diff --git a/lang/fr/gklearn/preimage/median_preimage_generator_cml.py b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py index e6bca92..e56b894 100644 --- a/lang/fr/gklearn/preimage/median_preimage_generator_cml.py +++ b/lang/fr/gklearn/preimage/median_preimage_generator_cml.py @@ -27,69 +27,69 @@ class MedianPreimageGeneratorCML(PreimageGenerator): 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 + 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 + 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 + 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 + 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 + 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) + 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): @@ -105,48 +105,48 @@ class MedianPreimageGeneratorCML(PreimageGenerator): start = time.time() # 1. precompute gram matrix. - if self.__gram_matrix_unnorm is None: + 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 + self._gram_matrix_unnorm = self._graph_kernel.gram_matrix_unnorm end_precompute_gm = time.time() - self.__runtime_precompute_gm = end_precompute_gm - start + self._runtime_precompute_gm = end_precompute_gm - start else: - if self.__runtime_precompute_gm is None: + 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 + 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)) + 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) + self._graph_kernel.gram_matrix = np.copy(self._gram_matrix_unnorm) end_precompute_gm = time.time() - start -= self.__runtime_precompute_gm + start -= self._runtime_precompute_gm -# if self.__fit_method != 'k-graphs' and self.__fit_method != 'whole-dataset': +# if self._fit_method != 'k-graphs' and self._fit_method != 'whole-dataset': # start = time.time() -# self.__runtime_precompute_gm = 0 +# self._runtime_precompute_gm = 0 # end_precompute_gm = start # 2. optimize edit cost constants. - self.__optimize_edit_cost_vector() + self._optimize_edit_cost_vector() end_optimize_ec = time.time() - self.__runtime_optimize_ec = end_optimize_ec - end_precompute_gm + 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() + 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 + 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) + 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() + self._compute_distances_to_true_median() # 5. print out results. if self._verbose: @@ -154,145 +154,145 @@ class MedianPreimageGeneratorCML(PreimageGenerator): 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('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['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() + 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): + def _optimize_edit_cost_vector(self): """Learn edit cost vector. """ # Initialize label costs randomly. - if self.__init_method == 'random': + if self._init_method == 'random': # Initialize label costs. - self.__initialize_label_costs() + self._initialize_label_costs() # Optimize edit cost matrices. - self.__optimize_ecm_by_kernel_distances() + self._optimize_ecm_by_kernel_distances() # Initialize all label costs with the same value. - elif self.__init_method == 'uniform': # random + 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': + 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] + 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 + self._edit_cost_constants[2] = 0 if self._dataset.edge_attrs == []: - self.__edit_cost_constants[5] = 0 + 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] + 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] + 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] + 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] + 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 + self._init_ecc[2] = 0 if self._dataset.edge_attrs == []: - self.__init_ecc[5] = 0 + self._init_ecc[5] = 0 else: - self.__init_ecc = [3, 3, 1, 3, 3, 1] + 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] + 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] + self._init_ecc = [3, 3, 1, 3, 3, 1] # optimizeon the whole set. - self.__optimize_ecc_by_kernel_distances() - elif self.__fit_method == 'precomputed': + 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_label_costs(self): + self._initialize_node_label_costs() + self._initialize_edge_label_costs() - def __initialize_node_label_costs(self): + 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 + self._node_label_costs = rand_costs - def __initialize_edge_label_costs(self): + 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 + self._edge_label_costs = rand_costs - def __optimize_ecm_by_kernel_distances(self): + def _optimize_ecm_by_kernel_distances(self): # compute distances in feature space. dis_k_mat, _, _, _ = self._graph_kernel.compute_distance_matrix() dis_k_vec = [] @@ -303,35 +303,35 @@ class MedianPreimageGeneratorCML(PreimageGenerator): 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. +# 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 + 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) + 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'] + 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):] + 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): + def _gmg_bcu(self): """ The local search algorithm based on block coordinate update (BCU) for estimating a generalized median graph (GMG). @@ -343,77 +343,77 @@ class MedianPreimageGeneratorCML(PreimageGenerator): # 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] + 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)) + 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']) + 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() + 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 + 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, + 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 = 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) + 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) + 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') + 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) + 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): + 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) + 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 + 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)), + 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) + 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 + 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)), + 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) @@ -424,19 +424,19 @@ class MedianPreimageGeneratorCML(PreimageGenerator): [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() + 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 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. +# 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. """ @@ -458,63 +458,63 @@ class MedianPreimageGeneratorCML(PreimageGenerator): @property def mge(self): - return self.__mge + return self._mge @property def ged_options(self): - return self.__ged_options + return self._ged_options @ged_options.setter def ged_options(self, value): - self.__ged_options = value + self._ged_options = value @property def mge_options(self): - return self.__mge_options + return self._mge_options @mge_options.setter def mge_options(self, value): - self.__mge_options = value + self._mge_options = value @property def fit_method(self): - return self.__fit_method + return self._fit_method @fit_method.setter def fit_method(self, value): - self.__fit_method = value + self._fit_method = value @property def init_ecc(self): - return self.__init_ecc + return self._init_ecc @init_ecc.setter def init_ecc(self, value): - self.__init_ecc = value + self._init_ecc = value @property def set_median(self): - return self.__set_median + return self._set_median @property def gen_median(self): - return self.__gen_median + return self._gen_median @property def best_from_dataset(self): - return self.__best_from_dataset + return self._best_from_dataset @property def gram_matrix_unnorm(self): - return self.__gram_matrix_unnorm + 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 + self._gram_matrix_unnorm = value \ No newline at end of file