New translations utils.py (French)

lang/fr/gklearn/experiments/papers/PRL_2020/utils.py

@@ -6,6 +6,8 @@ Created on Tue Sep 22 11:33:28 2020
 @author: ljia
 """
 import multiprocessing
+import numpy as np
+from gklearn.utils import model_selection_for_precomputed_kernel
 
 
 Graph_Kernel_List = ['PathUpToH', 'WLSubtree', 'SylvesterEquation', 'Marginalized', 'ShortestPath', 'Treelet', 'ConjugateGradient', 'FixedPoint', 'SpectralDecomposition', 'StructuralSP', 'CommonWalk']
@@ -109,4 +111,123 @@ def compute_graph_kernel(graphs, kernel_name, n_jobs=multiprocessing.cpu_count()
 	params['verbose'] = True
 	results = estimator(graphs, **params)
 	
+	return results[0], results[1]
+
+
+def cross_validate(graphs, targets, kernel_name, output_dir='outputs/', ds_name='synthesized', n_jobs=multiprocessing.cpu_count()):
+	
+	param_grid = None
+	
+	if kernel_name == 'CommonWalk':
+		from gklearn.kernels.commonWalkKernel import commonwalkkernel
+		estimator = commonwalkkernel
+		param_grid_precomputed = [{'compute_method': ['geo'], 
+							 'weight': np.linspace(0.01, 0.15, 15)}]
+		
+	elif kernel_name == 'Marginalized':
+		from gklearn.kernels.marginalizedKernel import marginalizedkernel
+		estimator = marginalizedkernel
+		param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.9, 9),
+                          'n_iteration': np.linspace(1, 19, 7), 
+                          'remove_totters': [False]}
+		
+	elif kernel_name == 'SylvesterEquation':
+		from gklearn.kernels.randomWalkKernel import randomwalkkernel
+		estimator = randomwalkkernel
+		param_grid_precomputed = {'compute_method': ['sylvester'],
+#                          'weight': np.linspace(0.01, 0.10, 10)}
+                          'weight': np.logspace(-1, -10, num=10, base=10)}
+		
+	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}
+		param_grid_precomputed = {'compute_method': ['conjugate'], 
+                          'node_kernels': [sub_kernel], 'edge_kernels': [sub_kernel],
+                          'weight': np.logspace(-1, -10, num=10, base=10)}
+		
+	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}
+		param_grid_precomputed = {'compute_method': ['fp'], 
+                          'node_kernels': [sub_kernel], 'edge_kernels': [sub_kernel],
+                          'weight': np.logspace(-3, -10, num=8, base=10)}
+		
+	elif kernel_name == 'SpectralDecomposition':
+		from gklearn.kernels.randomWalkKernel import randomwalkkernel
+		estimator = randomwalkkernel
+		param_grid_precomputed = {'compute_method': ['spectral'],
+                          'weight': np.logspace(-1, -10, num=10, base=10),
+                          'sub_kernel': ['geo', 'exp']}
+		
+	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}
+		param_grid_precomputed = {'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}
+		param_grid_precomputed = {'node_kernels': [sub_kernel], 'edge_kernels': [sub_kernel],
+                          'compute_method': ['naive']}
+		
+	elif kernel_name == 'PathUpToH':
+		from gklearn.kernels.untilHPathKernel import untilhpathkernel
+		estimator = untilhpathkernel
+		param_grid_precomputed = {'depth': np.linspace(1, 10, 10),   # [2], 
+                          'k_func': ['MinMax', 'tanimoto'], # ['MinMax'], # 
+                          'compute_method': ['trie']} # ['MinMax']}
+		
+	elif kernel_name == 'Treelet':
+		from gklearn.kernels.treeletKernel import treeletkernel
+		estimator = treeletkernel
+		from gklearn.utils.kernels import polynomialkernel
+		import functools
+		gkernels = [functools.partial(gaussiankernel, gamma=1 / ga) 
+		#            for ga in np.linspace(1, 10, 10)]
+		            for ga in np.logspace(0, 10, num=11, base=10)]
+		pkernels = [functools.partial(polynomialkernel, d=d, c=c) for d in range(1, 11)
+		             for c in np.logspace(0, 10, num=11, base=10)]
+		param_grid_precomputed = {'sub_kernel': pkernels + gkernels}
+		
+	elif kernel_name == 'WLSubtree':
+		from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel
+		estimator = weisfeilerlehmankernel
+		param_grid_precomputed = {'base_kernel': ['subtree'], 
+                          'height': np.linspace(0, 10, 11)}
+		param_grid = {'C': np.logspace(-10, 4, num=29, base=10)}
+		
+	if param_grid is None:
+		param_grid = {'C': np.logspace(-10, 10, num=41, base=10)}
+	
+	results = model_selection_for_precomputed_kernel(
+        graphs,
+        estimator,
+        param_grid_precomputed,
+        param_grid,
+        'classification',
+        NUM_TRIALS=28,
+        datafile_y=targets,
+        extra_params=None,
+        ds_name=ds_name,
+		output_dir=output_dir,
+        n_jobs=n_jobs,
+        read_gm_from_file=False,
+        verbose=True)
+	
 	return results[0], results[1]