diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..3e16c78 --- /dev/null +++ b/.gitignore @@ -0,0 +1,8 @@ +__pycache__ +.vscode +**/__pycache__ +**/data +**/PyCTBN.egg-info +**/dist +**/results_data +**/.scannerwork \ No newline at end of file diff --git a/CTBN_project_dominio.pdf b/CTBN_project_dominio.pdf new file mode 100644 index 0000000..6bceea1 Binary files /dev/null and b/CTBN_project_dominio.pdf differ diff --git a/PyCTBN/PyCTBN/__init__.py b/PyCTBN/PyCTBN/__init__.py new file mode 100644 index 0000000..023c0f1 --- /dev/null +++ b/PyCTBN/PyCTBN/__init__.py @@ -0,0 +1,8 @@ +import PyCTBN.estimators +from PyCTBN.estimators import * +import PyCTBN.optimizers +from PyCTBN.optimizers import * +import PyCTBN.structure_graph +from PyCTBN.structure_graph import * +import PyCTBN.utility +from PyCTBN.utility import * \ No newline at end of file diff --git a/PyCTBN/PyCTBN/estimators/__init__.py b/PyCTBN/PyCTBN/estimators/__init__.py new file mode 100644 index 0000000..112086f --- /dev/null +++ b/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 diff --git a/PyCTBN/PyCTBN/estimators/fam_score_calculator.py b/PyCTBN/PyCTBN/estimators/fam_score_calculator.py new file mode 100644 index 0000000..5b0b591 --- /dev/null +++ b/PyCTBN/PyCTBN/estimators/fam_score_calculator.py @@ -0,0 +1,272 @@ + +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 + """ + #print("------") + #print(self.marginal_likelihood_q(cims, + # tau_xu, + # alpha_xu)) + + #print(self.marginal_likelihood_theta(cims, + # alpha_xu, + # alpha_xxu)) + '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) diff --git a/PyCTBN/PyCTBN/estimators/parameters_estimator.py b/PyCTBN/PyCTBN/estimators/parameters_estimator.py new file mode 100644 index 0000000..4754d58 --- /dev/null +++ b/PyCTBN/PyCTBN/estimators/parameters_estimator.py @@ -0,0 +1,143 @@ +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() + + def init_sets_cims_container(self): + self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes, + self.net_graph.nodes_values, + self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(), + self.net_graph.p_combs) + + def compute_parameters(self): + #print(self.net_graph.get_nodes()) + #print(self.amalgamated_cims_struct.sets_of_cims) + #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)) + for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)): + #print(self.net_graph.time_filtering[indx]) + #print(self.net_graph.time_scalar_indexing_strucure[indx]) + self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times, + self.sample_path.trajectories.trajectory, + self.net_graph.time_filtering[indx], + self.net_graph.time_scalar_indexing_strucure[indx], + aggr[1]._state_residence_times) + #print(self.net_graph.transition_filtering[indx]) + #print(self.net_graph.transition_scalar_indexing_structure[indx]) + self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]), + self.sample_path.trajectories.complete_trajectory, + self.net_graph.transition_filtering[indx], + self.net_graph.transition_scalar_indexing_structure[indx], + aggr[1]._transition_matrices) + aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices) + + + + + + + + diff --git a/PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py b/PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py new file mode 100644 index 0000000..7d5721e --- /dev/null +++ b/PyCTBN/PyCTBN/estimators/structure_constraint_based_estimator.py @@ -0,0 +1,238 @@ + +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 + :_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: + if (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) + #list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes] + + return set(itertools.chain.from_iterable(list_edges_partial)) + + + def estimate_structure(self,disable_multiprocessing:bool=False): + return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing) + + + + diff --git a/PyCTBN/PyCTBN/estimators/structure_estimator.py b/PyCTBN/PyCTBN/estimators/structure_estimator.py new file mode 100644 index 0000000..fbf8ea9 --- /dev/null +++ b/PyCTBN/PyCTBN/estimators/structure_estimator.py @@ -0,0 +1,187 @@ + +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 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 + :_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) -> None: + """Save the estimated Structure to a .json file in the path where the data are loaded from. + The file is named as the input dataset but the `results_` word is appended to the results file. + """ + res = json_graph.node_link_data(self._complete_graph) + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.json' + file_name = 'results_' + name + with open(file_name, 'w') as f: + json.dump(res, f) + + + def remove_diagonal_elements(self, matrix): + m = matrix.shape[0] + strided = np.lib.stride_tricks.as_strided + s0, s1 = matrix.strides + return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1) + + + @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).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: + raise RuntimeError("Can not compute spurious edges with no prior net structure!") + 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) -> None: + """Plot the estimated structure in a graphical model style. + Spurious edges are colored in red. + """ + graph_to_draw = nx.DiGraph() + spurious_edges = self.spurious_edges() + non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges)) + print(non_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") + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.png' + plt.savefig(name) + plt.clf() + print("Estimated Structure Plot Saved At: ", os.path.abspath(name)) + + + + + diff --git a/PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py b/PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py new file mode 100644 index 0000000..2903db3 --- /dev/null +++ b/PyCTBN/PyCTBN/estimators/structure_score_based_estimator.py @@ -0,0 +1,244 @@ + +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. + + :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) + + + + #list_edges_partial = p.map(estimate_parents, self._nodes) + #list_edges_partial= estimate_parents('Q',max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) + + 'Concatenate all the edges list' + set_list_edges = set(itertools.chain.from_iterable(list_edges_partial)) + + #print('-------------------------') + + + '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(f"n archi reali non trovati: {n_missing_edges}") + # print(f"n archi non reali aggiunti: {n_added_fake_edges}") + print(true_edges) + print(set_list_edges) + print(f"precision: {precision} ") + print(f"recall: {recall} ") + except Exception as e: + print(f"errore: {e}") + + 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 + + + + diff --git a/PyCTBN/PyCTBN/optimizers/__init__.py b/PyCTBN/PyCTBN/optimizers/__init__.py new file mode 100644 index 0000000..4162bf1 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/optimizers/constraint_based_optimizer.py b/PyCTBN/PyCTBN/optimizers/constraint_based_optimizer.py new file mode 100644 index 0000000..65bc19c --- /dev/null +++ b/PyCTBN/PyCTBN/optimizers/constraint_based_optimizer.py @@ -0,0 +1,87 @@ + +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 + #tests_parents_numb = len(u) + #complete_frame = self.complete_graph_frame + #test_frame = complete_frame.loc[complete_frame['To'].isin([self.node_id])] + 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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/optimizers/hill_climbing_search.py b/PyCTBN/PyCTBN/optimizers/hill_climbing_search.py new file mode 100644 index 0000000..6783be0 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/optimizers/optimizer.py b/PyCTBN/PyCTBN/optimizers/optimizer.py new file mode 100644 index 0000000..36445c0 --- /dev/null +++ b/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 diff --git a/PyCTBN/PyCTBN/optimizers/tabu_search.py b/PyCTBN/PyCTBN/optimizers/tabu_search.py new file mode 100644 index 0000000..e15dd40 --- /dev/null +++ b/PyCTBN/PyCTBN/optimizers/tabu_search.py @@ -0,0 +1,199 @@ + +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_tabu_rules_durationength = 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 + #print('**************************') + current_score = self.structure_estimator.get_score_from_graph(graph,self.node_id) + + + # print("-------------------------------------------") + # print(f"Current new parent: {current_new_parent}") + # print(f"Current score: {current_score}") + # print(f"Current best score: {actual_best_score}") + # print(f"tabu list : {str(tabu_set)} length: {len(tabu_set)}") + # print(f"tabu queue : {str(tabu_queue)} length: {tabu_queue.qsize()}") + # print(f"graph edges: {graph.edges}") + + # print("-------------------------------------------") + # input() + 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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/structure_graph/__init__.py b/PyCTBN/PyCTBN/structure_graph/__init__.py new file mode 100644 index 0000000..85f18a2 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/structure_graph/conditional_intensity_matrix.py b/PyCTBN/PyCTBN/structure_graph/conditional_intensity_matrix.py new file mode 100644 index 0000000..4abfdd0 --- /dev/null +++ b/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) + diff --git a/PyCTBN/PyCTBN/structure_graph/network_graph.py b/PyCTBN/PyCTBN/structure_graph/network_graph.py new file mode 100644 index 0000000..623981d --- /dev/null +++ b/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 diff --git a/PyCTBN/PyCTBN/structure_graph/sample_path.py b/PyCTBN/PyCTBN/structure_graph/sample_path.py new file mode 100644 index 0000000..80b51d9 --- /dev/null +++ b/PyCTBN/PyCTBN/structure_graph/sample_path.py @@ -0,0 +1,91 @@ + + +import numpy as np +import pandas as pd + +from .structure import Structure +from .trajectory import Trajectory +from ..utility.abstract_importer import AbstractImporter + + + +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('The importer object has to contain the all processed data!') + if self._importer._df_variables.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, pd.DataFrame): + if self._importer._concatenated_samples.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, np.ndarray): + if self._importer._concatenated_samples.size == 0: + raise RuntimeError('The importer object has to contain the all processed data!') + 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) + + + + + + diff --git a/PyCTBN/PyCTBN/structure_graph/set_of_cims.py b/PyCTBN/PyCTBN/structure_graph/set_of_cims.py new file mode 100644 index 0000000..81caff5 --- /dev/null +++ b/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) + + + + diff --git a/PyCTBN/PyCTBN/structure_graph/structure.py b/PyCTBN/PyCTBN/structure_graph/structure.py new file mode 100644 index 0000000..a9d60cc --- /dev/null +++ b/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(tuple) + print(self._edges_list) + + def remove_edge(self,edge: tuple): + self._edges_list.remove(tuple) + + 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 + diff --git a/PyCTBN/PyCTBN/structure_graph/trajectory.py b/PyCTBN/PyCTBN/structure_graph/trajectory.py new file mode 100644 index 0000000..36899b3 --- /dev/null +++ b/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__() + + diff --git a/PyCTBN/PyCTBN/utility/__init__.py b/PyCTBN/PyCTBN/utility/__init__.py new file mode 100644 index 0000000..f79749c --- /dev/null +++ b/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 \ No newline at end of file diff --git a/PyCTBN/PyCTBN/utility/abstract_importer.py b/PyCTBN/PyCTBN/utility/abstract_importer.py new file mode 100644 index 0000000..1cad352 --- /dev/null +++ b/PyCTBN/PyCTBN/utility/abstract_importer.py @@ -0,0 +1,164 @@ + +import typing +from abc import ABC, abstractmethod + +import numpy as np +import pandas as pd + +import copy + +#from sklearn.utils import resample + + +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 diff --git a/PyCTBN/PyCTBN/utility/cache.py b/PyCTBN/PyCTBN/utility/cache.py new file mode 100644 index 0000000..8e0369b --- /dev/null +++ b/PyCTBN/PyCTBN/utility/cache.py @@ -0,0 +1,58 @@ + +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: + #print("Cache State:", self.list_of_sets_of_indxs) + #print("Look For:", parents_comb) + result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)] + #print("CACHE HIT!!!!", parents_comb) + return result + except ValueError: + return None + + def put(self, parents_comb: typing.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 + """ + #print("Putting in cache:", parents_comb) + 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[:] \ No newline at end of file diff --git a/PyCTBN/PyCTBN/utility/json_importer.py b/PyCTBN/PyCTBN/utility/json_importer.py new file mode 100644 index 0000000..edff212 --- /dev/null +++ b/PyCTBN/PyCTBN/utility/json_importer.py @@ -0,0 +1,176 @@ +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) -> None: + """Implements the abstract method of :class:`AbstractImporter`. + + :param indx: the index of the outer JsonArray to extract the data from + :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) + return data + + def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame: + """Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param key: the key for the Dicts from which exctract data + :type key: string + :return: A normalized dataframe + :rtype: pandas.Datframe + """ + return pd.DataFrame(raw_data[indx][key]) + + def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List: + """ + Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param trajectories_key: the key of the trajectories objects + :type trajectories_key: string + :return: A list of daframes containg the trajectories + :rtype: List + """ + dataframe = pd.DataFrame + smps = raw_data[indx][trajectories_key] + df_samples_list = [dataframe(sample) for sample in smps] + return df_samples_list + + def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: + """Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset. + """ + columns_header = list(sample_frame.columns.values) + columns_header.remove(self._time_key) + return columns_header + + def clear_data_frame_list(self) -> None: + """Removes all values present in the dataframes in the list ``_df_samples_list``. + """ + for indx in range(len(self._df_samples_list)): + self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0] + + def dataset_id(self) -> object: + return self._array_indx + + def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict: + """Imports the synthetic CIMS in the dataset in a dictionary, using variables labels + as keys for the set of CIMS of a particular node. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param cims_key: the key where the json object cims are placed + :type cims_key: string + :return: a dictionary containing the sampled CIMS for all the variables in the net + :rtype: Dictionary + """ + cims_for_all_vars = {} + for var in raw_data[indx][cims_key]: + sampled_cims_list = [] + cims_for_all_vars[var] = sampled_cims_list + for p_comb in raw_data[indx][cims_key][var]: + cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy()) + return cims_for_all_vars + + + diff --git a/PyCTBN/PyCTBN/utility/sample_importer.py b/PyCTBN/PyCTBN/utility/sample_importer.py new file mode 100644 index 0000000..05073c8 --- /dev/null +++ b/PyCTBN/PyCTBN/utility/sample_importer.py @@ -0,0 +1,65 @@ +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(variables,list) or isinstance(variables,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 + + if isinstance(samples_list, np.ndarray): + samples_list = samples_list.tolist() + + 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) -> object: + pass \ No newline at end of file diff --git a/PyCTBN/basic_main.py b/PyCTBN/basic_main.py new file mode 100644 index 0000000..b1288db --- /dev/null +++ b/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() diff --git a/PyCTBN/build/lib/PyCTBN/__init__.py b/PyCTBN/build/lib/PyCTBN/__init__.py new file mode 100644 index 0000000..023c0f1 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/__init__.py @@ -0,0 +1,8 @@ +import PyCTBN.estimators +from PyCTBN.estimators import * +import PyCTBN.optimizers +from PyCTBN.optimizers import * +import PyCTBN.structure_graph +from PyCTBN.structure_graph import * +import PyCTBN.utility +from PyCTBN.utility import * \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/estimators/__init__.py b/PyCTBN/build/lib/PyCTBN/estimators/__init__.py new file mode 100644 index 0000000..112086f --- /dev/null +++ b/PyCTBN/build/lib/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 diff --git a/PyCTBN/build/lib/PyCTBN/estimators/fam_score_calculator.py b/PyCTBN/build/lib/PyCTBN/estimators/fam_score_calculator.py new file mode 100644 index 0000000..5b0b591 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/estimators/fam_score_calculator.py @@ -0,0 +1,272 @@ + +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 + """ + #print("------") + #print(self.marginal_likelihood_q(cims, + # tau_xu, + # alpha_xu)) + + #print(self.marginal_likelihood_theta(cims, + # alpha_xu, + # alpha_xxu)) + '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) diff --git a/PyCTBN/build/lib/PyCTBN/estimators/parameters_estimator.py b/PyCTBN/build/lib/PyCTBN/estimators/parameters_estimator.py new file mode 100644 index 0000000..4754d58 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/estimators/parameters_estimator.py @@ -0,0 +1,143 @@ +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() + + def init_sets_cims_container(self): + self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes, + self.net_graph.nodes_values, + self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(), + self.net_graph.p_combs) + + def compute_parameters(self): + #print(self.net_graph.get_nodes()) + #print(self.amalgamated_cims_struct.sets_of_cims) + #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)) + for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)): + #print(self.net_graph.time_filtering[indx]) + #print(self.net_graph.time_scalar_indexing_strucure[indx]) + self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times, + self.sample_path.trajectories.trajectory, + self.net_graph.time_filtering[indx], + self.net_graph.time_scalar_indexing_strucure[indx], + aggr[1]._state_residence_times) + #print(self.net_graph.transition_filtering[indx]) + #print(self.net_graph.transition_scalar_indexing_structure[indx]) + self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]), + self.sample_path.trajectories.complete_trajectory, + self.net_graph.transition_filtering[indx], + self.net_graph.transition_scalar_indexing_structure[indx], + aggr[1]._transition_matrices) + aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices) + + + + + + + + diff --git a/PyCTBN/build/lib/PyCTBN/estimators/structure_constraint_based_estimator.py b/PyCTBN/build/lib/PyCTBN/estimators/structure_constraint_based_estimator.py new file mode 100644 index 0000000..7d5721e --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/estimators/structure_constraint_based_estimator.py @@ -0,0 +1,238 @@ + +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 + :_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: + if (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) + #list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes] + + return set(itertools.chain.from_iterable(list_edges_partial)) + + + def estimate_structure(self,disable_multiprocessing:bool=False): + return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing) + + + + diff --git a/PyCTBN/build/lib/PyCTBN/estimators/structure_estimator.py b/PyCTBN/build/lib/PyCTBN/estimators/structure_estimator.py new file mode 100644 index 0000000..fbf8ea9 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/estimators/structure_estimator.py @@ -0,0 +1,187 @@ + +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 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 + :_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) -> None: + """Save the estimated Structure to a .json file in the path where the data are loaded from. + The file is named as the input dataset but the `results_` word is appended to the results file. + """ + res = json_graph.node_link_data(self._complete_graph) + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.json' + file_name = 'results_' + name + with open(file_name, 'w') as f: + json.dump(res, f) + + + def remove_diagonal_elements(self, matrix): + m = matrix.shape[0] + strided = np.lib.stride_tricks.as_strided + s0, s1 = matrix.strides + return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1) + + + @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).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: + raise RuntimeError("Can not compute spurious edges with no prior net structure!") + 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) -> None: + """Plot the estimated structure in a graphical model style. + Spurious edges are colored in red. + """ + graph_to_draw = nx.DiGraph() + spurious_edges = self.spurious_edges() + non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges)) + print(non_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") + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.png' + plt.savefig(name) + plt.clf() + print("Estimated Structure Plot Saved At: ", os.path.abspath(name)) + + + + + diff --git a/PyCTBN/build/lib/PyCTBN/estimators/structure_score_based_estimator.py b/PyCTBN/build/lib/PyCTBN/estimators/structure_score_based_estimator.py new file mode 100644 index 0000000..2903db3 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/estimators/structure_score_based_estimator.py @@ -0,0 +1,244 @@ + +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. + + :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) + + + + #list_edges_partial = p.map(estimate_parents, self._nodes) + #list_edges_partial= estimate_parents('Q',max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) + + 'Concatenate all the edges list' + set_list_edges = set(itertools.chain.from_iterable(list_edges_partial)) + + #print('-------------------------') + + + '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(f"n archi reali non trovati: {n_missing_edges}") + # print(f"n archi non reali aggiunti: {n_added_fake_edges}") + print(true_edges) + print(set_list_edges) + print(f"precision: {precision} ") + print(f"recall: {recall} ") + except Exception as e: + print(f"errore: {e}") + + 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 + + + + diff --git a/PyCTBN/build/lib/PyCTBN/optimizers/__init__.py b/PyCTBN/build/lib/PyCTBN/optimizers/__init__.py new file mode 100644 index 0000000..4162bf1 --- /dev/null +++ b/PyCTBN/build/lib/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/optimizers/constraint_based_optimizer.py b/PyCTBN/build/lib/PyCTBN/optimizers/constraint_based_optimizer.py new file mode 100644 index 0000000..65bc19c --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/optimizers/constraint_based_optimizer.py @@ -0,0 +1,87 @@ + +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 + #tests_parents_numb = len(u) + #complete_frame = self.complete_graph_frame + #test_frame = complete_frame.loc[complete_frame['To'].isin([self.node_id])] + 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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/optimizers/hill_climbing_search.py b/PyCTBN/build/lib/PyCTBN/optimizers/hill_climbing_search.py new file mode 100644 index 0000000..6783be0 --- /dev/null +++ b/PyCTBN/build/lib/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/optimizers/optimizer.py b/PyCTBN/build/lib/PyCTBN/optimizers/optimizer.py new file mode 100644 index 0000000..36445c0 --- /dev/null +++ b/PyCTBN/build/lib/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 diff --git a/PyCTBN/build/lib/PyCTBN/optimizers/tabu_search.py b/PyCTBN/build/lib/PyCTBN/optimizers/tabu_search.py new file mode 100644 index 0000000..e15dd40 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/optimizers/tabu_search.py @@ -0,0 +1,199 @@ + +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_tabu_rules_durationength = 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 + #print('**************************') + current_score = self.structure_estimator.get_score_from_graph(graph,self.node_id) + + + # print("-------------------------------------------") + # print(f"Current new parent: {current_new_parent}") + # print(f"Current score: {current_score}") + # print(f"Current best score: {actual_best_score}") + # print(f"tabu list : {str(tabu_set)} length: {len(tabu_set)}") + # print(f"tabu queue : {str(tabu_queue)} length: {tabu_queue.qsize()}") + # print(f"graph edges: {graph.edges}") + + # print("-------------------------------------------") + # input() + 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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/__init__.py b/PyCTBN/build/lib/PyCTBN/structure_graph/__init__.py new file mode 100644 index 0000000..85f18a2 --- /dev/null +++ b/PyCTBN/build/lib/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/conditional_intensity_matrix.py b/PyCTBN/build/lib/PyCTBN/structure_graph/conditional_intensity_matrix.py new file mode 100644 index 0000000..4abfdd0 --- /dev/null +++ b/PyCTBN/build/lib/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) + diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/network_graph.py b/PyCTBN/build/lib/PyCTBN/structure_graph/network_graph.py new file mode 100644 index 0000000..623981d --- /dev/null +++ b/PyCTBN/build/lib/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 diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/sample_path.py b/PyCTBN/build/lib/PyCTBN/structure_graph/sample_path.py new file mode 100644 index 0000000..80b51d9 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/structure_graph/sample_path.py @@ -0,0 +1,91 @@ + + +import numpy as np +import pandas as pd + +from .structure import Structure +from .trajectory import Trajectory +from ..utility.abstract_importer import AbstractImporter + + + +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('The importer object has to contain the all processed data!') + if self._importer._df_variables.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, pd.DataFrame): + if self._importer._concatenated_samples.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, np.ndarray): + if self._importer._concatenated_samples.size == 0: + raise RuntimeError('The importer object has to contain the all processed data!') + 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) + + + + + + diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/set_of_cims.py b/PyCTBN/build/lib/PyCTBN/structure_graph/set_of_cims.py new file mode 100644 index 0000000..81caff5 --- /dev/null +++ b/PyCTBN/build/lib/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) + + + + diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/structure.py b/PyCTBN/build/lib/PyCTBN/structure_graph/structure.py new file mode 100644 index 0000000..a9d60cc --- /dev/null +++ b/PyCTBN/build/lib/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(tuple) + print(self._edges_list) + + def remove_edge(self,edge: tuple): + self._edges_list.remove(tuple) + + 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 + diff --git a/PyCTBN/build/lib/PyCTBN/structure_graph/trajectory.py b/PyCTBN/build/lib/PyCTBN/structure_graph/trajectory.py new file mode 100644 index 0000000..36899b3 --- /dev/null +++ b/PyCTBN/build/lib/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__() + + diff --git a/PyCTBN/build/lib/PyCTBN/utility/__init__.py b/PyCTBN/build/lib/PyCTBN/utility/__init__.py new file mode 100644 index 0000000..f79749c --- /dev/null +++ b/PyCTBN/build/lib/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/utility/abstract_importer.py b/PyCTBN/build/lib/PyCTBN/utility/abstract_importer.py new file mode 100644 index 0000000..1cad352 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/utility/abstract_importer.py @@ -0,0 +1,164 @@ + +import typing +from abc import ABC, abstractmethod + +import numpy as np +import pandas as pd + +import copy + +#from sklearn.utils import resample + + +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 diff --git a/PyCTBN/build/lib/PyCTBN/utility/cache.py b/PyCTBN/build/lib/PyCTBN/utility/cache.py new file mode 100644 index 0000000..8e0369b --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/utility/cache.py @@ -0,0 +1,58 @@ + +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: + #print("Cache State:", self.list_of_sets_of_indxs) + #print("Look For:", parents_comb) + result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)] + #print("CACHE HIT!!!!", parents_comb) + return result + except ValueError: + return None + + def put(self, parents_comb: typing.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 + """ + #print("Putting in cache:", parents_comb) + 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[:] \ No newline at end of file diff --git a/PyCTBN/build/lib/PyCTBN/utility/json_importer.py b/PyCTBN/build/lib/PyCTBN/utility/json_importer.py new file mode 100644 index 0000000..edff212 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/utility/json_importer.py @@ -0,0 +1,176 @@ +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) -> None: + """Implements the abstract method of :class:`AbstractImporter`. + + :param indx: the index of the outer JsonArray to extract the data from + :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) + return data + + def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame: + """Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param key: the key for the Dicts from which exctract data + :type key: string + :return: A normalized dataframe + :rtype: pandas.Datframe + """ + return pd.DataFrame(raw_data[indx][key]) + + def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List: + """ + Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param trajectories_key: the key of the trajectories objects + :type trajectories_key: string + :return: A list of daframes containg the trajectories + :rtype: List + """ + dataframe = pd.DataFrame + smps = raw_data[indx][trajectories_key] + df_samples_list = [dataframe(sample) for sample in smps] + return df_samples_list + + def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: + """Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset. + """ + columns_header = list(sample_frame.columns.values) + columns_header.remove(self._time_key) + return columns_header + + def clear_data_frame_list(self) -> None: + """Removes all values present in the dataframes in the list ``_df_samples_list``. + """ + for indx in range(len(self._df_samples_list)): + self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0] + + def dataset_id(self) -> object: + return self._array_indx + + def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict: + """Imports the synthetic CIMS in the dataset in a dictionary, using variables labels + as keys for the set of CIMS of a particular node. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param cims_key: the key where the json object cims are placed + :type cims_key: string + :return: a dictionary containing the sampled CIMS for all the variables in the net + :rtype: Dictionary + """ + cims_for_all_vars = {} + for var in raw_data[indx][cims_key]: + sampled_cims_list = [] + cims_for_all_vars[var] = sampled_cims_list + for p_comb in raw_data[indx][cims_key][var]: + cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy()) + return cims_for_all_vars + + + diff --git a/PyCTBN/build/lib/PyCTBN/utility/sample_importer.py b/PyCTBN/build/lib/PyCTBN/utility/sample_importer.py new file mode 100644 index 0000000..05073c8 --- /dev/null +++ b/PyCTBN/build/lib/PyCTBN/utility/sample_importer.py @@ -0,0 +1,65 @@ +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(variables,list) or isinstance(variables,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 + + if isinstance(samples_list, np.ndarray): + samples_list = samples_list.tolist() + + 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) -> object: + pass \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/__init__.py b/PyCTBN/build/lib/classes/__init__.py new file mode 100644 index 0000000..faff79c --- /dev/null +++ b/PyCTBN/build/lib/classes/__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 * \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/estimators/__init__.py b/PyCTBN/build/lib/classes/estimators/__init__.py new file mode 100644 index 0000000..112086f --- /dev/null +++ b/PyCTBN/build/lib/classes/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 diff --git a/PyCTBN/build/lib/classes/estimators/fam_score_calculator.py b/PyCTBN/build/lib/classes/estimators/fam_score_calculator.py new file mode 100644 index 0000000..5b0b591 --- /dev/null +++ b/PyCTBN/build/lib/classes/estimators/fam_score_calculator.py @@ -0,0 +1,272 @@ + +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 + """ + #print("------") + #print(self.marginal_likelihood_q(cims, + # tau_xu, + # alpha_xu)) + + #print(self.marginal_likelihood_theta(cims, + # alpha_xu, + # alpha_xxu)) + '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) diff --git a/PyCTBN/build/lib/classes/estimators/parameters_estimator.py b/PyCTBN/build/lib/classes/estimators/parameters_estimator.py new file mode 100644 index 0000000..4754d58 --- /dev/null +++ b/PyCTBN/build/lib/classes/estimators/parameters_estimator.py @@ -0,0 +1,143 @@ +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() + + def init_sets_cims_container(self): + self.sets_of_cims_struct = acims.SetsOfCimsContainer(self.net_graph.nodes, + self.net_graph.nodes_values, + self.net_graph.get_ordered_by_indx_parents_values_for_all_nodes(), + self.net_graph.p_combs) + + def compute_parameters(self): + #print(self.net_graph.get_nodes()) + #print(self.amalgamated_cims_struct.sets_of_cims) + #enumerate(zip(self.net_graph.get_nodes(), self.amalgamated_cims_struct.sets_of_cims)) + for indx, aggr in enumerate(zip(self.net_graph.nodes, self.sets_of_cims_struct.sets_of_cims)): + #print(self.net_graph.time_filtering[indx]) + #print(self.net_graph.time_scalar_indexing_strucure[indx]) + self.compute_state_res_time_for_node(self.net_graph.get_node_indx(aggr[0]), self.sample_path.trajectories.times, + self.sample_path.trajectories.trajectory, + self.net_graph.time_filtering[indx], + self.net_graph.time_scalar_indexing_strucure[indx], + aggr[1]._state_residence_times) + #print(self.net_graph.transition_filtering[indx]) + #print(self.net_graph.transition_scalar_indexing_structure[indx]) + self.compute_state_transitions_for_a_node(self.net_graph.get_node_indx(aggr[0]), + self.sample_path.trajectories.complete_trajectory, + self.net_graph.transition_filtering[indx], + self.net_graph.transition_scalar_indexing_structure[indx], + aggr[1]._transition_matrices) + aggr[1].build_cims(aggr[1]._state_residence_times, aggr[1]._transition_matrices) + + + + + + + + diff --git a/PyCTBN/build/lib/classes/estimators/structure_constraint_based_estimator.py b/PyCTBN/build/lib/classes/estimators/structure_constraint_based_estimator.py new file mode 100644 index 0000000..7d5721e --- /dev/null +++ b/PyCTBN/build/lib/classes/estimators/structure_constraint_based_estimator.py @@ -0,0 +1,238 @@ + +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 + :_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: + if (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) + #list_edges_partial = [ctpc_algo(n,total_vars_numb) for n in self._nodes] + + return set(itertools.chain.from_iterable(list_edges_partial)) + + + def estimate_structure(self,disable_multiprocessing:bool=False): + return self.ctpc_algorithm(disable_multiprocessing=disable_multiprocessing) + + + + diff --git a/PyCTBN/build/lib/classes/estimators/structure_estimator.py b/PyCTBN/build/lib/classes/estimators/structure_estimator.py new file mode 100644 index 0000000..fbf8ea9 --- /dev/null +++ b/PyCTBN/build/lib/classes/estimators/structure_estimator.py @@ -0,0 +1,187 @@ + +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 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 + :_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) -> None: + """Save the estimated Structure to a .json file in the path where the data are loaded from. + The file is named as the input dataset but the `results_` word is appended to the results file. + """ + res = json_graph.node_link_data(self._complete_graph) + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.json' + file_name = 'results_' + name + with open(file_name, 'w') as f: + json.dump(res, f) + + + def remove_diagonal_elements(self, matrix): + m = matrix.shape[0] + strided = np.lib.stride_tricks.as_strided + s0, s1 = matrix.strides + return strided(matrix.ravel()[1:], shape=(m - 1, m), strides=(s0 + s1, s1)).reshape(m, -1) + + + @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).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: + raise RuntimeError("Can not compute spurious edges with no prior net structure!") + 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) -> None: + """Plot the estimated structure in a graphical model style. + Spurious edges are colored in red. + """ + graph_to_draw = nx.DiGraph() + spurious_edges = self.spurious_edges() + non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges)) + print(non_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") + name = self._sample_path._importer.file_path.rsplit('/', 1)[-1] + name = name.split('.', 1)[0] + name += '_' + str(self._sample_path._importer.dataset_id()) + name += '.png' + plt.savefig(name) + plt.clf() + print("Estimated Structure Plot Saved At: ", os.path.abspath(name)) + + + + + diff --git a/PyCTBN/build/lib/classes/estimators/structure_score_based_estimator.py b/PyCTBN/build/lib/classes/estimators/structure_score_based_estimator.py new file mode 100644 index 0000000..2903db3 --- /dev/null +++ b/PyCTBN/build/lib/classes/estimators/structure_score_based_estimator.py @@ -0,0 +1,244 @@ + +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. + + :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) + + + + #list_edges_partial = p.map(estimate_parents, self._nodes) + #list_edges_partial= estimate_parents('Q',max_parents,iterations_number,patience,tabu_length,tabu_rules_duration,optimizer) + + 'Concatenate all the edges list' + set_list_edges = set(itertools.chain.from_iterable(list_edges_partial)) + + #print('-------------------------') + + + '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(f"n archi reali non trovati: {n_missing_edges}") + # print(f"n archi non reali aggiunti: {n_added_fake_edges}") + print(true_edges) + print(set_list_edges) + print(f"precision: {precision} ") + print(f"recall: {recall} ") + except Exception as e: + print(f"errore: {e}") + + 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 + + + + diff --git a/PyCTBN/build/lib/classes/optimizers/__init__.py b/PyCTBN/build/lib/classes/optimizers/__init__.py new file mode 100644 index 0000000..4162bf1 --- /dev/null +++ b/PyCTBN/build/lib/classes/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/optimizers/constraint_based_optimizer.py b/PyCTBN/build/lib/classes/optimizers/constraint_based_optimizer.py new file mode 100644 index 0000000..65bc19c --- /dev/null +++ b/PyCTBN/build/lib/classes/optimizers/constraint_based_optimizer.py @@ -0,0 +1,87 @@ + +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 + #tests_parents_numb = len(u) + #complete_frame = self.complete_graph_frame + #test_frame = complete_frame.loc[complete_frame['To'].isin([self.node_id])] + 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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/optimizers/hill_climbing_search.py b/PyCTBN/build/lib/classes/optimizers/hill_climbing_search.py new file mode 100644 index 0000000..6783be0 --- /dev/null +++ b/PyCTBN/build/lib/classes/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/optimizers/optimizer.py b/PyCTBN/build/lib/classes/optimizers/optimizer.py new file mode 100644 index 0000000..36445c0 --- /dev/null +++ b/PyCTBN/build/lib/classes/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 diff --git a/PyCTBN/build/lib/classes/optimizers/tabu_search.py b/PyCTBN/build/lib/classes/optimizers/tabu_search.py new file mode 100644 index 0000000..e15dd40 --- /dev/null +++ b/PyCTBN/build/lib/classes/optimizers/tabu_search.py @@ -0,0 +1,199 @@ + +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_tabu_rules_durationength = 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 + #print('**************************') + current_score = self.structure_estimator.get_score_from_graph(graph,self.node_id) + + + # print("-------------------------------------------") + # print(f"Current new parent: {current_new_parent}") + # print(f"Current score: {current_score}") + # print(f"Current best score: {actual_best_score}") + # print(f"tabu list : {str(tabu_set)} length: {len(tabu_set)}") + # print(f"tabu queue : {str(tabu_queue)} length: {tabu_queue.qsize()}") + # print(f"graph edges: {graph.edges}") + + # print("-------------------------------------------") + # input() + 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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/structure_graph/__init__.py b/PyCTBN/build/lib/classes/structure_graph/__init__.py new file mode 100644 index 0000000..85f18a2 --- /dev/null +++ b/PyCTBN/build/lib/classes/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/structure_graph/conditional_intensity_matrix.py b/PyCTBN/build/lib/classes/structure_graph/conditional_intensity_matrix.py new file mode 100644 index 0000000..4abfdd0 --- /dev/null +++ b/PyCTBN/build/lib/classes/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) + diff --git a/PyCTBN/build/lib/classes/structure_graph/network_graph.py b/PyCTBN/build/lib/classes/structure_graph/network_graph.py new file mode 100644 index 0000000..623981d --- /dev/null +++ b/PyCTBN/build/lib/classes/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 diff --git a/PyCTBN/build/lib/classes/structure_graph/sample_path.py b/PyCTBN/build/lib/classes/structure_graph/sample_path.py new file mode 100644 index 0000000..80b51d9 --- /dev/null +++ b/PyCTBN/build/lib/classes/structure_graph/sample_path.py @@ -0,0 +1,91 @@ + + +import numpy as np +import pandas as pd + +from .structure import Structure +from .trajectory import Trajectory +from ..utility.abstract_importer import AbstractImporter + + + +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('The importer object has to contain the all processed data!') + if self._importer._df_variables.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, pd.DataFrame): + if self._importer._concatenated_samples.empty: + raise RuntimeError('The importer object has to contain the all processed data!') + if isinstance(self._importer._concatenated_samples, np.ndarray): + if self._importer._concatenated_samples.size == 0: + raise RuntimeError('The importer object has to contain the all processed data!') + 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) + + + + + + diff --git a/PyCTBN/build/lib/classes/structure_graph/set_of_cims.py b/PyCTBN/build/lib/classes/structure_graph/set_of_cims.py new file mode 100644 index 0000000..81caff5 --- /dev/null +++ b/PyCTBN/build/lib/classes/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) + + + + diff --git a/PyCTBN/build/lib/classes/structure_graph/structure.py b/PyCTBN/build/lib/classes/structure_graph/structure.py new file mode 100644 index 0000000..a9d60cc --- /dev/null +++ b/PyCTBN/build/lib/classes/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(tuple) + print(self._edges_list) + + def remove_edge(self,edge: tuple): + self._edges_list.remove(tuple) + + 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 + diff --git a/PyCTBN/build/lib/classes/structure_graph/trajectory.py b/PyCTBN/build/lib/classes/structure_graph/trajectory.py new file mode 100644 index 0000000..36899b3 --- /dev/null +++ b/PyCTBN/build/lib/classes/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__() + + diff --git a/PyCTBN/build/lib/classes/utility/__init__.py b/PyCTBN/build/lib/classes/utility/__init__.py new file mode 100644 index 0000000..f79749c --- /dev/null +++ b/PyCTBN/build/lib/classes/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 \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/utility/abstract_importer.py b/PyCTBN/build/lib/classes/utility/abstract_importer.py new file mode 100644 index 0000000..1cad352 --- /dev/null +++ b/PyCTBN/build/lib/classes/utility/abstract_importer.py @@ -0,0 +1,164 @@ + +import typing +from abc import ABC, abstractmethod + +import numpy as np +import pandas as pd + +import copy + +#from sklearn.utils import resample + + +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 diff --git a/PyCTBN/build/lib/classes/utility/cache.py b/PyCTBN/build/lib/classes/utility/cache.py new file mode 100644 index 0000000..8e0369b --- /dev/null +++ b/PyCTBN/build/lib/classes/utility/cache.py @@ -0,0 +1,58 @@ + +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: + #print("Cache State:", self.list_of_sets_of_indxs) + #print("Look For:", parents_comb) + result = self._actual_cache[self._list_of_sets_of_parents.index(parents_comb)] + #print("CACHE HIT!!!!", parents_comb) + return result + except ValueError: + return None + + def put(self, parents_comb: typing.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 + """ + #print("Putting in cache:", parents_comb) + 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[:] \ No newline at end of file diff --git a/PyCTBN/build/lib/classes/utility/json_importer.py b/PyCTBN/build/lib/classes/utility/json_importer.py new file mode 100644 index 0000000..edff212 --- /dev/null +++ b/PyCTBN/build/lib/classes/utility/json_importer.py @@ -0,0 +1,176 @@ +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) -> None: + """Implements the abstract method of :class:`AbstractImporter`. + + :param indx: the index of the outer JsonArray to extract the data from + :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) + return data + + def one_level_normalizing(self, raw_data: typing.List, indx: int, key: str) -> pd.DataFrame: + """Extracts the one-level nested data in the list ``raw_data`` at the index ``indx`` at the key ``key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param key: the key for the Dicts from which exctract data + :type key: string + :return: A normalized dataframe + :rtype: pandas.Datframe + """ + return pd.DataFrame(raw_data[indx][key]) + + def normalize_trajectories(self, raw_data: typing.List, indx: int, trajectories_key: str) -> typing.List: + """ + Extracts the trajectories in ``raw_data`` at the index ``index`` at the key ``trajectories key``. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param trajectories_key: the key of the trajectories objects + :type trajectories_key: string + :return: A list of daframes containg the trajectories + :rtype: List + """ + dataframe = pd.DataFrame + smps = raw_data[indx][trajectories_key] + df_samples_list = [dataframe(sample) for sample in smps] + return df_samples_list + + def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: + """Implements the abstract method build_sorter of the :class:`AbstractImporter` for this dataset. + """ + columns_header = list(sample_frame.columns.values) + columns_header.remove(self._time_key) + return columns_header + + def clear_data_frame_list(self) -> None: + """Removes all values present in the dataframes in the list ``_df_samples_list``. + """ + for indx in range(len(self._df_samples_list)): + self._df_samples_list[indx] = self._df_samples_list[indx].iloc[0:0] + + def dataset_id(self) -> object: + return self._array_indx + + def import_sampled_cims(self, raw_data: typing.List, indx: int, cims_key: str) -> typing.Dict: + """Imports the synthetic CIMS in the dataset in a dictionary, using variables labels + as keys for the set of CIMS of a particular node. + + :param raw_data: List of Dicts + :type raw_data: List + :param indx: The index of the array from which the data have to be extracted + :type indx: int + :param cims_key: the key where the json object cims are placed + :type cims_key: string + :return: a dictionary containing the sampled CIMS for all the variables in the net + :rtype: Dictionary + """ + cims_for_all_vars = {} + for var in raw_data[indx][cims_key]: + sampled_cims_list = [] + cims_for_all_vars[var] = sampled_cims_list + for p_comb in raw_data[indx][cims_key][var]: + cims_for_all_vars[var].append(pd.DataFrame(raw_data[indx][cims_key][var][p_comb]).to_numpy()) + return cims_for_all_vars + + + diff --git a/PyCTBN/build/lib/classes/utility/sample_importer.py b/PyCTBN/build/lib/classes/utility/sample_importer.py new file mode 100644 index 0000000..05073c8 --- /dev/null +++ b/PyCTBN/build/lib/classes/utility/sample_importer.py @@ -0,0 +1,65 @@ +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(variables,list) or isinstance(variables,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 + + if isinstance(samples_list, np.ndarray): + samples_list = samples_list.tolist() + + 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) -> object: + pass \ No newline at end of file diff --git a/PyCTBN/build/lib/tests/__init__.py b/PyCTBN/build/lib/tests/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/PyCTBN/build/lib/tests/__init__.py @@ -0,0 +1 @@ + diff --git a/PyCTBN/setup.py b/PyCTBN/setup.py new file mode 100644 index 0000000..56dd72f --- /dev/null +++ b/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') diff --git a/PyCTBN/tests/__init__.py b/PyCTBN/tests/__init__.py new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/PyCTBN/tests/__init__.py @@ -0,0 +1 @@ + diff --git a/PyCTBN/tests/coverage.xml b/PyCTBN/tests/coverage.xml new file mode 100644 index 0000000..094af83 --- /dev/null +++ b/PyCTBN/tests/coverage.xml @@ -0,0 +1,963 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/PyCTBN/tests/estimators/test_parameters_estimator.py b/PyCTBN/tests/estimators/test_parameters_estimator.py new file mode 100644 index 0000000..5c438db --- /dev/null +++ b/PyCTBN/tests/estimators/test_parameters_estimator.py @@ -0,0 +1,67 @@ + +import unittest +import numpy as np +import glob +import os + +from ...PyCTBN.structure_graph.network_graph import NetworkGraph +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.structure_graph.set_of_cims import SetOfCims +from ...PyCTBN.estimators.parameters_estimator import ParametersEstimator +from ...PyCTBN.utility.json_importer import JsonImporter + + +class TestParametersEstimatior(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.read_files = glob.glob(os.path.join('./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json', "*.json")) + cls.array_indx = 0 + cls.importer = JsonImporter('./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json', 'samples', 'dyn.str', 'variables', 'Time', 'Name') + cls.importer.import_data(cls.array_indx) + cls.s1 = SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + print(cls.s1.structure.edges) + print(cls.s1.structure.nodes_values) + + def test_fast_init(self): + for node in self.s1.structure.nodes_labels: + g = NetworkGraph(self.s1.structure) + g.fast_init(node) + p1 = ParametersEstimator(self.s1.trajectories, g) + self.assertEqual(p1._trajectories, self.s1.trajectories) + self.assertEqual(p1._net_graph, g) + self.assertIsNone(p1._single_set_of_cims) + p1.fast_init(node) + self.assertIsInstance(p1._single_set_of_cims, SetOfCims) + + def test_compute_parameters_for_node(self): + for indx, node in enumerate(self.s1.structure.nodes_labels): + print(node) + g = NetworkGraph(self.s1.structure) + g.fast_init(node) + p1 = ParametersEstimator(self.s1.trajectories, g) + p1.fast_init(node) + sofc1 = p1.compute_parameters_for_node(node) + sampled_cims = self.aux_import_sampled_cims('dyn.cims') + sc = list(sampled_cims.values()) + self.equality_of_cims_of_node(sc[indx], sofc1._actual_cims) + + def equality_of_cims_of_node(self, sampled_cims, estimated_cims): + self.assertEqual(len(sampled_cims), len(estimated_cims)) + for c1, c2 in zip(sampled_cims, estimated_cims): + self.cim_equality_test(c1, c2.cim) + + def cim_equality_test(self, cim1, cim2): + for r1, r2 in zip(cim1, cim2): + self.assertTrue(np.all(np.isclose(r1, r2, 1e01))) + + def aux_import_sampled_cims(self, cims_label): + i1 = JsonImporter('./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json', '', '', '', '', '') + raw_data = i1.read_json_file() + return i1.import_sampled_cims(raw_data, self.array_indx, cims_label) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/estimators/test_structure_constraint_based_estimator.py b/PyCTBN/tests/estimators/test_structure_constraint_based_estimator.py new file mode 100644 index 0000000..96834dc --- /dev/null +++ b/PyCTBN/tests/estimators/test_structure_constraint_based_estimator.py @@ -0,0 +1,64 @@ + +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import psutil +from line_profiler import LineProfiler + +import json +import pandas as pd + + +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator +from ...PyCTBN.utility.sample_importer import SampleImporter + +import copy + + +class TestStructureConstraintBasedEstimator(unittest.TestCase): + @classmethod + def setUpClass(cls): + with open("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json") as f: + raw_data = json.load(f) + + trajectory_list_raw= raw_data[0]["samples"] + + trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] + + variables= pd.DataFrame(raw_data[0]["variables"]) + prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"]) + + + cls.importer = SampleImporter( + trajectory_list=trajectory_list, + variables=variables, + prior_net_structure=prior_net_structure + ) + + cls.importer.import_data() + #cls.s1 = sp.SamplePath(cls.importer) + + #cls.traj = cls.s1.concatenated_samples + + # print(len(cls.traj)) + cls.s1 = SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + + def test_structure(self): + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + se1 = StructureConstraintBasedEstimator(self.s1,0.1,0.1) + edges = se1.estimate_structure(disable_multiprocessing=False) + + + self.assertEqual(edges, true_edges) + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/estimators/test_structure_constraint_based_estimator_server.py b/PyCTBN/tests/estimators/test_structure_constraint_based_estimator_server.py new file mode 100644 index 0000000..a524515 --- /dev/null +++ b/PyCTBN/tests/estimators/test_structure_constraint_based_estimator_server.py @@ -0,0 +1,59 @@ + +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import psutil +from line_profiler import LineProfiler + +from ...PyCTBN.utility.cache import Cache +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.estimators.structure_constraint_based_estimator import StructureConstraintBasedEstimator +from ...PyCTBN.utility.json_importer import JsonImporter + +from multiprocessing import set_start_method + +import copy + + +class TestStructureConstraintBasedEstimator(unittest.TestCase): + @classmethod + def setUpClass(cls): + pass + + def test_structure(self): + #cls.read_files = glob.glob(os.path.join('../../data', "*.json")) + self.importer = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.s1 = SamplePath(self.importer) + self.s1.build_trajectories() + self.s1.build_structure() + + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + + se1 = StructureConstraintBasedEstimator(self.s1,0.1,0.1) + edges = se1.estimate_structure(disable_multiprocessing=False) + + + self.importer = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.s1 = SamplePath(self.importer) + self.s1.build_trajectories() + self.s1.build_structure() + + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + + se1 = StructureConstraintBasedEstimator(self.s1,0.1,0.1) + edges = se1.estimate_structure(disable_multiprocessing=True) + + + + self.assertEqual(edges, true_edges) + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/estimators/test_structure_score_based_estimator.py b/PyCTBN/tests/estimators/test_structure_score_based_estimator.py new file mode 100644 index 0000000..742fa21 --- /dev/null +++ b/PyCTBN/tests/estimators/test_structure_score_based_estimator.py @@ -0,0 +1,82 @@ +import sys +sys.path.append("../../PyCTBN/") +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import psutil +from line_profiler import LineProfiler +import copy + +from ...PyCTBN.utility.cache import Cache +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.estimators.structure_score_based_estimator import StructureScoreBasedEstimator +from ...PyCTBN.utility.json_importer import JsonImporter +from ...PyCTBN.utility.sample_importer import SampleImporter + +import json + +import pandas as pd + + + +class TestStructureScoreBasedEstimator(unittest.TestCase): + + @classmethod + def setUpClass(cls): + with open("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json") as f: + raw_data = json.load(f) + + trajectory_list_raw= raw_data[0]["samples"] + + trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] + + variables= pd.DataFrame(raw_data[0]["variables"]) + prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"]) + + + cls.importer = SampleImporter( + trajectory_list=trajectory_list, + variables=variables, + prior_net_structure=prior_net_structure + ) + + cls.importer.import_data() + #cls.s1 = sp.SamplePath(cls.importer) + + #cls.traj = cls.s1.concatenated_samples + + # print(len(cls.traj)) + cls.s1 = SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + + + + def test_structure(self): + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + + se1 = StructureScoreBasedEstimator(self.s1,known_edges = [('X','Q')]) + edges = se1.estimate_structure( + max_parents = None, + iterations_number = 100, + patience = 35, + tabu_length = 15, + tabu_rules_duration = 15, + optimizer = 'hill', + disable_multiprocessing=True + ) + + + self.assertEqual(edges, true_edges) + + + +if __name__ == '__main__': + unittest.main() + diff --git a/PyCTBN/tests/estimators/test_structure_score_based_estimator_server.py b/PyCTBN/tests/estimators/test_structure_score_based_estimator_server.py new file mode 100644 index 0000000..b21ea7a --- /dev/null +++ b/PyCTBN/tests/estimators/test_structure_score_based_estimator_server.py @@ -0,0 +1,79 @@ + +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import psutil +from line_profiler import LineProfiler +import copy + +from ...PyCTBN.utility.cache import Cache +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.estimators.structure_score_based_estimator import StructureScoreBasedEstimator +from ...PyCTBN.utility.json_importer import JsonImporter + + + +class TestStructureScoreBasedEstimator(unittest.TestCase): + + @classmethod + def setUpClass(cls): + pass + + + + def test_structure(self): + #cls.read_files = glob.glob(os.path.join('../../data', "*.json")) + self.importer = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.s1 = SamplePath(self.importer) + self.s1.build_trajectories() + self.s1.build_structure() + + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + + se1 = StructureScoreBasedEstimator(self.s1) + edges = se1.estimate_structure( + max_parents = None, + iterations_number = 100, + patience = 35, + tabu_length = 15, + tabu_rules_duration = 15, + optimizer = 'tabu', + disable_multiprocessing=False + ) + + + self.importer = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.s1 = SamplePath(self.importer) + self.s1.build_trajectories() + self.s1.build_structure() + + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + + se1 = StructureScoreBasedEstimator(self.s1) + edges = se1.estimate_structure( + max_parents = None, + iterations_number = 100, + patience = 35, + tabu_length = 15, + tabu_rules_duration = 15, + optimizer = 'tabu', + disable_multiprocessing=True + ) + + + + self.assertEqual(edges, true_edges) + + + +if __name__ == '__main__': + unittest.main() + diff --git a/PyCTBN/tests/optimizers/test_hill_climbing_search.py b/PyCTBN/tests/optimizers/test_hill_climbing_search.py new file mode 100644 index 0000000..2b05236 --- /dev/null +++ b/PyCTBN/tests/optimizers/test_hill_climbing_search.py @@ -0,0 +1,54 @@ + +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import psutil +from line_profiler import LineProfiler +import copy + + +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.estimators.structure_score_based_estimator import StructureScoreBasedEstimator +from ...PyCTBN.utility.json_importer import JsonImporter + + + +class TestHillClimbingSearch(unittest.TestCase): + + @classmethod + def setUpClass(cls): + #cls.read_files = glob.glob(os.path.join('../../data', "*.json")) + + + cls.importer = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + cls.importer.import_data(0) + cls.s1 = SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + + + + def test_structure(self): + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + se1 = StructureScoreBasedEstimator(self.s1) + edges = se1.estimate_structure( + max_parents = None, + iterations_number = 40, + patience = None, + optimizer = 'hill' + ) + + + self.assertEqual(edges, true_edges) + + + +if __name__ == '__main__': + unittest.main() + diff --git a/PyCTBN/tests/optimizers/test_tabu_search.py b/PyCTBN/tests/optimizers/test_tabu_search.py new file mode 100644 index 0000000..3349dd7 --- /dev/null +++ b/PyCTBN/tests/optimizers/test_tabu_search.py @@ -0,0 +1,84 @@ +import sys +sys.path.append("../../PyCTBN/") +import glob +import math +import os +import unittest + +import networkx as nx +import numpy as np +import pandas as pd +import psutil +from line_profiler import LineProfiler +import copy +import json + +import utility.cache as ch +import structure_graph.sample_path as sp +import estimators.structure_score_based_estimator as se +import utility.json_importer as ji +import utility.sample_importer as si + + + + + +class TestTabuSearch(unittest.TestCase): + + @classmethod + def setUpClass(cls): + #cls.read_files = glob.glob(os.path.join('../../data', "*.json")) + + with open("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json") as f: + raw_data = json.load(f) + + trajectory_list_raw= raw_data[0]["samples"] + + trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] + + variables= pd.DataFrame(raw_data[0]["variables"]) + prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"]) + + + cls.importer = si.SampleImporter( + trajectory_list=trajectory_list, + variables=variables, + prior_net_structure=prior_net_structure + ) + + cls.importer.import_data() + #cls.s1 = sp.SamplePath(cls.importer) + + #cls.traj = cls.s1.concatenated_samples + + # print(len(cls.traj)) + cls.s1 = sp.SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + #cls.s1.clear_memory() + + + + def test_structure(self): + true_edges = copy.deepcopy(self.s1.structure.edges) + true_edges = set(map(tuple, true_edges)) + + se1 = se.StructureScoreBasedEstimator(self.s1) + edges = se1.estimate_structure( + max_parents = None, + iterations_number = 100, + patience = 20, + tabu_length = 10, + tabu_rules_duration = 10, + optimizer = 'tabu', + disable_multiprocessing=False + ) + + + self.assertEqual(edges, true_edges) + + + +if __name__ == '__main__': + unittest.main() + diff --git a/PyCTBN/tests/structure_graph/test_cim.py b/PyCTBN/tests/structure_graph/test_cim.py new file mode 100644 index 0000000..a5fcd75 --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_cim.py @@ -0,0 +1,46 @@ + +import unittest +import numpy as np + +from ...PyCTBN.structure_graph.conditional_intensity_matrix import ConditionalIntensityMatrix + + +class TestConditionalIntensityMatrix(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.state_res_times = np.random.rand(1, 3)[0] + cls.state_res_times = cls.state_res_times * 1000 + cls.state_transition_matrix = np.random.randint(1, 10000, (3, 3)) + for i in range(0, len(cls.state_res_times)): + cls.state_transition_matrix[i, i] = 0 + cls.state_transition_matrix[i, i] = np.sum(cls.state_transition_matrix[i]) + + def test_init(self): + c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix) + self.assertTrue(np.array_equal(self.state_res_times, c1.state_residence_times)) + self.assertTrue(np.array_equal(self.state_transition_matrix, c1.state_transition_matrix)) + self.assertEqual(c1.cim.dtype, np.float) + self.assertEqual(self.state_transition_matrix.shape, c1.cim.shape) + + def test_compute_cim_coefficients(self): + c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix) + c2 = self.state_transition_matrix.astype(np.float) + np.fill_diagonal(c2, c2.diagonal() * -1) + for i in range(0, len(self.state_res_times)): + for j in range(0, len(self.state_res_times)): + c2[i, j] = (c2[i, j] + 1) / (self.state_res_times[i] + 1) + c1.compute_cim_coefficients() + for i in range(0, len(c1.state_residence_times)): + self.assertTrue(np.isclose(np.sum(c1.cim[i]), 0.0, 1e-02, 1e-01)) + for i in range(0, len(self.state_res_times)): + for j in range(0, len(self.state_res_times)): + self.assertTrue(np.isclose(c1.cim[i, j], c2[i, j], 1e-02, 1e-01)) + + def test_repr(self): + c1 = ConditionalIntensityMatrix(self.state_res_times, self.state_transition_matrix) + print(c1) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/structure_graph/test_networkgraph.py b/PyCTBN/tests/structure_graph/test_networkgraph.py new file mode 100644 index 0000000..b41c20a --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_networkgraph.py @@ -0,0 +1,190 @@ + +import unittest +import glob +import os +import networkx as nx +import numpy as np +import itertools + +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.structure_graph.network_graph import NetworkGraph +from ...PyCTBN.utility.json_importer import JsonImporter + + +class TestNetworkGraph(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.read_files = glob.glob(os.path.join('./PyCTBN/test_data', "*.json")) + cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + cls.importer.import_data(0) + cls.s1 = SamplePath(cls.importer) + cls.s1.build_trajectories() + cls.s1.build_structure() + + def test_init(self): + g1 = NetworkGraph(self.s1.structure) + self.assertEqual(self.s1.structure, g1._graph_struct) + self.assertIsInstance(g1._graph, nx.DiGraph) + self.assertIsNone(g1.time_scalar_indexing_strucure) + self.assertIsNone(g1.transition_scalar_indexing_structure) + self.assertIsNone(g1.transition_filtering) + self.assertIsNone(g1.p_combs) + + def test_add_nodes(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + for n1, n2 in zip(g1.nodes, self.s1.structure.nodes_labels): + self.assertEqual(n1, n2) + + def test_add_edges(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_edges(self.s1.structure.edges) + for e in self.s1.structure.edges: + self.assertIn(tuple(e), g1.edges) + + def test_fast_init(self): + g1 = NetworkGraph(self.s1.structure) + for node in self.s1.structure.nodes_labels: + g1.fast_init(node) + self.assertIsNotNone(g1._graph.nodes) + self.assertIsNotNone(g1._graph.edges) + self.assertIsInstance(g1._time_scalar_indexing_structure, np.ndarray) + self.assertIsInstance(g1._transition_scalar_indexing_structure, np.ndarray) + self.assertIsInstance(g1._time_filtering, np.ndarray) + self.assertIsInstance(g1._transition_filtering, np.ndarray) + self.assertIsInstance(g1._p_combs_structure, np.ndarray) + self.assertIsInstance(g1._aggregated_info_about_nodes_parents, tuple) + + def test_get_ordered_by_indx_set_of_parents(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + for indx in range(len(aggr_info[0]) - 1 ): + self.assertLess(g1.get_node_indx(aggr_info[0][indx]), g1.get_node_indx(aggr_info[0][indx + 1])) + for par, par_indx in zip(aggr_info[0], aggr_info[1]): + self.assertEqual(g1.get_node_indx(par), par_indx) + for par, par_val in zip(aggr_info[0], aggr_info[2]): + self.assertEqual(g1._graph_struct.get_states_number(par), par_val) + + def test_build_time_scalar_indexing_structure_for_a_node(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + self.aux_build_time_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1], + aggr_info[0], aggr_info[2]) + + def aux_build_time_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels, parents_vals): + node_states = graph.get_states_number(node_id) + time_scalar_indexing = NetworkGraph.build_time_scalar_indexing_structure_for_a_node(node_states, parents_vals) + self.assertEqual(len(time_scalar_indexing), len(parents_indxs) + 1) + merged_list = parents_labels[:] + merged_list.insert(0, node_id) + vals_list = [] + for node in merged_list: + vals_list.append(graph.get_states_number(node)) + t_vec = np.array(vals_list) + t_vec = t_vec.cumprod() + self.assertTrue(np.array_equal(time_scalar_indexing, t_vec)) + + def test_build_transition_scalar_indexing_structure_for_a_node(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + self.aux_build_transition_scalar_indexing_structure_for_a_node(g1, node, aggr_info[1], + aggr_info[0], aggr_info[2]) + + def aux_build_transition_scalar_indexing_structure_for_a_node(self, graph, node_id, parents_indxs, parents_labels, + parents_values): + node_states = graph.get_states_number(node_id) + transition_scalar_indexing = graph.build_transition_scalar_indexing_structure_for_a_node(node_states, + parents_values) + self.assertEqual(len(transition_scalar_indexing), len(parents_indxs) + 2) + merged_list = parents_labels[:] + merged_list.insert(0, node_id) + merged_list.insert(0, node_id) + vals_list = [] + for node_id in merged_list: + vals_list.append(graph.get_states_number(node_id)) + m_vec = np.array([vals_list]) + m_vec = m_vec.cumprod() + self.assertTrue(np.array_equal(transition_scalar_indexing, m_vec)) + + def test_build_time_columns_filtering_structure_for_a_node(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1]) + + def aux_build_time_columns_filtering_structure_for_a_node(self, graph, node_id, p_indxs): + graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id), p_indxs) + single_filter = [] + single_filter.append(graph.get_node_indx(node_id)) + single_filter.extend(p_indxs) + self.assertTrue(np.array_equal(graph.build_time_columns_filtering_for_a_node(graph.get_node_indx(node_id), + p_indxs),np.array(single_filter))) + def test_build_transition_columns_filtering_structure(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + self.aux_build_time_columns_filtering_structure_for_a_node(g1, node, aggr_info[1]) + + def aux_build_transition_columns_filtering_structure(self, graph, node_id, p_indxs): + single_filter = [] + single_filter.append(graph.get_node_indx(node_id) + graph._graph_struct.total_variables_number) + single_filter.append(graph.get_node_indx(node_id)) + single_filter.extend(p_indxs) + self.assertTrue(np.array_equal(graph.build_transition_filtering_for_a_node(graph.get_node_indx(node_id), + + p_indxs), np.array(single_filter))) + def test_build_p_combs_structure(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in self.s1.structure.nodes_labels: + aggr_info = g1.get_ordered_by_indx_set_of_parents(node) + self.aux_build_p_combs_structure(g1, aggr_info[2]) + + def aux_build_p_combs_structure(self, graph, p_vals): + p_combs = graph.build_p_comb_structure_for_a_node(p_vals) + p_possible_vals = [] + for val in p_vals: + vals = [v for v in range(val)] + p_possible_vals.extend(vals) + comb_struct = set(itertools.product(p_possible_vals,repeat=len(p_vals))) + for comb in comb_struct: + self.assertIn(np.array(comb), p_combs) + + def test_get_parents_by_id(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node in g1.nodes: + self.assertListEqual(g1.get_parents_by_id(node), list(g1._graph.predecessors(node))) + + def test_get_states_number(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node, val in zip(g1.nodes, g1.nodes_values): + self.assertEqual(val, g1.get_states_number(node)) + + def test_get_node_indx(self): + g1 = NetworkGraph(self.s1.structure) + g1.add_nodes(self.s1.structure.nodes_labels) + g1.add_edges(self.s1.structure.edges) + for node, indx in zip(g1.nodes, g1.nodes_indexes): + self.assertEqual(indx, g1.get_node_indx(node)) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/structure_graph/test_sample_path.py b/PyCTBN/tests/structure_graph/test_sample_path.py new file mode 100644 index 0000000..3d9635d --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_sample_path.py @@ -0,0 +1,72 @@ + +import unittest +import glob +import os +import random + +from ...PyCTBN.utility.json_importer import JsonImporter +from ...PyCTBN.structure_graph.sample_path import SamplePath +from ...PyCTBN.structure_graph.trajectory import Trajectory +from ...PyCTBN.structure_graph.structure import Structure + + +class TestSamplePath(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.read_files = glob.glob(os.path.join('./PyCTBN/test_data', "*.json")) + + def test_init_not_initialized_importer(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.assertRaises(RuntimeError, SamplePath, importer) + + def test_init_not_filled_dataframse(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + importer.clear_concatenated_frame() + self.assertRaises(RuntimeError, SamplePath, importer) + + def test_init(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + s1 = SamplePath(importer) + self.assertIsNone(s1.trajectories) + self.assertIsNone(s1.structure) + self.assertFalse(s1._importer.concatenated_samples.empty) + self.assertIsNone(s1._total_variables_count) + + def test_build_trajectories(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + s1 = SamplePath(importer) + s1.build_trajectories() + self.assertIsInstance(s1.trajectories, Trajectory) + + def test_build_structure(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + s1 = SamplePath(importer) + s1.build_structure() + self.assertIsInstance(s1.structure, Structure) + self.assertEqual(s1._total_variables_count, len(s1._importer.sorter)) + + def test_build_structure_bad_sorter(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + s1 = SamplePath(importer) + random.shuffle(importer._sorter) + self.assertRaises(RuntimeError, s1.build_structure) + + def test_build_saplepath_no_prior_net_structure(self): + importer = JsonImporter(self.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + importer._df_structure = None + s1 = SamplePath(importer) + s1.build_trajectories() + s1.build_structure() + self.assertFalse(s1.structure.edges) + + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/structure_graph/test_setofcims.py b/PyCTBN/tests/structure_graph/test_setofcims.py new file mode 100644 index 0000000..9f5fbe9 --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_setofcims.py @@ -0,0 +1,133 @@ + +import unittest +import numpy as np +import itertools + +from ...PyCTBN.structure_graph.set_of_cims import SetOfCims + + +class TestSetOfCims(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.node_id = 'X' + cls.possible_cardinalities = [2, 3] + cls.possible_states = [[0,1], [0, 1, 2]] + cls.node_states_number = range(2, 4) + + def test_init(self): + # empty parent set + for sn in self.node_states_number: + p_combs = self.build_p_comb_structure_for_a_node([]) + self.aux_test_init(self.node_id, [], sn, p_combs) + # one parent + for sn in self.node_states_number: + for p in itertools.product(self.possible_cardinalities, repeat=1): + p_combs = self.build_p_comb_structure_for_a_node(list(p)) + self.aux_test_init(self.node_id, list(p), sn, p_combs) + #two parents + for sn in self.node_states_number: + for p in itertools.product(self.possible_cardinalities, repeat=2): + p_combs = self.build_p_comb_structure_for_a_node(list(p)) + self.aux_test_init(self.node_id, list(p), sn, p_combs) + + def test_build_cims(self): + # empty parent set + for sn in self.node_states_number: + p_combs = self.build_p_comb_structure_for_a_node([]) + self.aux_test_build_cims(self.node_id, [], sn, p_combs) + # one parent + for sn in self.node_states_number: + for p in itertools.product(self.possible_cardinalities, repeat=1): + p_combs = self.build_p_comb_structure_for_a_node(list(p)) + self.aux_test_build_cims(self.node_id, list(p), sn, p_combs) + #two parents + for sn in self.node_states_number: + for p in itertools.product(self.possible_cardinalities, repeat=2): + p_combs = self.build_p_comb_structure_for_a_node(list(p)) + self.aux_test_build_cims(self.node_id, list(p), sn, p_combs) + + def test_filter_cims_with_mask(self): + p_combs = self.build_p_comb_structure_for_a_node(self.possible_cardinalities) + sofc1 = SetOfCims('X', self.possible_cardinalities, 3, p_combs) + state_res_times_list = [] + transition_matrices_list = [] + for i in range(len(p_combs)): + state_res_times = np.random.rand(1, 3)[0] + state_res_times = state_res_times * 1000 + state_transition_matrix = np.random.randint(1, 10000, (3, 3)) + state_res_times_list.append(state_res_times) + transition_matrices_list.append(state_transition_matrix) + sofc1.build_cims(np.array(state_res_times_list), np.array(transition_matrices_list)) + for length_of_mask in range(3): + for mask in list(itertools.permutations([True, False],r=length_of_mask)): + m = np.array(mask) + for parent_value in range(self.possible_cardinalities[0]): + cims = sofc1.filter_cims_with_mask(m, [parent_value]) + if length_of_mask == 0 or length_of_mask == 1: + self.assertTrue(np.array_equal(sofc1._actual_cims, cims)) + else: + indxs = self.another_filtering_method(p_combs, m, [parent_value]) + self.assertTrue(np.array_equal(cims, sofc1._actual_cims[indxs])) + + def aux_test_build_cims(self, node_id, p_values, node_states, p_combs): + state_res_times_list = [] + transition_matrices_list = [] + so1 = SetOfCims(node_id, p_values, node_states, p_combs) + for i in range(len(p_combs)): + state_res_times = np.random.rand(1, node_states)[0] + state_res_times = state_res_times * 1000 + state_transition_matrix = np.random.randint(1, 10000, (node_states, node_states)) + state_res_times_list.append(state_res_times) + transition_matrices_list.append(state_transition_matrix) + so1.build_cims(np.array(state_res_times_list), np.array(transition_matrices_list)) + self.assertEqual(len(state_res_times_list), so1.get_cims_number()) + self.assertIsInstance(so1._actual_cims, np.ndarray) + self.assertIsNone(so1._transition_matrices) + self.assertIsNone(so1._state_residence_times) + + def aux_test_init(self, node_id, parents_states_number, node_states_number, p_combs): + sofcims = SetOfCims(node_id, parents_states_number, node_states_number, p_combs) + self.assertEqual(sofcims._node_id, node_id) + self.assertTrue(np.array_equal(sofcims._p_combs, p_combs)) + self.assertTrue(np.array_equal(sofcims._parents_states_number, parents_states_number)) + self.assertEqual(sofcims._node_states_number, node_states_number) + self.assertFalse(sofcims._actual_cims) + self.assertEqual(sofcims._state_residence_times.shape[0], np.prod(np.array(parents_states_number))) + self.assertEqual(len(sofcims._state_residence_times[0]), node_states_number) + self.assertEqual(sofcims._transition_matrices.shape[0], np.prod(np.array(parents_states_number))) + self.assertEqual(len(sofcims._transition_matrices[0][0]), node_states_number) + + def build_p_comb_structure_for_a_node(self, parents_values): + """ + Builds the combinatory structure that contains the combinations of all the values contained in parents_values. + + Parameters: + parents_values: the cardinalities of the nodes + Returns: + a numpy matrix containing a grid of the combinations + """ + tmp = [] + for val in parents_values: + tmp.append([x for x in range(val)]) + if len(parents_values) > 0: + parents_comb = np.array(np.meshgrid(*tmp)).T.reshape(-1, len(parents_values)) + if len(parents_values) > 1: + tmp_comb = parents_comb[:, 1].copy() + parents_comb[:, 1] = parents_comb[:, 0].copy() + parents_comb[:, 0] = tmp_comb + else: + parents_comb = np.array([[]], dtype=np.int) + return parents_comb + + def another_filtering_method(self,p_combs, mask, parent_value): + masked_combs = p_combs[:, mask] + indxs = [] + for indx, val in enumerate(masked_combs): + if val == parent_value: + indxs.append(indx) + return np.array(indxs) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/structure_graph/test_structure.py b/PyCTBN/tests/structure_graph/test_structure.py new file mode 100644 index 0000000..a80b91c --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_structure.py @@ -0,0 +1,81 @@ + +import unittest +import numpy as np +from ...PyCTBN.structure_graph.structure import Structure + + +class TestStructure(unittest.TestCase): + @classmethod + def setUpClass(cls): + cls.labels = ['X','Y','Z'] + cls.indxs = np.array([0,1,2]) + cls.vals = np.array([3,3,3]) + cls.edges = [('X','Z'),('Y','Z'), ('Z','Y')] + cls.vars_numb = len(cls.labels) + + def test_init(self): + s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb) + self.assertListEqual(self.labels,s1.nodes_labels) + self.assertIsInstance(s1.nodes_indexes, np.ndarray) + self.assertTrue(np.array_equal(self.indxs, s1.nodes_indexes)) + self.assertIsInstance(s1.nodes_values, np.ndarray) + self.assertTrue(np.array_equal(self.vals, s1.nodes_values)) + self.assertListEqual(self.edges, s1.edges) + self.assertEqual(self.vars_numb, s1.total_variables_number) + + def test_get_node_id(self): + s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb) + for indx, var in enumerate(self.labels): + self.assertEqual(var, s1.get_node_id(indx)) + + def test_get_node_indx(self): + l2 = self.labels[:] + l2.remove('Y') + i2 = self.indxs.copy() + np.delete(i2, 1) + v2 = self.vals.copy() + np.delete(v2, 1) + e2 = [('X','Z')] + n2 = self.vars_numb - 1 + s1 = Structure(l2, i2, v2, e2, n2) + for indx, var in zip(i2, l2): + self.assertEqual(indx, s1.get_node_indx(var)) + + def test_get_positional_node_indx(self): + l2 = self.labels[:] + l2.remove('Y') + i2 = self.indxs.copy() + np.delete(i2, 1) + v2 = self.vals.copy() + np.delete(v2, 1) + e2 = [('X', 'Z')] + n2 = self.vars_numb - 1 + s1 = Structure(l2, i2, v2, e2, n2) + for indx, var in enumerate(s1.nodes_labels): + self.assertEqual(indx, s1.get_positional_node_indx(var)) + + def test_get_states_number(self): + l2 = self.labels[:] + l2.remove('Y') + i2 = self.indxs.copy() + np.delete(i2, 1) + v2 = self.vals.copy() + np.delete(v2, 1) + e2 = [('X', 'Z')] + n2 = self.vars_numb - 1 + s1 = Structure(l2, i2, v2, e2, n2) + for val, node in zip(v2, l2): + self.assertEqual(val, s1.get_states_number(node)) + + def test_equality(self): + s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb) + s2 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb) + self.assertEqual(s1, s2) + + def test_repr(self): + s1 = Structure(self.labels, self.indxs, self.vals, self.edges, self.vars_numb) + print(s1) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/structure_graph/test_trajectory.py b/PyCTBN/tests/structure_graph/test_trajectory.py new file mode 100644 index 0000000..b06b17b --- /dev/null +++ b/PyCTBN/tests/structure_graph/test_trajectory.py @@ -0,0 +1,27 @@ + +import unittest +import numpy as np +import glob + +from ...PyCTBN.structure_graph.trajectory import Trajectory +from ...PyCTBN.utility.json_importer import JsonImporter + +class TestTrajectory(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.read_files = glob.glob(os.path.join('./test_data', "*.json")) + cls.importer = JsonImporter(cls.read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + cls.importer.import_data(0) + + def test_init(self): + t1 = Trajectory(self.importer.build_list_of_samples_array(self.importer.concatenated_samples), + len(self.importer.sorter) + 1) + self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 0].to_numpy(), t1.times)) + self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:,1:].to_numpy(), t1.complete_trajectory)) + self.assertTrue(np.array_equal(self.importer.concatenated_samples.iloc[:, 1: len(self.importer.sorter) + 1], t1.trajectory)) + self.assertEqual(len(self.importer.sorter) + 1, t1._original_cols_number) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/utility/test_cache.py b/PyCTBN/tests/utility/test_cache.py new file mode 100644 index 0000000..5d9c766 --- /dev/null +++ b/PyCTBN/tests/utility/test_cache.py @@ -0,0 +1,57 @@ + +import unittest +import numpy as np + +from ...PyCTBN.utility.cache import Cache +from ...PyCTBN.structure_graph.set_of_cims import SetOfCims + + +class TestCache(unittest.TestCase): + + def test_init(self): + c1 = Cache() + self.assertFalse(c1._list_of_sets_of_parents) + self.assertFalse(c1._actual_cache) + + def test_put(self): + c1 = Cache() + pset1 = {'X', 'Y'} + sofc1 = SetOfCims('Z', [], 3, np.array([])) + c1.put(pset1, sofc1) + self.assertEqual(1, len(c1._actual_cache)) + self.assertEqual(1, len(c1._list_of_sets_of_parents)) + self.assertEqual(sofc1, c1._actual_cache[0]) + pset2 = {'X'} + sofc2 = SetOfCims('Z', [], 3, np.array([])) + c1.put(pset2, sofc2) + self.assertEqual(2, len(c1._actual_cache)) + self.assertEqual(2, len(c1._list_of_sets_of_parents)) + self.assertEqual(sofc2, c1._actual_cache[1]) + + def test_find(self): + c1 = Cache() + pset1 = {'X', 'Y'} + sofc1 = SetOfCims('Z', [], 3, np.array([])) + c1.put(pset1, sofc1) + self.assertEqual(1, len(c1._actual_cache)) + self.assertEqual(1, len(c1._list_of_sets_of_parents)) + self.assertIsInstance(c1.find(pset1), SetOfCims) + self.assertEqual(sofc1, c1.find(pset1)) + self.assertIsInstance(c1.find({'Y', 'X'}), SetOfCims) + self.assertEqual(sofc1, c1.find({'Y', 'X'})) + self.assertIsNone(c1.find({'X'})) + + def test_clear(self): + c1 = Cache() + pset1 = {'X', 'Y'} + sofc1 = SetOfCims('Z', [], 3, np.array([])) + c1.put(pset1, sofc1) + self.assertEqual(1, len(c1._actual_cache)) + self.assertEqual(1, len(c1._list_of_sets_of_parents)) + c1.clear() + self.assertFalse(c1._list_of_sets_of_parents) + self.assertFalse(c1._actual_cache) + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/utility/test_json_importer.py b/PyCTBN/tests/utility/test_json_importer.py new file mode 100644 index 0000000..9ae1aad --- /dev/null +++ b/PyCTBN/tests/utility/test_json_importer.py @@ -0,0 +1,176 @@ + +import unittest +import os +import glob +import numpy as np +import pandas as pd +from ...PyCTBN.utility.json_importer import JsonImporter + +import json + + + +class TestJsonImporter(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + cls.read_files = glob.glob(os.path.join('./PyCTBN/test_data', "*.json")) + + def test_init(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.assertEqual(j1._samples_label, 'samples') + self.assertEqual(j1._structure_label, 'dyn.str') + self.assertEqual(j1._variables_label, 'variables') + self.assertEqual(j1._time_key, 'Time') + self.assertEqual(j1._variables_key, 'Name') + self.assertEqual(j1._file_path, "./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json") + self.assertIsNone(j1._df_samples_list) + self.assertIsNone(j1.variables) + self.assertIsNone(j1.structure) + self.assertEqual(j1.concatenated_samples,[]) + self.assertIsNone(j1.sorter) + self.assertIsNone(j1._array_indx) + self.assertIsInstance(j1._raw_data, list) + + def test_read_json_file_found(self): + data_set = {"key1": [1, 2, 3], "key2": [4, 5, 6]} + with open('data.json', 'w') as f: + json.dump(data_set, f) + path = os.getcwd() + path = path + '/data.json' + j1 = JsonImporter(path, '', '', '', '', '') + self.assertTrue(self.ordered(data_set) == self.ordered(j1._raw_data)) + os.remove('data.json') + + def test_read_json_file_not_found(self): + path = os.getcwd() + path = path + '/data.json' + self.assertRaises(FileNotFoundError, JsonImporter, path, '', '', '', '', '') + + def test_build_sorter(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + df_samples_list = j1.normalize_trajectories(j1._raw_data, 0, j1._samples_label) + sorter = j1.build_sorter(df_samples_list[0]) + self.assertListEqual(sorter, list(df_samples_list[0].columns.values)[1:]) + + def test_normalize_trajectories(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + df_samples_list = j1.normalize_trajectories(j1._raw_data, 0, j1._samples_label) + self.assertEqual(len(df_samples_list), len(j1._raw_data[0][j1._samples_label])) + + def test_normalize_trajectories_wrong_indx(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.assertRaises(IndexError, j1.normalize_trajectories, j1._raw_data, 474, j1._samples_label) + + def test_normalize_trajectories_wrong_key(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'sample', 'dyn.str', 'variables', 'Time', 'Name') + self.assertRaises(KeyError, j1.normalize_trajectories, j1._raw_data, 0, j1._samples_label) + + def test_compute_row_delta_single_samples_frame(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1._array_indx = 0 + j1._df_samples_list = j1.import_trajectories(j1._raw_data) + sample_frame = j1._df_samples_list[0] + original_copy = sample_frame.copy() + columns_header = list(sample_frame.columns.values) + shifted_cols_header = [s + "S" for s in columns_header[1:]] + new_sample_frame = j1.compute_row_delta_sigle_samples_frame(sample_frame, columns_header[1:], + shifted_cols_header) + self.assertEqual(len(list(sample_frame.columns.values)) + len(shifted_cols_header), + len(list(new_sample_frame.columns.values))) + self.assertEqual(sample_frame.shape[0] - 1, new_sample_frame.shape[0]) + for indx, row in new_sample_frame.iterrows(): + self.assertAlmostEqual(row['Time'], + original_copy.iloc[indx + 1]['Time'] - original_copy.iloc[indx]['Time']) + for indx, row in new_sample_frame.iterrows(): + np.array_equal(np.array(row[columns_header[1:]],dtype=int), + np.array(original_copy.iloc[indx][columns_header[1:]],dtype=int)) + np.array_equal(np.array(row[shifted_cols_header], dtype=int), + np.array(original_copy.iloc[indx + 1][columns_header[1:]], dtype=int)) + + def test_compute_row_delta_in_all_frames(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1._array_indx = 0 + j1._df_samples_list = j1.import_trajectories(j1._raw_data) + j1._sorter = j1.build_sorter(j1._df_samples_list[0]) + j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list) + self.assertEqual(list(j1._df_samples_list[0].columns.values), + list(j1.concatenated_samples.columns.values)[:len(list(j1._df_samples_list[0].columns.values))]) + self.assertEqual(list(j1.concatenated_samples.columns.values)[0], j1._time_key) + + def test_compute_row_delta_in_all_frames_not_init_sorter(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1._array_indx = 0 + j1._df_samples_list = j1.import_trajectories(j1._raw_data) + self.assertRaises(RuntimeError, j1.compute_row_delta_in_all_samples_frames, j1._df_samples_list) + + def test_clear_data_frame_list(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1._array_indx = 0 + j1._df_samples_list = j1.import_trajectories(j1._raw_data) + j1._sorter = j1.build_sorter(j1._df_samples_list[0]) + j1.compute_row_delta_in_all_samples_frames(j1._df_samples_list) + j1.clear_data_frame_list() + for df in j1._df_samples_list: + self.assertTrue(df.empty) + + def test_clear_concatenated_frame(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1.import_data(0) + j1.clear_concatenated_frame() + self.assertTrue(j1.concatenated_samples.empty) + + def test_import_variables(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + sorter = ['X', 'Y', 'Z'] + raw_data = [{'variables':{"Name": ['X', 'Y', 'Z'], "value": [3, 3, 3]}}] + j1._array_indx = 0 + df_var = j1.import_variables(raw_data) + self.assertEqual(list(df_var[j1._variables_key]), sorter) + + def test_import_structure(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + raw_data = [{"dyn.str":[{"From":"X","To":"Z"},{"From":"Y","To":"Z"},{"From":"Z","To":"Y"}]}] + j1._array_indx = 0 + df_struct = j1.import_structure(raw_data) + self.assertIsInstance(df_struct, pd.DataFrame) + + def test_import_sampled_cims(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + raw_data = j1.read_json_file() + j1._array_indx = 0 + j1._df_samples_list = j1.import_trajectories(raw_data) + j1._sorter = j1.build_sorter(j1._df_samples_list[0]) + cims = j1.import_sampled_cims(raw_data, 0, 'dyn.cims') + self.assertEqual(list(cims.keys()), j1.sorter) + + def test_dataset_id(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + array_indx = 0 + j1.import_data(array_indx) + self.assertEqual(array_indx, j1.dataset_id()) + + def test_file_path(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + self.assertEqual(j1.file_path, "./PyCTBN/data/networks_and_trajectories_binary_data_01_3.json") + + def test_import_data(self): + j1 = JsonImporter("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json", 'samples', 'dyn.str', 'variables', 'Time', 'Name') + j1.import_data(0) + self.assertEqual(list(j1.variables[j1._variables_key]), + list(j1.concatenated_samples.columns.values[1:len(j1.variables[j1._variables_key]) + 1])) + print(j1.variables) + print(j1.structure) + print(j1.concatenated_samples) + + def ordered(self, obj): + if isinstance(obj, dict): + return sorted((k, self.ordered(v)) for k, v in obj.items()) + if isinstance(obj, list): + return sorted(self.ordered(x) for x in obj) + else: + return obj + + +if __name__ == '__main__': + unittest.main() diff --git a/PyCTBN/tests/utility/test_sample_importer.py b/PyCTBN/tests/utility/test_sample_importer.py new file mode 100644 index 0000000..596e5d2 --- /dev/null +++ b/PyCTBN/tests/utility/test_sample_importer.py @@ -0,0 +1,80 @@ + +import unittest +import os +import glob +import numpy as np +import pandas as pd +from ...PyCTBN.utility.sample_importer import SampleImporter +from ...PyCTBN.structure_graph.sample_path import SamplePath + +import json + + + +class TestSampleImporter(unittest.TestCase): + + @classmethod + def setUpClass(cls) -> None: + with open("./PyCTBN/test_data/networks_and_trajectories_binary_data_01_3.json") as f: + raw_data = json.load(f) + + trajectory_list_raw= raw_data[0]["samples"] + + cls.trajectory_list = [pd.DataFrame(sample) for sample in trajectory_list_raw] + + cls.variables= pd.DataFrame(raw_data[0]["variables"]) + cls.prior_net_structure = pd.DataFrame(raw_data[0]["dyn.str"]) + + + def test_init(self): + sample_importer = SampleImporter( + trajectory_list=self.trajectory_list, + variables=self.variables, + prior_net_structure=self.prior_net_structure + ) + + sample_importer.import_data() + + s1 = SamplePath(sample_importer) + s1.build_trajectories() + s1.build_structure() + s1.clear_memory() + + self.assertEqual(len(s1._importer._df_samples_list), 300) + self.assertIsInstance(s1._importer._df_samples_list,list) + self.assertIsInstance(s1._importer._df_samples_list[0],pd.DataFrame) + self.assertEqual(len(s1._importer._df_variables), 3) + self.assertIsInstance(s1._importer._df_variables,pd.DataFrame) + self.assertEqual(len(s1._importer._df_structure), 2) + self.assertIsInstance(s1._importer._df_structure,pd.DataFrame) + + def test_order(self): + sample_importer = SampleImporter( + trajectory_list=self.trajectory_list, + variables=self.variables, + prior_net_structure=self.prior_net_structure + ) + + sample_importer.import_data() + + s1 = SamplePath(sample_importer) + s1.build_trajectories() + s1.build_structure() + s1.clear_memory() + + for count,var in enumerate(s1._importer._df_samples_list[0].columns[1:]): + self.assertEqual(s1._importer._sorter[count],var) + + + + def ordered(self, obj): + if isinstance(obj, dict): + return sorted((k, self.ordered(v)) for k, v in obj.items()) + if isinstance(obj, list): + return sorted(self.ordered(x) for x in obj) + else: + return obj + + +if __name__ == '__main__': + unittest.main() diff --git a/README.md b/README.md index ba6427e..dfe1815 100644 --- a/README.md +++ b/README.md @@ -1 +1 @@ -# PyCTBN +# CTBN_Project \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d4bb2cb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/_build/doctrees/classes.doctree b/docs/_build/doctrees/classes.doctree new file mode 100644 index 0000000..5d6c6b6 Binary files /dev/null and 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differ diff --git a/docs/_build/doctrees/environment.pickle b/docs/_build/doctrees/environment.pickle new file mode 100644 index 0000000..fe70b1d Binary files /dev/null and b/docs/_build/doctrees/environment.pickle differ diff --git a/docs/_build/doctrees/examples.doctree b/docs/_build/doctrees/examples.doctree new file mode 100644 index 0000000..f16ef4e Binary files /dev/null and b/docs/_build/doctrees/examples.doctree differ diff --git a/docs/_build/doctrees/index.doctree b/docs/_build/doctrees/index.doctree new file mode 100644 index 0000000..8db117a Binary files /dev/null and b/docs/_build/doctrees/index.doctree differ diff --git a/docs/_build/doctrees/modules.doctree b/docs/_build/doctrees/modules.doctree new file mode 100644 index 0000000..af89f10 Binary files /dev/null and b/docs/_build/doctrees/modules.doctree differ diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo new file mode 100644 index 0000000..80fb6bc --- /dev/null +++ b/docs/_build/html/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: d42d832abe4d6a9e23dc099c95623ecc +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_sources/classes.estimators.rst.txt b/docs/_build/html/_sources/classes.estimators.rst.txt new file mode 100644 index 0000000..5ecab8b --- /dev/null +++ b/docs/_build/html/_sources/classes.estimators.rst.txt @@ -0,0 +1,53 @@ +PyCTBN.estimators package +========================== + +Submodules +---------- + +PyCTBN.estimators.fam\_score\_calculator module +------------------------------------------------ + +.. automodule:: PyCTBN.estimators.fam_score_calculator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.parameters\_estimator module +----------------------------------------------- + +.. automodule:: PyCTBN.estimators.parameters_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_constraint\_based\_estimator module +----------------------------------------------------------------- + +.. automodule:: PyCTBN.estimators.structure_constraint_based_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_estimator module +---------------------------------------------- + +.. automodule:: PyCTBN.estimators.structure_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_score\_based\_estimator module +------------------------------------------------------------ + +.. automodule:: PyCTBN.estimators.structure_score_based_estimator + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.estimators + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/classes.optimizers.rst.txt b/docs/_build/html/_sources/classes.optimizers.rst.txt new file mode 100644 index 0000000..0730b68 --- /dev/null +++ b/docs/_build/html/_sources/classes.optimizers.rst.txt @@ -0,0 +1,45 @@ +PyCTBN.optimizers package +========================== + +Submodules +---------- + +PyCTBN.optimizers.constraint\_based\_optimizer module +------------------------------------------------------ + +.. automodule:: PyCTBN.optimizers.constraint_based_optimizer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.hill\_climbing\_search module +------------------------------------------------ + +.. automodule:: PyCTBN.optimizers.hill_climbing_search + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.optimizer module +----------------------------------- + +.. automodule:: PyCTBN.optimizers.optimizer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.tabu\_search module +-------------------------------------- + +.. automodule:: PyCTBN.optimizers.tabu_search + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.optimizers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/classes.rst.txt b/docs/_build/html/_sources/classes.rst.txt new file mode 100644 index 0000000..0ff219f --- /dev/null +++ b/docs/_build/html/_sources/classes.rst.txt @@ -0,0 +1,21 @@ +PyCTBN package +=============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + PyCTBN.estimators + PyCTBN.optimizers + PyCTBN.structure_graph + PyCTBN.utility + +Module contents +--------------- + +.. automodule:: PyCTBN + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/classes.structure_graph.rst.txt b/docs/_build/html/_sources/classes.structure_graph.rst.txt new file mode 100644 index 0000000..489b7b6 --- /dev/null +++ b/docs/_build/html/_sources/classes.structure_graph.rst.txt @@ -0,0 +1,77 @@ +PyCTBN.structure\_graph package +================================ + +Submodules +---------- + +PyCTBN.structure\_graph.abstract\_sample\_path module +------------------------------------------------------ + +.. automodule:: PyCTBN.structure_graph.abstract_sample_path + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.conditional\_intensity\_matrix module +-------------------------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.conditional_intensity_matrix + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.network\_graph module +---------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.network_graph + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.sample\_path module +-------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.sample_path + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.set\_of\_cims module +--------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.set_of_cims + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.sets\_of\_cims\_container module +--------------------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.sets_of_cims_container + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.structure module +----------------------------------------- + +.. automodule:: PyCTBN.structure_graph.structure + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.trajectory module +------------------------------------------ + +.. automodule:: PyCTBN.structure_graph.trajectory + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.structure_graph + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/classes.utility.rst.txt b/docs/_build/html/_sources/classes.utility.rst.txt new file mode 100644 index 0000000..6285db8 --- /dev/null +++ b/docs/_build/html/_sources/classes.utility.rst.txt @@ -0,0 +1,53 @@ +PyCTBN.utility package +======================= + +Submodules +---------- + +PyCTBN.utility.abstract\_importer module +----------------------------------------- + +.. automodule:: PyCTBN.utility.abstract_importer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.cache module +---------------------------- + +.. automodule:: PyCTBN.utility.cache + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.decorators module +--------------------------------- + +.. automodule:: PyCTBN.utility.decorators + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.json\_importer module +------------------------------------- + +.. automodule:: PyCTBN.utility.json_importer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.sample\_importer module +--------------------------------------- + +.. automodule:: PyCTBN.utility.sample_importer + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.utility + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/examples.rst.txt b/docs/_build/html/_sources/examples.rst.txt new file mode 100644 index 0000000..b1e07c5 --- /dev/null +++ b/docs/_build/html/_sources/examples.rst.txt @@ -0,0 +1,121 @@ +Examples +======== + +Installation/Usage +****************** +Download the release in .tar.gz or .whl format and simply use pip install to install it:: + + $pip install PyCTBN-1.0.tar.gz + + +Implementing your own data importer +*********************************** +.. code-block:: python + + """This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format. + The net in exam has three ternary nodes and no prior net structure. + """ + + from PyCTBN import AbstractImporter + + class CSVImporter(AbstractImporter): + + def __init__(self, file_path): + self._df_samples_list = None + super(CSVImporter, self).__init__(file_path) + + def import_data(self): + self.read_csv_file() + self._sorter = self.build_sorter(self._df_samples_list[0]) + self.import_variables() + self.compute_row_delta_in_all_samples_frames(self._df_samples_list) + + def read_csv_file(self): + df = pd.read_csv(self._file_path) + df.drop(df.columns[[0]], axis=1, inplace=True) + self._df_samples_list = [df] + + def import_variables(self): + values_list = [3 for var in self._sorter] + # initialize dict of lists + data = {'Name':self._sorter, 'Value':values_list} + # Create the pandas DataFrame + self._df_variables = pd.DataFrame(data) + + def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: + return list(sample_frame.columns)[1:] + + def dataset_id(self) -> object: + pass + +Parameters Estimation Example +***************************** + +.. code-block:: python + + from PyCTBN import JsonImporter + from PyCTBN import SamplePath + from PyCTBN import NetworkGraph + from PyCTBN import ParametersEstimator + + + def main(): + read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir + #import data + importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + #Create a SamplePath Obj passing an already filled AbstractImporter object + s1 = SamplePath(importer) + #Build The trajectries and the structural infos + s1.build_trajectories() + s1.build_structure() + print(s1.structure.edges) + print(s1.structure.nodes_values) + #From The Structure Object build the Graph + g = NetworkGraph(s1.structure) + #Select a node you want to estimate the parameters + node = g.nodes[2] + print("Node", node) + #Init the _graph specifically for THIS node + g.fast_init(node) + #Use SamplePath and Grpah to create a ParametersEstimator Object + p1 = ParametersEstimator(s1.trajectories, 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) + +Structure Estimation Example +**************************** + +.. code-block:: python + + from PyCTBN import JsonImporter + from PyCTBN import SamplePath + from PyCTBN import StructureEstimator + + def structure_estimation_example(): + + # read the json files in ./data path + read_files = glob.glob(os.path.join('./data', "*.json")) + # initialize a JsonImporter object for the first file + importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + # import the data at index 0 of the outer json array + importer.import_data(0) + # construct a SamplePath Object passing a filled AbstractImporter + s1 = SamplePath(importer) + # build the trajectories + s1.build_trajectories() + # build the real structure + s1.build_structure() + # construct a StructureEstimator object + se1 = StructureEstimator(s1, 0.1, 0.1) + # call the ctpc algorithm + se1.ctpc_algorithm() + # the adjacency matrix of the estimated structure + print(se1.adjacency_matrix()) + # save results to a json file + se1.save_results() + diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt new file mode 100644 index 0000000..236bf53 --- /dev/null +++ b/docs/_build/html/_sources/index.rst.txt @@ -0,0 +1,21 @@ +.. PyCTBN documentation master file, created by + sphinx-quickstart on Wed Feb 24 18:06:35 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to PyCTBN's documentation! +================================== + +.. toctree:: + :maxdepth: 3 + :caption: Contents: + + examples + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/_build/html/_sources/modules.rst.txt b/docs/_build/html/_sources/modules.rst.txt new file mode 100644 index 0000000..5b5819a --- /dev/null +++ b/docs/_build/html/_sources/modules.rst.txt @@ -0,0 +1,7 @@ +PyCTBN +======= + +.. toctree:: + :maxdepth: 4 + + PyCTBN diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css new file mode 100644 index 0000000..24a49f0 --- /dev/null +++ b/docs/_build/html/_static/basic.css @@ -0,0 +1,856 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic 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+.wy-body-for-nav { + background-image: none !important; +} diff --git a/docs/_build/html/_static/css/pdj.css b/docs/_build/html/_static/css/pdj.css new file mode 100644 index 0000000..8445eca --- /dev/null +++ b/docs/_build/html/_static/css/pdj.css @@ -0,0 +1,494 @@ +@import url("theme.css"); + +.wy-side-nav-search{ + background-color: #595C5E; +} + +.wy-side-nav-search input[type=text] { + border-color: #595C5E; +} + +@media screen and (min-width: 768px) { +.wy-nav-side{ + width: 224px; +} +.wy-nav-content-wrap{ + margin-left: 200px; + background: #343131; +} +} +@media screen and (max-width: 768px) { + .wy-nav-content{ + background-color: #343131; + } +} + +.wy-nav-top{ + background-color: #595C5E; +} +.wy-nav-content{ + padding-top: 1%; + max-width: 100%; + background-color: #343131; +} + +.rst-content{ + margin-left: -3%; +} + +pre{ + font-size: 0.9em; + padding: 1%; +} + +pre > span, +pre > p{ + margin: 0; + font-size: 1em; +} + +pre > span{ + margin: 0; +} + +.hentry{ + padding-top: 2%; + padding-bottom: 5%; + padding-left: 3%; + padding-right: 3%; + border-bottom: 2px solid #343131; +} + +.entry-container{ + background-color: #fff; /*#EAEAEA; */ + border-radius: 3px; + /* -webkit-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ + /* -moz-box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ + /* box-shadow: 0 0 10px 8px rgba(50, 50, 50, 0.75); */ + -webkit-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); + -moz-box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); + box-shadow: 0 0 10px 8px rgba(2, 2, 2, 0.36); + position: relative; + z-index: 210; +} + +.entry-content{ + margin-right: 3%; +} + +.wy-side-nav-search{ + padding:0.639em +} + + +.wy-menu-vertical a:hover{ + -webkit-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); + -moz-box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); + box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); +} + +.wy-menu-vertical a{ + width: 90% +} + +#comments, +#comment-form{ + padding: 20px; +} + + +@media screen and (max-width: 1330px) { + .fancybox img{ + width: 310px; /* 88px / 633px */ + height: 206px; + } +} + +@media screen and (max-width: 1241px) { + .fancybox img{ + width: 280px; + height: 186px; + } +} + +@media screen and (max-width: 1154px) { + .fancybox img{ + width: 260px; + height: 173px; + } +} + +.expander { + position: absolute; + top: 5px; + left: 5px; + width: 16px; + height: 16px; + padding: 4px; + background: white url(/static/blog/img/fsbtn.png) center center no-repeat; + z-index: 99999; + cursor: pointer; +} + +/* 340 */ +/* .rst-content img { */ +/* margin-top: 8px; */ +/* margin-left: 10px; */ +/* margin-bottom: 5px; */ +/* } */ + +/* 260 */ +.rst-content .row img { + margin-top: 8px; + margin-left: 5px; + border-radius: 3px; + -webkit-box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36); + -moz-box-shadow: 0 0 10px 5px /*rgba(50, 50, 50, 0.75)*/ rgba(2, 2, 2, 0.36); + box-shadow: 0 0 10px 5px rgba(2, 2, 2, 0.36); + +} + +.rst-content{ + z-index: 210; + margin-top: -60px; +} + +h2 a, +h2 a:visited, +h2 a:hover{ + color: #404040; +} + +.wy-menu-vertical li.on a, .wy-menu-vertical li.current>a { + color: #b3b3b3; + background: #4e4a4a; + box-shadow: 0 0 5px 5px rgba(20, 20, 20, 0.30); + border: none; +} + +.wy-menu-vertical li.current { + background: #343131; + border: none; +} + +.wy-menu-vertical li.current a{ + border:none +} + +.wy-alert.wy-alert-info .wy-alert-title, .rst-content .note .wy-alert-title, .rst-content .wy-alert-info.attention .wy-alert-title, .rst-content .wy-alert-info.caution .wy-alert-title, .rst-content .wy-alert-info.danger .wy-alert-title, .rst-content .wy-alert-info.error .wy-alert-title, .rst-content .wy-alert-info.hint .wy-alert-title, .rst-content .wy-alert-info.important .wy-alert-title, .rst-content .wy-alert-info.tip .wy-alert-title, .rst-content .wy-alert-info.warning .wy-alert-title, .rst-content .seealso .wy-alert-title, .rst-content .wy-alert-info.admonition-todo .wy-alert-title, .wy-alert.wy-alert-info .rst-content .admonition-title, 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.mi, +.highlight .go{ + color: #57de4e !important; + font-size: 0.9em !important; +} + +.highlight .nt{ + color: #8080ec; +} + +.highlight .n, .highlight .nn, +.highlight .p, .highlight .o, +.highlight .nv, +.highlight .gp{ + color: #57de4e; +} + +.highlight .kn{ + color: #47ffff !important; +} + +.highlight .nc, +.highlight .nd{ + color: #1c909e; + font-weight: normal; +} + + +.highlight .k, .highlight .bp{ + color: #47ffff !important; +} + +.highlight .kc { + color: #ae39ff; + font-weight: normal !important; +} + +.highlight .nf, +.highlight .nb{ + color: #87CEFA; +} + +.highlight .s1, .highlight .s2, .highlight .s, +.highlight .sd, .highlight .si, +.highlight .se{ + color: #FDF5E6; +} + +.highlight .ow{ + color: #47ffff; +} + +.highlight .c1, +.highlight .c{ + color: #d80e04; +} + +.fundo-claro{ + height: 88px; + background-color: #595C5E; + margin-left: -10%; + margin-top: -1.1%; + margin-right: -10%; +} + + +@media screen and (max-width: 980px) { + pre{ + overflow-x: scroll; + } +} + + +@media screen and (max-width: 768px){ + .fundo-claro{ + display:none; + } + + .rst-content{ + z-index: 210; + margin-top: -20px; + } + +} + +.page{ + width: 3%; + float:left; +} + +.previous{ + width: 10% +} + +.paginator{ + margin-top: 50px; + padding-bottom: 25px; + padding-left: 20px; +} + +blockquote { + font-style: italic; + margin: 0 4.5em; + position: relative; +} + +blockquote, q { + quotes: "" ""; +} + +blockquote:before { + color: #807f7f; + content: "\201C"; + display: block; + font-family: "Droid Serif", "Times New Roman", serif; + font-size: 48px; + font-size: 4.8rem; + font-style: normal; + font-weight: bold; + line-height: 1; + position: absolute; + top: -15px; + left: -40px; +} + +.flatpage{ + +} + +.bkg-escuro{ + background-color: #343131; + color: #b3b3b3; +} + +.search-topo{ + float: right; + padding-right: 7%; + margin-top: -1.5; +} + +.fa-home:before, .icon-home:before { + content: url('../img/porao-branco.png'); +} + + +/* fundo preto */ + +.entry-container-escuro{ + background-color: #343131; + color: #b3b3b3; + padding: 15px; +} + +.entry-container-escuro h2, +.entry-container-escuro h2 a, +.entry-container-escuro h2 a:visited, +.entry-container-escuro h2 a:hover, +.entry-container-escuro h4, +.entry-container-escuro .entry-info abbr{ + color: #F9F4F4; +} + + +/* fim fundo preto */ + +.classe-correio textarea{ + width: 60%; + min-height: 300px; + margin-left: -39.5%; + border-radius: 15px; +} +.classe-correio { + background-color: #343131; +} + +.classe-correio .nome-email{ + width: 30%; + margin-bottom: 10px; +} + +/* cor dos links */ +a:visited{ + color: #2980B9; +} + +.wy-menu-vertical a { + color: #b3b3b3; +} + +.wy-side-nav-search>a, .wy-side-nav-search .wy-dropdown>a { + color: #fcfcfc; +} + +.highlight-yaml .nt{ + color: #eac648; +} + + +/* .rst-content dl:not(.docutils) { */ +/* margin-bottom: 24px; */ +/* background-color: #000; */ +/* } */ + +/* .rst-content dl:not(.docutils) code{ */ +/* color: #1bb41b; */ 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+ "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + */ +jQuery.urldecode = function(x) { + return decodeURIComponent(x).replace(/\+/g, ' '); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { + this.initOnKeyListeners(); + } + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + $(document).keydown(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box, textarea, dropdown or button + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' + && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey + && !event.shiftKey) { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { + Documentation.init(); +}); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js new file mode 100644 index 0000000..a7d0fd2 --- /dev/null +++ b/docs/_build/html/_static/documentation_options.js @@ -0,0 +1,12 @@ +var DOCUMENTATION_OPTIONS = { + URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), + VERSION: '2.0', + LANGUAGE: 'None', + COLLAPSE_INDEX: false, + BUILDER: 'html', + FILE_SUFFIX: '.html', + LINK_SUFFIX: '.html', + HAS_SOURCE: true, + SOURCELINK_SUFFIX: '.txt', + NAVIGATION_WITH_KEYS: false +}; \ No newline at end of file diff --git a/docs/_build/html/_static/file.png b/docs/_build/html/_static/file.png new file mode 100644 index 0000000..a858a41 Binary files /dev/null and b/docs/_build/html/_static/file.png differ diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.eot b/docs/_build/html/_static/fonts/fontawesome-webfont.eot new file mode 100644 index 0000000..7c79c6a Binary files /dev/null and b/docs/_build/html/_static/fonts/fontawesome-webfont.eot differ diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.svg b/docs/_build/html/_static/fonts/fontawesome-webfont.svg new file mode 100644 index 0000000..45fdf33 --- /dev/null +++ b/docs/_build/html/_static/fonts/fontawesome-webfont.svg @@ -0,0 +1,414 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.ttf b/docs/_build/html/_static/fonts/fontawesome-webfont.ttf new file mode 100644 index 0000000..e89738d Binary files /dev/null and b/docs/_build/html/_static/fonts/fontawesome-webfont.ttf differ diff --git a/docs/_build/html/_static/fonts/fontawesome-webfont.woff b/docs/_build/html/_static/fonts/fontawesome-webfont.woff new file mode 100644 index 0000000..8c1748a Binary files /dev/null and b/docs/_build/html/_static/fonts/fontawesome-webfont.woff differ diff --git a/docs/_build/html/_static/img/porao-branco.png b/docs/_build/html/_static/img/porao-branco.png new file mode 100644 index 0000000..9aa13b4 Binary files /dev/null and b/docs/_build/html/_static/img/porao-branco.png differ diff --git a/docs/_build/html/_static/jquery-3.5.1.js b/docs/_build/html/_static/jquery-3.5.1.js new file mode 100644 index 0000000..77d0b37 --- /dev/null +++ b/docs/_build/html/_static/jquery-3.5.1.js @@ -0,0 +1,10872 @@ +/*! + * jQuery JavaScript Library v3.5.1 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2020-05-04T22:49Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + return typeof obj === "function" && typeof obj.nodeType !== "number"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.5.1", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.5 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2020-03-14 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem.namespaceURI, + docElem = ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-PyCTBN + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +}; +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the master Deferred + master = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + master.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( master.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return master.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); + } + + return master.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
" ], + col: [ 2, "", "
" ], + tr: [ 2, "", "
" ], + td: [ 3, "", "
" ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var + rkeyEvent = /^key/, + rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, + rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + return result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + + which: function( event ) { + var button = event.button; + + // Add which for key events + if ( event.which == null && rkeyEvent.test( event.type ) ) { + return event.charCode != null ? event.charCode : event.keyCode; + } + + // Add which for click: 1 === left; 2 === middle; 3 === right + if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { + if ( button & 1 ) { + return 1; + } + + if ( button & 2 ) { + return 3; + } + + if ( button & 4 ) { + return 2; + } + + return 0; + } + + return event.which; + } +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px"; + tr.style.height = "1px"; + trChild.style.height = "9px"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function PyCTBNToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var PyCTBN, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + PyCTBN = PyCTBNToArray( value ); + + if ( PyCTBN.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = PyCTBN[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var PyCTBN, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + PyCTBN = PyCTBNToArray( value ); + + if ( PyCTBN.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = PyCTBN[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = PyCTBNToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( + dataPriv.get( cur, "events" ) || Object.create( null ) + )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) { + xml = undefined; + } + + if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { + jQuery.error( "Invalid XML: " + data ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ) + .filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ) + .map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script + if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+
+

PyCTBN.estimators package

+
+

Submodules

+
+
+

PyCTBN.estimators.fam_score_calculator module

+
+
+class PyCTBN.estimators.fam_score_calculator.FamScoreCalculator
+

Bases: object

+

Has the task of calculating the FamScore of a node by using a Bayesian score function

+
+
+get_fam_score(cims: numpy.array, tau_xu: float = 0.1, alpha_xu: float = 1)
+

Calculate the FamScore value of the node

+
+
Parameters
+
    +

  • cims (np.array) – np.array with all the node’s cims

    • +

  • tau_xu (float, optional) – hyperparameter over the CTBN’s q parameters, default to 0.1

    • +

  • alpha_xu (float, optional) – hyperparameter over the CTBN’s q parameters, default to 1

    • +
+
+
Returns
+

the FamScore value of the node

+
+
Return type
+

float

+
+
+
+ +
+
+marginal_likelihood_q(cims: numpy.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

+
+
Parameters
+
    +

  • cims (np.array) – np.array with all the node’s cims

    • +

  • tau_xu (float) – hyperparameter over the CTBN’s q parameters

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over q

+
+
Return type
+

float

+
+
+
+ +
+
+marginal_likelihood_theta(cims: PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix, alpha_xu: float, alpha_xxu: float)
+

Calculate the FamScore value of the node identified by the label node_id

+
+
Parameters
+
    +

  • cims (np.array) – np.array with all the node’s cims

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters, default to 0.1

    • +

  • alpha_xxu (float) – distribuited hyperparameter over the CTBN’s theta parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over theta

+
+
Return type
+

float

+
+
+
+ +
+
+single_cim_xu_marginal_likelihood_q(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

+
+
Parameters
+
    +

  • M_xu_suff_stats – value of the suffucient statistic M[x|u]

    • +

  • T_xu_suff_stats (float) – value of the suffucient statistic T[x|u]

    • +

  • cim (class:'ConditionalIntensityMatrix') – A conditional_intensity_matrix object with the sufficient statistics

    • +

  • tau_xu (float) – hyperparameter over the CTBN’s q parameters

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters

    • +
+
+
Returns
+

the value of the marginal likelihood of the node when assumes a specif value

+
+
Return type
+

float

+
+
+
+ +
+
+single_cim_xu_marginal_likelihood_theta(index: int, cim: PyCTBN.structure_graph.conditional_intensity_matrix.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

+
+
Parameters
+
    +

  • cim (class:'ConditionalIntensityMatrix') – A conditional_intensity_matrix object with the sufficient statistics

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters

    • +

  • alpha_xxu (float) – distribuited hyperparameter over the CTBN’s theta parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over theta when the node assumes a specif value

+
+
Return type
+

float

+
+
+
+ +
+
+single_internal_cim_xxu_marginal_likelihood_theta(M_xxu_suff_stats: float, alpha_xxu: float = 1)
+

Calculate the second part of the marginal likelihood over theta formula

+
+
Parameters
+
    +

  • M_xxu_suff_stats (float) – value of the suffucient statistic M[xx’|u]

    • +

  • alpha_xxu (float) – distribuited hyperparameter over the CTBN’s theta parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over theta when the node assumes a specif value

+
+
Return type
+

float

+
+
+
+ +
+
+variable_cim_xu_marginal_likelihood_q(cim: PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix, tau_xu: float = 0.1, alpha_xu: float = 1)
+

Calculate the value of the marginal likelihood over q given a cim

+
+
Parameters
+
    +

  • cim (class:'ConditionalIntensityMatrix') – A conditional_intensity_matrix object with the sufficient statistics

    • +

  • tau_xu (float) – hyperparameter over the CTBN’s q parameters

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over q

+
+
Return type
+

float

+
+
+
+ +
+
+variable_cim_xu_marginal_likelihood_theta(cim: PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix, alpha_xu: float, alpha_xxu: float)
+

Calculate the value of the marginal likelihood over theta given a cim

+
+
Parameters
+
    +

  • cim (class:'ConditionalIntensityMatrix') – A conditional_intensity_matrix object with the sufficient statistics

    • +

  • alpha_xu (float) – hyperparameter over the CTBN’s q parameters, default to 0.1

    • +

  • alpha_xxu (float) – distribuited hyperparameter over the CTBN’s theta parameters

    • +
+
+
Returns
+

the value of the marginal likelihood over theta

+
+
Return type
+

float

+
+
+
+ +
+ +
+
+

PyCTBN.estimators.parameters_estimator module

+
+
+class PyCTBN.estimators.parameters_estimator.ParametersEstimator(trajectories: PyCTBN.structure_graph.trajectory.Trajectory, net_graph: PyCTBN.structure_graph.network_graph.NetworkGraph)
+

Bases: object

+

Has the task of computing the cims of particular node given the trajectories and the net structure +in the graph _net_graph.

+
+
Parameters
+
+
+
_single_set_of_cims
+

the set of cims object that will hold the cims of the node

+
+
+
+
+compute_parameters()
+
+ +
+
+compute_parameters_for_node(node_id: str) → PyCTBN.structure_graph.set_of_cims.SetOfCims
+

Compute the CIMS of the node identified by the label node_id.

+
+
Parameters
+

node_id (string) – the node label

+
+
Returns
+

A SetOfCims object filled with the computed CIMS

+
+
Return type
+

SetOfCims

+
+
+
+ +
+
+static compute_state_res_time_for_node(times: numpy.ndarray, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexes_struct: numpy.ndarray, T: numpy.ndarray) → None
+

Compute the state residence times for a node and fill the matrix T with the results

+
+
Parameters
+
    +

  • node_indx (int) – the index of the node

    • +

  • times (numpy.array) – the times deltas vector

    • +

  • trajectory (numpy.ndArray) – the trajectory

    • +

  • cols_filter (numpy.array) – the columns filtering structure

    • +

  • scalar_indexes_struct (numpy.array) – the indexing structure

    • +

  • T (numpy.ndArray) – the state residence times vectors

    • +
+
+
+
+ +
+
+static compute_state_transitions_for_a_node(node_indx: int, trajectory: numpy.ndarray, cols_filter: numpy.ndarray, scalar_indexing: numpy.ndarray, M: numpy.ndarray) → None
+

Compute the state residence times for a node and fill the matrices M with the results.

+
+
Parameters
+
    +

  • node_indx (int) – the index of the node

    • +

  • trajectory (numpy.ndArray) – the trajectory

    • +

  • cols_filter (numpy.array) – the columns filtering structure

    • +

  • scalar_indexing (numpy.array) – the indexing structure

    • +

  • M (numpy.ndArray) – the state transitions matrices

    • +
+
+
+
+ +
+
+fast_init(node_id: str) → None
+

Initializes all the necessary structures for the parameters estimation for the node node_id.

+
+
Parameters
+

node_id (string) – the node label

+
+
+
+ +
+
+init_sets_cims_container()
+
+ +
+ +
+
+

PyCTBN.estimators.structure_constraint_based_estimator module

+
+
+class PyCTBN.estimators.structure_constraint_based_estimator.StructureConstraintBasedEstimator(sample_path: PyCTBN.structure_graph.sample_path.SamplePath, exp_test_alfa: float, chi_test_alfa: float, known_edges: List = [], thumb_threshold: int = 25)
+

Bases: PyCTBN.estimators.structure_estimator.StructureEstimator

+

Has the task of estimating the network structure given the trajectories in samplepath by using a constraint-based approach.

+
+
Parameters
+
    +

  • sample_path (SamplePath) – the _sample_path object containing the trajectories and the real structure

    • +

  • exp_test_alfa (float) – the significance level for the exponential Hp test

    • +

  • chi_test_alfa (float) – the significance level for the chi Hp test

    • +
+
+
_nodes
+

the nodes labels

+
+
_nodes_vals
+

the nodes cardinalities

+
+
_nodes_indxs
+

the nodes indexes

+
+
_complete_graph
+

the complete directed graph built using the nodes labels in _nodes

+
+
_cache
+

the Cache object

+
+
+
+
+complete_test(test_parent: str, test_child: str, parent_set: 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.

+
+
Parameters
+
    +

  • test_parent (string) – the node label of the test parent

    • +

  • test_child (string) – the node label of the child

    • +

  • parent_set (List) – the common parent set

    • +

  • child_states_numb (int) – the cardinality of the test_child

    • +

  • tot_vars_count (int) – the total number of variables in the net

    • +
+
+
Returns
+

True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise

+
+
Return type
+

bool

+
+
+
+ +
+
+compute_thumb_value(parent_val, child_val, parent_set_vals)
+

Compute the value to test against the thumb_threshold.

+
+
Parameters
+
    +

  • parent_val (int) – test parent’s variable cardinality

    • +

  • child_val (int) – test child’s variable cardinality

    • +

  • parent_set_vals (List) – the cardinalities of the nodes in the current sep-set

    • +
+
+
Returns
+

the thumb value for the current independence test

+
+
Return type
+

int

+
+
+
+ +
+
+ctpc_algorithm(disable_multiprocessing: bool = False)
+

Compute the CTPC algorithm over the entire net.

+
+ +
+
+estimate_structure(disable_multiprocessing: bool = False)
+

Abstract method to estimate the structure

+
+
Returns
+

List of estimated edges

+
+
Return type
+

Typing.List

+
+
+
+ +
+
+independence_test(child_states_numb: int, cim1: PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix, cim2: PyCTBN.structure_graph.conditional_intensity_matrix.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.

+
+
Parameters
+
+
+
Returns
+

True iff both tests do NOT reject the null hypothesis of independence. False otherwise.

+
+
Return type
+

bool

+
+
+
+ +
+
+one_iteration_of_CTPC_algorithm(var_id: str, tot_vars_count: int) → List
+

Performs an iteration of the CTPC algorithm using the node var_id as test_child.

+
+
Parameters
+

var_id (string) – the node label of the test child

+
+
+
+ +
+ +
+
+

PyCTBN.estimators.structure_estimator module

+
+
+class PyCTBN.estimators.structure_estimator.StructureEstimator(sample_path: PyCTBN.structure_graph.sample_path.SamplePath, known_edges: List = None)
+

Bases: object

+

Has the task of estimating the network structure given the trajectories in samplepath.

+
+
Parameters
+

sample_path (SamplePath) – the _sample_path object containing the trajectories and the real structure

+
+
_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

+
+
+
+
+adjacency_matrix() → numpy.ndarray
+

Converts the estimated structure _complete_graph to a boolean adjacency matrix representation.

+
+
Returns
+

The adjacency matrix of the graph _complete_graph

+
+
Return type
+

numpy.ndArray

+
+
+
+ +
+
+static build_complete_graph(node_ids: List) → networkx.PyCTBN.digraph.DiGraph
+

Builds a complete directed graph (no self loops) given the nodes labels in the list node_ids:

+
+
Parameters
+

node_ids (List) – the list of nodes labels

+
+
Returns
+

a complete Digraph Object

+
+
Return type
+

networkx.DiGraph

+
+
+
+ +
+
+build_removable_edges_matrix(known_edges: List)
+

Builds a boolean matrix who shows if a edge could be removed or not, based on prior knowledge given:

+
+
Parameters
+

known_edges (List) – the list of nodes labels

+
+
Returns
+

a boolean matrix

+
+
Return type
+

np.ndarray

+
+
+
+ +
+
+abstract estimate_structure() → List
+

Abstract method to estimate the structure

+
+
Returns
+

List of estimated edges

+
+
Return type
+

Typing.List

+
+
+
+ +
+
+static generate_possible_sub_sets_of_size(u: 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.

+
+
Parameters
+
    +

  • u (List) – the list of nodes

    • +

  • size (int) – the size of the subsets

    • +

  • parent_label (string) – the node to exclude in the subsets generation

    • +
+
+
Returns
+

an Iterator Object containing a list of lists

+
+
Return type
+

Iterator

+
+
+
+ +
+
+remove_diagonal_elements(matrix)
+
+ +
+
+save_plot_estimated_structure_graph() → None
+

Plot the estimated structure in a graphical model style. +Spurious edges are colored in red.

+
+ +
+
+save_results() → None
+

Save the estimated Structure to a .json file in the path where the data are loaded from. +The file is named as the input dataset but the results_ word is appended to the results file.

+
+ +
+
+spurious_edges() → List
+
+
Return the spurious edges present in the estimated structure, if a prior net structure is present in

_sample_path.structure.

+
+
+
+
Returns
+

A list containing the spurious edges

+
+
Return type
+

List

+
+
+
+ +
+ +
+
+

PyCTBN.estimators.structure_score_based_estimator module

+
+
+class PyCTBN.estimators.structure_score_based_estimator.StructureScoreBasedEstimator(sample_path: PyCTBN.structure_graph.sample_path.SamplePath, tau_xu: int = 0.1, alpha_xu: int = 1, known_edges: List = [])
+

Bases: PyCTBN.estimators.structure_estimator.StructureEstimator

+

Has the task of estimating the network structure given the trajectories in samplepath by +using a score based approach.

+
+
Parameters
+
    +

  • sample_path (SamplePath) – the _sample_path object containing the trajectories and the real structure

    • +

  • tau_xu (float, optional) – hyperparameter over the CTBN’s q parameters, default to 0.1

    • +

  • alpha_xu (float, optional) – hyperparameter over the CTBN’s q parameters, default to 1

    • +

  • known_edges (List, optional) – List of known edges, default to []

    • +
+
+
+
+
+estimate_parents(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

+
+
Parameters
+
    +

  • node_id (string) – current node’s id

    • +

  • max_parents (int, optional) – maximum number of parents for each variable. If None, disabled, default to None

    • +

  • iterations_number (int, optional) – maximum number of optimization algorithm’s iteration, default to 40

    • +

  • patience (int, optional) – number of iteration without any improvement before to stop the search.If None, disabled, default to None

    • +

  • tabu_length (int, optional) – maximum lenght of the data structures used in the optimization process, default to None

    • +

  • tabu_rules_duration (int, optional) – number of iterations in which each rule keeps its value, default to None

    • +

  • optimizer (string, optional) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’

    • +
+
+
Returns
+

A list of the best edges for the currente node

+
+
Return type
+

List

+
+
+
+ +
+
+estimate_structure(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

+
+
Parameters
+
    +

  • max_parents (int, optional) – maximum number of parents for each variable. If None, disabled, default to None

    • +

  • iterations_number (int, optional) – maximum number of optimization algorithm’s iteration, default to 40

    • +

  • patience (int, optional) – number of iteration without any improvement before to stop the search.If None, disabled, default to None

    • +

  • tabu_length (int, optional) – maximum lenght of the data structures used in the optimization process, default to None

    • +

  • tabu_rules_duration (int, optional) – number of iterations in which each rule keeps its value, default to None

    • +

  • optimizer (string, optional) – name of the optimizer algorithm. Possible values: ‘hill’ (Hill climbing),’tabu’ (tabu search), defualt to ‘tabu’

    • +

  • disable_multiprocessing (Boolean, optional) – true if you desire to disable the multiprocessing operations, default to False

    • +
+
+
+
+ +
+
+get_score_from_graph(graph: PyCTBN.structure_graph.network_graph.NetworkGraph, node_id: str)
+

Get the FamScore of a node

+
+
Parameters
+
    +

  • node_id (string) – current node’s id

    • +

  • graph (class:'NetworkGraph') – current graph to be computed

    • +
+
+
Returns
+

The FamSCore for this graph structure

+
+
Return type
+

float

+
+
+
+ +
+ +
+
+

Module contents

+
+
+ + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/classes.html b/docs/_build/html/classes.html new file mode 100644 index 0000000..c015456 --- /dev/null +++ b/docs/_build/html/classes.html @@ -0,0 +1,226 @@ + + + + + + + + + + + PyCTBN package — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+ + + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/classes.optimizers.html b/docs/_build/html/classes.optimizers.html new file mode 100644 index 0000000..e7f5954 --- /dev/null +++ b/docs/_build/html/classes.optimizers.html @@ -0,0 +1,317 @@ + + + + + + + + + + + PyCTBN.optimizers package — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+
+

PyCTBN.optimizers package

+
+

Submodules

+
+
+

PyCTBN.optimizers.constraint_based_optimizer module

+
+
+class PyCTBN.optimizers.constraint_based_optimizer.ConstraintBasedOptimizer(node_id: str, structure_estimator: PyCTBN.estimators.structure_estimator.StructureEstimator, tot_vars_count: int)
+

Bases: PyCTBN.optimizers.optimizer.Optimizer

+

Optimizer class that implement a CTPC Algorithm

+
+
Parameters
+
    +

  • node_id (string) – current node’s id

    • +

  • structure_estimator (class:'StructureEstimator') – a structure estimator object with the information about the net

    • +

  • tot_vars_count (int) – number of variables in the dataset

    • +
+
+
+
+
+optimize_structure()
+

Compute Optimization process for a structure_estimator by using a CTPC Algorithm

+
+
Returns
+

the estimated structure for the node

+
+
Return type
+

List

+
+
+
+ +
+ +
+ +
+

PyCTBN.optimizers.optimizer module

+
+
+class PyCTBN.optimizers.optimizer.Optimizer(node_id: str, structure_estimator: PyCTBN.estimators.structure_estimator.StructureEstimator)
+

Bases: abc.ABC

+

Interface class for all the optimizer’s child PyCTBN

+
+
Parameters
+
    +

  • node_id (string) – the node label

    • +

  • structure_estimator (class:'StructureEstimator') – A structureEstimator Object to predict the structure

    • +
+
+
+
+
+abstract optimize_structure() → List
+

Compute Optimization process for a structure_estimator

+
+
Returns
+

the estimated structure for the node

+
+
Return type
+

List

+
+
+
+ +
+ +
+ +
+

Module contents

+
+
+ + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/classes.structure_graph.html b/docs/_build/html/classes.structure_graph.html new file mode 100644 index 0000000..2bba96d --- /dev/null +++ b/docs/_build/html/classes.structure_graph.html @@ -0,0 +1,914 @@ + + + + + + + + + + + PyCTBN.structure_graph package — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+
+

PyCTBN.structure_graph package

+
+

Submodules

+
+
+

PyCTBN.structure_graph.abstract_sample_path module

+
+
+class PyCTBN.structure_graph.abstract_sample_path.AbstractSamplePath(importer: utility.abstract_importer.AbstractImporter)
+

Bases: abc.ABC

+
+
+abstract build_structure()
+

Builds the Structure object that aggregates all the infos about the net. +Assigns the Structure object to the instance attribuite _structure +Parameters:

+
+

void

+
+
+
Returns:

void

+
+
+
+ +
+
+abstract build_trajectories()
+

Builds the Trajectory object that will contain all the trajectories. +Assigns the Trajectoriy object to the instance attribute _trajectories +Clears all the unused dataframes in Importer Object

+
+
Parameters:

void

+
+
Returns:

void

+
+
+
+ +
+ +
+
+

PyCTBN.structure_graph.conditional_intensity_matrix module

+
+
+class PyCTBN.structure_graph.conditional_intensity_matrix.ConditionalIntensityMatrix(state_residence_times: numpy.array, state_transition_matrix: numpy.array)
+

Bases: 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.

+
+
Parameters
+
    +

  • state_residence_times (numpy.array) – state residence times vector

    • +

  • state_transition_matrix (numpy.ndArray) – the transitions count matrix

    • +
+
+
_cim
+

the actual cim of the node

+
+
+
+
+property cim
+
+ +
+
+compute_cim_coefficients() → 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

+
+ +
+
+property state_residence_times
+
+ +
+
+property state_transition_matrix
+
+ +
+ +
+
+

PyCTBN.structure_graph.network_graph module

+
+
+class PyCTBN.structure_graph.network_graph.NetworkGraph(graph_struct: PyCTBN.structure_graph.structure.Structure)
+

Bases: 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

+
+
Parameters
+

graph_struct (Structure) – the Structure object from which infos about the net will be extracted

+
+
_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

+
+
+
+
+add_edges(list_of_edges: List) → None
+

Add the edges to the _graph contained in the list list_of_edges.

+
+
Parameters
+

list_of_edges (List) – the list containing of tuples containing the edges

+
+
+
+ +
+
+add_nodes(list_of_nodes: 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)

+
+
Parameters
+

list_of_nodes (List) – the nodes to add to _graph

+
+
+
+ +
+
+static build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray
+

Builds the combinatorial structure that contains the combinations of all the values contained in +parents_values.

+
+
Parameters
+

parents_values (List) – the cardinalities of the nodes

+
+
Returns
+

A numpy matrix containing a grid of the combinations

+
+
Return type
+

numpy.ndArray

+
+
+
+ +
+
+static build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.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

+
+ +
+
+static build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray
+

Builds an indexing structure for the computation of state residence times values.

+
+
Parameters
+
    +

  • node_states (int) – the node cardinality

    • +

  • parents_vals (List) – the caridinalites of the node’s parents

    • +
+
+
Returns
+

The time indexing structure

+
+
Return type
+

numpy.ndArray

+
+
+
+ +
+
+static build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.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

+
+ +
+
+static build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray
+

Builds an indexing structure for the computation of state transitions values.

+
+
Parameters
+
    +

  • node_states_number (int) – the node cardinality

    • +

  • parents_vals (List) – the caridinalites of the node’s parents

    • +
+
+
Returns
+

The transition indexing structure

+
+
Return type
+

numpy.ndArray

+
+
+
+ +
+
+clear_indexing_filtering_structures() → None
+

Initialize all the filtering/indexing structures.

+
+ +
+
+property edges
+
+ +
+
+fast_init(node_id: str) → None
+

Initializes all the necessary structures for parameters estimation of the node identified by the label +node_id

+
+
Parameters
+

node_id (string) – the label of the node

+
+
+
+ +
+
+get_node_indx(node_id) → int
+
+ +
+
+get_ordered_by_indx_set_of_parents(node: str) → 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).

+
+
Parameters
+

node (string) – the label of the node

+
+
Returns
+

a tuple containing all the parent set infos

+
+
Return type
+

Tuple

+
+
+
+ +
+
+get_parents_by_id(node_id) → List
+

Returns a list of labels of the parents of the node node_id

+
+
Parameters
+

node_id (string) – the node label

+
+
Returns
+

a List of labels of the parents

+
+
Return type
+

List

+
+
+
+ +
+
+get_positional_node_indx(node_id) → int
+
+ +
+
+get_states_number(node_id) → int
+
+ +
+
+has_edge(edge: tuple) → bool
+

Check if the graph contains a specific edge

+
+
Parameters:

edge: a tuple that rappresents the edge

+
+
Returns:

bool

+
+
+
+ +
+
+init_graph()
+
+ +
+
+property nodes
+
+ +
+
+property nodes_indexes
+
+ +
+
+property nodes_values
+
+ +
+
+property p_combs
+
+ +
+
+remove_edges(list_of_edges: List) → None
+

Remove the edges to the graph contained in the list list_of_edges.

+
+
Parameters
+

list_of_edges (List) – The edges to remove from the graph

+
+
+
+ +
+
+remove_node(node_id: str) → None
+

Remove the node node_id from all the class members. +Initialize all the filtering/indexing structures.

+
+ +
+
+property time_filtering
+
+ +
+
+property time_scalar_indexing_strucure
+
+ +
+
+property transition_filtering
+
+ +
+
+property transition_scalar_indexing_structure
+
+ +
+ +
+
+

PyCTBN.structure_graph.sample_path module

+
+
+class PyCTBN.structure_graph.sample_path.SamplePath(importer: PyCTBN.utility.abstract_importer.AbstractImporter)
+

Bases: 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.

+
+
Parameters
+

importer (AbstractImporter) – the Importer object which contains the imported and processed data

+
+
_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

+
+
+
+
+build_structure() → None
+

Builds the Structure object that aggregates all the infos about the net.

+
+ +
+
+build_trajectories() → None
+

Builds the Trajectory object that will contain all the trajectories. +Clears all the unused dataframes in _importer Object

+
+ +
+
+clear_memory()
+
+ +
+
+property has_prior_net_structure
+
+ +
+
+property structure
+
+ +
+
+property total_variables_count
+
+ +
+
+property trajectories
+
+ +
+ +
+
+

PyCTBN.structure_graph.set_of_cims module

+
+
+class PyCTBN.structure_graph.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray)
+

Bases: object

+

Aggregates all the CIMS of the node identified by the label _node_id.

+
+
Parameters
+
    +

  • node_id – the node label

    • +

  • parents_states_number (List) – the cardinalities of the parents

    • +

  • node_states_number (int) – the caridinality of the node

    • +

  • p_combs (numpy.ndArray) – the p_comb structure bound to this node

    • +
+
+
_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

+
+
+
+
+property actual_cims
+
+ +
+
+build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.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.

+
+
Parameters
+
    +

  • state_res_times (numpy.ndArray) – the state residence times matrix

    • +

  • transition_matrices (numpy.ndArray) – the transition matrices

    • +
+
+
+
+ +
+
+build_times_and_transitions_structures() → None
+

Initializes at the correct dimensions the state residence times matrix and the state transition matrices.

+
+ +
+
+filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray
+

Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index +comb.

+
+
Parameters
+
    +

  • mask_arr (numpy.array) – the boolean mask that indicates which parent to consider

    • +

  • comb (numpy.array) – the state/s of the filtered parents

    • +
+
+
Returns
+

Array of ConditionalIntensityMatrix objects

+
+
Return type
+

numpy.array

+
+
+
+ +
+
+get_cims_number()
+
+ +
+
+property p_combs
+
+ +
+ +
+
+

PyCTBN.structure_graph.sets_of_cims_container module

+
+
+class PyCTBN.structure_graph.sets_of_cims_container.SetsOfCimsContainer(list_of_keys, states_number_per_node, list_of_parents_states_number, p_combs_list)
+

Bases: object

+

Aggrega un insieme di oggetti SetOfCims

+
+
+get_cims_of_node(node_indx, cim_indx)
+
+ +
+
+get_set_of_cims(node_indx)
+
+ +
+
+init_cims_structure(keys, states_number_per_node, list_of_parents_states_number, p_combs_list)
+

for indx, key in enumerate(keys): +self.sets_of_cims.append(

+
+

socim.SetOfCims(key, list_of_parents_states_number[indx], states_number_per_node[indx]))

+
+
+ +
+ +
+
+

PyCTBN.structure_graph.structure module

+
+
+class PyCTBN.structure_graph.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)
+

Bases: object

+

Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)

+
+
Parameters
+
    +

  • nodes_labels_list (List) – the symbolic names of the variables

    • +

  • nodes_indexes_arr (numpy.ndArray) – the indexes of the nodes

    • +

  • nodes_vals_arr (numpy.ndArray) – the cardinalites of the nodes

    • +

  • edges_list (List) – the edges of the network

    • +

  • total_variables_number (int) – the total number of variables in the dataset

    • +
+
+
+
+
+add_edge(edge: tuple)
+
+ +
+
+clean_structure_edges()
+
+ +
+
+contains_edge(edge: tuple) → bool
+
+ +
+
+property edges
+
+ +
+
+get_node_id(node_indx: int) → str
+

Given the node_index returns the node label.

+
+
Parameters
+

node_indx (int) – the node index

+
+
Returns
+

the node label

+
+
Return type
+

string

+
+
+
+ +
+
+get_node_indx(node_id: str) → int
+

Given the node_index returns the node label.

+
+
Parameters
+

node_id (string) – the node label

+
+
Returns
+

the node index

+
+
Return type
+

int

+
+
+
+ +
+
+get_positional_node_indx(node_id: str) → int
+
+ +
+
+get_states_number(node: str) → int
+

Given the node label node returns the cardinality of the node.

+
+
Parameters
+

node (string) – the node label

+
+
Returns
+

the node cardinality

+
+
Return type
+

int

+
+
+
+ +
+
+property nodes_indexes
+
+ +
+
+property nodes_labels
+
+ +
+
+property nodes_values
+
+ +
+
+remove_edge(edge: tuple)
+
+ +
+
+remove_node(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.

+
+ +
+
+property total_variables_number
+
+ +
+ +
+
+

PyCTBN.structure_graph.trajectory module

+
+
+class PyCTBN.structure_graph.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)
+

Bases: 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).

+
+
Parameters
+
    +

  • list_of_columns (List) – the list containing the times array and values matrix

    • +

  • original_cols_number (int) – total number of cols in the data

    • +
+
+
_actual_trajectory
+

the trajectory containing also the duplicated/shifted values

+
+
_times
+

the array containing the time deltas

+
+
+
+
+property complete_trajectory
+
+ +
+
+size()
+
+ +
+
+property times
+
+ +
+
+property trajectory
+
+ +
+ +
+
+

Module contents

+
+
+ + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/classes.utility.html b/docs/_build/html/classes.utility.html new file mode 100644 index 0000000..f9caabc --- /dev/null +++ b/docs/_build/html/classes.utility.html @@ -0,0 +1,649 @@ + + + + + + + + + + + PyCTBN.utility package — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+
+

PyCTBN.utility package

+
+

Submodules

+
+
+

PyCTBN.utility.abstract_importer module

+
+
+class PyCTBN.utility.abstract_importer.AbstractImporter(file_path: str = None, trajectory_list: Union[pandas.core.frame.DataFrame, numpy.ndarray] = None, variables: pandas.core.frame.DataFrame = None, prior_net_structure: pandas.core.frame.DataFrame = None)
+

Bases: abc.ABC

+

Abstract class that exposes all the necessary methods to process the trajectories and the net structure.

+
+
Parameters
+
    +

  • file_path (str) – the file path, or dataset name if you import already processed data

    • +

  • trajectory_list (typing.Union[pandas.DataFrame, numpy.ndarray]) – Dataframe or numpy array containing the concatenation of all the processed trajectories

    • +

  • variables (pandas.DataFrame) – Dataframe containing the nodes labels and cardinalities

    • +
+
+
Prior_net_structure
+

Dataframe containing the structure of the network (edges)

+
+
_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

+
+
+
+build_list_of_samples_array(concatenated_sample: pandas.core.frame.DataFrame) → List
+

Builds a List containing the the delta times numpy array, and the complete transitions matrix

+
+
Parameters
+

concatenated_sample (pandas.Dataframe) – the dataframe/array from which the time, and transitions matrix have to be extracted +and converted

+
+
Returns
+

the resulting list of numpy arrays

+
+
Return type
+

List

+
+
+
+ +
+
+abstract build_sorter(trajecory_header: object) → 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.

+
+
Parameters
+

trajecory_header (object) – an object that will be used to define the header

+
+
Returns
+

A list containing the processed header.

+
+
Return type
+

List

+
+
+
+ +
+
+clear_concatenated_frame() → None
+

Removes all values in the dataframe concatenated_samples.

+
+ +
+
+compute_row_delta_in_all_samples_frames(df_samples_list: 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

+
+
Parameters
+

df_samples_list (List) – the datframe’s list to be processed and concatenated

+
+
+
+

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

+
+
+ +
+
+compute_row_delta_sigle_samples_frame(sample_frame: pandas.core.frame.DataFrame, columns_header: List, shifted_cols_header: List) → pandas.core.frame.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.

+
+
Parameters
+
    +

  • sample_frame (pandas.Dataframe) – the traj to be processed

    • +

  • columns_header (List) – the original header of sample_frame

    • +

  • shifted_cols_header (List) – a copy of columns_header with changed names of the contents

    • +
+
+
Returns
+

The processed dataframe

+
+
Return type
+

pandas.Dataframe

+
+
+
+

Warning

+

the Dataframe sample_frame has to follow the column structure of this header: +Header of sample_frame = [Time | Variable values]

+
+
+ +
+
+property concatenated_samples
+
+ +
+
+abstract dataset_id() → object
+

If the original dataset contains multiple dataset, this method returns a unique id to identify the current +dataset

+
+ +
+
+property file_path
+
+ +
+
+property sorter
+
+ +
+
+property structure
+
+ +
+
+property variables
+
+ +
+ +
+
+

PyCTBN.utility.cache module

+
+
+class PyCTBN.utility.cache.Cache
+

Bases: object

+

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

+
+
+
+
+clear()
+

Clear the contents both of _actual_cache and _list_of_sets_of_parents.

+
+ +
+
+find(parents_comb: Set)
+

Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims +related to that parents_comb.

+
+
Parameters
+

parents_comb (Set) – the parents related to that SetOfCims

+
+
Returns
+

A SetOfCims object if the parents_comb index is found in _list_of_sets_of_parents. +None otherwise.

+
+
Return type
+

SetOfCims

+
+
+
+ +
+
+put(parents_comb: Set, socim: PyCTBN.structure_graph.set_of_cims.SetOfCims)
+

Place in cache the SetOfCims object, and the related symbolic index parents_comb in +_list_of_sets_of_parents.

+
+
Parameters
+
    +

  • parents_comb (Set) – the symbolic set index

    • +

  • socim (SetOfCims) – the related SetOfCims object

    • +
+
+
+
+ +
+ +
+
+

PyCTBN.utility.decorators module

+
+
+PyCTBN.utility.decorators.timing(f)
+
+ +
+
+PyCTBN.utility.decorators.timing_write(f)
+
+ +
+
+

PyCTBN.utility.json_importer module

+
+
+class PyCTBN.utility.json_importer.JsonImporter(file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, variables_key: str)
+

Bases: PyCTBN.utility.abstract_importer.AbstractImporter

+

Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare +the data in json extension.

+
+
Parameters
+
    +

  • file_path (string) – the path of the file that contains tha data to be imported

    • +

  • samples_label (string) – the reference key for the samples in the trajectories

    • +

  • structure_label (string) – the reference key for the structure of the network data

    • +

  • variables_label (string) – the reference key for the cardinalites of the nodes data

    • +

  • time_key (string) – the key used to identify the timestamps in each trajectory

    • +

  • variables_key (string) – the key used to identify the names of the variables in the net

    • +
+
+
_array_indx
+

the index of the outer JsonArray to extract the data from

+
+
_df_samples_list
+

a Dataframe list in which every dataframe contains a trajectory

+
+
_raw_data
+

The raw contents of the json file to import

+
+
+
+
+build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
+

Implements the abstract method build_sorter of the AbstractImporter for this dataset.

+
+ +
+
+clear_data_frame_list() → None
+

Removes all values present in the dataframes in the list _df_samples_list.

+
+ +
+
+dataset_id() → object
+

If the original dataset contains multiple dataset, this method returns a unique id to identify the current +dataset

+
+ +
+
+import_data(indx: int) → None
+

Implements the abstract method of AbstractImporter.

+
+
Parameters
+

indx (int) – the index of the outer JsonArray to extract the data from

+
+
+
+ +
+
+import_sampled_cims(raw_data: List, indx: int, cims_key: str) → 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.

+
+
Parameters
+
    +

  • raw_data (List) – List of Dicts

    • +

  • indx (int) – The index of the array from which the data have to be extracted

    • +

  • cims_key (string) – the key where the json object cims are placed

    • +
+
+
Returns
+

a dictionary containing the sampled CIMS for all the variables in the net

+
+
Return type
+

Dictionary

+
+
+
+ +
+
+import_structure(raw_data: List) → pandas.core.frame.DataFrame
+

Imports in a dataframe the data in the list raw_data at the key _structure_label

+
+
Parameters
+

raw_data (List) – List of Dicts

+
+
Returns
+

Dataframe containg the starting node a ending node of every arc of the network

+
+
Return type
+

pandas.Dataframe

+
+
+
+ +
+
+import_trajectories(raw_data: List) → List
+

Imports the trajectories from the list of dicts raw_data.

+
+
Parameters
+

raw_data (List) – List of Dicts

+
+
Returns
+

List of dataframes containing all the trajectories

+
+
Return type
+

List

+
+
+
+ +
+
+import_variables(raw_data: List) → pandas.core.frame.DataFrame
+

Imports the data in raw_data at the key _variables_label.

+
+
Parameters
+

raw_data (List) – List of Dicts

+
+
Returns
+

Datframe containg the variables simbolic labels and their cardinalities

+
+
Return type
+

pandas.Dataframe

+
+
+
+ +
+
+normalize_trajectories(raw_data: List, indx: int, trajectories_key: str) → List
+

Extracts the trajectories in raw_data at the index index at the key trajectories key.

+
+
Parameters
+
    +

  • raw_data (List) – List of Dicts

    • +

  • indx (int) – The index of the array from which the data have to be extracted

    • +

  • trajectories_key (string) – the key of the trajectories objects

    • +
+
+
Returns
+

A list of daframes containg the trajectories

+
+
Return type
+

List

+
+
+
+ +
+
+one_level_normalizing(raw_data: List, indx: int, key: str) → pandas.core.frame.DataFrame
+

Extracts the one-level nested data in the list raw_data at the index indx at the key key.

+
+
Parameters
+
    +

  • raw_data (List) – List of Dicts

    • +

  • indx (int) – The index of the array from which the data have to be extracted

    • +

  • key (string) – the key for the Dicts from which exctract data

    • +
+
+
Returns
+

A normalized dataframe

+
+
Return type
+

pandas.Datframe

+
+
+
+ +
+
+read_json_file() → List
+

Reads the JSON file in the path self.filePath.

+
+
Returns
+

The contents of the json file

+
+
Return type
+

List

+
+
+
+ +
+ +
+
+

PyCTBN.utility.sample_importer module

+
+
+class PyCTBN.utility.sample_importer.SampleImporter(trajectory_list: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, variables: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, prior_net_structure: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None)
+

Bases: PyCTBN.utility.abstract_importer.AbstractImporter

+

Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare +the data loaded directly by using DataFrame

+
+
Parameters
+
    +

  • trajectory_list (typing.Union[pd.DataFrame, np.ndarray, typing.List]) – the data that describes the trajectories

    • +

  • variables (typing.Union[pd.DataFrame, np.ndarray, typing.List]) – the data that describes the variables with name and cardinality

    • +

  • prior_net_structure (typing.Union[pd.DataFrame, np.ndarray, typing.List]) – the data of the real structure, if it exists

    • +
+
+
_df_samples_list
+

a Dataframe list in which every dataframe contains a trajectory

+
+
_raw_data
+

The raw contents of the json file to import

+
+
+
+
+build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
+

Implements the abstract method build_sorter of the AbstractImporter in order to get the ordered variables list.

+
+ +
+
+dataset_id() → object
+

If the original dataset contains multiple dataset, this method returns a unique id to identify the current +dataset

+
+ +
+
+import_data(header_column=None)
+
+ +
+ +
+
+

Module contents

+
+
+ + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/examples.html b/docs/_build/html/examples.html new file mode 100644 index 0000000..0b23f72 --- /dev/null +++ b/docs/_build/html/examples.html @@ -0,0 +1,298 @@ + + + + + + + + + + + Examples — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+
+

Examples

+
+

Installation/Usage

+

Download the release in .tar.gz or .whl format and simply use pip install to install it:

+
$pip install PyCTBN-1.0.tar.gz
+
+
+
+
+

Implementing your own data importer

+
"""This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format.
+The net in exam has three ternary nodes and no prior net structure.
+"""
+
+from PyCTBN import AbstractImporter
+
+class CSVImporter(AbstractImporter):
+
+    def __init__(self, file_path):
+        self._df_samples_list = None
+        super(CSVImporter, self).__init__(file_path)
+
+    def import_data(self):
+        self.read_csv_file()
+        self._sorter = self.build_sorter(self._df_samples_list[0])
+        self.import_variables()
+        self.compute_row_delta_in_all_samples_frames(self._df_samples_list)
+
+    def read_csv_file(self):
+        df = pd.read_csv(self._file_path)
+        df.drop(df.columns[[0]], axis=1, inplace=True)
+        self._df_samples_list = [df]
+
+    def import_variables(self):
+        values_list = [3 for var in self._sorter]
+        # initialize dict of lists
+        data = {'Name':self._sorter, 'Value':values_list}
+        # Create the pandas DataFrame
+        self._df_variables = pd.DataFrame(data)
+
+    def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List:
+        return list(sample_frame.columns)[1:]
+
+    def dataset_id(self) -> object:
+        pass
+
+
+
+
+

Parameters Estimation Example

+
from PyCTBN import JsonImporter
+from PyCTBN import SamplePath
+from PyCTBN import NetworkGraph
+from PyCTBN import ParametersEstimator
+
+
+def main():
+    read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir
+    #import data
+    importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+    importer.import_data(0)
+    #Create a SamplePath Obj passing an already filled AbstractImporter object
+    s1 = SamplePath(importer)
+    #Build The trajectries and the structural infos
+    s1.build_trajectories()
+    s1.build_structure()
+    print(s1.structure.edges)
+    print(s1.structure.nodes_values)
+    #From The Structure Object build the Graph
+    g = NetworkGraph(s1.structure)
+    #Select a node you want to estimate the parameters
+    node = g.nodes[2]
+    print("Node", node)
+    #Init the _graph specifically for THIS node
+    g.fast_init(node)
+    #Use SamplePath and Grpah to create a ParametersEstimator Object
+    p1 = ParametersEstimator(s1.trajectories, 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)
+
+
+
+
+

Structure Estimation Example

+
from PyCTBN import JsonImporter
+from PyCTBN import SamplePath
+from PyCTBN import StructureEstimator
+
+def structure_estimation_example():
+
+    # read the json files in ./data path
+    read_files = glob.glob(os.path.join('./data', "*.json"))
+    # initialize a JsonImporter object for the first file
+    importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name')
+    # import the data at index 0 of the outer json array
+    importer.import_data(0)
+    # construct a SamplePath Object passing a filled AbstractImporter
+    s1 = SamplePath(importer)
+    # build the trajectories
+    s1.build_trajectories()
+    # build the real structure
+    s1.build_structure()
+    # construct a StructureEstimator object
+    se1 = StructureEstimator(s1, 0.1, 0.1)
+    # call the ctpc algorithm
+    se1.ctpc_algorithm()
+    # the adjacency matrix of the estimated structure
+    print(se1.adjacency_matrix())
+    # save results to a json file
+    se1.save_results()
+
+
+
+
+ + +
+
+ + + + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html new file mode 100644 index 0000000..91cecdd --- /dev/null +++ b/docs/_build/html/genindex.html @@ -0,0 +1,923 @@ + + + + + + + + + + + Index — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+ +

Index

+ +
+ A + | B + | C + | D + | E + | F + | G + | H + | I + | J + | M + | N + | O + | P + | R + | S + | T + | V + +
+

A

+ + + +
+ +

B

+ + + +
+ +

C

+ + + +
+ +

D

+ + +
+ +

E

+ + + +
+ +

F

+ + + +
+ +

G

+ + + +
+ +

H

+ + + +
+ +

I

+ + + +
+ +

J

+ + +
+ +

M

+ + +
+ +

N

+ + + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

V

+ + + +
+ + + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/index.html b/docs/_build/html/index.html new file mode 100644 index 0000000..09124b3 --- /dev/null +++ b/docs/_build/html/index.html @@ -0,0 +1,202 @@ + + + + + + + + + + + Welcome to PyCTBN’s documentation! — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
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+ + + + + + + + + + + + +
+ +
+ +
+ +
+

Indices and tables

+ +
+ + +
+
+ + + + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
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+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/modules.html b/docs/_build/html/modules.html new file mode 100644 index 0000000..370654c --- /dev/null +++ b/docs/_build/html/modules.html @@ -0,0 +1,227 @@ + + + + + + + + + + + PyCTBN — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
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+ +
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+ + + + + + + + + + + + +
+ +
+ +
+ + + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/objects.inv b/docs/_build/html/objects.inv new file mode 100644 index 0000000..6d2cd99 Binary files /dev/null and b/docs/_build/html/objects.inv differ diff --git a/docs/_build/html/py-modindex.html b/docs/_build/html/py-modindex.html new file mode 100644 index 0000000..d4d863b --- /dev/null +++ b/docs/_build/html/py-modindex.html @@ -0,0 +1,322 @@ + + + + + + + + + + + Python Module Index — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+ +

Python Module Index

+ +
+ c +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
 
+ c
+ PyCTBN +
    + PyCTBN.estimators +
    + PyCTBN.estimators.fam_score_calculator +
    + PyCTBN.estimators.parameters_estimator +
    + PyCTBN.estimators.structure_constraint_based_estimator +
    + PyCTBN.estimators.structure_estimator +
    + PyCTBN.estimators.structure_score_based_estimator +
    + PyCTBN.optimizers +
    + PyCTBN.optimizers.constraint_based_optimizer +
    + PyCTBN.optimizers.hill_climbing_search +
    + PyCTBN.optimizers.optimizer +
    + PyCTBN.optimizers.tabu_search +
    + PyCTBN.structure_graph +
    + PyCTBN.structure_graph.abstract_sample_path +
    + PyCTBN.structure_graph.conditional_intensity_matrix +
    + PyCTBN.structure_graph.network_graph +
    + PyCTBN.structure_graph.sample_path +
    + PyCTBN.structure_graph.set_of_cims +
    + PyCTBN.structure_graph.sets_of_cims_container +
    + PyCTBN.structure_graph.structure +
    + PyCTBN.structure_graph.trajectory +
    + PyCTBN.utility +
    + PyCTBN.utility.abstract_importer +
    + PyCTBN.utility.cache +
    + PyCTBN.utility.decorators +
    + PyCTBN.utility.json_importer +
    + PyCTBN.utility.sample_importer +
+ + +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/search.html b/docs/_build/html/search.html new file mode 100644 index 0000000..1733d10 --- /dev/null +++ b/docs/_build/html/search.html @@ -0,0 +1,190 @@ + + + + + + + + + + + Search — PyCTBN 2.0 documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+ + + +
+
+
+
+
+ +
+
+ + + + + + + + + + + + +
+ +
+ +
+ + + +
+ +
+ +
+
+ + +
+ +
+

+ © Copyright 2021, Bregoli Alessandro, Martini Filippo, Moretti Luca. +

+
+ + Built with Sphinx using a theme provided by Porão do Juca. + +
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js new file mode 100644 index 0000000..8bd7326 --- /dev/null +++ b/docs/_build/html/searchindex.js @@ -0,0 +1 @@ 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package","PyCTBN.estimators package","PyCTBN.optimizers package","PyCTBN.structure_graph package","PyCTBN.utility package","Examples","Welcome to PyCTBN\u2019s documentation!","PyCTBN"],titleterms:{"class":[0,1,2,3,4,7],"import":5,abstract_import:4,abstract_sample_path:3,cach:4,conditional_intensity_matrix:3,constraint_based_optim:2,content:[0,1,2,3,4],data:5,decor:4,document:6,estim:[1,5],exampl:5,fam_score_calcul:1,hill_climbing_search:2,implement:5,indic:6,instal:5,json_import:4,modul:[0,1,2,3,4],network_graph:3,optim:2,own:5,packag:[0,1,2,3,4],paramet:5,parameters_estim:1,pyctbn:6,sample_import:4,sample_path:3,set_of_cim:3,sets_of_cims_contain:3,structur:[3,5],structure_constraint_based_estim:1,structure_estim:1,structure_graph:3,structure_score_based_estim:1,submodul:[1,2,3,4],subpackag:0,tabl:6,tabu_search:2,trajectori:3,usag:5,util:4,welcom:6,your:5}}) \ No newline at end of file diff --git a/docs/classes.estimators.rst b/docs/classes.estimators.rst new file mode 100644 index 0000000..5ecab8b --- /dev/null +++ b/docs/classes.estimators.rst @@ -0,0 +1,53 @@ +PyCTBN.estimators package +========================== + +Submodules +---------- + +PyCTBN.estimators.fam\_score\_calculator module +------------------------------------------------ + +.. automodule:: PyCTBN.estimators.fam_score_calculator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.parameters\_estimator module +----------------------------------------------- + +.. automodule:: PyCTBN.estimators.parameters_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_constraint\_based\_estimator module +----------------------------------------------------------------- + +.. automodule:: PyCTBN.estimators.structure_constraint_based_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_estimator module +---------------------------------------------- + +.. automodule:: PyCTBN.estimators.structure_estimator + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.estimators.structure\_score\_based\_estimator module +------------------------------------------------------------ + +.. automodule:: PyCTBN.estimators.structure_score_based_estimator + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.estimators + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/classes.optimizers.rst b/docs/classes.optimizers.rst new file mode 100644 index 0000000..0730b68 --- /dev/null +++ b/docs/classes.optimizers.rst @@ -0,0 +1,45 @@ +PyCTBN.optimizers package +========================== + +Submodules +---------- + +PyCTBN.optimizers.constraint\_based\_optimizer module +------------------------------------------------------ + +.. automodule:: PyCTBN.optimizers.constraint_based_optimizer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.hill\_climbing\_search module +------------------------------------------------ + +.. automodule:: PyCTBN.optimizers.hill_climbing_search + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.optimizer module +----------------------------------- + +.. automodule:: PyCTBN.optimizers.optimizer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.optimizers.tabu\_search module +-------------------------------------- + +.. automodule:: PyCTBN.optimizers.tabu_search + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.optimizers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/classes.rst b/docs/classes.rst new file mode 100644 index 0000000..0ff219f --- /dev/null +++ b/docs/classes.rst @@ -0,0 +1,21 @@ +PyCTBN package +=============== + +Subpackages +----------- + +.. toctree:: + :maxdepth: 4 + + PyCTBN.estimators + PyCTBN.optimizers + PyCTBN.structure_graph + PyCTBN.utility + +Module contents +--------------- + +.. automodule:: PyCTBN + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/classes.structure_graph.rst b/docs/classes.structure_graph.rst new file mode 100644 index 0000000..489b7b6 --- /dev/null +++ b/docs/classes.structure_graph.rst @@ -0,0 +1,77 @@ +PyCTBN.structure\_graph package +================================ + +Submodules +---------- + +PyCTBN.structure\_graph.abstract\_sample\_path module +------------------------------------------------------ + +.. automodule:: PyCTBN.structure_graph.abstract_sample_path + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.conditional\_intensity\_matrix module +-------------------------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.conditional_intensity_matrix + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.network\_graph module +---------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.network_graph + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.sample\_path module +-------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.sample_path + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.set\_of\_cims module +--------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.set_of_cims + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.sets\_of\_cims\_container module +--------------------------------------------------------- + +.. automodule:: PyCTBN.structure_graph.sets_of_cims_container + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.structure module +----------------------------------------- + +.. automodule:: PyCTBN.structure_graph.structure + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.structure\_graph.trajectory module +------------------------------------------ + +.. automodule:: PyCTBN.structure_graph.trajectory + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.structure_graph + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/classes.utility.rst b/docs/classes.utility.rst new file mode 100644 index 0000000..6285db8 --- /dev/null +++ b/docs/classes.utility.rst @@ -0,0 +1,53 @@ +PyCTBN.utility package +======================= + +Submodules +---------- + +PyCTBN.utility.abstract\_importer module +----------------------------------------- + +.. automodule:: PyCTBN.utility.abstract_importer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.cache module +---------------------------- + +.. automodule:: PyCTBN.utility.cache + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.decorators module +--------------------------------- + +.. automodule:: PyCTBN.utility.decorators + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.json\_importer module +------------------------------------- + +.. automodule:: PyCTBN.utility.json_importer + :members: + :undoc-members: + :show-inheritance: + +PyCTBN.utility.sample\_importer module +--------------------------------------- + +.. automodule:: PyCTBN.utility.sample_importer + :members: + :undoc-members: + :show-inheritance: + +Module contents +--------------- + +.. automodule:: PyCTBN.utility + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 0000000..2acb0f0 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,70 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) + +import os +import sys +sys.path.insert(0, os.path.abspath('../PyCTBN/')) +#sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN')) +sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN')) +sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN/estimators')) +sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN/optimizers')) +sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN/structure_graph')) +sys.path.insert(0, os.path.abspath('../PyCTBN/PyCTBN/utility')) + +print(sys.path) + +# -- Project information ----------------------------------------------------- + +project = 'PyCTBN' +copyright = '2021, Bregoli Alessandro, Martini Filippo, Moretti Luca' +author = 'Bregoli Alessandro, Martini Filippo, Moretti Luca' + +# The full version, including alpha/beta/rc tags +release = '2.0' + + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = ['sphinx.ext.autodoc' +] + +#autosummary_generate = True + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_pdj_theme' +import sphinx_pdj_theme +html_theme_path = [sphinx_pdj_theme.get_html_theme_path()] + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] \ No newline at end of file diff --git a/docs/examples.rst b/docs/examples.rst new file mode 100644 index 0000000..b1e07c5 --- /dev/null +++ b/docs/examples.rst @@ -0,0 +1,121 @@ +Examples +======== + +Installation/Usage +****************** +Download the release in .tar.gz or .whl format and simply use pip install to install it:: + + $pip install PyCTBN-1.0.tar.gz + + +Implementing your own data importer +*********************************** +.. code-block:: python + + """This example demonstrates the implementation of a simple data importer the extends the class abstract importer to import data in csv format. + The net in exam has three ternary nodes and no prior net structure. + """ + + from PyCTBN import AbstractImporter + + class CSVImporter(AbstractImporter): + + def __init__(self, file_path): + self._df_samples_list = None + super(CSVImporter, self).__init__(file_path) + + def import_data(self): + self.read_csv_file() + self._sorter = self.build_sorter(self._df_samples_list[0]) + self.import_variables() + self.compute_row_delta_in_all_samples_frames(self._df_samples_list) + + def read_csv_file(self): + df = pd.read_csv(self._file_path) + df.drop(df.columns[[0]], axis=1, inplace=True) + self._df_samples_list = [df] + + def import_variables(self): + values_list = [3 for var in self._sorter] + # initialize dict of lists + data = {'Name':self._sorter, 'Value':values_list} + # Create the pandas DataFrame + self._df_variables = pd.DataFrame(data) + + def build_sorter(self, sample_frame: pd.DataFrame) -> typing.List: + return list(sample_frame.columns)[1:] + + def dataset_id(self) -> object: + pass + +Parameters Estimation Example +***************************** + +.. code-block:: python + + from PyCTBN import JsonImporter + from PyCTBN import SamplePath + from PyCTBN import NetworkGraph + from PyCTBN import ParametersEstimator + + + def main(): + read_files = glob.glob(os.path.join('./data', "*.json")) #Take all json files in this dir + #import data + importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + importer.import_data(0) + #Create a SamplePath Obj passing an already filled AbstractImporter object + s1 = SamplePath(importer) + #Build The trajectries and the structural infos + s1.build_trajectories() + s1.build_structure() + print(s1.structure.edges) + print(s1.structure.nodes_values) + #From The Structure Object build the Graph + g = NetworkGraph(s1.structure) + #Select a node you want to estimate the parameters + node = g.nodes[2] + print("Node", node) + #Init the _graph specifically for THIS node + g.fast_init(node) + #Use SamplePath and Grpah to create a ParametersEstimator Object + p1 = ParametersEstimator(s1.trajectories, 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) + +Structure Estimation Example +**************************** + +.. code-block:: python + + from PyCTBN import JsonImporter + from PyCTBN import SamplePath + from PyCTBN import StructureEstimator + + def structure_estimation_example(): + + # read the json files in ./data path + read_files = glob.glob(os.path.join('./data', "*.json")) + # initialize a JsonImporter object for the first file + importer = JsonImporter(read_files[0], 'samples', 'dyn.str', 'variables', 'Time', 'Name') + # import the data at index 0 of the outer json array + importer.import_data(0) + # construct a SamplePath Object passing a filled AbstractImporter + s1 = SamplePath(importer) + # build the trajectories + s1.build_trajectories() + # build the real structure + s1.build_structure() + # construct a StructureEstimator object + se1 = StructureEstimator(s1, 0.1, 0.1) + # call the ctpc algorithm + se1.ctpc_algorithm() + # the adjacency matrix of the estimated structure + print(se1.adjacency_matrix()) + # save results to a json file + se1.save_results() + diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 0000000..236bf53 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,21 @@ +.. PyCTBN documentation master file, created by + sphinx-quickstart on Wed Feb 24 18:06:35 2021. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to PyCTBN's documentation! +================================== + +.. toctree:: + :maxdepth: 3 + :caption: Contents: + + examples + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..922152e --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/modules.rst b/docs/modules.rst new file mode 100644 index 0000000..5b5819a --- /dev/null +++ b/docs/modules.rst @@ -0,0 +1,7 @@ +PyCTBN +======= + +.. toctree:: + :maxdepth: 4 + + PyCTBN diff --git a/main_package/classes/__init__.py b/main_package/classes/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/main_package/data/esempio_dataset.csv b/main_package/data/esempio_dataset.csv deleted file mode 100644 index 09f00dd..0000000 --- a/main_package/data/esempio_dataset.csv +++ /dev/null @@ -1,1864 +0,0 @@ -,Time,X,Y,Z -0,0.0,2,2,2 -1,0.0795,1,2,2 -2,0.0979,1,2,1 -3,0.1675,1,1,1 -4,0.2181,1,0,1 -5,0.3233,1,0,2 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