graphkit-learn/notebooks/run_spkernel.py

from libs import *
from pygraph.kernels.spKernel import spkernel

dslist = [   
#     {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node_labeled
#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge_labeled
#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled
    {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # fully_labeled
#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',},

#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',
#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},
#     {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', 
#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',},
#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'},
#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'},    
    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},
#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'},
#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},
#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'},
#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'},
#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},

#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},
#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'},
#     {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',
#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},
#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'},
#     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',
#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},
#     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',
#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},
#     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',
#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',},
    
#     # 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
param_grid_precomputed = {}
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))
    print()