Merge pull request #25 from jajupmochi/v0.2

V0.2

Author: jajupmochi

Problems.md

@@ -0,0 +1,21 @@
+# About graph kenrels.
+
+## (Random walk) Sylvester equation kernel.
+
+### ImportError: cannot import name 'frange' from 'matplotlib.mlab'
+
+You are using an outdated `control` with a recent `matplotlib`. `mlab.frange` was removed in `matplotlib-3.1.0`, and `control` removed the call in `control-0.8.2`.
+
+Update your `control` package.
+
+### Intel MKL FATAL ERROR: Cannot load libmkl_avx2.so or libmkl_def.so.
+
+The Intel Math Kernel Library (MKL) is missing or not properly set. I assume the MKL is required by `control` module.
+
+Install MKL. Then add the following to your path:
+
+```
+export PATH=/opt/intel/bin:$PATH
+
+export LD_LIBRARY_PATH=/opt/intel/lib/intel64:/opt/intel/mkl/lib/intel64:$LD_LIBRARY_PATH
+```

README.md

@@ -12,12 +12,12 @@ A Python package for graph kernels, graph edit distances and graph pre-image pro
 * python>=3.5
 * numpy>=1.16.2
 * scipy>=1.1.0
-* matplotlib>=3.0.0
+* matplotlib>=3.1.0
 * networkx>=2.2
 * scikit-learn>=0.20.0
 * tabulate>=0.8.2
 * tqdm>=4.26.0
-* control==0.8.0 (for generalized random walk kernels only)
+* control>=0.8.2 (for generalized random walk kernels only)
 * slycot==0.3.3 (for generalized random walk kernels only, which requires a fortran compiler, gfortran for example)
 
 ## How to use?

gklearn/experiments/papers/PRL_2020/synthesized_graphs_N.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List, compute_graph_kernel
+
+
+def generate_graphs():
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=1000, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=0, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_dataset_size():
+	
+	# Generate graphs.
+	graphs = generate_graphs()
+	
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_N/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for num_graphs in [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000]:
+			print()
+			print('Number of graphs:', num_graphs)
+			
+			sub_graphs = [g.copy() for g in graphs[0:num_graphs]]
+			gram_matrix, run_time = compute_graph_kernel(sub_graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_graphs) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_dataset_size()

gklearn/experiments/papers/PRL_2020/synthesized_graphs_degrees.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List, compute_graph_kernel
+
+
+def generate_graphs(degree):
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=20, num_edges=int(10*degree), num_node_labels=0, num_edge_labels=0, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_degrees():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_degrees/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for degree in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
+			print()
+			print('Degree:', degree)
+			
+			# Generate graphs.
+			graphs = generate_graphs(degree)
+
+			# Compute Gram matrix.
+			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(degree) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_degrees()

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_el.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List_ESym, compute_graph_kernel
+
+
+def generate_graphs(num_el_alp):
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=20, num_edges=40, num_node_labels=0, num_edge_labels=num_el_alp, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_num_edge_label_alphabet():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_num_edge_label_alphabet/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List_ESym:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for num_el_alp in [0, 4, 8, 12, 16, 20, 24, 28, 32, 36, 40]:
+			print()
+			print('Number of edge label alphabet:', num_el_alp)
+			
+			# Generate graphs.
+			graphs = generate_graphs(num_el_alp)
+
+			# Compute Gram matrix.
+			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_el_alp) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_num_edge_label_alphabet()

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List_VSym, compute_graph_kernel
+
+
+def generate_graphs(num_nl_alp):
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=20, num_edges=40, num_node_labels=num_nl_alp, num_edge_labels=0, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_num_node_label_alphabet():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_num_node_label_alphabet/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List_VSym:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for num_nl_alp in [0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]:
+			print()
+			print('Number of node label alphabet:', num_nl_alp)
+			
+			# Generate graphs.
+			graphs = generate_graphs(num_nl_alp)
+
+			# Compute Gram matrix.
+			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_nl_alp) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_num_node_label_alphabet()

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nodes.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List, compute_graph_kernel
+
+
+def generate_graphs(num_nodes):
+	from gklearn.utils.graph_synthesizer import GraphSynthesizer
+	gsyzer = GraphSynthesizer()
+	graphs = gsyzer.unified_graphs(num_graphs=100, num_nodes=num_nodes, num_edges=int(num_nodes*2), num_node_labels=0, num_edge_labels=0, seed=None, directed=False)
+	return graphs
+
+
+def xp_synthesied_graphs_num_nodes():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/synthesized_graphs_num_nodes/'
+	if not os.path.exists(save_dir):
+		os.makedirs(save_dir)
+
+	run_times = {}
+	
+	for kernel_name in Graph_Kernel_List:
+		print()
+		print('Kernel:', kernel_name)
+		
+		run_times[kernel_name] = []
+		for num_nodes in [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]:
+			print()
+			print('Number of nodes:', num_nodes)
+			
+			# Generate graphs.
+			graphs = generate_graphs(num_nodes)
+
+			# Compute Gram matrix.
+			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
+			run_times[kernel_name].append(run_time)
+			
+			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_nodes) + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_synthesied_graphs_num_nodes()