diff --git a/lang/zh/gklearn/preimage/kernel_knn_cv.py b/lang/zh/gklearn/preimage/kernel_knn_cv.py index 073fa31..1ae25d1 100644 --- a/lang/zh/gklearn/preimage/kernel_knn_cv.py +++ b/lang/zh/gklearn/preimage/kernel_knn_cv.py @@ -33,35 +33,35 @@ def kernel_knn_cv(ds_name, train_examples, knn_options, mpg_options, kernel_opti if save_results: # create result files. print('creating output files...') - fn_output_detail, fn_output_summary = __init_output_file_knn(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save) + fn_output_detail, fn_output_summary = _init_output_file_knn(ds_name, kernel_options['name'], mpg_options['fit_method'], dir_save) else: fn_output_detail, fn_output_summary = None, None # 2. compute/load Gram matrix a priori. print('2. computing/loading Gram matrix...') - gram_matrix_unnorm, time_precompute_gm = __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all) + gram_matrix_unnorm, time_precompute_gm = _get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all) # 3. perform k-nn CV. print('3. performing k-nn CV...') if train_examples == 'k-graphs' or train_examples == 'expert' or train_examples == 'random': - __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + _kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) elif train_examples == 'best-dataset': - __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + _kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) elif train_examples == 'trainset': - __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) + _kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary) print('\ncomplete.\n') -def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): +def _kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kernel_options, mge_options, ged_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): Gn = dataset_all.graphs y_all = dataset_all.targets n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] # get shuffles. - train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size) + train_indices, test_indices, train_nums, y_app = _get_shuffles(y_all, n_splits, test_size) accuracies = [[], [], []] for trial in range(len(train_indices)): @@ -89,11 +89,11 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne mge_options['update_order'] = True mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy() mpg_options['runtime_precompute_gm'] = 0 - set_median, gen_median_uo = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) + set_median, gen_median_uo = _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) mge_options['update_order'] = False mpg_options['gram_matrix_unnorm'] = gm_unnorm_trial[i_start:i_end,i_start:i_end].copy() mpg_options['runtime_precompute_gm'] = 0 - _, gen_median = __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) + _, gen_median = _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options) medians[0].append(set_median) medians[1].append(gen_median) medians[2].append(gen_median_uo) @@ -104,10 +104,10 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne # compute dis_mat between medians. dataset = dataset_all.copy() dataset.load_graphs([g.copy() for g in G_app], targets=None) - gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy()) + gm_app_unnorm, _ = _compute_gram_matrix_unnorm(dataset, kernel_options.copy()) # compute the entire Gram matrix. - graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy()) + graph_kernel = _get_graph_kernel(dataset.copy(), kernel_options.copy()) kernels_to_medians = [] for g in G_app: kernels_to_median, _ = graph_kernel.compute(g, G_test, **kernel_options.copy()) @@ -161,13 +161,13 @@ def __kernel_knn_cv_median(dataset_all, ds_name, knn_options, mpg_options, kerne f_summary.close() -def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): +def _kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): Gn = dataset_all.graphs y_all = dataset_all.targets n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] # get shuffles. - train_indices, test_indices, train_nums, y_app = __get_shuffles(y_all, n_splits, test_size) + train_indices, test_indices, train_nums, y_app = _get_shuffles(y_all, n_splits, test_size) accuracies = [] for trial in range(len(train_indices)): @@ -204,10 +204,10 @@ def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, g # compute dis_mat between medians. dataset = dataset_all.copy() dataset.load_graphs([g.copy() for g in best_graphs], targets=None) - gm_app_unnorm, _ = __compute_gram_matrix_unnorm(dataset, kernel_options.copy()) + gm_app_unnorm, _ = _compute_gram_matrix_unnorm(dataset, kernel_options.copy()) # compute the entire Gram matrix. - graph_kernel = __get_graph_kernel(dataset.copy(), kernel_options.copy()) + graph_kernel = _get_graph_kernel(dataset.copy(), kernel_options.copy()) kernels_to_best_graphs = [] for g in best_graphs: kernels_to_best_graph, _ = graph_kernel.compute(g, G_test, **kernel_options.copy()) @@ -259,7 +259,7 @@ def __kernel_knn_cv_best_ds(dataset_all, ds_name, knn_options, kernel_options, g f_summary.close() -def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): +def _kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, gram_matrix_unnorm, time_precompute_gm, train_examples, save_results, dir_save, fn_output_detail, fn_output_summary): y_all = dataset_all.targets n_neighbors, n_splits, test_size = knn_options['n_neighbors'], knn_options['n_splits'], knn_options['test_size'] @@ -268,7 +268,7 @@ def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, dis_mat, _, _, _ = compute_distance_matrix(gram_matrix) # get shuffles. - train_indices, test_indices, _, _ = __get_shuffles(y_all, n_splits, test_size) + train_indices, test_indices, _, _ = _get_shuffles(y_all, n_splits, test_size) accuracies = [] for trial in range(len(train_indices)): @@ -317,7 +317,7 @@ def __kernel_knn_cv_trainset(dataset_all, ds_name, knn_options, kernel_options, f_summary.close() -def __get_shuffles(y_all, n_splits, test_size): +def _get_shuffles(y_all, n_splits, test_size): rs = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=0) train_indices = [[] for _ in range(n_splits)] test_indices = [[] for _ in range(n_splits)] @@ -335,7 +335,7 @@ def __get_shuffles(y_all, n_splits, test_size): return train_indices, test_indices, train_nums, keys -def __generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options): +def _generate_median_preimages(dataset, mpg_options, kernel_options, ged_options, mge_options): mpg = MedianPreimageGenerator() mpg.dataset = dataset.copy() mpg.set_options(**mpg_options.copy()) @@ -346,7 +346,7 @@ def __generate_median_preimages(dataset, mpg_options, kernel_options, ged_option return mpg.set_median, mpg.gen_median -def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all): +def _get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all): if load_gm == 'auto': gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' gmfile_exist = os.path.isfile(os.path.abspath(gm_fname)) @@ -355,10 +355,10 @@ def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all): gram_matrix_unnorm = gmfile['gram_matrix_unnorm'] time_precompute_gm = float(gmfile['run_time']) else: - gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options) + gram_matrix_unnorm, time_precompute_gm = _compute_gram_matrix_unnorm(dataset_all, kernel_options) np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm) elif not load_gm: - gram_matrix_unnorm, time_precompute_gm = __compute_gram_matrix_unnorm(dataset_all, kernel_options) + gram_matrix_unnorm, time_precompute_gm = _compute_gram_matrix_unnorm(dataset_all, kernel_options) np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=gram_matrix_unnorm, run_time=time_precompute_gm) else: gm_fname = dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm.npz' @@ -369,7 +369,7 @@ def __get_gram_matrix(load_gm, dir_save, ds_name, kernel_options, dataset_all): return gram_matrix_unnorm, time_precompute_gm -def __get_graph_kernel(dataset, kernel_options): +def _get_graph_kernel(dataset, kernel_options): from gklearn.utils.utils import get_graph_kernel_by_name graph_kernel = get_graph_kernel_by_name(kernel_options['name'], node_labels=dataset.node_labels, @@ -381,7 +381,7 @@ def __get_graph_kernel(dataset, kernel_options): return graph_kernel -def __compute_gram_matrix_unnorm(dataset, kernel_options): +def _compute_gram_matrix_unnorm(dataset, kernel_options): from gklearn.utils.utils import get_graph_kernel_by_name graph_kernel = get_graph_kernel_by_name(kernel_options['name'], node_labels=dataset.node_labels, @@ -397,7 +397,7 @@ def __compute_gram_matrix_unnorm(dataset, kernel_options): return gram_matrix_unnorm, run_time -def __init_output_file_knn(ds_name, gkernel, fit_method, dir_output): +def _init_output_file_knn(ds_name, gkernel, fit_method, dir_output): if not os.path.exists(dir_output): os.makedirs(dir_output) fn_output_detail = 'results_detail_knn.' + ds_name + '.' + gkernel + '.csv'