From e013752e0643db8efff116e03ac93fd5841110d5 Mon Sep 17 00:00:00 2001
From: linlin <jajupmochi@gmail.com>
Date: Mon, 5 Oct 2020 16:38:45 +0200
Subject: [PATCH] New translations utils.py (French)

---
 .../gklearn/experiments/papers/PRL_2020/utils.py   | 112 +++++++++++++++++++++
 1 file changed, 112 insertions(+)
 create mode 100644 lang/fr/gklearn/experiments/papers/PRL_2020/utils.py

diff --git a/lang/fr/gklearn/experiments/papers/PRL_2020/utils.py b/lang/fr/gklearn/experiments/papers/PRL_2020/utils.py
new file mode 100644
index 0000000..07c82f7
--- /dev/null
+++ b/lang/fr/gklearn/experiments/papers/PRL_2020/utils.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+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']
+
+
+Graph_Kernel_List_VSym = ['PathUpToH', 'WLSubtree', 'Marginalized', 'ShortestPath', 'Treelet', 'ConjugateGradient', 'FixedPoint', 'StructuralSP', 'CommonWalk'] 
+
+
+Graph_Kernel_List_ESym = ['PathUpToH', 'Marginalized', 'Treelet', 'ConjugateGradient', 'FixedPoint', 'StructuralSP', 'CommonWalk']
+
+
+Graph_Kernel_List_VCon = ['ShortestPath', 'ConjugateGradient', 'FixedPoint', 'StructuralSP']
+
+
+Graph_Kernel_List_ECon = ['ConjugateGradient', 'FixedPoint', 'StructuralSP']
+
+
+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(), chunksize=None):
+	
+	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['parallel'] = None
+	params['n_jobs'] = n_jobs
+	params['chunksize'] = chunksize
+	params['verbose'] = True
+	results = estimator(graphs, **params)
+	
+	return results[0], results[1]
\ No newline at end of file