Add xp.

Author: jajupmochi

gklearn/experiments/papers/PRL_2020/synthesized_graphs_N.py

@@ -5,8 +5,8 @@
 
 @author: ljia
 """
-Graph_Kernel_List = ['PathUpToH', 'WLSubtree', 'SylvesterEquation', 'Marginalized', 'ShortestPath', 'Treelet', 'ConjugateGradient', 'FixedPoint', 'SpectralDecomposition', 'CommonWalk'] 
-# Graph_Kernel_List = ['CommonWalk', 'Marginalized', 'SylvesterEquation', 'ConjugateGradient', 'FixedPoint', 'SpectralDecomposition', 'ShortestPath', 'StructuralSP', 'PathUpToH', 'Treelet', 'WLSubtree']
+from utils import Graph_Kernel_List, compute_graph_kernel
+
 
 def generate_graphs():
 	from gklearn.utils.graph_synthesizer import GraphSynthesizer
@@ -15,90 +15,6 @@ def generate_graphs():
 	return graphs
 
 
-def compute_graph_kernel(graphs, kernel_name):
-	import multiprocessing
-	
-	if kernel_name == 'CommonWalk':
-		from gklearn.kernels.commonWalkKernel import commonwalkkernel
-		estimator = commonwalkkernel
-		params = {'compute_method': 'geo', 'weight': 0.1}
-		
-	elif kernel_name == 'Marginalized':
-		from gklearn.kernels.marginalizedKernel import marginalizedkernel
-		estimator = marginalizedkernel
-		params = {'p_quit': 0.5, 'n_iteration': 5, 'remove_totters': False}
-		
-	elif kernel_name == 'SylvesterEquation':
-		from gklearn.kernels.randomWalkKernel import randomwalkkernel
-		estimator = randomwalkkernel
-		params = {'compute_method': 'sylvester', 'weight': 0.1}
-		
-	elif kernel_name == 'ConjugateGradient':
-		from gklearn.kernels.randomWalkKernel import randomwalkkernel
-		estimator = randomwalkkernel
-		from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-		import functools
-		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
-		sub_kernel = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
-		params = {'compute_method': 'conjugate', 'weight': 0.1, 'node_kernels': sub_kernel, 'edge_kernels': sub_kernel}
-		
-	elif kernel_name == 'FixedPoint':
-		from gklearn.kernels.randomWalkKernel import randomwalkkernel
-		estimator = randomwalkkernel
-		from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-		import functools
-		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
-		sub_kernel = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
-		params = {'compute_method': 'fp', 'weight': 1e-3, 'node_kernels': sub_kernel, 'edge_kernels': sub_kernel}
-		
-	elif kernel_name == 'SpectralDecomposition':
-		from gklearn.kernels.randomWalkKernel import randomwalkkernel
-		estimator = randomwalkkernel
-		params = {'compute_method': 'spectral', 'sub_kernel': 'geo', 'weight': 0.1}
-		
-	elif kernel_name == 'ShortestPath':
-		from gklearn.kernels.spKernel import spkernel
-		estimator = spkernel
-		from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-		import functools
-		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
-		sub_kernel = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
-		params = {'node_kernels': sub_kernel}
-		
-	elif kernel_name == 'StructuralSP':
-		from gklearn.kernels.structuralspKernel import structuralspkernel
-		estimator = structuralspkernel
-		from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
-		import functools
-		mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
-		sub_kernel = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}
-		params = {'node_kernels': sub_kernel, 'edge_kernels': sub_kernel}
-		
-	elif kernel_name == 'PathUpToH':
-		from gklearn.kernels.untilHPathKernel import untilhpathkernel
-		estimator = untilhpathkernel
-		params = {'depth': 5, 'k_func': 'MinMax', 'compute_method': 'trie'}
-		
-	elif kernel_name == 'Treelet':
-		from gklearn.kernels.treeletKernel import treeletkernel
-		estimator = treeletkernel
-		from gklearn.utils.kernels import polynomialkernel
-		import functools
-		sub_kernel = functools.partial(polynomialkernel, d=4, c=1e+8)
-		params = {'sub_kernel': sub_kernel}
-		
-	elif kernel_name == 'WLSubtree':
-		from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel
-		estimator = weisfeilerlehmankernel
-		params = {'base_kernel': 'subtree', 'height': 5}
-		
-	params['n_jobs'] = multiprocessing.cpu_count()
-	params['verbose'] = True
-	results = estimator(graphs, **params)
-	
-	return results[0], results[1]
-
-
 def xp_synthesied_graphs_dataset_size():
 
 	# Generate graphs.

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()