New translations run_rwalk_symonly.py (Chinese Simplified)

lang/zh/notebooks/else/run_rwalk_symonly.py

@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 23 16:56:44 2018
+
+@author: ljia
+"""
+
+import functools
+from libs import *
+import multiprocessing
+
+from gklearn.kernels.rwalk_sym import randomwalkkernel
+from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
+
+import numpy as np
+
+
+dslist = [
+    {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},
+    # node nsymb
+    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
+    # node symb/nsymb
+]
+estimator = randomwalkkernel
+param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},
+              {'alpha': np.logspace(-10, 10, num=41, base=10)}]
+
+for ds in dslist:
+    print()
+    print(ds['name'])
+    for compute_method in ['conjugate', 'fp']:
+        if compute_method == 'sylvester':
+            param_grid_precomputed = {'compute_method': ['sylvester'],
+#                          'weight': np.linspace(0.01, 0.10, 10)}
+                          'weight': np.logspace(-1, -10, num=10, base=10)}
+        elif compute_method == 'conjugate':
+            mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+            param_grid_precomputed = {'compute_method': ['conjugate'], 
+                          'node_kernels': 
+                          [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
+                          'edge_kernels': 
+                          [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
+                          'weight': np.logspace(-1, -10, num=10, base=10)}
+        elif compute_method == 'fp':
+            mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
+            param_grid_precomputed = {'compute_method': ['fp'], 
+                          'node_kernels': 
+                          [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
+                          'edge_kernels': 
+                          [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],
+                          'weight': np.logspace(-3, -10, num=8, base=10)}
+        elif compute_method == 'spectral':
+            param_grid_precomputed = {'compute_method': ['spectral'],
+                          'weight': np.logspace(-1, -10, num=10, base=10),
+                          'sub_kernel': ['geo', 'exp']}
+        model_selection_for_precomputed_kernel(
+            ds['dataset'],
+            estimator,
+            param_grid_precomputed,
+            (param_grid[1] if ('task' in ds and ds['task']
+                               == 'regression') else param_grid[0]),
+            (ds['task'] if 'task' in ds else 'classification'),
+            NUM_TRIALS=30,
+            datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),
+            extra_params=(ds['extra_params'] if 'extra_params' in ds else None),
+            ds_name=ds['name'],
+            n_jobs=multiprocessing.cpu_count(),
+            read_gm_from_file=False)
+    print()