diff --git a/notebooks/run_spkernel.ipynb b/notebooks/run_spkernel.ipynb index 63ffaf7..5209609 100644 --- a/notebooks/run_spkernel.ipynb +++ b/notebooks/run_spkernel.ipynb @@ -2,128 +2,6 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[Parallel(n_jobs=8)]: Using backend LokyBackend with 8 concurrent workers.\n", - "[Parallel(n_jobs=8)]: Done 2 out of 9 | elapsed: 2.8min remaining: 9.9min\n", - "[Parallel(n_jobs=8)]: Done 3 out of 9 | elapsed: 3.2min remaining: 6.4min\n", - "[Parallel(n_jobs=8)]: Done 4 out of 9 | elapsed: 4.0min remaining: 5.0min\n", - "[Parallel(n_jobs=8)]: Done 5 out of 9 | elapsed: 7.9min remaining: 6.3min\n", - "[Parallel(n_jobs=8)]: Done 6 out of 9 | elapsed: 147.0min remaining: 73.5min\n", - "[Parallel(n_jobs=8)]: Done 7 out of 9 | elapsed: 397.8min remaining: 113.7min\n", - "[Parallel(n_jobs=8)]: Done 9 out of 9 | elapsed: 1098.6min remaining: 0.0s\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_cores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompute_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdslist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 961\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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 963\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 964\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 863\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 864\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 865\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\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 866\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 867\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 513\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 514\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 516\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/joblib/externals/loky/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 426\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 428\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib/python3.5/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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 294\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "# # test parallel computing\n", - "# import psutil\n", - "# # logical=True counts threads, but we are interested in cores\n", - "# psutil.()# .cpu_count(logical=False)\n", - "%load_ext line_profiler\n", - "%matplotlib inline\n", - "import functools\n", - "from libs import *\n", - "from sklearn.metrics.pairwise import rbf_kernel\n", - "from joblib import Parallel, delayed\n", - "import multiprocessing\n", - "\n", - "from pygraph.kernels.spKernel import spkernel\n", - "from pygraph.utils.kernels import deltakernel, kernelsum\n", - "\n", - "num_cores = multiprocessing.cpu_count()\n", - "\n", - "dslist = [ \n", - " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n", - "# {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", - " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n", - " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb\n", - " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n", - " 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb\n", - " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n", - " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',}, # contains single node graph, node symb\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': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb\n", - " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n", - "# {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", - " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\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': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n", - " 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb\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 = spkernel\n", - "mixkernel = functools.partial(kernelsum, deltakernel, rbf_kernel)\n", - "param_grid_precomputed = {'node_kernels': [{'symb': deltakernel, 'nsymb': rbf_kernel, 'mix': mixkernel}]}\n", - "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n", - " {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n", - " \n", - "def compute_ds(ds):\n", - " print()\n", - " print(ds['name'])\n", - " model_selection_for_precomputed_kernel(\n", - " ds['dataset'], estimator, param_grid_precomputed, \n", - " (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n", - " (ds['task'] if 'task' in ds else 'classification'), 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", - "# %lprun -f spkernel \\\n", - "# model_selection_for_precomputed_kernel( \\\n", - "# ds['dataset'], estimator, param_grid_precomputed, \\\n", - "# (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \\\n", - "# (ds['task'] if 'task' in ds else 'classification'), 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", - " print()\n", - " \n", - "Parallel(n_jobs=num_cores, verbose=10)(delayed(compute_ds)(ds) for ds in dslist)" - ] - }, - { - "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false @@ -134,538 +12,38 @@ "output_type": "stream", "text": [ "\n", - "Acyclic\n", - "\n", - "--- This is a regression problem ---\n", - "\n", - "\n", - "I. 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", - "\n", - " --- shortest path kernel matrix of size 183 built in 3.4878082275390625 seconds ---\n", - "\n", - "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} is: \n", - "[[1. 0.47140452 0.33333333 ... 0.30151134 0.30512858 0.27852425]\n", - " [0.47140452 1. 0. ... 0.14213381 0.11986583 0.17232809]\n", - " [0.33333333 0. 1. ... 0.36851387 0.37293493 0.34815531]\n", - " ...\n", - " [0.30151134 0.14213381 0.36851387 ... 1. 0.96429344 0.95175317]\n", - " [0.30512858 0.11986583 0.37293493 ... 0.96429344 1. 0.96671243]\n", - " [0.27852425 0.17232809 0.34815531 ... 0.95175317 0.96671243 1. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "4. Getting final performance...\n", - "best_params_out: [{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}]\n", - "best_params_in: [{'alpha': 0.01}]\n", - "\n", - "best_val_perf: 10.48016704845543\n", - "best_val_std: 0.4581423960367689\n", - "final_performance: [11.856860325044012]\n", - "final_confidence: [1.6523186100392606]\n", - "train_performance: [7.279597258509724]\n", - "train_std: [0.24128809947271068]\n", - "\n", - "time to calculate gram matrix with different hyper-params: 3.49±nans\n", - "time to calculate best gram matrix: 3.49±nans\n", - "total training time with all hyper-param choices: 46.81s\n", - "\n", - "params train_perf valid_perf test_perf gram_matrix_time\n", - "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------ ------------ ----------- ------------------\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-10'} 7.36±0.64 11.98±3.12 11.65±3.32 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-10'} 7.40±0.78 12.14±3.95 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-09'} 7.40±0.78 12.14±3.95 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-09'} 7.40±0.78 12.14±3.95 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-08'} 7.40±0.78 12.14±3.95 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-08'} 7.40±0.78 12.14±3.95 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-07'} 7.40±0.79 12.14±3.96 11.71±3.52 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-07'} 7.40±0.79 12.14±3.99 11.71±3.53 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-06'} 7.40±0.81 12.16±4.08 11.72±3.55 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-06'} 7.41±0.86 12.21±4.37 11.74±3.63 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-05'} 7.46±1.11 12.45±5.69 11.83±4.01 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-05'} 10.64±18.49 27.50±88.21 17.94±36.68 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-04'} 7.29±0.48 11.60±1.69 11.42±3.09 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-04'} 7.19±0.41 11.18±0.77 11.16±3.00 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-03'} 7.13±0.39 10.90±0.70 11.02±2.97 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-03'} 7.12±0.34 10.65±0.56 11.27±2.50 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-02'} 7.28±0.24 10.48±0.46 11.86±1.65 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-02'} 7.94±0.11 10.67±0.43 12.51±1.10 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-01'} 9.62±0.12 11.78±0.48 13.65±2.20 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-01'} 12.75±0.25 14.41±0.54 16.51±2.92 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+00'} 17.44±0.33 18.67±0.46 21.11±3.92 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+00'} 56.07±67.67 55.81±64.64 65.83±86.28 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+01'} 33.91±0.57 34.15±0.67 36.33±5.23 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+01'} 48.93±0.50 48.71±0.65 50.50±3.37 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+02'} 76.07±0.23 75.65±0.40 76.53±2.14 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+02'} 108.05±0.18 107.63±0.29 107.88±2.37 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+03'} 128.34±0.26 127.93±0.32 127.95±2.62 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+03'} 136.90±0.30 136.48±0.35 136.43±2.72 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+04'} 139.89±0.31 139.47±0.36 139.39±2.75 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+04'} 140.87±0.32 140.45±0.36 140.36±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+05'} 141.18±0.32 140.76±0.36 140.67±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+05'} 141.28±0.32 140.86±0.36 140.77±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+06'} 141.31±0.32 140.89±0.36 140.80±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+06'} 141.32±0.32 140.90±0.36 140.81±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+07'} 141.32±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+07'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+08'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+08'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+09'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+09'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+10'} 141.33±0.32 140.91±0.36 140.82±2.76 3.49\n", - "\n", - "\n", - "Alkane\n", - "\n", - "--- This is a regression problem ---\n", - "\n", - "\n", - "I. 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", - "\n", - " 1 graphs are removed as they don't contain edges.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n", - " keepdims=keepdims)\n", - "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n", - " ret = ret.dtype.type(ret / rcount)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " --- shortest path kernel matrix of size 149 built in 3.3240325450897217 seconds ---\n", - "\n", - "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} is: \n", - "[[1. 0.89442719 0.70710678 ... 0.47902167 0.46852129 0.53311399]\n", - " [0.89442719 1. 0.9486833 ... 0.642675 0.62858727 0.68875683]\n", - " [0.70710678 0.9486833 1. ... 0.67743894 0.66258916 0.71205164]\n", - " ...\n", - " [0.47902167 0.642675 0.67743894 ... 1. 0.99747487 0.97420128]\n", - " [0.46852129 0.62858727 0.66258916 ... 0.99747487 1. 0.96209727]\n", - " [0.53311399 0.68875683 0.71205164 ... 0.97420128 0.96209727 1. ]]\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n", - "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n", - " warnings.warn(\"Singular matrix in solving dual problem. Using \"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "4. Getting final performance...\n", - "best_params_out: [{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}]\n", - "best_params_in: [{'alpha': 0.03162277660168379}]\n", - "\n", - "best_val_perf: 8.650257813261417\n", - "best_val_std: 0.42968288406182015\n", - "final_performance: [9.361116361154078]\n", - "final_confidence: [2.218550782316567]\n", - "train_performance: [7.8343217840551755]\n", - "train_std: [0.25589398275456354]\n", - "\n", - "time to calculate gram matrix with different hyper-params: 3.32±nans\n", - "time to calculate best gram matrix: 3.32±nans\n", - "total training time with all hyper-param choices: 30.35s\n", - "\n", - "params train_perf valid_perf test_perf gram_matrix_time\n", - "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------ ------------ ----------- ------------------\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-10'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-10'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-09'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-09'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-08'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-08'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-07'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-07'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-06'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-06'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-05'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-05'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-04'} 7.71±0.25 8.68±0.49 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-04'} 7.71±0.25 8.68±0.48 9.41±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-03'} 7.71±0.25 8.68±0.48 9.40±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-03'} 7.72±0.25 8.68±0.48 9.40±2.17 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-02'} 7.74±0.26 8.67±0.47 9.39±2.19 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-02'} 7.83±0.26 8.65±0.43 9.36±2.22 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e-01'} 8.17±0.26 8.71±0.35 9.33±2.26 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e-01'} 14.40±13.12 14.84±14.51 15.04±13.59 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+00'} 13.74±0.26 14.14±0.28 14.65±1.73 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+00'} 21.84±0.21 22.04±0.27 24.33±2.16 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+01'} 31.21±0.32 31.06±0.37 33.91±2.91 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+01'} 44.70±0.43 44.64±0.45 43.78±3.41 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+02'} 72.78±0.22 72.71±0.21 66.94±6.71 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+02'} 106.35±0.56 106.24±0.55 98.16±8.32 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+03'} 127.45±0.82 127.32±0.81 118.43±8.82 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+03'} 136.29±0.93 136.16±0.93 127.00±8.97 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+04'} 139.38±0.97 139.24±0.96 130.00±9.02 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+04'} 140.39±0.98 140.25±0.98 130.98±9.03 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+05'} 140.72±0.98 140.57±0.98 131.30±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+05'} 140.82±0.99 140.68±0.98 131.40±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+06'} 140.85±0.99 140.71±0.98 131.43±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+06'} 140.86±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+07'} 140.86±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+07'} 140.86±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+08'} 140.87±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+08'} 140.87±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+09'} 140.87±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '3.16e+09'} 140.87±0.99 140.72±0.98 131.44±9.04 3.32\n", - "{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }, 'alpha': '1.00e+10'} 140.87±0.99 140.72±0.98 131.44±9.04 3.32\n", - "\n", - "\n", - "MAO\n", - "\n", - "--- This is a classification problem ---\n", - "\n", - "\n", - "I. Loading dataset from file...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "2. Calculating gram matrices. This could take a while...\n", - "\n", - " None edge weight specified. Set all weight to 1.\n", - "\n", - "\n", - " --- shortest path kernel matrix of size 68 built in 7.607230186462402 seconds ---\n", - "\n", - "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} is: \n", - "[[1. 0.98449615 0.91863253 ... 0.90803004 0.88073949 0.74163265]\n", - " [0.98449615 1. 0.96352874 ... 0.95770189 0.93322371 0.82803429]\n", - " [0.91863253 0.96352874 1. ... 0.98530439 0.97703823 0.92845585]\n", - " ...\n", - " [0.90803004 0.95770189 0.98530439 ... 1. 0.99204562 0.94363326]\n", - " [0.88073949 0.93322371 0.97703823 ... 0.99204562 1. 0.96718938]\n", - " [0.74163265 0.82803429 0.92845585 ... 0.94363326 0.96718938 1. ]]\n" - ] - }, - { - "data": { - "image/png": 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dzVvnIh1g5mF9zkUA/masDr0F4eFRBpRimzOKe0RENwJYzcyPQqm7/zURMdQS44v68tMA/ISIRqBWpzcVE3ntJwgPjwkGg47w3RxTWw7uETPfII4fhM2rKs/5A4AzR5ePhbJOEKnKbBCRaQKvksJUNDwHwDokzbICAM7S++aSO7FROO8u1r6uqPqT5ion08KaA0FZUnf58rDtuzZmv8xTk+q4M29DkmcIJ6YpHxixjtHuEbtcqXVwHlriYRN1YyYcXGRMcABorbXOuZReItXGJDcj7FB8btBKrbfElPnscpACQFyXd+Us3Vg6Rg0W1NolSOeIug9NMXe4dr+uj0X02aHDxXsybidla0I5FBsFP+b3/WrJGeX4NJAcEYkG7aR8a8QGZpnxbRuwgXXJaXbMxhn73JC6n8NceOk0GiovxtTcMPQWhIfHhGNyyskVg7JOENl8LNBqMCHZ8t0iGZBmq1I6HI1lIBmX5s2o6hMF63sGw2+qar2dtSBhz0sKh2QMZrvVraNgyy0DsT5mx1zteJMlHevRBanwNmdnzgZW5QSrcUgzEyU/Me54AOckDgXHLXFlkplt3dHoZcWkbBXh3LX5cLDWhpy1dGZrC0daXBJNcbXNGSXYOqNCsToHcmFLBQCGtRU5ILa/Z+ht64qYexvT3O8qcrM3q/R10x0Bc3MrrWWQJrmtrb6vtoSqTzrYs4XgLQgPD4+C8BaEh4eHE8zkLYhiED8UDyTgjNKT1GuQgVWGASl5DMbhKJcNp6Ws6tHz2h8YVZ94RGk4/tM5ll8w9xJlUn9EaxsAwNODVsFontYiqI1Zx2KvcN6Z8leHrI5ClXDaLRRaBgYuj/aSVDjY6nMvvi84jm+xS5xYVr2NVs6cZ/txLFGWCOWs54aUA64t4WYimmXAvJR14Gb5UOi8H//2w8Hxu67YBgA4M70zdB4AzNM6Ci79CwBo1OxVF4sUAB48rBSpzqi07Zs2Mwn3suaJ/pMAAGdVuLWLhlgtM+cl7FKqJa7GJ5cdvzq8JDheojkq769Uz1XlURgDJ6JgjIeHRxEo5TZnueEnCA+PCYZyUnofxJiINWcD5WkjMGtk4gCr5wDYQJ29Qr7c8hzsec8LmsFY9dUrVN+XtGwLypbXqKXF9pxdipwpzFPLg8iFymT50gprBksehAuzE+HlxEN9C0Jln1psRWk3ts0Ijg0PYlmV/RySCm7w5IDdcVicUvyTKB7E1rxSxX6p/+SgrCcf/hyXv/f14NgsLaJ4EE8NnBLZDgCs7VW0+51905z1Pz/1PgBH8iD+/pCievfk3G3+eeMfAETzIAyF+uWh+UGZGd+awzbM4YY5/xocGx7E4wNK6PbwiHtJVAhTVbR2zFET0V1EtJ+I3hRljUS0kog26//d37CHhwcYhBEu7meyoRgL4h4A/wDgZ6LsegBPMfNNOuXe9QC+OVZDngeh2/c8CAAnDg8CmLqitWOOmpmfh9K2k1gO4F59fC+AosQnPDxORCg9iJIoSpUdR+uDmMHMe/TxXgAzok6UmbUSzeHsTx4exzsYdIQFOJVwzE5KZmYdWhpVH2TWqm5qY8+D8DwIgxOKB3GCMSn3EdEsZt5DRLMAjN+t6+FxgmAqb3MerefkUQDX6uNrATxSmuF4eByPUFTrYn4mG4i5sMY/Ef0SSqWmGcA+AN8B8DCABwDMBbAdwCeZxw6SX7okxU89pmLqTSLdm0R2b4ndA8pfYWTiAKvnIHcjzLIBsDyHqPpV378VALA/b3NV/O0BtdT5p+ffbcd5tpXpMzsGew7b9HCz6qwpasrn1FsR1N3iXFfm69q68HKi6ldh/8y3v3dPcHymMP11tnp8eduVQdn2Q+Gd5soHRZtXq+sPHHKn3ovtVPesaY19HkzWa4nbb/1xcPyVrZ8AAOzsdiffrfq16r+iNyK7d4cy2Wn9Vmd9y1PKwN3cbXUt6B51nIrIGJ7+hkql2BExpv796vtofskuUdI96lms3mGfi1Nus+kZ13WrnZvD9ynexsZHfoT+AzuLNglaFjfz8p99tKhz7zzv3leOUbS2pBhzicHMV0VUXVrisXh4HJcwuxhTEeWVvWfCpqxSGTpNO9BkIl3JNjPS9FJgVipBGUiHo2RIuuqN5TBdCOF+uO4NAMCaM216gRUzXw6OOzLKudfbaK2S2rilZ5pyKYK6r9G+uTcP2DTzBo3JcLath88/P1R2jrAaZKanXm31XdZsHasbqsKKVE++yyZR+myr4rm93dgSOg8AXq1uBQAciNk3b7I3zCWYEbff0YpZ6j5tbJgdOg8AHjhLO6J73Y9ZRZuytOrmnOGsX16v2Ix1Cev0/e3Z6n4m+tw8h/88Q33OzXXh+w4Ar+vPuWtQMFN71LPW22otv+sa1gbH03WGsrvOVfc49ztn0wUxGZcPxcDHYnh4TDAYhJyfIDw8PFyYyrsYZZ0gDmRr8dP97wcAXN3yAgDgw9W7g/qkyChhKMEmb4UqU/9Xi6xHhscA2MCrqHrjkDTLCgBYVqkcVDMW/FNQdnLSitIOV+0DAGTZksKTon1ZbtAvynrqwvv1UmzWYNql4eS920VA0rphuwTqHVHLmmvq1gVlXTVhBfPZl1rOw+enqcCvg3XuB3XNNNX+83NODcr2DoYdms8IIdzlNYqfcIkIGpMYvkQ9XruG3A7Dvf3KpO844K5fXqOWn5dUvxWUZS9R975zqMZ5zTV16hnor13nrH+zQS0Zn2pZHJTtGVJLwl39dmn4gbR1RJ9Xoe5d/TLlXP7bW6xDulj4JYaHh4cbkzQQqxiUdYLoO1yJ3/9OSfO/fX4zAOC7Cx8N6qU8usmVKTNeGWl6GVglGZAmZDuq/tKVXwdwpEPSWA6ScSm3Qbv1kFpE0NXuvLUATHnniN0erBLPQqPDWhhw7Cx/vO7VUNmtB94fHD/bYcOwBweUhbPsQungDT+AHxWh9CZDWDqC9NqWVJaWdBp3VtWFzvvh1suC4yWLfq7bdDaJC2rVdvHutDvYd6BWfY6NtW6m/qsZ9YzIvKMX1ytrorPavV27V9/uVMSYDMv1otrNQdkuPb7hOuv4fDNrG0jpP5OP1iir5I5YeJu6EBgoVeo9ENEVAG6GSpxzBzPfNKr+JKhsWi1QMVTXMHOHrrsWwLf1qd9n5nsxBqam3ePhMcVQinBvIooDuAXAhwEsBnAVES0eddr/AfAzZj4LwI0A/lpf2wjFYboAwPkAvlOMTIOfIDw8JhjGSVkCPYjzAWxh5q3MnAFwP1RktcRiAE/r42dE/eUAVjJzFzMfArASwBVjdVhe0drqHKado4KX/qztRQDAgqR1BsnZqkVnr5KJdI3ugNRrkIFVRgkqqt4wJCXPwTgk5bJC8iRiUOUVZG9Vo9AiMOUz4m5mX+9IuDzpeA6eHQgrSl3ZuMqOKWUD2fZnlHndLLQyuhyZpv5toD04vrpWLR26HU5VADiYV06/NwdbgzLjvJO4du4LwfHshLp3vSPuZLZvDKh7H+WkPDis7vPWg03O+m/OelK3b4Pjnuk9DQCwf9i9xDCOW9cyDgCGdAY0+Tm3D6r+D2Xscvaz9YE+UsD6/cOgYvX2szv4LAoqmrPod3EzEa0Wv9+uAx4BYA4AGRnXAWURSKyByu59M4A/AVBLRE0R187BGPBOSg+PMmAcPogDx0i1/jqAfyCiz0Al790FYPwKNxp+gvDwmGhwyXgQuwC0id9bdZntink3lAUBIqoB8HFm7iaiXVAxVfLaZ8fqsKwTBIERjylzrSdfFapPi6XBEOf0/9ZWNGKxMSG2Nk/oLYxVbwKvDH0asDyHbmF5m2UFADTr5cawkD1rjtkliCnvEjsbA+JhqHI8F/UOiTaXZsLmYZtgdu+wNff788rklq20xcNLh3kp+9kNN8MlgQcAMxNqb785aZcyLmm1HeLe7ctvAQDUktt8nluhdkYkNV1iI9Tn662tcNZX6f5nJO31M/SSNEr2zTwvrnsMAG36c+4VNPYqremxAZauPiSWYiZZs7lHyXG+kEtIlFoFoJ2I5kNNDCsAXC1PIKJmAF3MPALgW1A7GgDwJID/JRyTH9L1BeGdlB4eZUApnJTMnAPwJag/9g0AHmDmdUR0IxF9TJ+2DMAmInoLSuntr/S1XQC+BzXJrAJwYzER2OVN3puLY1+X2lvf1aIcV93CuTZEkl+g3gRSzr0zr/awpYCsVHoyEvRR9SY0WwZeGSak5DlIh6SxECqECKq0Jkx5imw/kheQpvAtziPsQTNvJ4nOnOUhdGWsxdU1rI6lbGve4XxsEMpaKX0fkxFr4bS+941aaDYKO4cag2Pz3VTF3IFTaX1PkhQWCwaA+qTiE9Sm3LL5Xdo5aaxJAKjR1khUIhq3C1bWqzGnpaNZH7cIR7C8twMj6rdG7YiOR6QOiIJRtS4FmPkxAI+NKrtBHD8I4MGIa++CtSiKgvdBeHiUAeyZlB4eHi4ww0dzFoNkIo8ZjcrJNKdCBRI1xKz5KJcTMX3cOxJ2FknIPXKZ8cpVb5SgpNPMBF5J+rTkORiHpGtZIcszwpl6pJPSfj4DVx6JvbnCit+NKbtcqIirNqVhn3Y4CrtH7LIkw/r6iBfZkDbZu/I2CKonF3Yk1whthgH9meMj7hwVQ6zufdRyoCerloK9GbeTslEvD2WWrL68Wq705t3LlrH+DGN6eWf4EAAwrI87M5ZbIe+tWUJ16XEeTSLeqWpBFJNZq42IniGi9US0joi+rMt9di0Pj6IwdTNrFTMV5gB8jZkXA3g3gC9q/rfJrtUO4Cn9u4eHhwPMVNTPZEMxmpR7AOzRx71EtAGKorkclnhxLxTpomD6Pb+LoeB3MRROnF2ME0QwhojmATgbwEsoMruWzKwV95m1PE5EsHJUTkUUPUFo2uY/A/gKMx8m8WYvlF3riMxa7bO4sV6xFNvShTka5i3bJSbrgcCxZOPxZUarpRU7C9YbaXopMGsg9Rxk4JVhSEoLQTokTbkM8MqyZFWGA5mSCL/9tmbCIqvvSm+3bU6z1xzQSX2l07bHETC1LWvVn86vULmN+kbcb2uXk3JPJhxkdV7NO8FxfUxZBtKikujRyX/3ZNwvBmMV7e1zB17NiKvPJJ2+PXllHe7PuK9pjKlHWiaClsjre98tEhObzymdlPLeGgtis/6OhsfmF4VQKj2IcqOoCYKIklCTw33M/Gtd7LNreXgUAQYhX3w056RCMbsYBOBOABuY+YeiymfX8vAoEszF/Uw2FGNBXATgzwCsJaLXddl/B3ATgAeI6HPQ2bXGaiiXj+PAQWXGbZup5MQ609bUS1NWHCsHmzTGu0eUeVkvHEwyUe5Y9SbjlcxbYYKYXEFVgDVvpeNRmrymXC4rjOAuAKQdt1g63QxczkHzeQDgnWG7XNgzpJ2t0+yyon8k/HQ1xMP5N2Lk/qAp7d5riFvHZl887FzcnZW72eE8JUf2pdqMCqyqSajxVyXdS5R9+TCPwgRWVcajuBeqryg3YlZzGNLSqayfl7qEXZpKjQvD96jVUnPxMV2hYUzGHYpiUMwuxu8RSa/x2bU8PMaCsg6O0wnCw8Pj2HFCbHMeM3IEdKudiG19Slegu9YuMaRJ3K+dOnGxOVIbU3vgUtNgodB7MIiqN4l0ZTo8k7dCqk9LmTiz9JB8BkmfNuVyt0IuKyR/wiDmcP20Jw+FylYOnBIcb+61S4wDg2qnQWpl1Dp0HtqTNifIsDbTqygVOg8AZsSV+bwgZX3Ncrlh8PihM4PjnnqVns7sZozGQt2Wqx0A2JdVS72ovJX9mqrdELPUeKNxEdVmr15qNcbdPIkWvTOyUHzOOs2t2J+0vJMesWSr1/d2of6OKhz0+bEwGf0LxcBbEB4eEwwGYWSK7mL4CcLDowyYogaEnyA8PCYc3knp4eFREFPUhCjvBJEYQaxJOYlmVynasxRrlc6otN5Dz4odVuNgSkImz7XOqNl6HzuqvrZO1TcmrTPUJNKVeRRk3gojfioDrKSegymX9GnJc3A5JCVPwqBnJOzMrBYcjpmVNpAopangchwu5+PuvJsr4IJxyw6MWIrxkGNMTckwt+JoYdrPOHJ6AMBM7bRuELdwa1adGxWsZXgpLjq7qlfPVd7xvfTQYVAtAAAgAElEQVQJjQnp9DVOWHM/XWkOx4K3IDw8PCLhdzE8PDycYHgLwsPDIwoM8IifIDw8PKLglxhjI3WQ0PqPqsuVy84GAGSXWWdSQ9Ky4xamFdNtaXpHULYxozIfSbbfkpQNsHmob0HB+qpfKebew+efH5RNu1T1+fG6V4MymUjXOFGl4pMUmDXlUs9BBl65GJIuh+TSirBw61WrPxIcj2yyOg2JfvU2euham2C2PbU3dP27K6zj8qlB1f68ZHfoPMAGxUn2ZSYRHvtfPv6p4PiUK/bqse8MnQcAbQnVV5RK1Rzd/qnpPc76O7suAgCcVWXbNwmaswk38ehf+hX79KyIMRkHdJsYU4tW3mpLWJ2Hu7uXBsdLKtUzeGmlOq8yIuAtGqWTkyOiK6AS88YB3MHMN42qnwul8Nagz7memR/TYk8bAGzSp77IzF8Yqz9vQXh4lAMlsCCIKA7gFgCXQWXnXkVEjzLzenHat6Eybt2qtWMfAzBP173NzEsxDkxN/qeHx1QCl0y09nwAW5h5KzNnANwPpQ07qjeYoJJ6ALuPZeh+gvDwKAe4yB+gmYhWi5/Pi1bmAJBrpw5dJvFdANcQUQeU9XCdqJtPRK8R0XNE9L5ihu2XGB4eZUHRPogDzHzuMXR0FYB7mPlviehCAD8nojOglOnnMvNBInoXgIeJ6HRmDqeVFyjrBFHX2ocP/eB5AMDltSpUeN3w7KBehnYbdan7D10QlNXEFbOwM2fFRT/3op0IP7X4lYL1P/7ePQCAc0Tq9+05pdp064H3B2VXNq4KjjcPz9Rt2lBgCVMuBWalEpQM2TaQDEkD6ZA02HDRz4PjQ++2Dty9mva4TwjMvj50Uuj6z61eFhz/+JwHAABP9i0OnQcAL/XMV/9vmxeU5XrDztR1V/5dcPxWVn1fGzMznW1+e/UfAwDyjnYAINGjXKPVu9x/PN+97mcAgE1Ds4KyG15WSaxH+txt/uSyuwEArw/Ndda/2d8KAFi59dSgLNOjHLjxXuswf+TjPwqO1+pn9OyXrgQAvNN/u7Ptghi/CJULuwC0id9bdZnE5wBcAQDM/AIRpQE0M/N+AMO6/BUiehvAKQBWF+rQLzE8PCYaDICpuJ/CWAWgnYjmE1EKwAoobViJHdBKb0R0GoA0gE4iatFOThDRAgDtALaO1WExorVpInqZiNbo1Hv/U5fPJ6KXiGgLEf1KD9jDw8OBUojWMnMOwJcAPAm1ZfkAM68johuJ6GP6tK8B+HMiWgPglwA+w8wM4GIAb2hd2QcBfIF5bP3+YpYYwwAuYeY+LX//eyJ6HMBXAfyIme8notugTJtbCzVUHRvGuVVq0jKZnHZkmoN6mRR1QGdVenHfvKCstVbtq+dGrCkY32LN+Y1tMwrWn/letbSIi33sdcPKx/Nsx8lB2XSRYWnvsOI8yMxWMpGuKZd5K6TArFSCMpCBVwaS52AglxXT4rb/JKmgtsf75gVla/tG+6oAfsu2+Uz7aQCATYed+Y2wab/iccj7lT4cfqPJe/esXj6t7W11thnfqoKcKnsjsnkdUH8R9VuHnPXb9LOxsd8uYVJ6fMnwLQQAvHzRQgDA+t5Zzvp3DqvMYPyOVTKr6lHjq+y0f6HrxLLptQG1fMu8pZaTPOQOBCuIEhGlmPkxKOejLLtBHK+HEpoefd0/Q6WuGBfGtCBYwbBKkvqHAVwCNRMBipjxx+Pt3MPjhEFplhhlR1E+CCKKa9NkP4CVAN4G0K1NHsC93WKu/bzZsunpGr+Wn4fH8QDi4n4mG4raxWDmPIClRNQA4CEAi4rtQKbeO/2sFLdpanJ1LJwzoULkszicU+bpYMZ6q1Nau2Eob8tiWSq6Pq+/gF6x2Osd0f0MWKqzTOvWr3MzmIS5AFARtxOdKT8gdk5M3grACsxKpOLhPBGGPi2xV5xmlhUAUBMhEjsa8WHbZn9Ofb7BnNv7n80qs7liyF6TcFj+Jg0dABzIqs/ck3WPJ3jgIzz4MX0bYxl33gwjUixzYBi92FjEu8YsU6NS3WVycd2nLdMyI0gM2edCamEM6ucpPqjbHO+OhOU4TDmMa5uTmbuJ6BkAFwJoIKKEtiJc2y0eHh4AAAKmaDQn8RiuUyJqAZDVk0MlgN8C+AFUur1/Fk7KN5j5/xZqq+7UGXzebZ8GAHxituIsXFr1VlAvVXzM2mdfPibq1ZtGvgNXDswLjpdVbStY/+A+xT+5rHlDUHZVnaKxd+TsXNks3ljGHSW1mWT7plwme5VZmYYc91cqQRk81BfmMcxLWr7Gq4PzQvVfbbS7VAOO5L2P9luH5MdrVFtRyXs3ZdX4/zDQHpS5ku6eXmXfA39So7gfWXa/Un/d167bCScBBoAdg8phuP6Q23H61Jm/BAAMC4Wuu3vOAADsy7p5KTdOX6XH5LZKtufU9/Hi4Hw7joxKwdAxaLOG/d+2Z4JjoxD2eL/iQ3z7T9dh61qHyReBipPaeNb1Xy7q3O3/5RuvHCNRqqQoxoKYBeBevYcag9pa+Q0RrQdwPxF9H8BrUPk7PTw8XDhelxjM/AaAsx3lW6GCRzw8PArBEKWmIMpKtc7k49h1SJmt6xuUuXZ6RUdQn4fVbojrKTcruBFVWnA0LhxQMkNSld6jj6rffkiZkBuq7B55V8163be9pkuIqLbFtcipMKPTZMdkyntG3Il0XRmvXAKzLj0HSZ928RwGGjbaNmPhNheJNgf0OGtiYd0JwAr+nlxhr5mR7Amd96pYCr2vUi1xGmPuzbCFqX0AgKYIPQjjoI5yKHbpe9oilm9mfM0JdwhBV14toRrj7s9phI/bxedsSqjxVQnPZWd+WFxjtDTUMi11FJm1JuMORTHwwVoeHuXAFJ0gfCyGh4dHJLwF4eFRBtAU3eb0E4SHx0TjRCFKHXNnB+No+oUKkvnNRWqrN3OxHUKLCJKapcVVF6ftvvtzgyrwaY4QU12Sso6lJwdmFayvfFA5SJ98l5Xlm32p6uejtWuCsn8TXIAg3XzMBk51j1hWpSnflrVBWQ1xm31KisAauDJeSYFZA6nnIAOvDEPy0WusaO0ih5NTCuE+Nai4FwsSYcejxJnCqZt1OA9vePwTwfGpl2vRWqGFITFP9zUU4aScqUVtz6h0C8zeeUhtkp2t+S0A0K4dhdmke3X8WP/Jekw7nPUmq9mChB3THD0+KVp7X4/duDu7UvX/fi1aW3U0xoCfIDw8PKLgdzE8PDyi4SeIsZFryuPg1cr8/nT7awCAj9W9FtQ3iH3oes0f6BWcApO/oCVuy54bagqOF2szO6oeVyvz9LOt1jT//DRF+ZaU6KtrNwXH/Zo/kBI6CBm2yw1Tfn6FNc0lhiOSzI6GyVshYWTiAKvnANjAK0OfBizP4cg2bd+XVuokxSNuXZ93cqr+lSGraLYnOy103hc/+Nvg+No6Q7V205of6Vecid1ZN9XaaG282T3bWX9n+/0AjoyNelrnLNmdCY8NAL7WpL7bfMQfZEdejfWlIdvndq07Ienb/6PlxeDYUMl/P6SC0/qOxuHoJwgPDw8XiP0uRlHgXAyDh5Qi0OY+pWDUWV0rzpAyQcqayAgmpUndbhxNgM3eVEz9gUOqr7cbrUPxYB3pa+0U3y3extXakklKh504NOUyCComrA0Xa9IFV8YrKTArlaBMyHbfTNuniyEpHZLGcnAxLgGgRTtWJfO0IT4QOu9fuqzzrqvuDQDRrMU27aCtjQ066w1zMRthZfVoZbDGmLVQTJuusQFjMykb9OMkA+GMiHBN3Ma3d+YtW7IxrsbRptmbKToKBVpvQXh4eETBOyk9PDyi4ScIDw8PJyapnFwx8BOEh0c5MEUnCB+s5eFRDhSfm7MgiOgKItqk89Fc76ifS0TP6BycbxDRR0Tdt/R1m4jo8mKGXVYLgjKEyh3KA/9Gk9qHXlNnU6RNS1iKsqHhmhR8ABDX3uNetueNCCGOrfn6gvWxnUpc9dVqm8dhzTSls9AmKNEHRUq7mXonIC3EdYeE192Uy7KU2LmfEQ978F2sAZcf36TDA2zeCsAKzG6abz31Rs8hCobn0CJo4BLT4yZPhPgOHDsF3+u0uhRrmxQXwbUDAwDtCbU70B1z7ziY3QP5HUsYTobcWWnXGhX9I+7EGGszhcc0P6GegWTC7gA1xvTOiBjnGpEXY4He8Yjr9+nRGAOlWGJoVbdbAFwGpSS/ioge1bkwDL4Npfp2KxEthsqhMU8frwBwOoDZAH5HRKdoQepIFG1BaOn714joN/p3n1nLw6NYlMaCOB/AFmbeyswZAPcDWO7oyTC+6gHs1sfLAdzPzMPM/A6ALShCEW48FsSXodJ9mc5/gHFm1koOMFpeU/vLe6pVMy80LQjq61L2LdiaVm+AuRX2zd6VU2/21pQtmycS8b6kA3Wi6pvWqG/gQMwy+56fo5K4Xlxn2ZNvDloLo1mncGoUAUddwsIw5bJM7tEvSIUZlgMj4T16V1CXTKQrM14Zafo/nGWDyqQSlIEMvHK9jY+EshysJQE0xcL7/Yc2NQbHL7Sp/rvSe5wt/kmN6qs25n5JtWmLsd2hXAUA39ujrOB91VY896vTNgMARuLuv6YHegqPqVerXC1NWan+Kj2+2Qn7eb+512Z22Fup2jpHB21FKWBFYnxOymYikgl1b9epIwCVe0ZGtnUAuABH4rsAfktE1wGoBvBBce2L4rzIXDYSxSbOaQXwRwDu0L8TfGYtD4/iUbwFcYCZzxU/400lfhWAe5i5FcBHAPyciI7a11isBfFjAP8NgKE9NmEcmbUAfB4AKirdnHwPj+MepdnF2AWgTfzuykfzOQBXAAAzv0BEaQDNRV4bwpgTBBF9FMB+Zn6FiJaNdf5oyMxadbVzONWt6LXJwzoj1aA1aTMi6W5SOyQrRAolQxeuzdtrsmy1H3rylQXrK3p0Xo1em9li76Ca8zqrbKDOniFr0srMX0E/uapQmcz90Be35quLEiyzNhlkhIaFQU6MUybSNRmvZN4Kl8Cs1HMwgVdRFGXjkJTLirjjxZPstWW7dbCVq2/ABnFJ6rtEXgfI1TvyhABA55Bats1O2/uZ0y7ehNOtO/aYTCa1EZHpxLQ1Iv6K94rsaNP1MnOIk/q88S0xCCXjQawC0E5E86H+uFcAuHrUOTsAXArgHiI6DUAaQCeARwH8goh+COWkbAfw8lgdFmNBXATgY3q7JA3lg7gZPrOWh0fxKMEEwcw5IvoSgCehNr7uYuZ1RHQjgNXM/CiArwH4KRH9he71M6yyY60jogcArAeQA/DFsXYwgOLyYnwLwLcAQFsQX2fmTxPRPwG4EsqTei2AR8b9iT08TgQwcDTxXc6mmB+D2rqUZTeI4/VQL3XXtX8F4K/G09+xEKW+CeCrRLQFyifhM2t5eEShRESpcmO8yXufBfCsPvaZtTw8isRUjcUYM3lvKdF+ZiXf/MhCAMBizVV4ov+UoF7qApjjJ/adHpQtqA1zBZ78vRWgvfy9rxesf/xP/xYAMCNuDadntBDuD7deFpRdO/eF4Ngkdu3LWe5CjWDhmfLzat4JynYLJaZNA+HEtE3JMJvxV8+9J1S29sq/C47jQmNiYEQ52B7pn2fP7bfcDYOH/83O30YJakP/rNB5APC6ZkhKnoN0SBps+LzNz/xAn3IIbhh0b6f/7KmLI9sBgAqtEVu3w70U/sJNahf99X7Ltn34d+8GACQicud+fcWvAQBvDc101r/epe7T5nV2zMkeNb70QdvmzdfdFhybxMl//7yiFOz965sxvL2jaE9l5aw2XnDtV4s6d/0Pvjrlkvd6eHgcCybp8qEY+AnCw6MMmKpLjLJOELuGGvCXGxR1/DMLXgIAfLB6Q1AfF3exRUu9La+xArOdmqI8O25N/HddsS04PjO9s2D9V7aqnA4rZtnt3+U16poli34elM1O2OXEvvwWADYxMAAMiGWZKa+P2b16wNK7e+rXohicckWYKv1W1vbz7IBdih3IKu7GN5rt5zCJdCVM3grACswambjRMIFXhj4NWE6BhFlWAMAnaxTXYKCq09lm/IPKdb9zyC0wa3gOW7qanfXvq1RjPrPC7qBXXa54NLuH3KS7a+rU99lb87azfn294si8NGNhULZ9UPV/IGP5Mxem7TO0OKW+w5M+qL7X628Jc1bGhJ8gPDw8olCqbc5yo6wTRD4fQ3e3mqU3DSgn0gVVW4L6NGRItWJQxoUrqEmHB9cK5p2xGoqp/3H3JQCAjQ1W8vwSnbUpLfrpHbHy+7WaTVgVs6zG+Ihl4ZnyYXaHLB9pWURjaUU4u9RGEXK8ttc6IXuyqs1sk33qGmNhR6DMeGVYjVFiriY8WgY5udiI0iFpLIcoIdxTdFsyjF9iW8JtORgM6VD9RsGmXVixr2CbxoEbdd/bdHBdd4X9nI06BN44pIEjn4F6/flMWoWo8PRIeB+Eh4dHFAgYb/znpIGfIDw8ygFvQXh4eETB72J4eHhEw08QHh4ekfATRBGd9cQw/THlRX/8gErhNvweO4SGpNUqaE4qb3Nrqiso69c8iCYh/yZTqD2luQJR9VW/Vnv4D5xlg92GL1H9X1Br983fGLDUXiN5lybr1R5i67U35T1CgyIm9rSiJd6OhEwRaPDt1VakK77VeuWNufrrT1jOwkItpSYxT6TeM4l02xzSdoAVmDUycYA7Ke+SB75ix6R5DqdEyLsZnkSWu5z1w3VKPq6zKeesv6f7At2+5XN8ulaNLwd3n7d2q3SF7Q4JPgCYl1Df1x9V2WckBzXOIbZ0+R90XhgcL6pUso6nV6j/j0pyzm9zenh4RMH7IIpALM+oOGxUnVTXewatcs9g3nINhkfCQzNvZill36ITqgJWUSqqvqL3yL4Bxe4EgN3paaEyAKjVCV2TZN/gMqjMlEt1J6lCFaXgNBpSFNcgLxSlKnvFW0u/jaSKVZPj+iFRtjurzo1KpGuk6aXArEsJSgZeGYZkFCfBWA5RilKGO9Lg4HAAli3ZnJBve2UZRClK7RourJxlpO1zgnNj27KWTId4Bqan1DMUqFHxUWxa+gnCw8MjCt6C8PDwcMMzKT08PArieJ4giGgbgF6orHE5Zj6XiBoB/ArAPADbAHySmY8izM3D4/gGYeruYoxHk/IDzLxUqN1cD+ApZm4H8JT+3cPDwwFiLupnsuFYlhjLASzTx/dCaVV+s9AFseE8qt7RyXAXqEi+Pb21Qf1hkQ5toFJ58GuEtoPxLM+osIlbG4WHe23vnIL1VR3Kg13RZndO9var44FaG+V4cNhyGjZCRVTWJ633vydr0+CZ8q6MzZVRk7CciX3ZsKaCKy/GHEdejESPSBJ8wD48Jrhxx6CQh3Pk75gpuBV7tbZDVSwTOg+wiXTbxI5E3vHAVghKg9FziIrKNDyHqEjXGh1xOTDiHtOWw6rdI3YNGlT070iEzb5zUO1iyHwqEhlWj/wZKakXoc6tIfsM7Oq3uxiVcTX+tmSXPtu9gxKJKeyDKNaCYKh8f6/oTFkAMIOZDVtlL4Cw+CJUZi0iWk1EqzP54rb8PDyONxAX9zPZUKwF8V5m3kVE0wGsJKKNspKZmcj98Y7IrEWNnF//FgCg7hQ1Q+/cb9/mPZV21u+pMnvOdg7ryeg3Ts4yGRemLfNvZ9+0gvW0Xqku1c05IyjrOKDGsbHWzm9bDwpdAG1Z1KasJdObsW8aU763z1pCVUn7xsw79swzDo7HqQ42YvUue2391qHgOJZR1sL6Q3bMLnbfGZVWY+LNbqWBITkcEkbjQCbSdWW8kgKzUUpQBoYhGcVzMJZDlJ5ER6f6PmPi0erMD+s23Y/upgPTC47JWCNdVW8FZbVavWwQ1pLZ2Wl5Mab/zhr1Hef4KLJFlOiPn4iugEpcFQdwBzPfNKr+RwA+oH+tAjCdmRt0XR6AkTjbwcwfG6u/oiYIZt6l/99PRA9Byd3vI6JZzLyHiGYBKI5T7OFxAqIU1gERxQHcAuAyqHy4q4joUZ0sBwDAzH8hzr8OwNmiiUFmXopxYMypkIiqiajWHAP4EIA3oXL9XatP85m1PDwKoTSJc84HsIWZtzJzBiqr3fIC518F4JfHMuxiLIgZAB4iJc6aAPALZn6CiFYBeICIPgdgO4BPHstAPDyOW4wvWKuZiFaL32/Xy3QAmANAahN2ALjA1QgRnQRgPoCnRXFat50DcBMzPzzWYIrJzbkVwBJH+UGoLMIeHh4FMM7s3gdKlDhnBYAHRyXoPUn7EhcAeJqI1jKzW/5b41hyc3p4eBQL5uJ+CmMXgDbxe6suc2EFRi0vhC9xKxQt4ezwZUeirFTr1KIYWu9Ve+f/oekXAID3pK2OQZWI+hvQE9/WrOVGtCYU52BY3McHD9vP+PNT7ytY3/KU+rjL6/81KFteswkA8GrGeuS/OetJMSY1jq4R62lvFFwCUz4jLrgPeXtuP4c99DPj4ejHO7vCCZm/e93PguNtYny1MbWjcVXtDjGOMJfgzkM29d6d7fcDAHpG3LsYrwyp5+57ey4PygzPQcKkwwNs3oqhiOhGo+cQlcPC8Bw6Ot15MzYvuwcA0Ddid3Cu36OM1v3D4bEBwIvnqvwmPRHciteH1Vhu63pvULZnSO2kbTtsd69ef99Pg+MBzeO4u+csAED+KN6rJdrCXAWgnYjmQ00MKwBcHeqLaBGAaQBeEGXTAAww8zARNUNlAP+bsTr0sRgeHhONEhGlmDlHRF8C8CTUNuddzLyOiG4EsJqZH9WnrgBwPx+ZePc0AD8hohGolcNNcvcjCmWdIIbyCWzsVvvUcyuValN70mZlkvkPhvRHy4v9/Ua9Xz4gmHlyr3+s+oc71BugTiTfvaRa7YfHxTfYK6yFGckhPR47NrNvLssHxFtUcg0aYvbtZ8tCRTirKpwXY9OQTbS7sd/myDDMvj+tsdm0Whz5Ls7WOT+AQEICjTF3olyjfLWv2jI/Z6fDuSVkIl2T8aoxgrVolKCknoOE4STEIl6vxnKoETkuTq5SFmdLqtd5jbEcaijMVgWANq2ydXplR1A2K6WsEZnZTVpk5t4uTqvPW0lu66QQShWLwcyPAXhsVNkNo37/ruO6PwA4c7z9eQvCw6McmIQsyWLgJwgPj4kGAzQyNWeI8k4QnQnEb1OOqX+88P0AgHUXWTO6PmnN8dlpFWg0J2WDmH7fr4KoZEo4KUr794dOL1hP97QAAH57tqXjZi9R9v7F9ZZ6+0zvacHxjKSSG6uJ27H15a3Ja8qN3B1wZEDUvFQ4se3WbPi2ywS1Bje8bJmwqS22fdIW/d1XWfLqyQ6R1nbx2Z8eWACggGitvmdfnbY5KJOybAZn3Pdfg2OTSNekwxsNKzDrFpA1gVeGPj0axiFplhUA8JVp2wAAeXbb7D84eA4AYEGFm9i7QC+lzNgAYJiVGG28wX72b+z5QHDcXqnOXaKdslFLokKYjHEWxcBbEB4e5YCfIMZGLA+ketTrL9mnuu4ctNtVmbxDqFbcWSNkWxGzTshMwjoEe3KVBetTWjA30WcdWGYrr7PaBlvtH7bHJoxaOh5782EB2/0Ze41xIgJu8VRXwFQ2EfZcjohxJoVPzvgE92VtoFuzEOcN2kzaNndnCou59o+oDkbi9n67hGET/dYZa7Yvo0RrjTR9lMCsCdmOCrwyW5nSIWkshzhFCN3qbczGCMdod75K920twgpKHDEe1bf9Pqfr/k2YPo9TtHacRKlJBW9BeHhMNIojQU1K+AnCw6MM8BaEh4dHNPwE4eHh4QQDlJ+aM4SfIDw8yoGpOT+Ud4LgmTnkvqWEP7/W9iIA4CPVW4L6JFnvcJWmyu7Lh2mt1YLq/IROSgsAf974h4L16W+o/e7/POPNoOyaug0AgL1iy/+aunXB8ZB2Lsldd+k/txRmeyuHRIRtr4Mgk3Y4wf+l/5RQ2U8uuzs4fvmihcFxXkueXd+8JijrcnAJHus/OTj+WtObkecBwFq9y/FAj00IvHs4LLj79RW/Do6vqVP08IERtyitSaRr0uGNhhGYjZKJcwVeGZ6D2a0YjX+Y85Iek5sO/UZG7aj8/SH7OXcMK/HfvUN2V+jn81YGx30j6p6tHFScndy/X7BW2eEtCA+PcsDvYoyNbD6OPYfULL2xWc3G51VuC+objgj6Cb+VTJBUlQjEOUswEMeq/5tu9dbZXGffWP21ylpIibf6gPguXcKtLkirQVobjXFHAlwHL+CsinCw1utDNjBqfa9lnBqB2mzTq6KfcLDW0rQNBzdLYNd5ADAvqZirXUI8VzJSDd4askFjvTVKa6Q+Fg7qAoB2ze6M4l5ESdMbuAKvDEMyiucwlhDubH2dCbwCLIfk7YQVAe4RIebm8y1KKUanEfgdD6aqBVGUrUREDUT0IBFtJKINRHQhETUS0Uoi2qz/d9uRHh4nOorVo5yEk0ixi6mbATzBzIug5Oc2wGfW8vAoCopJOTUzaxWjal0P4GIAdwIAM2eYuRtKTfdefdq9AP54ogbp4THVQXku6meyoRgLYj6ATgB3E9FrRHSHlr8fd2at/GE3Z9/D47jGcb7ESAA4B8CtzHw2gH6MWk5oaavIzFrMfC4znxuvq3ad4uFxnKNIwdpJuMQoZhejA0AHM7+kf38QaoIYd2YtzsaQ262i6V6onw8AWFRpveZNwjNdG1MCtVIKrkGXVYlozSG2HvQO7RSPqu/fryao16tbg7I3G5r0NZYfIJPrGokymdpORpia8rzYmciK1Gwt8fB+fNqhP5Z0cCPe7LfjfOewTdSbyam+ts+x43D1kxQiwB15tcvikrsDgPkJdZ96U1Z7oXckvDvx0B4rAry+Xt3Ptrh7R2GeTmLcEHPvYphEuiMR0ZFGYNZ8B4DVczBRmaNheA6zI3Y55iZUhGiWrS5GSzwsX7c+a19m83RbWS1AzI40h2PhuN3FYOa9AHYS0XFs1C4AAAmfSURBVKm66FIA6+Eza3l4FI/j2IIAgOsA3EdEKQBbAXwWanIZV2atZB8wQxEosYcVF+G5essgbEzZN82slHprtKZsvvm3RtQe/HShfTAvYd8ELw/NL1jf/JJ6u+watO6Sp1oU2++iWqsm9OagfXPvTSlVprS0SoSFYcq78/aNkxaipkYMVsIlm+56C6/cempwzO/Y9o1g1Ysnzw/K2h2KUgvEW/SlIZW8VypsSSS1kO/SlLUaRhxclM3r5tg2Zyh2Z3dFOPEwAPxRlerfpUwFAGekFI9CJtKVMNL0UmDWKEFJPQcJw5CUPAcJYzksTNaIMjW+U5O2n5sOnGfHqftfrD8njddZML7MWpMKxSbvfR2AK9uPz6zl4VEMvCalh4dHFCYjx6EYlHWCoDyjokeZc6nDOhhrwAbIZEbscHKODFAxh53WErfLCSkc66pP96jrUz227T1DKiBpV9oSQbcP2gxLRoBWytgNiyWGKd+TscFDcjlSF3ebwqFxOhx5mR5Li67qsY4x44/ckbHjbHI45eaIZct2nZmrOuYO1mqMKdO7SuTNcEnFJXvs8mj7oGqz0ZEpDABy6Ilsx5wBHJlnRMJkvDJ5KwArMGtk4kbDBF65JPgA65DMCmq8ceYOi3wqu0Q2sFkpRUMf0N/7yFE4KUvlXyCiK6CIi3EAdzDzTaPqfwTAKO5WAZjOzA267loA39Z132fmezEGvAXh4THRYBwZoHOUIKI4gFsAXAa1u7iKiB6VGbKY+S/E+ddB598kokYA34FyFTCAV/S1h1AA5RWtHc6h8m31pkqfrByO+/vs26EvawNsctXqTdWQtG/WbQPqjTm30joupUNyzeG2gvXVO9SbrrfVWi27+pUFMVxnrYJDGWuJbIAKkpLCqZ1CoNaUy7I6nUMUAPYnbV/B58yHtw/bEl2hsnivffNWdgoxWZ12rGPQWj1Sat/VphG4rYmwaMxW5OyEfZJHHM649EH79jyQUY5TaclIDPE7+sgdlFVDykIahDs02+TKlBmvjDS9a2yADdmWgVcuSIeksRxkBq+OfmtBVMaVY9akMMjz+MK9CSWjUZ8PYItOvgsiuh+K0RyVQu8qqEkBAC4HsJKZu/S1KwFcgVEJfkfDZ/f28CgHit/mbDbMY/3zedHKHAAy7LdDl4VARCdBsaCfHu+1En6J4eFRDhRvQRxgZteO4XixAsCDzOzeYy4SZZ0g4vPzqLtLOXy+MP0JAMB5IgNSXChKVei8Bx3COk1OU+ZvWpicvzq8JDi+Yc6/Fqw/5bZNAIDrGtYGZR9IqyXIm1nb92frreLUkM7DIBkBMi2sKZ8hdBZ6hZpRj2N7y+WUu7t7aajskY//KDhel7E6DIaH8alayz9wZae6r8eyHv9Hy4v6PLe5v0a3/829i4IyqbBkcPN1twXHF6ZVn70R6k0/6LwQANAx5FZ/2qXN+J2dbqWA19/3UwBHJtI1Ga9k3goJowQl9RwkDENS8hyMQ1IuK1ae9i/BsUkifO9hxbEY5u3OtiNROk3KXQDaxO+tusyFFQC+OOraZaOufXasDv0Sw8OjHCgNk3IVgHYimq9JiyugGM1HgIgWAZgG4AVR/CSADxHRNK3d8iFdVhB+ieHhMeEoDY2amXNE9CWoP+w4gLuYeR0R3QhgNTObyWIFgPt1EKW5touIvgc1yQDAjcZhWQhlnSCGcwls7Vae6TdqlZyaTDBbHbMe9LxeOsWEkdOiTfOYSLu2RMiqjVX/xF6V3He62JE4r+IVAEBK3IpekRjWLB2kMGtVzC4yTLmsHxAPQ71jOeGSaFtSuSNUtnZ4dnD82oAV3x3Mq/6X11ifU4tDSu5sIeeX1Z/JJYEHAAv097BXBM9NT4aDmF4dnBccL06ppVp9hLzbokrFWZiecnMSTIrCqGS4A3p3QX42k0hXfocSRmA2SgbPBF6dIejbhudgditUO3aJYnY3lqbV0qIqgksSCUbJeBDM/BiAx0aV3TDq9+9GXHsXgLvG05+3IDw8yoHjORajVKCDCaTuVky3u963DAAwcLF9+9TE7cx8UoV6oy2tsDP9c0MtAI7c339/peVJPD4wvWD9X96n3hB3nWsFYOuXKc7CR2us1P0fBq0faKYONW6MW2dwV8a+0Uz55owVwjWh6gCwMBnmoezOh4OgLq0MMynPfunK4DjzlnUYxgeVVfKeq23KAFcQlvzsvx9STr22CIZhXFtq5wirY4iTofOufcLuup30QdXn4lQ4UAwATq9QFoQrbBwA2pLqe+qscTsc7+45S7UvAq+WVKq3uAyYkzDS9ItE2LqECdleLALMDEPS8BwA65AErOVwUVrdoxo6mnBvT7X28PCIgp8gPDw8nGAG8lNzjUFcxpmNiDqhJOvcogSlRXOZ+ilnX76fydHXSczcUuzJ9emZ/J62/1jUuU9s+d+vlIgoVRKUN/UecwsRrS7HDShXP+Xsy/czNfpywi8xPDw8nGB4wRgPD48oMMBT0wfx7zFB3H6c9VPOvnw/U6OvMKboEqOsTkoPjxMR9akZ/J6ZVxV17hM7bz5xnZQeHicsRvwSw8PDw4nJmfOiGPgJwsNjosHwFoSHh0cBeAvCw8MjEn6C8PDwcIM9UcrDwyMCDLAnSnl4eERiikZz+gnCw2Oiwex3MTw8PArAOyk9PDyiwN6C8PDwcMMzKT08PKIwhfUgfGYtD48JBgPgfL6on7FARFcQ0SYi2kJE10ec80kiWk9E64joF6I8T0Sv659QRi4XvAXh4THR4NIIxhBRHMAtAC6Dys69iogeZeb14px2AN8CcBEzHyKi6aKJQWYOJ4EtAG9BeHiUATzCRf2MgfMBbGHmrcycAXA/gOWjzvlzALcw8yEAYOb9OAZ4C8LDY4LRi0NP/m7kgeYiT08T0Wrx++3MbNSw5gDYKeo6AFww6vpTAICI/h9U/s7vMvMTo9rOAbiJmR8eazB+gvDwmGAw8xVl7C4BoB3AMgCtAJ4nojOZuRtKrn8XES0A8DQRrWXmtws15pcYHh5TB7sAtInfW3WZRAeAR5k5y8zvAHgLasIAM+/S/28F8CyAs8fq0E8QHh5TB6sAtBPRfCJKAVgBYPRuxMNQ1gOIqBlqybGViKYRUYUovwjAeowBv8Tw8JgiYOYcEX0JwJNQ/oW7mHkdEd0IYDUzP6rrPkRE6wHkAXyDmQ8S0XsA/ISIRqAMg5vk7kcUvKq1h4dHJPwSw8PDIxJ+gvDw8IiEnyA8PDwi4ScIDw+PSPgJwsPDIxJ+gvDw8IiEnyA8PDwi8f8Bettlkz9i/QIAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\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", - "\n", - "4. Getting final performance...\n", - "best_params_out: [{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}]\n", - "best_params_in: [{'C': 3.1622776601683795}]\n", - "\n", - "best_val_perf: 0.5635714285714286\n", - "best_val_std: 0.020692049669866652\n", - "final_performance: [0.5376190476190476]\n", - "final_confidence: [0.07997917861814137]\n", - "train_performance: [0.5574466891133556]\n", - "train_std: [0.008328075153960232]\n", - "\n", - "time to calculate gram matrix with different hyper-params: 7.61±nans\n", - "time to calculate best gram matrix: 7.61±nans\n", - "total training time with all hyper-param choices: 9.71s\n", - "\n", - "params train_perf valid_perf test_perf gram_matrix_time\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------ ------------ ----------- ------------------\n", - "{'n_jobs': 8, 'C': '1.00e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '1.00e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.55±0.05 0.54±0.11 7.61\n", - "{'n_jobs': 8, 'C': '3.16e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.41±0.07 0.37±0.07 0.35±0.05 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.49±0.04 0.49±0.04 0.42±0.15 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.56±0.01 0.56±0.02 0.54±0.08 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.48±0.01 0.49±0.02 0.50±0.15 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.46±0.02 0.46±0.02 0.50±0.16 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '3.16e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "{'n_jobs': 8, 'C': '1.00e+10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.47±0.03 0.47±0.03 0.54±0.21 7.61\n", - "\n", - "\n", - "PAH\n", + "Letter-med\n", "\n", "--- This is a classification problem ---\n", "\n", "\n", - "I. Loading dataset from file...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "I. 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", "\n", - " --- shortest path kernel matrix of size 94 built in 6.5481321811676025 seconds ---\n", - "\n", - "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} is: \n", - "[[1. 0.96353531 0.96592281 ... 0.8622094 0.87997676 0.87988951]\n", - " [0.96353531 1. 0.9971178 ... 0.96212799 0.97024435 0.97178508]\n", - " [0.96592281 0.9971178 1. ... 0.95944325 0.96816017 0.97260121]\n", - " ...\n", - " [0.8622094 0.96212799 0.95944325 ... 1. 0.99889548 0.99345489]\n", - " [0.87997676 0.97024435 0.96816017 ... 0.99889548 1. 0.9934214 ]\n", - " [0.87988951 0.97178508 0.97260121 ... 0.99345489 0.9934214 1. ]]\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\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", - "\n", - "4. Getting final performance...\n", - "best_params_out: [{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}, {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}]\n", - "best_params_in: [{'C': 31.622776601683793}, {'C': 100.0}]\n", - "\n", - "best_val_perf: 0.6420833333333335\n", - "best_val_std: 0.015945233736988702\n", - "final_performance: [0.6130000000000001, 0.6133333333333334]\n", - "final_confidence: [0.1274457288146741, 0.1279367659898984]\n", - "train_performance: [0.6412754385964912, 0.6412754385964912]\n", - "train_std: [0.015228857126704994, 0.015228857126704994]\n", - "\n", - "time to calculate gram matrix with different hyper-params: 6.55±nans\n", - "time to calculate best gram matrix: 6.55±0.00s\n", - "total training time with all hyper-param choices: 8.87s\n", - "\n", - "params train_perf valid_perf test_perf gram_matrix_time\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------ ------------ ----------- ------------------\n", - "{'n_jobs': 8, 'C': '1.00e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.63±0.02 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.01 0.64±0.02 0.57±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '3.16e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "{'n_jobs': 8, 'C': '1.00e+10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.02 0.64±0.02 0.61±0.13 6.55\n", - "\n", - "\n", - "MUTAG\n", - "\n", - "--- This is a classification problem ---\n", - "\n", - "\n", - "I. Loading dataset from file...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "2. Calculating gram matrices. This could take a while...\n", - "\n", - " None edge weight specified. Set all weight to 1.\n", + " 9 graphs are removed as they don't contain edges.\n", "\n", + "getting sp graphs: 2241it [00:01, 2167.27it/s]\n", + "calculating kernels: 2512161it [35:21, 1183.88it/s]\n", "\n", - " --- shortest path kernel matrix of size 188 built in 67.91289067268372 seconds ---\n", + " --- shortest path kernel matrix of size 2241 built in 2123.1229825019836 seconds ---\n", "\n", - "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} is: \n", - "[[1. 0.68780488 0.977912 ... 0.72072063 0.79304207 0.6640214 ]\n", - " [0.68780488 1. 0.72921233 ... 0.79419383 0.80547177 0.77837484]\n", - " [0.977912 0.72921233 1. ... 0.79338054 0.87106629 0.74397578]\n", + "the gram matrix with parameters {'n_jobs': 8, 'node_kernels': {'symb': , 'mix': functools.partial(, , ), 'nsymb': }} is: \n", + "[[1. 0.12737447 0.24414863 ... 0.08575717 0.24779506 0.13389497]\n", + " [0.12737447 1. 0.26175695 ... 0.45546182 0.49016833 0.67892735]\n", + " [0.24414863 0.26175695 1. ... 0.3161496 0.27686542 0.26213761]\n", " ...\n", - " [0.72072063 0.79419383 0.79338054 ... 1. 0.95662951 0.94918589]\n", - " [0.79304207 0.80547177 0.87106629 ... 0.95662951 1. 0.93460209]\n", - " [0.6640214 0.77837484 0.74397578 ... 0.94918589 0.93460209 1. ]]\n" + " [0.08575717 0.45546182 0.3161496 ... 1. 0.47821266 0.36507203]\n", + " [0.24779506 0.49016833 0.27686542 ... 0.47821266 1. 0.58909 ]\n", + " [0.13389497 0.67892735 0.26213761 ... 0.36507203 0.58909 1. ]]\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -681,87 +59,8 @@ "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", - "\n", - "4. Getting final performance...\n", - "best_params_out: [{'n_jobs': 8, 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }}]\n", - "best_params_in: [{'C': 0.1}]\n", - "\n", - "best_val_perf: 0.7621936274509803\n", - "best_val_std: 0.019636735759586195\n", - "final_performance: [0.8019298245614036]\n", - "final_confidence: [0.09742587536592802]\n", - "train_performance: [0.7818095688567825]\n", - "train_std: [0.015873629836738855]\n", - "\n", - "time to calculate gram matrix with different hyper-params: 67.91±nans\n", - "time to calculate best gram matrix: 67.91±nans\n", - "total training time with all hyper-param choices: 71.04s\n", - "\n", - "params train_perf valid_perf test_perf gram_matrix_time\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- ------------ ------------ ----------- ------------------\n", - "{'n_jobs': 8, 'C': '1.00e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.66±0.01 0.66±0.01 0.72±0.06 67.91\n", - "{'n_jobs': 8, 'C': '1.00e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.78±0.02 0.76±0.02 0.80±0.10 67.91\n", - "{'n_jobs': 8, 'C': '3.16e-01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.45±0.03 0.46±0.03 0.41±0.11 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.35±0.01 0.35±0.01 0.30±0.07 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+00', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.46±0.02 0.46±0.02 0.41±0.11 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.54±0.03 0.54±0.03 0.49±0.09 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+01', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.58±0.02 0.58±0.03 0.58±0.13 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.60±0.03 0.60±0.03 0.62±0.15 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+02', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.65±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+03', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+04', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+05', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+06', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+07', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+08', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '3.16e+09', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "{'n_jobs': 8, 'C': '1.00e+10', 'node_kernels': {'mix': functools.partial(, , ), 'symb': , 'nsymb': }} 0.64±0.07 0.64±0.08 0.66±0.19 67.91\n", - "\n", - "\n", - "Letter-med\n", - "\n", - "--- This is a classification problem ---\n", - "\n", - "\n", - "I. Loading dataset from file...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "2. Calculating gram matrices. This could take a while...\n", - "\n", - " None edge weight specified. Set all weight to 1.\n", - "\n", - "\n", - " 9 graphs are removed as they don't contain edges.\n", - "\n" + "\r", + "cross validation: 0%| | 0/30 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 81\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m \u001b[0mParallel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_cores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelayed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcompute_ds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdslist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 960\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 961\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 962\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\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 963\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 964\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 291\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 292\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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 294\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "# # test parallel computing\n", + "# import psutil\n", + "# # logical=True counts threads, but we are interested in cores\n", + "# psutil.()# .cpu_count(logical=False)\n", + "%load_ext line_profiler\n", + "%matplotlib inline\n", + "import functools\n", + "from libs import *\n", + "from sklearn.metrics.pairwise import rbf_kernel\n", + "from joblib import Parallel, delayed\n", + "import multiprocessing\n", + "\n", + "from pygraph.kernels.spKernel import spkernel\n", + "from pygraph.utils.kernels import deltakernel, kernelsum\n", + "\n", + "num_cores = multiprocessing.cpu_count()\n", + "\n", + "dslist = [ \n", + " {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n", + "# {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n", + " {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n", + " {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb\n", + " {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n", + " 'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb\n", + " {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n", + " 'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',}, # contains single node graph, node symb\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': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb\n", + " {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n", + "# {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n", + " {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\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': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n", + " 'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb\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 = spkernel\n", + "mixkernel = functools.partial(kernelsum, deltakernel, rbf_kernel)\n", + "param_grid_precomputed = {'node_kernels': [{'symb': deltakernel, 'nsymb': rbf_kernel, 'mix': mixkernel}]}\n", + "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n", + " {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n", + " \n", + "def compute_ds(ds):\n", + " print()\n", + " print(ds['name'])\n", + " model_selection_for_precomputed_kernel(\n", + " ds['dataset'], estimator, param_grid_precomputed, \n", + " (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n", + " (ds['task'] if 'task' in ds else 'classification'), 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", + "# %lprun -f spkernel \\\n", + "# model_selection_for_precomputed_kernel( \\\n", + "# ds['dataset'], estimator, param_grid_precomputed, \\\n", + "# (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \\\n", + "# (ds['task'] if 'task' in ds else 'classification'), 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", + " print()\n", + " \n", + "Parallel(n_jobs=num_cores, verbose=10)(delayed(compute_ds)(ds) for ds in dslist)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { "name": "stdout", "output_type": "stream", "text": [ diff --git a/pygraph/kernels/spKernel.py b/pygraph/kernels/spKernel.py index d286d37..cbc9d9f 100644 --- a/pygraph/kernels/spKernel.py +++ b/pygraph/kernels/spKernel.py @@ -8,7 +8,7 @@ import pathlib sys.path.insert(0, "../") from tqdm import tqdm import time -from itertools import combinations_with_replacement, product +from itertools import combinations, combinations_with_replacement, product from functools import partial from joblib import Parallel, delayed from multiprocessing import Pool @@ -77,207 +77,108 @@ def spkernel(*args, if len(Gn) != len_gn: print('\n %d graphs are removed as they don\'t contain edges.\n' % (len_gn - len(Gn))) + start_time = time.time() - pool = Pool(n_jobs) + pool = Pool(n_jobs) # get shortest path graphs of Gn getsp_partial = partial(wrap_getSPGraph, Gn, edge_weight) - result_sp = pool.map(getsp_partial, range(0, len(Gn))) - for i in result_sp: - Gn[i[0]] = i[1] + if len(Gn) < 100: + # use default chunksize as pool.map when iterable is less than 100 + chunksize, extra = divmod(len(Gn), n_jobs * 4) + if extra: + chunksize += 1 + else: + chunksize = 100 + # chunksize = 300 # int(len(list(itr)) / n_jobs) + for i, g in tqdm( + pool.imap_unordered(getsp_partial, range(0, len(Gn)), chunksize), + desc='getting sp graphs', + file=sys.stdout): + Gn[i] = g - # Gn = [ - # getSPGraph(G, edge_weight=edge_weight) - # for G in tqdm(Gn, desc='getting sp graphs', file=sys.stdout) - # ] + # # ---- use pool.map to parallel ---- + # result_sp = pool.map(getsp_partial, range(0, len(Gn))) + # for i in result_sp: + # Gn[i[0]] = i[1] + # or + # getsp_partial = partial(wrap_getSPGraph, Gn, edge_weight) + # for i, g in tqdm( + # pool.map(getsp_partial, range(0, len(Gn))), + # desc='getting sp graphs', + # file=sys.stdout): + # Gn[i] = g + + # # ---- only for the Fast Computation of Shortest Path Kernel (FCSP) + # sp_ml = [0] * len(Gn) # shortest path matrices + # for i in result_sp: + # sp_ml[i[0]] = i[1] + # edge_x_g = [[] for i in range(len(sp_ml))] + # edge_y_g = [[] for i in range(len(sp_ml))] + # edge_w_g = [[] for i in range(len(sp_ml))] + # for idx, item in enumerate(sp_ml): + # for i1 in range(len(item)): + # for i2 in range(i1 + 1, len(item)): + # if item[i1, i2] != np.inf: + # edge_x_g[idx].append(i1) + # edge_y_g[idx].append(i2) + # edge_w_g[idx].append(item[i1, i2]) + # print(len(edge_x_g[0])) + # print(len(edge_y_g[0])) + # print(len(edge_w_g[0])) Kmatrix = np.zeros((len(Gn), len(Gn))) + # ---- use pool.imap_unordered to parallel and track progress. ---- do_partial = partial(spkernel_do, Gn, ds_attrs, node_label, node_kernels) itr = combinations_with_replacement(range(0, len(Gn)), 2) - # chunksize = 2000 # int(len(list(itr)) / n_jobs) - # for i, j, kernel in tqdm(pool.imap_unordered(do_partial, itr, chunksize)): - # Kmatrix[i][j] = kernel - # Kmatrix[j][i] = kernel - - result_perf = pool.map(do_partial, itr) + len_itr = int(len(Gn) * (len(Gn) + 1) / 2) + if len_itr < 100: + chunksize, extra = divmod(len_itr, n_jobs * 4) + if extra: + chunksize += 1 + else: + chunksize = 100 + for i, j, kernel in tqdm( + pool.imap_unordered(do_partial, itr, chunksize), + desc='calculating kernels', + file=sys.stdout): + Kmatrix[i][j] = kernel + Kmatrix[j][i] = kernel pool.close() pool.join() + # # ---- use pool.map to parallel. ---- + # # result_perf = pool.map(do_partial, itr) + # do_partial = partial(spkernel_do, Gn, ds_attrs, node_label, node_kernels) + # itr = combinations_with_replacement(range(0, len(Gn)), 2) + # for i, j, kernel in tqdm( + # pool.map(do_partial, itr), desc='calculating kernels', + # file=sys.stdout): + # Kmatrix[i][j] = kernel + # Kmatrix[j][i] = kernel + # pool.close() + # pool.join() + + # # ---- use joblib.Parallel to parallel and track progress. ---- # result_perf = Parallel( # n_jobs=n_jobs, verbose=10)( # delayed(do_partial)(ij) # for ij in combinations_with_replacement(range(0, len(Gn)), 2)) - # result_perf = [ # do_partial(ij) # for ij in combinations_with_replacement(range(0, len(Gn)), 2) # ] + # for i in result_perf: + # Kmatrix[i[0]][i[1]] = i[2] + # Kmatrix[i[1]][i[0]] = i[2] - for i in result_perf: - Kmatrix[i[0]][i[1]] = i[2] - Kmatrix[i[1]][i[0]] = i[2] - - # pbar = tqdm( - # total=((len(Gn) + 1) * len(Gn) / 2), - # desc='calculating kernels', - # file=sys.stdout) - # if ds_attrs['node_labeled']: - # # node symb and non-synb labeled - # if ds_attrs['node_attr_dim'] > 0: - # if ds_attrs['is_directed']: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['mix'] - # try: - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn(n11[node_label], n21[node_label], [ - # n11['attributes'] - # ], [n21['attributes']]) * kn( - # n12[node_label], n22[node_label], - # [n12['attributes']], [n22['attributes']]) - # Kmatrix[i][j] += kn1 - # except KeyError: # missing labels or attributes - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - - # else: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['mix'] - # try: - # # each edge walk is counted twice, starting from both its extreme nodes. - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn(n11[node_label], n21[node_label], [ - # n11['attributes'] - # ], [n21['attributes']]) * kn( - # n12[node_label], n22[node_label], - # [n12['attributes']], [n22['attributes']]) - # kn2 = kn(n11[node_label], n22[node_label], [ - # n11['attributes'] - # ], [n22['attributes']]) * kn( - # n12[node_label], n21[node_label], - # [n12['attributes']], [n21['attributes']]) - # Kmatrix[i][j] += kn1 + kn2 - # except KeyError: # missing labels or attributes - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - # # node symb labeled - # else: - # if ds_attrs['is_directed']: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['symb'] - # try: - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn(n11[node_label], - # n21[node_label]) * kn( - # n12[node_label], n22[node_label]) - # Kmatrix[i][j] += kn1 - # except KeyError: # missing labels - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - - # else: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['symb'] - # try: - # # each edge walk is counted twice, starting from both its extreme nodes. - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn(n11[node_label], - # n21[node_label]) * kn( - # n12[node_label], n22[node_label]) - # kn2 = kn(n11[node_label], - # n22[node_label]) * kn( - # n12[node_label], n21[node_label]) - # Kmatrix[i][j] += kn1 + kn2 - # except KeyError: # missing labels - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - # else: - # # node non-synb labeled - # if ds_attrs['node_attr_dim'] > 0: - # if ds_attrs['is_directed']: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['nsymb'] - # try: - # # each edge walk is counted twice, starting from both its extreme nodes. - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn([n11['attributes']], - # [n21['attributes']]) * kn( - # [n12['attributes']], - # [n22['attributes']]) - # Kmatrix[i][j] += kn1 - # except KeyError: # missing attributes - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - # else: - # for i, j in combinations_with_replacement( - # range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # kn = node_kernels['nsymb'] - # try: - # # each edge walk is counted twice, starting from both its extreme nodes. - # n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - # i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - # j].nodes[e2[1]] - # kn1 = kn([n11['attributes']], - # [n21['attributes']]) * kn( - # [n12['attributes']], - # [n22['attributes']]) - # kn2 = kn([n11['attributes']], - # [n22['attributes']]) * kn( - # [n12['attributes']], - # [n21['attributes']]) - # Kmatrix[i][j] += kn1 + kn2 - # except KeyError: # missing attributes - # pass - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) - - # # node unlabeled - # else: - # for i, j in combinations_with_replacement(range(0, len(Gn)), 2): - # for e1, e2 in product( - # Gn[i].edges(data=True), Gn[j].edges(data=True)): - # if e1[2]['cost'] == e2[2]['cost']: - # Kmatrix[i][j] += 1 - # Kmatrix[j][i] = Kmatrix[i][j] - # pbar.update(1) + # # ---- direct running, normally use single CPU core. ---- + # itr = combinations_with_replacement(range(0, len(Gn)), 2) + # for gs in tqdm(itr, desc='calculating kernels', file=sys.stdout): + # i, j, kernel = spkernel_do(Gn, ds_attrs, node_label, node_kernels, gs) + # Kmatrix[i][j] = kernel + # Kmatrix[j][i] = kernel run_time = time.time() - start_time print( @@ -291,130 +192,271 @@ def spkernel_do(Gn, ds_attrs, node_label, node_kernels, ij): i = ij[0] j = ij[1] + g1 = Gn[i] + g2 = Gn[j] Kmatrix = 0 - if ds_attrs['node_labeled']: - # node symb and non-synb labeled - if ds_attrs['node_attr_dim'] > 0: - if ds_attrs['is_directed']: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['mix'] - try: - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn( - n11[node_label], n21[node_label], - [n11['attributes']], [n21['attributes']]) * kn( - n12[node_label], n22[node_label], - [n12['attributes']], [n22['attributes']]) - Kmatrix += kn1 - except KeyError: # missing labels or attributes - pass + + try: + # compute shortest path matrices first, method borrowed from FCSP. + if ds_attrs['node_labeled']: + # node symb and non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['mix'] + vk_dict = {} # shortest path matrices dict + for n1, n2 in product( + g1.nodes(data=True), g2.nodes(data=True)): + vk_dict[(n1[0], n2[0])] = kn( + n1[1][node_label], n2[1][node_label], + [n1[1]['attributes']], [n2[1]['attributes']]) + # node symb labeled else: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['mix'] - try: - # each edge walk is counted twice, starting from both its extreme nodes. - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn( - n11[node_label], n21[node_label], - [n11['attributes']], [n21['attributes']]) * kn( - n12[node_label], n22[node_label], - [n12['attributes']], [n22['attributes']]) - kn2 = kn( - n11[node_label], n22[node_label], - [n11['attributes']], [n22['attributes']]) * kn( - n12[node_label], n21[node_label], - [n12['attributes']], [n21['attributes']]) - Kmatrix += kn1 + kn2 - except KeyError: # missing labels or attributes - pass - # node symb labeled + kn = node_kernels['symb'] + vk_dict = {} # shortest path matrices dict + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn(n1[1][node_label], + n2[1][node_label]) else: - if ds_attrs['is_directed']: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['symb'] - try: - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn(n11[node_label], n21[node_label]) * kn( - n12[node_label], n22[node_label]) - Kmatrix += kn1 - except KeyError: # missing labels - pass - else: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['symb'] - try: - # each edge walk is counted twice, starting from both its extreme nodes. - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn(n11[node_label], n21[node_label]) * kn( - n12[node_label], n22[node_label]) - kn2 = kn(n11[node_label], n22[node_label]) * kn( - n12[node_label], n21[node_label]) - Kmatrix += kn1 + kn2 - except KeyError: # missing labels - pass - else: - # node non-synb labeled - if ds_attrs['node_attr_dim'] > 0: - if ds_attrs['is_directed']: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): - if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['nsymb'] - try: - # each edge walk is counted twice, starting from both its extreme nodes. - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn( - [n11['attributes']], [n21['attributes']]) * kn( - [n12['attributes']], [n22['attributes']]) - Kmatrix += kn1 - except KeyError: # missing attributes - pass + # node non-synb labeled + if ds_attrs['node_attr_dim'] > 0: + kn = node_kernels['nsymb'] + vk_dict = {} # shortest path matrices dict + for n1 in g1.nodes(data=True): + for n2 in g2.nodes(data=True): + vk_dict[(n1[0], n2[0])] = kn([n1[1]['attributes']], + [n2[1]['attributes']]) + # node unlabeled else: for e1, e2 in product( Gn[i].edges(data=True), Gn[j].edges(data=True)): if e1[2]['cost'] == e2[2]['cost']: - kn = node_kernels['nsymb'] - try: - # each edge walk is counted twice, starting from both its extreme nodes. - n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ - i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ - j].nodes[e2[1]] - kn1 = kn( - [n11['attributes']], [n21['attributes']]) * kn( - [n12['attributes']], [n22['attributes']]) - kn2 = kn( - [n11['attributes']], [n22['attributes']]) * kn( - [n12['attributes']], [n21['attributes']]) - Kmatrix += kn1 + kn2 - except KeyError: # missing attributes - pass - # node unlabeled + Kmatrix += 1 + return i, j, Kmatrix + + # compute graph kernels + if ds_attrs['is_directed']: + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): + if e1[2]['cost'] == e2[2]['cost']: + # each edge walk is counted twice, starting from both its extreme nodes. + nk11, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + Kmatrix += kn1 + kn2 else: - for e1, e2 in product( - Gn[i].edges(data=True), Gn[j].edges(data=True)): + for e1, e2 in product(g1.edges(data=True), g2.edges(data=True)): if e1[2]['cost'] == e2[2]['cost']: - Kmatrix += 1 + # each edge walk is counted twice, starting from both its extreme nodes. + nk11, nk12, nk21, nk22 = vk_dict[(e1[0], e2[0])], vk_dict[( + e1[0], e2[1])], vk_dict[(e1[1], + e2[0])], vk_dict[(e1[1], + e2[1])] + kn1 = nk11 * nk22 + kn2 = nk12 * nk21 + Kmatrix += kn1 + kn2 + + # # ---- exact implementation of the Fast Computation of Shortest Path Kernel (FCSP), reference [2], sadly it is slower than the current implementation + # # compute vertex kernel matrix + # try: + # vk_mat = np.zeros((nx.number_of_nodes(g1), + # nx.number_of_nodes(g2))) + # g1nl = enumerate(g1.nodes(data=True)) + # g2nl = enumerate(g2.nodes(data=True)) + # for i1, n1 in g1nl: + # for i2, n2 in g2nl: + # vk_mat[i1][i2] = kn( + # n1[1][node_label], n2[1][node_label], + # [n1[1]['attributes']], [n2[1]['attributes']]) + + # range1 = range(0, len(edge_w_g[i])) + # range2 = range(0, len(edge_w_g[j])) + # for i1 in range1: + # x1 = edge_x_g[i][i1] + # y1 = edge_y_g[i][i1] + # w1 = edge_w_g[i][i1] + # for i2 in range2: + # x2 = edge_x_g[j][i2] + # y2 = edge_y_g[j][i2] + # w2 = edge_w_g[j][i2] + # ke = (w1 == w2) + # if ke > 0: + # kn1 = vk_mat[x1][x2] * vk_mat[y1][y2] + # kn2 = vk_mat[x1][y2] * vk_mat[y1][x2] + # Kmatrix += kn1 + kn2 + except KeyError: # missing labels or attributes + pass return i, j, Kmatrix def wrap_getSPGraph(Gn, weight, i): - return i, getSPGraph(Gn[i], edge_weight=weight) \ No newline at end of file + return i, getSPGraph(Gn[i], edge_weight=weight) + # return i, nx.floyd_warshall_numpy(Gn[i], weight=weight) + + +# def spkernel_do(Gn, ds_attrs, node_label, node_kernels, ij): + +# i = ij[0] +# j = ij[1] +# g1 = Gn[i] +# g2 = Gn[j] +# Kmatrix = 0 +# if ds_attrs['node_labeled']: +# # node symb and non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# if ds_attrs['is_directed']: +# for e1, e2 in product( +# Gn[i].edges(data=True), Gn[j].edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# kn = node_kernels['mix'] +# try: +# n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ +# i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ +# j].nodes[e2[1]] +# kn1 = kn( +# n11[node_label], n21[node_label], +# [n11['attributes']], [n21['attributes']]) * kn( +# n12[node_label], n22[node_label], +# [n12['attributes']], [n22['attributes']]) +# Kmatrix += kn1 +# except KeyError: # missing labels or attributes +# pass +# else: +# kn = node_kernels['mix'] +# try: +# # compute shortest path matrices first, method borrowed from FCSP. +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn( +# n1[1][node_label], n2[1][node_label], +# [n1[1]['attributes']], [n2[1]['attributes']]) + +# for e1, e2 in product( +# g1.edges(data=True), g2.edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# # each edge walk is counted twice, starting from both its extreme nodes. +# nk11, nk12, nk21, nk22 = vk_dict[( +# e1[0], +# e2[0])], vk_dict[(e1[0], e2[1])], vk_dict[( +# e1[1], e2[0])], vk_dict[(e1[1], e2[1])] +# kn1 = nk11 * nk22 +# kn2 = nk12 * nk21 +# Kmatrix += kn1 + kn2 + +# # # ---- exact implementation of the Fast Computation of Shortest Path Kernel (FCSP), reference [2], sadly it is slower than the current implementation +# # # compute vertex kernel matrix +# # try: +# # vk_mat = np.zeros((nx.number_of_nodes(g1), +# # nx.number_of_nodes(g2))) +# # g1nl = enumerate(g1.nodes(data=True)) +# # g2nl = enumerate(g2.nodes(data=True)) +# # for i1, n1 in g1nl: +# # for i2, n2 in g2nl: +# # vk_mat[i1][i2] = kn( +# # n1[1][node_label], n2[1][node_label], +# # [n1[1]['attributes']], [n2[1]['attributes']]) + +# # range1 = range(0, len(edge_w_g[i])) +# # range2 = range(0, len(edge_w_g[j])) +# # for i1 in range1: +# # x1 = edge_x_g[i][i1] +# # y1 = edge_y_g[i][i1] +# # w1 = edge_w_g[i][i1] +# # for i2 in range2: +# # x2 = edge_x_g[j][i2] +# # y2 = edge_y_g[j][i2] +# # w2 = edge_w_g[j][i2] +# # ke = (w1 == w2) +# # if ke > 0: +# # kn1 = vk_mat[x1][x2] * vk_mat[y1][y2] +# # kn2 = vk_mat[x1][y2] * vk_mat[y1][x2] +# # Kmatrix += kn1 + kn2 + +# except KeyError: # missing labels or attributes +# pass + +# # node symb labeled +# else: +# if ds_attrs['is_directed']: +# for e1, e2 in product( +# Gn[i].edges(data=True), Gn[j].edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# kn = node_kernels['symb'] +# try: +# n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ +# i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ +# j].nodes[e2[1]] +# kn1 = kn(n11[node_label], n21[node_label]) * kn( +# n12[node_label], n22[node_label]) +# Kmatrix += kn1 +# except KeyError: # missing labels +# pass +# else: +# kn = node_kernels['symb'] +# try: +# # compute shortest path matrices first, method borrowed from FCSP. +# vk_dict = {} # shortest path matrices dict +# for n1 in g1.nodes(data=True): +# for n2 in g2.nodes(data=True): +# vk_dict[(n1[0], n2[0])] = kn( +# n1[1][node_label], n2[1][node_label]) + +# for e1, e2 in product( +# g1.edges(data=True), g2.edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# # each edge walk is counted twice, starting from both its extreme nodes. +# nk11, nk12, nk21, nk22 = vk_dict[( +# e1[0], +# e2[0])], vk_dict[(e1[0], e2[1])], vk_dict[( +# e1[1], e2[0])], vk_dict[(e1[1], e2[1])] +# kn1 = nk11 * nk22 +# kn2 = nk12 * nk21 +# Kmatrix += kn1 + kn2 +# except KeyError: # missing labels +# pass +# else: +# # node non-synb labeled +# if ds_attrs['node_attr_dim'] > 0: +# if ds_attrs['is_directed']: +# for e1, e2 in product( +# Gn[i].edges(data=True), Gn[j].edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# kn = node_kernels['nsymb'] +# try: +# # each edge walk is counted twice, starting from both its extreme nodes. +# n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ +# i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ +# j].nodes[e2[1]] +# kn1 = kn( +# [n11['attributes']], [n21['attributes']]) * kn( +# [n12['attributes']], [n22['attributes']]) +# Kmatrix += kn1 +# except KeyError: # missing attributes +# pass +# else: +# for e1, e2 in product( +# Gn[i].edges(data=True), Gn[j].edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# kn = node_kernels['nsymb'] +# try: +# # each edge walk is counted twice, starting from both its extreme nodes. +# n11, n12, n21, n22 = Gn[i].nodes[e1[0]], Gn[ +# i].nodes[e1[1]], Gn[j].nodes[e2[0]], Gn[ +# j].nodes[e2[1]] +# kn1 = kn( +# [n11['attributes']], [n21['attributes']]) * kn( +# [n12['attributes']], [n22['attributes']]) +# kn2 = kn( +# [n11['attributes']], [n22['attributes']]) * kn( +# [n12['attributes']], [n21['attributes']]) +# Kmatrix += kn1 + kn2 +# except KeyError: # missing attributes +# pass +# # node unlabeled +# else: +# for e1, e2 in product( +# Gn[i].edges(data=True), Gn[j].edges(data=True)): +# if e1[2]['cost'] == e2[2]['cost']: +# Kmatrix += 1 + +# return i, j, Kmatrix diff --git a/pygraph/utils/model_selection_precomputed.py b/pygraph/utils/model_selection_precomputed.py index 9522e80..3ff01b5 100644 --- a/pygraph/utils/model_selection_precomputed.py +++ b/pygraph/utils/model_selection_precomputed.py @@ -190,24 +190,44 @@ def model_selection_for_precomputed_kernel(datafile, ) pool = Pool(n_jobs) trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type) - result_perf = pool.map(trial_do_partial, range(NUM_TRIALS)) - train_pref = [item[0] for item in result_perf] - val_pref = [item[1] for item in result_perf] - test_pref = [item[2] for item in result_perf] + train_pref = [] + val_pref = [] + test_pref = [] + if NUM_TRIALS < 100: + chunksize, extra = divmod(NUM_TRIALS, n_jobs * 4) + if extra: + chunksize += 1 + else: + chunksize = 100 + for o1, o2, o3 in tqdm(pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout): + train_pref.append(o1) + val_pref.append(o2) + test_pref.append(o3) pool.close() pool.join() + # # ---- use pool.map to parallel. ---- + # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS)) + # train_pref = [item[0] for item in result_perf] + # val_pref = [item[1] for item in result_perf] + # test_pref = [item[2] for item in result_perf] + + # # ---- use joblib.Parallel to parallel and track progress. ---- # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type) # result_perf = Parallel(n_jobs=n_jobs, verbose=10)(delayed(trial_do_partial)(trial) for trial in range(NUM_TRIALS)) # train_pref = [item[0] for item in result_perf] # val_pref = [item[1] for item in result_perf] # test_pref = [item[2] for item in result_perf] - - # pbar.clear() - # np.save(results_name_pre + 'train_pref.dt', train_pref) - # np.save(results_name_pre + 'val_pref.dt', val_pref) - # np.save(results_name_pre + 'test_pref.dt', test_pref) + # # ---- direct running, normally use single CPU core. ---- + # train_pref = [] + # val_pref = [] + # test_pref = [] + # for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout): + # o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i) + # train_pref.append(o1) + # val_pref.append(o2) + # test_pref.append(o3) print() print('4. Getting final performance...') @@ -479,4 +499,4 @@ def trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, t test_pref[index_out][index_in] = np.mean( current_test_perf) - return train_pref, val_pref, test_pref \ No newline at end of file + return train_pref, val_pref, test_pref