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New translations run_marginalizedkernel.ipynb (Chinese Simplified)

l10n_v0.2.x
linlin 4 years ago
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Acyclic\n",
"\n",
"--- This is a regression problem ---\n",
"\n",
"\n",
"1. Loading dataset from file...\n",
"\n",
"2. Calculating gram matrices. This could take a while...\n",
"calculating kernels: 16836it [00:00, 18811.60it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0535125732421875 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.1, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18469.31it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.037832498550415 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.2, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 17905.98it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.030360460281372 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.30000000000000004, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 17494.74it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0369200706481934 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.4, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18481.51it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0335497856140137 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.5, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18173.29it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0338375568389893 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.6, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18516.39it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0297644138336182 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.7000000000000001, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18708.47it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0404298305511475 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.8, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:00, 18376.82it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 1.0408570766448975 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 1.0, 'p_quit': 0.9, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 16836it [00:08, 1984.14it/s]\n",
"\n",
" --- marginalized kernel matrix of size 183 built in 8.540878295898438 seconds ---\n",
"\n",
"the gram matrix with parameters {'n_iteration': 4.0, 'p_quit': 0.1, 'remove_totters': False, 'n_jobs': 8} is: \n",
"\n",
"\n",
"calculating kernels: 14901it [00:07, 1221.99it/s]"
]
}
],
"source": [
"# %load_ext line_profiler\n",
"# %matplotlib inline\n",
"from libs import *\n",
"import multiprocessing\n",
"\n",
"from gklearn.kernels.marginalizedKernel import marginalizedkernel\n",
"\n",
"dslist = [\n",
" {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n",
" 'task': 'regression'}, # node symb\n",
" {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n",
" 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt'}, \n",
" # contains single node graph, node symb\n",
" {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds'}, # node/edge symb\n",
" {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds'}, # unlabeled\n",
" {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG_A.txt'}, # node/edge symb\n",
" {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n",
" # node nsymb\n",
" {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",
" # node symb/nsymb\n",
"# {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n",
"# # node/edge symb\n",
"# {'name': 'D&D', 'dataset': '../datasets/DD/DD_A.txt'}, # node symb\n",
"\n",
" # {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n",
" # # # {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n",
" # # # {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n",
" # {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n",
" #\n",
" # # {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n",
" # # {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n",
" # # {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n",
" # # {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n",
" # # {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n",
"\n",
" # # {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n",
" # # {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n",
" # # {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n",
" # {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
" # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
" # {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
" # 'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
" # {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
" # 'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n",
"\n",
" # # not working below\n",
" # {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",
" # {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n",
" # {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n",
" # {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
"]\n",
"estimator = marginalizedkernel\n",
"#param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.3, 3),\n",
"# 'n_iteration': np.linspace(1, 1, 1),\n",
"param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.9, 9),\n",
" 'n_iteration': np.linspace(1, 19, 7), \n",
" 'remove_totters': [False]}\n",
"param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n",
" {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n",
"\n",
"for ds in dslist:\n",
" print()\n",
" print(ds['name'])\n",
" model_selection_for_precomputed_kernel(\n",
" ds['dataset'],\n",
" estimator,\n",
" param_grid_precomputed,\n",
" (param_grid[1] if ('task' in ds and ds['task']\n",
" == 'regression') else param_grid[0]),\n",
" (ds['task'] if 'task' in ds else 'classification'),\n",
" NUM_TRIALS=30,\n",
" datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
" extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
" ds_name=ds['name'],\n",
" n_jobs=multiprocessing.cpu_count(),\n",
" read_gm_from_file=False,\n",
" verbose=True)\n",
" print()"
]
}
],
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"language": "python",
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"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
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},
"nbformat": 4,
"nbformat_minor": 2
}

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