OpenI
/
graphkit-learn

import functools
from libs import *
from pygraph.kernels.spKernel import spkernel
from pygraph.utils.kernels import deltakernel, kernelsum
from sklearn.metrics.pairwise import rbf_kernel

# dslist = [
#      {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb
# #     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb
#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled
#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb
#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',
#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb
#     {'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': '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': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb
#     {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb
# #     {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},
#     {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_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': 'D&D', 'dataset': '../datasets/D&D/DD.mat',
#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb
# #     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb
# #     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',
# #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb
# #     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',
# #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb
# #     {'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',},
# ]

import ast
ds = ast.literal_eval(sys.argv[1])

estimator = spkernel
mixkernel = functools.partial(kernelsum, deltakernel, rbf_kernel)
param_grid_precomputed = {
    'node_kernels': [{
        'symb': deltakernel,
        'nsymb': rbf_kernel,
        'mix': mixkernel
    }]
}
param_grid = [{
    'C': np.logspace(-10, 10, num=41, base=10)
}, {
    'alpha': np.logspace(-10, 10, num=41, base=10)
}]

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'])

#     %lprun -f spkernel \
#         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))
print()

# import functools
# from libs import *
# from pygraph.kernels.spKernel import spkernel
# from pygraph.utils.kernels import deltakernel, kernelsum
# from sklearn.metrics.pairwise import rbf_kernel

# dslist = [
#     {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb
# #     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb
# #    {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled
# #    {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb
# #    {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',
# #        'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb
# #    {'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': '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': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb
# #    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb
# #     {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},
# #    {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_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': 'D&D', 'dataset': '../datasets/D&D/DD.mat',
# #     'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb
# #     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb
# #     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',
# #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb
# #     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',
# #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb
# #     {'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 = spkernel
# mixkernel = functools.partial(kernelsum, deltakernel, rbf_kernel)
# param_grid_precomputed = {'node_kernels': [{'symb': deltakernel, 'nsymb': rbf_kernel, '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'])

# #     %lprun -f spkernel \
# #         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))
#     print()