graphkit-learn/notebooks/run_untilhpathkernel.ipynb

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     "text": [
      "\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 paths: 183it [00:00, 33583.79it/s]\n",
      "calculating kernels: 16836it [00:00, 382919.33it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 183 built in 0.28138017654418945 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 49932.19it/s]\n",
      "calculating kernels: 16836it [00:00, 339040.24it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 183 built in 0.2915959358215332 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 13100.71it/s]\n",
      "calculating kernels: 16836it [00:00, 195915.25it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 183 built in 0.39291882514953613 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 15186.23it/s]\n",
      "calculating kernels: 16836it [00:00, 216679.82it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 183 built in 0.2922053337097168 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 8410.48it/s]\n",
      "calculating kernels: 16836it [00:00, 146690.73it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 183 built in 0.3915746212005615 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 13951.28it/s]\n",
      "calculating kernels: 16836it [00:00, 201673.88it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 183 built in 0.3854410648345947 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 10054.46it/s]\n",
      "calculating kernels: 16836it [00:00, 70713.10it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 183 built in 0.48105573654174805 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 1418.94it/s]\n",
      "calculating kernels: 16836it [00:00, 144898.57it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 183 built in 0.5477819442749023 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 15604.25it/s]\n",
      "calculating kernels: 16836it [00:00, 103300.82it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 183 built in 0.3788299560546875 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 9795.27it/s]\n",
      "calculating kernels: 16836it [00:00, 121689.68it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 183 built in 0.3888108730316162 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 7163.19it/s]\n",
      "calculating kernels: 16836it [00:00, 89262.17it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 183 built in 0.39624905586242676 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 16751.59it/s]\n",
      "calculating kernels: 16836it [00:00, 100004.39it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 183 built in 0.388913631439209 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 10090.81it/s]\n",
      "calculating kernels: 16836it [00:00, 91172.28it/s] \n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 183 built in 0.4908461570739746 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 2997.78it/s]\n",
      "calculating kernels: 16836it [00:00, 104945.65it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 183 built in 0.36611366271972656 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 6353.90it/s]\n",
      "calculating kernels: 16836it [00:00, 80425.25it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 183 built in 0.5061323642730713 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 9427.60it/s]\n",
      "calculating kernels: 16836it [00:00, 93863.88it/s] \n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 183 built in 0.3872077465057373 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 7575.20it/s]\n",
      "calculating kernels: 16836it [00:00, 82517.07it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 183 built in 0.48129963874816895 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 6563.74it/s]\n",
      "calculating kernels: 16836it [00:00, 94045.02it/s] \n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 183 built in 0.39592933654785156 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 6069.81it/s]\n",
      "calculating kernels: 16836it [00:00, 77447.83it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 183 built in 0.47420382499694824 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 183it [00:00, 9481.17it/s]\n",
      "calculating kernels: 16836it [00:00, 58253.60it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 183 built in 0.4869115352630615 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\n",
      "20 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 [01:07,  1.10s/it]\n",
      "\n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8}]\n",
      "best_params_in:  [{'alpha': 0.01}]\n",
      "\n",
      "best_val_perf:  6.8347760734601675\n",
      "best_val_std:  0.26187601855914455\n",
      "final_performance:  [6.844597847292873]\n",
      "final_confidence:  [1.3282917788841784]\n",
      "train_performance: [2.2886614412566524]\n",
      "train_std:  [0.11697823620293107]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 0.41±0.07s\n",
      "time to calculate best gram matrix: 0.39±nans\n",
      "total training time with all hyper-param choices: 82.00s\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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 paths: 150it [00:00, 38060.83it/s]\n",
      "calculating kernels: 11325it [00:00, 447307.64it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29852986335754395 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 16860.39it/s]\n",
      "calculating kernels: 11325it [00:00, 522115.40it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 1 of size 150 built in 0.29816317558288574 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 1.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 18149.30it/s]\n",
      "calculating kernels: 11325it [00:00, 383173.55it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 150 built in 0.29796385765075684 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 23172.10it/s]\n",
      "calculating kernels: 11325it [00:00, 427074.37it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 2 of size 150 built in 0.3109288215637207 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 2.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 13243.78it/s]\n",
      "calculating kernels: 11325it [00:00, 269283.28it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 150 built in 0.29997825622558594 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 20671.78it/s]\n",
      "calculating kernels: 11325it [00:00, 312080.29it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 3 of size 150 built in 0.29572534561157227 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 3.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 15393.45it/s]\n",
      "calculating kernels: 11325it [00:00, 362928.87it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 150 built in 0.30132484436035156 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 16957.65it/s]\n",
      "calculating kernels: 11325it [00:00, 84503.61it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 4 of size 150 built in 0.4178507328033447 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 4.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 14440.54it/s]\n",
      "calculating kernels: 11325it [00:00, 276894.92it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 150 built in 0.29338693618774414 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 15619.30it/s]\n",
      "calculating kernels: 11325it [00:00, 254676.58it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 5 of size 150 built in 0.29663729667663574 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 5.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 14585.84it/s]\n",
      "calculating kernels: 11325it [00:00, 242964.30it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 150 built in 0.29677391052246094 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 11555.83it/s]\n",
      "calculating kernels: 11325it [00:00, 330949.31it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 6 of size 150 built in 0.2948622703552246 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 6.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 10424.60it/s]\n",
      "calculating kernels: 11325it [00:00, 238514.96it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 150 built in 0.3041496276855469 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 12318.80it/s]\n",
      "calculating kernels: 11325it [00:00, 251979.97it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 7 of size 150 built in 0.3013496398925781 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 7.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 10722.00it/s]\n",
      "calculating kernels: 11325it [00:00, 232363.74it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 150 built in 0.294144868850708 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 18965.59it/s]\n",
      "calculating kernels: 11325it [00:00, 424638.55it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 8 of size 150 built in 0.2961091995239258 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 8.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 13945.38it/s]\n",
      "calculating kernels: 11325it [00:00, 286344.19it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 150 built in 0.30029296875 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 9525.87it/s]\n",
      "calculating kernels: 11325it [00:00, 231776.43it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 9 of size 150 built in 0.29835057258605957 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 9.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 16916.15it/s]\n",
      "calculating kernels: 11325it [00:00, 85396.60it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 150 built in 0.42621588706970215 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'MinMax', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "getting paths: 150it [00:00, 16108.40it/s]\n",
      "calculating kernels: 11325it [00:00, 328896.12it/s]\n",
      "\n",
      " --- kernel matrix of path kernel up to 10 of size 150 built in 0.301084041595459 seconds ---\n",
      "\n",
      "the gram matrix with parameters {'compute_method': 'trie', 'depth': 10.0, 'k_func': 'tanimoto', 'n_jobs': 8} is: \n",
      "\n",
      "\n",
      "\n",
      "20 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": [
    "# %load_ext line_profiler\n",
    "# %matplotlib inline\n",
    "from libs import *\n",
    "import multiprocessing\n",
    "\n",
    "from pygraph.kernels.untilHPathKernel import untilhpathkernel\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 = untilhpathkernel\n",
    "param_grid_precomputed = {'depth': np.linspace(1, 10, 10),   # [2], \n",
    "                          'k_func': ['MinMax', 'tanimoto'],\n",
    "                          'compute_method': ['trie']} # ['MinMax']}\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",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'line_profiler'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-cf4da93eeb50>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_ext'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'line_profiler'\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      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\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[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpygraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mkernel_train_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[0;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[1;32m   2283\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'local_ns'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_locals\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2284\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2285\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   2286\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<decorator-gen-65>\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\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    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\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/python3.6/dist-packages/IPython/core/magics/extension.py\u001b[0m in \u001b[0;36mload_ext\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     31\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mmodule_str\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mUsageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Missing module name.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshell\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextension_manager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_extension\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\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     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'already loaded'\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/python3.6/dist-packages/IPython/core/extensions.py\u001b[0m in \u001b[0;36mload_extension\u001b[0;34m(self, module_str)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmodule_str\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mprepended_to_syspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\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---> 80\u001b[0;31m                     \u001b[0mmod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimport_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule_str\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     81\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mmod\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mipython_extension_dir\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[1;32m     82\u001b[0m                         print((\"Loading extensions from {dir} is deprecated. \"\n",
      "\u001b[0;32m/usr/lib/python3.6/importlib/__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    124\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    125\u001b[0m             \u001b[0mlevel\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 126\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_bootstrap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_gcd_import\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpackage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\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    127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/importlib/_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'line_profiler'"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.untildPathKernel import untildpathkernel\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "datafile = '../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', labeled = True, k_func = 'tanimoto')\n",
    "\n",
    "# kernel_train_test(datafile, kernel_file_path, treeletkernel, kernel_para, normalize = False)\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = True)\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 20, 21), normalize = False)\n",
    "\n",
    "kernel_para['k_func'] = 'minmax'\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = True)\n",
    "kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, \\\n",
    "    hyper_name = 'depth', hyper_range = np.linspace(0, 10, 11), normalize = False)\n",
    "\n",
    "# # kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)\n",
    "\n",
    "# kernel_para['depth'] = 10\n",
    "# %lprun -f untildpathkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, untildpathkernel, kernel_para, normalize = False)"
   ]
  }
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