graphkit-learn/notebooks/run_commonwalkkernel.ipynb

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      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "calculating kernels: 16836it [00:04, 3664.86it/s]\n",
      "\n",
      " --- kernel matrix of common walk kernel of size 183 built in 4.640817880630493 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.01, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "calculating kernels: 16836it [00:04, 3737.97it/s]\n",
      "\n",
      " --- kernel matrix of common walk kernel of size 183 built in 4.647830486297607 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.019999999999999997, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "calculating kernels: 16836it [00:04, 3385.68it/s]\n",
      "\n",
      " --- kernel matrix of common walk kernel of size 183 built in 5.04192042350769 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.03, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "calculating kernels: 16836it [00:04, 3624.84it/s]\n",
      "\n",
      " --- kernel matrix of common walk kernel of size 183 built in 4.778176546096802 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.039999999999999994, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "calculating kernels: 16836it [00:04, 3619.18it/s]\n",
      "\n",
      " --- kernel matrix of common walk kernel of size 183 built in 4.761460542678833 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'geo', 'weight': 0.049999999999999996, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "calculating kernels: 10201it [00:02, 3556.34it/s]"
     ]
    }
   ],
   "source": [
    "# %load_ext line_profiler\n",
    "# %matplotlib inline\n",
    "from libs import *\n",
    "import multiprocessing\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "\n",
    "from pygraph.kernels.commonWalkKernel import commonwalkkernel\n",
    "from pygraph.utils.kernels import deltakernel, kernelproduct\n",
    "\n",
    "dslist = [\n",
    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds',\n",
    "        'task': 'regression'},  # node symb\n",
    "    {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression',\n",
    "             'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt', },  \n",
    "    # contains single node graph, node symb\n",
    "    {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds', },  # node/edge symb\n",
    "    {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds', },  # unlabeled\n",
    "    {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",
    "             'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},  # node/edge symb\n",
    "    {'name': 'Letter-med', 'dataset': '../datasets/Letter-med/Letter-med_A.txt'},\n",
    "    # node nsymb\n",
    "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",
    "    # node symb/nsymb\n",
    "#    {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'},\n",
    "#    # node/edge symb\n",
    "#    {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",
    "#     'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},  # node symb\n",
    "\n",
    "    #     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n",
    "    # # #     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n",
    "    # # #     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n",
    "    #     {'name': 'Fingerprint', 'dataset': '../datasets/Fingerprint/Fingerprint_A.txt'},\n",
    "    #\n",
    "    # #     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n",
    "    # #     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n",
    "    # #     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n",
    "    # #     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n",
    "    # #     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n",
    "\n",
    "    # #     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n",
    "    # #     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n",
    "    # #     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n",
    "    #     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
    "    #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
    "    #     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
    "    #         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
    "    #     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
    "    #         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n",
    "\n",
    "    #     # not working below\n",
    "    #     {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",
    "    #     {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n",
    "    #     {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n",
    "    #     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
    "]\n",
    "estimator = commonwalkkernel\n",
    "param_grid_precomputed = [{'compute_method': ['geo'], \n",
    "                           'weight': np.linspace(0.01, 0.15, 15)},\n",
    "#                           'weight': np.logspace(-1, -10, num=10, base=10)},\n",
    "                          {'compute_method': ['exp'], 'weight': range(0, 15)}]\n",
    "param_grid = [{'C': np.logspace(-10, 10, num=41, base=10)},\n",
    "              {'alpha': np.logspace(-10, 10, num=41, base=10)}]\n",
    "\n",
    "for ds in dslist:\n",
    "    print()\n",
    "    print(ds['name'])\n",
    "    model_selection_for_precomputed_kernel(\n",
    "        ds['dataset'],\n",
    "        estimator,\n",
    "        param_grid_precomputed,\n",
    "        (param_grid[1] if ('task' in ds and ds['task']\n",
    "                           == 'regression') else param_grid[0]),\n",
    "        (ds['task'] if 'task' in ds else 'classification'),\n",
    "        NUM_TRIALS=30,\n",
    "        datafile_y=(ds['dataset_y'] if 'dataset_y' in ds else None),\n",
    "        extra_params=(ds['extra_params'] if 'extra_params' in ds else None),\n",
    "        ds_name=ds['name'],\n",
    "        n_jobs=multiprocessing.cpu_count(),\n",
    "        read_gm_from_file=False,\n",
    "        verbose=True)\n",
    "    print()"
   ]
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