From 0c906ca37efa25dd395a9a076768440cd379d2a3 Mon Sep 17 00:00:00 2001 From: linlin Date: Sun, 4 Oct 2020 19:16:21 +0200 Subject: [PATCH] New translations visualization.py (French) --- lang/fr/gklearn/preimage/visualization.py | 585 ++++++++++++++++++++++++++++++ 1 file changed, 585 insertions(+) create mode 100644 lang/fr/gklearn/preimage/visualization.py diff --git a/lang/fr/gklearn/preimage/visualization.py b/lang/fr/gklearn/preimage/visualization.py new file mode 100644 index 0000000..81b814b --- /dev/null +++ b/lang/fr/gklearn/preimage/visualization.py @@ -0,0 +1,585 @@ +#!/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() \ No newline at end of file