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run_treeletkernel.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 21 11:19:33 2019
@author: ljia
"""
from libs import *
import multiprocessing
import functools
from gklearn.kernels.treeletKernel import treeletkernel
from gklearn.utils.kernels import gaussiankernel, polynomialkernel
dslist = [
# {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',
# 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'},
# # contains single node graph, node symb
# {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',
# 'task': 'regression'}, # node symb
# {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb
# {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled
# {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb
# {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1_A.txt'}, # node symb
# {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109_A.txt'}, # node symb
# {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb
# {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
# {'name': 'monoterpenoides', 'dataset': '../datasets/monoterpenoides/dataset_10+.ds'}, # node/edge symb
# node symb/nsymb
{'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb
{'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},
# node nsymb
#
# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},
# # node/edge symb
# {'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': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',
# 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb
# # not working below
# {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},
# {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},
# {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},
# {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},
]
estimator = treeletkernel
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}
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,
verbose=True)
print()