OpenI
/
graphkit-learn

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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",
      "\n",
      " None edge weight specified. Set all weight to 1.\n",
      "\n",
      "getting sp graphs: 183it [00:00, 5345.48it/s]\n",
      "calculating kernels: 16836it [00:01, 16066.90it/s]\n",
      "\n",
      " --- shortest path kernel matrix of size 183 built in 1.2855160236358643 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\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",
      "cross validation: 30it [00:03,  8.63it/s]\n",
      "\n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
      "best_params_in:  [{'alpha': 0.0001}]\n",
      "\n",
      "best_val_perf:  9.674788994813262\n",
      "best_val_std:  0.6229031522274688\n",
      "final_performance:  [9.590999824754439]\n",
      "final_confidence:  [2.911796096257332]\n",
      "train_performance: [6.16594412531739]\n",
      "train_std:  [0.2739093211154806]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 1.29±nans\n",
      "time to calculate best gram matrix: 1.29±nans\n",
      "total training time with all hyper-param choices: 5.15s\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",
      "\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"
     ]
    },
    {
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     "text": [
      "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:140: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  keepdims=keepdims)\n",
      "/usr/local/lib/python3.6/dist-packages/numpy/core/_methods.py:132: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
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     "text": [
      "getting sp graphs: 149it [00:00, 6510.18it/s]\n",
      "calculating kernels: 11175it [00:00, 18881.68it/s]\n",
      "\n",
      " --- shortest path kernel matrix of size 149 built in 0.8007419109344482 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\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",
      "cross validation: 30it [00:02, 10.52it/s]\n",
      "\n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
      "best_params_in:  [{'alpha': 3.162277660168379e-07}]\n",
      "\n",
      "best_val_perf:  8.784264102873752\n",
      "best_val_std:  0.2656887278835053\n",
      "final_performance:  [8.059911355753659]\n",
      "final_confidence:  [1.9620843656589473]\n",
      "train_performance: [7.8406202266920575]\n",
      "train_std:  [0.2177862360087283]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 0.80±nans\n",
      "time to calculate best gram matrix: 0.80±nans\n",
      "total training time with all hyper-param choices: 4.02s\n",
      "\n",
      "\n",
      "\n",
      "MAO\n",
      "\n",
      "--- This is a classification problem ---\n",
      "\n",
      "\n",
      "1. 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",
      "getting sp graphs: 68it [00:00, 1095.77it/s]\n",
      "calculating kernels: 2346it [00:02, 813.63it/s]\n",
      "\n",
      " --- shortest path kernel matrix of size 68 built in 3.110588550567627 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\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",
      "cross validation: 30it [00:02, 10.97it/s]\n",
      "\n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8}]\n",
      "best_params_in:  [{'C': 3162.2776601683795}]\n",
      "\n",
      "best_val_perf:  0.8798412698412699\n",
      "best_val_std:  0.02062186442241262\n",
      "final_performance:  [0.9042857142857144]\n",
      "final_confidence:  [0.07343487734322982]\n",
      "train_performance: [0.9709180695847363]\n",
      "train_std:  [0.005927396388634032]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 3.11±nans\n",
      "time to calculate best gram matrix: 3.11±nans\n",
      "total training time with all hyper-param choices: 6.21s\n",
      "\n",
      "\n",
      "\n",
      "PAH\n",
      "\n",
      "--- This is a classification problem ---\n",
      "\n",
      "\n",
      "1. 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",
      "getting sp graphs: 94it [00:00, 2190.46it/s]\n",
      "calculating kernels: 4465it [00:05, 763.81it/s]\n",
      "\n",
      " --- shortest path kernel matrix of size 94 built in 6.083932399749756 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f4d3eb29620>, 'nsymb': <function gaussiankernel at 0x7f4d3eb296a8>, 'mix': functools.partial(<function kernelproduct at 0x7f4d3eb297b8>, <function deltakernel at 0x7f4d3eb29620>, <function gaussiankernel at 0x7f4d3eb296a8>)}, 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\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",
      "cross validation: 0it [00:00, ?it/s]"
     ]
    }
   ],
   "source": [
    "import functools\n",
    "from libs import *\n",
    "import multiprocessing\n",
    "\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "from pygraph.utils.kernels import deltakernel, gaussiankernel, kernelproduct\n",
    "#from pygraph.utils.model_selection_precomputed import trial_do\n",
    "\n",
    "# datasets\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",
    "\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",
    "#    {'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 = spkernel\n",
    "# hyper-parameters\n",
    "mixkernel = functools.partial(kernelproduct, deltakernel, gaussiankernel)\n",
    "param_grid_precomputed = {'node_kernels': [\n",
    "    {'symb': deltakernel, 'nsymb': gaussiankernel, '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",
    "# for each dataset, do model selection.\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",
    "    print()"
   ]
  }
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