{ "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", "getting paths: 183it [00:00, 22697.39it/s]\n", "calculating kernels: 16836it [00:00, 371524.56it/s]\n", "\n", " --- kernel matrix of path kernel up to 1 of size 183 built in 0.27962422370910645 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 35988.26it/s]\n", "calculating kernels: 16836it [00:00, 444708.75it/s]\n", "\n", " --- kernel matrix of path kernel up to 1 of size 183 built in 0.284440279006958 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 26474.81it/s]\n", "calculating kernels: 16836it [00:00, 215084.65it/s]\n", "\n", " --- kernel matrix of path kernel up to 2 of size 183 built in 0.2832369804382324 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 18360.43it/s]\n", "calculating kernels: 16836it [00:00, 254309.18it/s]\n", "\n", " --- kernel matrix of path kernel up to 2 of size 183 built in 0.28844165802001953 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 8687.30it/s]\n", "calculating kernels: 16836it [00:00, 168741.96it/s]\n", "\n", " --- kernel matrix of path kernel up to 3 of size 183 built in 0.38907885551452637 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 11379.65it/s]\n", "calculating kernels: 16836it [00:00, 195770.23it/s]\n", "\n", " --- kernel matrix of path kernel up to 3 of size 183 built in 0.39213061332702637 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 8062.50it/s]\n", "calculating kernels: 16836it [00:00, 72349.59it/s]\n", "\n", " --- kernel matrix of path kernel up to 4 of size 183 built in 0.512467622756958 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 10578.68it/s]\n", "calculating kernels: 16836it [00:00, 133704.13it/s]\n", "\n", " --- kernel matrix of path kernel up to 4 of size 183 built in 0.3866546154022217 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 9220.91it/s]\n", "calculating kernels: 16836it [00:00, 98386.86it/s] \n", "\n", " --- kernel matrix of path kernel up to 5 of size 183 built in 0.38112974166870117 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 8493.03it/s]\n", "calculating kernels: 16836it [00:00, 119698.11it/s]\n", "\n", " --- kernel matrix of path kernel up to 5 of size 183 built in 0.38007307052612305 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 7385.55it/s]\n", "calculating kernels: 16836it [00:00, 88347.09it/s]\n", "\n", " --- kernel matrix of path kernel up to 6 of size 183 built in 0.3929023742675781 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 5394.24it/s]\n", "calculating kernels: 16836it [00:00, 100946.78it/s]\n", "\n", " --- kernel matrix of path kernel up to 6 of size 183 built in 0.3824801445007324 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 12457.52it/s]\n", "calculating kernels: 16836it [00:00, 68995.02it/s]\n", "\n", " --- kernel matrix of path kernel up to 7 of size 183 built in 0.49313783645629883 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 2829.00it/s]\n", "calculating kernels: 16836it [00:00, 105515.66it/s]\n", "\n", " --- kernel matrix of path kernel up to 7 of size 183 built in 0.35750555992126465 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 7427.43it/s]\n", "calculating kernels: 16836it [00:00, 81607.79it/s]\n", "\n", " --- kernel matrix of path kernel up to 8 of size 183 built in 0.4937615394592285 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 5660.08it/s]\n", "calculating kernels: 16836it [00:00, 90014.85it/s]\n", "\n", " --- kernel matrix of path kernel up to 8 of size 183 built in 0.36504673957824707 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 7548.83it/s]\n", "calculating kernels: 16836it [00:00, 79498.55it/s]\n", "\n", " --- kernel matrix of path kernel up to 9 of size 183 built in 0.47993040084838867 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 7319.90it/s]\n", "calculating kernels: 16836it [00:00, 92310.24it/s]\n", "\n", " --- kernel matrix of path kernel up to 9 of size 183 built in 0.3970515727996826 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 8318.60it/s]\n", "calculating kernels: 16836it [00:00, 89934.38it/s] \n", "\n", " --- kernel matrix of path kernel up to 10 of size 183 built in 0.4861469268798828 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 183it [00:00, 2635.72it/s]\n", "calculating kernels: 16836it [00:00, 90123.30it/s]\n", "\n", " --- kernel matrix of path kernel up to 10 of size 183 built in 0.367603063583374 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "\n", "20 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 [01:06, 1.11s/it]\n", "\n", "4. Getting final performance...\n", "best_params_out: [{'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True}]\n", "best_params_in: [{'alpha': 0.01}]\n", "\n", "best_val_perf: 6.842702754673377\n", "best_val_std: 0.3600238142615252\n", "final_performance: [7.557191252340816]\n", "final_confidence: [2.5849069582911595]\n", "train_performance: [2.276370048287339]\n", "train_std: [0.13830866732067562]\n", "\n", "time to calculate gram matrix with different hyper-params: 0.39±0.07s\n", "time to calculate best gram matrix: 0.28±0.00s\n", "total training time with all hyper-param choices: 79.82s\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", "getting paths: 150it [00:00, 31366.32it/s]\n", "calculating kernels: 11325it [00:00, 509820.58it/s]\n", "\n", " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29791831970214844 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 150it [00:00, 30330.50it/s]\n", "calculating kernels: 11325it [00:00, 655613.27it/s]\n", "\n", " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29232001304626465 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 150it [00:00, 27568.71it/s]\n", "calculating kernels: 11325it [00:00, 780628.98it/s]\n", "\n", " --- kernel matrix of path kernel up to 2 of size 150 built in 0.2590019702911377 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n", "getting paths: 150it [00:00, 17554.29it/s]\n", "calculating kernels: 11325it [00:00, 320784.55it/s]\n", "\n", " --- kernel matrix of path kernel up to 2 of size 150 built in 0.3091611862182617 seconds ---\n", "\n", "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8, 'verbose': True} is: \n", "\n", "\n" ] } ], "source": [ "# %load_ext line_profiler\n", "# %matplotlib inline\n", "from libs import *\n", "import multiprocessing\n", "\n", "from gklearn.kernels.untilHPathKernel import untilhpathkernel\n", "from gklearn.utils.kernels import deltakernel, 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\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 = untilhpathkernel\n", "param_grid_precomputed = {'depth': np.linspace(1, 10, 10), # [2], \n", " 'k_func': ['MinMax', 'tanimoto'],\n", " 'compute_method': ['trie']} # ['MinMax']}\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()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'line_profiler'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'line_profiler'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m 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'line_profiler'" ] } ], "source": [ "%load_ext line_profiler\n", "\n", "import sys\n", "sys.path.insert(0, \"../\")\n", "from gklearn.utils.utils import kernel_train_test\n", "from gklearn.kernels.untildPathKernel import untildpathkernel\n", "\n", "import numpy as np\n", "\n", "datafile = '../../datasets/acyclic/Acyclic/dataset_bps.ds'\n", "kernel_file_path = 'kernelmatrices_path_acyclic/'\n", "\n", "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, k_func = 'tanimoto')\n", "\n", "# kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = False)\n", "\n", "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", " hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = True)\n", "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", " hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = False)\n", "\n", "kernel_para['k_func'] = 'minmax'\n", "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", " hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = True)\n", "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n", " hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = False)\n", "\n", "# # kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)\n", "\n", "# kernel_para['depth'] = 10\n", "# %lprun -f untildpathkernel \\\n", "# kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)" ] } ], "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 }