Update exps.

gklearn/experiments/papers/PRL_2020/runtimes_28cores.py

@@ -0,0 +1,64 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Sep 21 10:34:26 2020
+
+@author: ljia
+"""
+from utils import Graph_Kernel_List, Dataset_List, compute_graph_kernel
+from gklearn.utils.graphdataset import load_predefined_dataset
+import logging
+
+
+# def get_graphs(ds_name):
+# 	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_runtimes_of_all_7cores():
+		
+	# Run and save.
+	import pickle
+	import os
+	save_dir = 'outputs/runtimes_of_all_7cores/'
+	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 ds_name in Dataset_List:
+			print()
+			print('Dataset:', ds_name)
+			
+			# get graphs.
+			graphs, _ = load_predefined_dataset(ds_name)
+
+			# Compute Gram matrix.
+			try:
+				gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name, n_jobs=28)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
+			
+			pickle.dump(run_time, open(save_dir + 'run_time.' + kernel_name + '.' + ds_name + '.pkl', 'wb'))
+		
+	# Save all.	
+	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	
+	
+	return
+
+
+if __name__ == '__main__':
+	xp_runtimes_of_all_7cores()

gklearn/experiments/papers/PRL_2020/synthesized_graphs_N.py

@@ -6,6 +6,7 @@ Created on Mon Sep 21 10:34:26 2020
 @author: ljia
 """
 from utils import Graph_Kernel_List, compute_graph_kernel
+import logging
 
 
 def generate_graphs():
@@ -39,10 +40,19 @@ def xp_synthesied_graphs_dataset_size():
 			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'))
+			try:
+				gram_matrix, run_time = compute_graph_kernel(sub_graphs, kernel_name, n_jobs=1)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
+			
+			pickle.dump(run_time, 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'))	

gklearn/experiments/papers/PRL_2020/synthesized_graphs_degrees.py

@@ -6,6 +6,7 @@ Created on Mon Sep 21 10:34:26 2020
 @author: ljia
 """
 from utils import Graph_Kernel_List, compute_graph_kernel
+import logging
 
 
 def generate_graphs(degree):
@@ -39,10 +40,18 @@ def xp_synthesied_graphs_degrees():
 			graphs = generate_graphs(degree)
 
 			# Compute Gram matrix.
-			gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name)
-			run_times[kernel_name].append(run_time)
+			try:
+				gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name, n_jobs=1)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
 			
-			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(degree) + '.pkl', 'wb'))
+			pickle.dump(run_time, open(save_dir + 'run_time.' + kernel_name + '.' + str(degree) + '.pkl', 'wb'))
 		
 	# Save all.	
 	pickle.dump(run_times, open(save_dir + 'run_times.pkl', 'wb'))	

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_el.py

@@ -6,6 +6,7 @@ Created on Mon Sep 21 10:34:26 2020
 @author: ljia
 """
 from utils import Graph_Kernel_List_ESym, compute_graph_kernel
+import logging
 
 
 def generate_graphs(num_el_alp):
@@ -39,10 +40,18 @@ def xp_synthesied_graphs_num_edge_label_alphabet():
 			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)
+			try:
+				gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name, n_jobs=1)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
 			
-			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_el_alp) + '.pkl', 'wb'))
+			pickle.dump(run_time, 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'))	

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nl.py

@@ -6,6 +6,7 @@ Created on Mon Sep 21 10:34:26 2020
 @author: ljia
 """
 from utils import Graph_Kernel_List_VSym, compute_graph_kernel
+import logging
 
 
 def generate_graphs(num_nl_alp):
@@ -39,10 +40,18 @@ def xp_synthesied_graphs_num_node_label_alphabet():
 			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)
+			try:
+				gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name, n_jobs=1)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
 			
-			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_nl_alp) + '.pkl', 'wb'))
+			pickle.dump(run_time, 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'))	

gklearn/experiments/papers/PRL_2020/synthesized_graphs_num_nodes.py

@@ -6,6 +6,7 @@ Created on Mon Sep 21 10:34:26 2020
 @author: ljia
 """
 from utils import Graph_Kernel_List, compute_graph_kernel
+import logging
 
 
 def generate_graphs(num_nodes):
@@ -39,10 +40,18 @@ def xp_synthesied_graphs_num_nodes():
 			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)
+			try:
+				gram_matrix, run_time = compute_graph_kernel(graphs, kernel_name, n_jobs=1)
+				run_times[kernel_name].append(run_time)
+			except Exception as exp:
+				run_times[kernel_name].append('error')
+				print('An exception occured when running this experiment:')
+				LOG_FILENAME = save_dir + 'error.txt'
+				logging.basicConfig(filename=LOG_FILENAME, level=logging.DEBUG)
+				logging.exception('')
+				print(repr(exp))
 			
-			pickle.dump(run_times, open(save_dir + 'run_time.' + kernel_name + '.' + str(num_nodes) + '.pkl', 'wb'))
+			pickle.dump(run_time, 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'))	

gklearn/experiments/papers/PRL_2020/utils.py

@@ -5,6 +5,9 @@ Created on Tue Sep 22 11:33:28 2020
 
 @author: ljia
 """
+import multiprocessing
+
+
 Graph_Kernel_List = ['PathUpToH', 'WLSubtree', 'SylvesterEquation', 'Marginalized', 'ShortestPath', 'Treelet', 'ConjugateGradient', 'FixedPoint', 'SpectralDecomposition', 'StructuralSP', 'CommonWalk']
 # Graph_Kernel_List = ['CommonWalk', 'Marginalized', 'SylvesterEquation', 'ConjugateGradient', 'FixedPoint', 'SpectralDecomposition', 'ShortestPath', 'StructuralSP', 'PathUpToH', 'Treelet', 'WLSubtree']
 
@@ -21,8 +24,10 @@ Graph_Kernel_List_VCon = ['ShortestPath', 'ConjugateGradient', 'FixedPoint', 'St
 Graph_Kernel_List_ECon = ['ConjugateGradient', 'FixedPoint', 'StructuralSP']
 
 
-def compute_graph_kernel(graphs, kernel_name):
-	import multiprocessing
+Dataset_List = ['Alkane', 'Acyclic', 'MAO', 'PAH', 'MUTAG', 'Letter-med', 'ENZYMES', 'AIDS', 'NCI1', 'NCI109', 'DD']
+
+
+def compute_graph_kernel(graphs, kernel_name, n_jobs=multiprocessing.cpu_count()):
 	
 	if kernel_name == 'CommonWalk':
 		from gklearn.kernels.commonWalkKernel import commonwalkkernel
@@ -99,7 +104,7 @@ def compute_graph_kernel(graphs, kernel_name):
 		params = {'base_kernel': 'subtree', 'height': 5}
 		
 # 	params['parallel'] = None
-	params['n_jobs'] = multiprocessing.cpu_count()
+	params['n_jobs'] = n_jobs
 	params['verbose'] = True
 	results = estimator(graphs, **params)