graphkit-learn/notebooks/else/run_sp_symonly.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 21 17:59:28 2018

@author: ljia
"""

import functools
from libs import *
import multiprocessing

from gklearn.kernels.sp_sym import spkernel
from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct
#from gklearn.utils.model_selection_precomputed import trial_do

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

    #     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb
    # # #     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb
    # # #     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb
    #     {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},
    #
    # #     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb
    # #     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb
    # #     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb
    # #     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb
    # #     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb

    # #     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb
    # #     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb
    # #     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb
]
estimator = spkernel
mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)
param_grid_precomputed = {'node_kernels': [
    {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}]}
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'])
    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()