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- #!/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()
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