Add build and dist directories

.gitignore

@@ -3,7 +3,6 @@ __pycache__
 **/__pycache__
 **/data
 **/PyCTBN.egg-info
-**/dist
 **/results_data
 **/.scannerwork
-**/build
+

build/lib/PyCTBN/PyCTBN/__init__.py

@@ -0,0 +1,8 @@
+import PyCTBN.PyCTBN.estimators
+from PyCTBN.PyCTBN.estimators import *
+import PyCTBN.PyCTBN.optimizers
+from PyCTBN.PyCTBN.optimizers import *
+import PyCTBN.PyCTBN.structure_graph
+from PyCTBN.PyCTBN.structure_graph import *
+import PyCTBN.PyCTBN.utility
+from PyCTBN.PyCTBN.utility import *

build/lib/PyCTBN/PyCTBN/estimators/__init__.py

@@ -0,0 +1,5 @@
+from .fam_score_calculator import FamScoreCalculator
+from .parameters_estimator import ParametersEstimator
+from .structure_estimator import StructureEstimator
+from .structure_constraint_based_estimator import StructureConstraintBasedEstimator
+from .structure_score_based_estimator import StructureScoreBasedEstimator

build/lib/PyCTBN/PyCTBN/estimators/fam_score_calculator.py

@@ -0,0 +1,265 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+from networkx.readwrite import json_graph
+
+from math import log
+
+from scipy.special import loggamma
+from random import choice
+
+from ..structure_graph.set_of_cims import SetOfCims
+from ..structure_graph.network_graph import NetworkGraph
+from ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
+
+
+'''
+
+'''
+
+
+class FamScoreCalculator:
+    """
+    Has the task of calculating the FamScore of a node by using a Bayesian score function
+    """
+
+    def __init__(self):
+        #np.seterr('raise')
+        pass
+
+    # region theta
+
+    def marginal_likelihood_theta(self,
+                        cims: ConditionalIntensityMatrix,
+                        alpha_xu: float,
+                        alpha_xxu: float):
+        """
+        Calculate the FamScore value of the node identified by the label node_id
+
+        :param cims: np.array with all the node's cims
+        :type cims: np.array
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters, default to 0.1
+        :type alpha_xu: float
+        :param alpha_xxu: distribuited hyperparameter over the CTBN’s theta parameters
+        :type alpha_xxu: float
+
+        :return: the value of the marginal likelihood over theta
+        :rtype: float
+        """
+        return np.sum(
+                        [self.variable_cim_xu_marginal_likelihood_theta(cim,
+                                                                    alpha_xu,
+                                                                    alpha_xxu)
+                        for cim in cims])
+
+    def variable_cim_xu_marginal_likelihood_theta(self,
+                        cim: ConditionalIntensityMatrix,
+                        alpha_xu: float,
+                        alpha_xxu: float):
+        """
+        Calculate the value of the marginal likelihood over theta given a cim
+
+        :param cim: A conditional_intensity_matrix object with the sufficient statistics
+        :type cim: class:'ConditionalIntensityMatrix'
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters, default to 0.1
+        :type alpha_xu: float
+        :param alpha_xxu: distribuited hyperparameter over the CTBN’s theta parameters
+        :type alpha_xxu: float
+
+        :return: the value of the marginal likelihood over theta
+        :rtype: float
+        """
+
+        'get cim length'
+        values = len(cim._state_residence_times)
+
+        'compute the marginal likelihood for the current cim'
+        return np.sum([
+                    self.single_cim_xu_marginal_likelihood_theta(
+                                                index,
+                                                cim,
+                                                alpha_xu,
+                                                alpha_xxu)
+                    for index in range(values)])
+
+    def single_cim_xu_marginal_likelihood_theta(self,
+                    index: int,
+                    cim: ConditionalIntensityMatrix,
+                    alpha_xu: float,
+                    alpha_xxu: float):
+        """
+        Calculate the marginal likelihood on q of the node when assumes a specif value
+        and a specif parents's assignment
+
+        :param cim: A conditional_intensity_matrix object with the sufficient statistics
+        :type cim: class:'ConditionalIntensityMatrix'
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters
+        :type alpha_xu: float
+        :param alpha_xxu: distribuited hyperparameter over the CTBN’s theta parameters
+        :type alpha_xxu: float
+
+        :return: the value of the marginal likelihood over theta when the node assumes a specif value
+        :rtype: float
+        """
+
+        values = list(range(len(cim._state_residence_times)))
+
+        'remove the index because of the x != x^ condition in the summation '
+        values.remove(index)
+
+        'uncomment for alpha xx not uniform'
+        #alpha_xxu = alpha_xu * cim.state_transition_matrix[index,index_x_first] / cim.state_transition_matrix[index, index])
+
+        return (loggamma(alpha_xu) - loggamma(alpha_xu + cim.state_transition_matrix[index, index])) \
+                + \
+                np.sum([self.single_internal_cim_xxu_marginal_likelihood_theta(
+                                                                        cim.state_transition_matrix[index,index_x_first],
+                                                                        alpha_xxu)
+                for index_x_first in values])
+
+
+    def single_internal_cim_xxu_marginal_likelihood_theta(self,
+                                                M_xxu_suff_stats: float,
+                                                alpha_xxu: float=1):
+        """Calculate the second part of the marginal likelihood over theta formula
+        
+        :param M_xxu_suff_stats: value of the suffucient statistic M[xx'|u]
+        :type M_xxu_suff_stats: float
+        :param alpha_xxu: distribuited hyperparameter over the CTBN’s theta parameters
+        :type alpha_xxu: float
+
+        :return: the value of the marginal likelihood over theta when the node assumes a specif value
+        :rtype: float
+        """
+        return loggamma(alpha_xxu+M_xxu_suff_stats) - loggamma(alpha_xxu)
+
+    # endregion
+
+    # region q
+
+    def marginal_likelihood_q(self,
+                        cims: np.array,
+                        tau_xu: float=0.1,
+                        alpha_xu: float=1):
+        """
+        Calculate the value of the marginal likelihood over q of the node identified by the label node_id
+        
+        :param cims: np.array with all the node's cims
+        :type cims: np.array
+        :param tau_xu: hyperparameter over the CTBN’s q parameters
+        :type tau_xu: float
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters
+        :type alpha_xu: float
+
+
+        :return: the value of the marginal likelihood over q
+        :rtype: float
+        """
+
+        return np.sum([self.variable_cim_xu_marginal_likelihood_q(cim, tau_xu, alpha_xu) for cim in cims])
+
+    def variable_cim_xu_marginal_likelihood_q(self,
+                        cim: ConditionalIntensityMatrix,
+                        tau_xu: float=0.1,
+                        alpha_xu: float=1):
+        """
+        Calculate the value of the marginal likelihood over q given a cim
+        
+        :param cim: A conditional_intensity_matrix object with the sufficient statistics
+        :type cim: class:'ConditionalIntensityMatrix'
+        :param tau_xu: hyperparameter over the CTBN’s q parameters
+        :type tau_xu: float
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters
+        :type alpha_xu: float
+
+
+        :return: the value of the marginal likelihood over q
+        :rtype: float
+        """
+
+        'get cim length'
+        values=len(cim._state_residence_times)
+
+        'compute the marginal likelihood for the current cim'
+        return np.sum([
+                    self.single_cim_xu_marginal_likelihood_q(
+                                                cim.state_transition_matrix[index, index],
+                                                cim._state_residence_times[index],
+                                                tau_xu,
+                                                alpha_xu)
+                    for index in range(values)])
+
+
+    def single_cim_xu_marginal_likelihood_q(self,
+                        M_xu_suff_stats: float,
+                        T_xu_suff_stats: float,
+                        tau_xu: float=0.1,
+                        alpha_xu: float=1):
+        """
+        Calculate the marginal likelihood on q of the node when assumes a specif value
+        and a specif parents's assignment
+        
+        :param M_xu_suff_stats: value of the suffucient statistic M[x|u]
+        :type M_xxu_suff_stats: float
+        :param T_xu_suff_stats: value of the suffucient statistic T[x|u]
+        :type T_xu_suff_stats: float
+        :param cim: A conditional_intensity_matrix object with the sufficient statistics
+        :type cim: class:'ConditionalIntensityMatrix'
+        :param tau_xu: hyperparameter over the CTBN’s q parameters
+        :type tau_xu: float
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters
+        :type alpha_xu: float
+
+
+        :return: the value of the marginal likelihood of the node when assumes a specif value
+        :rtype: float
+        """
+        return (
+                loggamma(alpha_xu + M_xu_suff_stats + 1) + 
+                                                        (log(tau_xu)
+                                                        *
+                                                        (alpha_xu+1))
+                ) \
+                - \
+                (loggamma(alpha_xu + 1)+(
+                                    log(tau_xu + T_xu_suff_stats) 
+                                    *
+                                    (alpha_xu + M_xu_suff_stats + 1))
+                )
+
+    # end region
+
+    def get_fam_score(self,
+                cims: np.array,
+                tau_xu: float=0.1,
+                alpha_xu: float=1):
+        """
+        Calculate the FamScore value of the node
+        
+        
+        :param cims: np.array with all the node's cims
+        :type cims: np.array
+        :param tau_xu: hyperparameter over the CTBN’s q parameters, default to 0.1
+        :type tau_xu: float, optional
+        :param alpha_xu: hyperparameter over the CTBN’s q parameters, default to 1
+        :type alpha_xu: float, optional
+
+
+        :return: the FamScore value of the node
+        :rtype: float
+        """
+
+        'calculate alpha_xxu as a uniform distribution'                                
+        alpha_xxu = alpha_xu /(len(cims[0]._state_residence_times) - 1)
+
+        return self.marginal_likelihood_q(cims,
+                                    tau_xu,
+                                    alpha_xu) \
+               + \
+               self.marginal_likelihood_theta(cims, 
+                                        alpha_xu,
+                                        alpha_xxu)

build/lib/PyCTBN/PyCTBN/estimators/parameters_estimator.py

@@ -0,0 +1,117 @@
+import sys
+sys.path.append('../')
+import numpy as np
+
+from ..structure_graph.network_graph import NetworkGraph
+from ..structure_graph.set_of_cims import SetOfCims
+from ..structure_graph.trajectory import Trajectory
+
+
+class ParametersEstimator(object):
+    """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
+        ParametersEstimator.compute_state_res_time_for_node(self._trajectories.times,
+                                             self._trajectories.trajectory,
+                                             self._net_graph.time_filtering,
+                                             self._net_graph.time_scalar_indexing_strucure,
+                                             state_res_times)
+        ParametersEstimator.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
+
+    @staticmethod
+    def compute_state_res_time_for_node(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])
+
+    @staticmethod
+    def compute_state_transitions_for_a_node(node_indx: int, trajectory: np.ndarray, cols_filter: np.ndarray,
+                                             scalar_indexing: np.ndarray, M: np.ndarray) -> None:
+        """Compute the state residence times for a node and fill the matrices ``M`` with the results.
+
+        :param node_indx: the index of the node
+        :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()
+
+
+
+
+
+
+
+
+

build/lib/PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py

@@ -0,0 +1,246 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+from networkx.readwrite import json_graph
+import os
+from scipy.stats import chi2 as chi2_dist
+from scipy.stats import f as f_dist
+from tqdm import tqdm
+
+from ..utility.cache import Cache
+from ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
+from ..structure_graph.network_graph import NetworkGraph
+from .parameters_estimator import ParametersEstimator
+from .structure_estimator import StructureEstimator
+from ..structure_graph.sample_path import SamplePath
+from ..structure_graph.structure import Structure
+from ..optimizers.constraint_based_optimizer import ConstraintBasedOptimizer
+
+import concurrent.futures
+
+
+
+import multiprocessing
+from multiprocessing import Pool
+
+
+class StructureConstraintBasedEstimator(StructureEstimator):
+    """
+    Has the task of estimating the network structure given the trajectories in samplepath by using a constraint-based approach.
+
+    :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
+    :param known_edges: the prior known edges in the net structure if present
+    :type known_edges: List
+    :param thumb_threshold: the threshold value to consider a valid independence test
+    :type thumb_threshold: int
+    :_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,known_edges: typing.List= [],
+                 thumb_threshold:int = 25):
+        super().__init__(sample_path,known_edges)
+        self._exp_test_sign = exp_test_alfa
+        self._chi_test_alfa = chi_test_alfa
+        self._thumb_threshold = thumb_threshold
+        self._cache = Cache()
+
+    def complete_test(self, test_parent: str, test_child: str, parent_set: typing.List, child_states_numb: int,
+                      tot_vars_count: int, parent_indx, child_indx) -> 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
+
+        p_set.insert(0, test_parent)
+        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)
+
+        del p_set[0]
+        sofc1 = self._cache.find(set(p_set))
+        if not sofc1:
+            g2.remove_node(test_parent)
+            g2.fast_init(test_child)
+            p2 = ParametersEstimator(self._sample_path.trajectories, g2)
+            p2.fast_init(test_child)
+            sofc1 = p2.compute_parameters_for_node(test_child)
+            self._cache.put(set(p_set), sofc1)
+        thumb_value = 0.0
+        if child_states_numb > 2:
+            parent_val = self._sample_path.structure.get_states_number(test_parent)
+            bool_mask_vals = np.isin(self._nodes, parent_set)
+            parents_vals = self._nodes_vals[bool_mask_vals]
+            thumb_value = self.compute_thumb_value(parent_val, child_states_numb, parents_vals)
+        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, thumb_value, parent_indx, child_indx):
+                    return False
+        return True
+
+    def independence_test(self, child_states_numb: int, cim1: ConditionalIntensityMatrix,
+                          cim2: ConditionalIntensityMatrix, thumb_value: float, parent_indx, child_indx) -> 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 independence. 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
+        if child_states_numb > 2 and (np.sum(np.diagonal(M1)) / thumb_value) < self._thumb_threshold:
+                self._removable_edges_matrix[parent_indx][child_indx] = False
+                return False
+        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 compute_thumb_value(self, parent_val, child_val, parent_set_vals):
+        """Compute the value to test against the thumb_threshold.
+        
+        :param parent_val: test parent's variable cardinality
+        :type parent_val: int
+        :param child_val: test child's variable cardinality
+        :type child_val: int
+        :param parent_set_vals: the cardinalities of the nodes in the current sep-set
+        :type parent_set_vals: List
+        :return: the thumb value for the current independence test
+        :rtype: int
+        """
+        df = (child_val - 1) ** 2
+        df = df * parent_val
+        for v in parent_set_vals:
+            df = df * v
+        return df
+        
+    def one_iteration_of_CTPC_algorithm(self, var_id: str, tot_vars_count: int)-> typing.List:
+        """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
+        """
+        optimizer_obj = ConstraintBasedOptimizer(
+                                                            node_id = var_id,
+                                                            structure_estimator = self,
+                                                            tot_vars_count = tot_vars_count)
+        return optimizer_obj.optimize_structure()
+
+    
+    def ctpc_algorithm(self,disable_multiprocessing:bool= False ):
+        """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
+
+        n_nodes= len(self._nodes)
+
+        total_vars_numb_array =  [total_vars_numb] *  n_nodes
+
+        'get the number of CPU'
+        cpu_count = multiprocessing.cpu_count()
+
+
+
+        'Remove all the edges from the structure'   
+        self._sample_path.structure.clean_structure_edges()
+
+        'Estimate the best parents for each node'
+        #with multiprocessing.Pool(processes=cpu_count) as pool:
+        #with get_context("spawn").Pool(processes=cpu_count) as pool:
+        if disable_multiprocessing:
+            print("DISABILITATO")
+            cpu_count = 1
+            list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes]
+        else:
+            with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_count) as executor:
+                list_edges_partial = executor.map(ctpc_algo,
+                                                                 self._nodes,
+                                                                 total_vars_numb_array)
+            
+        'Update the graph'
+        edges = set(itertools.chain.from_iterable(list_edges_partial))
+        self._complete_graph = nx.DiGraph()
+        self._complete_graph.add_edges_from(edges)
+
+        return edges
+
+        
+    def estimate_structure(self,disable_multiprocessing:bool=False):
+        return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing)
+
+    
+
+

build/lib/PyCTBN/PyCTBN/estimators/structure_estimator.py

@@ -0,0 +1,183 @@
+
+import itertools
+import json
+import typing
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import numpy as np
+from networkx.readwrite import json_graph
+
+from abc import ABC
+import os
+import abc
+
+from ..utility.cache import Cache
+from ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
+from ..structure_graph.network_graph import NetworkGraph
+from .parameters_estimator import ParametersEstimator
+from ..structure_graph.sample_path import SamplePath
+from ..structure_graph.structure import Structure
+
+
+class StructureEstimator(object):
+    """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 known_edges: the prior known edges in the net structure if present
+    :type known_edges: List
+    :_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``
+    """
+
+    def __init__(self, sample_path: SamplePath, known_edges: typing.List = None):
+        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._removable_edges_matrix = self.build_removable_edges_matrix(known_edges)
+        self._complete_graph = StructureEstimator.build_complete_graph(self._sample_path.structure.nodes_labels)
+        
+
+    def build_removable_edges_matrix(self, known_edges: typing.List):
+        """Builds a boolean matrix who shows if a edge could be removed or not, based on prior knowledge given:
+
+        :param known_edges: the list of nodes labels
+        :type known_edges: List
+        :return: a boolean matrix
+        :rtype: np.ndarray
+        """
+        tot_vars_count = self._sample_path.total_variables_count
+        complete_adj_matrix = np.full((tot_vars_count, tot_vars_count), True)
+        if known_edges:
+            for edge in known_edges:
+                i = self._sample_path.structure.get_node_indx(edge[0])
+                j = self._sample_path.structure.get_node_indx(edge[1])
+                complete_adj_matrix[i][j] = False
+        return complete_adj_matrix
+
+    @staticmethod
+    def build_complete_graph(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
+
+
+    @staticmethod
+    def generate_possible_sub_sets_of_size( u: typing.List, size: int, parent_label: str):
+        """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 save_results(self, file_path: str) -> None:
+        """Save the estimated Structure to a .json file in file_path.
+
+        :param file_path: the path including the file name with .json extension
+        :type file_path: string
+        """
+        res = json_graph.node_link_data(self._complete_graph)
+        with open(file_path, 'w') as f:
+            json.dump(res, f)
+
+
+    #def remove_diagonal_elements(self, matrix):
+    #   m = matrix.shape[0]
+    #    strided = np.lib.stride_tricks.as_strided
+    #    s0, s1 = matrix.strides
+    #    return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1)
+
+
+    @abc.abstractmethod
+    def estimate_structure(self) -> typing.List:
+        """Abstract method to estimate the structure
+
+        :return: List of estimated edges
+        :rtype: Typing.List
+        """
+        pass
+
+    def adjacency_matrix(self) -> np.ndarray:
+        """Converts the estimated structure ``_complete_graph`` to a boolean adjacency matrix representation.
+
+        :return: The adjacency matrix of the graph ``_complete_graph``
+        :rtype: numpy.ndArray
+        """
+        return nx.adj_matrix(self._complete_graph, self._nodes).toarray().astype(bool)
+
+    def spurious_edges(self) -> typing.List:
+        """Return the spurious edges present in the estimated structure, if a prior net structure is present in
+            ``_sample_path.structure``.
+
+        :return: A list containing the spurious edges
+        :rtype: List
+        """
+        if not self._sample_path.has_prior_net_structure:
+            return []
+        real_graph = nx.DiGraph()
+        real_graph.add_nodes_from(self._sample_path.structure.nodes_labels)
+        real_graph.add_edges_from(self._sample_path.structure.edges)
+        return nx.difference(real_graph, self._complete_graph).edges
+
+    def save_plot_estimated_structure_graph(self, file_path: str) -> None:  
+            """Plot the estimated structure in a graphical model style, use .png extension.
+            Spurious edges are colored in red if a prior structure is present.
+
+            :param file_path: path to save the file to
+            :type: string
+            """
+            graph_to_draw = nx.DiGraph()
+            spurious_edges = self.spurious_edges()
+            non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges))
+            edges_colors = ['red' if edge in spurious_edges else 'black' for edge in self._complete_graph.edges]
+            graph_to_draw.add_edges_from(spurious_edges)
+            graph_to_draw.add_edges_from(non_spurious_edges)
+            pos = nx.spring_layout(graph_to_draw, k=0.5*1/np.sqrt(len(graph_to_draw.nodes())), iterations=50,scale=10)
+            options = {
+                "node_size": 2000,
+                "node_color": "white",
+                "edgecolors": "black",
+                'linewidths':2,
+                "with_labels":True,
+                "font_size":13,
+                'connectionstyle': 'arc3, rad = 0.1',
+                "arrowsize": 15,
+                "arrowstyle": '<|-',
+                "width": 1,
+                "edge_color":edges_colors,
+            }
+
+            nx.draw(graph_to_draw, pos, **options)
+            ax = plt.gca()
+            ax.margins(0.20)
+            plt.axis("off")
+            plt.savefig(file_path)
+            plt.clf()
+            print("Estimated Structure Plot Saved At: ", os.path.abspath(file_path))
+
+
+
+
+

build/lib/PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py

@@ -0,0 +1,236 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+from networkx.readwrite import json_graph
+
+from random import choice
+
+import concurrent.futures
+
+import copy
+
+from ..structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix
+from ..structure_graph.network_graph import NetworkGraph
+from .parameters_estimator import ParametersEstimator
+from .structure_estimator import StructureEstimator
+from ..structure_graph.sample_path import SamplePath
+from ..structure_graph.structure import Structure
+from .fam_score_calculator import FamScoreCalculator
+from ..optimizers.hill_climbing_search import HillClimbing
+from ..optimizers.tabu_search import TabuSearch
+
+
+import multiprocessing
+from multiprocessing import Pool
+
+
+
+
+class StructureScoreBasedEstimator(StructureEstimator):
+    """
+    Has the task of estimating the network structure given the trajectories in samplepath by
+    using a score based approach and differt kinds of optimization algorithms.
+
+    :param sample_path: the _sample_path object containing the trajectories and the real structure
+    :type sample_path: SamplePath
+    :param tau_xu: hyperparameter over the CTBN’s q parameters, default to 0.1
+    :type tau_xu: float, optional
+    :param alpha_xu: hyperparameter over the CTBN’s q parameters, default to 1
+    :type alpha_xu: float, optional
+    :param known_edges: List of known edges, default to []
+    :type known_edges: List, optional
+
+    """
+
+    def __init__(self, sample_path: SamplePath, tau_xu:int=0.1, alpha_xu:int = 1,known_edges: typing.List= []):
+        super().__init__(sample_path,known_edges)
+        self.tau_xu=tau_xu
+        self.alpha_xu=alpha_xu
+
+
+    def estimate_structure(self, max_parents:int = None, iterations_number:int= 40,
+                         patience:int = None, tabu_length:int = None, tabu_rules_duration:int = None,
+                         optimizer: str = 'tabu',disable_multiprocessing:bool= False ):
+        """
+        Compute the score-based algorithm to find the optimal structure
+
+        :param max_parents: maximum number of parents for each variable. If None, disabled, default to None
+        :type max_parents: int, optional
+        :param iterations_number: maximum number of optimization algorithm's iteration, default to 40
+        :type iterations_number: int, optional
+        :param patience: number of iteration without any improvement before to stop the search.If None, disabled, default to None
+        :type patience: int, optional
+        :param tabu_length: maximum lenght of the data structures used in the optimization process, default to None
+        :type tabu_length: int, optional
+        :param tabu_rules_duration: number of iterations in which each rule keeps its value, default to None
+        :type tabu_rules_duration: int, optional
+        :param optimizer: name of the optimizer algorithm. Possible values: 'hill' (Hill climbing),'tabu' (tabu search), defualt to 'tabu'
+        :type optimizer: string, optional
+        :param disable_multiprocessing: true if you desire to disable the multiprocessing operations, default to False
+        :type disable_multiprocessing: Boolean, optional
+        """
+        'Save the true edges structure in tuples'
+        true_edges = copy.deepcopy(self._sample_path.structure.edges)
+        true_edges = set(map(tuple, true_edges))
+
+        'Remove all the edges from the structure'   
+        self._sample_path.structure.clean_structure_edges()
+
+        estimate_parents = self.estimate_parents
+
+        n_nodes= len(self._nodes)
+        
+        l_max_parents= [max_parents] * n_nodes
+        l_iterations_number = [iterations_number] * n_nodes
+        l_patience = [patience] * n_nodes
+        l_tabu_length = [tabu_length] * n_nodes
+        l_tabu_rules_duration = [tabu_rules_duration] * n_nodes
+        l_optimizer = [optimizer] * n_nodes
+
+
+        'get the number of CPU'
+        cpu_count = multiprocessing.cpu_count()
+        print(f"CPU COUNT: {cpu_count}")
+
+        if disable_multiprocessing:
+            cpu_count = 1
+
+        
+
+
+
+        #with get_context("spawn").Pool(processes=cpu_count) as pool:
+        #with multiprocessing.Pool(processes=cpu_count) as pool:
+
+        'Estimate the best parents for each node'
+        if disable_multiprocessing:
+            list_edges_partial = [estimate_parents(n,max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) for n in self._nodes]
+        else:
+            with concurrent.futures.ProcessPoolExecutor(max_workers=cpu_count) as executor:
+                list_edges_partial = executor.map(estimate_parents, 
+                                                            self._nodes,
+                                                            l_max_parents,
+                                                            l_iterations_number,
+                                                            l_patience,
+                                                            l_tabu_length,
+                                                            l_tabu_rules_duration,
+                                                            l_optimizer)
+
+
+        'Concatenate all the edges list'
+        set_list_edges =  set(itertools.chain.from_iterable(list_edges_partial))
+
+
+        'calculate precision and recall'
+        n_missing_edges = 0
+        n_added_fake_edges = 0
+
+        try:
+            n_added_fake_edges = len(set_list_edges.difference(true_edges))
+
+            n_missing_edges = len(true_edges.difference(set_list_edges))
+
+            n_true_positive = len(true_edges) - n_missing_edges
+
+            precision = n_true_positive / (n_true_positive + n_added_fake_edges)
+
+            recall = n_true_positive / (n_true_positive + n_missing_edges)
+        
+            print(true_edges)
+            print(set_list_edges)
+            print(f"precision: {precision} ")
+            print(f"recall: {recall} ")
+        except Exception as e: 
+            print(f"errore: {e}")
+
+
+        'Update the graph'
+        self._complete_graph = nx.DiGraph()
+        self._complete_graph.add_edges_from(set_list_edges)
+
+        return set_list_edges
+    
+
+    def estimate_parents(self,node_id:str, max_parents:int = None, iterations_number:int= 40,
+                            patience:int = 10, tabu_length:int = None, tabu_rules_duration:int=5, 
+                            optimizer:str = 'hill' ):
+        """
+        Use the FamScore of a node in order to find the best parent nodes
+        
+        :param node_id: current node's id
+        :type node_id: string
+        :param max_parents: maximum number of parents for each variable. If None, disabled, default to None
+        :type max_parents: int, optional
+        :param iterations_number: maximum number of optimization algorithm's iteration, default to 40
+        :type iterations_number: int, optional
+        :param patience: number of iteration without any improvement before to stop the search.If None, disabled, default to None
+        :type patience: int, optional
+        :param tabu_length: maximum lenght of the data structures used in the optimization process, default to None
+        :type tabu_length: int, optional
+        :param tabu_rules_duration: number of iterations in which each rule keeps its value, default to None
+        :type tabu_rules_duration: int, optional
+        :param optimizer: name of the optimizer algorithm. Possible values: 'hill' (Hill climbing),'tabu' (tabu search), defualt to 'tabu'
+        :type optimizer: string, optional
+
+        :return: A list of the best edges for the currente node
+        :rtype: List
+        """
+
+        "choose the optimizer algotithm"
+        if optimizer == 'tabu':
+            optimizer = TabuSearch(
+                        node_id = node_id,
+                        structure_estimator = self,
+                        max_parents = max_parents,
+                        iterations_number = iterations_number,
+                        patience = patience,
+                        tabu_length = tabu_length,
+                        tabu_rules_duration = tabu_rules_duration)
+        else: #if optimizer == 'hill':
+            optimizer = HillClimbing(
+                                    node_id = node_id,
+                                    structure_estimator = self,
+                                    max_parents = max_parents,
+                                    iterations_number = iterations_number,
+                                    patience = patience)
+
+        "call the optmizer's function that calculates the current node's parents"
+        return optimizer.optimize_structure()
+
+    
+    def get_score_from_graph(self,
+                            graph: NetworkGraph,
+                            node_id:str):
+        """
+        Get the FamScore of a node 
+        
+        :param node_id: current node's id
+        :type node_id: string
+        :param graph: current graph to be computed
+        :type graph: class:'NetworkGraph'
+
+
+        :return: The FamSCore for this graph structure
+        :rtype: float
+        """
+
+        'inizialize the graph for a single node'
+        graph.fast_init(node_id) 
+
+        params_estimation = ParametersEstimator(self._sample_path.trajectories, graph)
+
+        'Inizialize and compute parameters for node'
+        params_estimation.fast_init(node_id)
+        SoCims = params_estimation.compute_parameters_for_node(node_id)
+
+        'calculate the FamScore for the node'
+        fam_score_obj = FamScoreCalculator()
+
+        score = fam_score_obj.get_fam_score(SoCims.actual_cims,tau_xu = self.tau_xu,alpha_xu=self.alpha_xu)
+        
+        #print(f" lo score per {node_id} risulta: {score} ")
+        return score 

build/lib/PyCTBN/PyCTBN/optimizers/__init__.py

@@ -0,0 +1,4 @@
+from .optimizer import Optimizer
+from .tabu_search import TabuSearch
+from .hill_climbing_search import HillClimbing
+from .constraint_based_optimizer import ConstraintBasedOptimizer

build/lib/PyCTBN/PyCTBN/optimizers/constraint_based_optimizer.py

@@ -0,0 +1,84 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+
+from random import choice
+
+from abc import ABC
+
+import copy
+
+
+from .optimizer import Optimizer
+from ..estimators.structure_estimator import StructureEstimator
+from ..structure_graph.network_graph import NetworkGraph
+
+
+class ConstraintBasedOptimizer(Optimizer):
+    """
+    Optimizer class that implement a CTPC Algorithm
+
+    :param node_id: current node's id
+    :type node_id: string
+    :param structure_estimator: a structure estimator object with the information about the net
+    :type structure_estimator: class:'StructureEstimator' 
+    :param tot_vars_count: number of variables in the dataset
+    :type tot_vars_count: int
+    """
+    def __init__(self,
+                node_id:str,
+                structure_estimator: StructureEstimator,
+                tot_vars_count:int
+                ):
+        """
+        Constructor
+        """
+        super().__init__(node_id, structure_estimator)
+        self.tot_vars_count = tot_vars_count
+        
+
+
+    def optimize_structure(self):
+        """
+        Compute Optimization process for a structure_estimator by using a CTPC Algorithm
+
+        :return: the estimated structure for the node
+        :rtype: List
+        """
+        print("##################TESTING VAR################", self.node_id)
+
+        graph = NetworkGraph(self.structure_estimator._sample_path.structure)
+
+        other_nodes =  [node for node in self.structure_estimator._sample_path.structure.nodes_labels if node != self.node_id]
+        
+        for possible_parent in other_nodes:
+            graph.add_edges([(possible_parent,self.node_id)])
+
+        
+        u = other_nodes
+        child_states_numb = self.structure_estimator._sample_path.structure.get_states_number(self.node_id)
+        b = 0
+        while b < len(u):
+            parent_indx = 0
+            while parent_indx < len(u):
+                removed = False
+                test_parent = u[parent_indx]
+               	i = self.structure_estimator._sample_path.structure.get_node_indx(test_parent)
+                j = self.structure_estimator._sample_path.structure.get_node_indx(self.node_id)
+                if self.structure_estimator._removable_edges_matrix[i][j]:
+                    S = StructureEstimator.generate_possible_sub_sets_of_size(u, b, test_parent)
+                    for parents_set in S:
+	                    if self.structure_estimator.complete_test(test_parent, self.node_id, parents_set, child_states_numb, self.tot_vars_count,i,j):
+	                        graph.remove_edges([(test_parent, self.node_id)])
+	                        u.remove(test_parent)
+	                        removed = True
+	                        break
+                if not removed:
+                    parent_indx += 1
+            b += 1
+        self.structure_estimator._cache.clear()
+        return graph.edges

build/lib/PyCTBN/PyCTBN/optimizers/hill_climbing_search.py

@@ -0,0 +1,135 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+
+from random import choice
+
+from abc import ABC
+
+
+from .optimizer import Optimizer
+from ..estimators.structure_estimator import StructureEstimator
+from ..structure_graph.network_graph import NetworkGraph
+
+
+class HillClimbing(Optimizer):
+    """
+    Optimizer class that implement Hill Climbing Search
+    
+
+    :param node_id: current node's id
+    :type node_id: string
+    :param structure_estimator: a structure estimator object with the information about the net
+    :type structure_estimator: class:'StructureEstimator' 
+    :param max_parents: maximum number of parents for each variable. If None, disabled, default to None
+    :type max_parents: int, optional
+    :param iterations_number: maximum number of optimization algorithm's iteration, default to 40
+    :type iterations_number: int, optional
+    :param patience: number of iteration without any improvement before to stop the search.If None, disabled, default to None
+    :type patience: int, optional
+
+
+    
+    """
+    def __init__(self,
+                node_id:str,
+                structure_estimator: StructureEstimator,
+                max_parents:int = None,
+                iterations_number:int= 40,
+                patience:int = None
+                ):
+        """
+        Constructor
+        """
+        super().__init__(node_id, structure_estimator)
+        self.max_parents = max_parents
+        self.iterations_number = iterations_number
+        self.patience = patience
+        
+
+
+    def optimize_structure(self) -> typing.List:
+        """
+        Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm
+
+        :return: the estimated structure for the node
+        :rtype: List
+        """
+
+        #'Create the graph for the single node'
+        graph = NetworkGraph(self.structure_estimator._sample_path.structure)
+
+        'get the index for the current node'
+        node_index = self.structure_estimator._sample_path._structure.get_node_indx(self.node_id)
+
+        'list of prior edges'
+        prior_parents = set()
+
+        'Add the edges from prior knowledge'
+        for i in range(len(self.structure_estimator._removable_edges_matrix)):
+            if not self.structure_estimator._removable_edges_matrix[i][node_index]:
+                parent_id= self.structure_estimator._sample_path._structure.get_node_id(i)
+                prior_parents.add(parent_id)
+
+                'Add the node to the starting structure'
+                graph.add_edges([(parent_id, self.node_id)])
+
+
+
+        'get all the possible parents'
+        other_nodes =  [node for node in 
+                                            self.structure_estimator._sample_path.structure.nodes_labels if
+                                                                                            node != self.node_id and
+                                                                                            not prior_parents.__contains__(node)]
+        
+        actual_best_score = self.structure_estimator.get_score_from_graph(graph,self.node_id)
+
+        patince_count = 0
+        for i in range(self.iterations_number):
+            'choose a new random edge'
+            current_new_parent = choice(other_nodes)
+            current_edge =  (current_new_parent,self.node_id)
+            added = False
+            parent_removed = None 
+            
+
+            if graph.has_edge(current_edge):
+                graph.remove_edges([current_edge])
+            else:
+                'check the max_parents constraint'
+                if self.max_parents is not None:
+                    parents_list = graph.get_parents_by_id(self.node_id)
+                    if len(parents_list) >= self.max_parents :
+                        parent_removed = (choice(parents_list), self.node_id)
+                        graph.remove_edges([parent_removed])
+                graph.add_edges([current_edge])
+                added = True
+            #print('**************************')
+            current_score =  self.structure_estimator.get_score_from_graph(graph,self.node_id)
+
+
+            if current_score > actual_best_score:
+                'update current best score' 
+                actual_best_score = current_score
+                patince_count = 0
+            else:
+                'undo the last update'
+                if added:
+                    graph.remove_edges([current_edge])
+                    'If a parent was removed, add it again to the graph'
+                    if parent_removed is not None:
+                        graph.add_edges([parent_removed])
+                else:
+                    graph.add_edges([current_edge])
+                'update patience count'
+                patince_count += 1
+
+            if self.patience is not None and patince_count > self.patience:
+                break
+
+        print(f"finito variabile: {self.node_id}")
+        return graph.edges

build/lib/PyCTBN/PyCTBN/optimizers/optimizer.py

@@ -0,0 +1,39 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+
+import abc 
+
+from ..estimators.structure_estimator import StructureEstimator
+
+
+
+class Optimizer(abc.ABC):
+    """
+    Interface class for all the optimizer's child PyCTBN
+
+    :param node_id: the node label
+    :type node_id: string
+    :param structure_estimator: A structureEstimator Object to predict the structure
+    :type structure_estimator: class:'StructureEstimator'
+    
+    """
+
+    def __init__(self, node_id:str, structure_estimator: StructureEstimator):
+        self.node_id = node_id
+        self.structure_estimator = structure_estimator
+        
+
+    @abc.abstractmethod
+    def optimize_structure(self) -> typing.List:
+        """
+        Compute Optimization process for a structure_estimator
+
+        :return: the estimated structure for the node
+        :rtype: List
+        """
+        pass

build/lib/PyCTBN/PyCTBN/optimizers/tabu_search.py

@@ -0,0 +1,188 @@
+
+import itertools
+import json
+import typing
+
+import networkx as nx
+import numpy as np
+
+from random import choice,sample
+
+from abc import ABC
+
+
+from .optimizer import Optimizer
+from ..estimators.structure_estimator import StructureEstimator
+from ..structure_graph.network_graph import NetworkGraph
+
+import queue
+
+
+class TabuSearch(Optimizer):
+    """
+    Optimizer class that implement Tabu Search
+
+
+    :param node_id: current node's id
+    :type node_id: string
+    :param structure_estimator: a structure estimator object with the information about the net
+    :type structure_estimator: class:'StructureEstimator' 
+    :param max_parents: maximum number of parents for each variable. If None, disabled, default to None
+    :type max_parents: int, optional
+    :param iterations_number: maximum number of optimization algorithm's iteration, default to 40
+    :type iterations_number: int, optional
+    :param patience: number of iteration without any improvement before to stop the search.If None, disabled, default to None
+    :type patience: int, optional
+    :param tabu_length: maximum lenght of the data structures used in the optimization process, default to None
+    :type tabu_length: int, optional
+    :param tabu_rules_duration: number of iterations in which each rule keeps its value, default to None
+    :type tabu_rules_duration: int, optional
+
+    
+    """
+    def __init__(self,
+                node_id:str,
+                structure_estimator: StructureEstimator,
+                max_parents:int = None,
+                iterations_number:int= 40,
+                patience:int = None,
+                tabu_length:int = None,
+                tabu_rules_duration = None
+                ):
+        """
+        Constructor
+        """
+        super().__init__(node_id, structure_estimator)
+        self.max_parents = max_parents
+        self.iterations_number = iterations_number
+        self.patience = patience
+        self.tabu_length = tabu_length
+        self.tabu_rules_duration = tabu_rules_duration
+
+
+    def optimize_structure(self) -> typing.List:
+        """
+        Compute Optimization process for a structure_estimator by using a Hill Climbing Algorithm
+
+        :return: the estimated structure for the node
+        :rtype: List
+        """
+        print(f"tabu search is processing the structure of {self.node_id}")
+
+        'Create the graph for the single node'
+        graph = NetworkGraph(self.structure_estimator._sample_path.structure)
+
+        'get the index for the current node'
+        node_index = self.structure_estimator._sample_path._structure.get_node_indx(self.node_id)
+
+        'list of prior edges'
+        prior_parents = set()
+
+        'Add the edges from prior knowledge'
+        for i in range(len(self.structure_estimator._removable_edges_matrix)):
+            if not self.structure_estimator._removable_edges_matrix[i][node_index]:
+                parent_id= self.structure_estimator._sample_path._structure.get_node_id(i)
+                prior_parents.add(parent_id)
+
+                'Add the node to the starting structure'
+                graph.add_edges([(parent_id, self.node_id)])
+
+
+
+        'get all the possible parents'
+        other_nodes =  set([node for node in 
+                                            self.structure_estimator._sample_path.structure.nodes_labels if
+                                                                                            node != self.node_id and
+                                                                                            not prior_parents.__contains__(node)])
+
+        'calculate the score for the node without parents'
+        actual_best_score = self.structure_estimator.get_score_from_graph(graph,self.node_id)
+
+
+        'initialize tabu_length and tabu_rules_duration if None'
+        if self.tabu_length is None:
+            self.tabu_length = len(other_nodes)
+
+        if self.tabu_rules_duration is None:
+            self.tabu_rules_duration = len(other_nodes)
+
+        'inizialize the data structures'
+        tabu_set = set()
+        tabu_queue = queue.Queue()
+
+        patince_count = 0
+        tabu_count = 0 
+        for i in range(self.iterations_number):
+            
+            current_possible_nodes = other_nodes.difference(tabu_set)
+
+            'choose a new random edge according to tabu restiction'
+            if(len(current_possible_nodes) > 0):
+                current_new_parent = sample(current_possible_nodes,k=1)[0]
+            else:
+                current_new_parent = tabu_queue.get()
+                tabu_set.remove(current_new_parent)
+
+
+            
+            current_edge =  (current_new_parent,self.node_id)
+            added = False
+            parent_removed = None 
+
+            if graph.has_edge(current_edge):
+                graph.remove_edges([current_edge])
+            else:
+                'check the max_parents constraint'
+                if self.max_parents is not None:
+                    parents_list = graph.get_parents_by_id(self.node_id)
+                    if len(parents_list) >= self.max_parents :
+                        parent_removed = (choice(parents_list), self.node_id)
+                        graph.remove_edges([parent_removed])
+                graph.add_edges([current_edge])
+                added = True
+           
+            current_score =  self.structure_estimator.get_score_from_graph(graph,self.node_id)
+
+            if current_score > actual_best_score:
+                'update current best score' 
+                actual_best_score = current_score
+                patince_count = 0
+                'update tabu list'
+
+
+            else:
+                'undo the last update'
+                if added:
+                    graph.remove_edges([current_edge])
+                    'If a parent was removed, add it again to the graph'
+                    if parent_removed is not None:
+                        graph.add_edges([parent_removed])
+                else:
+                    graph.add_edges([current_edge])
+                'update patience count'
+                patince_count += 1
+                
+            
+            if tabu_queue.qsize() >= self.tabu_length:
+                    current_removed = tabu_queue.get()
+                    tabu_set.remove(current_removed)
+            'Add the node on the tabu list'
+            tabu_queue.put(current_new_parent)
+            tabu_set.add(current_new_parent)
+
+            tabu_count += 1
+
+            'Every tabu_rules_duration step remove an item from the tabu list '
+            if tabu_count % self.tabu_rules_duration == 0:
+                if tabu_queue.qsize() > 0:
+                    current_removed = tabu_queue.get()
+                    tabu_set.remove(current_removed)
+                    tabu_count = 0
+                else:
+                    tabu_count = 0
+
+            if self.patience is not None and patince_count > self.patience:
+                break
+
+        print(f"finito variabile: {self.node_id}")
+        return graph.edges

build/lib/PyCTBN/PyCTBN/structure_graph/__init__.py

@@ -0,0 +1,6 @@
+from .conditional_intensity_matrix import ConditionalIntensityMatrix
+from .network_graph import NetworkGraph
+from .sample_path import SamplePath
+from .set_of_cims import SetOfCims
+from .structure import Structure
+from .trajectory import Trajectory

build/lib/PyCTBN/PyCTBN/structure_graph/conditional_intensity_matrix.py

@@ -0,0 +1,42 @@
+import numpy as np
+
+
+class ConditionalIntensityMatrix(object):
+    """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)
+

build/lib/PyCTBN/PyCTBN/structure_graph/network_graph.py

@@ -0,0 +1,293 @@
+
+import typing
+
+import networkx as nx
+import numpy as np
+
+from .structure import Structure
+
+
+class NetworkGraph(object):
+    """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 init_graph(self):
+    #    self.add_nodes(self._nodes_labels)
+    #    self.add_edges(self.graph_struct.edges)
+    #    self.aggregated_info_about_nodes_parents = self.get_ord_set_of_par_of_all_nodes()
+    #    self._fancy_indexing = self.build_fancy_indexing_structure(0)
+    #    self.build_scalar_indexing_structures()
+    #    self.build_time_columns_filtering_structure()
+    #    self.build_transition_columns_filtering_structure()
+    #    self._p_combs_structure = self.build_p_combs_structure()
+
+    def fast_init(self, node_id: str) -> 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]
+        node_states = self.get_states_number(node_id)
+        node_indx = self.get_node_indx(node_id)
+        cols_number = self._graph_struct.total_variables_number
+        self._time_scalar_indexing_structure = NetworkGraph.\
+            build_time_scalar_indexing_structure_for_a_node(node_states, p_vals)
+        self._transition_scalar_indexing_structure = NetworkGraph.\
+            build_transition_scalar_indexing_structure_for_a_node(node_states, p_vals)
+        self._time_filtering = NetworkGraph.build_time_columns_filtering_for_a_node(node_indx, p_indxs)
+        self._transition_filtering = NetworkGraph.build_transition_filtering_for_a_node(node_indx, p_indxs, cols_number)
+        self._p_combs_structure = NetworkGraph.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 has_edge(self,edge:tuple)-> bool:
+        """
+        Check if the graph contains a specific edge
+
+        Parameters:
+            edge: a tuple that rappresents the edge
+        Returns:
+            bool
+        """
+        return self._graph.has_edge(edge[0],edge[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 remove_node(self, node_id: str) -> None:
+        """Remove the node ``node_id`` from all the class members.
+        Initialize all the filtering/indexing structures.
+        """
+        self._graph.remove_node(node_id)
+        self._graph_struct.remove_node(node_id)
+        self.clear_indexing_filtering_structures()
+
+    def clear_indexing_filtering_structures(self) -> None:
+        """Initialize all the filtering/indexing structures.
+        """
+        self._aggregated_info_about_nodes_parents = None
+        self._time_scalar_indexing_structure = None
+        self._transition_scalar_indexing_structure = None
+        self._time_filtering = None
+        self._transition_filtering = None
+        self._p_combs_structure = None
+
+    def get_ordered_by_indx_set_of_parents(self, node: str) -> typing.Tuple:
+        """Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
+        (parents_labels, parents_indexes, parents_cardinalities).
+
+        :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 remove_edges(self, list_of_edges: typing.List) -> None:
+        """Remove the edges to the graph contained in the list list_of_edges.
+        
+        :param list_of_edges: The edges to remove from the graph
+        :type list_of_edges: List
+        """
+        self._graph.remove_edges_from(list_of_edges)
+
+    @staticmethod
+    def build_time_scalar_indexing_structure_for_a_node(node_states: int,
+                                                        parents_vals: typing.List) -> np.ndarray:
+        """Builds an indexing structure for the computation of state residence times values.
+
+        :param node_states: the node cardinality
+        :type node_states: int
+        :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([node_states])
+        T_vector = np.append(T_vector, parents_vals)
+        T_vector = T_vector.cumprod().astype(np.int)
+        return T_vector
+
+    @staticmethod
+    def build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: typing.List) \
+            -> np.ndarray:
+        """Builds an indexing structure for the computation of state transitions values.
+
+        :param node_states_number: the node cardinality
+        :type node_states_number: int
+        :param parents_vals: the caridinalites of the node's parents
+        :type parents_vals: List
+        :return: The transition indexing structure
+        :rtype: numpy.ndArray
+        """
+        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
+
+    @staticmethod
+    def build_time_columns_filtering_for_a_node(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)
+
+    @staticmethod
+    def build_transition_filtering_for_a_node(node_indx: int, p_indxs: typing.List, nodes_number: int) \
+            -> 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
+        :param nodes_number: the total number of nodes in the dataset
+        :type nodes_number: int
+        :return: The filtering structure for transitions estimation
+        :rtype: numpy.ndArray
+        """
+        return np.array([node_indx + nodes_number, node_indx, *p_indxs], dtype=np.int)
+
+    @staticmethod
+    def build_p_comb_structure_for_a_node(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

build/lib/PyCTBN/PyCTBN/structure_graph/sample_path.py

@@ -0,0 +1,93 @@
+
+
+import numpy as np
+import pandas as pd
+
+from .structure import Structure
+from .trajectory import Trajectory
+from ..utility.abstract_importer import AbstractImporter
+
+
+MESSAGE_HAS_TO_CONTAIN_EXCEPTION = 'The importer object has to contain the all processed data!'
+
+
+class SamplePath(object):
+    """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 object which contains the imported and processed data
+    :type importer: AbstractImporter
+    :_trajectories: the ``Trajectory`` object that will contain all the concatenated trajectories
+    :_structure: the ``Structure`` Object that will contain all the structural infos about the net
+    :_total_variables_count: the number of variables in the net
+    """
+    def __init__(self, importer: AbstractImporter):
+        """Constructor Method
+        """
+        self._importer = importer
+        if self._importer._df_variables is None or self._importer._concatenated_samples is None:
+            raise RuntimeError()
+        if self._importer._df_variables.empty:
+            raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
+        if isinstance(self._importer._concatenated_samples, pd.DataFrame) and\
+            self._importer._concatenated_samples.empty:
+                raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
+        if isinstance(self._importer._concatenated_samples, np.ndarray) and\
+            self._importer._concatenated_samples.size == 0:
+                raise RuntimeError(MESSAGE_HAS_TO_CONTAIN_EXCEPTION)
+        self._trajectories = None
+        self._structure = None
+        self._total_variables_count = None
+
+    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()
+        if self._importer.structure is None or self._importer.structure.empty:
+            edges = []
+        else:
+            edges = list(self._importer.structure.to_records(index=False))
+        self._structure = Structure(labels, indxs, vals, edges,
+                                       self._total_variables_count)
+
+    def clear_memory(self):
+        self._importer._raw_data = [] 
+
+    @property
+    def trajectories(self) -> Trajectory:
+        return self._trajectories
+
+    @property
+    def structure(self) -> Structure:
+        return self._structure
+
+    @property
+    def total_variables_count(self) -> int:
+        return self._total_variables_count
+
+    @property
+    def has_prior_net_structure(self) -> bool:
+        return bool(self._structure.edges)
+
+
+
+
+
+

build/lib/PyCTBN/PyCTBN/structure_graph/set_of_cims.py

@@ -0,0 +1,97 @@
+
+
+import typing
+
+import numpy as np
+
+from .conditional_intensity_matrix import ConditionalIntensityMatrix
+
+
+class SetOfCims(object):
+    """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)
+
+
+
+

build/lib/PyCTBN/PyCTBN/structure_graph/structure.py

@@ -0,0 +1,124 @@
+
+import typing as ty
+
+import numpy as np
+
+
+class Structure(object):
+    """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 dataset
+    :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
+
+    def remove_node(self, node_id: str) -> None:
+        """Remove the node ``node_id`` from all the class members.
+        The class member ``_total_variables_number`` since it refers to the total number of variables in the dataset.
+        """
+        node_positional_indx = self._nodes_labels_list.index(node_id)
+        del self._nodes_labels_list[node_positional_indx]
+        self._nodes_indexes_arr = np.delete(self._nodes_indexes_arr, node_positional_indx)
+        self._nodes_vals_arr = np.delete(self._nodes_vals_arr, node_positional_indx)
+        self._edges_list = [(from_node, to_node) for (from_node, to_node) in self._edges_list if (from_node != node_id
+                                                                                        and to_node != node_id)]
+
+    @property
+    def edges(self) -> ty.List:
+        return self._edges_list
+
+    @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 clean_structure_edges(self):
+        self._edges_list = list()
+
+    def add_edge(self,edge: tuple):
+        self._edges_list.append(edge)
+
+    
+    def remove_edge(self,edge: tuple):
+        self._edges_list.remove(edge)
+
+    def contains_edge(self,edge:tuple) -> bool:
+        return edge in self._edges_list
+
+    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 False
+

build/lib/PyCTBN/PyCTBN/structure_graph/trajectory.py

@@ -0,0 +1,45 @@
+
+import typing
+
+import numpy as np
+
+
+class Trajectory(object):
+    """ 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
+        """
+        self._times = list_of_columns[0]
+        self._actual_trajectory = list_of_columns[1]
+        self._original_cols_number = original_cols_number
+
+    @property
+    def trajectory(self) -> np.ndarray:
+        return self._actual_trajectory[:, :self._original_cols_number - 1]
+
+    @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__()
+
+

build/lib/PyCTBN/PyCTBN/utility/__init__.py

@@ -0,0 +1,4 @@
+from .abstract_importer import AbstractImporter
+from .cache import Cache
+from .json_importer import JsonImporter
+from .sample_importer import SampleImporter

build/lib/PyCTBN/PyCTBN/utility/abstract_importer.py

@@ -0,0 +1,163 @@
+
+import typing
+from abc import ABC, abstractmethod
+
+import numpy as np
+import pandas as pd
+
+import copy
+
+
+
+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, or dataset name if you import already processed data
+    :type file_path: str
+    :param trajectory_list: Dataframe or numpy array containing the concatenation of all the processed trajectories
+    :type trajectory_list: typing.Union[pandas.DataFrame, numpy.ndarray]
+    :param variables: Dataframe containing the nodes labels and cardinalities
+    :type variables: pandas.DataFrame
+    :prior_net_structure: Dataframe containing the structure of the network (edges)
+    :type prior_net_structure: pandas.DataFrame
+    :_sorter: A list containing the variables labels in the SAME order as the columns in ``concatenated_samples``
+
+    .. warning::
+        The parameters ``variables`` and ``prior_net_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]
+        See the tutorial on how to construct a correct ``concatenated_samples`` Dataframe/ndarray.
+
+    .. note::
+        See :class:``JsonImporter`` for an example implementation
+
+    """
+
+    def __init__(self, file_path: str = None, trajectory_list: typing.Union[pd.DataFrame, np.ndarray] = None,
+                 variables: pd.DataFrame = None, prior_net_structure: pd.DataFrame = None):
+        """Constructor
+        """
+        self._file_path = file_path
+        self._df_samples_list = trajectory_list
+        self._concatenated_samples = []
+        self._df_variables = variables
+        self._df_structure = prior_net_structure
+        self._sorter = None
+        super().__init__()
+
+    @abstractmethod
+    def build_sorter(self, trajecory_header: object) -> 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 trajecory_header: an object that will be used to define the header
+        :type trajecory_header: object
+        :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 = copy.deepcopy(sample_frame)
+        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, concatenated_sample: pd.DataFrame) -> typing.List:
+        """Builds a List containing the the delta times numpy array, and the complete transitions matrix
+
+        :param concatenated_sample: the dataframe/array from which the time, and transitions matrix have to be extracted
+            and converted
+        :type concatenated_sample: pandas.Dataframe
+        :return: the resulting list of numpy arrays
+        :rtype: List
+        """
+        
+        concatenated_array = concatenated_sample.to_numpy()
+        columns_list = [concatenated_array[:, 0], concatenated_array[:, 1:].astype(int)]
+
+        return columns_list
+
+    def clear_concatenated_frame(self) -> None:
+        """Removes all values in the dataframe concatenated_samples.
+         """
+        if isinstance(self._concatenated_samples, pd.DataFrame):
+            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

build/lib/PyCTBN/PyCTBN/utility/cache.py

@@ -0,0 +1,54 @@
+
+import typing
+
+from ..structure_graph.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): #typing.Union[typing.Set, str]
+        """
+        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):
+        """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):
+        """Clear the contents both of ``__actual_cache`` and ``__list_of_sets_of_parents``.
+        """
+        del self._list_of_sets_of_parents[:]
+        del self._actual_cache[:]

build/lib/PyCTBN/PyCTBN/utility/json_importer.py

@@ -0,0 +1,181 @@
+import json
+import typing
+
+import pandas as pd
+
+
+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 extension.
+
+    :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
+    :_array_indx: the index of the outer JsonArray to extract the data from
+    :type _array_indx: int
+    :_df_samples_list: a Dataframe list in which every dataframe contains a trajectory
+    :_raw_data: The raw contents of the json file to import
+    :type _raw_data: List
+    """
+
+    def __init__(self, file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str,
+                 variables_key: str):
+        """Constructor method
+
+        .. note::
+            This constructor calls also the method ``read_json_file()``, so after the construction of the object
+            the class member ``_raw_data`` will contain the raw imported json data.
+
+        """
+        self._samples_label = samples_label
+        self._structure_label = structure_label
+        self._variables_label = variables_label
+        self._time_key = time_key
+        self._variables_key = variables_key
+        self._df_samples_list = None
+        self._array_indx = None
+        super(JsonImporter, self).__init__(file_path)
+        self._raw_data = self.read_json_file()
+
+    def import_data(self, indx: int = 0) -> None:
+        """Implements the abstract method of :class:`AbstractImporter`.
+
+        :param indx: the index of the outer JsonArray to extract the data from, default to 0
+        :type indx: int
+        """
+        self._array_indx = indx
+        self._df_samples_list = self.import_trajectories(self._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(self._raw_data)
+        self._df_variables = self.import_variables(self._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)
+
+            if (isinstance(data,list)):
+                return data
+            else:
+                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
+
+
+

build/lib/PyCTBN/PyCTBN/utility/sample_importer.py

@@ -0,0 +1,62 @@
+import json
+import typing
+
+import pandas as pd
+import numpy as np
+
+from .abstract_importer import AbstractImporter
+
+
+
+class SampleImporter(AbstractImporter):
+    """Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare
+    the data loaded directly by using DataFrame
+
+    :param trajectory_list: the data that describes the trajectories
+    :type trajectory_list: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+    :param variables: the data that describes the variables with name and cardinality
+    :type variables: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+    :param prior_net_structure: the data of the real structure, if it exists
+    :type prior_net_structure: typing.Union[pd.DataFrame, np.ndarray, typing.List]
+
+    :_df_samples_list: a Dataframe list in which every dataframe contains a trajectory
+    :_raw_data: The raw contents of the json file to import
+    :type _raw_data: List
+    """
+
+    def __init__(self, 
+                trajectory_list: typing.Union[pd.DataFrame, np.ndarray, typing.List] = None,
+                variables: typing.Union[pd.DataFrame, np.ndarray, typing.List] = None,
+                prior_net_structure: typing.Union[pd.DataFrame, np.ndarray,typing.List] = None):
+
+        'If the data are not DataFrame, it will be converted'
+        if isinstance(variables,list) or isinstance(variables,np.ndarray):
+            variables = pd.DataFrame(variables)
+        if isinstance(prior_net_structure,list) or isinstance(prior_net_structure,np.ndarray):
+            prior_net_structure=pd.DataFrame(prior_net_structure)
+
+        super(SampleImporter, self).__init__(trajectory_list =trajectory_list,
+                                        variables= variables,
+                                        prior_net_structure=prior_net_structure)
+
+    def import_data(self, header_column = None):
+
+        if header_column is not None:
+            self._sorter = header_column
+        else:    
+            self._sorter = self.build_sorter(self._df_samples_list[0])
+
+        samples_list= self._df_samples_list
+
+        self.compute_row_delta_in_all_samples_frames(samples_list)
+
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        """Implements the abstract method build_sorter of the :class:`AbstractImporter` in order to get the ordered variables list.
+        """
+        columns_header = list(sample_frame.columns.values)
+        del columns_header[0]
+        return columns_header
+
+
+    def dataset_id(self) -> str:
+        return str("")

build/lib/PyCTBN/__init__.py

@@ -0,0 +1,2 @@
+import PyCTBN.PyCTBN
+from PyCTBN.PyCTBN import *

build/lib/PyCTBN/basic_main.py

@@ -0,0 +1,39 @@
+import glob
+import os
+
+import sys
+sys.path.append("./PyCTBN/")
+
+import structure_graph.network_graph as ng
+import structure_graph.sample_path as sp
+import structure_graph.set_of_cims as sofc
+import estimators.parameters_estimator as pe
+import utility.json_importer as ji
+
+
+def main():
+    read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
+    #import data
+    importer = ji.JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+    #Create a SamplePath Obj
+    s1 = sp.SamplePath(importer)
+    #Build The trajectries and the structural infos
+    s1.build_trajectories()
+    s1.build_structure()
+    #From The Structure Object build the Graph
+    g = ng.NetworkGraph(s1.structure)
+    #Select a node you want to estimate the parameters
+    node = g.nodes[1]
+    #Init the graph specifically for THIS node
+    g.fast_init(node)
+    #Use SamplePath and Grpah to create a ParametersEstimator Object
+    p1 = pe.ParametersEstimator(s1, g)
+    #Init the peEst specifically for THIS node
+    p1.fast_init(node)
+    #Compute the parameters
+    sofc1 = p1.compute_parameters_for_node(node)
+    #The est CIMS are inside the resultant SetOfCIms Obj
+    print(sofc1.actual_cims)
+
+if __name__ == "__main__":
+    main()

build/lib/PyCTBN/setup.py

@@ -0,0 +1,20 @@
+from setuptools import setup, find_packages
+
+
+setup(name='PyCTBN',
+      version='1.0',
+      url='https://github.com/philipMartini/PyCTBN',
+      license='MIT',
+      author=['Alessandro Bregoli', 'Filippo Martini','Luca Moretti'],
+      author_email=['a.bregoli1@campus.unimib.it', 'f.martini@campus.unimib.it','lucamoretti96@gmail.com'],
+      description='A Continuous Time Bayesian Networks Library',
+      packages=find_packages('.', exclude=['tests']),
+      #packages=['PyCTBN.PyCTBN'],
+      install_requires=[
+          'numpy', 'pandas', 'networkx', 'scipy', 'matplotlib', 'tqdm'],
+      dependency_links=['https://github.com/numpy/numpy', 'https://github.com/pandas-dev/pandas',
+                        'https://github.com/networkx/networkx', 'https://github.com/scipy/scipy',
+                        'https://github.com/tqdm/tqdm'],
+      #long_description=open('../README.md').read(),
+      zip_safe=False,
+      python_requires='>=3.6')