diff --git a/PyCTBN/.scannerwork/.sonar_lock b/PyCTBN/.scannerwork/.sonar_lock new file mode 100644 index 0000000..e69de29 diff --git a/PyCTBN/.scannerwork/report-task.txt b/PyCTBN/.scannerwork/report-task.txt new file mode 100644 index 0000000..5394593 --- /dev/null +++ b/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 diff --git a/PyCTBN/PyCTBN/__init__.py b/PyCTBN/PyCTBN/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/PyCTBN/PyCTBN/abstract_importer.py b/PyCTBN/PyCTBN/abstract_importer.py new file mode 100644 index 0000000..283841a --- /dev/null +++ b/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 diff --git a/PyCTBN/PyCTBN/cache.py b/PyCTBN/PyCTBN/cache.py new file mode 100644 index 0000000..592fcf4 --- /dev/null +++ b/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[:] \ No newline at end of file diff --git a/PyCTBN/PyCTBN/conditional_intensity_matrix.py b/PyCTBN/PyCTBN/conditional_intensity_matrix.py new file mode 100644 index 0000000..96a3ae2 --- /dev/null +++ b/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) + diff --git a/PyCTBN/PyCTBN/deprecated/sets_of_cims_container.py b/PyCTBN/PyCTBN/deprecated/sets_of_cims_container.py new file mode 100644 index 0000000..cf1cc82 --- /dev/null +++ b/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) + diff --git a/PyCTBN/PyCTBN/json_importer.py b/PyCTBN/PyCTBN/json_importer.py new file mode 100644 index 0000000..283057d --- /dev/null +++ b/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 + + + diff --git a/PyCTBN/PyCTBN/network_graph.py b/PyCTBN/PyCTBN/network_graph.py new file mode 100644 index 0000000..6ac6812 --- /dev/null +++ b/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 + """ \ No newline at end of file diff --git a/PyCTBN/PyCTBN/original_ctpc_algorithm.py b/PyCTBN/PyCTBN/original_ctpc_algorithm.py new file mode 100644 index 0000000..45e539a --- /dev/null +++ b/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 + + diff --git a/PyCTBN/PyCTBN/parameters_estimator.py b/PyCTBN/PyCTBN/parameters_estimator.py new file mode 100644 index 0000000..d1c0ddb --- /dev/null +++ b/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) + """ + + + + + + + + diff --git a/PyCTBN/PyCTBN/sample_path.py b/PyCTBN/PyCTBN/sample_path.py new file mode 100644 index 0000000..1d6c6c0 --- /dev/null +++ b/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 + + + + + diff --git a/PyCTBN/PyCTBN/set_of_cims.py b/PyCTBN/PyCTBN/set_of_cims.py new file mode 100644 index 0000000..bbbd746 --- /dev/null +++ b/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) + + + diff --git a/PyCTBN/PyCTBN/simple_cvs_importer.py b/PyCTBN/PyCTBN/simple_cvs_importer.py new file mode 100644 index 0000000..2f04fb6 --- /dev/null +++ b/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() + diff --git a/PyCTBN/PyCTBN/structure.py b/PyCTBN/PyCTBN/structure.py new file mode 100644 index 0000000..c2a5692 --- /dev/null +++ b/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 + diff --git a/PyCTBN/PyCTBN/structure_estimator.py b/PyCTBN/PyCTBN/structure_estimator.py new file mode 100644 index 0000000..284e94d --- /dev/null +++ b/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) + + diff --git a/PyCTBN/PyCTBN/trajectory.py b/PyCTBN/PyCTBN/trajectory.py new file mode 100644 index 0000000..ae3ff93 --- /dev/null +++ b/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__() + + diff --git a/PyCTBN/__init__.py b/PyCTBN/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/PyCTBN/tests/__init__.py b/PyCTBN/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/PyCTBN/tests/coverage.xml b/PyCTBN/tests/coverage.xml new file mode 100644 index 0000000..cef006f --- /dev/null +++ b/PyCTBN/tests/coverage.xml @@ -0,0 +1,963 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/PyCTBN/tests/test_cache.py b/PyCTBN/tests/test_cache.py new file mode 100644 index 0000000..7cb6706 --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_cim.py b/PyCTBN/tests/test_cim.py new file mode 100644 index 0000000..e3228db --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_json_importer.py b/PyCTBN/tests/test_json_importer.py new file mode 100644 index 0000000..f806ebd --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_networkgraph.py b/PyCTBN/tests/test_networkgraph.py new file mode 100644 index 0000000..6bb819b --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_parameters_estimator.py b/PyCTBN/tests/test_parameters_estimator.py new file mode 100644 index 0000000..9314f84 --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_sample_path.py b/PyCTBN/tests/test_sample_path.py new file mode 100644 index 0000000..e2f10b1 --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_setofcims.py b/PyCTBN/tests/test_setofcims.py new file mode 100644 index 0000000..daed9f0 --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_structure.py b/PyCTBN/tests/test_structure.py new file mode 100644 index 0000000..b6b4493 --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_structure_estimator.py b/PyCTBN/tests/test_structure_estimator.py new file mode 100644 index 0000000..ca21cab --- /dev/null +++ b/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() diff --git a/PyCTBN/tests/test_trajectory.py b/PyCTBN/tests/test_trajectory.py new file mode 100644 index 0000000..f826632 --- /dev/null +++ b/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() diff --git a/documentation/conf.py b/documentation/conf.py index 6ddffbc..64776d2 100644 --- a/documentation/conf.py +++ b/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) diff --git a/setup.py b/setup.py index 1f722eb..1cc662f 100644 --- a/setup.py +++ b/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'],