rename main_pack to PyCtbn; addinit file

PyCTBN/.scannerwork/report-task.txt

@@ -0,0 +1,6 @@
+projectKey=Ctbn_Project
+serverUrl=http://localhost:9000
+serverVersion=8.4.1.35646
+dashboardUrl=http://localhost:9000/dashboard?id=Ctbn_Project
+ceTaskId=AXPs4gCNB9mzoAo2hiLI
+ceTaskUrl=http://localhost:9000/api/ce/task?id=AXPs4gCNB9mzoAo2hiLI

PyCTBN/PyCTBN/abstract_importer.py

@@ -0,0 +1,149 @@
+
+from abc import ABC, abstractmethod
+import pandas as pd
+import typing
+
+
+class AbstractImporter(ABC):
+    """Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
+
+    :param file_path: the file path
+    :type file_path: str
+    :_concatenated_samples: Dataframe containing the concatenation of all the processed trajectories
+    :_df_structure: Dataframe containing the structure of the network (edges)
+    :_df_variables: Dataframe containing the nodes cardinalities
+    :_sorter: A list containing the columns header (excluding the time column) of the `_concatenated_samples`
+    """
+
+    def __init__(self, file_path: str):
+        """Constructor
+        """
+        self._file_path = file_path
+        self._df_variables = None
+        self._df_structure = None
+        self._concatenated_samples = None
+        self._sorter = None
+        super().__init__()
+
+    @abstractmethod
+    def import_data(self) -> None:
+        """Imports all the trajectories, variables cardinalities, and net edges.
+
+        .. warning::
+            The class members ``_df_variables`` and ``_df_structure`` HAVE to be properly constructed
+            as Pandas Dataframes with the following structure:
+            Header of _df_structure = [From_Node | To_Node]
+            Header of _df_variables = [Variable_Label | Variable_Cardinality]
+        .. note::
+            See :class:``JsonImporter`` for an example of implementation of this method.
+        """
+        pass
+
+    @abstractmethod
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        """Initializes the ``_sorter`` class member from a trajectory dataframe, exctracting the header of the frame
+        and keeping ONLY the variables symbolic labels, cutting out the time label in the header.
+
+        :param sample_frame: The dataframe from which extract the header
+        :type sample_frame: pandas.DataFrame
+        :return: A list containing the processed header.
+        :rtype: List
+        """
+        pass
+
+    def compute_row_delta_sigle_samples_frame(self, sample_frame: pd.DataFrame,
+                                              columns_header: typing.List, shifted_cols_header: typing.List) \
+            -> pd.DataFrame:
+        """Computes the difference between each value present in th time column.
+        Copies and shift by one position up all the values present in the remaining columns.
+
+        :param sample_frame: the traj to be processed
+        :type sample_frame: pandas.Dataframe
+        :param columns_header: the original header of sample_frame
+        :type columns_header: List
+        :param shifted_cols_header: a copy of columns_header with changed names of the contents
+        :type shifted_cols_header: List
+        :return: The processed dataframe
+        :rtype: pandas.Dataframe
+
+        .. warning::
+            the Dataframe ``sample_frame`` has to follow the column structure of this header:
+            Header of sample_frame = [Time | Variable values]
+        """
+        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
+        sample_frame = sample_frame.assign(**shifted_cols)
+        sample_frame.drop(sample_frame.tail(1).index, inplace=True)
+        return sample_frame
+
+    def compute_row_delta_in_all_samples_frames(self, df_samples_list: typing.List) -> None:
+        """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``
+
+        :param df_samples_list: the datframe's list to be processed and concatenated
+        :type df_samples_list: List
+
+        .. warning::
+            The Dataframe sample_frame has to follow the column structure of this header:
+            Header of sample_frame = [Time | Variable values]
+            The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)
+        .. note::
+            After the call of this method the class member ``concatanated_samples`` will contain all processed
+            and merged trajectories
+        """
+        if not self._sorter:
+            raise RuntimeError("The class member self._sorter has to be INITIALIZED!")
+        shifted_cols_header = [s + "S" for s in self._sorter]
+        compute_row_delta = self.compute_row_delta_sigle_samples_frame
+        proc_samples_list = [compute_row_delta(sample, self._sorter, shifted_cols_header)
+                                for sample in df_samples_list]
+        self._concatenated_samples = pd.concat(proc_samples_list)
+        complete_header = self._sorter[:]
+        complete_header.insert(0,'Time')
+        complete_header.extend(shifted_cols_header)
+        self._concatenated_samples = self._concatenated_samples[complete_header]
+
+    def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
+        """Builds a List containing the columns of data_frame and converts them to a numpy array.
+
+        :param data_frame: the dataframe from which the columns have to be extracted and converted
+        :type data_frame: pandas.Dataframe
+        :return: the resulting list of numpy arrays
+        :rtype: List
+        """
+        columns_list = [data_frame[column].to_numpy() for column in data_frame]
+        return columns_list
+
+    def clear_concatenated_frame(self) -> None:
+        """Removes all values in the dataframe concatenated_samples.
+         """
+        self._concatenated_samples = self._concatenated_samples.iloc[0:0]
+
+    @abstractmethod
+    def dataset_id(self) -> object:
+        """If the original dataset contains multiple dataset, this method returns a unique id to identify the current
+        dataset
+        """
+        pass
+
+    @property
+    def concatenated_samples(self) -> pd.DataFrame:
+        return self._concatenated_samples
+
+    @property
+    def variables(self) -> pd.DataFrame:
+        return self._df_variables
+
+    @property
+    def structure(self) -> pd.DataFrame:
+        return self._df_structure
+
+    @property
+    def sorter(self) -> typing.List:
+        return self._sorter
+
+    @property
+    def file_path(self) -> str:
+        return self._file_path

PyCTBN/PyCTBN/cache.py

@@ -0,0 +1,54 @@
+
+import typing
+#import set_of_cims as sofc
+from .set_of_cims import SetOfCims
+
+
+class Cache:
+    """This class acts as a cache of ``SetOfCims`` objects for a node.
+
+    :_list_of_sets_of_parents: a list of ``Sets`` objects of the parents to which the cim in cache at SAME
+        index is related
+    :_actual_cache: a list of setOfCims objects
+    """
+
+    def __init__(self):
+        """Constructor Method
+        """
+        self._list_of_sets_of_parents = []
+        self._actual_cache = []
+
+    def find(self, parents_comb: typing.Set) -> SetOfCims:
+        """
+        Tries to find in cache given the symbolic parents combination ``parents_comb`` the ``SetOfCims``
+        related to that ``parents_comb``.
+
+        :param parents_comb: the parents related to that ``SetOfCims``
+        :type parents_comb: Set
+        :return: A ``SetOfCims`` object if the ``parents_comb`` index is found in ``_list_of_sets_of_parents``.
+            None otherwise.
+        :rtype: SetOfCims
+        """
+        try:
+            result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)]
+            return result
+        except ValueError:
+            return None
+
+    def put(self, parents_comb: typing.Set, socim: SetOfCims) -> None:
+        """Place in cache the ``SetOfCims`` object, and the related symbolic index ``parents_comb`` in
+        ``_list_of_sets_of_parents``.
+
+        :param parents_comb: the symbolic set index
+        :type parents_comb: Set
+        :param socim: the related SetOfCims object
+        :type socim: SetOfCims
+        """
+        self._list_of_sets_of_parents.append(parents_comb)
+        self._actual_cache.append(socim)
+
+    def clear(self) -> None:
+        """Clear the contents both of ``_actual_cache`` and ``_list_of_sets_of_parents``.
+        """
+        del self._list_of_sets_of_parents[:]
+        del self._actual_cache[:]

PyCTBN/PyCTBN/conditional_intensity_matrix.py

@@ -0,0 +1,42 @@
+import numpy as np
+
+
+class ConditionalIntensityMatrix:
+    """Abstracts the Conditional Intesity matrix of a node as aggregation of the state residence times vector
+    and state transition matrix and the actual CIM matrix.
+
+    :param state_residence_times: state residence times vector
+    :type state_residence_times: numpy.array
+    :param state_transition_matrix: the transitions count matrix
+    :type state_transition_matrix: numpy.ndArray
+    :_cim: the actual cim of the node
+    """
+    def __init__(self, state_residence_times: np.array, state_transition_matrix: np.array):
+        """Constructor Method
+        """
+        self._state_residence_times = state_residence_times
+        self._state_transition_matrix = state_transition_matrix
+        self._cim = self.state_transition_matrix.astype(np.float64)
+
+    def compute_cim_coefficients(self) -> None:
+        """Compute the coefficients of the matrix _cim by using the following equality q_xx' = M[x, x'] / T[x].
+        The class member ``_cim`` will contain the computed cim
+        """
+        np.fill_diagonal(self._cim, self._cim.diagonal() * -1)
+        self._cim = ((self._cim.T + 1) / (self._state_residence_times + 1)).T
+
+    @property
+    def state_residence_times(self) -> np.ndarray:
+        return self._state_residence_times
+
+    @property
+    def state_transition_matrix(self) -> np.ndarray:
+        return self._state_transition_matrix
+
+    @property
+    def cim(self) -> np.ndarray:
+        return self._cim
+
+    def __repr__(self):
+        return 'CIM:\n' + str(self.cim)
+

PyCTBN/PyCTBN/deprecated/sets_of_cims_container.py

@@ -0,0 +1,25 @@
+import set_of_cims as socim
+
+
+class SetsOfCimsContainer:
+    """
+    Aggrega un insieme di oggetti SetOfCims
+    """
+    def __init__(self, list_of_keys, states_number_per_node, list_of_parents_states_number, p_combs_list):
+        self.sets_of_cims = None
+        self.init_cims_structure(list_of_keys, states_number_per_node, list_of_parents_states_number, p_combs_list)
+        #self.states_per_variable = states_number
+
+    def init_cims_structure(self, keys, states_number_per_node, list_of_parents_states_number, p_combs_list):
+        """for indx, key in enumerate(keys):
+            self.sets_of_cims.append(
+                socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))"""
+        self.sets_of_cims = [socim.SetOfCims(pair[1], list_of_parents_states_number[pair[0]], states_number_per_node[pair[0]], p_combs_list[pair[0]])
+                             for pair in enumerate(keys)]
+
+    def get_set_of_cims(self, node_indx):
+        return self.sets_of_cims[node_indx]
+
+    def get_cims_of_node(self, node_indx, cim_indx):
+        return self.sets_of_cims[node_indx].get_cim(cim_indx)
+

PyCTBN/PyCTBN/json_importer.py

@@ -0,0 +1,169 @@
+
+import json
+import typing
+import pandas as pd
+
+#import abstract_importer as ai
+from .abstract_importer import AbstractImporter
+
+
+class JsonImporter(AbstractImporter):
+    """Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare the data in json ext.
+    with the following structure:
+    [0]
+        |_ dyn.cims
+        |_ dyn.str
+        |_ samples
+        |_ variabels
+    :param file_path: the path of the file that contains tha data to be imported
+    :type file_path: string
+    :param samples_label: the reference key for the samples in the trajectories
+    :type samples_label: string
+    :param structure_label: the reference key for the structure of the network data
+    :type structure_label: string
+    :param variables_label: the reference key for the cardinalites of the nodes data
+    :type variables_label: string
+    :param time_key: the key used to identify the timestamps in each trajectory
+    :type time_key: string
+    :param variables_key: the key used to identify the names of the variables in the net
+    :type variables_key: string
+    :param array_indx: the index of the outer JsonArray to exctract the data from
+    :type array_indx: int
+    :_df_samples_list: a Dataframe list in which every dataframe contains a trajectory
+    """
+
+    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
+                 variables_key: str, array_indx: int):
+        """Constructor method
+        """
+        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) -> None:
+        """Implements the abstract method of :class:`AbstractImporter`
+        """
+        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.clear_data_frame_list()
+        self._df_structure = self.import_structure(raw_data)
+        self._df_variables = self.import_variables(raw_data)
+
+    def import_trajectories(self, raw_data: typing.List) -> typing.List:
+        """Imports the trajectories from the list of dicts ``raw_data``.
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :return: List of dataframes containing all the trajectories
+        :rtype: List
+        """
+        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``
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :return: Dataframe containg the starting node a ending node of every arc of the network
+        :rtype: pandas.Dataframe
+        """
+        return self.one_level_normalizing(raw_data, self._array_indx, self._structure_label)
+
+    def import_variables(self, raw_data: typing.List) -> pd.DataFrame:
+        """Imports the data in ``raw_data`` at the key ``_variables_label``.
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :return: Datframe containg the variables simbolic labels and their cardinalities
+        :rtype: pandas.Dataframe
+        """
+        return self.one_level_normalizing(raw_data, self._array_indx, self._variables_label)
+
+    def read_json_file(self) -> typing.List:
+        """Reads the JSON file in the path self.filePath
+
+        :return: The contents of the json file
+        :rtype: List
+        """
+        with open(self._file_path) as f:
+            data = json.load(f)
+            return data
+
+    def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
+        """Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``.
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :param indx: The index of the array from which the data have to be extracted
+        :type indx: int
+        :param key: the key for the Dicts from which exctract data
+        :type key: string
+        :return: A normalized dataframe
+        :rtype: pandas.Datframe
+        """
+        return pd.DataFrame(raw_data[indx][key])
+
+    def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List:
+        """
+        Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``.
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :param indx: The index of the array from which the data have to be extracted
+        :type indx: int
+        :param trajectories_key: the key of the trajectories objects
+        :type trajectories_key: string
+        :return: A list of daframes containg the trajectories
+        :rtype: List
+        """
+        dataframe = pd.DataFrame
+        smps = raw_data[indx][trajectories_key]
+        df_samples_list = [dataframe(sample) for sample in smps]
+        return df_samples_list
+
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        """Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset
+        """
+        columns_header = list(sample_frame.columns.values)
+        columns_header.remove(self._time_key)
+        return columns_header
+
+    def clear_data_frame_list(self) -> None:
+        """Removes all values present in the dataframes in the list ``_df_samples_list``.
+        """
+        for indx in range(len(self._df_samples_list)):
+            self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0]
+
+    def dataset_id(self) -> object:
+        return self._array_indx
+
+    def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict:
+        """Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
+        as keys for the set of CIMS of a particular node.
+
+        :param raw_data: List of Dicts
+        :type raw_data: List
+        :param indx: The index of the array from which the data have to be extracted
+        :type indx: int
+        :param cims_key: the key where the json object cims are placed
+        :type cims_key: string
+        :return: a dictionary containing the sampled CIMS for all the variables in the net
+        :rtype: Dictionary
+        """
+        cims_for_all_vars = {}
+        for var in raw_data[indx][cims_key]:
+            sampled_cims_list = []
+            cims_for_all_vars[var] = sampled_cims_list
+            for p_comb in raw_data[indx][cims_key][var]:
+                cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy())
+        return cims_for_all_vars
+
+
+

PyCTBN/PyCTBN/network_graph.py

@@ -0,0 +1,297 @@
+
+import typing
+import networkx as nx
+import numpy as np
+
+#import structure as st
+from .structure import Structure
+
+
+class NetworkGraph:
+    """Abstracts the infos contained in the Structure class in the form of a directed graph.
+    Has the task of creating all the necessary filtering and indexing structures for parameters estimation
+
+    :param graph_struct: the ``Structure`` object from which infos about the net will be extracted
+    :type graph_struct: Structure
+    :_graph: directed graph
+    :_aggregated_info_about_nodes_parents: a structure that contains all the necessary infos
+        about every parents of the node of which all the indexing and filtering structures will be constructed.
+    :_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
+    :_p_combs_structure: all the possible parents states combination for the node of interest
+    """
+
+    def __init__(self, graph_struct: Structure):
+        """Constructor Method
+        """
+        self._graph_struct = graph_struct
+        self._graph = nx.DiGraph()
+        self._aggregated_info_about_nodes_parents = None
+        self._time_scalar_indexing_structure = None
+        self._transition_scalar_indexing_structure = None
+        self._time_filtering = None
+        self._transition_filtering = None
+        self._p_combs_structure = None
+
+    def fast_init(self, node_id: str) -> None:
+        """Initializes all the necessary structures for parameters estimation of the node identified by the label
+        node_id
+
+        :param node_id: the label of the node
+        :type node_id: string
+        """
+        self.add_nodes(self._graph_struct.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)
+        p_indxs = self._aggregated_info_about_nodes_parents[1]
+        p_vals = self._aggregated_info_about_nodes_parents[2]
+        self._time_scalar_indexing_structure = self.build_time_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                    p_vals)
+        self._transition_scalar_indexing_structure = self.build_transition_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                                p_vals)
+        node_indx = self.get_node_indx(node_id)
+        self._time_filtering = self.build_time_columns_filtering_for_a_node(node_indx, p_indxs)
+        self._transition_filtering = self.build_transition_filtering_for_a_node(node_indx, p_indxs)
+        self._p_combs_structure = self.build_p_comb_structure_for_a_node(p_vals)
+
+    def add_nodes(self, list_of_nodes: typing.List) -> None:
+        """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)
+
+        :param list_of_nodes: the nodes to add to ``_graph``
+        :type list_of_nodes: List
+        """
+        nodes_indxs = self._graph_struct.nodes_indexes
+        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)
+            pos += 1
+
+    def add_edges(self, list_of_edges: typing.List) -> None:
+        """Add the edges to the ``_graph`` contained in the list ``list_of_edges``.
+
+        :param list_of_edges: the list containing of tuples containing the edges
+        :type list_of_edges: List
+        """
+        self._graph.add_edges_from(list_of_edges)
+
+    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).
+
+        :param node: the label of the node
+        :type node: string
+        :return: a tuple containing all the parent set infos
+        :rtype: Tuple
+        """
+        parents = self.get_parents_by_id(node)
+        nodes = self._graph_struct.nodes_labels
+        d = {v: i for i, v in enumerate(nodes)}
+        sorted_parents = sorted(parents, key=lambda v: d[v])
+        get_node_indx = self.get_node_indx
+        p_indxes = [get_node_indx(node) for node in sorted_parents]
+        p_values = [self.get_states_number(node) for node in sorted_parents]
+        return (sorted_parents, p_indxes, p_values)
+
+    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.
+
+        :param node_id: the node label
+        :type node_id: string
+        :param parents_vals: the caridinalites of the node's parents
+        :type parents_vals: List
+        :return: The time indexing structure
+        :rtype: numpy.ndArray
+        """
+        T_vector = np.array([self.get_states_number(node_id)])
+        T_vector = np.append(T_vector, parents_vals)
+        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:
+        """Builds an indexing structure for the computation of state transitions values.
+
+        :param node_id: the node label
+        :type node_id: string
+        :param parents_vals: the caridinalites of the node's parents
+        :type parents_vals: List
+        :return: The transition indexing structure
+        :rtype: numpy.ndArray
+        """
+        node_states_number = self.get_states_number(node_id)
+        M_vector = np.array([node_states_number,
+                             node_states_number])
+        M_vector = np.append(M_vector, parents_vals)
+        M_vector = M_vector.cumprod().astype(np.int)
+        return M_vector
+
+    def build_time_columns_filtering_for_a_node(self, node_indx: int, p_indxs: typing.List) -> np.ndarray:
+        """
+        Builds the necessary structure to filter the desired columns indicated by ``node_indx`` and ``p_indxs``
+        in the dataset.
+        This structute will be used in the computation of the state res times.
+        :param node_indx: the index of the node
+        :type node_indx: int
+        :param p_indxs: the indexes of the node's parents
+        :type p_indxs: List
+        :return: The filtering structure for times estimation
+        :rtype: numpy.ndArray
+        """
+        return np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
+
+    def build_transition_filtering_for_a_node(self, node_indx: int, p_indxs: typing.List) -> np.ndarray:
+        """Builds the necessary structure to filter the desired columns indicated by ``node_indx`` and ``p_indxs``
+        in the dataset.
+        This structure will be used in the computation of the state transitions values.
+        :param node_indx: the index of the node
+        :type node_indx: int
+        :param p_indxs: the indexes of the node's parents
+        :type p_indxs: List
+        :return: The filtering structure for transitions estimation
+        :rtype: numpy.ndArray
+        """
+        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:
+        """
+        Builds the combinatorial structure that contains the combinations of all the values contained in
+        ``parents_values``.
+
+        :param parents_values: the cardinalities of the nodes
+        :type parents_values: List
+        :return: A numpy matrix containing a grid of the combinations
+        :rtype: numpy.ndArray
+        """
+        tmp = []
+        for val in parents_values:
+            tmp.append([x for x in range(val)])
+        if len(parents_values) > 0:
+            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
+            if len(parents_values) > 1:
+                tmp_comb = parents_comb[:, 1].copy()
+                parents_comb[:, 1] = parents_comb[:, 0].copy()
+                parents_comb[:, 0] = tmp_comb
+        else:
+            parents_comb = np.array([[]], dtype=np.int)
+        return parents_comb
+
+    def get_parents_by_id(self, node_id) -> typing.List:
+        """Returns a list of labels of the parents of the node ``node_id``
+
+        :param node_id: the node label
+        :type node_id: string
+        :return: a List of labels of the parents
+        :rtype: List
+        """
+        return list(self._graph.predecessors(node_id))
+
+    def get_states_number(self, node_id) -> int:
+        return self._graph.nodes[node_id]['val']
+
+    def get_node_indx(self, node_id) -> int:
+        return nx.get_node_attributes(self._graph, 'indx')[node_id]
+
+    def get_positional_node_indx(self, node_id) -> int:
+        return self._graph.nodes[node_id]['pos_indx']
+
+    @property
+    def nodes(self) -> typing.List:
+        return self._graph_struct.nodes_labels
+
+    @property
+    def edges(self) -> typing.List:
+        return list(self._graph.edges)
+
+    @property
+    def nodes_indexes(self) -> np.ndarray:
+        return self._graph_struct.nodes_indexes
+
+    @property
+    def nodes_values(self) -> np.ndarray:
+        return self._graph_struct.nodes_values
+
+    @property
+    def time_scalar_indexing_strucure(self) -> np.ndarray:
+        return self._time_scalar_indexing_structure
+
+    @property
+    def time_filtering(self) -> np.ndarray:
+        return self._time_filtering
+
+    @property
+    def transition_scalar_indexing_structure(self) -> np.ndarray:
+        return self._transition_scalar_indexing_structure
+
+    @property
+    def transition_filtering(self) -> np.ndarray:
+        return self._transition_filtering
+
+    @property
+    def p_combs(self) -> np.ndarray:
+        return self._p_combs_structure
+
+    """
+    ##############These Methods are actually unused but could become useful in the near future################
+
+    def init_graph(self):
+        self.add_nodes(self._nodes_labels)
+        self.add_edges(self._graph_struct.edges)
+        self._aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
+        self._fancy_indexing = self.build_fancy_indexing_structure(0)
+        self.build_scalar_indexing_structures()
+        self.build_time_columns_filtering_structure()
+        self.build_transition_columns_filtering_structure()
+        self._p_combs_structure = self.build_p_combs_structure()
+
+    def build_time_columns_filtering_structure(self):
+        nodes_indxs = self._nodes_indexes
+        self._time_filtering = [np.append(np.array([node_indx], dtype=np.int), p_indxs).astype(np.int)
+            for node_indx, p_indxs in zip(nodes_indxs, self._fancy_indexing)]
+
+    def build_transition_columns_filtering_structure(self):
+        nodes_number = self._graph_struct.total_variables_number
+        nodes_indxs = self._nodes_indexes
+        self._transition_filtering = [np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
+                                      for node_indx, p_indxs in zip(nodes_indxs,
+                                                                    self._fancy_indexing)]
+
+    def build_scalar_indexing_structures(self):
+        parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
+        build_transition_scalar_indexing_structure_for_a_node = \
+            self.build_transition_scalar_indexing_structure_for_a_node
+        build_time_scalar_indexing_structure_for_a_node = self.build_time_scalar_indexing_structure_for_a_node
+        aggr = [(build_transition_scalar_indexing_structure_for_a_node(node_id, p_vals),
+                 build_time_scalar_indexing_structure_for_a_node(node_id, p_vals))
+                                                      for node_id, p_vals in
+                                                      zip(self._nodes_labels,
+                                                          parents_values_for_all_nodes)]
+        self._transition_scalar_indexing_structure = [i[0] for i in aggr]
+        self._time_scalar_indexing_structure = [i[1] for i in aggr]
+
+    def build_p_combs_structure(self):
+        parents_values_for_all_nodes = self.get_ordered_by_indx_parents_values_for_all_nodes()
+        p_combs_struct = [self.build_p_comb_structure_for_a_node(p_vals) for p_vals in parents_values_for_all_nodes]
+        return p_combs_struct
+
+    def get_ord_set_of_par_of_all_nodes(self):
+        get_ordered_by_indx_set_of_parents = self.get_ordered_by_indx_set_of_parents
+        result = [get_ordered_by_indx_set_of_parents(node) for node in self._nodes_labels]
+        return result
+
+    def get_ordered_by_indx_parents_values_for_all_nodes(self):
+        pars_values = [i[2] for i in self._aggregated_info_about_nodes_parents]
+        return pars_values
+
+    def build_fancy_indexing_structure(self, start_indx):
+        if start_indx > 0:
+            pass
+        else:
+            fancy_indx = [i[1] for i in self._aggregated_info_about_nodes_parents]
+            return fancy_indx
+    """

PyCTBN/PyCTBN/original_ctpc_algorithm.py

@@ -0,0 +1,495 @@
+import glob
+import json
+import os
+from itertools import combinations
+import typing
+
+import numpy as np
+import pandas as pd
+from line_profiler import LineProfiler
+from scipy.stats import chi2 as chi2_dist
+from scipy.stats import f as f_dist
+from tqdm import tqdm
+
+from .abstract_importer import AbstractImporter
+
+
+class OriginalCTPCAlgorithm(AbstractImporter):
+    """
+    Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare the data in json ext.
+    with the following structure:
+    [0]
+        |_ dyn.cims
+        |_ dyn.str
+        |_ 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
+    """
+
+    def dataset_id(self) -> object:
+        pass
+
+    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_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
+        self.trajectories = None
+        self._array_indx  = array_indx
+        self.matrix = None
+        super(OriginalCTPCAlgorithm, self).__init__(file_path)
+
+    def import_data(self):
+        """
+        Imports and prepares all data present needed for subsequent processing.
+        Parameters:
+            :void
+        Returns:
+            _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.clear_data_frame_list()
+        self._df_structure = self.import_structure(raw_data)
+        self._df_variables = self.import_variables(raw_data, self._sorter)
+
+
+    def import_trajectories(self, raw_data: typing.List):
+        """
+        Imports the trajectories in the list of dicts raw_data.
+        Parameters:
+            :raw_data: List of Dicts
+        Returns:
+            :List of dataframes containing all the trajectories
+        """
+        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
+
+        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, self._array_indx, self.structure_label)
+
+    def import_variables(self, raw_data: typing.List, sorter: typing.List) -> pd.DataFrame:
+        """
+        Imports the data in raw_data at the key _variables_label.
+        Sorts the row of the dataframe df_variables using the list sorter.
+
+        Parameters:
+            :raw_data: the data
+            :sorter: the header of the dataset containing only variables symbolic labels
+        Returns:
+            :Datframe containg the variables simbolic labels and their cardinalities
+        """
+        return self.one_level_normalizing(raw_data, self._array_indx, 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)
+        """
+
+    def read_json_file(self) -> typing.List:
+        """
+        Reads the JSON file in the path self.filePath
+
+        Parameters:
+              :void
+        Returns:
+              :data: the contents of the json file
+
+        """
+        with open(self._file_path) as f:
+            data = json.load(f)
+            return data
+
+    def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame:
+        """
+        Extracts the one-level nested data in the list raw_data at the index indx at the key key
+
+        Parameters:
+            :raw_data: List of Dicts
+            :indx: The index of the array from which the data have to be extracted
+            :key: the key for the Dicts from which exctract data
+        Returns:
+            :a normalized dataframe:
+
+        """
+        return pd.DataFrame(raw_data[indx][key])
+
+    def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str):
+        """
+        Extracts the traj in raw_data at the index index at the key trajectories key.
+
+        Parameters:
+            :raw_data: the data
+            :indx: the index of the array from which extract data
+            :trajectories_key: the key of the trajectories objects
+        Returns:
+            :A list of daframes containg the trajectories
+        """
+        dataframe = pd.DataFrame
+        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)
+        return columns_header
+
+    def clear_data_frame_list(self):
+        """
+        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]
+
+
+    def prepare_trajectories(self, trajectories, variables):
+        """
+            Riformula le traiettorie per rendere più efficiente la fase di computazione delle cim
+
+            Parameters
+            -------------
+            trajectories: [pandas.DataFrame]
+                Un array di pandas dataframe contenente tutte le traiettorie. Ogni array avrà una
+                colonna per il timestamp (sempre la prima) e n colonne una per ogni variabili
+                presente nella rete.
+            variables: pandas.DataFrame
+                Pandas dataframe contenente due colonne: il nome della variabile e cardinalità
+                della variabile
+        """
+        dimensions = np.array([x.shape[0] - 1 for x in trajectories], dtype=np.int)
+        ret_array = np.zeros([dimensions.sum(), trajectories[0].shape[1] * 2])
+        cum_dim = np.zeros(len(trajectories) + 1, dtype=np.int)
+        cum_dim[1:] = dimensions.cumsum()
+        dimensions.cumsum()
+        for it in range(len(trajectories)):
+            tmp = trajectories[it].to_numpy()
+            dim = tmp.shape[1]
+            ret_array[cum_dim[it]:cum_dim[it + 1], 0:dim] = tmp[:-1]
+            ret_array[cum_dim[it]:cum_dim[it + 1], dim] = np.diff(tmp[:, 0])
+            ret_array[cum_dim[it]:cum_dim[it + 1], dim + 1:] = np.roll(tmp[:, 1:], -1, axis=0)[:-1]
+        self.trajectories = ret_array
+        #self.variables = variables
+
+    @staticmethod
+    def _compute_cim(trajectories, child_id, parents_id, T_vector, M_vector, parents_comb, M, T):
+        """Funzione interna per calcolare le CIM
+
+        Parameters:
+        -----------
+        trajectories: np.array
+            Array contenente le traiettorie. (self.trajectories)
+
+        child_id: int
+            Indice del nodo di cui si vogliono calcolare le cim
+
+        parents:id: [int]
+            Array degli indici dei genitori nel nodo child_id
+
+        T_vector: np.array
+            Array numpy per l'indicizzazione dell'array T
+
+        M_vector: np.array
+            Array numpy per l'indicizzazione dell'array M
+
+        parents_comb: [(int)]
+            Array di tuple contenenti tutte le possibili combinazioni dei genitori di child_id
+
+        M: np.array
+            Array numpy contenente  la statistica sufficiente M
+
+        T: np.array
+            Array numpy contenente la statistica sufficiente T
+
+        Returns:
+        ---------
+        CIM: np.array
+            Array numpy contenente le CIM
+        """
+        #print(T)
+        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)
+        #print(diag_indices)
+        T_filter = np.array([child_id, *parents_id], dtype=np.int) + 1
+        #print("TFilter",T_filter)
+        #print("TVector", T_vector)
+        #print("Trajectories", trajectories)
+        #print("Actual TVect",T_vector / T_vector[0])
+        #print("Masked COlumns", trajectories[:, T_filter])  # Colonne non shiftate dei values
+        #print("Masked Multiplied COlumns",trajectories[:, T_filter] * (T_vector / T_vector[0]) )
+        #print("Summing",np.sum(trajectories[:, T_filter] * (T_vector / T_vector[0]), axis=1))
+        #print("Deltas",trajectories[:, int(trajectories.shape[1] / 2)]) # i delta times
+        assert np.sum(trajectories[:, T_filter] * (T_vector / T_vector[0]), axis=1).size == \
+               trajectories[:, int(trajectories.shape[1] / 2)].size
+        #print(T_vector[-1])
+        T[:] = np.bincount(np.sum(trajectories[:, T_filter] * T_vector / T_vector[0], axis=1).astype(np.int), \
+                           trajectories[:, int(trajectories.shape[1] / 2)], minlength=T_vector[-1]).reshape(-1,
+                                                                                                            T.shape[1])
+        #print("Shape", T.shape[1])
+        #print(np.bincount(np.sum(trajectories[:, T_filter] * T_vector / T_vector[0], axis=1).astype(np.int), \
+                           #trajectories[:, int(trajectories.shape[1] / 2)], minlength=T_vector[-1]))
+        ###### Transitions #######
+
+        #print("Shifted Node column", trajectories[:, int(trajectories.shape[1] / 2) + 1 + child_id].astype(np.int))
+        #print("Step 2", trajectories[:, int(trajectories.shape[1] / 2) + 1 + child_id].astype(np.int) >= 0)
+        trj_tmp = trajectories[trajectories[:, int(trajectories.shape[1] / 2) + 1 + child_id].astype(np.int) >= 0]
+        #print("Trj Temp", trj_tmp)
+
+
+        M_filter = np.array([child_id, child_id, *parents_id], dtype=np.int) + 1
+        #print("MFilter", M_filter)
+        M_filter[0] += int(trj_tmp.shape[1] / 2)
+        #print("MFilter", M_filter)
+        #print("MVector", M_vector)
+        #print("Division", M_vector / M_vector[0])
+        #print("Masked Traj temp", (trj_tmp[:, M_filter]))
+        #print("Masked Multiplied Traj temp", trj_tmp[:, M_filter] * M_vector / M_vector[0])
+        #print("Summing", np.sum(trj_tmp[:, M_filter] * M_vector / M_vector[0], axis=1))
+        #print(M.shape[2])
+
+        M[:] = np.bincount(np.sum(trj_tmp[:, M_filter] * M_vector / M_vector[0], axis=1).astype(np.int), \
+                           minlength=M_vector[-1]).reshape(-1, M.shape[1], M.shape[2])
+        #print("M!!!!!!!", M)
+        M_raveled = M.ravel()
+        #print("Raveled", M_raveled)
+        M_raveled[diag_indices] = 0
+        M_raveled[diag_indices] = np.sum(M, axis=2).ravel()
+        #print("Raveled", M_raveled)
+        q = (M.ravel()[diag_indices].reshape(-1, M.shape[1]) + 1) / (T + 1)
+        theta = (M + 1) / (M.ravel()[diag_indices].reshape(-1, M.shape[2], 1) + 1)
+        negate_main_diag = np.ones((M.shape[1], M.shape[2]))
+        np.fill_diagonal(negate_main_diag, -1)
+        theta = np.multiply(theta, negate_main_diag)
+        return theta * q.reshape(-1, M.shape[2], 1)
+
+    def compute_cim(self, child_id, parents_id):
+        """Metodo utilizzato per calcolare le CIM di un nodo dati i suoi genitori
+
+        Parameters:
+        -----------
+        child_id: int
+            Indice del nodo di cui si vogliono calcolare le cim
+
+        parents:id: [int]
+            Array degli indici dei genitori nel nodo child_id
+
+        Return:
+        ----------
+        Restituisce una tupla contenente:
+            parents_comb: [(int)]
+                Array di tuple contenenti tutte le possibili combinazioni dei genitori di child_id
+
+            M: np.array
+                Array numpy contenente  la statistica sufficiente M
+
+            T: np.array
+                Array numpy contenente la statistica sufficiente T
+
+            CIM: np.array
+                Array numpy contenente le CIM
+
+        """
+        tmp = []
+        child_id = int(child_id)
+        parents_id = np.array(parents_id, dtype=np.int)
+        parents_id.sort()
+        #print("Parents id",parents_id)
+        #breakpoint()
+        for idx in parents_id:
+            tmp.append([x for x in range(self.variables.loc[idx, "Value"])])
+        #print("TIMP", tmp)
+        if len(parents_id) > 0:
+            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_id))
+            #print(np.argsort(parents_comb))
+            #print("PArents COmb", parents_comb)
+            if len(parents_id) > 1:
+                tmp_comb = parents_comb[:, 1].copy()
+                #print(tmp_comb)
+                parents_comb[:, 1] = parents_comb[:, 0].copy()
+                parents_comb[:, 0] = tmp_comb
+        else:
+            parents_comb = np.array([[]], dtype=np.int)
+        #print("PARENTS COMB ", parents_comb)
+        M = np.zeros([max(1, parents_comb.shape[0]), \
+                      self.variables.loc[child_id, "Value"], \
+                      self.variables.loc[child_id, "Value"]], dtype=np.int)
+        #print(M)
+
+        T = np.zeros([max(1, parents_comb.shape[0]), \
+                      self.variables.loc[child_id, "Value"]], dtype=np.float)
+        #print(T)
+        #print("T Vector")
+        #print(child_id)
+        T_vector = np.array([self.variables.iloc[child_id, 1].astype(np.int)])
+        #print(T_vector)
+        #for x in parents_id:
+            #print(self.variables.iloc[x, 1])
+        T_vector = np.append(T_vector, [self.variables.iloc[x, 1] for x in parents_id])
+        #print(T_vector)
+        T_vector = T_vector.cumprod().astype(np.int)
+        #print(T_vector)
+
+        #print("M Vector")
+        M_vector = np.array([self.variables.iloc[child_id, 1], self.variables.iloc[child_id, 1].astype(np.int)])
+        #print(M_vector)
+        M_vector = np.append(M_vector, [self.variables.iloc[x, 1] for x in parents_id])
+
+        #for x in parents_id:
+            #print(self.variables.iloc[x, 1])
+        M_vector = M_vector.cumprod().astype(np.int)
+        #print("MVECTOR", M_vector)
+
+        CIM = self._compute_cim(self.trajectories, child_id, parents_id, T_vector, M_vector, parents_comb, M, T)
+        return parents_comb, M, T, CIM
+
+    def independence_test(self, to_var, from_var, sep_set, alpha_exp, alpha_chi2, thumb_threshold):
+        #print("To var", to_var)
+        #print("From var", from_var)
+        #print("sep set", sep_set)
+        parents = np.array(sep_set)
+        parents = np.append(parents, from_var)
+        parents.sort()
+        #print("PARENTS", parents)
+        parents_no_from_mask = parents != from_var
+        #print("Parents Comb NO Mask", parents_no_from_mask)
+
+
+        parents_comb_from, M_from, T_from, CIM_from = self.compute_cim(to_var, parents)
+        #print("Parents Comb From", parents_comb_from)
+
+        #print("C2:", CIM_from)
+
+        if self.variables.loc[to_var, "Value"] > 2:
+            df = (self.variables.loc[to_var, "Value"] - 1) ** 2
+            df = df * (self.variables.loc[from_var, "Value"])
+            for v in sep_set:
+                df = df * (self.variables.loc[v, "Value"])
+
+            if np.all(np.sum(np.diagonal(M_from, axis1=1, axis2=2), axis=1) / df < thumb_threshold):
+                return False
+            #print("Before CHi quantile", self.variables.loc[to_var, "Value"] - 1)
+            chi_2_quantile = chi2_dist.ppf(1 - alpha_chi2, self.variables.loc[to_var, "Value"] - 1)
+            #print("Chi Quantile", chi_2_quantile)
+
+        parents_comb, M, T, CIM = self.compute_cim(to_var, parents[parents_no_from_mask])
+
+        #print("C1", CIM)
+
+
+        for comb_id in range(parents_comb.shape[0]):
+            # Bad code, inefficient
+            #print("COMB ID", comb_id)
+
+            if parents.shape[0] > 1:
+                #print("STEP 0", parents_comb_from[:, parents_no_from_mask])
+                #print("STEP 1", np.all(parents_comb_from[:, parents_no_from_mask] == parents_comb[comb_id], axis=1))
+                #print("STEP 2", np.argwhere(
+                    #np.all(parents_comb_from[:, parents_no_from_mask] == parents_comb[comb_id], axis=1)).ravel())
+                tmp_parents_comb_from_ids = np.argwhere(
+                    np.all(parents_comb_from[:, parents_no_from_mask] == parents_comb[comb_id], axis=1)).ravel()
+            else:
+                tmp_parents_comb_from_ids = np.array([x for x in range(parents_comb_from.shape[0])])
+
+            #print("TMP PAR COMB IDSSSS:", tmp_parents_comb_from_ids)
+            for comb_from_id in tmp_parents_comb_from_ids:
+                #print("COMB ID FROM", comb_from_id)
+                diag = np.diag(CIM[comb_id])
+                diag_from = np.diag(CIM_from[comb_from_id])
+                #print("Diag C2", diag_from)
+                #print("Diag C1", diag)
+                r1 = np.diag(M[comb_id])
+                r2 = np.diag(M_from[comb_from_id])
+                stats = diag_from / diag
+                #print("Exponential Test", stats, r1, r2)
+                for id_diag in range(diag.shape[0]):
+                    if stats[id_diag] < f_dist.ppf(alpha_exp / 2, r1[id_diag], r2[id_diag]) or \
+                            stats[id_diag] > f_dist.ppf(1 - alpha_exp / 2, r1[id_diag], r2[id_diag]):
+                        return False
+
+                if diag.shape[0] > 2:
+
+                    # https://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm
+                    K_from = np.sqrt(M[comb_id].diagonal() / M_from[comb_from_id].diagonal())
+                    K = np.sqrt(M_from[comb_from_id].diagonal() / M[comb_id].diagonal())
+                    #print("K From", K_from)
+                    #print("K ", K)
+
+                    M_no_diag = M[comb_id][~np.eye(diag.shape[0], dtype=np.bool)].reshape(diag.shape[0], -1)
+                    M_from_no_diag = M_from[comb_from_id][~np.eye(diag.shape[0], dtype=np.bool)].reshape(diag.shape[0],
+                                                                                                         -1)
+                    #print("M No Diag", M_no_diag)
+                    #print("M From No Diag", M_from_no_diag)
+                    chi_stats = np.sum((np.power((M_no_diag.T * K).T - (M_from_no_diag.T * K_from).T, 2) \
+                                        / (M_no_diag + M_from_no_diag)), axis=1)
+                    #print("Chi stats", chi_stats)
+                    #print("Chi Quantile", chi_2_quantile)
+                    if np.any(chi_stats > chi_2_quantile):
+                        return False
+
+        return True
+
+    def cb_structure_algo(self, alpha_exp=0.1, alpha_chi2=0.1, thumb_threshold=25):
+        adj_matrix = np.ones((self.variables.shape[0], self.variables.shape[0]), dtype=np.bool)
+        np.fill_diagonal(adj_matrix, False)
+        for to_var in tqdm(range(self.variables.shape[0])):
+            n = 0
+            tested_variables = np.argwhere(adj_matrix[:, to_var]).ravel()
+            while n < tested_variables.shape[0]:
+                for from_var in tested_variables:
+                    if from_var not in tested_variables:
+                        continue
+                    if n >= tested_variables.shape[0]:
+                        break
+                    sep_set_vars = tested_variables[tested_variables != from_var]
+                    for comb in combinations(sep_set_vars, n):
+                        if self.independence_test(to_var, from_var, comb, alpha_exp, alpha_chi2, thumb_threshold):
+                            #print("######REMOVING EDGE #############", from_var, to_var)
+                            adj_matrix[from_var, to_var] = False
+                            tested_variables = np.argwhere(adj_matrix[:, to_var]).ravel()
+                            break
+                n += 1
+        #print("MATRIZ:", adj_matrix)
+        self.matrix =  adj_matrix
+
+

PyCTBN/PyCTBN/parameters_estimator.py

@@ -0,0 +1,143 @@
+
+import numpy as np
+
+#import network_graph as ng
+#import trajectory as tr
+#import set_of_cims as sofc
+from .trajectory import Trajectory
+from .set_of_cims import SetOfCims
+from .network_graph import NetworkGraph
+
+
+
+class ParametersEstimator:
+    """Has the task of computing the cims of particular node given the trajectories and the net structure
+    in the graph ``_net_graph``.
+
+    :param trajectories:  the trajectories
+    :type trajectories: Trajectory
+    :param net_graph: the net structure
+    :type net_graph: NetworkGraph
+    :_single_set_of_cims: the set of cims object that will hold the cims of the node
+    """
+
+    def __init__(self, trajectories: Trajectory, net_graph: NetworkGraph):
+        """Constructor Method
+        """
+        self._trajectories = trajectories
+        self._net_graph = net_graph
+        self._single_set_of_cims = None
+
+    def fast_init(self, node_id: str) -> None:
+        """Initializes all the necessary structures for the parameters estimation for the node ``node_id``.
+
+        :param node_id: the node label
+        :type node_id: string
+        """
+        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 = SetOfCims(node_id, p_vals, node_states_number, self._net_graph.p_combs)
+
+    def compute_parameters_for_node(self, node_id: str) -> SetOfCims:
+        """Compute the CIMS of the node identified by the label ``node_id``.
+
+        :param node_id: the node label
+        :type node_id: string
+        :return: A SetOfCims object filled with the computed CIMS
+        :rtype: SetOfCims
+        """
+        node_indx = self._net_graph.get_node_indx(node_id)
+        state_res_times = self._single_set_of_cims._state_residence_times
+        transition_matrices = self._single_set_of_cims._transition_matrices
+        self.compute_state_res_time_for_node(node_indx, self._trajectories.times,
+                                             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._trajectories.complete_trajectory,
+                                                  self._net_graph.transition_filtering,
+                                                  self._net_graph.transition_scalar_indexing_structure,
+                                                  transition_matrices)
+        self._single_set_of_cims.build_cims(state_res_times, transition_matrices)
+        return self._single_set_of_cims
+
+    def compute_state_res_time_for_node(self, node_indx: int, times: np.ndarray, trajectory: np.ndarray,
+                                        cols_filter: np.ndarray, scalar_indexes_struct: np.ndarray,
+                                        T: np.ndarray) -> None:
+        """Compute the state residence times for a node and fill the matrix ``T`` with the results
+
+        :param node_indx: the index of the node
+        :type node_indx: int
+        :param times: the times deltas vector
+        :type times: numpy.array
+        :param trajectory: the trajectory
+        :type trajectory: numpy.ndArray
+        :param cols_filter: the columns filtering structure
+        :type cols_filter: numpy.array
+        :param scalar_indexes_struct: the indexing structure
+        :type scalar_indexes_struct: numpy.array
+        :param T: the state residence times vectors
+        :type T: numpy.ndArray
+        """
+        T[:] = np.bincount(np.sum(trajectory[:, cols_filter] * scalar_indexes_struct / scalar_indexes_struct[0], axis=1)
+                           .astype(np.int), \
+                           times,
+                           minlength=scalar_indexes_struct[-1]).reshape(-1, T.shape[1])
+
+    def compute_state_transitions_for_a_node(self, node_indx: int, trajectory: np.ndarray, cols_filter: np.ndarray,
+                                             scalar_indexing: np.ndarray, M: np.ndarray):
+        """Compute the state residence times for a node and fill the matrices ``M`` with the results.
+
+        :param node_indx: the index of the node
+        :type node_indx: int
+        :param trajectory: the trajectory
+        :type trajectory: numpy.ndArray
+        :param cols_filter: the columns filtering structure
+        :type cols_filter: numpy.array
+        :param scalar_indexing: the indexing structure
+        :type scalar_indexing: numpy.array
+        :param M: the state transitions matrices
+        :type M: numpy.ndArray
+        """
+        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]
+        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)
+
+    def compute_parameters(self):
+        for indx, aggr in enumerate(zip(self._net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)):
+            self.compute_state_res_time_for_node(self._net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times,
+                                                 self.sample_path.trajectories.trajectory,
+                                                 self._net_graph.time_filtering[indx],
+                                                 self._net_graph.time_scalar_indexing_strucure[indx],
+                                                 aggr[1]._state_residence_times)
+            self.compute_state_transitions_for_a_node(self._net_graph.get_node_indx(aggr[0]),
+                                                      self.sample_path.trajectories.complete_trajectory,
+                                                      self._net_graph.transition_filtering[indx],
+                                                      self._net_graph.transition_scalar_indexing_structure[indx],
+                                                      aggr[1]._transition_matrices)
+            aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices)
+    """
+
+
+
+
+
+
+
+

PyCTBN/PyCTBN/sample_path.py

@@ -0,0 +1,69 @@
+
+#import abstract_importer as imp
+#import structure as st
+#import trajectory as tr
+from .abstract_importer import AbstractImporter
+from .structure import Structure
+from .trajectory import Trajectory
+
+
+class SamplePath:
+    """Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
+    cardinalites. Has the task of creating the objects ``Trajectory`` and ``Structure`` that will
+    contain the mentioned data.
+
+    :param importer: the Importer objects that will import ad process data
+    :type importer: AbstractImporter
+    :_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
+    """
+    def __init__(self, importer: AbstractImporter):
+        """Constructor Method
+        """
+        self._importer = importer
+        self._trajectories = None
+        self._structure = None
+        self._total_variables_count = None
+        self._importer.import_data()
+
+    def build_trajectories(self) -> None:
+        """Builds the Trajectory object that will contain all the trajectories.
+        Clears all the unused dataframes in ``_importer`` Object
+        """
+        self._trajectories = \
+            Trajectory(self._importer.build_list_of_samples_array(self._importer.concatenated_samples),
+                len(self._importer.sorter) + 1)
+        self._importer.clear_concatenated_frame()
+
+    def build_structure(self) -> None:
+        """
+        Builds the ``Structure`` object that aggregates all the infos about the net.
+        """
+        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.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 = Structure(labels, indxs, vals, edges,
+                                       self._total_variables_count)
+
+    @property
+    def trajectories(self) -> Trajectory:
+        return self._trajectories
+
+    @property
+    def structure(self) -> Structure:
+        return self._structure
+
+    @property
+    def total_variables_count(self):
+        return self._total_variables_count
+
+
+
+
+

PyCTBN/PyCTBN/set_of_cims.py

@@ -0,0 +1,94 @@
+import typing
+import numpy as np
+
+#import conditional_intensity_matrix as cim
+from .conditional_intensity_matrix import ConditionalIntensityMatrix
+
+
+class SetOfCims:
+    """Aggregates all the CIMS of the node identified by the label _node_id.
+
+    :param node_id: the node label
+    :type node_ind: string
+    :param parents_states_number: the cardinalities of the parents
+    :type parents_states_number: List
+    :param node_states_number: the caridinality of the node
+    :type node_states_number: int
+    :param p_combs: the p_comb structure bound to this node
+    :type p_combs: numpy.ndArray
+    :_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
+    :_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):
+        """Constructor Method
+        """
+        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) -> None:
+        """Initializes at the correct dimensions the state residence times matrix and the state transition matrices.
+        """
+        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)
+
+    def build_cims(self, state_res_times: np.ndarray, transition_matrices: np.ndarray) -> None:
+        """Build the ``ConditionalIntensityMatrix`` objects given the state residence times and transitions matrices.
+        Compute the cim coefficients.The class member ``_actual_cims`` will contain the computed cims.
+
+        :param state_res_times: the state residence times matrix
+        :type state_res_times: numpy.ndArray
+        :param transition_matrices: the transition matrices
+        :type transition_matrices: numpy.ndArray
+        """
+        for state_res_time_vector, transition_matrix in zip(state_res_times, transition_matrices):
+            cim_to_add = 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
+
+    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``.
+
+        :param mask_arr: the boolean mask that indicates which parent to consider
+        :type mask_arr: numpy.array
+        :param comb: the state/s of the filtered parents
+        :type comb: numpy.array
+        :return: Array of ``ConditionalIntensityMatrix`` objects
+        :rtype: numpy.array
+        """
+        if mask_arr.size <= 1:
+            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]
+
+    @property
+    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)
+
+
+

PyCTBN/PyCTBN/simple_cvs_importer.py

@@ -0,0 +1,62 @@
+import pandas as pd
+import glob
+import os
+
+import typing
+
+#import abstract_importer as ai
+#import sample_path as sp
+from .abstract_importer import AbstractImporter
+from .sample_path import SamplePath
+
+
+class CSVImporter(AbstractImporter):
+
+    def __init__(self, file_path):
+        self._df_samples_list = None
+        super(CSVImporter, self).__init__(file_path)
+
+    def import_data(self):
+        self.read_csv_file()
+        self._sorter = self.build_sorter(self._df_samples_list[0])
+        self.import_variables()
+        self.import_structure()
+        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.drop(df.columns[[0]], axis=1, inplace=True)
+        self._df_samples_list = [df]
+
+    def import_variables(self):
+        values_list = [3 for var in self._sorter]
+        # initialize dict of lists
+        data = {'Name':self._sorter, 'Value':values_list}
+        # Create the pandas DataFrame
+        self._df_variables = pd.DataFrame(data)
+
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        return list(sample_frame.columns)[1:]
+
+    def import_structure(self):
+        data = {'From':['X','Y','Z'], 'To':['Z','Z','Y']}
+        self._df_structure = pd.DataFrame(data)
+
+    def dataset_id(self) -> object:
+        pass
+
+
+def main():
+    read_files = glob.glob(os.path.join('../data', "*.csv"))
+    print(read_files[0])
+    csvimp = CSVImporter(read_files[0])
+    s1 = SamplePath(csvimp)
+    s1.build_trajectories()
+    s1.build_structure()
+    print(s1.structure)
+    print(s1.trajectories)
+
+
+if __name__ == "__main__":
+    main()
+

PyCTBN/PyCTBN/structure.py

@@ -0,0 +1,98 @@
+import typing as ty
+import numpy as np
+
+
+class Structure:
+    """Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)
+
+    :param nodes_labels_list: the symbolic names of the variables
+    :type nodes_labels_list: List
+    :param nodes_indexes_arr: the indexes of the nodes
+    :type nodes_indexes_arr: numpy.ndArray
+    :param nodes_vals_arr: the cardinalites of the nodes
+    :type nodes_vals_arr: numpy.ndArray
+    :param edges_list: the edges of the network
+    :type edges_list: List
+    :param total_variables_number: the total number of variables in the net
+    :type total_variables_number: int
+    """
+
+    def __init__(self, nodes_labels_list: ty.List, nodes_indexes_arr: np.ndarray, nodes_vals_arr: np.ndarray,
+                 edges_list: ty.List, total_variables_number: int):
+        """Constructor Method
+        """
+        self._nodes_labels_list = nodes_labels_list
+        self._nodes_indexes_arr = nodes_indexes_arr
+        self._nodes_vals_arr = nodes_vals_arr
+        self._edges_list = edges_list
+        self._total_variables_number = total_variables_number
+
+    @property
+    def edges(self) -> ty.List:
+        return self._edges_list
+
+    @property
+    def nodes_labels(self) -> ty.List:
+        return self._nodes_labels_list
+
+    @property
+    def nodes_indexes(self) -> np.ndarray:
+        return self._nodes_indexes_arr
+
+    @property
+    def nodes_values(self) -> np.ndarray:
+        return self._nodes_vals_arr
+
+    @property
+    def total_variables_number(self) -> int:
+        return self._total_variables_number
+
+    def get_node_id(self, node_indx: int) -> str:
+        """Given the ``node_index`` returns the node label.
+
+        :param node_indx: the node index
+        :type node_indx: int
+        :return: the node label
+        :rtype: string
+        """
+        return self._nodes_labels_list[node_indx]
+
+    def get_node_indx(self, node_id: str) -> int:
+        """Given the ``node_index`` returns the node label.
+
+        :param node_id: the node label
+        :type node_id: string
+        :return: the node index
+        :rtype: int
+        """
+        pos_indx = self._nodes_labels_list.index(node_id)
+        return self._nodes_indexes_arr[pos_indx]
+
+    def get_positional_node_indx(self, node_id: str) -> int:
+        return self._nodes_labels_list.index(node_id)
+
+    def get_states_number(self, node: str) -> int:
+        """Given the node label ``node`` returns the cardinality of the node.
+
+        :param node: the node label
+        :type node: string
+        :return: the node cardinality
+        :rtype: int
+        """
+        pos_indx = self._nodes_labels_list.index(node)
+        return self._nodes_vals_arr[pos_indx]
+
+    def __repr__(self):
+        return "Variables:\n" + str(self._nodes_labels_list) +"\nValues:\n"+ str(self._nodes_vals_arr) +\
+               "\nEdges: \n" + str(self._edges_list)
+
+    def __eq__(self, other):
+        """Overrides the default implementation"""
+        if isinstance(other, Structure):
+            return set(self._nodes_labels_list) == set(other._nodes_labels_list) and \
+                   np.array_equal(self._nodes_vals_arr, other._nodes_vals_arr) and \
+                   np.array_equal(self._nodes_indexes_arr, other._nodes_indexes_arr) and \
+                   self._edges_list == other._edges_list
+
+        return NotImplemented
+

PyCTBN/PyCTBN/structure_estimator.py

@@ -0,0 +1,240 @@
+
+
+from tqdm import tqdm
+import itertools
+import json
+import typing
+import networkx as nx
+import numpy as np
+from networkx.readwrite import json_graph
+from scipy.stats import chi2 as chi2_dist
+from scipy.stats import f as f_dist
+
+#import cache as ch
+#import conditional_intensity_matrix as condim
+#import network_graph as ng
+#import parameters_estimator as pe
+#import sample_path as sp
+#import structure as st
+from .cache import Cache
+from .conditional_intensity_matrix import ConditionalIntensityMatrix
+from .network_graph import NetworkGraph
+from .parameters_estimator import ParametersEstimator
+from .sample_path import SamplePath
+from .structure import Structure
+
+
+class StructureEstimator:
+    """Has the task of estimating the network structure given the trajectories in ``samplepath``.
+
+    :param sample_path: the _sample_path object containing the trajectories and the real structure
+    :type sample_path: SamplePath
+    :param exp_test_alfa: the significance level for the exponential Hp test
+    :type exp_test_alfa: float
+    :param chi_test_alfa: the significance level for the chi Hp test
+    :type chi_test_alfa: float
+    :_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: SamplePath, exp_test_alfa: float, chi_test_alfa: float):
+        """Constructor Method
+        """
+        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 = 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``:
+
+        :param node_ids: the list of nodes labels
+        :type node_ids: List
+        :return: a complete Digraph Object
+        :rtype: networkx.DiGraph
+        """
+        complete_graph = nx.DiGraph()
+        complete_graph.add_nodes_from(node_ids)
+        complete_graph.add_edges_from(itertools.permutations(node_ids, 2))
+        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) -> bool:
+        """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.
+
+        :param test_parent: the node label of the test parent
+        :type test_parent: string
+        :param test_child: the node label of the child
+        :type test_child: string
+        :param parent_set: the common parent set
+        :type parent_set: List
+        :param child_states_numb: the cardinality of the ``test_child``
+        :type child_states_numb: int
+        :param tot_vars_count: the total number of variables in the net
+        :type tot_vars_count: int
+        :return: True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise
+        :rtype: bool
+        """
+        p_set = parent_set[:]
+        complete_info = parent_set[:]
+        complete_info.append(test_child)
+
+        parents = np.array(parent_set)
+        parents = np.append(parents, test_parent)
+        sorted_parents = self._nodes[np.isin(self._nodes, parents)]
+        cims_filter = sorted_parents != test_parent
+        sofc1 = self._cache.find(set(p_set))
+
+        if not sofc1:
+            bool_mask1 = np.isin(self._nodes, complete_info)
+            l1 = list(self._nodes[bool_mask1])
+            indxs1 = self._nodes_indxs[bool_mask1]
+            vals1 = self._nodes_vals[bool_mask1]
+            eds1 = list(itertools.product(parent_set,test_child))
+            s1 = Structure(l1, indxs1, vals1, eds1, tot_vars_count)
+            g1 = NetworkGraph(s1)
+            g1.fast_init(test_child)
+            p1 = ParametersEstimator(self._sample_path.trajectories, g1)
+            p1.fast_init(test_child)
+            sofc1 = p1.compute_parameters_for_node(test_child)
+            self._cache.put(set(p_set), sofc1)
+        sofc2 = None
+        p_set.insert(0, test_parent)
+        if p_set:
+            sofc2 = self._cache.find(set(p_set))
+        if not sofc2:
+            complete_info.append(test_parent)
+            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 = Structure(l2, indxs2, vals2, eds2, tot_vars_count)
+            g2 = NetworkGraph(s2)
+            g2.fast_init(test_child)
+            p2 = 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)
+        for cim1, p_comb in zip(sofc1.actual_cims, sofc1.p_combs):
+            cond_cims = sofc2.filter_cims_with_mask(cims_filter, p_comb)
+            for cim2 in cond_cims:
+                if not self.independence_test(child_states_numb, cim1, cim2):
+                    return False
+        return True
+
+    def independence_test(self, child_states_numb: int, cim1: ConditionalIntensityMatrix,
+                          cim2: 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,
+        it is performed also the chi_test.
+
+        :param child_states_numb: the cardinality of the test child
+        :type child_states_numb: int
+        :param cim1: a cim belonging to the graph without test parent
+        :type cim1: ConditionalIntensityMatrix
+        :param cim2: a cim belonging to the graph with test parent
+        :type cim2: ConditionalIntensityMatrix
+        :return:True iff both tests do NOT reject the null hypothesis of indipendence. False otherwise.
+        :rtype: bool
+        """
+        M1 = cim1.state_transition_matrix
+        M2 = cim2.state_transition_matrix
+        r1s = M1.diagonal()
+        r2s = M2.diagonal()
+        C1 = cim1.cim
+        C2 = cim2.cim
+        F_stats = C2.diagonal() / C1.diagonal()
+        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]):
+                return False
+        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(r1s / r2s)
+        Ls = np.sqrt(r2s / r1s)
+        for val in range(0, child_states_numb):
+            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]))
+            if Chi > chi_2_quantile:
+                return False
+        return True
+
+    def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int) -> None:
+        """Performs an iteration of the CTPC algorithm using the node ``var_id`` as ``test_child``.
+
+        :param var_id: the node label of the test child
+        :type var_id: string
+        :param tot_vars_count: the number of _nodes in the net
+        :type tot_vars_count: int
+        """
+        #print("##################TESTING VAR################", 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):
+            parent_indx = 0
+            while parent_indx < len(u):
+                removed = False
+                S = self.generate_possible_sub_sets_of_size(u, b, u[parent_indx])
+                test_parent = u[parent_indx]
+                for parents_set in S:
+                    if self.complete_test(test_parent, var_id, parents_set, child_states_numb, tot_vars_count):
+                        self._complete_graph.remove_edge(test_parent, var_id)
+                        u.remove(test_parent)
+                        removed = True
+                        break
+                if not removed:
+                    parent_indx += 1
+            b += 1
+        self._cache.clear()
+
+    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``.
+
+        :param u: the list of nodes
+        :type u: List
+        :param size: the size of the subsets
+        :type size: int
+        :param parent_label: the node to exclude in the subsets generation
+        :type parent_label: string
+        :return: an Iterator Object containing a list of lists
+        :rtype: Iterator
+        """
+        list_without_test_parent = u[:]
+        list_without_test_parent.remove(parent_label)
+        return map(list, itertools.combinations(list_without_test_parent, size))
+
+    def ctpc_algorithm(self) -> None:
+        """Compute the CTPC algorithm over the entire net.
+        """
+        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 tqdm(self._nodes)]
+
+    def save_results(self) -> None:
+        """Save the estimated Structure to a .json file in the path where the data are loaded from.
+        The file is named as the input dataset but the results_ word is appendend to the results file.
+
+        """
+        res = json_graph.node_link_data(self._complete_graph)
+        name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + str(self._sample_path._importer.dataset_id())
+        name = 'results_' + name
+        with open(name, 'w') as f:
+            json.dump(res, f)
+
+

PyCTBN/PyCTBN/trajectory.py

@@ -0,0 +1,46 @@
+
+import numpy as np
+import typing
+
+
+class Trajectory:
+    """ Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles (the time deltas)
+    and a numpy matrix of ints (the changes of states).
+
+    :param list_of_columns: the list containing the times array and values matrix
+    :type list_of_columns: List
+    :param original_cols_number: total number of cols in the data
+    :type original_cols_number: int
+    :_actual_trajectory: the trajectory containing also the duplicated/shifted values
+    :_times: the array containing the time deltas
+    """
+
+    def __init__(self, list_of_columns: typing.List, original_cols_number: int):
+        """Constructor Method
+        """
+        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._actual_trajectory = np.array(list_of_columns[1:], dtype=np.int).T
+        self._times = np.array(list_of_columns[0], dtype=np.float)
+
+    @property
+    def trajectory(self) -> np.ndarray:
+        return self._actual_trajectory[:, :self._original_cols_number]
+
+    @property
+    def complete_trajectory(self) -> np.ndarray:
+        return self._actual_trajectory
+
+    @property
+    def times(self):
+        return self._times
+
+    def size(self):
+        return self._actual_trajectory.shape[0]
+
+    def __repr__(self):
+        return "Complete Trajectory Rows: " + str(self.size()) + "\n" + self.complete_trajectory.__repr__() + \
+               "\nTimes Rows:" + str(self.times.size) + "\n" + self.times.__repr__()
+
+

PyCTBN/tests/coverage.xml

@@ -0,0 +1,963 @@
+<?xml version="1.0" ?>
+<coverage version="5.2" timestamp="1597406229874" lines-valid="891" lines-covered="638" line-rate="0.716" branches-covered="0" branches-valid="0" branch-rate="0" complexity="0">
+	<!-- Generated by coverage.py: https://coverage.readthedocs.io -->
+	<!-- Based on https://raw.githubusercontent.com/cobertura/web/master/htdocs/xml/coverage-04.dtd -->
+	<sources>
+		<source></source>
+	</sources>
+	<packages>
+		<package name="." line-rate="1" branch-rate="0" complexity="0">
+			<classes>
+				<class name="test_json_importer.py" filename="test_json_importer.py" complexity="0" line-rate="1" branch-rate="0">
+					<methods/>
+					<lines>
+						<line number="1" hits="1"/>
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+				</class>
+				<class name="test_sample_path.py" filename="test_sample_path.py" complexity="0" line-rate="1" branch-rate="0">
+					<methods/>
+					<lines>
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+				</class>
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+					<methods/>
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+				</class>
+			</classes>
+		</package>
+	</packages>
+</coverage>

PyCTBN/tests/test_cache.py

@@ -0,0 +1,57 @@
+
+import unittest
+import numpy as np
+
+from ..PyCTBN.cache import Cache
+from ..PyCTBN.set_of_cims import SetOfCims
+
+
+class TestCache(unittest.TestCase):
+
+    def test_init(self):
+        c1 = Cache()
+        self.assertFalse(c1._list_of_sets_of_parents)
+        self.assertFalse(c1._actual_cache)
+
+    def test_put(self):
+        c1 = Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1._actual_cache))
+        self.assertEqual(1, len(c1._list_of_sets_of_parents))
+        self.assertEqual(sofc1, c1._actual_cache[0])
+        pset2 = {'X'}
+        sofc2 = SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset2, sofc2)
+        self.assertEqual(2, len(c1._actual_cache))
+        self.assertEqual(2, len(c1._list_of_sets_of_parents))
+        self.assertEqual(sofc2, c1._actual_cache[1])
+
+    def test_find(self):
+        c1 = Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1._actual_cache))
+        self.assertEqual(1, len(c1._list_of_sets_of_parents))
+        self.assertIsInstance(c1.find(pset1), SetOfCims)
+        self.assertEqual(sofc1, c1.find(pset1))
+        self.assertIsInstance(c1.find({'Y', 'X'}), SetOfCims)
+        self.assertEqual(sofc1, c1.find({'Y', 'X'}))
+        self.assertIsNone(c1.find({'X'}))
+
+    def test_clear(self):
+        c1 = Cache()
+        pset1 = {'X', 'Y'}
+        sofc1 = SetOfCims('Z', [], 3, np.array([]))
+        c1.put(pset1, sofc1)
+        self.assertEqual(1, len(c1._actual_cache))
+        self.assertEqual(1, len(c1._list_of_sets_of_parents))
+        c1.clear()
+        self.assertFalse(c1._list_of_sets_of_parents)
+        self.assertFalse(c1._actual_cache)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_cim.py

@@ -0,0 +1,46 @@
+
+import unittest
+import numpy as np
+
+from ..PyCTBN.conditional_intensity_matrix import ConditionalIntensityMatrix
+
+
+class TestConditionalIntensityMatrix(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.state_res_times = np.random.rand(1, 3)[0]
+        cls.state_res_times = cls.state_res_times * 1000
+        cls.state_transition_matrix = np.random.randint(1, 10000, (3, 3))
+        for i in range(0, len(cls.state_res_times)):
+            cls.state_transition_matrix[i, i] = 0
+            cls.state_transition_matrix[i, i] = np.sum(cls.state_transition_matrix[i])
+
+    def test_init(self):
+        c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
+        self.assertTrue(np.array_equal(self.state_res_times, c1.state_residence_times))
+        self.assertTrue(np.array_equal(self.state_transition_matrix, c1.state_transition_matrix))
+        self.assertEqual(c1.cim.dtype, np.float)
+        self.assertEqual(self.state_transition_matrix.shape, c1.cim.shape)
+
+    def test_compute_cim_coefficients(self):
+        c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
+        c2 = self.state_transition_matrix.astype(np.float)
+        np.fill_diagonal(c2, c2.diagonal() * -1)
+        for i in range(0, len(self.state_res_times)):
+            for j in range(0, len(self.state_res_times)):
+                c2[i, j] = (c2[i, j] + 1) / (self.state_res_times[i] + 1)
+        c1.compute_cim_coefficients()
+        for i in range(0, len(c1.state_residence_times)):
+            self.assertTrue(np.isclose(np.sum(c1.cim[i]), 0.0, 1e-02, 1e-01))
+        for i in range(0, len(self.state_res_times)):
+            for j in range(0, len(self.state_res_times)):
+                self.assertTrue(np.isclose(c1.cim[i, j], c2[i, j], 1e-02, 1e-01))
+
+    def test_repr(self):
+        c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix)
+        print(c1)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_json_importer.py

@@ -0,0 +1,175 @@
+
+import unittest
+import os
+import glob
+import numpy as np
+import pandas as pd
+import json
+
+from ..PyCTBN.json_importer import JsonImporter
+
+
+class TestJsonImporter(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.read_files = glob.glob(os.path.join('./data', "*.json"))
+        #print(os.path.join('../data'))
+
+    def test_init(self):
+        j1 = 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)
+        self.assertIsNone(j1.sorter)
+
+    def test_read_json_file_found(self):
+        data_set = {"key1": [1, 2, 3], "key2": [4, 5, 6]}
+        with open('data.json', 'w') as f:
+            json.dump(data_set, f)
+        path = os.getcwd()
+        path = path + '/data.json'
+        j1 = JsonImporter(path, '', '', '', '', '', 0)
+        imported_data = j1.read_json_file()
+        self.assertTrue(self.ordered(data_set) == self.ordered(imported_data))
+        os.remove('data.json')
+
+    def test_read_json_file_not_found(self):
+        path = os.getcwd()
+        path = path + '/data.json'
+        j1 = JsonImporter(path, '', '', '', '', '', 0)
+        self.assertRaises(FileNotFoundError, j1.read_json_file)
+
+    def test_normalize_trajectories(self):
+        j1 = 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)
+
+    def test_normalize_trajectories_wrong_indx(self):
+        j1 = 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)
+
+    def test_normalize_trajectories_wrong_key(self):
+        j1 = 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)
+
+    def test_compute_row_delta_single_samples_frame(self):
+        j1 = 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]
+        original_copy = sample_frame.copy()
+        columns_header = list(sample_frame.columns.values)
+        shifted_cols_header = [s + "S" for s in columns_header[1:]]
+        new_sample_frame = j1.compute_row_delta_sigle_samples_frame(sample_frame, columns_header[1:],
+                                                                    shifted_cols_header)
+        self.assertEqual(len(list(sample_frame.columns.values)) + len(shifted_cols_header),
+                         len(list(new_sample_frame.columns.values)))
+        self.assertEqual(sample_frame.shape[0] - 1, new_sample_frame.shape[0])
+        for indx, row in new_sample_frame.iterrows():
+            self.assertAlmostEqual(row['Time'],
+                                   original_copy.iloc[indx + 1]['Time'] - original_copy.iloc[indx]['Time'])
+        for indx, row in new_sample_frame.iterrows():
+            np.array_equal(np.array(row[columns_header[1:]],dtype=int),
+                  np.array(original_copy.iloc[indx][columns_header[1:]],dtype=int))
+            np.array_equal(np.array(row[shifted_cols_header], dtype=int),
+                           np.array(original_copy.iloc[indx + 1][columns_header[1:]], dtype=int))
+
+    def test_compute_row_delta_in_all_frames(self):
+        j1 = 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)
+
+    def test_clear_data_frame_list(self):
+        j1 = 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.clear_data_frame_list()
+        for df in j1._df_samples_list:
+            self.assertTrue(df.empty)
+
+    def test_clear_concatenated_frame(self):
+        j1 = 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)
+
+    def test_build_list_of_samples_array(self):
+        data_set = {"key1": [1, 2, 3], "key2": [4.1, 5.2, 6.3]}
+        with open('data.json', 'w') as f:
+            json.dump(data_set, f)
+        path = os.getcwd()
+        path = path + '/data.json'
+        j1 = JsonImporter(path, '', '', '', '', '', 0)
+        raw_data = j1.read_json_file()
+        frame = pd.DataFrame(raw_data)
+        col_list = j1.build_list_of_samples_array(frame)
+        forced_list = []
+        for key in data_set:
+            forced_list.append(np.array(data_set[key]))
+        for a1, a2 in zip(col_list, forced_list):
+            self.assertTrue(np.array_equal(a1, a2))
+        os.remove('data.json')
+
+    def test_import_variables(self):
+        j1 = 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)
+        self.assertEqual(list(df_var[j1._variables_key]), sorter)
+
+    def test_import_structure(self):
+        j1 = 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 = 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])
+        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 = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 2)
+        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]))
+        print(j1.variables)
+        print(j1.structure)
+        print(j1.concatenated_samples)
+
+    def ordered(self, obj):
+        if isinstance(obj, dict):
+            return sorted((k, self.ordered(v)) for k, v in obj.items())
+        if isinstance(obj, list):
+            return sorted(self.ordered(x) for x in obj)
+        else:
+            return obj
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_networkgraph.py

@@ -0,0 +1,187 @@
+
+import unittest
+import glob
+import os
+import networkx as nx
+import numpy as np
+import itertools
+
+from ..PyCTBN.sample_path import SamplePath
+from ..PyCTBN.network_graph import NetworkGraph
+from ..PyCTBN.json_importer import JsonImporter
+
+
+class TestNetworkGraph(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.read_files = glob.glob(os.path.join('./data', "*.json"))
+        cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
+        cls.s1 = SamplePath(cls.importer)
+        cls.s1.build_trajectories()
+        cls.s1.build_structure()
+
+    def test_init(self):
+        g1 = NetworkGraph(self.s1.structure)
+        self.assertEqual(self.s1.structure, g1._graph_struct)
+        self.assertIsInstance(g1._graph, nx.DiGraph)
+        self.assertIsNone(g1.time_scalar_indexing_strucure)
+        self.assertIsNone(g1.transition_scalar_indexing_structure)
+        self.assertIsNone(g1.transition_filtering)
+        self.assertIsNone(g1.p_combs)
+
+    def test_add_nodes(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        for n1, n2 in zip(g1.nodes, self.s1.structure.nodes_labels):
+            self.assertEqual(n1, n2)
+
+    def test_add_edges(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_edges(self.s1.structure.edges)
+        for e in self.s1.structure.edges:
+            self.assertIn(tuple(e), g1.edges)
+
+    def test_fast_init(self):
+        g1 = NetworkGraph(self.s1.structure)
+        for node in self.s1.structure.nodes_labels:
+            g1.fast_init(node)
+            self.assertIsNotNone(g1._graph.nodes)
+            self.assertIsNotNone(g1._graph.edges)
+            self.assertIsInstance(g1._time_scalar_indexing_structure, np.ndarray)
+            self.assertIsInstance(g1._transition_scalar_indexing_structure, np.ndarray)
+            self.assertIsInstance(g1._time_filtering, np.ndarray)
+            self.assertIsInstance(g1._transition_filtering, np.ndarray)
+            self.assertIsInstance(g1._p_combs_structure, np.ndarray)
+            self.assertIsInstance(g1._aggregated_info_about_nodes_parents, tuple)
+
+    def test_get_ordered_by_indx_set_of_parents(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            for indx in range(len(aggr_info[0]) - 1 ):
+                self.assertLess(g1.get_node_indx(aggr_info[0][indx]), g1.get_node_indx(aggr_info[0][indx + 1]))
+            for par, par_indx in zip(aggr_info[0], aggr_info[1]):
+                self.assertEqual(g1.get_node_indx(par), par_indx)
+            for par, par_val in zip(aggr_info[0], aggr_info[2]):
+                self.assertEqual(g1._graph_struct.get_states_number(par), par_val)
+
+    def test_build_time_scalar_indexing_structure_for_a_node(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1],
+                                                                     aggr_info[0], aggr_info[2])
+
+    def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels, parents_vals):
+        time_scalar_indexing = graph.build_time_scalar_indexing_structure_for_a_node(node_id, parents_vals)
+        self.assertEqual(len(time_scalar_indexing), len(parents_indxs) + 1)
+        merged_list = parents_labels[:]
+        merged_list.insert(0, node_id)
+        vals_list = []
+        for node in merged_list:
+            vals_list.append(graph.get_states_number(node))
+        t_vec = np.array(vals_list)
+        t_vec = t_vec.cumprod()
+        self.assertTrue(np.array_equal(time_scalar_indexing, t_vec))
+
+    def test_build_transition_scalar_indexing_structure_for_a_node(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1],
+                                                                        aggr_info[0], aggr_info[2])
+
+    def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels,
+                                                                  parents_values):
+        transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_id,
+                                                                                                 parents_values)
+        self.assertEqual(len(transition_scalar_indexing), len(parents_indxs) + 2)
+        merged_list = parents_labels[:]
+        merged_list.insert(0, node_id)
+        merged_list.insert(0, node_id)
+        vals_list = []
+        for node_id in merged_list:
+            vals_list.append(graph.get_states_number(node_id))
+        m_vec = np.array([vals_list])
+        m_vec = m_vec.cumprod()
+        self.assertTrue(np.array_equal(transition_scalar_indexing, m_vec))
+
+    def test_build_time_columns_filtering_structure_for_a_node(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1])
+
+    def aux_build_time_columns_filtering_structure_for_a_node(self, graph, node_id, p_indxs):
+        graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id), p_indxs)
+        single_filter = []
+        single_filter.append(graph.get_node_indx(node_id))
+        single_filter.extend(p_indxs)
+        self.assertTrue(np.array_equal(graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id),
+                                                                                     p_indxs),np.array(single_filter)))
+    def test_build_transition_columns_filtering_structure(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1])
+
+    def aux_build_transition_columns_filtering_structure(self, graph, node_id, p_indxs):
+        single_filter = []
+        single_filter.append(graph.get_node_indx(node_id) + graph._graph_struct.total_variables_number)
+        single_filter.append(graph.get_node_indx(node_id))
+        single_filter.extend(p_indxs)
+        self.assertTrue(np.array_equal(graph.build_transition_filtering_for_a_node(graph.get_node_indx(node_id),
+
+                                                                                     p_indxs), np.array(single_filter)))
+    def test_build_p_combs_structure(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in self.s1.structure.nodes_labels:
+            aggr_info = g1.get_ordered_by_indx_set_of_parents(node)
+            self.aux_build_p_combs_structure(g1, aggr_info[2])
+
+    def aux_build_p_combs_structure(self, graph, p_vals):
+        p_combs = graph.build_p_comb_structure_for_a_node(p_vals)
+        p_possible_vals = []
+        for val in p_vals:
+            vals = [v for v in range(val)]
+            p_possible_vals.extend(vals)
+            comb_struct = set(itertools.product(p_possible_vals,repeat=len(p_vals)))
+            for comb in comb_struct:
+                self.assertIn(np.array(comb), p_combs)
+
+    def test_get_parents_by_id(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node in g1.nodes:
+            self.assertListEqual(g1.get_parents_by_id(node), list(g1._graph.predecessors(node)))
+
+    def test_get_states_number(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node, val in zip(g1.nodes, g1.nodes_values):
+            self.assertEqual(val, g1.get_states_number(node))
+
+    def test_get_node_indx(self):
+        g1 = NetworkGraph(self.s1.structure)
+        g1.add_nodes(self.s1.structure.nodes_labels)
+        g1.add_edges(self.s1.structure.edges)
+        for node, indx in zip(g1.nodes, g1.nodes_indexes):
+            self.assertEqual(indx, g1.get_node_indx(node))
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_parameters_estimator.py

@@ -0,0 +1,67 @@
+
+import unittest
+import numpy as np
+import glob
+import os
+
+from ..PyCTBN.network_graph import NetworkGraph
+from ..PyCTBN.sample_path import SamplePath
+from ..PyCTBN.set_of_cims import SetOfCims
+from ..PyCTBN.parameters_estimator import ParametersEstimator
+from ..PyCTBN.json_importer import JsonImporter
+
+
+class TestParametersEstimatior(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.read_files = glob.glob(os.path.join('./data', "*.json"))
+        cls.array_indx = 0
+        cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name',
+                                       cls.array_indx)
+        cls.s1 = SamplePath(cls.importer)
+        cls.s1.build_trajectories()
+        cls.s1.build_structure()
+        print(cls.s1.structure.edges)
+        print(cls.s1.structure.nodes_values)
+
+    def test_fast_init(self):
+        for node in self.s1.structure.nodes_labels:
+            g = NetworkGraph(self.s1.structure)
+            g.fast_init(node)
+            p1 = 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, SetOfCims)
+
+    def test_compute_parameters_for_node(self):
+        for indx, node in enumerate(self.s1.structure.nodes_labels):
+            print(node)
+            g = NetworkGraph(self.s1.structure)
+            g.fast_init(node)
+            p1 = 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())
+            self.equality_of_cims_of_node(sc[indx], sofc1._actual_cims)
+
+    def equality_of_cims_of_node(self, sampled_cims, 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)
+
+    def cim_equality_test(self, cim1, cim2):
+        for r1, r2 in zip(cim1, cim2):
+            self.assertTrue(np.all(np.isclose(r1, r2, 1e-01, 1e-01) == True))
+
+    def aux_import_sampled_cims(self, cims_label):
+        i1 = JsonImporter(self.read_files[0], '', '', '', '', '', self.array_indx)
+        raw_data = i1.read_json_file()
+        return i1.import_sampled_cims(raw_data, self.array_indx, cims_label)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_sample_path.py

@@ -0,0 +1,39 @@
+
+import unittest
+import glob
+import os
+
+from ..PyCTBN.json_importer import JsonImporter
+from ..PyCTBN.sample_path import SamplePath
+from ..PyCTBN.trajectory import Trajectory
+from ..PyCTBN.structure import Structure
+
+
+class TestSamplePath(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.read_files = glob.glob(os.path.join('./data', "*.json"))
+        cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
+
+    def test_init(self):
+        s1 = SamplePath(self.importer)
+        self.assertIsNone(s1.trajectories)
+        self.assertIsNone(s1.structure)
+        self.assertFalse(s1._importer.concatenated_samples.empty)
+        self.assertIsNone(s1._total_variables_count)
+
+    def test_build_trajectories(self):
+        s1 = SamplePath(self.importer)
+        s1.build_trajectories()
+        self.assertIsInstance(s1.trajectories, Trajectory)
+
+    def test_build_structure(self):
+        s1 = SamplePath(self.importer)
+        s1.build_structure()
+        self.assertIsInstance(s1.structure, Structure)
+        self.assertEqual(s1._total_variables_count, len(s1._importer.sorter))
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_setofcims.py

@@ -0,0 +1,133 @@
+
+import unittest
+import numpy as np
+import itertools
+
+from ..PyCTBN.set_of_cims import SetOfCims
+
+
+class TestSetOfCims(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.node_id = 'X'
+        cls.possible_cardinalities = [2, 3]
+        cls.possible_states = [[0,1], [0, 1, 2]]
+        cls.node_states_number = range(2, 4)
+
+    def test_init(self):
+        # empty parent set
+        for sn in self.node_states_number:
+            p_combs = self.build_p_comb_structure_for_a_node([])
+            self.aux_test_init(self.node_id, [], sn, p_combs)
+        # one parent
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=1):
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_init(self.node_id, list(p), sn, p_combs)
+        #two parents
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=2):
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_init(self.node_id, list(p), sn, p_combs)
+
+    def test_build_cims(self):
+        # empty parent set
+        for sn in self.node_states_number:
+            p_combs = self.build_p_comb_structure_for_a_node([])
+            self.aux_test_build_cims(self.node_id, [], sn, p_combs)
+        # one parent
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=1):
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_build_cims(self.node_id, list(p), sn, p_combs)
+        #two parents
+        for sn in self.node_states_number:
+            for p in itertools.product(self.possible_cardinalities, repeat=2):
+                p_combs = self.build_p_comb_structure_for_a_node(list(p))
+                self.aux_test_build_cims(self.node_id, list(p), sn, p_combs)
+
+    def test_filter_cims_with_mask(self):
+        p_combs = self.build_p_comb_structure_for_a_node(self.possible_cardinalities)
+        sofc1 = SetOfCims('X', self.possible_cardinalities, 3, p_combs)
+        state_res_times_list = []
+        transition_matrices_list = []
+        for i in range(len(p_combs)):
+            state_res_times = np.random.rand(1, 3)[0]
+            state_res_times = state_res_times * 1000
+            state_transition_matrix = np.random.randint(1, 10000, (3, 3))
+            state_res_times_list.append(state_res_times)
+            transition_matrices_list.append(state_transition_matrix)
+        sofc1.build_cims(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))
+                    else:
+                        indxs = self.another_filtering_method(p_combs, m, [parent_value])
+                        self.assertTrue(np.array_equal(cims, sofc1._actual_cims[indxs]))
+
+    def aux_test_build_cims(self, node_id, p_values, node_states, p_combs):
+        state_res_times_list = []
+        transition_matrices_list = []
+        so1 = SetOfCims(node_id, p_values, node_states, p_combs)
+        for i in range(len(p_combs)):
+            state_res_times = np.random.rand(1, node_states)[0]
+            state_res_times = state_res_times * 1000
+            state_transition_matrix = np.random.randint(1, 10000, (node_states, node_states))
+            state_res_times_list.append(state_res_times)
+            transition_matrices_list.append(state_transition_matrix)
+        so1.build_cims(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)
+
+    def aux_test_init(self, node_id, parents_states_number, node_states_number, p_combs):
+        sofcims = 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)
+
+    def build_p_comb_structure_for_a_node(self, parents_values):
+        """
+        Builds the combinatory structure that contains the combinations of all the values contained in parents_values.
+
+        Parameters:
+            parents_values: the cardinalities of the nodes
+        Returns:
+            a numpy matrix containing a grid of the combinations
+        """
+        tmp = []
+        for val in parents_values:
+            tmp.append([x for x in range(val)])
+        if len(parents_values) > 0:
+            parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values))
+            if len(parents_values) > 1:
+                tmp_comb = parents_comb[:, 1].copy()
+                parents_comb[:, 1] = parents_comb[:, 0].copy()
+                parents_comb[:, 0] = tmp_comb
+        else:
+            parents_comb = np.array([[]], dtype=np.int)
+        return parents_comb
+
+    def another_filtering_method(self,p_combs, mask, parent_value):
+        masked_combs = p_combs[:, mask]
+        indxs = []
+        for indx, val in enumerate(masked_combs):
+            if val == parent_value:
+                indxs.append(indx)
+        return np.array(indxs)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_structure.py

@@ -0,0 +1,81 @@
+
+import unittest
+import numpy as np
+from ..PyCTBN.structure import Structure
+
+
+class TestStructure(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.labels = ['X','Y','Z']
+        cls.indxs = np.array([0,1,2])
+        cls.vals = np.array([3,3,3])
+        cls.edges = [('X','Z'),('Y','Z'), ('Z','Y')]
+        cls.vars_numb = len(cls.labels)
+
+    def test_init(self):
+        s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
+        self.assertListEqual(self.labels,s1.nodes_labels)
+        self.assertIsInstance(s1.nodes_indexes, np.ndarray)
+        self.assertTrue(np.array_equal(self.indxs, s1.nodes_indexes))
+        self.assertIsInstance(s1.nodes_values, np.ndarray)
+        self.assertTrue(np.array_equal(self.vals, s1.nodes_values))
+        self.assertListEqual(self.edges, s1.edges)
+        self.assertEqual(self.vars_numb, s1.total_variables_number)
+
+    def test_get_node_id(self):
+        s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
+        for indx, var in enumerate(self.labels):
+            self.assertEqual(var, s1.get_node_id(indx))
+
+    def test_get_node_indx(self):
+        l2 = self.labels[:]
+        l2.remove('Y')
+        i2 = self.indxs.copy()
+        np.delete(i2, 1)
+        v2 = self.vals.copy()
+        np.delete(v2, 1)
+        e2 = [('X','Z')]
+        n2 = self.vars_numb - 1
+        s1 = Structure(l2, i2, v2, e2, n2)
+        for indx, var in zip(i2, l2):
+            self.assertEqual(indx, s1.get_node_indx(var))
+
+    def test_get_positional_node_indx(self):
+        l2 = self.labels[:]
+        l2.remove('Y')
+        i2 = self.indxs.copy()
+        np.delete(i2, 1)
+        v2 = self.vals.copy()
+        np.delete(v2, 1)
+        e2 = [('X', 'Z')]
+        n2 = self.vars_numb - 1
+        s1 = Structure(l2, i2, v2, e2, n2)
+        for indx, var in enumerate(s1.nodes_labels):
+            self.assertEqual(indx, s1.get_positional_node_indx(var))
+
+    def test_get_states_number(self):
+        l2 = self.labels[:]
+        l2.remove('Y')
+        i2 = self.indxs.copy()
+        np.delete(i2, 1)
+        v2 = self.vals.copy()
+        np.delete(v2, 1)
+        e2 = [('X', 'Z')]
+        n2 = self.vars_numb - 1
+        s1 = Structure(l2, i2, v2, e2, n2)
+        for val, node in zip(v2, l2):
+            self.assertEqual(val, s1.get_states_number(node))
+
+    def test_equality(self):
+        s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
+        s2 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
+        self.assertEqual(s1, s2)
+
+    def test_repr(self):
+        s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb)
+        print(s1)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_structure_estimator.py

@@ -0,0 +1,103 @@
+
+import glob
+import math
+import os
+import unittest
+
+import networkx as nx
+import numpy as np
+import psutil
+from line_profiler import LineProfiler
+import timeit
+
+from ..PyCTBN.cache import Cache
+from ..PyCTBN.sample_path import SamplePath
+from ..PyCTBN.structure_estimator import StructureEstimator
+from ..PyCTBN.json_importer import JsonImporter
+
+
+class TestStructureEstimator(unittest.TestCase):
+
+    @classmethod
+    def setUpClass(cls):
+        cls.read_files = glob.glob(os.path.join('./data', "*.json"))
+        cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name', 0)
+        cls.s1 = SamplePath(cls.importer)
+        cls.s1.build_trajectories()
+        cls.s1.build_structure()
+
+    def test_init(self):
+        exp_alfa = 0.1
+        chi_alfa = 0.1
+        se1 = 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, Cache)
+
+    def test_build_complete_graph(self):
+        exp_alfa = 0.1
+        chi_alfa = 0.1
+        nodes_numb = len(self.s1.structure.nodes_labels)
+        se1 = StructureEstimator(self.s1, exp_alfa, chi_alfa)
+        cg = se1.build_complete_graph(self.s1.structure.nodes_labels)
+        self.assertEqual(len(cg.edges), nodes_numb*(nodes_numb - 1))
+        for node in self.s1.structure.nodes_labels:
+            no_self_loops = self.s1.structure.nodes_labels[:]
+            no_self_loops.remove(node)
+            for n2 in no_self_loops:
+                self.assertIn((node, n2), cg.edges)
+
+    def test_generate_possible_sub_sets_of_size(self):
+        exp_alfa = 0.1
+        chi_alfa = 0.1
+        nodes_numb = len(self.s1.structure.nodes_labels)
+        se1 = StructureEstimator(self.s1, exp_alfa, chi_alfa)
+
+        for node in self.s1.structure.nodes_labels:
+            for b in range(nodes_numb):
+                sets = se1.generate_possible_sub_sets_of_size(self.s1.structure.nodes_labels, b, node)
+                sets2 = se1.generate_possible_sub_sets_of_size(self.s1.structure.nodes_labels, b, node)
+                self.assertEqual(len(list(sets)), math.floor(math.factorial(nodes_numb - 1) /
+                                                             (math.factorial(b)*math.factorial(nodes_numb -1 - b))))
+                for sset in sets2:
+                    self.assertFalse(node in sset)
+
+    def test_time(self):
+        se1 = StructureEstimator(self.s1, 0.1, 0.1)
+        lp = LineProfiler()
+        #lp.add_function(se1.complete_test)
+        #lp.add_function(se1.one_iteration_of_CTPC_algorithm)
+        #lp.add_function(se1.independence_test)
+        lp_wrapper = lp(se1.ctpc_algorithm)
+        lp_wrapper()
+        lp.print_stats()
+        #print("Last time", lp.dump_stats())
+        #print("Exec Time", timeit.timeit(se1.ctpc_algorithm, number=1))
+        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)
+        tuples_edges = [tuple(rec) for rec in self.s1.structure.edges]
+        spurious_edges = []
+        for ed in se1._complete_graph.edges:
+            if not(ed in tuples_edges):
+                spurious_edges.append(ed)
+        print("Spurious Edges:",spurious_edges)
+        print("Adj Matrix:", nx.adj_matrix(se1._complete_graph).toarray().astype(bool))
+        #se1.save_results()
+
+    def test_memory(self):
+        se1 = StructureEstimator(self.s1, 0.1, 0.1)
+        se1.ctpc_algorithm()
+        current_process = psutil.Process(os.getpid())
+        mem = current_process.memory_info().rss
+        print("Average Memory Usage in MB:", mem / 10**6)
+
+
+if __name__ == '__main__':
+    unittest.main()

PyCTBN/tests/test_trajectory.py

@@ -0,0 +1,46 @@
+
+import unittest
+import numpy as np
+
+from ..PyCTBN.trajectory import Trajectory
+
+
+class TestTrajectory(unittest.TestCase):
+
+    def test_init(self):
+        cols_list = [np.array([1.2,1.3,.14]), np.arange(1,4), np.arange(4,7)]
+        t1 = Trajectory(cols_list, len(cols_list) - 2)
+        self.assertTrue(np.array_equal(cols_list[0], t1.times))
+        self.assertTrue(np.array_equal(np.ravel(t1.complete_trajectory[:, : 1]), cols_list[1]))
+        self.assertTrue(np.array_equal(np.ravel(t1.complete_trajectory[:, 1: 2]), cols_list[2]))
+        self.assertEqual(len(cols_list) - 1, t1.complete_trajectory.shape[1])
+        self.assertEqual(t1.size(), t1.times.size)
+
+    def test_init_first_array_not_float_type(self):
+        cols_list = [np.arange(1, 4), np.arange(4, 7), np.array([1.2, 1.3, .14])]
+        self.assertRaises(TypeError, Trajectory, cols_list, len(cols_list))
+
+    def test_complete_trajectory(self):
+        cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
+        t1 = Trajectory(cols_list, len(cols_list) - 2)
+        complete = np.column_stack((cols_list[1], cols_list[2]))
+        self.assertTrue(np.array_equal(t1.complete_trajectory, complete))
+
+    def test_trajectory(self):
+        cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
+        t1 = Trajectory(cols_list, len(cols_list) - 2)
+        self.assertTrue(np.array_equal(cols_list[1], t1.trajectory.ravel()))
+
+    def test_times(self):
+        cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
+        t1 = Trajectory(cols_list, len(cols_list) - 2)
+        self.assertTrue(np.array_equal(cols_list[0], t1.times))
+
+    def test_repr(self):
+        cols_list = [np.array([1.2, 1.3, .14]), np.arange(1, 4), np.arange(4, 7)]
+        t1 = Trajectory(cols_list, len(cols_list) - 2)
+        print(t1)
+
+
+if __name__ == '__main__':
+    unittest.main()

documentation/conf.py

@@ -12,7 +12,7 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('../main_package/PyCTBN'))
+sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN'))
 print(sys.path)
 
 

setup.py

@@ -2,11 +2,11 @@ from setuptools import setup, find_packages
 
 setup(name='PyCTBN',
       version='1.0',
-      url='https://github.com/philipMartini/CTBN_Project',
+      url='https://github.com/philipMartini/PyCTBN',
       license='MIT',
-      author='Filippo Martini',
-      author_email='f.martini@campus.unimib.it',
-      description='A Continuous Time Bayesian Network Library',
+      author=['Alessandro Bregoli', 'Filippo Martini'],
+      author_email=['a.bregoli1@campus.unimib.it', 'f.martini@campus.unimib.it'],
+      description='A Continuous Time Bayesian Networks Library',
       packages=find_packages(exclude=['tests', 'data']),
       install_requires=[
           'numpy', 'pandas', 'networkx'],