graphkit-learn/notebooks/run_treeletkernel.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",
      "getting canonkeys: 183it [00:00, 2869.32it/s]\n",
      "calculating kernels: 16836it [00:00, 289967.90it/s]\n",
      "\n",
      " --- treelet kernel matrix of size 183 built in 0.2736480236053467 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'sub_kernel': <function gaussiankernel at 0x7f12f14ec730>, 'n_jobs': 8, 'verbose': True} is: \n",
      "\n",
      "\n",
      "getting canonkeys: 183it [00:00, 2431.05it/s]\n",
      "calculating kernels: 16836it [00:00, 225177.06it/s]\n",
      "\n",
      " --- treelet kernel matrix of size 183 built in 0.2614881992340088 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'sub_kernel': <function polynomialkernel at 0x7f12f14ec7b8>, 'n_jobs': 8, 'verbose': True} is: \n",
      "\n",
      "\n",
      "\n",
      "2 gram matrices are calculated, 0 of which are ignored.\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "cross validation: 30it [00:06,  4.34it/s]\n",
      "\n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'sub_kernel': <function polynomialkernel at 0x7f12f14ec7b8>, 'n_jobs': 8, 'verbose': True}]\n",
      "best_params_in:  [{'alpha': 0.01}]\n",
      "\n",
      "best_val_perf:  8.699254729880051\n",
      "best_val_std:  0.6859488791023038\n",
      "final_performance:  [10.449041034883777]\n",
      "final_confidence:  [5.005824863496953]\n",
      "train_performance: [1.3405521528763233]\n",
      "train_std:  [0.0923786919637616]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 0.27±0.01s\n",
      "time to calculate best gram matrix: 0.26±0.00s\n",
      "total training time with all hyper-param choices: 8.18s\n",
      "\n",
      "\n",
      "\n",
      "Alkane\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "getting canonkeys: 150it [00:00, 1460.40it/s]\n",
      "calculating kernels: 11325it [00:00, 188753.18it/s]\n",
      "\n",
      " --- treelet kernel matrix of size 150 built in 0.452197790145874 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'sub_kernel': <function gaussiankernel at 0x7f12f14ec730>, 'n_jobs': 8, 'verbose': True} is: \n",
      "\n",
      "\n",
      "getting canonkeys: 150it [00:00, 3273.02it/s]\n",
      "calculating kernels: 11325it [00:00, 223074.04it/s]\n",
      "\n",
      " --- treelet kernel matrix of size 150 built in 0.2638716697692871 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'sub_kernel': <function polynomialkernel at 0x7f12f14ec7b8>, 'n_jobs': 8, 'verbose': True} is: \n",
      "\n",
      "\n",
      "\n",
      "2 gram matrices are calculated, 0 of which are ignored.\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "cross validation: 1it [00:01,  1.41s/it]"
     ]
    }
   ],
   "source": [
    "from libs import *\n",
    "import multiprocessing\n",
    "\n",
    "from gklearn.kernels.treeletKernel import treeletkernel\n",
    "from gklearn.utils.kernels import gaussiankernel, polynomialkernel\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 = treeletkernel\n",
    "param_grid_precomputed = {'sub_kernel': [gaussiankernel, polynomialkernel]}\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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