Update the RPG experiment.

gklearn/preimage/experiments/xp_random_preimage_generation.py

@@ -9,6 +9,7 @@ import multiprocessing
 import numpy as np
 import networkx as nx
 import os
+from gklearn.utils.graphfiles import saveGXL
 from gklearn.preimage import RandomPreimageGenerator
 from gklearn.utils import Dataset
 
@@ -16,7 +17,7 @@ from gklearn.utils import Dataset
 dir_root = '../results/xp_random_preimage_generation/'
 
 
-def xp_random_preimage_generation():
+def xp_random_preimage_generation(kernel_name):
 	"""
 	Experiment similar to the one in Bakir's paper. A test to check if RandomPreimageGenerator class works correctly.
 
@@ -40,15 +41,26 @@ def xp_random_preimage_generation():
 					   'alphas': None,
 					   'parallel': True,
 					   'verbose': 2}
-		kernel_options = {'name': 'PathUpToH',
-						  'depth': 2, #
-						  'k_func': 'MinMax', #
-						  'compute_method': 'trie',
-	 					  'parallel': 'imap_unordered', 
-	                      # 'parallel': None, 
-						  'n_jobs': multiprocessing.cpu_count(),
-						  'normalize': True,
-						  'verbose': 0}
+		if kernel_name == 'PathUpToH':
+			kernel_options = {'name': 'PathUpToH',
+							  'depth': 2, #
+							  'k_func': 'MinMax', #
+							  'compute_method': 'trie',
+		 					  'parallel': 'imap_unordered', 
+		                      # 'parallel': None, 
+							  'n_jobs': multiprocessing.cpu_count(),
+							  'normalize': True,
+							  'verbose': 0}
+		elif kernel_name == 'Marginalized':
+			kernel_options = {'name': 'Marginalized',
+							  'p_quit': 0.8, #
+							  'n_iteration': 7, #
+							  'remove_totters': False,
+		 					  'parallel': 'imap_unordered', 
+		                      # 'parallel': None, 
+							  'n_jobs': multiprocessing.cpu_count(),
+							  'normalize': True,
+							  'verbose': 0}
 		edge_required = True
 		irrelevant_labels = {'edge_labels': ['label_0']}
 		cut_range = None
@@ -74,25 +86,30 @@ def xp_random_preimage_generation():
 		if cut_range is not None:
 			dataset_all.cut_graphs(cut_range)
 		
-		# add two "random" graphs.
-		g1 = nx.Graph()
-		g1.add_nodes_from(range(0, 16), label_0='0')
-		g1.add_nodes_from(range(16, 25), label_0='1')
-		g1.add_node(25, label_0='2')
-		g1.add_nodes_from([26, 27], label_0='3')
-		g1.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (5, 0), (4, 9), (12, 3), (10, 13), (13, 14), (14, 15), (15, 8), (0, 16), (1, 17), (2, 18), (12, 19), (11, 20), (13, 21), (15, 22), (7, 23), (6, 24), (14, 25), (25, 26), (25, 27)])
-		g2 = nx.Graph()
-		g2.add_nodes_from(range(0, 12), label_0='0')
-		g2.add_nodes_from(range(12, 19), label_0='1')
-		g2.add_nodes_from([19, 20, 21], label_0='2')
-		g2.add_nodes_from([22, 23], label_0='3')
-		g2.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 19), (19, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 20), (20, 7), (5, 0), (4, 8), (0, 12), (1, 13), (2, 14), (9, 15), (10, 16), (11, 17), (6, 18), (3, 21), (21, 22), (21, 23)])
-		dataset_all.load_graphs([g1, g2] + dataset_all.graphs, targets=None)
+# 		# add two "random" graphs.
+# 		g1 = nx.Graph()
+# 		g1.add_nodes_from(range(0, 16), label_0='0')
+# 		g1.add_nodes_from(range(16, 25), label_0='1')
+# 		g1.add_node(25, label_0='2')
+# 		g1.add_nodes_from([26, 27], label_0='3')
+# 		g1.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 12), (5, 0), (4, 9), (12, 3), (10, 13), (13, 14), (14, 15), (15, 8), (0, 16), (1, 17), (2, 18), (12, 19), (11, 20), (13, 21), (15, 22), (7, 23), (6, 24), (14, 25), (25, 26), (25, 27)])
+# 		g2 = nx.Graph()
+# 		g2.add_nodes_from(range(0, 12), label_0='0')
+# 		g2.add_nodes_from(range(12, 19), label_0='1')
+# 		g2.add_nodes_from([19, 20, 21], label_0='2')
+# 		g2.add_nodes_from([22, 23], label_0='3')
+# 		g2.add_edges_from([(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 19), (19, 7), (7, 8), (8, 9), (9, 10), (10, 11), (11, 20), (20, 7), (5, 0), (4, 8), (0, 12), (1, 13), (2, 14), (9, 15), (10, 16), (11, 17), (6, 18), (3, 21), (21, 22), (21, 23)])
+# 		dataset_all.load_graphs([g1, g2] + dataset_all.graphs, targets=None)
 		
 		# 2. initialize rpg and setting parameters.
 		print('2. initializing rpg and setting parameters...')
-		nb_graphs = len(dataset_all.graphs) - 2
-		rpg_options['alphas'] = [alpha1, 1 - alpha1] + [0] * nb_graphs
+# 		nb_graphs = len(dataset_all.graphs) - 2
+# 		rpg_options['alphas'] = [alpha1, 1 - alpha1] + [0] * nb_graphs
+		nb_graphs = len(dataset_all.graphs)
+		alphas = [0] * nb_graphs
+		alphas[1] = alpha1
+		alphas[6] = 1 - alpha1
+		rpg_options['alphas'] = alphas
 		if gmfile_exist:
 			rpg_options['gram_matrix_unnorm'] = gram_matrix_unnorm
 			rpg_options['runtime_precompute_gm'] = time_precompute_gm
@@ -114,11 +131,129 @@ def xp_random_preimage_generation():
 		# write Gram matrices to file.
 		if not gmfile_exist:
 			np.savez(dir_save + 'gram_matrix_unnorm.' + ds_name + '.' + kernel_options['name'] + '.gm', gram_matrix_unnorm=rpg.gram_matrix_unnorm, run_time=results['runtime_precompute_gm'])
-
+		
+		# save graphs.
+		fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3]
+		saveGXL(rpg.best_from_dataset, fn_best_dataset + '.gxl', method='default', 
+			  node_labels=dataset_all.node_labels, edge_labels=dataset_all.edge_labels,	
+			  node_attrs=dataset_all.node_attrs, edge_attrs=dataset_all.edge_attrs)
+		fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3]
+		saveGXL(rpg.preimage, fn_preimage + '.gxl', method='default', 
+			  node_labels=dataset_all.node_labels, edge_labels=dataset_all.edge_labels,	
+			  node_attrs=dataset_all.node_attrs, edge_attrs=dataset_all.edge_attrs)
+		
+		# draw graphs.
+		__draw_graph(rpg.best_from_dataset, fn_best_dataset)
+		__draw_graph(rpg.preimage, fn_preimage)
+		
+	# plot results figure.
+	__plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save)
+		
 	print('\ncomplete.\n')
 	
 	return k_dis_datasets, k_dis_preimages, bests_from_dataset, preimages
 
 
+def __draw_graph(graph, file_prefix):
+# 	import matplotlib
+# 	matplotlib.use('agg')
+	import matplotlib.pyplot as plt
+	plt.figure()
+	pos = nx.spring_layout(graph)
+	nx.draw(graph, pos, node_size=500, labels=nx.get_node_attributes(graph, 'label_0'), font_color='w', width=3, with_labels=True)
+	plt.savefig(file_prefix + '.eps', format='eps', dpi=300)
+#	plt.show()
+	plt.clf()
+	plt.close()
+
+
+def __plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save):
+	import matplotlib.pyplot as plt
+	fig, ax = plt.subplots(1, 1, figsize=(7, 4.5))
+
+	ind = np.arange(len(alpha1_list))    # the x locations for the groups
+	width = 0.35       # the width of the bars: can also be len(x) sequence
+	
+	p1 = ax.bar(ind, k_dis_preimages, width, label='Reconstructed pre-image', zorder=3, color='#133AAC')
+	
+	ax.set_xlabel(r'$\alpha \in [0,1]$')
+	ax.set_ylabel(r'$d(g_i,g^\star(\alpha))$')
+	#ax.set_title('Runtime of the shortest path kernel on all datasets')
+	plt.xticks(ind, [str(i)[0:3] for i in alpha1_list])
+	#ax.set_yticks(np.logspace(-16, -3, num=20, base=10))
+	#ax.set_ylim(bottom=1e-15)
+	ax.grid(axis='y', zorder=0)
+	ax.spines['top'].set_visible(False)
+	ax.spines['bottom'].set_visible(False)
+	ax.spines['left'].set_visible(False)
+	ax.spines['right'].set_visible(False)
+	ax.xaxis.set_ticks_position('none')
+
+	p2 = ax.plot(ind, k_dis_datasets, 'b.-', label=r'Nearest neighbor in $D_N$', color='orange', zorder=4)
+	ax.yaxis.set_ticks_position('none')
+	
+	fig.subplots_adjust(bottom=.2)
+	fig.legend(loc='lower center', ncol=2, frameon=False) # , ncol=5, labelspacing=0.1, handletextpad=0.4, columnspacing=0.6)
+	
+	plt.savefig(dir_save + 'distances in kernel space.eps', format='eps', dpi=300,
+	            transparent=True, bbox_inches='tight')
+	plt.show()
+	plt.clf()
+	plt.close()	
+	
+
 if __name__ == '__main__':
-	k_dis_datasets, k_dis_preimages, bests_from_dataset, preimages = xp_random_preimage_generation()
+# 	kernel_name = 'PathUpToH'
+	kernel_name = 'Marginalized'
+	k_dis_datasets, k_dis_preimages, bests_from_dataset, preimages = xp_random_preimage_generation(kernel_name)
+	
+# 	# save graphs.	
+# 	dir_save = dir_root + 'MUTAG.PathUpToH/'
+# 	for i, alpha1 in enumerate(np.linspace(0, 1, 11)):
+# 		fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3]
+# 		saveGXL(bests_from_dataset[i], fn_best_dataset + '.gxl', method='default', 
+# 			  node_labels=['label_0'], edge_labels=[],	
+# 			  node_attrs=[], edge_attrs=[])
+# 		fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3]
+# 		saveGXL(preimages[i], fn_preimage + '.gxl', method='default', 
+# 			  node_labels=['label_0'], edge_labels=[],	
+# 			  node_attrs=[], edge_attrs=[])
+
+# 	# draw graphs.
+# 	dir_save = dir_root + 'MUTAG.PathUpToH/'
+# 	for i, alpha1 in enumerate(np.linspace(0, 1, 11)):
+# 		fn_best_dataset = dir_save + 'g_best_dataset.' + 'alpha1_' + str(alpha1)[0:3]
+# 		__draw_graph(bests_from_dataset[i], fn_best_dataset)
+# 		fn_preimage = dir_save + 'g_preimage.' + 'alpha1_' + str(alpha1)[0:3]
+# 		__draw_graph(preimages[i], fn_preimage)
+
+# 	# plot results figure.
+# 	alpha1_list = np.linspace(0, 1, 11)
+# 	dir_save = dir_root + 'MUTAG.PathUpToH/'
+# 	__plot_results(alpha1_list, k_dis_datasets, k_dis_preimages, dir_save)
+
+		
+		
+# k_dis_datasets = [0.0,
+#  0.08882515554098754,
+#  0.17765031108197632,
+#  0.2664754666229643,
+#  0.35530062216395264,
+#  0.44412577770494066,
+#  0.35530062216395236,
+#  0.2664754666229643,
+#  0.17765031108197632,
+#  0.08882515554098878,
+#  0.0]
+
+# k_dis_preimages = [0.0,
+#  0.08882515554098754,
+#  0.17765031108197632,
+#  0.2664754666229643,
+#  0.35530062216395264,
+#  0.44412577770494066,
+#  0.35530062216395236,
+#  0.2664754666229643,
+#  0.17765031108197632,
+#  0.08882515554098878,
+#  0.0]