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