{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "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", "\n", " None edge weight specified. Set all weight to 1.\n", "\n", "getting shortest paths: 183it [00:00, 5323.35it/s]\n", "calculating kernels: 16836it [00:02, 5980.75it/s]\n", "\n", " --- shortest path kernel matrix of size 183 built in 3.0884954929351807 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "\n", "1 gram matrices are calculated, 0 of which are ignored.\n", "\n", "3. Fitting and predicting using nested cross validation. This could really take a while...\n", "cross validation: 30it [00:03, 8.90it/s]\n", "\n", "4. Getting final performance...\n", "best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", "best_params_in: [{'alpha': 0.001}]\n", "\n", "best_val_perf: 12.857015647214508\n", "best_val_std: 0.8860388066269581\n", "final_performance: [12.157314781928168]\n", "final_confidence: [2.5739406086892296]\n", "train_performance: [3.773093745028789]\n", "train_std: [0.12430822644728814]\n", "\n", "time to calculate gram matrix with different hyper-params: 3.09±0.00s\n", "time to calculate best gram matrix: 3.09±0.00s\n", "total training time with all hyper-param choices: 6.84s\n", "\n", "\n", "\n", "Alkane\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", "\n", " None edge weight specified. Set all weight to 1.\n", "\n", "getting shortest paths: 150it [00:00, 5191.83it/s]\n", "calculating kernels: 11325it [00:01, 7143.18it/s]\n", "\n", " --- shortest path kernel matrix of size 150 built in 1.7898523807525635 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "\n", "1 gram matrices are calculated, 0 of which are ignored.\n", "\n", "3. Fitting and predicting using nested cross validation. This could really take a while...\n", "cross validation: 30it [00:02, 10.59it/s]\n", "\n", "4. Getting final performance...\n", "best_params_out: [{'compute_method': 'naive', 'edge_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'node_kernels': {'symb': , 'nsymb': , 'mix': functools.partial(, , )}, 'n_jobs': 8, 'verbose': True}]\n", "best_params_in: [{'alpha': 0.1}]\n", "\n", "best_val_perf: 11.040598123045763\n", "best_val_std: 0.31492017111536147\n", "final_performance: [8.138193149138093]\n", "final_confidence: [1.6238744767195439]\n", "train_performance: [7.9412913127748235]\n", "train_std: [0.18726339675217385]\n", "\n", "time to calculate gram matrix with different hyper-params: 1.79±0.00s\n", "time to calculate best gram matrix: 1.79±0.00s\n", "total training time with all hyper-param choices: 5.00s\n", "\n", "\n", "\n", "MAO\n", "\n", "--- This is a classification problem ---\n", "\n", "\n", "1. Loading dataset from file...\n", "\n", "2. Calculating gram matrices. This could take a while...\n", "\n", " None edge weight specified. Set all weight to 1.\n", "\n", "getting shortest paths: 68it [00:00, 536.19it/s]\n", "calculating kernels: 0it [00:00, ?it/s]" ] } ], "source": [ "import functools\n", "from libs import *\n", "import multiprocessing\n", "\n", "from gklearn.kernels.structuralspKernel import structuralspkernel\n", "from gklearn.utils.kernels import deltakernel, gaussiankernel, kernelproduct\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, missing values\n", "# {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb, missing values\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 = structuralspkernel\n", "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n", "param_grid_precomputed = {'node_kernels': \n", " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", " 'edge_kernels': \n", " [{'symb': deltakernel, 'nsymb': gaussiankernel, 'mix': mixkernel}],\n", " 'compute_method': ['naive']}\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()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }