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New translations visualization.py (French)

l10n_v0.2.x
linlin 5 years ago
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#!/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()

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