diff --git a/lang/fr/gklearn/preimage/median_preimage_generator_cml.py b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py new file mode 100644 index 0000000..e6bca92 --- /dev/null +++ b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py @@ -0,0 +1,520 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Jun 16 16:04:46 2020 + +@author: ljia +""" +import numpy as np +import time +import random +import multiprocessing +import networkx as nx +from gklearn.preimage import PreimageGenerator +from gklearn.preimage.utils import compute_k_dis +from gklearn.ged.env import GEDEnv +from gklearn.ged.learning import CostMatricesLearner +from gklearn.ged.median import MedianGraphEstimatorCML +from gklearn.ged.median import constant_node_costs, mge_options_to_string +from gklearn.utils.utils import get_graph_kernel_by_name +from gklearn.ged.util import label_costs_to_matrix + + +class MedianPreimageGeneratorCML(PreimageGenerator): + """Generator median preimages by cost matrices learning using the pure Python version of GEDEnv. Works only for symbolic labeled graphs. + """ + + def __init__(self, dataset=None): + PreimageGenerator.__init__(self, dataset=dataset) + ### arguments to set. + self.__mge = None + self.__ged_options = {} + self.__mge_options = {} +# self.__fit_method = 'k-graphs' + self.__init_method = 'random' + self.__init_ecc = None + self.__parallel = True + self.__n_jobs = multiprocessing.cpu_count() + self.__ds_name = None + # for cml. + self.__time_limit_in_sec = 0 + self.__max_itrs = 100 + self.__max_itrs_without_update = 3 + self.__epsilon_residual = 0.01 + self.__epsilon_ec = 0.1 + self.__allow_zeros = True +# self.__triangle_rule = True + ### values to compute. + self.__runtime_optimize_ec = None + self.__runtime_generate_preimage = None + self.__runtime_total = None + self.__set_median = None + self.__gen_median = None + self.__best_from_dataset = None + self.__sod_set_median = None + self.__sod_gen_median = None + self.__k_dis_set_median = None + self.__k_dis_gen_median = None + self.__k_dis_dataset = None + self.__node_label_costs = None + self.__edge_label_costs = None + # for cml. + self.__itrs = 0 + self.__converged = False + self.__num_updates_ecs = 0 + ### values that can be set or to be computed. + self.__edit_cost_constants = [] + self.__gram_matrix_unnorm = None + self.__runtime_precompute_gm = None + + + def set_options(self, **kwargs): + self._kernel_options = kwargs.get('kernel_options', {}) + self._graph_kernel = kwargs.get('graph_kernel', None) + self._verbose = kwargs.get('verbose', 2) + self.__ged_options = kwargs.get('ged_options', {}) + self.__mge_options = kwargs.get('mge_options', {}) +# self.__fit_method = kwargs.get('fit_method', 'k-graphs') + self.__init_method = kwargs.get('init_method', 'random') + self.__init_ecc = kwargs.get('init_ecc', None) + self.__edit_cost_constants = kwargs.get('edit_cost_constants', []) + self.__parallel = kwargs.get('parallel', True) + self.__n_jobs = kwargs.get('n_jobs', multiprocessing.cpu_count()) + self.__ds_name = kwargs.get('ds_name', None) + self.__time_limit_in_sec = kwargs.get('time_limit_in_sec', 0) + self.__max_itrs = kwargs.get('max_itrs', 100) + self.__max_itrs_without_update = kwargs.get('max_itrs_without_update', 3) + self.__epsilon_residual = kwargs.get('epsilon_residual', 0.01) + self.__epsilon_ec = kwargs.get('epsilon_ec', 0.1) + self.__gram_matrix_unnorm = kwargs.get('gram_matrix_unnorm', None) + self.__runtime_precompute_gm = kwargs.get('runtime_precompute_gm', None) + self.__allow_zeros = kwargs.get('allow_zeros', True) +# self.__triangle_rule = kwargs.get('triangle_rule', True) + + + def run(self): + self._graph_kernel = get_graph_kernel_by_name(self._kernel_options['name'], + node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs, + ds_infos=self._dataset.get_dataset_infos(keys=['directed']), + kernel_options=self._kernel_options) + + # record start time. + start = time.time() + + # 1. precompute gram matrix. + if self.__gram_matrix_unnorm is None: + gram_matrix, run_time = self._graph_kernel.compute(self._dataset.graphs, **self._kernel_options) + self.__gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm + end_precompute_gm = time.time() + self.__runtime_precompute_gm = end_precompute_gm - start + else: + if self.__runtime_precompute_gm is None: + raise Exception('Parameter "runtime_precompute_gm" must be given when using pre-computed Gram matrix.') + self._graph_kernel.gram_matrix_unnorm = self.__gram_matrix_unnorm + if self._kernel_options['normalize']: + self._graph_kernel.gram_matrix = self._graph_kernel.normalize_gm(np.copy(self.__gram_matrix_unnorm)) + else: + self._graph_kernel.gram_matrix = np.copy(self.__gram_matrix_unnorm) + end_precompute_gm = time.time() + start -= self.__runtime_precompute_gm + +# if self.__fit_method != 'k-graphs' and self.__fit_method != 'whole-dataset': +# start = time.time() +# self.__runtime_precompute_gm = 0 +# end_precompute_gm = start + + # 2. optimize edit cost constants. + self.__optimize_edit_cost_vector() + end_optimize_ec = time.time() + self.__runtime_optimize_ec = end_optimize_ec - end_precompute_gm + + # 3. compute set median and gen median using optimized edit costs. + if self._verbose >= 2: + print('\nstart computing set median and gen median using optimized edit costs...\n') + self.__gmg_bcu() + end_generate_preimage = time.time() + self.__runtime_generate_preimage = end_generate_preimage - end_optimize_ec + self.__runtime_total = end_generate_preimage - start + if self._verbose >= 2: + print('medians computed.') + print('SOD of the set median: ', self.__sod_set_median) + print('SOD of the generalized median: ', self.__sod_gen_median) + + # 4. compute kernel distances to the true median. + if self._verbose >= 2: + print('\nstart computing distances to true median....\n') + self.__compute_distances_to_true_median() + + # 5. print out results. + if self._verbose: + print() + print('================================================================================') + print('Finished generation of preimages.') + print('--------------------------------------------------------------------------------') + print('The optimized edit costs:', self.__edit_cost_constants) + print('SOD of the set median:', self.__sod_set_median) + print('SOD of the generalized median:', self.__sod_gen_median) + print('Distance in kernel space for set median:', self.__k_dis_set_median) + print('Distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('Minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('Time to pre-compute Gram matrix:', self.__runtime_precompute_gm) + print('Time to optimize edit costs:', self.__runtime_optimize_ec) + print('Time to generate pre-images:', self.__runtime_generate_preimage) + print('Total time:', self.__runtime_total) + print('Total number of iterations for optimizing:', self.__itrs) + print('Total number of updating edit costs:', self.__num_updates_ecs) + print('Is optimization of edit costs converged:', self.__converged) + print('================================================================================') + print() + + + def get_results(self): + results = {} + results['edit_cost_constants'] = self.__edit_cost_constants + results['runtime_precompute_gm'] = self.__runtime_precompute_gm + results['runtime_optimize_ec'] = self.__runtime_optimize_ec + results['runtime_generate_preimage'] = self.__runtime_generate_preimage + results['runtime_total'] = self.__runtime_total + results['sod_set_median'] = self.__sod_set_median + results['sod_gen_median'] = self.__sod_gen_median + results['k_dis_set_median'] = self.__k_dis_set_median + results['k_dis_gen_median'] = self.__k_dis_gen_median + results['k_dis_dataset'] = self.__k_dis_dataset + results['itrs'] = self.__itrs + results['converged'] = self.__converged + results['num_updates_ecc'] = self.__num_updates_ecs + results['mge'] = {} + results['mge']['num_decrease_order'] = self.__mge.get_num_times_order_decreased() + results['mge']['num_increase_order'] = self.__mge.get_num_times_order_increased() + results['mge']['num_converged_descents'] = self.__mge.get_num_converged_descents() + return results + + + def __optimize_edit_cost_vector(self): + """Learn edit cost vector. + """ + # Initialize label costs randomly. + if self.__init_method == 'random': + # Initialize label costs. + self.__initialize_label_costs() + + # Optimize edit cost matrices. + self.__optimize_ecm_by_kernel_distances() + # Initialize all label costs with the same value. + elif self.__init_method == 'uniform': # random + pass + + elif self.__fit_method == 'random': # random + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = random.sample(range(1, 1000), 3) + self.__edit_cost_constants = [item * 0.001 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'LETTER2': + random.seed(time.time()) + self.__edit_cost_constants = random.sample(range(1, 1000), 5) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._dataset.node_attrs == []: + self.__edit_cost_constants[2] = 0 + if self._dataset.edge_attrs == []: + self.__edit_cost_constants[5] = 0 + else: + self.__edit_cost_constants = random.sample(range(1, 1000), 6) + self.__edit_cost_constants = [item * 0.01 for item in self.__edit_cost_constants] + if self._verbose >= 2: + print('edit cost constants used:', self.__edit_cost_constants) + elif self.__fit_method == 'expert': # expert + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__edit_cost_constants = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__edit_cost_constants = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__edit_cost_constants = [3, 3, 1, 3, 3, 1] + else: + self.__edit_cost_constants = self.__init_ecc + elif self.__fit_method == 'k-graphs': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + elif self.__ged_options['edit_cost'] == 'NON_SYMBOLIC': + self.__init_ecc = [0, 0, 1, 1, 1, 0] + if self._dataset.node_attrs == []: + self.__init_ecc[2] = 0 + if self._dataset.edge_attrs == []: + self.__init_ecc[5] = 0 + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimize on the k-graph subset. + self.__optimize_ecm_by_kernel_distances() + elif self.__fit_method == 'whole-dataset': + if self.__init_ecc is None: + if self.__ged_options['edit_cost'] == 'LETTER': + self.__init_ecc = [0.9, 1.7, 0.75] + elif self.__ged_options['edit_cost'] == 'LETTER2': + self.__init_ecc = [0.675, 0.675, 0.75, 0.425, 0.425] + else: + self.__init_ecc = [3, 3, 1, 3, 3, 1] + # optimizeon the whole set. + self.__optimize_ecc_by_kernel_distances() + elif self.__fit_method == 'precomputed': + pass + + + def __initialize_label_costs(self): + self.__initialize_node_label_costs() + self.__initialize_edge_label_costs() + + + def __initialize_node_label_costs(self): + # Get list of node labels. + nls = self._dataset.get_all_node_labels() + # Generate random costs. + nb_nl = int((len(nls) * (len(nls) - 1)) / 2 + 2 * len(nls)) + rand_costs = random.sample(range(1, 10 * nb_nl + 1), nb_nl) + rand_costs /= np.max(rand_costs) # @todo: maybe not needed. + self.__node_label_costs = rand_costs + + + def __initialize_edge_label_costs(self): + # Get list of edge labels. + els = self._dataset.get_all_edge_labels() + # Generate random costs. + nb_el = int((len(els) * (len(els) - 1)) / 2 + 2 * len(els)) + rand_costs = random.sample(range(1, 10 * nb_el + 1), nb_el) + rand_costs /= np.max(rand_costs) # @todo: maybe not needed. + self.__edge_label_costs = rand_costs + + + def __optimize_ecm_by_kernel_distances(self): + # compute distances in feature space. + dis_k_mat, _, _, _ = self._graph_kernel.compute_distance_matrix() + dis_k_vec = [] + for i in range(len(dis_k_mat)): + # for j in range(i, len(dis_k_mat)): + for j in range(i + 1, len(dis_k_mat)): + dis_k_vec.append(dis_k_mat[i, j]) + dis_k_vec = np.array(dis_k_vec) + + # Set GEDEnv options. +# graphs = [self.__clean_graph(g) for g in self._dataset.graphs] +# self.__edit_cost_constants = self.__init_ecc + options = self.__ged_options.copy() + options['edit_cost_constants'] = self.__edit_cost_constants # @todo: not needed. + options['node_labels'] = self._dataset.node_labels + options['edge_labels'] = self._dataset.edge_labels +# options['node_attrs'] = self._dataset.node_attrs +# options['edge_attrs'] = self._dataset.edge_attrs + options['node_label_costs'] = self.__node_label_costs + options['edge_label_costs'] = self.__edge_label_costs + + # Learner cost matrices. + # Initialize cost learner. + cml = CostMatricesLearner(edit_cost='CONSTANT', triangle_rule=False, allow_zeros=True, parallel=self.__parallel, verbose=self._verbose) # @todo + cml.set_update_params(time_limit_in_sec=self.__time_limit_in_sec, max_itrs=self.__max_itrs, max_itrs_without_update=self.__max_itrs_without_update, epsilon_residual=self.__epsilon_residual, epsilon_ec=self.__epsilon_ec) + # Run cost learner. + cml.update(dis_k_vec, self._dataset.graphs, options) + + # Get results. + results = cml.get_results() + self.__converged = results['converged'] + self.__itrs = results['itrs'] + self.__num_updates_ecs = results['num_updates_ecs'] + cost_list = results['cost_list'] + self.__node_label_costs = cost_list[-1][0:len(self.__node_label_costs)] + self.__edge_label_costs = cost_list[-1][len(self.__node_label_costs):] + + + def __gmg_bcu(self): + """ + The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG). + + Returns + ------- + None. + + """ + # Set up the ged environment. + ged_env = GEDEnv() # @todo: maybe create a ged_env as a private varible. + # gedlibpy.restart_env() + ged_env.set_edit_cost(self.__ged_options['edit_cost'], edit_cost_constants=self.__edit_cost_constants) + graphs = [self.__clean_graph(g) for g in self._dataset.graphs] + for g in graphs: + ged_env.add_nx_graph(g, '') + graph_ids = ged_env.get_all_graph_ids() + + node_labels = ged_env.get_all_node_labels() + edge_labels = ged_env.get_all_edge_labels() + node_label_costs = label_costs_to_matrix(self.__node_label_costs, len(node_labels)) + edge_label_costs = label_costs_to_matrix(self.__edge_label_costs, len(edge_labels)) + ged_env.set_label_costs(node_label_costs, edge_label_costs) + + set_median_id = ged_env.add_graph('set_median') + gen_median_id = ged_env.add_graph('gen_median') + ged_env.init(init_type=self.__ged_options['init_option']) + + # Set up the madian graph estimator. + self.__mge = MedianGraphEstimatorCML(ged_env, constant_node_costs(self.__ged_options['edit_cost'])) + self.__mge.set_refine_method(self.__ged_options['method'], self.__ged_options) + options = self.__mge_options.copy() + if not 'seed' in options: + options['seed'] = int(round(time.time() * 1000)) # @todo: may not work correctly for possible parallel usage. + options['parallel'] = self.__parallel + + # Select the GED algorithm. + self.__mge.set_options(mge_options_to_string(options)) + self.__mge.set_label_names(node_labels=self._dataset.node_labels, + edge_labels=self._dataset.edge_labels, + node_attrs=self._dataset.node_attrs, + edge_attrs=self._dataset.edge_attrs) + ged_options = self.__ged_options.copy() + if self.__parallel: + ged_options['threads'] = 1 + self.__mge.set_init_method(ged_options['method'], ged_options) + self.__mge.set_descent_method(ged_options['method'], ged_options) + + # Run the estimator. + self.__mge.run(graph_ids, set_median_id, gen_median_id) + + # Get SODs. + self.__sod_set_median = self.__mge.get_sum_of_distances('initialized') + self.__sod_gen_median = self.__mge.get_sum_of_distances('converged') + + # Get median graphs. + self.__set_median = ged_env.get_nx_graph(set_median_id) + self.__gen_median = ged_env.get_nx_graph(gen_median_id) + + + def __compute_distances_to_true_median(self): + # compute distance in kernel space for set median. + kernels_to_sm, _ = self._graph_kernel.compute(self.__set_median, self._dataset.graphs, **self._kernel_options) + kernel_sm, _ = self._graph_kernel.compute(self.__set_median, self.__set_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_sm = [kernels_to_sm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_sm) for i in range(len(kernels_to_sm))] # normalize + kernel_sm = 1 + # @todo: not correct kernel value + gram_with_sm = np.concatenate((np.array([kernels_to_sm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_sm = np.concatenate((np.array([[kernel_sm] + kernels_to_sm]).T, gram_with_sm), axis=1) + self.__k_dis_set_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_sm, withterm3=False) + + # compute distance in kernel space for generalized median. + kernels_to_gm, _ = self._graph_kernel.compute(self.__gen_median, self._dataset.graphs, **self._kernel_options) + kernel_gm, _ = self._graph_kernel.compute(self.__gen_median, self.__gen_median, **self._kernel_options) + if self._kernel_options['normalize']: + kernels_to_gm = [kernels_to_gm[i] / np.sqrt(self.__gram_matrix_unnorm[i, i] * kernel_gm) for i in range(len(kernels_to_gm))] # normalize + kernel_gm = 1 + gram_with_gm = np.concatenate((np.array([kernels_to_gm]), np.copy(self._graph_kernel.gram_matrix)), axis=0) + gram_with_gm = np.concatenate((np.array([[kernel_gm] + kernels_to_gm]).T, gram_with_gm), axis=1) + self.__k_dis_gen_median = compute_k_dis(0, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False) + + # compute distance in kernel space for each graph in median set. + k_dis_median_set = [] + for idx in range(len(self._dataset.graphs)): + k_dis_median_set.append(compute_k_dis(idx+1, range(1, 1+len(self._dataset.graphs)), + [1 / len(self._dataset.graphs)] * len(self._dataset.graphs), + gram_with_gm, withterm3=False)) + idx_k_dis_median_set_min = np.argmin(k_dis_median_set) + self.__k_dis_dataset = k_dis_median_set[idx_k_dis_median_set_min] + self.__best_from_dataset = self._dataset.graphs[idx_k_dis_median_set_min].copy() + + if self._verbose >= 2: + print() + print('distance in kernel space for set median:', self.__k_dis_set_median) + print('distance in kernel space for generalized median:', self.__k_dis_gen_median) + print('minimum distance in kernel space for each graph in median set:', self.__k_dis_dataset) + print('distance in kernel space for each graph in median set:', k_dis_median_set) + + +# def __clean_graph(self, G, node_labels=[], edge_labels=[], node_attrs=[], edge_attrs=[]): + def __clean_graph(self, G): # @todo: this may not be needed when datafile is updated. + """ + Cleans node and edge labels and attributes of the given graph. + """ + G_new = nx.Graph(**G.graph) + for nd, attrs in G.nodes(data=True): + G_new.add_node(str(nd)) # @todo: should we keep this as str()? + for l_name in self._dataset.node_labels: + G_new.nodes[str(nd)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.node_attrs: + G_new.nodes[str(nd)][a_name] = str(attrs[a_name]) + for nd1, nd2, attrs in G.edges(data=True): + G_new.add_edge(str(nd1), str(nd2)) + for l_name in self._dataset.edge_labels: + G_new.edges[str(nd1), str(nd2)][l_name] = str(attrs[l_name]) + for a_name in self._dataset.edge_attrs: + G_new.edges[str(nd1), str(nd2)][a_name] = str(attrs[a_name]) + return G_new + + + @property + def mge(self): + return self.__mge + + @property + def ged_options(self): + return self.__ged_options + + @ged_options.setter + def ged_options(self, value): + self.__ged_options = value + + + @property + def mge_options(self): + return self.__mge_options + + @mge_options.setter + def mge_options(self, value): + self.__mge_options = value + + + @property + def fit_method(self): + return self.__fit_method + + @fit_method.setter + def fit_method(self, value): + self.__fit_method = value + + + @property + def init_ecc(self): + return self.__init_ecc + + @init_ecc.setter + def init_ecc(self, value): + self.__init_ecc = value + + + @property + def set_median(self): + return self.__set_median + + + @property + def gen_median(self): + return self.__gen_median + + + @property + def best_from_dataset(self): + return self.__best_from_dataset + + + @property + def gram_matrix_unnorm(self): + return self.__gram_matrix_unnorm + + @gram_matrix_unnorm.setter + def gram_matrix_unnorm(self, value): + self.__gram_matrix_unnorm = value \ No newline at end of file