{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      "gram matrix with parameters {'p_quit': 0.10000000000000001, 'itr': 20} is: \n",
      "removing tottering: 100%|██████████| 183/183 [00:00<00:00, 1678.94it/s]\n",
      "calculating kernels:   8%|▊         | 1318/16836.0 [02:48<28:58,  8.93it/s]  "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-c8ad7685a8b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'task'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'task'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m'classification'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNUM_TRIALS\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mdatafile_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset_y'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'dataset_y'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mds\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m         extra_params=(ds['extra_params'] if 'extra_params' in ds else None))\n\u001b[0m\u001b[1;32m     61\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/utils/model_selection_precomputed.py\u001b[0m in \u001b[0;36mmodel_selection_for_precomputed_kernel\u001b[0;34m(datafile, estimator, param_grid_precomputed, param_grid, model_type, NUM_TRIALS, datafile_y, extra_params)\u001b[0m\n\u001b[1;32m     99\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'gram matrix with parameters'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams_out\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'is: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         \u001b[0mKmatrix\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcurrent_run_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams_out\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m         \u001b[0mKmatrix_diag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiagonal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/marginalizedKernel.py\u001b[0m in \u001b[0;36mmarginalizedkernel\u001b[0;34m(node_label, edge_label, p_quit, itr, remove_totters, *args)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGn\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     84\u001b[0m             Kmatrix[i][j] = _marginalizedkernel_do(Gn[i], Gn[j], node_label,\n\u001b[0;32m---> 85\u001b[0;31m                                                    edge_label, p_quit, itr)\n\u001b[0m\u001b[1;32m     86\u001b[0m             \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKmatrix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0mpbar\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\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/media/ljia/DATA/research-repo/codes/Linlin/py-graph/pygraph/kernels/marginalizedKernel.py\u001b[0m in \u001b[0;36m_marginalizedkernel_do\u001b[0;34m(G1, G2, node_label, edge_label, p_quit, itr)\u001b[0m\n\u001b[1;32m    147\u001b[0m                     \u001b[0;32mfor\u001b[0m \u001b[0mneighbor2\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mneighbor_n2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m                         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp_trans_n1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mp_trans_n2\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m                             \u001b[0mdeltakernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighbor1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode_label\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mG2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighbor2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnode_label\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m                             \u001b[0mdeltakernel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mneighbor_n1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighbor1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0medge_label\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mneighbor_n2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mneighbor2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0medge_label\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/networkx/classes/graph.py\u001b[0m in \u001b[0;36mnodes\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    717\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    718\u001b[0m         \"\"\"\n\u001b[0;32m--> 719\u001b[0;31m         \u001b[0mnodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNodeView\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    720\u001b[0m         \u001b[0;31m# Lazy View creation: overload the (class) property on the instance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    721\u001b[0m         \u001b[0;31m# Then future G.nodes use the existing View\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/dist-packages/networkx/classes/reportviews.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, graph)\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'_nodes'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 167\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\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    168\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_node\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.marginalizedKernel import marginalizedkernel\n",
    "\n",
    "dslist = [   \n",
    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node_labeled\n",
    "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge_labeled\n",
    "#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n",
    "#     {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # fully_labeled\n",
    "#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',},\n",
    "\n",
    "#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}},\n",
    "#     {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n",
    "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',},\n",
    "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'},\n",
    "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'},    \n",
    "    {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'},\n",
    "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'},\n",
    "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'},\n",
    "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'},\n",
    "#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'},\n",
    "#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'},\n",
    "\n",
    "#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'},\n",
    "#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'},\n",
    "#     {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",
    "#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}},\n",
    "#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'},\n",
    "#     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
    "#     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}},\n",
    "#     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
    "#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',},\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, 0.9, 9), 'itr': [20]}\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'], estimator, param_grid_precomputed, \n",
    "        (param_grid[1] if ('task' in ds and ds['task'] == 'regression') else param_grid[0]), \n",
    "        (ds['task'] if 'task' in ds else 'classification'), 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",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "1. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      "gram matrix with parameters {'p_quit': 0.10000000000000001} is: \n",
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:46:28<00:00,  1.48it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6388.502187728882 seconds ---\n",
      "[[ 1.          0.64549125  0.1238602  ...,  0.18744115  0.18784508\n",
      "   0.18052003]\n",
      " [ 0.64549125  1.          0.13569668 ...,  0.20535363  0.20579615\n",
      "   0.19777109]\n",
      " [ 0.1238602   0.13569668  1.         ...,  0.27603195  0.27457716\n",
      "   0.29886586]\n",
      " ..., \n",
      " [ 0.18744115  0.20535363  0.27603195 ...,  1.          0.99990821\n",
      "   0.99626713]\n",
      " [ 0.18784508  0.20579615  0.27457716 ...,  0.99990821  1.          0.99550561]\n",
      " [ 0.18052003  0.19777109  0.29886586 ...,  0.99626713  0.99550561  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38bcb28f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.20000000000000001} is: \n",
      "\n",
      "calculate kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 5/16836.0 [00:00<06:53, 40.74it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 8/16836.0 [00:00<07:50, 35.79it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 11/16836.0 [00:00<08:48, 31.86it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 23/16836.0 [00:00<12:26, 22.52it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-4:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/usr/lib/python3.5/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:46:43<00:00,  1.47it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6403.860609769821 seconds ---\n",
      "\n",
      "[[ 1.          0.77431936  0.23044786 ...,  0.31951339  0.31935955\n",
      "   0.31647445]\n",
      " [ 0.77431936  1.          0.20441831 ...,  0.28342371  0.28328725\n",
      "   0.28072802]\n",
      " [ 0.23044786  0.20441831  1.         ...,  0.42016303  0.4182119\n",
      "   0.43719159]\n",
      " ..., \n",
      " [ 0.31951339  0.28342371  0.42016303 ...,  1.          0.999968\n",
      "   0.99865875]\n",
      " [ 0.31935955  0.28328725  0.4182119  ...,  0.999968    1.          0.99840553]\n",
      " [ 0.31647445  0.28072802  0.43719159 ...,  0.99865875  0.99840553  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38bc220978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.30000000000000004} is: \n",
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:49:18<00:00,  1.47it/s]  \n",
      " --- marginalized kernel matrix of size 183 built in 6559.1021230220795 seconds ---\n",
      "[[ 1.          0.8408897   0.32640042 ...,  0.43422274  0.43379622\n",
      "   0.43375138]\n",
      " [ 0.8408897   1.          0.26207831 ...,  0.34865261  0.34831015\n",
      "   0.34827414]\n",
      " [ 0.32640042  0.26207831  1.         ...,  0.53322928  0.53137761\n",
      "   0.54559482]\n",
      " ..., \n",
      " [ 0.43422274  0.34865261  0.53322928 ...,  1.          0.99998698\n",
      "   0.99941502]\n",
      " [ 0.43379622  0.34831015  0.53137761 ...,  0.99998698  1.          0.99931551]\n",
      " [ 0.43375138  0.34827414  0.54559482 ...,  0.99941502  0.99931551  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38b471f7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.40000000000000002} is: \n",
      "\n",
      "calculate kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 5/16836.0 [00:00<06:52, 40.78it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 14/16836.0 [00:00<09:52, 28.41it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-6:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/usr/lib/python3.5/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:42:47<00:00,  1.45it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6167.1879296302795 seconds ---\n",
      "\n",
      "[[ 1.          0.87992527  0.4096     ...,  0.53183212  0.53136236\n",
      "   0.5326506 ]\n",
      " [ 0.87992527  1.          0.31083675 ...,  0.40359611  0.40323962\n",
      "   0.40421723]\n",
      " [ 0.4096      0.31083675  1.         ...,  0.62445239  0.62295134\n",
      "   0.63333087]\n",
      " ..., \n",
      " [ 0.53183212  0.40359611  0.62445239 ...,  1.          0.99999372\n",
      "   0.99971846]\n",
      " [ 0.53136236  0.40323962  0.62295134 ...,  0.99999372  1.          0.99966936]\n",
      " [ 0.5326506   0.40421723  0.63333087 ...,  0.99971846  0.99966936  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38b46bbcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.5} is: \n",
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:43:19<00:00,  1.47it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6199.830270290375 seconds ---\n",
      "[[ 1.          0.90464676  0.48       ...,  0.6133451   0.61295001\n",
      "   0.6145831 ]\n",
      " [ 0.90464676  1.          0.35164363 ...,  0.44933104  0.44904159\n",
      "   0.45023798]\n",
      " [ 0.48        0.35164363  1.         ...,  0.69841949  0.69732936\n",
      "   0.70457945]\n",
      " ..., \n",
      " [ 0.6133451   0.44933104  0.69841949 ...,  1.          0.99999665\n",
      "   0.99985882]\n",
      " [ 0.61295001  0.44904159  0.69732936 ...,  0.99999665  1.          0.99983035]\n",
      " [ 0.6145831   0.45023798  0.70457945 ...,  0.99985882  0.99983035  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38b468c240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.59999999999999998} is: \n",
      "\n",
      "calculate kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 5/16836.0 [00:00<06:48, 41.18it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 8/16836.0 [00:00<07:47, 36.03it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 11/16836.0 [00:00<08:42, 32.18it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 14/16836.0 [00:00<09:48, 28.56it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 17/16836.0 [00:00<11:18, 24.78it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 20/16836.0 [00:00<11:23, 24.59it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 23/16836.0 [00:00<12:15, 22.87it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 26/16836.0 [00:01<12:43, 22.01it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 29/16836.0 [00:01<13:27, 20.81it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 31/16836.0 [00:01<14:38, 19.14it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 33/16836.0 [00:01<15:52, 17.65it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 35/16836.0 [00:01<16:25, 17.05it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 37/16836.0 [00:01<15:52, 17.64it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 39/16836.0 [00:01<15:30, 18.04it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 41/16836.0 [00:01<16:02, 17.46it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 43/16836.0 [00:02<16:27, 17.01it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 45/16836.0 [00:02<16:32, 16.92it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 47/16836.0 [00:02<16:48, 16.65it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 49/16836.0 [00:02<17:11, 16.28it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 51/16836.0 [00:02<17:27, 16.02it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 53/16836.0 [00:02<17:52, 15.65it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 55/16836.0 [00:02<18:35, 15.04it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 57/16836.0 [00:03<19:30, 14.33it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 59/16836.0 [00:03<20:24, 13.70it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 61/16836.0 [00:03<20:25, 13.69it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 63/16836.0 [00:03<20:19, 13.76it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 65/16836.0 [00:03<19:21, 14.44it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 67/16836.0 [00:03<18:43, 14.93it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 69/16836.0 [00:03<18:30, 15.10it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 71/16836.0 [00:03<18:20, 15.23it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 73/16836.0 [00:04<18:55, 14.77it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 75/16836.0 [00:04<18:55, 14.76it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 77/16836.0 [00:04<19:46, 14.12it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 79/16836.0 [00:04<20:30, 13.62it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 81/16836.0 [00:04<20:41, 13.49it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 83/16836.0 [00:04<20:27, 13.65it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 85/16836.0 [00:04<20:28, 13.64it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 87/16836.0 [00:05<20:34, 13.57it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 89/16836.0 [00:05<21:26, 13.01it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 91/16836.0 [00:05<21:55, 12.73it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 93/16836.0 [00:05<22:18, 12.51it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 95/16836.0 [00:05<22:08, 12.60it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 97/16836.0 [00:05<23:05, 12.08it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 99/16836.0 [00:06<23:13, 12.01it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 101/16836.0 [00:06<23:01, 12.11it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 103/16836.0 [00:06<23:18, 11.97it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 105/16836.0 [00:06<22:14, 12.54it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 107/16836.0 [00:06<22:01, 12.66it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 109/16836.0 [00:06<22:07, 12.60it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 111/16836.0 [00:07<21:58, 12.68it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 113/16836.0 [00:07<21:49, 12.77it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 115/16836.0 [00:07<22:03, 12.64it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 117/16836.0 [00:07<22:28, 12.40it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 119/16836.0 [00:07<23:25, 11.89it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 121/16836.0 [00:07<23:59, 11.61it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 123/16836.0 [00:08<25:30, 10.92it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 125/16836.0 [00:08<25:19, 11.00it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 127/16836.0 [00:08<26:18, 10.59it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 129/16836.0 [00:08<27:03, 10.29it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 131/16836.0 [00:08<27:38, 10.07it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 133/16836.0 [00:09<26:28, 10.51it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 135/16836.0 [00:09<25:51, 10.77it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 137/16836.0 [00:09<26:24, 10.54it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 139/16836.0 [00:09<25:57, 10.72it/s]\u001b[A\n",
      "calculate kernels:   1%|          | 143/16836.0 [00:10<27:21, 10.17it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-7:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/usr/lib/python3.5/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:41:44<00:00,  1.48it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6104.786992549896 seconds ---\n",
      "\n",
      "[[ 1.          0.92084364  0.5376     ...,  0.67938428  0.67910614\n",
      "   0.68051945]\n",
      " [ 0.92084364  1.          0.38486347 ...,  0.48636568  0.48616656\n",
      "   0.48717834]\n",
      " [ 0.5376      0.38486347  1.         ...,  0.7573247   0.75661569\n",
      "   0.7613079 ]\n",
      " ..., \n",
      " [ 0.67938428  0.48636568  0.7573247  ...,  1.          0.99999819\n",
      "   0.999929  ]\n",
      " [ 0.67910614  0.48616656  0.75661569 ...,  0.99999819  1.          0.99991171]\n",
      " [ 0.68051945  0.48717834  0.7613079  ...,  0.999929    0.99991171  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38bc0538d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.70000000000000007} is: \n",
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:43:44<00:00,  1.44it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6225.047295093536 seconds ---\n",
      "[[ 1.          0.93146048  0.5824     ...,  0.73036764  0.73020428\n",
      "   0.73115317]\n",
      " [ 0.93146048  1.          0.41064428 ...,  0.51497474  0.51485956\n",
      "   0.51552861]\n",
      " [ 0.5824      0.41064428  1.         ...,  0.80233943  0.80194044\n",
      "   0.80461114]\n",
      " ..., \n",
      " [ 0.73036764  0.51497474  0.80233943 ...,  1.          0.99999909\n",
      "   0.99996617]\n",
      " [ 0.73020428  0.51485956  0.80194044 ...,  0.99999909  1.          0.9999564 ]\n",
      " [ 0.73115317  0.51552861  0.80461114 ...,  0.99996617  0.9999564   1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38bc206048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.80000000000000004} is: \n",
      "\n",
      "calculate kernels:   0%|          | 0/16836.0 [00:00<?, ?it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 5/16836.0 [00:00<06:50, 40.97it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 8/16836.0 [00:00<07:50, 35.77it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 11/16836.0 [00:00<08:47, 31.87it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 14/16836.0 [00:00<09:55, 28.27it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 17/16836.0 [00:00<11:22, 24.63it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 20/16836.0 [00:00<11:25, 24.54it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 23/16836.0 [00:00<12:20, 22.69it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 26/16836.0 [00:01<12:50, 21.81it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 29/16836.0 [00:01<13:32, 20.68it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 31/16836.0 [00:01<14:44, 18.99it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 33/16836.0 [00:01<16:06, 17.39it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 35/16836.0 [00:01<16:36, 16.87it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 37/16836.0 [00:01<16:00, 17.50it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 39/16836.0 [00:01<15:37, 17.91it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 41/16836.0 [00:01<16:14, 17.24it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 43/16836.0 [00:02<16:38, 16.82it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 45/16836.0 [00:02<16:47, 16.66it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 47/16836.0 [00:02<17:02, 16.42it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 49/16836.0 [00:02<17:33, 15.93it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 51/16836.0 [00:02<17:46, 15.74it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 53/16836.0 [00:02<18:08, 15.42it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 55/16836.0 [00:02<18:45, 14.91it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 57/16836.0 [00:03<19:42, 14.19it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 59/16836.0 [00:03<20:29, 13.65it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 61/16836.0 [00:03<20:34, 13.58it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 63/16836.0 [00:03<20:45, 13.47it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 65/16836.0 [00:03<19:36, 14.26it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 67/16836.0 [00:03<19:03, 14.66it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 69/16836.0 [00:03<18:52, 14.80it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 71/16836.0 [00:04<18:46, 14.88it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 73/16836.0 [00:04<19:13, 14.54it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 75/16836.0 [00:04<19:14, 14.51it/s]\u001b[A\n",
      "calculate kernels:   0%|          | 81/16836.0 [00:04<20:40, 13.50it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-8:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/usr/lib/python3.5/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:42:54<00:00,  1.46it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6174.926059484482 seconds ---\n",
      "\n",
      "[[ 1.          0.9381379   0.6144     ...,  0.76658748  0.76651379\n",
      "   0.76698253]\n",
      " [ 0.9381379   1.          0.42904385 ...,  0.53531843  0.53526696\n",
      "   0.53559429]\n",
      " [ 0.6144      0.42904385  1.         ...,  0.83412902  0.83395285\n",
      "   0.83515141]\n",
      " ..., \n",
      " [ 0.76658748  0.53531843  0.83412902 ...,  1.          0.99999963\n",
      "   0.99998645]\n",
      " [ 0.76651379  0.53526696  0.83395285 ...,  0.99999963  1.          0.99998201]\n",
      " [ 0.76698253  0.53559429  0.83515141 ...,  0.99998645  0.99998201  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38bc2065f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "gram matrix with parameters {'p_quit': 0.90000000000000002} is: \n",
      "calculate kernels: 100%|██████████| 16836/16836.0 [1:42:56<00:00,  1.47it/s]\n",
      " --- marginalized kernel matrix of size 183 built in 6176.7943415641785 seconds ---\n",
      "[[ 1.          0.94181611  0.6336     ...,  0.78824197  0.7882235\n",
      "   0.78834754]\n",
      " [ 0.94181611  1.          0.44008114 ...,  0.5474912   0.54747837\n",
      "   0.54756452]\n",
      " [ 0.6336      0.44008114  1.         ...,  0.85307284  0.8530291\n",
      "   0.85333062]\n",
      " ..., \n",
      " [ 0.78824197  0.5474912   0.85307284 ...,  1.          0.99999991\n",
      "   0.99999679]\n",
      " [ 0.7882235   0.54747837  0.8530291  ...,  0.99999991  1.          0.99999566]\n",
      " [ 0.78834754  0.54756452  0.85333062 ...,  0.99999679  0.99999566  1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38b47b31d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "9 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",
      "\n",
      "calculate performance:   0%|          | 0/11070 [00:00<?, ?it/s]\u001b[A\n",
      "\u001b[A                                                             \n",
      "calculate performance:   0%|          | 2/11070 [00:00<15:40, 11.77it/s]\u001b[A\n",
      "\u001b[A                                                                     "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ljia/.local/lib/python3.5/site-packages/sklearn/linear_model/ridge.py:154: UserWarning: Singular matrix in solving dual problem. Using least-squares solution instead.\n",
      "  warnings.warn(\"Singular matrix in solving dual problem. Using \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[A\n",
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      "\u001b[A                                                                     \n",
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      "\u001b[A                                                                     \n",
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      "\u001b[A                                                                     \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
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      "\u001b[A                                                                      \n",
      "\u001b[A\n",
      "\u001b[A\n",
      "\u001b[A\n",
      "                                                                         \r"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-9:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.5/threading.py\", line 914, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/local/lib/python3.5/dist-packages/tqdm/_tqdm.py\", line 144, in run\n",
      "    for instance in self.tqdm_cls._instances:\n",
      "  File \"/usr/lib/python3.5/_weakrefset.py\", line 60, in __iter__\n",
      "    for itemref in self.data:\n",
      "RuntimeError: Set changed size during iteration\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                            \n",
      "4. Getting final performances...\n",
      "best_params_out:  [{'p_quit': 0.10000000000000001}]\n",
      "best_params_in:  [{'alpha': 0.0005623413251903491}]\n",
      "\n",
      "best_val_perf:  26.062653165\n",
      "best_val_std:  1.8303539525\n",
      "final_performance:  26.8380192095\n",
      "final_confidence:  4.81048260006\n",
      "train_performance:  20.6534557877\n",
      "train_std:  0.444474306713\n",
      "\n",
      "time to calculate gram matrix with different hyperpapams: 6266.67±149.16\n",
      "time to calculate best gram matrix:  6388.502187728882 s\n",
      "\n",
      "params                                                train_perf      valid_perf      test_perf         gram_matrix_time\n",
      "----------------------------------------------------  --------------  --------------  --------------  ------------------\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e-05'}  25.69±37.46     320.73±1476.47  144.12±515.91              6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e-05'}  20.68±1.75      51.88±46.68     43.91±60.09                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e-05'}  20.63±0.67      30.50±5.11      28.34±6.29                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e-05'}  22.77±0.45      28.04±1.83      28.24±4.58                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e-05'}                  24.49±0.50      28.93±1.46      29.39±6.24                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e-05'}  25.58±0.53      29.37±1.11      30.63±5.60                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e-05'}  27.64±0.45      30.31±0.80      31.39±4.18                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e-05'}  29.80±0.64      32.09±1.06      32.18±5.49                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e-05'}  30.92±0.45      32.70±0.70      33.38±3.69                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e-05'}  19.33±2.20      54.35±50.99     48.59±33.17                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e-05'}  20.43±1.68      55.88±102.49    49.24±91.81                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e-05'}  20.97±0.53      28.43±2.75      26.53±4.56                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e-05'}  23.37±0.47      28.23±1.76      28.54±4.57                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e-05'}                  24.87±0.50      29.20±1.38      29.54±6.26                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e-05'}  26.04±0.55      29.53±1.06      30.87±5.36                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e-05'}  28.23±0.44      30.68±0.68      31.28±3.96                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e-05'}  30.09±0.64      32.29±1.03      32.43±5.40                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e-05'}  31.30±0.43      32.92±0.65      33.54±3.55                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e-05'}  19.24±1.44      44.43±29.32     43.39±30.26                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e-05'}  19.98±0.64      32.41±12.05     31.03±21.70                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e-05'}  21.60±0.49      27.83±1.93      26.22±4.41                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e-05'}  23.92±0.47      28.49±1.69      28.85±4.63                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e-05'}                  25.30±0.50      29.43±1.26      29.65±6.18                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e-05'}  26.56±0.56      29.65±1.01      31.12±4.95                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e-05'}  28.82±0.44      31.11±0.60      31.34±3.84                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e-05'}  30.35±0.63      32.41±0.99      32.65±5.29                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e-05'}  31.88±0.41      33.36±0.61      33.89±3.51                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e-05'}  19.24±1.23      45.98±56.76     44.60±49.03                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e-05'}  20.14±0.49      28.62±4.27      27.79±11.63                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e-05'}  22.37±0.48      27.84±1.60      26.57±4.24                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e-05'}  24.45±0.47      28.77±1.60      29.13±4.71                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e-05'}                  25.77±0.50      29.52±1.09      29.66±5.89                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e-05'}  27.15±0.55      29.83±0.95      31.45±4.52                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e-05'}  29.36±0.44      31.53±0.56      31.53±3.74                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e-05'}  30.59±0.64      32.47±0.94      32.83±5.16                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e-05'}  32.62±0.40      33.98±0.57      34.40±3.58                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e-04'}  19.18±0.87      37.36±30.90     37.62±28.55                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e-04'}  20.65±0.43      27.13±2.11      26.24±7.01                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e-04'}  23.15±0.47      28.07±1.44      27.12±4.08                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e-04'}  24.98±0.46      29.02±1.43      29.31±4.73                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e-04'}                  26.27±0.51      29.48±0.93      29.58±5.41                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e-04'}  27.81±0.53      30.16±0.88      31.93±4.22                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e-04'}  29.84±0.44      31.88±0.54      31.77±3.63                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e-04'}  30.86±0.63      32.53±0.87      33.04±5.01                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e-04'}  33.41±0.40      34.67±0.56      34.98±3.70                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e-04'}  19.37±0.67      34.48±35.60     34.92±30.50                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e-04'}  21.44±0.41      26.67±1.41      25.63±4.90                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e-04'}  23.90±0.46      28.36±1.32      27.71±4.01                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e-04'}  25.54±0.45      29.14±1.20      29.32±4.65                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e-04'}                  26.83±0.50      29.45±0.80      29.53±4.90                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e-04'}  28.50±0.52      30.60±0.82      32.54±4.08                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e-04'}  30.26±0.43      32.15±0.53      32.01±3.51                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e-04'}  31.25±0.62      32.71±0.81      33.36±4.88                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e-04'}  34.12±0.41      35.32±0.56      35.53±3.81                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e-04'}  19.80±0.50      27.23±5.01      28.31±6.53                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e-04'}  22.41±0.41      26.74±1.04      25.65±4.03                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e-04'}  24.64±0.45      28.62±1.19      28.28±4.09                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e-04'}  26.10±0.44      29.12±0.93      29.21±4.50                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e-04'}                  27.52±0.49      29.61±0.70      29.66±4.55                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e-04'}  29.19±0.51      31.09±0.76      33.19±4.07                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e-04'}  30.67±0.43      32.37±0.51      32.29±3.40                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e-04'}  31.84±0.60      33.14±0.75      33.93±4.81                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e-04'}  34.72±0.43      35.89±0.58      36.03±3.88                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e-04'}  20.65±0.44      26.06±1.83      26.84±4.81                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e-04'}  23.49±0.41      27.09±0.85      26.06±3.62                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e-04'}  25.39±0.45      28.78±1.02      28.81±4.29                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e-04'}  26.73±0.44      29.11±0.73      29.16±4.38                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e-04'}                  28.35±0.47      30.09±0.64      30.07±4.38                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e-04'}  29.85±0.51      31.57±0.72      33.82±4.14                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e-04'}  31.17±0.41      32.69±0.49      32.74±3.32                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e-04'}  32.63±0.58      33.87±0.71      34.73±4.80                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e-04'}  35.23±0.44      36.38±0.60      36.48±3.92                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e-03'}  21.89±0.45      26.28±1.39      26.65±4.75                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e-03'}  24.64±0.40      27.68±0.75      26.72±3.39                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e-03'}  26.16±0.46      28.89±0.84      29.34±4.47                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e-03'}  27.50±0.44      29.38±0.61      29.48±4.26                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e-03'}                  29.30±0.46      30.87±0.60      30.74±4.30                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e-03'}  30.54±0.51      32.09±0.70      34.46±4.28                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e-03'}  31.82±0.39      33.24±0.48      33.49±3.33                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e-03'}  33.52±0.57      34.73±0.70      35.62±4.85                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e-03'}  35.64±0.45      36.76±0.61      36.85±3.97                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e-03'}  23.49±0.48      27.44±1.38      27.59±4.85                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e-03'}  25.87±0.40      28.51±0.67      27.57±3.34                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e-03'}  27.03±0.47      29.15±0.70      29.98±4.49                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e-03'}  28.52±0.44      30.12±0.59      30.35±4.05                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e-03'}                  30.33±0.46      31.84±0.55      31.60±4.35                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e-03'}  31.31±0.51      32.76±0.68      35.17±4.47                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e-03'}  32.65±0.36      34.03±0.48      34.54±3.48                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e-03'}  34.35±0.57      35.55±0.72      36.44±4.95                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e-03'}  35.95±0.46      37.02±0.62      37.10±4.04                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e-03'}  25.34±0.52      29.13±1.28      29.32±4.77                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e-03'}  27.19±0.39      29.57±0.60      28.57±3.41                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e-03'}  28.10±0.48      29.87±0.64      30.87±4.32                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e-03'}  29.77±0.44      31.31±0.62      31.69±3.83                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e-03'}                  31.41±0.45      32.93±0.51      32.58±4.55                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e-03'}  32.20±0.52      33.62±0.67      36.00±4.66                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e-03'}  33.55±0.36      34.89±0.50      35.70±3.70                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e-03'}  35.03±0.59      36.19±0.75      37.07±5.06                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e-03'}  36.17±0.46      37.16±0.62      37.25±4.15                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e-03'}  27.25±0.54      30.90±1.10      31.33±4.62                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e-03'}  28.57±0.38      30.79±0.53      29.66±3.55                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e-03'}  29.42±0.48      31.14±0.61      32.05±4.09                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e-03'}  31.14±0.43      32.73±0.63      33.19±3.76                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e-03'}                  32.51±0.46      34.05±0.49      33.62±4.84                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e-03'}  33.12±0.52      34.54±0.68      36.88±4.83                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e-03'}  34.37±0.37      35.65±0.53      36.74±3.94                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e-03'}  35.53±0.60      36.63±0.78      37.50±5.18                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e-03'}  36.34±0.47      37.24±0.63      37.32±4.29                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e-02'}  29.03±0.55      32.44±0.93      33.15±4.51                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e-02'}  29.97±0.39      32.11±0.51      30.81±3.74                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e-02'}  30.93±0.46      32.74±0.59      33.39±3.93                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e-02'}  32.46±0.41      34.08±0.65      34.57±3.84                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e-02'}                  33.52±0.48      35.07±0.50      34.56±5.13                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e-02'}  33.97±0.54      35.34±0.70      37.66±4.97                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e-02'}  35.03±0.40      36.22±0.55      37.57±4.15                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e-02'}  35.88±0.62      36.89±0.80      37.75±5.28                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e-02'}  36.50±0.47      37.29±0.63      37.37±4.46                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e-02'}  30.53±0.54      33.62±0.79      34.57±4.42                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e-02'}  31.35±0.41      33.43±0.54      31.96±3.95                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e-02'}  32.41±0.44      34.28±0.58      34.65±3.87                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e-02'}  33.58±0.41      35.19±0.67      35.63±3.93                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e-02'}                  34.35±0.51      35.85±0.53      35.30±5.31                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e-02'}  34.66±0.56      35.93±0.72      38.28±5.08                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e-02'}  35.51±0.43      36.59±0.57      38.16±4.31                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e-02'}  36.11±0.63      37.02±0.81      37.89±5.38                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e-02'}  36.67±0.48      37.36±0.64      37.44±4.64                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e-02'}  31.72±0.53      34.47±0.71      35.58±4.32                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e-02'}  32.64±0.43      34.64±0.56      33.05±4.11                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e-02'}  33.63±0.43      35.47±0.57      35.61±3.86                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e-02'}  34.42±0.42      35.96±0.68      36.33±3.96                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e-02'}                  35.00±0.54      36.38±0.55      35.83±5.37                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e-02'}  35.19±0.58      36.33±0.72      38.72±5.18                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e-02'}  35.84±0.46      36.80±0.58      38.57±4.44                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e-02'}  36.29±0.63      37.08±0.81      37.96±5.46                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e-02'}  36.86±0.50      37.46±0.66      37.53±4.79                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e-02'}  32.68±0.52      35.13±0.66      36.29±4.23                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e-02'}  33.76±0.44      35.62±0.58      34.00±4.22                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e-02'}  34.52±0.43      36.25±0.56      36.22±3.83                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e-02'}  35.03±0.43      36.44±0.68      36.71±3.94                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e-02'}                  35.50±0.56      36.72±0.56      36.20±5.38                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e-02'}  35.59±0.60      36.57±0.72      39.01±5.29                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e-02'}  36.08±0.48      36.91±0.59      38.87±4.57                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e-02'}  36.46±0.64      37.13±0.82      38.03±5.54                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e-02'}  37.03±0.51      37.56±0.67      37.62±4.90                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e-01'}  33.51±0.52      35.67±0.64      36.84±4.19                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e-01'}  34.65±0.45      36.32±0.57      34.73±4.29                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e-01'}  35.14±0.43      36.69±0.55      36.53±3.76                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e-01'}  35.48±0.43      36.73±0.67      36.88±3.90                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e-01'}                  35.89±0.57      36.92±0.56      36.45±5.40                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e-01'}  35.88±0.61      36.71±0.71      39.19±5.42                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e-01'}  36.26±0.50      36.96±0.60      39.12±4.71                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e-01'}  36.64±0.64      37.20±0.82      38.11±5.62                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e-01'}  37.16±0.53      37.64±0.69      37.67±4.97                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e-01'}  34.27±0.51      36.15±0.63      37.31±4.19                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e-01'}  35.30±0.46      36.75±0.57      35.26±4.34                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e-01'}  35.59±0.42      36.92±0.52      36.65±3.68                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e-01'}  35.85±0.43      36.92±0.64      36.93±3.90                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e-01'}                  36.21±0.58      37.04±0.57      36.61±5.44                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e-01'}  36.11±0.63      36.78±0.71      39.31±5.55                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e-01'}  36.42±0.51      37.00±0.62      39.35±4.86                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e-01'}  36.80±0.65      37.26±0.83      38.19±5.70                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e-01'}  37.26±0.54      37.68±0.69      37.66±5.01                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e-01'}  34.96±0.51      36.55±0.62      37.71±4.23                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e-01'}  35.79±0.47      37.00±0.55      35.64±4.41                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e-01'}  35.97±0.42      37.08±0.50      36.68±3.59                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e-01'}  36.18±0.43      37.07±0.61      36.94±3.96                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e-01'}                  36.46±0.58      37.12±0.59      36.68±5.52                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e-01'}  36.30±0.65      36.84±0.72      39.39±5.68                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e-01'}  36.57±0.53      37.04±0.63      39.56±5.00                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e-01'}  36.95±0.65      37.32±0.83      38.26±5.80                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e-01'}  37.34±0.54      37.69±0.70      37.62±5.04                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e-01'}  35.57±0.51      36.89±0.61      38.02±4.29                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e-01'}  36.20±0.47      37.18±0.54      35.96±4.53                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e-01'}  36.32±0.41      37.22±0.47      36.69±3.53                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e-01'}  36.48±0.44      37.19±0.58      36.94±4.06                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e-01'}                  36.67±0.59      37.19±0.62      36.69±5.63                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e-01'}  36.49±0.66      36.90±0.73      39.50±5.79                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e-01'}  36.73±0.55      37.11±0.65      39.77±5.13                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e-01'}  37.11±0.66      37.39±0.83      38.33±5.95                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e-01'}  37.78±1.92      37.90±1.20      37.86±5.31                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+00'}  36.14±0.51      37.22±0.59      38.30±4.41                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+00'}  36.60±0.48      37.38±0.55      36.29±4.71                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+00'}  36.73±0.53      37.43±0.53      36.81±3.55                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+00'}  36.77±0.45      37.32±0.55      36.95±4.19                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+00'}                  71.89±184.57    72.25±184.67    68.43±164.21               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+00'}  42.43±20.29     42.98±21.61     45.31±19.68                6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+00'}  40.30±7.01      40.50±6.73      43.14±9.78                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+00'}  42.62±13.20     42.67±13.81     42.75±11.15                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+00'}  45.20±22.37     46.28±25.41     46.45±28.85                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e+00'}  38.01±2.61      38.89±2.62      39.67±5.21                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e+00'}  38.53±6.04      39.19±6.69      38.02±7.38                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e+00'}  45.48±18.11     45.53±18.60     44.85±18.08                6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e+00'}  38.55±3.45      38.94±3.50      38.02±5.09                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e+00'}                  100.19±335.45   92.34±291.45    107.06±377.80              6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e+00'}  881.87±4583.21  983.45±5139.24  769.30±3955.79             6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e+00'}  38.47±1.73      38.70±1.74      41.05±5.75                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e+00'}  38.32±0.81      38.41±0.93      39.62±6.67                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e+00'}  38.95±2.04      39.04±1.94      38.54±5.27                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e+00'}  46.36±32.53     46.63±31.15     47.94±31.59                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e+00'}  87.44±173.24    88.05±172.65    89.97±184.65               6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e+00'}  62.43±93.87     62.42±93.50     61.32±88.07                6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e+00'}  38.56±0.93      38.75±1.01      37.89±4.74                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e+00'}                  38.39±0.70      38.49±0.72      37.54±5.79                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e+00'}  38.31±0.67      38.35±0.72      41.38±6.11                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e+00'}  38.50±0.59      38.62±0.67      41.34±5.32                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e+00'}  38.98±0.66      38.97±0.82      39.97±6.94                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e+00'}  39.29±0.55      39.32±0.67      38.98±5.45                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e+00'}  39.95±1.14      40.34±1.08      41.39±6.05                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e+00'}  39.40±0.62      39.65±0.71      39.36±6.19                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e+00'}  39.21±0.48      39.41±0.45      38.38±3.92                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e+00'}  39.41±0.49      39.49±0.50      38.61±4.85                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e+00'}                  39.62±0.60      39.62±0.69      38.42±6.04                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e+00'}  39.63±0.66      39.58±0.70      42.79±6.05                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e+00'}  39.94±0.58      39.97±0.66      42.61±5.28                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e+00'}  40.44±0.69      40.35±0.84      41.34±7.31                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e+00'}  40.78±0.54      40.73±0.67      40.40±5.73                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+01'}  41.25±0.61      41.47±0.57      42.49±6.72                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+01'}  41.14±0.52      41.25±0.56      41.22±6.48                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+01'}  41.19±0.45      41.22±0.43      39.94±4.63                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+01'}  41.46±0.50      41.41±0.47      40.35±5.10                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+01'}                  41.76±0.58      41.65±0.68      40.17±6.43                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+01'}  41.76±0.64      41.62±0.67      44.97±6.17                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+01'}  42.11±0.57      42.05±0.65      44.49±5.34                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+01'}  42.57±0.72      42.40±0.86      43.48±7.63                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+01'}  42.92±0.54      42.79±0.67      42.58±6.17                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e+01'}  44.73±0.64      44.71±0.59      45.82±7.71                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e+01'}  44.52±0.52      44.46±0.54      44.86±7.02                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e+01'}  44.59±0.44      44.44±0.44      42.97±5.81                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e+01'}  44.80±0.50      44.62±0.46      43.38±5.51                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e+01'}                  45.07±0.56      44.85±0.66      43.00±7.18                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e+01'}  44.99±0.61      44.75±0.64      48.14±6.59                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e+01'}  45.32±0.57      45.15±0.64      47.32±5.52                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e+01'}  45.70±0.75      45.44±0.87      46.68±7.94                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e+01'}  46.01±0.55      45.79±0.65      45.86±6.82                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e+01'}  50.85±0.66      50.60±0.61      51.81±8.66                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e+01'}  50.38±0.51      50.13±0.53      51.12±7.73                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e+01'}  50.31±0.44      49.97±0.47      48.49±7.37                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e+01'}  50.35±0.48      50.04±0.46      48.67±6.22                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e+01'}                  50.49±0.54      50.15±0.65      47.84±8.32                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e+01'}  50.27±0.57      49.91±0.62      53.18±7.43                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e+01'}  50.52±0.54      50.24±0.59      52.05±5.93                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e+01'}  50.78±0.73      50.40±0.82      51.87±8.28                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e+01'}  51.03±0.55      50.70±0.61      51.18±7.61                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e+01'}  60.83±0.65      60.42±0.62      61.69±9.37                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e+01'}  59.97±0.50      59.58±0.54      61.24±8.43                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e+01'}  59.69±0.45      59.20±0.52      57.92±8.88                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e+01'}  59.50±0.45      59.09±0.47      57.68±7.17                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e+01'}                  59.46±0.53      59.02±0.64      56.30±9.59                 6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e+01'}  59.08±0.52      58.62±0.62      61.58±8.55                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e+01'}  59.21±0.50      58.83±0.51      60.20±6.62                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e+01'}  59.33±0.68      58.83±0.72      60.54±8.64                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e+01'}  59.49±0.54      59.06±0.55      60.04±8.33                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+02'}  74.85±0.66      74.40±0.66      75.65±9.75                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+02'}  73.66±0.54      73.21±0.59      75.46±8.97                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+02'}  73.20±0.52      72.66±0.62      71.75±9.86                 6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+02'}  72.84±0.47      72.39±0.51      71.02±8.03                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+02'}                  72.65±0.59      72.18±0.67      69.22±10.57                6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+02'}  72.13±0.55      71.64±0.67      74.20±9.58                 6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+02'}  72.17±0.47      71.74±0.47      72.66±7.34                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+02'}  72.14±0.64      71.59±0.63      73.45±8.96                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+02'}  72.21±0.57      71.73±0.54      73.15±8.78                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e+02'}  91.05±0.72      90.60±0.73      91.81±9.92                 6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e+02'}  89.74±0.64      89.29±0.69      91.97±9.32                 6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e+02'}  89.24±0.66      88.71±0.75      88.20±10.31                6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e+02'}  88.82±0.57      88.38±0.61      87.10±8.61                 6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e+02'}                  88.59±0.73      88.13±0.78      85.07±11.14                6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e+02'}  87.99±0.68      87.51±0.79      89.73±10.27                6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e+02'}  87.98±0.52      87.55±0.53      88.11±7.85                 6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e+02'}  87.85±0.68      87.30±0.63      89.21±9.15                 6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e+02'}  87.85±0.65      87.37±0.62      89.08±9.00                 6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e+02'}  106.40±0.82     105.98±0.83     107.14±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e+02'}  105.20±0.78     104.78±0.82     107.75±9.53                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e+02'}  104.82±0.82     104.32±0.89     104.13±10.47               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e+02'}  104.46±0.71     104.05±0.74     102.85±8.92                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e+02'}                  104.27±0.90     103.85±0.93     100.77±11.39               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e+02'}  103.65±0.85     103.20±0.95     105.19±10.63               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e+02'}  103.65±0.64     103.25±0.65     103.56±8.14                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e+02'}  103.46±0.77     102.96±0.71     104.87±9.23                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e+02'}  103.43±0.75     102.98±0.73     104.86±9.07                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e+02'}  118.72±0.92     118.31±0.93     119.43±9.98                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e+02'}  117.75±0.90     117.34±0.95     120.50±9.65                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e+02'}  117.54±0.95     117.06±1.02     117.11±10.51               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e+02'}  117.31±0.84     116.92±0.86     115.78±9.07                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e+02'}                  117.21±1.05     116.80±1.07     113.73±11.49               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e+02'}  116.61±1.00     116.18±1.10     118.03±10.80               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e+02'}  116.65±0.75     116.27±0.77     116.43±8.29                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e+02'}  116.44±0.87     115.97±0.81     117.87±9.24                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e+02'}  116.40±0.85     115.97±0.84     117.93±9.07                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+03'}  127.40±1.00     127.01±1.01     128.11±9.98                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+03'}  126.67±1.00     126.28±1.04     129.55±9.72                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+03'}  126.63±1.05     126.17±1.11     126.37±10.51               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+03'}  126.52±0.93     126.15±0.95     125.06±9.15                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+03'}                  126.52±1.16     126.13±1.17     123.07±11.52               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+03'}  125.95±1.11     125.54±1.20     127.32±10.88               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+03'}  126.04±0.83     125.68±0.86     125.74±8.36                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+03'}  125.83±0.95     125.38±0.89     127.27±9.23                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+03'}  125.79±0.93     125.38±0.92     127.38±9.05                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e+03'}  133.02±1.06     132.63±1.06     133.71±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e+03'}  132.46±1.07     132.08±1.11     135.43±9.76                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e+03'}  132.56±1.11     132.11±1.17     132.40±10.50               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e+03'}  132.55±0.99     132.18±1.01     131.12±9.19                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e+03'}                  132.61±1.23     132.24±1.25     129.18±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e+03'}  132.08±1.19     131.68±1.27     133.41±10.91               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e+03'}  132.21±0.89     131.85±0.91     131.86±8.39                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e+03'}  132.00±1.00     131.56±0.94     133.44±9.22                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e+03'}  131.96±0.98     131.56±0.97     133.59±9.04                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e+03'}  136.44±1.09     136.06±1.10     137.14±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e+03'}  136.01±1.11     135.63±1.15     139.02±9.78                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e+03'}  136.20±1.15     135.75±1.20     136.10±10.49               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e+03'}  136.25±1.03     135.89±1.05     134.84±9.20                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e+03'}                  136.36±1.28     135.99±1.29     132.94±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e+03'}  135.85±1.23     135.46±1.31     137.16±10.93               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e+03'}  136.01±0.93     135.66±0.95     135.64±8.41                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e+03'}  135.80±1.03     135.37±0.98     137.25±9.21                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e+03'}  135.77±1.01     135.38±1.00     137.41±9.03                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e+03'}  138.46±1.12     138.08±1.12     139.16±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e+03'}  138.11±1.14     137.73±1.18     141.15±9.79                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e+03'}  138.35±1.17     137.90±1.23     138.29±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e+03'}  138.44±1.05     138.08±1.07     137.04±9.21                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e+03'}                  138.59±1.31     138.22±1.32     135.17±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e+03'}  138.09±1.26     137.70±1.34     139.39±10.94               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e+03'}  138.26±0.95     137.91±0.97     137.88±8.42                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e+03'}  138.06±1.05     137.63±1.00     139.51±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e+03'}  138.03±1.03     137.64±1.02     139.68±9.02                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+04'}  139.63±1.13     139.25±1.13     140.32±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+04'}  139.32±1.15     138.94±1.19     142.38±9.80                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+04'}  139.59±1.19     139.15±1.24     139.55±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+04'}  139.71±1.06     139.35±1.08     138.32±9.22                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+04'}                  139.88±1.32     139.51±1.33     136.47±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+04'}  139.39±1.27     139.00±1.36     140.68±10.94               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+04'}  139.57±0.96     139.22±0.99     139.18±8.43                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+04'}  139.37±1.06     138.94±1.01     140.82±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+04'}  139.34±1.04     138.95±1.03     141.00±9.01                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.78e+04'}  140.30±1.13     139.92±1.14     140.99±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.78e+04'}  140.01±1.16     139.64±1.20     143.08±9.80                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.78e+04'}  140.31±1.19     139.86±1.25     140.28±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.78e+04'}  140.44±1.07     140.08±1.09     139.05±9.22                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.78e+04'}                  140.61±1.33     140.25±1.34     137.21±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.78e+04'}  140.13±1.28     139.74±1.36     141.42±10.94               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.78e+04'}  140.32±0.97     139.97±0.99     139.92±8.43                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.78e+04'}  140.11±1.07     139.69±1.02     141.57±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.78e+04'}  140.09±1.05     139.70±1.04     141.75±9.01                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '3.16e+04'}  140.68±1.14     140.30±1.14     141.37±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '3.16e+04'}  140.41±1.17     140.03±1.20     143.48±9.80                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '3.16e+04'}  140.71±1.20     140.27±1.25     140.69±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '3.16e+04'}  140.85±1.07     140.49±1.09     139.47±9.22                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '3.16e+04'}                  141.03±1.33     140.67±1.34     137.63±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '3.16e+04'}  140.55±1.29     140.16±1.37     141.84±10.94               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '3.16e+04'}  140.74±0.97     140.40±1.00     140.34±8.43                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '3.16e+04'}  140.54±1.07     140.12±1.02     142.00±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '3.16e+04'}  140.52±1.05     140.13±1.04     142.18±9.01                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '5.62e+04'}  140.89±1.14     140.51±1.15     141.58±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '5.62e+04'}  140.63±1.17     140.26±1.21     143.70±9.80                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '5.62e+04'}  140.94±1.20     140.50±1.25     140.92±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '5.62e+04'}  141.09±1.07     140.73±1.10     139.70±9.23                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '5.62e+04'}                  141.27±1.34     140.90±1.35     137.86±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '5.62e+04'}  140.79±1.29     140.40±1.37     142.08±10.95               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '5.62e+04'}  140.98±0.97     140.64±1.00     140.58±8.43                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '5.62e+04'}  140.78±1.08     140.36±1.02     142.24±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '5.62e+04'}  140.76±1.06     140.37±1.05     142.42±9.01                6176.79\n",
      "{'p_quit': 0.10000000000000001, 'alpha': '1.00e+05'}  141.01±1.14     140.63±1.15     141.70±9.97                6388.5\n",
      "{'p_quit': 0.20000000000000001, 'alpha': '1.00e+05'}  140.75±1.17     140.38±1.21     143.83±9.80                6403.86\n",
      "{'p_quit': 0.30000000000000004, 'alpha': '1.00e+05'}  141.07±1.20     140.63±1.25     141.05±10.48               6559.1\n",
      "{'p_quit': 0.40000000000000002, 'alpha': '1.00e+05'}  141.22±1.08     140.86±1.10     139.83±9.23                6167.19\n",
      "{'p_quit': 0.5, 'alpha': '1.00e+05'}                  141.40±1.34     141.04±1.35     138.00±11.53               6199.83\n",
      "{'p_quit': 0.59999999999999998, 'alpha': '1.00e+05'}  140.92±1.29     140.54±1.37     142.21±10.95               6104.79\n",
      "{'p_quit': 0.70000000000000007, 'alpha': '1.00e+05'}  141.12±0.98     140.77±1.00     140.72±8.43                6225.05\n",
      "{'p_quit': 0.80000000000000004, 'alpha': '1.00e+05'}  140.92±1.08     140.49±1.02     142.37±9.20                6174.93\n",
      "{'p_quit': 0.90000000000000002, 'alpha': '1.00e+05'}  140.90±1.06     140.51±1.05     142.56±9.01                6176.79\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance: 100%|██████████| 11070/11070 [07:58<00:00, 23.15it/s]\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel\n",
    "from pygraph.kernels.marginalizedKernel import marginalizedkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = marginalizedkernel\n",
    "param_grid_precomputed = {'p_quit': np.linspace(0.1, 0.9, 9)}\n",
    "param_grid = {'alpha': np.logspace(-5, 5, num = 41, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The line_profiler extension is already loaded. To reload it, use:\n",
      "  %reload_ext line_profiler\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.1 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 258.76952958106995 seconds ---\n",
      "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
      "   0.00606061]\n",
      " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
      "   0.00454545]\n",
      " [ 0.00444444  0.00333333  0.0287062  ...,  0.00819912  0.00819912\n",
      "   0.00975875]\n",
      " ..., \n",
      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02846735  0.02836907\n",
      "   0.02896354]\n",
      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02836907  0.02831424\n",
      "   0.0288712 ]\n",
      " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
      "   0.02987915]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.186285\n",
      "With standard deviation: 7.038988\n",
      "\n",
      " Mean performance on test set: 18.024312\n",
      "With standard deviation: 6.292466\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.2 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 256.3271746635437 seconds ---\n",
      "[[ 0.06171557  0.03856471  0.01777778 ...,  0.02424242  0.02424242\n",
      "   0.02424242]\n",
      " [ 0.03856471  0.03579176  0.01333333 ...,  0.01818182  0.01818182\n",
      "   0.01818182]\n",
      " [ 0.01777778  0.01333333  0.06171557 ...,  0.02994207  0.02994207\n",
      "   0.03262072]\n",
      " ..., \n",
      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07442109  0.07434207\n",
      "   0.07383563]\n",
      " [ 0.02424242  0.01818182  0.02994207 ...,  0.07434207  0.07430377\n",
      "   0.07376068]\n",
      " [ 0.02424242  0.01818182  0.03262072 ...,  0.07383563  0.07376068\n",
      "   0.07366354]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 13.955359\n",
      "With standard deviation: 7.544068\n",
      "\n",
      " Mean performance on test set: 18.337589\n",
      "With standard deviation: 5.854545\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.3 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 255.61398577690125 seconds ---\n",
      "[[ 0.09803909  0.07202114  0.04       ...,  0.05454545  0.05454545\n",
      "   0.05454545]\n",
      " [ 0.07202114  0.06853421  0.03       ...,  0.04090909  0.04090909\n",
      "   0.04090909]\n",
      " [ 0.04        0.03        0.09803909 ...,  0.06368916  0.06368916\n",
      "   0.06678704]\n",
      " ..., \n",
      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12892852  0.12891455\n",
      "   0.12734365]\n",
      " [ 0.05454545  0.04090909  0.06368916 ...,  0.12891455  0.12892664\n",
      "   0.12733207]\n",
      " [ 0.05454545  0.04090909  0.06678704 ...,  0.12734365  0.12733207\n",
      "   0.1261675 ]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 13.939071\n",
      "With standard deviation: 7.958123\n",
      "\n",
      " Mean performance on test set: 18.495992\n",
      "With standard deviation: 5.734918\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.4 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 254.89703965187073 seconds ---\n",
      "[[ 0.13888889  0.11120616  0.07111111 ...,  0.0969697   0.0969697\n",
      "   0.0969697 ]\n",
      " [ 0.11120616  0.10756609  0.05333333 ...,  0.07272727  0.07272727\n",
      "   0.07272727]\n",
      " [ 0.07111111  0.05333333  0.13888889 ...,  0.10909713  0.10909713\n",
      "   0.11216176]\n",
      " ..., \n",
      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19178929  0.19182091\n",
      "   0.18963212]\n",
      " [ 0.0969697   0.07272727  0.10909713 ...,  0.19182091  0.19186661\n",
      "   0.18966477]\n",
      " [ 0.0969697   0.07272727  0.11216176 ...,  0.18963212  0.18966477\n",
      "   0.18786824]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 16.259313\n",
      "With standard deviation: 6.693580\n",
      "\n",
      " Mean performance on test set: 19.449149\n",
      "With standard deviation: 5.371295\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.5 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 256.75693798065186 seconds ---\n",
      "[[ 0.18518519  0.15591398  0.11111111 ...,  0.15151515  0.15151515\n",
      "   0.15151515]\n",
      " [ 0.15591398  0.15254237  0.08333333 ...,  0.11363636  0.11363636\n",
      "   0.11363636]\n",
      " [ 0.11111111  0.08333333  0.18518519 ...,  0.16617791  0.16617791\n",
      "   0.16890214]\n",
      " ..., \n",
      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26386999  0.26391515\n",
      "   0.26158184]\n",
      " [ 0.15151515  0.11363636  0.16617791 ...,  0.26391515  0.26396688\n",
      "   0.26162729]\n",
      " [ 0.15151515  0.11363636  0.16890214 ...,  0.26158184  0.26162729\n",
      "   0.25964592]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 17.018055\n",
      "With standard deviation: 6.844372\n",
      "\n",
      " Mean performance on test set: 19.785683\n",
      "With standard deviation: 5.550543\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.6 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 256.5566437244415 seconds ---\n",
      "[[ 0.23809524  0.20664506  0.16       ...,  0.21818182  0.21818182\n",
      "   0.21818182]\n",
      " [ 0.20664506  0.20385906  0.12       ...,  0.16363636  0.16363636\n",
      "   0.16363636]\n",
      " [ 0.16        0.12        0.23809524 ...,  0.2351024   0.2351024\n",
      "   0.23727718]\n",
      " ..., \n",
      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34658956  0.34662512\n",
      "   0.34454945]\n",
      " [ 0.21818182  0.16363636  0.2351024  ...,  0.34662512  0.34666325\n",
      "   0.34458505]\n",
      " [ 0.21818182  0.16363636  0.23727718 ...,  0.34454945  0.34458505\n",
      "   0.34279503]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 17.661762\n",
      "With standard deviation: 6.567179\n",
      "\n",
      " Mean performance on test set: 20.192158\n",
      "With standard deviation: 5.591223\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.7 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 254.9531705379486 seconds ---\n",
      "[[ 0.2991453   0.26444601  0.21777778 ...,  0.2969697   0.2969697\n",
      "   0.2969697 ]\n",
      " [ 0.26444601  0.26246188  0.16333333 ...,  0.22272727  0.22272727\n",
      "   0.22272727]\n",
      " [ 0.21777778  0.16333333  0.2991453  ...,  0.31614548  0.31614548\n",
      "   0.31765009]\n",
      " ..., \n",
      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44189997  0.44191814\n",
      "   0.44038348]\n",
      " [ 0.2969697   0.22272727  0.31614548 ...,  0.44191814  0.44193708\n",
      "   0.44040164]\n",
      " [ 0.2969697   0.22272727  0.31765009 ...,  0.44038348  0.44040164\n",
      "   0.43906772]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 20.588213\n",
      "With standard deviation: 5.746009\n",
      "\n",
      " Mean performance on test set: 21.661372\n",
      "With standard deviation: 6.026849\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.8 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 252.80415797233582 seconds ---\n",
      "[[ 0.37037037  0.33093141  0.28444444 ...,  0.38787879  0.38787879\n",
      "   0.38787879]\n",
      " [ 0.33093141  0.32983023  0.21333333 ...,  0.29090909  0.29090909\n",
      "   0.29090909]\n",
      " [ 0.28444444  0.21333333  0.37037037 ...,  0.4096795   0.4096795\n",
      "   0.41049599]\n",
      " ..., \n",
      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55242487  0.55243009\n",
      "   0.5515636 ]\n",
      " [ 0.38787879  0.29090909  0.4096795  ...,  0.55243009  0.55243545\n",
      "   0.55156881]\n",
      " [ 0.38787879  0.29090909  0.41049599 ...,  0.5515636   0.55156881\n",
      "   0.55081257]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 23.594332\n",
      "With standard deviation: 3.806374\n",
      "\n",
      " Mean performance on test set: 22.996018\n",
      "With standard deviation: 6.083466\n",
      "\n",
      "\n",
      " #--- calculating kernel matrix when p_quit = 0.9 ---#\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 256.7384788990021 seconds ---\n",
      "[[ 0.45454545  0.40839542  0.36       ...,  0.49090909  0.49090909\n",
      "   0.49090909]\n",
      " [ 0.40839542  0.40805534  0.27       ...,  0.36818182  0.36818182\n",
      "   0.36818182]\n",
      " [ 0.36        0.27        0.45454545 ...,  0.51619708  0.51619708\n",
      "   0.51644564]\n",
      " ..., \n",
      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172189  0.68172233\n",
      "   0.68145294]\n",
      " [ 0.49090909  0.36818182  0.51619708 ...,  0.68172233  0.68172277\n",
      "   0.68145338]\n",
      " [ 0.49090909  0.36818182  0.51644564 ...,  0.68145294  0.68145338\n",
      "   0.68121781]]\n",
      "\n",
      " Saving kernel matrix to file...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Mean performance on train set: 25.808155\n",
      "With standard deviation: 3.312074\n",
      "\n",
      " Mean performance on test set: 24.424089\n",
      "With standard deviation: 4.951191\n",
      "\n",
      "\n",
      "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
      "--------  -----------  ----------  ------------  -----------  --------\n",
      "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
      "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
      "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
      "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
      "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
      "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
      "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
      "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
      "     0.9      24.4241     4.95119       25.8082      3.31207   256.738\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.marginalizedKernel import marginalizedkernel, _marginalizedkernel_do\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', itr = 20)\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
    "    hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)\n",
    "\n",
    "# %lprun -f _marginalizedkernel_do \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
    "#     hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results\n",
    "\n",
    "# with y normalization\n",
    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "--------  -----------  ----------  ------------  -----------  --------\n",
    "     0.1      18.0192     6.27867       12.1642      6.99821   266.905\n",
    "     0.2      18.3374     5.84775       13.9376      7.51398   256.288\n",
    "     0.3      18.4955     5.73774       13.9291      7.9416    254.441\n",
    "     0.4      19.4498     5.37509       16.2538      6.68378   257.581\n",
    "     0.5      19.7851     5.55018       17.0142      6.83653   248.562\n",
    "     0.6      20.1911     5.58951       17.6595      6.56211   249.667\n",
    "     0.7      21.6606     6.02589       20.5872      5.74395   243.046\n",
    "     0.8      22.9959     6.08344       23.5941      3.80595   252.36\n",
    "     0.9      24.424      4.9512        25.8082      3.31202   248.077\n",
    "\n",
    "# without y normalization\n",
    "  p_quit    RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "--------  -----------  ----------  ------------  -----------  --------\n",
    "     0.1      18.0243     6.29247       12.1863      7.03899   258.77\n",
    "     0.2      18.3376     5.85454       13.9554      7.54407   256.327\n",
    "     0.3      18.496      5.73492       13.9391      7.95812   255.614\n",
    "     0.4      19.4491     5.3713        16.2593      6.69358   254.897\n",
    "     0.5      19.7857     5.55054       17.0181      6.84437   256.757\n",
    "     0.6      20.1922     5.59122       17.6618      6.56718   256.557\n",
    "     0.7      21.6614     6.02685       20.5882      5.74601   254.953\n",
    "     0.8      22.996      6.08347       23.5943      3.80637   252.804\n",
    "     0.9      24.4241     4.95119       25.8082      3.31207   256.738"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      "\n",
      " Loading dataset from file...\n",
      "\n",
      " Calculating kernel matrix, this could take a while...\n",
      "\n",
      " --- marginalized kernel matrix of size 185 built in 1133.0229969024658 seconds ---\n",
      "[[ 0.0287062   0.0124634   0.00444444 ...,  0.00606061  0.00606061\n",
      "   0.00606061]\n",
      " [ 0.0124634   0.01108958  0.00333333 ...,  0.00454545  0.00454545\n",
      "   0.00454545]\n",
      " [ 0.00444444  0.00333333  0.0287062  ...,  0.00819912  0.00819912\n",
      "   0.00975875]\n",
      " ..., \n",
      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02846735  0.02836907\n",
      "   0.02896354]\n",
      " [ 0.00606061  0.00454545  0.00819912 ...,  0.02836907  0.02831424\n",
      "   0.0288712 ]\n",
      " [ 0.00606061  0.00454545  0.00975875 ...,  0.02896354  0.0288712\n",
      "   0.02987915]]\n",
      "\n",
      " Saving kernel matrix to file...\n",
      "\n",
      " Mean performance on train set: 12.186285\n",
      "With standard deviation: 7.038988\n",
      "\n",
      " Mean performance on test set: 18.024312\n",
      "With standard deviation: 6.292466\n",
      "\n",
      "\n",
      "  rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-----------  ----------  ------------  -----------  --------\n",
      "    18.0243     6.29247       12.1863      7.03899   1133.02\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import numpy as np\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.marginalizedKernel import marginalizedkernel, _marginalizedkernel_do\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_weisfeilerlehman_subtree_acyclic/'\n",
    "\n",
    "kernel_para = dict(node_label = 'atom', edge_label = 'bond_type', itr = 20, p_quit = 0.1)\n",
    "\n",
    "# kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
    "#     hyper_name = 'p_quit', hyper_range = np.linspace(0.1, 0.9, 9), normalize = False)\n",
    "\n",
    "%lprun -f _marginalizedkernel_do \\\n",
    "    kernel_train_test(datafile, kernel_file_path, marginalizedkernel, kernel_para, \\\n",
    "    normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Timer unit: 1e-06 s\n",
    "\n",
    "Total time: 828.879 s\n",
    "File: ../pygraph/kernels/marginalizedKernel.py\n",
    "Function: _marginalizedkernel_do at line 67\n",
    "\n",
    "Line #      Hits         Time  Per Hit   % Time  Line Contents\n",
    "==============================================================\n",
    "    67                                           def _marginalizedkernel_do(G1, G2, node_label, edge_label, p_quit, itr):\n",
    "    68                                               \"\"\"Calculate marginalized graph kernel between 2 graphs.\n",
    "    69                                               \n",
    "    70                                               Parameters\n",
    "    71                                               ----------\n",
    "    72                                               G1, G2 : NetworkX graphs\n",
    "    73                                                   2 graphs between which the kernel is calculated.\n",
    "    74                                               node_label : string\n",
    "    75                                                   node attribute used as label.\n",
    "    76                                               edge_label : string\n",
    "    77                                                   edge attribute used as label.\n",
    "    78                                               p_quit : integer\n",
    "    79                                                   the termination probability in the random walks generating step.\n",
    "    80                                               itr : integer\n",
    "    81                                                   time of iterations to calculate R_inf.\n",
    "    82                                                   \n",
    "    83                                               Return\n",
    "    84                                               ------\n",
    "    85                                               kernel : float\n",
    "    86                                                   Marginalized Kernel between 2 graphs.\n",
    "    87                                               \"\"\"\n",
    "    88                                               # init parameters\n",
    "    89     17205      12886.0      0.7      0.0      kernel = 0\n",
    "    90     17205      52542.0      3.1      0.0      num_nodes_G1 = nx.number_of_nodes(G1)\n",
    "    91     17205      28240.0      1.6      0.0      num_nodes_G2 = nx.number_of_nodes(G2)\n",
    "    92     17205      15595.0      0.9      0.0      p_init_G1 = 1 / num_nodes_G1 # the initial probability distribution in the random walks generating step (uniform distribution over |G|)\n",
    "    93     17205      11587.0      0.7      0.0      p_init_G2 = 1 / num_nodes_G2\n",
    "    94                                           \n",
    "    95     17205      11663.0      0.7      0.0      q = p_quit * p_quit\n",
    "    96     17205      10728.0      0.6      0.0      r1 = q\n",
    "    97                                           \n",
    "    98                                               # initial R_inf\n",
    "    99     17205      38412.0      2.2      0.0      R_inf = np.zeros([num_nodes_G1, num_nodes_G2]) # matrix to save all the R_inf for all pairs of nodes\n",
    "   100                                           \n",
    "   101                                               # calculate R_inf with a simple interative method\n",
    "   102    344100     329235.0      1.0      0.0      for i in range(1, itr):\n",
    "   103    326895     900354.0      2.8      0.1          R_inf_new = np.zeros([num_nodes_G1, num_nodes_G2])\n",
    "   104    326895    2287346.0      7.0      0.3          R_inf_new.fill(r1)\n",
    "   105                                           \n",
    "   106                                                   # calculate R_inf for each pair of nodes\n",
    "   107   2653464    3667117.0      1.4      0.4          for node1 in G1.nodes(data = True):\n",
    "   108   2326569    7522840.0      3.2      0.9              neighbor_n1 = G1[node1[0]]\n",
    "   109   2326569    3492118.0      1.5      0.4              p_trans_n1 = (1 - p_quit) / len(neighbor_n1) # the transition probability distribution in the random walks generating step (uniform distribution over the vertices adjacent to the current vertex)\n",
    "   110  24024379   27775021.0      1.2      3.4              for node2 in G2.nodes(data = True):\n",
    "   111  21697810   69471941.0      3.2      8.4                  neighbor_n2 = G2[node2[0]]\n",
    "   112  21697810   32446626.0      1.5      3.9                  p_trans_n2 = (1 - p_quit) / len(neighbor_n2)    \n",
    "   113                                           \n",
    "   114  59095092   52545370.0      0.9      6.3                  for neighbor1 in neighbor_n1:\n",
    "   115 104193150   92513935.0      0.9     11.2                      for neighbor2 in neighbor_n2:\n",
    "   116                                           \n",
    "   117                                                                   t = p_trans_n1 * p_trans_n2 * \\\n",
    "   118  66795868  285324518.0      4.3     34.4                              deltakernel(G1.node[neighbor1][node_label] == G2.node[neighbor2][node_label]) * \\\n",
    "   119  66795868  137934393.0      2.1     16.6                              deltakernel(neighbor_n1[neighbor1][edge_label] == neighbor_n2[neighbor2][edge_label])\n",
    "   120  66795868  106834143.0      1.6     12.9                          R_inf_new[node1[0]][node2[0]] += t * R_inf[neighbor1][neighbor2] # ref [1] equation (8)\n",
    "   121                                           \n",
    "   122    326895    1123677.0      3.4      0.1          R_inf[:] = R_inf_new\n",
    "   123                                           \n",
    "   124                                               # add elements of R_inf up and calculate kernel\n",
    "   125    139656     330283.0      2.4      0.0      for node1 in G1.nodes(data = True):\n",
    "   126   1264441    1435263.0      1.1      0.2          for node2 in G2.nodes(data = True):                \n",
    "   127   1141990    1377134.0      1.2      0.2              s = p_init_G1 * p_init_G2 * deltakernel(node1[1][node_label] == node2[1][node_label])\n",
    "   128   1141990    1375456.0      1.2      0.2              kernel += s * R_inf[node1[0]][node2[0]] # ref [1] equation (6)\n",
    "   129                                           \n",
    "   130     17205      10801.0      0.6      0.0      return kernel"
   ]
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   "source": []
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