Refactor class members as protected;Refactors on comments and methods return types

main_package/basic_main.py

@@ -6,7 +6,6 @@ sys.path.append("./classes/")
 
 import network_graph as ng
 import sample_path as sp
-import set_of_cims as sofc
 import parameters_estimator as pe
 import json_importer as ji
 
@@ -14,20 +13,23 @@ import json_importer as ji
 def main():
     read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
     #import data
-    importer = ji.JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+    importer = ji.JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 1)
     #Create a SamplePath Obj
     s1 = sp.SamplePath(importer)
     #Build The trajectries and the structural infos
     s1.build_trajectories()
     s1.build_structure()
+    print(s1.structure.edges)
+    print(s1.structure.nodes_values)
     #From The Structure Object build the Graph
     g = ng.NetworkGraph(s1.structure)
     #Select a node you want to estimate the parameters
     node = g.nodes[1]
-    #Init the graph specifically for THIS node
+    print("NOde", node)
+    #Init the _graph specifically for THIS node
     g.fast_init(node)
     #Use SamplePath and Grpah to create a ParametersEstimator Object
-    p1 = pe.ParametersEstimator(s1, g)
+    p1 = pe.ParametersEstimator(s1.trajectories, g)
     #Init the peEst specifically for THIS node
     p1.fast_init(node)
     #Compute the parameters

main_package/classes/abstract_importer.py

@@ -7,21 +7,21 @@ class AbstractImporter(ABC):
     """
     Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
 
-    :file_path: the file path
+    :_file_path: the file path
     :_concatenated_samples: the concatenation of all the processed trajectories
-    :df_structure: Dataframe containing the structure of the network (edges)
-    :df_variables: Dataframe containing the nodes cardinalities
-    :df_concatenated_samples: the concatenation and processing of all the trajectories present
+    :_df_structure: Dataframe containing the structure of the network (edges)
+    :_df_variables: Dataframe containing the nodes cardinalities
+    :_df_concatenated_samples: the concatenation and processing of all the trajectories present
     in the list df_samples list
-    :sorter: the columns header(excluding the time column) of the Dataframe concatenated_samples
+    :_sorter: the columns header(excluding the time column) of the Dataframe concatenated_samples
     """
 
     def __init__(self, file_path: str):
         """
         Parameters:
-            :file_path: the path to the file containing the data
+            :_file_path: the path to the file containing the data
         """
-        self.file_path = file_path
+        self._file_path = file_path
         self._df_variables = None
         self._df_structure = None
         self._concatenated_samples = None
@@ -71,7 +71,6 @@ class AbstractImporter(ABC):
         pre: the Dataframe sample_frame has to follow the column structure of this header:
             Header of sample_frame = [Time | Variable values]
         """
-        #sample_frame[time_header_label] = sample_frame[time_header_label].diff().shift(-1)
         sample_frame.iloc[:, 0] = sample_frame.iloc[:, 0].diff().shift(-1)
         shifted_cols = sample_frame[columns_header].shift(-1).fillna(0).astype('int32')
         shifted_cols.columns = shifted_cols_header
@@ -81,7 +80,7 @@ class AbstractImporter(ABC):
 
     def compute_row_delta_in_all_samples_frames(self, df_samples_list: typing.List):
         """
-        Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list df_samples_list.
+        Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list _df_samples_list.
         Concatenates the result in the dataframe concatanated_samples
         Parameters:
             time_header_label: the label of the time column
@@ -103,9 +102,7 @@ class AbstractImporter(ABC):
         complete_header = self._sorter[:]
         complete_header.insert(0,'Time')
         complete_header.extend(shifted_cols_header)
-        #print("Complete Header", complete_header)
         self._concatenated_samples = self._concatenated_samples[complete_header]
-        #print("Concat Samples",self._concatenated_samples)
 
     def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
         """
@@ -143,3 +140,7 @@ class AbstractImporter(ABC):
     @property
     def sorter(self) -> typing.List:
         return self._sorter
+
+    @property
+    def file_path(self) -> str:
+        return self._file_path

main_package/classes/cache.py

@@ -7,28 +7,28 @@ 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
+    :_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_parents = []
-        self.actual_cache = []
+        self._list_of_sets_of_parents = []
+        self._actual_cache = []
 
-    def find(self, parents_comb: typing.Set): #typing.Union[typing.Set, str]
+    def find(self, parents_comb: typing.Set) -> sofc.SetOfCims:
         """
         Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims related to that parents_comb.
         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.
+            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)]
+            result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)]
             print("CACHE HIT!!!!", parents_comb)
             return result
         except ValueError:
@@ -36,7 +36,7 @@ class Cache:
 
     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
+        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
@@ -45,9 +45,9 @@ class Cache:
         Returns:
             void
         """
-        #print("Putting in cache:", parents_comb)
-        self.list_of_sets_of_parents.append(parents_comb)
-        self.actual_cache.append(socim)
+        #print("Putting in _cache:", parents_comb)
+        self._list_of_sets_of_parents.append(parents_comb)
+        self._actual_cache.append(socim)
 
     def clear(self):
         """
@@ -58,5 +58,5 @@ class Cache:
         Returns:
             void
         """
-        del self.list_of_sets_of_parents[:]
-        del self.actual_cache[:]
+        del self._list_of_sets_of_parents[:]
+        del self._actual_cache[:]

main_package/classes/conditional_intensity_matrix.py

@@ -11,6 +11,11 @@ class ConditionalIntensityMatrix:
     :_cim: the actual cim of the node
     """
     def __init__(self, state_residence_times: np.array, state_transition_matrix: np.array):
+        """
+        Parameters:
+            :_state_residence_times: state residence times vector
+            :_state_transition_matrix: the transitions count matrix
+        """
         self._state_residence_times = state_residence_times
         self._state_transition_matrix = state_transition_matrix
         self._cim = self.state_transition_matrix.astype(np.float64)
@@ -28,15 +33,15 @@ class ConditionalIntensityMatrix:
         self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
 
     @property
-    def state_residence_times(self):
+    def state_residence_times(self) -> np.ndarray:
         return self._state_residence_times
 
     @property
-    def state_transition_matrix(self):
+    def state_transition_matrix(self) -> np.ndarray:
         return self._state_transition_matrix
 
     @property
-    def cim(self):
+    def cim(self) -> np.ndarray:
         return self._cim
 
     def __repr__(self):

main_package/classes/json_importer.py

@@ -16,31 +16,33 @@ class JsonImporter(ai.AbstractImporter):
         |_ samples
         |_ variabels
     :file_path: the path of the file that contains tha data to be imported
-    :samples_label: the reference key for the samples in the trajectories
-    :structure_label: the reference key for the structure of the network data
-    :variables_label: the reference key for the cardinalites of the nodes data
-    :time_key: the key used to identify the timestamps in each trajectory
-    :variables_key: the key used to identify the names of the variables in the net
-    :df_samples_list: a Dataframe list in which every df contains a trajectory
+    :_samples_label: the reference key for the samples in the trajectories
+    :_structure_label: the reference key for the structure of the network data
+    :_variables_label: the reference key for the cardinalites of the nodes data
+    :_time_key: the key used to identify the timestamps in each trajectory
+    :_variables_key: the key used to identify the names of the variables in the net
+    :_df_samples_list: a Dataframe list in which every df contains a trajectory
     """
 
     def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
-                 variables_key: str):
+                 variables_key: str, array_indx: int):
         """
         Parameters:
-            file_path: the path of the file that contains tha data to be imported
-            :samples_label: the reference key for the samples in the trajectories
-            :structure_label: the reference key for the structure of the network data
-            :variables_label: the reference key for the cardinalites of the nodes data
-            :time_key: the key used to identify the timestamps in each trajectory
-            :variables_key: the key used to identify the names of the variables in the net
-        """
-        self.samples_label = samples_label
-        self.structure_label = structure_label
-        self.variables_label = variables_label
-        self.time_key = time_key
-        self.variables_key = variables_key
-        self.df_samples_list = None
+            :file_path: the path of the file that contains tha data to be imported
+            :_samples_label: the reference key for the samples in the trajectories
+            :_structure_label: the reference key for the structure of the network data
+            :_variables_label: the reference key for the cardinalites of the nodes data
+            :_time_key: the key used to identify the timestamps in each trajectory
+            :_variables_key: the key used to identify the names of the variables in the net
+            :_array_indx: the index of the outer json array from which import all the data
+        """
+        self._samples_label = samples_label
+        self._structure_label = structure_label
+        self._variables_label = variables_label
+        self._time_key = time_key
+        self._variables_key = variables_key
+        self._df_samples_list = None
+        self._array_indx = array_indx
         super(JsonImporter, self).__init__(file_path)
 
     def import_data(self):
@@ -52,14 +54,14 @@ class JsonImporter(ai.AbstractImporter):
             _void
         """
         raw_data = self.read_json_file()
-        self.df_samples_list = self.import_trajectories(raw_data)
-        self._sorter = self.build_sorter(self.df_samples_list[0])
-        self.compute_row_delta_in_all_samples_frames(self.df_samples_list)
+        self._df_samples_list = self.import_trajectories(raw_data)
+        self._sorter = self.build_sorter(self._df_samples_list[0])
+        self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
         self.clear_data_frame_list()
         self._df_structure = self.import_structure(raw_data)
-        self._df_variables = self.import_variables(raw_data, self._sorter)
+        self._df_variables = self.import_variables(raw_data)
 
-    def import_trajectories(self, raw_data: typing.List):
+    def import_trajectories(self, raw_data: typing.List) -> typing.List:
         """
         Imports the trajectories in the list of dicts raw_data.
         Parameters:
@@ -67,22 +69,22 @@ class JsonImporter(ai.AbstractImporter):
         Returns:
             :List of dataframes containing all the trajectories
         """
-        return self.normalize_trajectories(raw_data, 0, self.samples_label)
+        return self.normalize_trajectories(raw_data, self._array_indx, self._samples_label)
 
     def import_structure(self, raw_data: typing.List) -> pd.DataFrame:
         """
-        Imports in a dataframe the data in the list raw_data at the key structure_label
+        Imports in a dataframe the data in the list raw_data at the key _structure_label
 
         Parameters:
             :raw_data: the data
         Returns:
             :Daframe containg the starting node a ending node of every arc of the network
         """
-        return self.one_level_normalizing(raw_data, 0, self.structure_label)
+        return self.one_level_normalizing(raw_data, self._array_indx, self._structure_label)
 
-    def import_variables(self, raw_data: typing.List, sorter: typing.List) -> pd.DataFrame:
+    def import_variables(self, raw_data: typing.List) -> pd.DataFrame:
         """
-        Imports the data in raw_data at the key variables_label.
+        Imports the data in raw_data at the key _variables_label.
         Sorts the row of the dataframe df_variables using the list sorter.
 
         Parameters:
@@ -91,16 +93,7 @@ class JsonImporter(ai.AbstractImporter):
         Returns:
             :Datframe containg the variables simbolic labels and their cardinalities
         """
-        return self.one_level_normalizing(raw_data, 0, self.variables_label)
-        #TODO Usando come Pre-requisito l'ordinamento del frame _df_variables uguale a quello presente in
-        #TODO self _sorter questo codice risulta inutile
-        """self._df_variables[self.variables_key] = self._df_variables[self.variables_key].astype("category")
-        self._df_variables[self.variables_key] = self._df_variables[self.variables_key].cat.set_categories(sorter)
-        self._df_variables = self._df_variables.sort_values([self.variables_key])
-        self._df_variables.reset_index(inplace=True)
-        self._df_variables.drop('index', axis=1, inplace=True)
-        #print("Var Frame", self._df_variables)
-        """
+        return self.one_level_normalizing(raw_data, self._array_indx, self._variables_label)
 
     def read_json_file(self) -> typing.List:
         """
@@ -112,7 +105,7 @@ class JsonImporter(ai.AbstractImporter):
               :data: the contents of the json file
 
         """
-        with open(self.file_path) as f:
+        with open(self._file_path) as f:
             data = json.load(f)
             return data
 
@@ -130,7 +123,7 @@ class JsonImporter(ai.AbstractImporter):
         """
         return pd.DataFrame(raw_data[indx][key])
 
-    def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str):
+    def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List:
         """
         Extracts the traj in raw_data at the index index at the key trajectories key.
 
@@ -145,28 +138,25 @@ class JsonImporter(ai.AbstractImporter):
         smps = raw_data[indx][trajectories_key]
         df_samples_list = [dataframe(sample) for sample in smps]
         return df_samples_list
-        #columns_header = list(self.df_samples_list[0].columns.values)
-        #columns_header.remove(self.time_key)
-        #self._sorter = columns_header
 
     def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
         """
         Implements the abstract method build_sorter for this dataset
         """
         columns_header = list(sample_frame.columns.values)
-        columns_header.remove(self.time_key)
+        columns_header.remove(self._time_key)
         return columns_header
 
     def clear_data_frame_list(self):
         """
-        Removes all values present in the dataframes in the list df_samples_list
+        Removes all values present in the dataframes in the list _df_samples_list
         Parameters:
             :void
         Returns:
             :void
         """
-        for indx in range(len(self.df_samples_list)):
-            self.df_samples_list[indx] = self.df_samples_list[indx].iloc[0:0]
+        for indx in range(len(self._df_samples_list)):
+            self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0]
 
     def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
         """

main_package/classes/network_graph.py

@@ -1,37 +1,42 @@
 
 import typing
-
+import structure as st
 import networkx as nx
 import numpy as np
 
 
 class NetworkGraph:
     """
-    Abstracts the infos contained in the Structure class in the form of a directed graph.
+    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
+    :_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
+    :_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
+    :_transition_filtering: the columns filtering structure used in the computation of the transition
+        from one state to another
     :self._p_combs_structure: all the possible parents states combination for every node in the net
     """
 
-    def __init__(self, graph_struct):
-        self.graph_struct = graph_struct
-        self.graph = nx.DiGraph()
-        self._nodes_indexes = self.graph_struct.nodes_indexes
-        self._nodes_labels = self.graph_struct.nodes_labels
-        self._nodes_values = self.graph_struct.nodes_values
-        self.aggregated_info_about_nodes_parents = None
+    def __init__(self, graph_struct: st.Structure):
+        """
+        Parameters:
+            :graph_struct:the Structure object from which infos about the net will be extracted
+        """
+        self._graph_struct = graph_struct
+        self._graph = nx.DiGraph()
+        self._nodes_indexes = self._graph_struct.nodes_indexes
+        self._nodes_labels = self._graph_struct.nodes_labels
+        self._nodes_values = self._graph_struct.nodes_values
+        self._aggregated_info_about_nodes_parents = None
         self._fancy_indexing = None
         self._time_scalar_indexing_structure = None
         self._transition_scalar_indexing_structure = None
@@ -39,16 +44,6 @@ class NetworkGraph:
         self._transition_filtering = None
         self._p_combs_structure = None
 
-    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 fast_init(self, node_id: str):
         """
         Initializes all the necessary structures for parameters estimation of the node identified by the label node_id
@@ -58,11 +53,11 @@ class NetworkGraph:
             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]
+        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]
+        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,
@@ -74,33 +69,33 @@ class NetworkGraph:
 
     def add_nodes(self, list_of_nodes: typing.List):
         """
-        Adds the nodes to the graph contained in the list of nodes list_of_nodes.
+        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
+            list_of_nodes: the nodes to add to _graph
         Returns:
             void
         """
         nodes_indxs = self._nodes_indexes
-        nodes_vals = self.graph_struct.nodes_values
+        nodes_vals = self._graph_struct.nodes_values
         pos = 0
         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)
+            self._graph.add_node(id, indx=node_indx, val=node_val, pos_indx=pos)
             pos += 1
 
     def add_edges(self, list_of_edges: typing.List):
         """
-        Add the edges to the graph contained in the list list_of_edges.
+        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)
+        self._graph.add_edges_from(list_of_edges)
 
-    def get_ordered_by_indx_set_of_parents(self, node: str):
+    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).
@@ -121,22 +116,6 @@ class NetworkGraph:
         p_values = [self.get_states_number(node) for node in sorted_parents]
         return (sorted_parents, p_indxes, p_values)
 
-    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
-
     def build_time_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
         """
         Builds an indexing structure for the computation of state residence times values.
@@ -153,8 +132,8 @@ class NetworkGraph:
         T_vector = T_vector.cumprod().astype(np.int)
         return T_vector
 
-
-    def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) -> np.ndarray:
+    def build_transition_scalar_indexing_structure_for_a_node(self, node_id: str, parents_vals: typing.List) \
+            -> np.ndarray:
         """
         Builds an indexing structure for the computation of state transitions values.
 
@@ -194,7 +173,7 @@ class NetworkGraph:
         Returns:
             a numpy array
         """
-        nodes_number = self.graph_struct.total_variables_number
+        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) -> np.ndarray:
@@ -219,81 +198,108 @@ class NetworkGraph:
             parents_comb = np.array([[]], dtype=np.int)
         return parents_comb
 
-    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_parents_by_id(self, node_id):
-        return list(self.graph.predecessors(node_id))
+        return list(self._graph.predecessors(node_id))
 
     def get_states_number(self, node_id):
-        return self.graph.nodes[node_id]['val']
+        return self._graph.nodes[node_id]['val']
 
     def get_node_indx(self, node_id):
-        return nx.get_node_attributes(self.graph, 'indx')[node_id]
+        return nx.get_node_attributes(self._graph, 'indx')[node_id]
 
     def get_positional_node_indx(self, node_id):
-        return self.graph.nodes[node_id]['pos_indx']
+        return self._graph.nodes[node_id]['pos_indx']
 
     @property
-    def nodes(self):
+    def nodes(self) -> typing.List:
         return self._nodes_labels
 
     @property
-    def edges(self):
-        return list(self.graph.edges)
+    def edges(self) -> typing.List:
+        return list(self._graph.edges)
 
     @property
-    def nodes_indexes(self):
+    def nodes_indexes(self) -> np.ndarray:
         return self._nodes_indexes
 
     @property
-    def nodes_values(self):
+    def nodes_values(self) -> np.ndarray:
         return self._nodes_values
 
     @property
-    def time_scalar_indexing_strucure(self):
+    def time_scalar_indexing_strucure(self) -> np.ndarray:
         return self._time_scalar_indexing_structure
 
     @property
-    def time_filtering(self):
+    def time_filtering(self) -> np.ndarray:
         return self._time_filtering
 
     @property
-    def transition_scalar_indexing_structure(self):
+    def transition_scalar_indexing_structure(self) -> np.ndarray:
         return self._transition_scalar_indexing_structure
 
     @property
-    def transition_filtering(self):
+    def transition_filtering(self) -> np.ndarray:
         return self._transition_filtering
 
     @property
-    def p_combs(self):
+    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

main_package/classes/parameters_estimator.py

@@ -2,7 +2,7 @@
 import numpy as np
 
 import network_graph as ng
-import sample_path as sp
+import trajectory as tr
 import set_of_cims as sofc
 import sets_of_cims_container as acims
 
@@ -10,18 +10,24 @@ import sets_of_cims_container as acims
 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
+    in the graph _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
+    :trajectories:  the trajectories
+    :_net_graph: the net structure
+    :_single_set_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__(self, trajectories: tr.Trajectory, net_graph: ng.NetworkGraph):
+        """
+        Parameters:
+            :trajectories:  the trajectories
+            :_net_graph: the net structure
+        """
+        #self.sample_path = sample_path
+        self._trajectories = trajectories
+        self._net_graph = net_graph
+        #self.sets_of_cims_struct = None
+        self._single_set_of_cims = None
 
     def fast_init(self, node_id: str):
         """
@@ -32,9 +38,9 @@ class ParametersEstimator:
         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)
+        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_for_node(self, node_id: str) -> sofc.SetOfCims:
         """
@@ -45,22 +51,22 @@ class ParametersEstimator:
         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,
+        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._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,
-                                                  self.sample_path.trajectories.complete_trajectory,
-                                                  self.net_graph.transition_filtering,
-                                                  self.net_graph.transition_scalar_indexing_structure,
+                                                  self._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
+        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):
@@ -99,38 +105,44 @@ class ParametersEstimator:
         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("Trajectory", trajectory)
+        #print("Step 1", trajectory[:, int(trajectory.shape[1] / 2) + node_indx])
+        #print("Step 2", trajectory[:, int(trajectory.shape[1] / 2) + node_indx].astype(np.int) >= 0)
+        #print("TrTemp", trj_tmp)
+        #print("Cols Filter", cols_filter)
+        #print("Filtered Tr Temp", trj_tmp[:, cols_filter])
+        #print("Actual Indexing", scalar_indexing / scalar_indexing[0])
+        #print("PreBins",trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0] )
+        #print("Bins", np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1))
+        #print("After BinCount", np.bincount(np.sum(trj_tmp[:, cols_filter] * scalar_indexing / scalar_indexing[0], axis=1).astype(np.int)))
         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])
         M_raveled = M.ravel()
         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)
+        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):
-        #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.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,
+        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)
-            #print(self.net_graph.transition_filtering[indx])
-            #print(self.net_graph.transition_scalar_indexing_structure[indx])
-            self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]),
+                                                 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)
+                                                      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)
 
 
 

main_package/classes/sample_path.py

@@ -10,10 +10,10 @@ class SamplePath:
     cardinalites.
     Has the task of creating the objects that will contain the mentioned data.
 
-    :importer: the Importer objects that will import ad process data
+    :_importer: the Importer objects that will import ad process data
     :trajectories: the Trajectory object that will contain all the concatenated trajectories
     :structure: the Structure Object that will contain all the structurral infos about the net
-    :total_variables_count: the number of variables in the net
+    :_total_variables_count: the number of variables in the net
 
     """
     def __init__(self, importer: imp.AbstractImporter):
@@ -21,11 +21,11 @@ class SamplePath:
         Parameters:
             :importer: the Importer objects that will import ad process data
         """
-        self.importer = importer
+        self._importer = importer
         self._trajectories = None
         self._structure = None
-        self.total_variables_count = None
-        self.importer.import_data()
+        self._total_variables_count = None
+        self._importer.import_data()
 
     def build_trajectories(self):
         """
@@ -37,12 +37,12 @@ class SamplePath:
         Returns:
             :void
         """
-        #self.importer.import_data()
+        #self._importer.import_data()
         self._trajectories = \
-            tr.Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples),
-                          len(self.importer.sorter) + 1)
+            tr.Trajectory(self._importer.build_list_of_samples_array(self._importer.concatenated_samples),
+                          len(self._importer.sorter) + 1)
         #self.trajectories.append(trajectory)
-        self.importer.clear_concatenated_frame()
+        self._importer.clear_concatenated_frame()
 
     def build_structure(self):
         """
@@ -52,19 +52,16 @@ class SamplePath:
         Returns:
             :void
         """
-        if self.importer.sorter != self.importer.variables.iloc[:, 0].to_list():
+        if self._importer.sorter != self._importer.variables.iloc[:, 0].to_list():
             raise RuntimeError("The Dataset columns order have to match the order of labels in the variables Frame!")
 
-        self.total_variables_count = len(self.importer.sorter)
-        #labels = self.importer.variables[self.importer.variables_key].to_list()
-        #print("SAMPLE PATH LABELS",labels)
-        #print(self.importer.variables)
-        labels = self.importer.variables.iloc[:, 0].to_list()
-        indxs = self.importer.variables.index.to_numpy()
-        vals = self.importer.variables.iloc[:, 1].to_numpy()
-        edges = list(self.importer.structure.to_records(index=False))
+        self._total_variables_count = len(self._importer.sorter)
+        labels = self._importer.variables.iloc[:, 0].to_list()
+        indxs = self._importer.variables.index.to_numpy()
+        vals = self._importer.variables.iloc[:, 1].to_numpy()
+        edges = list(self._importer.structure.to_records(index=False))
         self._structure = st.Structure(labels, indxs, vals, edges,
-                                       self.total_variables_count)
+                                       self._total_variables_count)
 
     @property
     def trajectories(self) -> tr.Trajectory:
@@ -74,8 +71,9 @@ class SamplePath:
     def structure(self) -> st.Structure:
         return self._structure
 
+    @property
     def total_variables_count(self):
-        return self.total_variables_count
+        return self._total_variables_count
 
 
 

main_package/classes/set_of_cims.py

@@ -7,26 +7,33 @@ import conditional_intensity_matrix as cim
 
 class SetOfCims:
     """
-    Aggregates all the CIMS of the node identified by the label node_id.
-
-    :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
+    Aggregates all the CIMS of the node identified by the label _node_id.
 
+    :_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
+    :_transition_matrices: matrix containing all the transition matrices for the node
+    :_actual_cims: the cims of the node
     """
 
     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
-        self.actual_cims = []
-        self.state_residence_times = None
-        self.transition_matrices = None
-        self.p_combs = p_combs
+        """
+        Parameters:
+        :_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
+
+        """
+        self._node_id = node_id
+        self._parents_states_number = parents_states_number
+        self._node_states_number = node_states_number
+        self._actual_cims = []
+        self._state_residence_times = None
+        self._transition_matrices = None
+        self._p_combs = p_combs
         self.build_times_and_transitions_structures()
 
     def build_times_and_transitions_structures(self):
@@ -34,73 +41,64 @@ class SetOfCims:
         Initializes at the correct dimensions the state residence times matrix and the state transition matrices
 
         Parameters:
-            void
+            :void
         Returns:
-            void
+            :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)
+        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)
         else:
-            self.state_residence_times = \
-                np.zeros((np.prod(self.parents_states_number), self.node_states_number), dtype=np.float)
-            self.transition_matrices = np.zeros([np.prod(self.parents_states_number), self.node_states_number,
-                                                 self.node_states_number], dtype=np.int)
+            self._state_residence_times = \
+                np.zeros((np.prod(self._parents_states_number), self._node_states_number), dtype=np.float)
+            self._transition_matrices = np.zeros([np.prod(self._parents_states_number), self._node_states_number,
+                                                  self._node_states_number], dtype=np.int)
 
-    def build_cims(self, state_res_times: typing.List, transition_matrices: typing.List):
+    def build_cims(self, state_res_times: np.ndarray, transition_matrices: np.ndarray):
         """
         Build the ConditionalIntensityMatrix object given the state residence times and transitions matrices.
         Compute the cim coefficients.
 
         Parameters:
-            state_res_times: the state residence times matrix
-            transition_matrices: the transition matrices
+            :state_res_times: the state residence times matrix
+            :_transition_matrices: the transition matrices
         Returns:
-            void
+            :void
         """
         for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
             cim_to_add = cim.ConditionalIntensityMatrix(state_res_time_vector, transition_matrix)
             cim_to_add.compute_cim_coefficients()
-            self.actual_cims.append(cim_to_add)
-        self.actual_cims = np.array(self.actual_cims)
-        self.transition_matrices = None
-        self.state_residence_times = None
+            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) -> np.ndarray:
         """
-        Filter the cims contained in the array actual_cims given the boolean mask mask_arr and the index comb.
+        Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index comb.
         Parameters:
-            mask_arr: the boolean mask
-            comb: the indexes of the selected cims
+            :mask_arr: the boolean mask
+            :comb: the indexes of the selected cims
 
         Returns:
-            Array of ConditionalIntensityMatrix
+            :Array of ConditionalIntensityMatrix
         """
         if mask_arr.size <= 1:
-            return self.actual_cims
+            return self._actual_cims
         else:
-            flat_indxs = np.argwhere(np.all(self.p_combs[:, mask_arr] == comb, axis=1)).ravel()
-            return self.actual_cims[flat_indxs]
+            flat_indxs = np.argwhere(np.all(self._p_combs[:, mask_arr] == comb, axis=1)).ravel()
+            return self._actual_cims[flat_indxs]
 
     @property
-    def get_cims(self):
-        return self.actual_cims
+    def actual_cims(self) -> np.ndarray:
+        return self._actual_cims
+
+    @property
+    def p_combs(self) -> np.ndarray:
+        return self._p_combs
 
     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 indexes_converter(self, indexes):
-        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"""
+        return len(self._actual_cims)
+
 
 

main_package/classes/simple_cvs_importer.py

@@ -19,7 +19,7 @@ class CSVImporter(ai.AbstractImporter):
         self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
 
     def read_csv_file(self):
-        df = pd.read_csv(self.file_path)
+        df = pd.read_csv(self._file_path)
         df.drop(df.columns[[0]], axis=1, inplace=True)
         self._df_samples_list = [df]
 

main_package/classes/structure.py

@@ -16,6 +16,15 @@ class Structure:
 
     def __init__(self, nodes_label_list: ty.List, node_indexes_arr: np.ndarray, nodes_vals_arr: np.ndarray,
                  edges_list: ty.List, total_variables_number: int):
+        """
+        Parameters:
+            :nodes_labels_list: the symbolic names of the variables
+            :nodes_indexes_arr: the indexes of the nodes
+            :nodes_vals_arr: the cardinalites of the nodes
+            :edges_list: the edges of the network
+            :total_variables_number: the total number of variables in the net
+
+        """
         self._nodes_labels_list = nodes_label_list
         self._nodes_indexes_arr = node_indexes_arr
         self._nodes_vals_arr = nodes_vals_arr
@@ -23,13 +32,11 @@ class Structure:
         self._total_variables_number = total_variables_number
 
     @property
-    def edges(self):
-        #records = self.structure_frame.to_records(index=False)
-        #edges_list = list(records)
+    def edges(self) -> ty.List:
         return self._edges_list
 
     @property
-    def nodes_labels(self):
+    def nodes_labels(self) -> ty.List:
         return self._nodes_labels_list
 
     @property
@@ -41,7 +48,7 @@ class Structure:
         return self._nodes_vals_arr
 
     @property
-    def total_variables_number(self):
+    def total_variables_number(self) -> int:
         return self._total_variables_number
 
     def get_node_id(self, node_indx: int) -> str:

main_package/classes/structure_estimator.py

@@ -21,28 +21,35 @@ 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
+    :_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: 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)
-        self.nodes_vals = self.sample_path.structure.nodes_values
-        self.nodes_indxs = self.sample_path.structure.nodes_indexes
-        self.complete_graph = self.build_complete_graph(self.sample_path.structure.nodes_labels)
-        self.exp_test_sign = exp_test_alfa
-        self.chi_test_alfa = chi_test_alfa
-        self.cache = ch.Cache()
+        """
+        Parameters:
+            :_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
 
-    def build_complete_graph(self, node_ids: typing.List):
+        """
+        self._sample_path = sample_path
+        self._nodes = np.array(self._sample_path.structure.nodes_labels)
+        self._nodes_vals = self._sample_path.structure.nodes_values
+        self._nodes_indxs = self._sample_path.structure.nodes_indexes
+        self._complete_graph = self.build_complete_graph(self._sample_path.structure.nodes_labels)
+        self._exp_test_sign = exp_test_alfa
+        self._chi_test_alfa = chi_test_alfa
+        self._cache = ch.Cache()
+
+    def build_complete_graph(self, node_ids: typing.List) -> nx.DiGraph:
         """
         Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:
 
@@ -57,9 +64,9 @@ class StructureEstimator:
         return complete_graph
 
     def complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
-                      tot_vars_count: int):
+                      tot_vars_count: int) -> bool:
         """
-        Permorms a complete independence test on the directed graphs G1 = test_child U parent_set
+        Performs a complete independence test on the directed graphs G1 = test_child U parent_set
         G2 = G1 U test_parent (added as an additional parent of the test_child).
         Generates all the necessary structures and datas to perform the tests.
 
@@ -68,98 +75,61 @@ class StructureEstimator:
             test_child: the node label of the child
             parent_set: the common parent set
             child_states_numb: the cardinality of the test_child
-            tot_vars_count_ the total number of variables in the net
+            tot_vars_count: the total number of variables in the net
         Returns:
             True iff test_child and test_parent are independent given the sep_set parent_set
             False otherwise
         """
-        #print("Test Parent:", test_parent)
-        #print("Sep Set", parent_set)
         p_set = parent_set[:]
         complete_info = parent_set[:]
         complete_info.append(test_child)
 
         parents = np.array(parent_set)
         parents = np.append(parents, test_parent)
-        #print("PARENTS", parents)
-        #parents.sort()
-        sorted_parents = self.nodes[np.isin(self.nodes, parents)]
-        #print("SORTED PARENTS", sorted_parents)
+        sorted_parents = self._nodes[np.isin(self._nodes, parents)]
         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)
-            #sofc1 = self.cache.find(test_child)
-        #else:
-        sofc1 = self.cache.find(set(p_set))
+        sofc1 = self._cache.find(set(p_set))
 
         if not 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])
-            #print("L1", l1)
-            indxs1 = self.nodes_indxs[bool_mask1]
-            #print("INDXS 1", indxs1)
-            vals1 = self.nodes_vals[bool_mask1]
+            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 = st.Structure(l1, indxs1, vals1, eds1, tot_vars_count)
             g1 = ng.NetworkGraph(s1)
             g1.fast_init(test_child)
-            p1 = pe.ParametersEstimator(self.sample_path, g1)
+            p1 = pe.ParametersEstimator(self._sample_path.trajectories, g1)
             p1.fast_init(test_child)
             sofc1 = p1.compute_parameters_for_node(test_child)
-            #if not p_set:
-                #self.cache.put(test_child, sofc1)
-            #else:
-            self.cache.put(set(p_set), sofc1)
+            self._cache.put(set(p_set), sofc1)
         sofc2 = None
-        #p_set.append(test_parent)
         p_set.insert(0, test_parent)
         if 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)
-            sofc2 = self.cache.find(set(p_set))
-            #if sofc2:
-                #print("Sofc2 in CACHE ", sofc2.actual_cims)
-            #print(self.cache.list_of_sets_of_indxs)
+            sofc2 = self._cache.find(set(p_set))
         if not sofc2:
-            #print("Cache MISSS SOFC2")
             complete_info.append(test_parent)
-            bool_mask2 = np.isin(self.nodes, complete_info)
-            #print("BOOL MASK 2",bool_mask2)
-            l2 = list(self.nodes[bool_mask2])
-            #print("L2", l2)
-            indxs2 = self.nodes_indxs[bool_mask2]
-            #print("INDXS 2", indxs2)
-            vals2 = self.nodes_vals[bool_mask2]
+            bool_mask2 = np.isin(self._nodes, complete_info)
+            l2 = list(self._nodes[bool_mask2])
+            indxs2 = self._nodes_indxs[bool_mask2]
+            vals2 = self._nodes_vals[bool_mask2]
             eds2 = list(itertools.product(p_set, test_child))
             s2 = st.Structure(l2, indxs2, vals2, eds2, tot_vars_count)
             g2 = ng.NetworkGraph(s2)
             g2.fast_init(test_child)
-            p2 = pe.ParametersEstimator(self.sample_path, g2)
+            p2 = pe.ParametersEstimator(self._sample_path.trajectories, g2)
             p2.fast_init(test_child)
             sofc2 = p2.compute_parameters_for_node(test_child)
-            self.cache.put(set(p_set), sofc2)
+            self._cache.put(set(p_set), sofc2)
         for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
-            #print("GETTING THIS P COMB", p_comb)
-            #if len(parent_set) > 1:
             cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
-            #else:
-                #cond_cims = sofc2.actual_cims
-            #print("COnd Cims", cond_cims)
             for cim2 in cond_cims:
-                #cim2 = sofc2.actual_cims[j]
-                #print(indx)
-                #print("Run Test", i, j)
                 if not self.independence_test(child_states_numb, cim1, cim2):
                     return False
         return True
 
     def independence_test(self, child_states_numb: int, cim1: condim.ConditionalIntensityMatrix,
-                          cim2: condim.ConditionalIntensityMatrix):
+                          cim2: condim.ConditionalIntensityMatrix) -> bool:
         """
         Compute the actual independence test using two cims.
         It is performed first the exponential test and if the null hypothesis is not rejected,
@@ -181,38 +151,23 @@ class StructureEstimator:
         C1 = cim1.cim
         C2 = cim2.cim
         F_stats = C2.diagonal() / C1.diagonal()
-        exp_alfa = self.exp_test_sign
+        exp_alfa = self._exp_test_sign
         for val in range(0, child_states_numb):
             if F_stats[val] < f_dist.ppf(exp_alfa / 2, r1s[val], r2s[val]) or \
                     F_stats[val] > f_dist.ppf(1 - exp_alfa / 2, r1s[val], r2s[val]):
                 #print("CONDITIONALLY DEPENDENT EXP")
                 return False
-        #M1_no_diag = self.remove_diagonal_elements(cim1.state_transition_matrix)
-        #M2_no_diag = self.remove_diagonal_elements(cim2.state_transition_matrix)
         M1_no_diag = M1[~np.eye(M1.shape[0], dtype=bool)].reshape(M1.shape[0], -1)
         M2_no_diag = M2[~np.eye(M2.shape[0], dtype=bool)].reshape(
             M2.shape[0], -1)
-        chi_2_quantile = chi2_dist.ppf(1 - self.chi_test_alfa, child_states_numb - 1)
-        """
-        Ks = np.sqrt(cim1.state_transition_matrix.diagonal() / cim2.state_transition_matrix.diagonal())
-        Ls = np.reciprocal(Ks)
-        chi_stats = np.sum((np.power((M2_no_diag.T * Ks).T - (M1_no_diag.T * Ls).T, 2) \
-                            / (M1_no_diag + M2_no_diag)), axis=1)"""
+        chi_2_quantile = chi2_dist.ppf(1 - self._chi_test_alfa, child_states_numb - 1)
         Ks = np.sqrt(r1s / r2s)
         Ls = np.sqrt(r2s / r1s)
         for val in range(0, child_states_numb):
-            #K = math.sqrt(cim1.state_transition_matrix[val][val] / cim2.state_transition_matrix[val][val])
-            #L = 1 / K
             Chi = np.sum(np.power(Ks[val] * M2_no_diag[val] - Ls[val] *M1_no_diag[val], 2) /
                          (M1_no_diag[val] + M2_no_diag[val]))
-
-            #print("Chi Stats", Chi)
-            #print("Chi Quantile", chi_2_quantile)
             if Chi > chi_2_quantile:
-        #if np.any(chi_stats > chi_2_quantile):
-                #print("CONDITIONALLY DEPENDENT CHI")
                 return False
-            #print("Chi test", Chi)
         return True
 
     def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int):
@@ -221,54 +176,45 @@ class StructureEstimator:
 
         Parameters:
             var_id: the node label of the test child
-            tot_vars_count: the number of nodes in the net
+            tot_vars_count: the number of _nodes in the net
         Returns:
             void
         """
         print("##################TESTING VAR################", var_id)
-        u = list(self.complete_graph.predecessors(var_id))
-        #tests_parents_numb = len(u)
-        #complete_frame = self.complete_graph_frame
-        #test_frame = complete_frame.loc[complete_frame['To'].isin([var_id])]
-        child_states_numb = self.sample_path.structure.get_states_number(var_id)
+        u = list(self._complete_graph.predecessors(var_id))
+        child_states_numb = self._sample_path.structure.get_states_number(var_id)
         b = 0
         while b < len(u):
-            #for parent_id in u:
             parent_indx = 0
             while parent_indx < len(u):
                 removed = False
-                #if not list(self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])):
-                    #break
                 S = self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])
-                #print("U Set", u)
-                #print("S", S)
                 test_parent = u[parent_indx]
                 #print("Test Parent", test_parent)
                 for parents_set in S:
-                    #print("Parent Set", parents_set)
-                    #print("Test Parent", test_parent)
+                    print("Parent Set", parents_set)
+                    print("Test Parent", test_parent)
                     if self.complete_test(test_parent, var_id, parents_set, child_states_numb, tot_vars_count):
                         #print("Removing EDGE:", test_parent, var_id)
-                        self.complete_graph.remove_edge(test_parent, var_id)
+                        self._complete_graph.remove_edge(test_parent, var_id)
                         u.remove(test_parent)
                         removed = True
                         break
-                    #else:
-                        #parent_indx += 1
                 if not removed:
                     parent_indx += 1
             b += 1
-        self.cache.clear()
+        self._cache.clear()
 
-    def generate_possible_sub_sets_of_size(self, u: typing.List, size: int, parent_label: str):
+    def generate_possible_sub_sets_of_size(self, u: typing.List, size: int, parent_label: str) -> \
+            typing.Iterator:
         """
         Creates a list containing all possible subsets of the list u of size size,
         that do not contains a the node identified by parent_label.
 
         Parameters:
-            u: the list of nodes
+            u: the list of _nodes
             size: the size of the subsets
-            parent_label: the nodes to exclude in the subsets generation
+            parent_label: the _nodes to exclude in the subsets generation
         Returns:
             a Map Object containing a list of lists
 
@@ -286,8 +232,8 @@ class StructureEstimator:
             void
         """
         ctpc_algo = self.one_iteration_of_CTPC_algorithm
-        total_vars_numb = self.sample_path.total_variables_count
-        [ctpc_algo(n, total_vars_numb) for n in self.nodes]
+        total_vars_numb = self._sample_path.total_variables_count
+        [ctpc_algo(n, total_vars_numb) for n in self._nodes]
 
     def save_results(self):
         """
@@ -298,17 +244,10 @@ class StructureEstimator:
         Returns:
             void
         """
-        res = json_graph.node_link_data(self.complete_graph)
-        name = self.sample_path.importer.file_path.rsplit('/',1)[-1]
-        #print(name)
-        name = 'results_' + name
+        res = json_graph.node_link_data(self._complete_graph)
+        name = self._sample_path._importer.file_path.rsplit('/', 1)[-1]
+        name = 'results_' + name #TODO va aggiunto anche l'indice di array
         with open(name, 'w') as f:
             json.dump(res, f)
 
 
-    def remove_diagonal_elements(self, matrix):
-        m = matrix.shape[0]
-        strided = np.lib.stride_tricks.as_strided
-        s0, s1 = matrix.strides
-        return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
-

main_package/classes/trajectory.py

@@ -15,9 +15,15 @@ class Trajectory:
     """
 
     def __init__(self, list_of_columns, original_cols_number):
+        """
+        Parameters:
+            :list_of_columns: the list containing the times array and values matrix
+            :original_cols_numb: total number of cols in the data
+
+        """
         if type(list_of_columns[0][0]) != np.float64:
             raise TypeError('The first array in the list has to be Times')
-        self.original_cols_number = original_cols_number
+        self._original_cols_number = original_cols_number
         self._actual_trajectory = np.array(list_of_columns[1:], dtype=np.int).T
         self._times = np.array(list_of_columns[0], dtype=np.float)
 
@@ -29,7 +35,7 @@ class Trajectory:
         Returns:
             a numpy matrix containing ONLY the original columns values, not the shifted ones
         """
-        return self._actual_trajectory[:, :self.original_cols_number]
+        return self._actual_trajectory[:, :self._original_cols_number]
 
     @property
     def complete_trajectory(self) -> np.ndarray:

main_package/tests/test_cache.py

@@ -11,31 +11,31 @@ class TestCache(unittest.TestCase):
 
     def test_init(self):
         c1 = ch.Cache()
-        self.assertFalse(c1.list_of_sets_of_parents)
-        self.assertFalse(c1.actual_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])
+        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])
+        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.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)
@@ -47,11 +47,11 @@ class TestCache(unittest.TestCase):
         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(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)
+        self.assertFalse(c1._list_of_sets_of_parents)
+        self.assertFalse(c1._actual_cache)
 
 
 

main_package/tests/test_json_importer.py

@@ -16,14 +16,14 @@ class TestJsonImporter(unittest.TestCase):
         cls.read_files = glob.glob(os.path.join('../data', "*.json"))
 
     def test_init(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
-        self.assertEqual(j1.samples_label, 'samples')
-        self.assertEqual(j1.structure_label, 'dyn.str')
-        self.assertEqual(j1.variables_label, 'variables')
-        self.assertEqual(j1.time_key, 'Time')
-        self.assertEqual(j1.variables_key, 'Name')
-        self.assertEqual(j1.file_path, self.read_files[0])
-        self.assertIsNone(j1.df_samples_list)
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
+        self.assertEqual(j1._samples_label, 'samples')
+        self.assertEqual(j1._structure_label, 'dyn.str')
+        self.assertEqual(j1._variables_label, 'variables')
+        self.assertEqual(j1._time_key, 'Time')
+        self.assertEqual(j1._variables_key, 'Name')
+        self.assertEqual(j1._file_path, self.read_files[0])
+        self.assertIsNone(j1._df_samples_list)
         self.assertIsNone(j1.variables)
         self.assertIsNone(j1.structure)
         self.assertIsNone(j1.concatenated_samples)
@@ -35,7 +35,7 @@ class TestJsonImporter(unittest.TestCase):
             json.dump(data_set, f)
         path = os.getcwd()
         path = path + '/data.json'
-        j1 = ji.JsonImporter(path, '', '', '', '', '')
+        j1 = ji.JsonImporter(path, '', '', '', '', '', 0)
         imported_data = j1.read_json_file()
         self.assertTrue(self.ordered(data_set) == self.ordered(imported_data))
         os.remove('data.json')
@@ -43,32 +43,32 @@ class TestJsonImporter(unittest.TestCase):
     def test_read_json_file_not_found(self):
         path = os.getcwd()
         path = path + '/data.json'
-        j1 = ji.JsonImporter(path, '', '', '', '', '')
+        j1 = ji.JsonImporter(path, '', '', '', '', '', 0)
         self.assertRaises(FileNotFoundError, j1.read_json_file)
 
     def test_normalize_trajectories(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
         #print(raw_data)
-        df_samples_list = j1.normalize_trajectories(raw_data, 0, j1.samples_label)
-        self.assertEqual(len(df_samples_list), len(raw_data[0][j1.samples_label]))
-        #self.assertEqual(list(j1.df_samples_list[0].columns.values)[1:], j1.sorter)
+        df_samples_list = j1.normalize_trajectories(raw_data, 0, j1._samples_label)
+        self.assertEqual(len(df_samples_list), len(raw_data[0][j1._samples_label]))
+        #self.assertEqual(list(j1._df_samples_list[0].columns.values)[1:], j1.sorter)
 
     def test_normalize_trajectories_wrong_indx(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        self.assertRaises(IndexError, j1.normalize_trajectories, raw_data, 474, j1.samples_label)
+        self.assertRaises(IndexError, j1.normalize_trajectories, raw_data, 474, j1._samples_label)
 
     def test_normalize_trajectories_wrong_key(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'sample', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'sample', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        self.assertRaises(KeyError, j1.normalize_trajectories, raw_data, 0, j1.samples_label)
+        self.assertRaises(KeyError, j1.normalize_trajectories, raw_data, 0, j1._samples_label)
 
     def test_compute_row_delta_single_samples_frame(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        j1.df_samples_list = j1.import_trajectories(raw_data)
-        sample_frame = j1.df_samples_list[0]
+        j1._df_samples_list = j1.import_trajectories(raw_data)
+        sample_frame = j1._df_samples_list[0]
         original_copy = sample_frame.copy()
         columns_header = list(sample_frame.columns.values)
         shifted_cols_header = [s + "S" for s in columns_header[1:]]
@@ -87,27 +87,27 @@ class TestJsonImporter(unittest.TestCase):
                            np.array(original_copy.iloc[indx + 1][columns_header[1:]], dtype=int))
 
     def test_compute_row_delta_in_all_frames(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        j1.df_samples_list = j1.import_trajectories(raw_data)
-        j1._sorter = j1.build_sorter(j1.df_samples_list[0])
-        j1.compute_row_delta_in_all_samples_frames(j1.df_samples_list)
-        self.assertEqual(list(j1.df_samples_list[0].columns.values),
-                         list(j1.concatenated_samples.columns.values)[:len(list(j1.df_samples_list[0].columns.values))])
-        self.assertEqual(list(j1.concatenated_samples.columns.values)[0], j1.time_key)
+        j1._df_samples_list = j1.import_trajectories(raw_data)
+        j1._sorter = j1.build_sorter(j1._df_samples_list[0])
+        j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list)
+        self.assertEqual(list(j1._df_samples_list[0].columns.values),
+                         list(j1.concatenated_samples.columns.values)[:len(list(j1._df_samples_list[0].columns.values))])
+        self.assertEqual(list(j1.concatenated_samples.columns.values)[0], j1._time_key)
 
     def test_clear_data_frame_list(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        j1.df_samples_list = j1.import_trajectories(raw_data)
-        j1._sorter = j1.build_sorter(j1.df_samples_list[0])
-        j1.compute_row_delta_in_all_samples_frames(j1.df_samples_list)
+        j1._df_samples_list = j1.import_trajectories(raw_data)
+        j1._sorter = j1.build_sorter(j1._df_samples_list[0])
+        j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list)
         j1.clear_data_frame_list()
-        for df in j1.df_samples_list:
+        for df in j1._df_samples_list:
             self.assertTrue(df.empty)
 
     def test_clear_concatenated_frame(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         j1.import_data()
         j1.clear_concatenated_frame()
         self.assertTrue(j1.concatenated_samples.empty)
@@ -118,7 +118,7 @@ class TestJsonImporter(unittest.TestCase):
             json.dump(data_set, f)
         path = os.getcwd()
         path = path + '/data.json'
-        j1 = ji.JsonImporter(path, '', '', '', '', '')
+        j1 = ji.JsonImporter(path, '', '', '', '', '', 0)
         raw_data = j1.read_json_file()
         frame = pd.DataFrame(raw_data)
         col_list = j1.build_list_of_samples_array(frame)
@@ -130,39 +130,33 @@ class TestJsonImporter(unittest.TestCase):
         os.remove('data.json')
 
     def test_import_variables(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         sorter = ['X', 'Y', 'Z']
         raw_data = [{'variables':{"Name": ['X', 'Y', 'Z'], "value": [3, 3, 3]}}]
-        df_var = j1.import_variables(raw_data, sorter)
-        self.assertEqual(list(df_var[j1.variables_key]), sorter)
+        df_var = j1.import_variables(raw_data)
+        self.assertEqual(list(df_var[j1._variables_key]), sorter)
 
     def test_import_structure(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = [{"dyn.str":[{"From":"X","To":"Z"},{"From":"Y","To":"Z"},{"From":"Z","To":"Y"}]}]
         df_struct = j1.import_structure(raw_data)
         #print(raw_data[0]['dyn.str'][0].items())
         self.assertIsInstance(df_struct, pd.DataFrame)
 
     def test_import_sampled_cims(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         raw_data = j1.read_json_file()
-        j1.df_samples_list = j1.import_trajectories(raw_data)
-        j1._sorter = j1.build_sorter(j1.df_samples_list[0])
+        j1._df_samples_list = j1.import_trajectories(raw_data)
+        j1._sorter = j1.build_sorter(j1._df_samples_list[0])
         cims = j1.import_sampled_cims(raw_data, 0, 'dyn.cims')
         #j1.import_variables(raw_data, j1.sorter)
         self.assertEqual(list(cims.keys()), j1.sorter)
 
     def test_import_data(self):
-        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        j1 = ji.JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 2)
         j1.import_data()
-        #lp = LineProfiler()
-
-        #lp_wrapper = lp(j1.import_data)
-        #lp_wrapper()
-        #lp.print_stats()
-        #j1.import_data()
-        self.assertEqual(list(j1.variables[j1.variables_key]),
-                         list(j1.concatenated_samples.columns.values[1:len(j1.variables[j1.variables_key]) + 1]))
+        self.assertEqual(list(j1.variables[j1._variables_key]),
+                         list(j1.concatenated_samples.columns.values[1:len(j1.variables[j1._variables_key]) + 1]))
         print(j1.variables)
         print(j1.structure)
         print(j1.concatenated_samples)

main_package/tests/test_networkgraph.py

@@ -12,20 +12,19 @@ import network_graph as ng
 import json_importer as ji
 
 
-
 class TestNetworkGraph(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls.read_files = glob.glob(os.path.join('../data', "*.json"))
-        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
 
     def test_init(self):
         g1 = ng.NetworkGraph(self.s1.structure)
-        self.assertEqual(self.s1.structure, g1.graph_struct)
-        self.assertIsInstance(g1.graph, nx.DiGraph)
+        self.assertEqual(self.s1.structure, g1._graph_struct)
+        self.assertIsInstance(g1._graph, nx.DiGraph)
         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))
@@ -59,7 +58,7 @@ class TestNetworkGraph(unittest.TestCase):
                     self.assertEqual(graph.get_node_indx(node), node_indx)
             else:
                 for node, node_val in zip(sorted_par_list_aggregated_info[0], sorted_par_list_aggregated_info[2]):
-                    self.assertEqual(graph.graph_struct.get_states_number(node), node_val)
+                    self.assertEqual(graph._graph_struct.get_states_number(node), node_val)
 
     def test_get_ord_set_of_par_of_all_nodes(self):
         g1 = ng.NetworkGraph(self.s1.structure)
@@ -68,36 +67,7 @@ class TestNetworkGraph(unittest.TestCase):
         sorted_list_of_par_lists = g1.get_ord_set_of_par_of_all_nodes()
         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())
-        g1.add_edges(self.s1.structure.list_of_edges())
-        g1.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
-        #print(g1.get_ordered_by_indx_parents_values_for_all_nodes())
-        parents_values_list = g1.get_ordered_by_indx_parents_values_for_all_nodes()
-        for pv1, aggr in zip(parents_values_list, g1.aggregated_info_about_nodes_parents):
-            self.assertEqual(pv1, aggr[2])
-
-    def test_get_states_number_of_all_nodes_sorted(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())
-        nodes_cardinality_list = g1.get_states_number_of_all_nodes_sorted()
-        for val, node in zip(nodes_cardinality_list, g1.get_nodes_sorted_by_indx()):
-            self.assertEqual(val, g1.get_states_number(node))
-
-    def test_build_fancy_indexing_structure_no_offset(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.aggregated_info_about_nodes_parents = g1.get_ord_set_of_par_of_all_nodes()
-        fancy_indx = g1.build_fancy_indexing_structure(0)
-        for par_indxs, aggr in zip(fancy_indx, g1.aggregated_info_about_nodes_parents):
-            self.assertEqual(par_indxs, aggr[1])
 
-    def test_build_fancy_indexing_structure_offset(self):
-        pass #TODO il codice di netgraph deve gestire questo caso"""
 
     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)
@@ -130,30 +100,30 @@ class TestNetworkGraph(unittest.TestCase):
         g1 = ng.NetworkGraph(self.s1.structure)
         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]
+        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)
-        for node_id, p_i ,p_l, p_v in zip(g1.graph_struct.nodes_labels, fancy_indx, p_labels, p_vals):
+        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.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._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_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, p_labels, p_v in zip(g1.graph_struct.nodes_labels, fancy_indx, p_labels, p_vals):
+        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)
 
     def test_build_time_columns_filtering_structure(self):
         g1 = ng.NetworkGraph(self.s1.structure)
         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._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()
         t_filter = []
@@ -170,13 +140,13 @@ class TestNetworkGraph(unittest.TestCase):
         g1 = ng.NetworkGraph(self.s1.structure)
         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._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()
         m_filter = []
         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) + 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))
@@ -187,7 +157,7 @@ class TestNetworkGraph(unittest.TestCase):
         g1 = ng.NetworkGraph(self.s1.structure)
         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._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()
 
@@ -219,7 +189,7 @@ class TestNetworkGraph(unittest.TestCase):
         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)))
+            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)

main_package/tests/test_parameters_estimator.py

@@ -17,10 +17,14 @@ class TestParametersEstimatior(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         cls.read_files = glob.glob(os.path.join('../data', "*.json"))
-        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.array_indx = 8
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name',
+                                       cls.array_indx)
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
+        print(cls.s1.structure.edges)
+        print(cls.s1.structure.nodes_values)
         cls.g1 = ng.NetworkGraph(cls.s1.structure)
         cls.g1.init_graph()
 
@@ -28,29 +32,26 @@ class TestParametersEstimatior(unittest.TestCase):
         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 = pe.ParametersEstimator(self.s1.trajectories, g)
+            self.assertEqual(p1._trajectories, self.s1.trajectories)
+            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)
+            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 = pe.ParametersEstimator(self.s1.trajectories, 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)
+            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)
@@ -60,30 +61,9 @@ class TestParametersEstimatior(unittest.TestCase):
             self.assertTrue(np.all(np.isclose(r1, r2, 1e-01, 1e-01) == True))
 
     def aux_import_sampled_cims(self, cims_label):
-        i1 = ji.JsonImporter(self.read_files[0], '', '', '', '', '')
+        i1 = ji.JsonImporter(self.read_files[0], '', '', '', '', '', self.array_indx)
         raw_data = i1.read_json_file()
-        return i1.import_sampled_cims(raw_data, 0, cims_label)
+        return i1.import_sampled_cims(raw_data, self.array_indx, 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_sample_path.py

@@ -14,14 +14,14 @@ class TestSamplePath(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
         cls.read_files = glob.glob(os.path.join('../data', "*.json"))
-        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
 
     def test_init(self):
         s1 = sp.SamplePath(self.importer)
         self.assertIsNone(s1.trajectories)
         self.assertIsNone(s1.structure)
-        self.assertFalse(s1.importer.concatenated_samples.empty)
-        self.assertIsNone(s1.total_variables_count)
+        self.assertFalse(s1._importer.concatenated_samples.empty)
+        self.assertIsNone(s1._total_variables_count)
 
     def test_build_trajectories(self):
         s1 = sp.SamplePath(self.importer)
@@ -32,7 +32,7 @@ class TestSamplePath(unittest.TestCase):
         s1 = sp.SamplePath(self.importer)
         s1.build_structure()
         self.assertIsInstance(s1.structure, st.Structure)
-        self.assertEqual(s1.total_variables_count, len(s1.importer.sorter))
+        self.assertEqual(s1._total_variables_count, len(s1._importer.sorter))
 
 if __name__ == '__main__':
     unittest.main()

main_package/tests/test_setofcims.py

@@ -59,17 +59,17 @@ class TestSetOfCims(unittest.TestCase):
             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)
+        sofc1.build_cims(np.array(state_res_times_list), np.array(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))
+                        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]))
+                        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 = []
@@ -81,23 +81,23 @@ class TestSetOfCims(unittest.TestCase):
             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)
+        so1.build_cims(np.array(state_res_times_list), np.array(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)
+        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, 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)
-        self.assertEqual(sofcims.state_residence_times.shape[0], np.prod(np.array(parents_states_number)))
-        self.assertEqual(len(sofcims.state_residence_times[0]),node_states_number)
-        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)
+        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)
+        self.assertEqual(sofcims._state_residence_times.shape[0], np.prod(np.array(parents_states_number)))
+        self.assertEqual(len(sofcims._state_residence_times[0]), node_states_number)
+        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)

main_package/tests/test_structure_estimator.py

@@ -21,7 +21,7 @@ class TestStructureEstimator(unittest.TestCase):
     @classmethod
     def setUpClass(cls):
         cls.read_files = glob.glob(os.path.join('../data', "*.json"))
-        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+        cls.importer = ji.JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 3)
         cls.s1 = sp.SamplePath(cls.importer)
         cls.s1.build_trajectories()
         cls.s1.build_structure()
@@ -30,14 +30,14 @@ class TestStructureEstimator(unittest.TestCase):
         exp_alfa = 0.1
         chi_alfa = 0.1
         se1 = se.StructureEstimator(self.s1, exp_alfa, chi_alfa)
-        self.assertEqual(self.s1, se1.sample_path)
-        self.assertTrue(np.array_equal(se1.nodes, np.array(self.s1.structure.nodes_labels)))
-        self.assertTrue(np.array_equal(se1.nodes_indxs, self.s1.structure.nodes_indexes))
-        self.assertTrue(np.array_equal(se1.nodes_vals, self.s1.structure.nodes_values))
-        self.assertEqual(se1.exp_test_sign, exp_alfa)
-        self.assertEqual(se1.chi_test_alfa, chi_alfa)
-        self.assertIsInstance(se1.complete_graph, nx.DiGraph)
-        self.assertIsInstance(se1.cache, ch.Cache)
+        self.assertEqual(self.s1, se1._sample_path)
+        self.assertTrue(np.array_equal(se1._nodes, np.array(self.s1.structure.nodes_labels)))
+        self.assertTrue(np.array_equal(se1._nodes_indxs, self.s1.structure.nodes_indexes))
+        self.assertTrue(np.array_equal(se1._nodes_vals, self.s1.structure.nodes_values))
+        self.assertEqual(se1._exp_test_sign, exp_alfa)
+        self.assertEqual(se1._chi_test_alfa, chi_alfa)
+        self.assertIsInstance(se1._complete_graph, nx.DiGraph)
+        self.assertIsInstance(se1._cache, ch.Cache)
 
     def test_build_complete_graph(self):
         exp_alfa = 0.1
@@ -76,17 +76,18 @@ class TestStructureEstimator(unittest.TestCase):
         lp_wrapper = lp(se1.ctpc_algorithm)
         lp_wrapper()
         lp.print_stats()
-        print(se1.complete_graph.edges)
+        print(se1._complete_graph.edges)
         print(self.s1.structure.edges)
         for ed in self.s1.structure.edges:
-            self.assertIn(tuple(ed), se1.complete_graph.edges)
+            self.assertIn(tuple(ed), se1._complete_graph.edges)
         tuples_edges = [tuple(rec) for rec in self.s1.structure.edges]
         spurious_edges = []
-        for ed in se1.complete_graph.edges:
+        for ed in se1._complete_graph.edges:
             if not(ed in tuples_edges):
                 spurious_edges.append(ed)
         print("Spurious Edges:",spurious_edges)
-        se1.save_results()
+        print("Adj Matrix:", nx.adj_matrix(se1._complete_graph).toarray().astype(bool))
+        #se1.save_results()
 
     def test_memory(self):
         se1 = se.StructureEstimator(self.s1, 0.1, 0.1)
@@ -95,5 +96,6 @@ class TestStructureEstimator(unittest.TestCase):
         mem = current_process.memory_info().rss
         print("Average Memory Usage in MB:", mem / 10**6)
 
+
 if __name__ == '__main__':
     unittest.main()