#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Thu Dec 19 17:16:23 2019 @author: ljia """ import numpy as np from sklearn.manifold import TSNE, Isomap import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset from tqdm import tqdm from gklearn.utils.graphfiles import loadDataset, loadGXL from gklearn.preimage.utils import kernel_distance_matrix, compute_kernel, dis_gstar, get_same_item_indices def visualize_graph_dataset(dis_measure, visual_method, draw_figure, draw_params={}, dis_mat=None, Gn=None, median_set=None): def draw_zoomed_axes(Gn_embedded, ax): margin = 0.01 if dis_measure == 'graph-kernel': index = -2 elif dis_measure == 'ged': index = -1 x1 = np.min(Gn_embedded[median_set + [index], 0]) - margin * np.max(Gn_embedded) x2 = np.max(Gn_embedded[median_set + [index], 0]) + margin * np.max(Gn_embedded) y1 = np.min(Gn_embedded[median_set + [index], 1]) - margin * np.max(Gn_embedded) y2 = np.max(Gn_embedded[median_set + [index], 1]) + margin * np.max(Gn_embedded) if (x1 < 0 and y1 < 0) or ((x1 > 0 and y1 > 0)): loc = 2 else: loc = 3 axins = zoomed_inset_axes(ax, 4, loc=loc) # zoom-factor: 2.5, location: upper-left draw_figure(axins, Gn_embedded, dis_measure=dis_measure, median_set=median_set, **draw_params) axins.set_xlim(x1, x2) # apply the x-limits axins.set_ylim(y1, y2) # apply the y-limits plt.yticks(visible=False) plt.xticks(visible=False) loc1 = 1 if loc == 2 else 3 mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.5") if dis_mat is None: if dis_measure == 'graph-kernel': gkernel = 'untilhpathkernel' node_label = 'atom' edge_label = 'bond_type' dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, Kmatrix=None, gkernel=gkernel) elif dis_measure == 'ged': pass if visual_method == 'tsne': Gn_embedded = TSNE(n_components=2, metric='precomputed').fit_transform(dis_mat) elif visual_method == 'isomap': Gn_embedded = Isomap(n_components=2, metric='precomputed').fit_transform(dis_mat) print(Gn_embedded.shape) fig, ax = plt.subplots() draw_figure(plt, Gn_embedded, dis_measure=dis_measure, legend=True, median_set=median_set, **draw_params) # draw_zoomed_axes(Gn_embedded, ax) plt.show() plt.clf() return def draw_figure(ax, Gn_embedded, dis_measure=None, y_idx=None, legend=False, median_set=None): from matplotlib import colors as mcolors colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) # colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', # '#c6dbef', '#deebf7'] # for i, values in enumerate(y_idx.values()): # for item in values: ## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') # ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') # ax.scatter(Gn_embedded[:,0], Gn_embedded[:,1], c='b') h1 = ax.scatter(Gn_embedded[median_set, 0], Gn_embedded[median_set, 1], c='b') if dis_measure == 'graph-kernel': h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi h3 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='gold') # gen median h4 = ax.scatter(Gn_embedded[-3, 0], Gn_embedded[-3, 1], c='r') #c='g', marker='+') # set median elif dis_measure == 'ged': h3 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='gold') # gen median h4 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='r') #c='g', marker='+') # set median if legend: # fig.subplots_adjust(bottom=0.17) if dis_measure == 'graph-kernel': ax.legend([h1, h2, h3, h4], ['k closest graphs', 'true median', 'gen median', 'set median']) elif dis_measure == 'ged': ax.legend([h1, h3, h4], ['k closest graphs', 'gen median', 'set median']) # fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) # plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, # bbox_inches='tight') # plt.show() ############################################################################### def visualize_distances_in_kernel(): ds = {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb Gn, y_all = loadDataset(ds['dataset']) # Gn = Gn[0:50] fname_medians = 'expert.treelet' # add set median. fname_sm = 'results/test_k_closest_graphs/set_median.' + fname_medians + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = 'results/test_k_closest_graphs/gen_median.' + fname_medians + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute distance matrix median_set = [22, 29, 54, 74] gkernel = 'treeletkernel' node_label = 'atom' edge_label = 'bond_type' Gn_median_set = [Gn[i].copy() for i in median_set] Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, edge_label, True) Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, Kmatrix=Kmatrix, gkernel=gkernel) print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) # add distances for the image of exact median \psi. dis_k_median_list = [] for idx, g in enumerate(Gn): dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), [1 / len(Gn_median_set)] * len(Gn_median_set), Kmatrix_median, withterm3=False)) dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) for i in range(len(Gn)): for j in range(i, len(Gn)): dis_mat_median[i, j] = dis_mat[i, j] dis_mat_median[j, i] = dis_mat_median[i, j] for i in range(len(Gn)): dis_mat_median[i, -1] = dis_k_median_list[i] dis_mat_median[-1, i] = dis_k_median_list[i] # get indices by classes. y_idx = get_same_item_indices(y_all) # visualization. # visualize_graph_dataset('graph-kernel', 'tsne', Gn) # visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, # draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, median_set=median_set) def visualize_distances_in_ged(): from gklearn.preimage.fitDistance import compute_geds from gklearn.preimage.ged import GED ds = {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'} # node/edge symb Gn, y_all = loadDataset(ds['dataset']) # Gn = Gn[0:50] # add set median. fname_medians = 'expert.treelet' fname_sm = 'preimage/results/test_k_closest_graphs/set_median.' + fname_medians + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = 'preimage/results/test_k_closest_graphs/gen_median.' + fname_medians + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute/load ged matrix. # # compute. ## k = 4 ## edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] # edit_costs = [3, 3, 1, 3, 3, 1] ## edit_costs = [7, 3, 5, 9, 2, 6] # algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' # params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', # 'algo_options': algo_options, 'stabilizer': None, # 'edit_cost_constant': edit_costs} # _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) # np.savez('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm', ged_mat=ged_mat) # load from file. gmfile = np.load('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm.npz') ged_mat = gmfile['ged_mat'] # # change medians. # edit_costs = [3, 3, 1, 3, 3, 1] # algo_options = '--threads 8 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' # params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', # 'algo_options': algo_options, 'stabilizer': None, # 'edit_cost_constant': edit_costs} # for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): # dis, _, _ = GED(Gn[idx], set_median, **params_ged) # ged_mat[idx, -2] = dis # ged_mat[-2, idx] = dis # dis, _, _ = GED(Gn[idx], gen_median, **params_ged) # ged_mat[idx, -1] = dis # ged_mat[-1, idx] = dis # np.savez('results/test_k_closest_graphs/ged_mat.' + fname_medians + '.with_medians.gm', # ged_mat=ged_mat) # get indices by classes. y_idx = get_same_item_indices(y_all) # visualization. median_set = [22, 29, 54, 74] visualize_graph_dataset('ged', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=ged_mat, median_set=median_set) ############################################################################### def visualize_distances_in_kernel_monoterpenoides(): import os ds = {'dataset': '../datasets/monoterpenoides/dataset_10+.ds', 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb Gn_original, y_all = loadDataset(ds['dataset']) # Gn = Gn[0:50] # compute distance matrix # median_set = [22, 29, 54, 74] gkernel = 'treeletkernel' fit_method = 'expert' node_label = 'atom' edge_label = 'bond_type' ds_name = 'monoterpenoides' fname_medians = fit_method + '.' + gkernel dir_output = 'results/xp_monoterpenoides/' repeat = 0 # get indices by classes. y_idx = get_same_item_indices(y_all) for i, (y, values) in enumerate(y_idx.items()): print('\ny =', y) k = len(values) Gn = [Gn_original[g].copy() for g in values] # add set median. fname_sm = dir_output + 'medians/' + str(int(y)) + '/set_median.k' + str(int(k)) \ + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = dir_output + 'medians/' + str(int(y)) + '/gen_median.k' + str(int(k)) \ + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute distance matrix median_set = range(0, len(values)) Gn_median_set = [Gn[i].copy() for i in median_set] Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, edge_label, False) Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, Kmatrix=Kmatrix, gkernel=gkernel) print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) # add distances for the image of exact median \psi. dis_k_median_list = [] for idx, g in enumerate(Gn): dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), [1 / len(Gn_median_set)] * len(Gn_median_set), Kmatrix_median, withterm3=False)) dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) for i in range(len(Gn)): for j in range(i, len(Gn)): dis_mat_median[i, j] = dis_mat[i, j] dis_mat_median[j, i] = dis_mat_median[i, j] for i in range(len(Gn)): dis_mat_median[i, -1] = dis_k_median_list[i] dis_mat_median[-1, i] = dis_k_median_list[i] # visualization. # visualize_graph_dataset('graph-kernel', 'tsne', Gn) # visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, # draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, median_set=median_set) def visualize_distances_in_ged_monoterpenoides(): from gklearn.preimage.fitDistance import compute_geds from gklearn.preimage.ged import GED import os ds = {'dataset': '../datasets/monoterpenoides/dataset_10+.ds', 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '../../datasets/monoterpenoides/'} # node/edge symb Gn_original, y_all = loadDataset(ds['dataset']) # Gn = Gn[0:50] # compute distance matrix # median_set = [22, 29, 54, 74] gkernel = 'treeletkernel' fit_method = 'expert' ds_name = 'monoterpenoides' fname_medians = fit_method + '.' + gkernel dir_output = 'results/xp_monoterpenoides/' repeat = 0 # edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] edit_costs = [3, 3, 1, 3, 3, 1] # edit_costs = [7, 3, 5, 9, 2, 6] # get indices by classes. y_idx = get_same_item_indices(y_all) for i, (y, values) in enumerate(y_idx.items()): print('\ny =', y) k = len(values) Gn = [Gn_original[g].copy() for g in values] # add set median. fname_sm = dir_output + 'medians/' + str(int(y)) + '/set_median.k' + str(int(k)) \ + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = dir_output + 'medians/' + str(int(y)) + '/gen_median.k' + str(int(k)) \ + '.y' + str(int(y)) + '.repeat' + str(repeat) + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute/load ged matrix. # compute. algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' params_ged = {'dataset': ds_name, 'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', 'algo_options': algo_options, 'stabilizer': None, 'edit_cost_constant': edit_costs} _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) \ + '.with_medians.gm', ged_mat=ged_mat) # # load from file. # gmfile = np.load('dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) + '.with_medians.gm.npz') # ged_mat = gmfile['ged_mat'] # # change medians. # algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' # params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', # 'algo_options': algo_options, 'stabilizer': None, # 'edit_cost_constant': edit_costs} # for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): # dis, _, _ = GED(Gn[idx], set_median, **params_ged) # ged_mat[idx, -2] = dis # ged_mat[-2, idx] = dis # dis, _, _ = GED(Gn[idx], gen_median, **params_ged) # ged_mat[idx, -1] = dis # ged_mat[-1, idx] = dis # np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + str(int(y)) + '.with_medians.gm', # ged_mat=ged_mat) # visualization. median_set = range(0, len(values)) visualize_graph_dataset('ged', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=ged_mat, median_set=median_set) ############################################################################### def visualize_distances_in_kernel_letter_h(): ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb Gn_original, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) # Gn = Gn[0:50] # compute distance matrix # median_set = [22, 29, 54, 74] gkernel = 'structuralspkernel' fit_method = 'expert' node_label = None edge_label = None ds_name = 'letter-h' fname_medians = fit_method + '.' + gkernel dir_output = 'results/xp_letter_h/' k = 150 repeat = 0 # get indices by classes. y_idx = get_same_item_indices(y_all) for i, (y, values) in enumerate(y_idx.items()): print('\ny =', y) Gn = [Gn_original[g].copy() for g in values] # add set median. fname_sm = dir_output + 'medians/' + y + '/set_median.k' + str(int(k)) \ + '.y' + y + '.repeat' + str(repeat) + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = dir_output + 'medians/' + y + '/gen_median.k' + str(int(k)) \ + '.y' + y + '.repeat' + str(repeat) + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute distance matrix median_set = range(0, len(values)) Gn_median_set = [Gn[i].copy() for i in median_set] Kmatrix_median = compute_kernel(Gn + Gn_median_set, gkernel, node_label, edge_label, False) Kmatrix = Kmatrix_median[0:len(Gn), 0:len(Gn)] dis_mat, _, _, _ = kernel_distance_matrix(Gn, node_label, edge_label, Kmatrix=Kmatrix, gkernel=gkernel) print('average distances: ', np.mean(np.mean(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('min distances: ', np.min(np.min(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) print('max distances: ', np.max(np.max(dis_mat[0:len(Gn)-2, 0:len(Gn)-2]))) # add distances for the image of exact median \psi. dis_k_median_list = [] for idx, g in enumerate(Gn): dis_k_median_list.append(dis_gstar(idx, range(len(Gn), len(Gn) + len(Gn_median_set)), [1 / len(Gn_median_set)] * len(Gn_median_set), Kmatrix_median, withterm3=False)) dis_mat_median = np.zeros((len(Gn) + 1, len(Gn) + 1)) for i in range(len(Gn)): for j in range(i, len(Gn)): dis_mat_median[i, j] = dis_mat[i, j] dis_mat_median[j, i] = dis_mat_median[i, j] for i in range(len(Gn)): dis_mat_median[i, -1] = dis_k_median_list[i] dis_mat_median[-1, i] = dis_k_median_list[i] # visualization. # visualize_graph_dataset('graph-kernel', 'tsne', Gn) # visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, # draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median) visualize_graph_dataset('graph-kernel', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=dis_mat_median, median_set=median_set) def visualize_distances_in_ged_letter_h(): from fitDistance import compute_geds from preimage.test_k_closest_graphs import reform_attributes ds = {'dataset': 'cpp_ext/data/collections/Letter.xml', 'graph_dir': os.path.dirname(os.path.realpath(__file__)) + '/cpp_ext/data/datasets/Letter/HIGH/'} # node/edge symb Gn_original, y_all = loadDataset(ds['dataset'], extra_params=ds['graph_dir']) # Gn = Gn[0:50] # compute distance matrix # median_set = [22, 29, 54, 74] gkernel = 'structuralspkernel' fit_method = 'expert' ds_name = 'letter-h' fname_medians = fit_method + '.' + gkernel dir_output = 'results/xp_letter_h/' k = 150 repeat = 0 # edit_costs = [0.16229209837639536, 0.06612870523413916, 0.04030113378793905, 0.20723547009415202, 0.3338607220394598, 0.27054392518077297] edit_costs = [3, 3, 1, 3, 3, 1] # edit_costs = [7, 3, 5, 9, 2, 6] # get indices by classes. y_idx = get_same_item_indices(y_all) for i, (y, values) in enumerate(y_idx.items()): print('\ny =', y) Gn = [Gn_original[g].copy() for g in values] # add set median. fname_sm = dir_output + 'medians/' + y + '/set_median.k' + str(int(k)) \ + '.y' + y + '.repeat' + str(repeat) + '.gxl' set_median = loadGXL(fname_sm) Gn.append(set_median) # add generalized median (estimated pre-image.) fname_gm = dir_output + 'medians/' + y + '/gen_median.k' + str(int(k)) \ + '.y' + y + '.repeat' + str(repeat) + '.gxl' gen_median = loadGXL(fname_gm) Gn.append(gen_median) # compute/load ged matrix. # compute. algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' params_ged = {'dataset': 'Letter', 'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', 'algo_options': algo_options, 'stabilizer': None, 'edit_cost_constant': edit_costs} for g in Gn: reform_attributes(g) _, ged_mat, _ = compute_geds(Gn, params_ged=params_ged, parallel=True) np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm', ged_mat=ged_mat) # # load from file. # gmfile = np.load('dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm.npz') # ged_mat = gmfile['ged_mat'] # # change medians. # algo_options = '--threads 1 --initial-solutions 40 --ratio-runs-from-initial-solutions 1' # params_ged = {'lib': 'gedlibpy', 'cost': 'CONSTANT', 'method': 'IPFP', # 'algo_options': algo_options, 'stabilizer': None, # 'edit_cost_constant': edit_costs} # for idx in tqdm(range(len(Gn) - 2), desc='computing GEDs', file=sys.stdout): # dis, _, _ = GED(Gn[idx], set_median, **params_ged) # ged_mat[idx, -2] = dis # ged_mat[-2, idx] = dis # dis, _, _ = GED(Gn[idx], gen_median, **params_ged) # ged_mat[idx, -1] = dis # ged_mat[-1, idx] = dis # np.savez(dir_output + 'ged_mat.' + fname_medians + '.y' + y + '.with_medians.gm', # ged_mat=ged_mat) # visualization. median_set = range(0, len(values)) visualize_graph_dataset('ged', 'tsne', draw_figure, draw_params={'y_idx': y_idx}, dis_mat=ged_mat, median_set=median_set) if __name__ == '__main__': visualize_distances_in_kernel_letter_h() # visualize_distances_in_ged_letter_h() # visualize_distances_in_kernel_monoterpenoides() # visualize_distances_in_kernel_monoterpenoides() # visualize_distances_in_kernel() # visualize_distances_in_ged() #def draw_figure_dis_k(ax, Gn_embedded, y_idx=None, legend=False): # from matplotlib import colors as mcolors # colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) ## colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', ## '#c6dbef', '#deebf7'] # for i, values in enumerate(y_idx.values()): # for item in values: ## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') # ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') # h1 = ax.scatter(Gn_embedded[[12, 13, 22, 29], 0], Gn_embedded[[12, 13, 22, 29], 1], c='r') # h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi # h3 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='gold') # gen median # h4 = ax.scatter(Gn_embedded[-3, 0], Gn_embedded[-3, 1], c='r', marker='+') # set median # if legend: ## fig.subplots_adjust(bottom=0.17) # ax.legend([h1, h2, h3, h4], ['k clostest graphs', 'true median', 'gen median', 'set median']) ## fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) ## plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, ## bbox_inches='tight') ## plt.show() #def draw_figure_ged(ax, Gn_embedded, y_idx=None, legend=False): # from matplotlib import colors as mcolors # colors = list(dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)) ## colors = ['#08306b', '#08519c', '#2171b5', '#4292c6', '#6baed6', '#9ecae1', ## '#c6dbef', '#deebf7'] # for i, values in enumerate(y_idx.values()): # for item in values: ## ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c=colors[i]) # , c='b') # ax.scatter(Gn_embedded[item,0], Gn_embedded[item,1], c='b') # h1 = ax.scatter(Gn_embedded[[12, 13, 22, 29], 0], Gn_embedded[[12, 13, 22, 29], 1], c='r') ## h2 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='darkorchid') # \psi # h3 = ax.scatter(Gn_embedded[-1, 0], Gn_embedded[-1, 1], c='gold') # gen median # h4 = ax.scatter(Gn_embedded[-2, 0], Gn_embedded[-2, 1], c='r', marker='+') # set median # if legend: ## fig.subplots_adjust(bottom=0.17) # ax.legend([h1, h3, h4], ['k clostest graphs', 'gen median', 'set median']) ## fig.legend(handles, labels, loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6) ## plt.savefig('symbolic_and_non_comparison_vertical_short.eps', format='eps', dpi=300, transparent=True, ## bbox_inches='tight') ## plt.show()