, sampled_cims, estimated_cims):
+        #print(sampled_cims)
+        print(estimated_cims)
         self.assertEqual(len(sampled_cims), len(estimated_cims))
         for c1, c2 in zip(sampled_cims, estimated_cims):
             self.cim_equality_test(c1, c2.cim)
@@ -56,27 +55,31 @@ class TestParametersEstimatior(unittest.TestCase):
         for r1, r2 in zip(cim1, cim2):
             self.assertTrue(np.all(np.isclose(r1, r2, 1e-01, 1e-01) == True))
 
-    def test_compute_parameters_for_node(self):#TODO Questo non è un test
-        pe1 = pe.ParametersEstimator(self.s1, self.g1)
-        #pe1.init_sets_cims_container()
-        lp = LineProfiler()
-        lp_wrapper = lp(pe1.init_sets_cims_container)
-        #lp.add_function(pe1.sets_of_cims_struct.init_cims_structure)
-        lp_wrapper()
-        lp.print_stats()
-        #pe1.init_sets_cims_container()
-        #pe1.compute_parameters_for_node('Y')
-        print(pe1.sets_of_cims_struct.get_set_of_cims(0).actual_cims)
-
-
     def aux_import_sampled_cims(self, cims_label):
         i1 = ji.JsonImporter('../data', '', '', '', '', '')
         raw_data = i1.read_json_file()
         return i1.import_sampled_cims(raw_data, 0, cims_label)
 
+    """
+    def test_init(self):
+        self.aux_test_init(self.s1, self.g1)
 
+    def test_init_sets_of_cims_container(self):
+        self.aux_test_init_sets_cims_container(self.s1, self.g1)
 
+    def aux_test_init(self, sample_p, graph):
+        pe1 = pe.ParametersEstimator(sample_p, graph)
+        self.assertEqual(sample_p, pe1.sample_path)
+        self.assertEqual(graph, pe1.net_graph)
+        self.assertIsNone(pe1.sets_of_cims_struct)
 
+    def aux_test_init_sets_cims_container(self, sample_p, graph):
+        pe1 = pe.ParametersEstimator(sample_p, graph)
+        pe1.init_sets_cims_container()
+        self.assertIsInstance(pe1.sets_of_cims_struct, scc.SetsOfCimsContainer)
 
+    def test_compute_parameters(self):
+        self.aux_test_compute_parameters(self.s1, self.g1)
+        """
 if __name__ == '__main__':
     unittest.main()

main_package/tests/test_setofcims.py

@@ -11,64 +11,84 @@ class TestSetOfCims(unittest.TestCase):
     def setUpClass(cls) -> None:
         cls.node_id = 'X'
         cls.possible_cardinalities = [2, 3]
-        #cls.possible_states = [[0,1], [0, 1, 2]]
+        cls.possible_states = [[0,1], [0, 1, 2]]
         cls.node_states_number = range(2, 4)
 
     def test_init(self):
         # empty parent set
         for sn in self.node_states_number:
-            self.aux_test_init(self.node_id, [], sn)
+            p_combs = self.build_p_comb_structure_for_a_node([])
+            self.aux_test_init(self.node_id, [], sn, p_combs)
         # one parent
         for sn in self.node_states_number:
             for p in itertools.product(self.possible_cardinalities, repeat=1):
-                self.aux_test_init(self.node_id, list(p), sn)
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_init(self.node_id, list(p), sn, p_combs)
         #two parents
         for sn in self.node_states_number:
             for p in itertools.product(self.possible_cardinalities, repeat=2):
-                self.aux_test_init(self.node_id, list(p), sn)
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_init(self.node_id, list(p), sn, p_combs)
 
-    def test_indexes_converter(self):
+    def test_build_cims(self):
         # empty parent set
         for sn in self.node_states_number:
-            self.aux_test_indexes_converter(self.node_id, [], sn)
+            p_combs = self.build_p_comb_structure_for_a_node([])
+            self.aux_test_build_cims(self.node_id, [], sn, p_combs)
         # one parent
         for sn in self.node_states_number:
             for p in itertools.product(self.possible_cardinalities, repeat=1):
-                self.aux_test_init(self.node_id, list(p), sn)
-        # two parents
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_build_cims(self.node_id, list(p), sn, p_combs)
+        #two parents
         for sn in self.node_states_number:
             for p in itertools.product(self.possible_cardinalities, repeat=2):
-                self.aux_test_init(self.node_id, list(p), sn)
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_build_cims(self.node_id, list(p), sn, p_combs)
 
-    def aux_test_indexes_converter(self, node_id, parents_states_number, node_states_number):
-        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number)
-        if not parents_states_number:
-            self.assertEqual(sofcims.indexes_converter([]), 0)
-        else:
-            parents_possible_values = []
-            for cardi in parents_states_number:
-                parents_possible_values.extend(range(0, cardi))
-            for p in itertools.permutations(parents_possible_values, len(parents_states_number)):
-                self.assertEqual(sofcims.indexes_converter(list(p)), np.ravel_multi_index(list(p), parents_states_number))
-
-    def test_build_cims(self):
+    def test_filter_cims_with_mask(self):
+        p_combs = self.build_p_comb_structure_for_a_node(self.possible_cardinalities)
+        sofc1 = soci.SetOfCims('X', self.possible_cardinalities, 3, p_combs)
         state_res_times_list = []
         transition_matrices_list = []
-        so1 = soci.SetOfCims('X',[3], 3)
-        for i in range(0, 3):
+        for i in range(len(p_combs)):
             state_res_times = np.random.rand(1, 3)[0]
             state_res_times = state_res_times * 1000
             state_transition_matrix = np.random.randint(1, 10000, (3, 3))
             state_res_times_list.append(state_res_times)
             transition_matrices_list.append(state_transition_matrix)
+        sofc1.build_cims(state_res_times_list, transition_matrices_list)
+        for length_of_mask in range(3):
+            for mask in list(itertools.permutations([True, False],r=length_of_mask)):
+                m = np.array(mask)
+                for parent_value in range(self.possible_cardinalities[0]):
+                    cims = sofc1.filter_cims_with_mask(m, [parent_value])
+                    if length_of_mask == 0 or length_of_mask == 1:
+                        self.assertTrue(np.array_equal(sofc1.actual_cims, cims))
+                    else:
+                        indxs = self.another_filtering_method(p_combs, m, [parent_value])
+                        self.assertTrue(np.array_equal(cims, sofc1.actual_cims[indxs]))
+
+    def aux_test_build_cims(self, node_id, p_values, node_states, p_combs):
+        state_res_times_list = []
+        transition_matrices_list = []
+        so1 = soci.SetOfCims(node_id, p_values, node_states, p_combs)
+        for i in range(len(p_combs)):
+            state_res_times = np.random.rand(1, node_states)[0]
+            state_res_times = state_res_times * 1000
+            state_transition_matrix = np.random.randint(1, 10000, (node_states, node_states))
+            state_res_times_list.append(state_res_times)
+            transition_matrices_list.append(state_transition_matrix)
         so1.build_cims(state_res_times_list, transition_matrices_list)
         self.assertEqual(len(state_res_times_list), so1.get_cims_number())
+        self.assertIsInstance(so1.actual_cims, np.ndarray)
         self.assertIsNone(so1.transition_matrices)
         self.assertIsNone(so1.state_residence_times)
 
-    def aux_test_init(self, node_id, parents_states_number, node_states_number):
-        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number)
+    def aux_test_init(self, node_id, parents_states_number, node_states_number, p_combs):
+        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number, p_combs)
         self.assertEqual(sofcims.node_id, node_id)
+        self.assertTrue(np.array_equal(sofcims.p_combs, p_combs))
         self.assertTrue(np.array_equal(sofcims.parents_states_number, parents_states_number))
         self.assertEqual(sofcims.node_states_number, node_states_number)
         self.assertFalse(sofcims.actual_cims)
@@ -77,7 +97,46 @@ class TestSetOfCims(unittest.TestCase):
         self.assertEqual(sofcims.transition_matrices.shape[0], np.prod(np.array(parents_states_number)))
         self.assertEqual(len(sofcims.transition_matrices[0][0]), node_states_number)
 
+    def aux_test_indexes_converter(self, node_id, parents_states_number, node_states_number):
+        sofcims = soci.SetOfCims(node_id, parents_states_number, node_states_number)
+        if not parents_states_number:
+            self.assertEqual(sofcims.indexes_converter([]), 0)
+        else:
+            parents_possible_values = []
+            for cardi in parents_states_number:
+                parents_possible_values.extend(range(0, cardi))
+            for p in itertools.permutations(parents_possible_values, len(parents_states_number)):
+                self.assertEqual(sofcims.indexes_converter(list(p)), np.ravel_multi_index(list(p), parents_states_number))
+
+    def build_p_comb_structure_for_a_node(self, parents_values):
+        """
+        Builds the combinatory structure that contains the combinations of all the values contained in parents_values.
+
+        Parameters:
+            parents_values: the cardinalities of the nodes
+        Returns:
+            a numpy matrix containing a grid of the combinations
+        """
+        tmp = []
+        for val in parents_values:
+            tmp.append([x for x in range(val)])
+        if len(parents_values) > 0:
+            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
+            if len(parents_values) > 1:
+                tmp_comb = parents_comb[:, 1].copy()
+                parents_comb[:, 1] = parents_comb[:, 0].copy()
+                parents_comb[:, 0] = tmp_comb
+        else:
+            parents_comb = np.array([[]], dtype=np.int)
+        return parents_comb
 
+    def another_filtering_method(self,p_combs, mask, parent_value):
+        masked_combs = p_combs[:, mask]
+        indxs = []
+        for indx, val in enumerate(masked_combs):
+            if val == parent_value:
+                indxs.append(indx)
+        return np.array(indxs)
 
 if __name__ == '__main__':
     unittest.main()