#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Jan 14 15:39:29 2020 @author: ljia """ import multiprocessing import functools from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct from gklearn.preimage.utils import generate_median_preimages_by_class from gklearn.utils import compute_gram_matrices_by_class def xp_median_preimage_9_1(): """xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only. """ # set parameters. ds_name = 'MAO' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [4, 4, 2, 1, 1, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_9_2(): """xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only. """ # set parameters. ds_name = 'MAO' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [4, 4, 2, 1, 1, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} kernel_options = {'name': 'PathUpToH', 'depth': 9, # 'k_func': 'MinMax', # 'compute_method': 'trie', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = {'node_attrs': ['x', 'y', 'z'], 'edge_labels': ['bond_stereo']} # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_8_1(): """xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT. """ # set parameters. ds_name = 'Monoterpenoides' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_8_2(): """xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT. """ # set parameters. ds_name = 'Monoterpenoides' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [4, 4, 2, 1, 1, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} kernel_options = {'name': 'PathUpToH', 'depth': 7, # 'k_func': 'MinMax', # 'compute_method': 'trie', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_7_1(): """xp 7_1: MUTAG, StructuralSP, using CONSTANT. """ # set parameters. ds_name = 'MUTAG' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [4, 4, 2, 1, 1, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_7_2(): """xp 7_2: MUTAG, PathUpToH, using CONSTANT. """ # set parameters. ds_name = 'MUTAG' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [4, 4, 2, 1, 1, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, # 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} kernel_options = {'name': 'PathUpToH', 'depth': 2, # 'k_func': 'MinMax', # 'compute_method': 'trie', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'CONSTANT', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_6_1(): """xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC. """ # set parameters. ds_name = 'COIL-RAG' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'NON_SYMBOLIC', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_6_2(): """xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC. """ # set parameters. ds_name = 'COIL-RAG' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3, 1], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'NON_SYMBOLIC', # 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = True # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_5_1(): """xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC. """ # set parameters. ds_name = 'FRANKENSTEIN' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3, 0], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'NON_SYMBOLIC', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_4_1(): """xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC. """ # set parameters. ds_name = 'COLORS-3' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3, 0], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'NON_SYMBOLIC', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_3_2(): """xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs. """ # set parameters. ds_name = 'Fingerprint' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.525, 0.525, 0.001, 0.125, 0.125], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = {'edge_attrs': ['orient', 'angle']} # edge_required = True # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_3_1(): """xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs. """ # set parameters. ds_name = 'Fingerprint' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.525, 0.525, 0.001, 0.125, 0.125], # 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = {'edge_attrs': ['orient', 'angle']} # edge_required = False # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_2_1(): """xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs. """ # set parameters. ds_name = 'COIL-DEL' # mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [3, 3, 1, 3, 3], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = {'edge_labels': ['valence']} # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # # compute gram matrices for each class a priori. # print('Compute gram matrices for each class a priori.') # compute_gram_matrices_by_class(ds_name, kernel_options, save_results=True, dir_save=dir_save, irrelevant_labels=irrelevant_labels) # generate preimages. for fit_method in ['k-graphs'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels) def xp_median_preimage_1_1(): """xp 1_1: Letter-high, StructuralSP. """ # set parameters. ds_name = 'Letter-high' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save='../results/xp_median_preimage/') def xp_median_preimage_1_2(): """xp 1_2: Letter-high, ShortestPath. """ # set parameters. ds_name = 'Letter-high' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.675, 0.675, 0.75, 0.425, 0.425], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = True # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_10_1(): """xp 10_1: Letter-med, StructuralSP. """ # set parameters. ds_name = 'Letter-med' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save='../results/xp_median_preimage/') def xp_median_preimage_10_2(): """xp 10_2: Letter-med, ShortestPath. """ # set parameters. ds_name = 'Letter-med' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.525, 0.525, 0.75, 0.475, 0.475], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = True # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) def xp_median_preimage_11_1(): """xp 11_1: Letter-low, StructuralSP. """ # set parameters. ds_name = 'Letter-low' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'StructuralSP', 'edge_weight': None, 'node_kernels': sub_kernels, 'edge_kernels': sub_kernels, 'compute_method': 'naive', 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save='../results/xp_median_preimage/') def xp_median_preimage_11_2(): """xp 11_2: Letter-low, ShortestPath. """ # set parameters. ds_name = 'Letter-low' mpg_options = {'fit_method': 'k-graphs', 'init_ecc': [0.075, 0.075, 0.25, 0.075, 0.075], 'ds_name': ds_name, 'parallel': True, # False 'time_limit_in_sec': 0, 'max_itrs': 100, 'max_itrs_without_update': 3, 'epsilon_residual': 0.01, 'epsilon_ec': 0.1, 'verbose': 2} mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel) sub_kernels = {'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel} kernel_options = {'name': 'ShortestPath', 'edge_weight': None, 'node_kernels': sub_kernels, 'parallel': 'imap_unordered', # 'parallel': None, 'n_jobs': multiprocessing.cpu_count(), 'normalize': True, 'verbose': 2} ged_options = {'method': 'IPFP', 'initialization_method': 'RANDOM', # 'NODE' 'initial_solutions': 10, # 1 'edit_cost': 'LETTER2', 'attr_distance': 'euclidean', 'ratio_runs_from_initial_solutions': 1, 'threads': multiprocessing.cpu_count(), 'init_option': 'EAGER_WITHOUT_SHUFFLED_COPIES'} mge_options = {'init_type': 'MEDOID', 'random_inits': 10, 'time_limit': 600, 'verbose': 2, 'refine': False} save_results = True dir_save='../results/xp_median_preimage/' irrelevant_labels = None # edge_required = True # # print settings. print('parameters:') print('dataset name:', ds_name) print('mpg_options:', mpg_options) print('kernel_options:', kernel_options) print('ged_options:', ged_options) print('mge_options:', mge_options) print('save_results:', save_results) print('irrelevant_labels:', irrelevant_labels) print() # generate preimages. for fit_method in ['k-graphs', 'expert'] + ['random'] * 10: print('\n-------------------------------------') print('fit method:', fit_method, '\n') mpg_options['fit_method'] = fit_method generate_median_preimages_by_class(ds_name, mpg_options, kernel_options, ged_options, mge_options, save_results=save_results, save_medians=True, plot_medians=True, load_gm='auto', dir_save=dir_save, irrelevant_labels=irrelevant_labels, edge_required=edge_required) if __name__ == "__main__": #### xp 1_1: Letter-high, StructuralSP. # xp_median_preimage_1_1() #### xp 1_2: Letter-high, ShortestPath. # xp_median_preimage_1_2() #### xp 10_1: Letter-med, StructuralSP. # xp_median_preimage_10_1() #### xp 10_2: Letter-med, ShortestPath. # xp_median_preimage_10_2() #### xp 11_1: Letter-low, StructuralSP. # xp_median_preimage_11_1() #### xp 11_2: Letter-low, ShortestPath. # xp_median_preimage_11_2() #### xp 2_1: COIL-DEL, StructuralSP, using LETTER2, only node attrs. # xp_median_preimage_2_1() #### xp 3_1: Fingerprint, StructuralSP, using LETTER2, only node attrs. # xp_median_preimage_3_1() #### xp 3_2: Fingerprint, ShortestPath, using LETTER2, only node attrs. # xp_median_preimage_3_2() #### xp 4_1: COLORS-3, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_4_1() #### xp 5_1: FRANKENSTEIN, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_5_1() #### xp 6_1: COIL-RAG, StructuralSP, using NON_SYMBOLIC. # xp_median_preimage_6_1() #### xp 6_2: COIL-RAG, ShortestPath, using NON_SYMBOLIC. # xp_median_preimage_6_2() #### xp 7_1: MUTAG, StructuralSP, using CONSTANT. # xp_median_preimage_7_1() #### xp 7_2: MUTAG, PathUpToH, using CONSTANT. xp_median_preimage_7_2() #### xp 8_1: Monoterpenoides, StructuralSP, using CONSTANT. # xp_median_preimage_8_1() #### xp 8_2: Monoterpenoides, PathUpToH, using CONSTANT. # xp_median_preimage_8_2() #### xp 9_1: MAO, StructuralSP, using CONSTANT, symbolic only. # xp_median_preimage_9_1() #### xp 9_2: MAO, PathUpToH, using CONSTANT, symbolic only. # xp_median_preimage_9_2()