graphkit-learn/notebooks/test_modelselection.ipynb

{
 "cells": [
  {
   "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",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.35580015182495117 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'subtree', 'height': 0} is: \n",
      "[[  5.   6.   4. ...  20.  20.  20.]\n",
      " [  6.   8.   4. ...  20.  20.  20.]\n",
      " [  4.   4.   5. ...  21.  21.  21.]\n",
      " ...\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]\n",
      " [ 20.  20.  21. ... 101. 101. 101.]]\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.812713623046875 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'subtree', 'height': 1} is: \n",
      "[[ 10.  10.   4. ...  20.  20.  20.]\n",
      " [ 10.  16.   4. ...  20.  20.  20.]\n",
      " [  4.   4.  10. ...  22.  22.  24.]\n",
      " ...\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  22. ... 130. 130. 122.]\n",
      " [ 20.  20.  24. ... 122. 122. 154.]]\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 1.3875529766082764 seconds ---\n",
      "\n",
      "gram matrix with parameters {'base_kernel': 'subtree', 'height': 2} is: \n",
      "[[ 15.   4.   0. ...   0.   0.   0.]\n",
      " [  4.  24.   0. ...   0.   0.   0.]\n",
      " [  0.   0.  15. ...   1.   1.   5.]\n",
      " ...\n",
      " [  0.   0.   1. ...  87.  58.  23.]\n",
      " [  0.   0.   1. ...  58.  67.  23.]\n",
      " [  0.   0.   5. ...  23.  23. 101.]]\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "cross validation:   0%|          | 0/2 [00:00<?, ?it/s]"
     ]
    },
    {
     "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": [
      "cross validation: 100%|██████████| 2/2 [00:05<00:00,  2.59s/it]\n",
      "\n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{'base_kernel': 'subtree', 'height': 1}]\n",
      "best_params_in:  [{'alpha': 0.1}]\n",
      "best_val_perf:  7.73126255070956\n",
      "best_val_std:  0.8236027186435332\n",
      "final_performance:  8.100438203861017\n",
      "final_confidence:  1.6117563441893499\n",
      "train_performance:  5.811202158923287\n",
      "train_std:  0.09870913101519103\n",
      "time to calculate gram matrix:  0.812713623046875 s\n",
      "\n",
      "params                                                        train_perf       valid_perf        test_perf            gram_matrix_time\n",
      "------------------------------------------------------------  ---------------  ----------------  -----------------  ------------------\n",
      "{'alpha': '1.00e-05', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '1.00e-05', 'base_kernel': 'subtree', 'height': 1}  5.80±0.12        7.96±0.93         8.22±2.19                        0.81\n",
      "{'alpha': '1.00e-05', 'base_kernel': 'subtree', 'height': 2}  1127.64±340.41   4609.15±493.42    10761.03±9656.76                 1.39\n",
      "{'alpha': '3.16e-05', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '3.16e-05', 'base_kernel': 'subtree', 'height': 1}  5.80±0.12        7.96±0.93         8.22±2.19                        0.81\n",
      "{'alpha': '3.16e-05', 'base_kernel': 'subtree', 'height': 2}  1220.47±403.93   4954.89±698.03    11489.71±10063.97                1.39\n",
      "{'alpha': '1.00e-04', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '1.00e-04', 'base_kernel': 'subtree', 'height': 1}  5.80±0.12        7.96±0.93         8.22±2.19                        0.81\n",
      "{'alpha': '1.00e-04', 'base_kernel': 'subtree', 'height': 2}  1714.12±822.45   6781.81±2091.09   14869.83±11268.67                1.39\n",
      "{'alpha': '3.16e-04', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '3.16e-04', 'base_kernel': 'subtree', 'height': 1}  5.79±0.12        7.96±0.93         8.22±2.19                        0.81\n",
      "{'alpha': '3.16e-04', 'base_kernel': 'subtree', 'height': 2}  3250.76±1213.21  14755.58±8697.38  51359.38±62440.28                1.39\n",
      "{'alpha': '1.00e-03', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '1.00e-03', 'base_kernel': 'subtree', 'height': 1}  5.79±0.12        7.95±0.93         8.21±2.19                        0.81\n",
      "{'alpha': '1.00e-03', 'base_kernel': 'subtree', 'height': 2}  565.64±45.38     2445.20±662.45    6164.83±6985.06                  1.39\n",
      "{'alpha': '3.16e-03', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '3.16e-03', 'base_kernel': 'subtree', 'height': 1}  5.79±0.12        7.94±0.92         8.21±2.17                        0.81\n",
      "{'alpha': '3.16e-03', 'base_kernel': 'subtree', 'height': 2}  283.42±117.79    1422.79±3.88      1903.82±1864.60                  1.39\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'subtree', 'height': 1}  5.77±0.12        7.91±0.92         8.18±2.12                        0.81\n",
      "{'alpha': '1.00e-02', 'base_kernel': 'subtree', 'height': 2}  286.66±209.93    1159.91±78.57     988.69±808.97                    1.39\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'subtree', 'height': 1}  5.75±0.12        7.83±0.89         8.13±1.97                        0.81\n",
      "{'alpha': '3.16e-02', 'base_kernel': 'subtree', 'height': 2}  284.15±43.86     1945.20±1117.63   1904.89±2084.73                  1.39\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'subtree', 'height': 0}  16.74±0.29       16.90±0.36        17.82±1.89                       0.36\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'subtree', 'height': 1}  5.81±0.10        7.73±0.82         8.10±1.61                        0.81\n",
      "{'alpha': '1.00e-01', 'base_kernel': 'subtree', 'height': 2}  139.72±133.69    500.80±459.26     1621.79±2207.89                  1.39\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'subtree', 'height': 0}  16.75±0.28       16.90±0.36        17.83±1.88                       0.36\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'subtree', 'height': 1}  6.56±0.07        8.11±0.65         8.54±1.10                        0.81\n",
      "{'alpha': '3.16e-01', 'base_kernel': 'subtree', 'height': 2}  55.98±54.26      179.40±145.43     414.41±558.08                    1.39\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'subtree', 'height': 0}  16.76±0.28       16.91±0.36        17.86±1.87                       0.36\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'subtree', 'height': 1}  8.47±0.09        9.59±0.31         9.96±0.71                        0.81\n",
      "{'alpha': '1.00e+00', 'base_kernel': 'subtree', 'height': 2}  32.46±30.32      191.14±230.43     237.78±317.96                    1.39\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'subtree', 'height': 0}  16.85±0.28       16.96±0.36        17.99±1.83                       0.36\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'subtree', 'height': 1}  10.42±0.11       11.26±0.06        11.57±0.48                       0.81\n",
      "{'alpha': '3.16e+00', 'base_kernel': 'subtree', 'height': 2}  11.64±2.85       22.00±5.10        19.56±11.47                      1.39\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'subtree', 'height': 0}  17.30±0.25       17.34±0.35        18.46±1.75                       0.36\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'subtree', 'height': 1}  11.81±0.09       12.55±0.01        12.48±0.58                       0.81\n",
      "{'alpha': '1.00e+01', 'base_kernel': 'subtree', 'height': 2}  11.52±0.13       18.07±0.41        16.26±3.04                       1.39\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'subtree', 'height': 0}  19.04±0.18       18.99±0.32        20.07±1.62                       0.36\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'subtree', 'height': 1}  13.68±0.12       14.42±0.09        12.86±0.95                       0.81\n",
      "{'alpha': '3.16e+01', 'base_kernel': 'subtree', 'height': 2}  15.52±0.58       20.50±1.23        18.90±0.01                       1.39\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'subtree', 'height': 0}  23.30±0.13       23.15±0.32        23.86±1.32                       0.36\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'subtree', 'height': 1}  18.14±0.18       18.74±0.16        14.65±0.94                       0.81\n",
      "{'alpha': '1.00e+02', 'base_kernel': 'subtree', 'height': 2}  22.73±0.35       26.06±1.22        23.39±2.70                       1.39\n",
      "{'alpha': '3.16e+02', 'base_kernel': 'subtree', 'height': 0}  30.44±0.77       30.05±0.23        30.76±1.35                       0.36\n",
      "{'alpha': '3.16e+02', 'base_kernel': 'subtree', 'height': 1}  26.50±1.30       26.32±0.74        20.33±1.51                       0.81\n",
      "{'alpha': '3.16e+02', 'base_kernel': 'subtree', 'height': 2}  35.92±0.59       37.63±0.09        34.99±1.30                       1.39\n",
      "{'alpha': '1.00e+03', 'base_kernel': 'subtree', 'height': 0}  36.99±0.02       36.66±0.38        37.05±0.23                       0.36\n",
      "{'alpha': '1.00e+03', 'base_kernel': 'subtree', 'height': 1}  34.98±0.34       34.78±0.44        26.83±2.94                       0.81\n",
      "{'alpha': '1.00e+03', 'base_kernel': 'subtree', 'height': 2}  58.07±0.32       59.15±0.06        59.11±0.27                       1.39\n",
      "{'alpha': '3.16e+03', 'base_kernel': 'subtree', 'height': 0}  54.41±0.09       54.10±0.26        55.04±1.86                       0.36\n",
      "{'alpha': '3.16e+03', 'base_kernel': 'subtree', 'height': 1}  50.94±0.11       50.64±0.27        43.83±4.10                       0.81\n",
      "{'alpha': '3.16e+03', 'base_kernel': 'subtree', 'height': 2}  90.84±0.04       91.41±0.10        94.50±0.21                       1.39\n",
      "{'alpha': '1.00e+04', 'base_kernel': 'subtree', 'height': 0}  87.06±0.10       86.83±0.18        88.37±3.26                       0.36\n",
      "{'alpha': '1.00e+04', 'base_kernel': 'subtree', 'height': 1}  81.55±0.13       81.40±0.03        78.23±2.73                       0.81\n",
      "{'alpha': '1.00e+04', 'base_kernel': 'subtree', 'height': 2}  118.69±0.03      118.82±0.10       123.68±0.47                      1.39\n",
      "{'alpha': '3.16e+04', 'base_kernel': 'subtree', 'height': 0}  116.84±0.25      116.60±0.04       118.56±3.91                      0.36\n",
      "{'alpha': '3.16e+04', 'base_kernel': 'subtree', 'height': 1}  112.91±0.13      112.77±0.06       112.72±1.03                      0.81\n",
      "{'alpha': '3.16e+04', 'base_kernel': 'subtree', 'height': 2}  132.74±0.06      132.64±0.28       138.21±0.84                      1.39\n",
      "{'alpha': '1.00e+05', 'base_kernel': 'subtree', 'height': 0}  132.26±0.39      132.00±0.08       134.17±4.12                      0.36\n",
      "{'alpha': '1.00e+05', 'base_kernel': 'subtree', 'height': 1}  130.53±0.04      130.36±0.02       131.90±0.15                      0.81\n",
      "{'alpha': '1.00e+05', 'base_kernel': 'subtree', 'height': 2}  138.05±0.10      137.85±0.36       143.68±0.99                      1.39\n"
     ]
    }
   ],
   "source": [
    "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.weisfeilerLehmanKernel import weisfeilerlehmankernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = weisfeilerlehmankernel\n",
    "param_grid_precomputed = {'height': [0,1,2], 'base_kernel': ['subtree']}\n",
    "param_grid = {\"alpha\": np.logspace(-5, 5, num = 21, base = 10)}\n",
    "\n",
    "model_selection_for_precomputed_kernel(datafile, estimator, param_grid_precomputed, param_grid, \n",
    "                                       'regression', NUM_TRIALS=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.36398959159851074 seconds ---\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 0.779775857925415 seconds ---\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 1.3914790153503418 seconds ---\n",
      "\n",
      " --- Weisfeiler-Lehman subtree kernel matrix of size 183 built in 1.8359944820404053 seconds ---\n",
      "\n",
      "trial:  0\n"
     ]
    },
    {
     "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",
      "trial:  1\n",
      "\n",
      "trial:  2\n",
      "train_pref:  [[[1.64395812e+01 1.64395817e+01 1.64395831e+01 1.64395878e+01\n",
      "   1.64396026e+01 1.64396493e+01 1.64397975e+01 1.64402709e+01\n",
      "   1.64418144e+01 1.64471515e+01 1.64683137e+01 1.65705015e+01\n",
      "   1.70895040e+01 1.90223508e+01 2.33598999e+01 2.99853599e+01\n",
      "   3.67295194e+01 5.42288176e+01 8.69252459e+01 1.16959004e+02\n",
      "   1.32613718e+02]\n",
      "  [5.77765862e+00 5.77759863e+00 5.77740915e+00 5.77681231e+00\n",
      "   5.77494842e+00 5.76928684e+00 5.75363997e+00 5.72465380e+00\n",
      "   5.78313368e+00 6.51218941e+00 8.39096505e+00 1.03098836e+01\n",
      "   1.16946602e+01 1.35325781e+01 1.79390214e+01 3.84839547e+01\n",
      "   3.43518311e+01 5.04124908e+01 8.10456130e+01 1.12514300e+02\n",
      "   1.30245200e+02]\n",
      "  [5.12408199e+02 5.27989892e+02 5.90994031e+02 1.21199906e+03\n",
      "   4.39901897e+02 2.48607914e+02 1.86727618e+02 2.32184246e+03\n",
      "   1.30354659e+02 2.96474073e+01 1.66211830e+01 1.01189335e+01\n",
      "   1.14327553e+01 1.55065515e+01 2.27833348e+01 3.57862455e+01\n",
      "   5.81765248e+01 9.14268180e+01 1.20053019e+02 1.34624520e+02\n",
      "   1.40151065e+02]\n",
      "  [8.83038185e+02 8.81789356e+02 8.77929201e+02 8.66557020e+02\n",
      "   8.37478355e+02 7.87523948e+02 7.96920902e+02 1.45339650e+03\n",
      "   7.91021504e+02 1.19570790e+02 6.59838250e+01 4.23578888e+01\n",
      "   3.99828703e+01 4.42751978e+01 6.83688467e+01 9.59145989e+01\n",
      "   1.21009120e+02 1.33546139e+02 1.38303952e+02 1.39906844e+02\n",
      "   1.40424313e+02]]\n",
      "\n",
      " [[1.71239968e+01 1.71239973e+01 1.71239987e+01 1.71240034e+01\n",
      "   1.71240182e+01 1.71240648e+01 1.71242128e+01 1.71246849e+01\n",
      "   1.71262194e+01 1.71314791e+01 1.71519371e+01 1.72481740e+01\n",
      "   1.77297069e+01 1.95500192e+01 2.38130857e+01 2.93381416e+01\n",
      "   3.69391496e+01 5.39977286e+01 8.62574846e+01 1.15858071e+02\n",
      "   1.31234224e+02]\n",
      "  [5.31737049e+00 5.31733420e+00 5.31721967e+00 5.31685964e+00\n",
      "   5.31574244e+00 5.31242054e+00 5.30395762e+00 5.29557922e+00\n",
      "   5.40013457e+00 6.16202166e+00 7.96877967e+00 9.83008650e+00\n",
      "   1.12551423e+01 1.31867242e+01 1.76786191e+01 3.73936703e+02\n",
      "   3.43372622e+01 5.04292890e+01 8.13535351e+01 1.13286452e+02\n",
      "   1.31351520e+02]\n",
      "  [6.58327599e+02 5.47082824e+02 5.83004689e+02 1.23332386e+03\n",
      "   6.74467019e+02 5.90938305e+02 3.61573189e+02 2.27964292e+02\n",
      "   9.10415399e+01 4.57028839e+01 1.81541707e+01 1.09581096e+01\n",
      "   1.10469223e+01 1.48460696e+01 2.22693331e+01 3.56014718e+01\n",
      "   5.80796053e+01 9.13010528e+01 1.19594147e+02 1.33925437e+02\n",
      "   1.39351226e+02]\n",
      "  [9.49547590e+03 1.01230715e+04 1.40491951e+04 1.17200196e+04\n",
      "   5.62457449e+03 4.49145884e+03 3.98063058e+03 6.88626312e+03\n",
      "   3.40201079e+04 3.04308252e+03 1.86845375e+02 5.73954968e+01\n",
      "   4.28632616e+01 4.54852901e+01 1.62517000e+02 9.58512977e+01\n",
      "   1.21409011e+02 1.34534898e+02 1.39599417e+02 1.41317164e+02\n",
      "   1.41873000e+02]]\n",
      "\n",
      " [[1.73737868e+01 1.73737871e+01 1.73737883e+01 1.73737921e+01\n",
      "   1.73738040e+01 1.73738418e+01 1.73739615e+01 1.73743442e+01\n",
      "   1.73755942e+01 1.73799380e+01 1.73973532e+01 1.74828906e+01\n",
      "   1.79268500e+01 1.96454061e+01 2.37679665e+01 2.97118446e+01\n",
      "   3.68844341e+01 5.41657107e+01 8.70891610e+01 1.17261955e+02\n",
      "   1.32916666e+02]\n",
      "  [6.02834530e+00 6.02828449e+00 6.02809251e+00 6.02748769e+00\n",
      "   6.02559783e+00 6.01984709e+00 6.00384925e+00 5.97311903e+00\n",
      "   6.02158179e+00 6.71208516e+00 8.54470850e+00 1.04361310e+01\n",
      "   1.18107153e+01 1.36916581e+01 1.80847408e+01 2.50702394e+01\n",
      "   3.38056400e+01 4.97106657e+01 8.06726656e+01 1.12826923e+02\n",
      "   1.31055998e+02]\n",
      "  [1.50083368e+02 1.49118741e+02 1.46185237e+02 1.37942788e+02\n",
      "   1.19190281e+02 9.21797744e+01 1.94073465e+02 8.05022034e+02\n",
      "   2.19770162e+01 1.10870120e+01 8.45748379e+00 8.22717134e+00\n",
      "   1.05980139e+01 1.51812716e+01 2.25095621e+01 3.53416138e+01\n",
      "   5.78284948e+01 9.13799499e+01 1.20023525e+02 1.34522150e+02\n",
      "   1.40007906e+02]\n",
      "  [3.66827666e+04 3.64958256e+04 3.62582530e+04 3.70990121e+04\n",
      "   2.79786816e+04 3.17581179e+04 1.34248933e+04 2.74329658e+03\n",
      "   1.51407484e+03 5.03262381e+02 2.27465513e+03 5.79719793e+01\n",
      "   4.01346008e+01 4.29577406e+01 6.53835532e+01 9.65725952e+01\n",
      "   1.22020504e+02 1.34771896e+02 1.39631358e+02 1.41271673e+02\n",
      "   1.41801590e+02]]]\n",
      "val_pref:  [[[1.66563570e+01 1.66563576e+01 1.66563593e+01 1.66563648e+01\n",
      "   1.66563822e+01 1.66564373e+01 1.66566121e+01 1.66571693e+01\n",
      "   1.66589765e+01 1.66651347e+01 1.66887574e+01 1.67972965e+01\n",
      "   1.73239060e+01 1.92260151e+01 2.33985563e+01 2.99680328e+01\n",
      "   3.61694283e+01 5.36072562e+01 8.64502439e+01 1.16526055e+02\n",
      "   1.32180335e+02]\n",
      "  [7.20462312e+00 7.20453201e+00 7.20424424e+00 7.20333731e+00\n",
      "   7.20049935e+00 7.19182262e+00 7.16727575e+00 7.11593903e+00\n",
      "   7.14373101e+00 7.87332333e+00 9.74706759e+00 1.16082347e+01\n",
      "   1.28707079e+01 1.45598867e+01 1.87328728e+01 3.86282094e+01\n",
      "   3.43001781e+01 5.01804641e+01 8.08571425e+01 1.12321451e+02\n",
      "   1.30023500e+02]\n",
      "  [5.54501850e+03 5.77517332e+03 6.68817182e+03 1.53219778e+04\n",
      "   4.84721745e+03 1.78170405e+03 1.43195034e+03 5.88411585e+03\n",
      "   9.86756803e+02 1.67505861e+02 6.07029693e+01 2.60232355e+01\n",
      "   1.82396027e+01 1.98730075e+01 2.58435706e+01 3.73801826e+01\n",
      "   5.93566639e+01 9.22139935e+01 1.20322941e+02 1.34586694e+02\n",
      "   1.39990660e+02]\n",
      "  [3.28948288e+04 3.28414181e+04 3.26765162e+04 3.21925096e+04\n",
      "   3.09704564e+04 2.89852445e+04 3.05280244e+04 5.04416608e+04\n",
      "   1.19260432e+04 2.50647232e+03 8.03343253e+02 2.65317419e+02\n",
      "   1.12251465e+02 7.92660971e+01 8.76822683e+01 1.08731659e+02\n",
      "   1.26412570e+02 1.35379717e+02 1.38804640e+02 1.39960946e+02\n",
      "   1.40334495e+02]]\n",
      "\n",
      " [[1.74180651e+01 1.74180661e+01 1.74180686e+01 1.74180768e+01\n",
      "   1.74181027e+01 1.74181844e+01 1.74184433e+01 1.74192666e+01\n",
      "   1.74219140e+01 1.74307152e+01 1.74625368e+01 1.75948037e+01\n",
      "   1.81664394e+01 2.00095699e+01 2.38152166e+01 2.88557921e+01\n",
      "   3.56482903e+01 5.28816517e+01 8.56055457e+01 1.15365043e+02\n",
      "   1.30776221e+02]\n",
      "  [7.24940190e+00 7.24930565e+00 7.24900178e+00 7.24804311e+00\n",
      "   7.24503575e+00 7.23576531e+00 7.20877413e+00 7.14453088e+00\n",
      "   7.09731685e+00 7.56380784e+00 9.09509858e+00 1.08012626e+01\n",
      "   1.21209817e+01 1.39402757e+01 1.81564673e+01 4.02500220e+02\n",
      "   3.39283566e+01 4.98525861e+01 8.08576874e+01 1.12799445e+02\n",
      "   1.30839370e+02]\n",
      "  [3.00556216e+03 2.54524766e+03 2.74578856e+03 6.94078143e+03\n",
      "   1.91925719e+03 8.56680617e+02 3.64656403e+02 7.01149782e+02\n",
      "   2.08267769e+02 1.16068921e+02 7.11007099e+01 2.35394235e+01\n",
      "   1.73831851e+01 1.95223254e+01 2.51442527e+01 3.69011891e+01\n",
      "   5.88099898e+01 9.16925454e+01 1.19683873e+02 1.33850739e+02\n",
      "   1.39212448e+02]\n",
      "  [3.80498024e+05 4.20226806e+05 6.67616175e+05 5.23409686e+05\n",
      "   1.50190381e+05 1.00203373e+05 9.73802633e+04 9.47715799e+04\n",
      "   3.37537163e+05 3.04793818e+04 1.35909249e+03 3.09845594e+02\n",
      "   1.21971730e+02 8.04084934e+01 1.52183702e+02 1.08032029e+02\n",
      "   1.26713342e+02 1.36377146e+02 1.40115999e+02 1.41384994e+02\n",
      "   1.41795699e+02]]\n",
      "\n",
      " [[1.74720175e+01 1.74720182e+01 1.74720199e+01 1.74720253e+01\n",
      "   1.74720426e+01 1.74720971e+01 1.74722701e+01 1.74728209e+01\n",
      "   1.74746027e+01 1.74806278e+01 1.75033521e+01 1.76054720e+01\n",
      "   1.80963131e+01 1.98739921e+01 2.38416705e+01 2.94745596e+01\n",
      "   3.61201562e+01 5.34652882e+01 8.67069158e+01 1.17020936e+02\n",
      "   1.32718374e+02]\n",
      "  [7.90046831e+00 7.90037596e+00 7.90008416e+00 7.89916368e+00\n",
      "   7.89627610e+00 7.88737467e+00 7.86145277e+00 7.79961723e+00\n",
      "   7.75150867e+00 8.18750597e+00 9.68730638e+00 1.13717302e+01\n",
      "   1.26311868e+01 1.45335717e+01 1.88382522e+01 2.54282316e+01\n",
      "   3.37942994e+01 4.96476734e+01 8.06297257e+01 1.12781316e+02\n",
      "   1.30996885e+02]\n",
      "  [1.06786703e+03 1.06491189e+03 1.05599183e+03 1.03155280e+03\n",
      "   9.81206384e+02 9.48799728e+02 7.78043475e+03 1.30516231e+04\n",
      "   2.93400834e+02 1.03073499e+02 4.32760770e+01 2.26383793e+01\n",
      "   1.74924966e+01 1.95735062e+01 2.54895748e+01 3.70907895e+01\n",
      "   5.89631309e+01 9.21232956e+01 1.20332626e+02 1.34569447e+02\n",
      "   1.39949840e+02]\n",
      "  [5.11933179e+06 5.06625505e+06 4.90519176e+06 4.45479720e+06\n",
      "   3.41856155e+06 1.90951963e+06 6.52663063e+05 1.33026791e+05\n",
      "   2.58074166e+04 8.80888038e+03 2.50953641e+04 3.21965338e+02\n",
      "   1.13659736e+02 7.66890430e+01 8.48964695e+01 1.08040190e+02\n",
      "   1.26581399e+02 1.35927473e+02 1.39495126e+02 1.40699821e+02\n",
      "   1.41089041e+02]]]\n",
      "test_pref:  [[[1.98909292e+01 1.98909260e+01 1.98909153e+01 1.98908815e+01\n",
      "   1.98907750e+01 1.98904383e+01 1.98893743e+01 1.98860181e+01\n",
      "   1.98754882e+01 1.98430137e+01 1.97482390e+01 1.95186552e+01\n",
      "   1.92838909e+01 2.04519736e+01 2.54795925e+01 3.34885599e+01\n",
      "   4.12626656e+01 5.75573157e+01 8.75597936e+01 1.15485889e+02\n",
      "   1.30127795e+02]\n",
      "  [7.33557631e+00 7.33566981e+00 7.33596572e+00 7.33690245e+00\n",
      "   7.33987460e+00 7.34937124e+00 7.38032765e+00 7.48594096e+00\n",
      "   7.86140565e+00 8.99826745e+00 1.09814464e+01 1.27474074e+01\n",
      "   1.37965165e+01 1.47045206e+01 1.73757897e+01 3.68397143e+01\n",
      "   3.20435147e+01 4.90878096e+01 8.18806930e+01 1.15185401e+02\n",
      "   1.33860837e+02]\n",
      "  [5.83282303e+03 6.04096499e+03 6.86717017e+03 1.46993022e+04\n",
      "   3.57700695e+03 1.40735117e+03 5.42895023e+03 3.69559057e+04\n",
      "   1.38410627e+03 1.04070644e+02 4.49080885e+01 2.57086018e+01\n",
      "   2.10354099e+01 1.98336526e+01 2.00647243e+01 2.88091902e+01\n",
      "   5.02414501e+01 8.13809926e+01 1.06760928e+02 1.19410711e+02\n",
      "   1.24175773e+02]\n",
      "  [5.42745284e+03 5.42067614e+03 5.39966540e+03 5.33715005e+03\n",
      "   5.17180350e+03 4.84595751e+03 4.62404049e+03 4.64905682e+03\n",
      "   1.86149018e+03 6.72622779e+02 2.38674443e+02 1.06471283e+02\n",
      "   6.47474790e+01 6.40209385e+01 8.38806206e+01 1.11193390e+02\n",
      "   1.31180566e+02 1.40961352e+02 1.44647834e+02 1.45886906e+02\n",
      "   1.46286618e+02]]\n",
      "\n",
      " [[1.41910971e+01 1.41910935e+01 1.41910820e+01 1.41910455e+01\n",
      "   1.41909302e+01 1.41905657e+01 1.41894139e+01 1.41857808e+01\n",
      "   1.41743849e+01 1.41392649e+01 1.40370388e+01 1.37924876e+01\n",
      "   1.35787461e+01 1.51091319e+01 2.13268361e+01 2.91482353e+01\n",
      "   4.28427623e+01 6.43532719e+01 9.81604040e+01 1.27734362e+02\n",
      "   1.42929303e+02]\n",
      "  [1.04367375e+01 1.04368617e+01 1.04372542e+01 1.04384962e+01\n",
      "   1.04424305e+01 1.04549365e+01 1.04950846e+01 1.06267442e+01\n",
      "   1.10607505e+01 1.22655236e+01 1.42390167e+01 1.59403160e+01\n",
      "   1.68849386e+01 1.74628039e+01 1.98402194e+01 3.78774334e+02\n",
      "   3.16357845e+01 4.41650511e+01 7.31225404e+01 1.04554280e+02\n",
      "   1.22505115e+02]\n",
      "  [4.42038020e+03 3.82200217e+03 3.78552427e+03 5.85207029e+03\n",
      "   3.45842209e+03 2.15035036e+03 1.09261821e+03 7.12493084e+02\n",
      "   1.35719291e+02 4.72621411e+01 4.52752513e+01 1.86644957e+01\n",
      "   1.97416275e+01 2.31128959e+01 2.88569517e+01 3.92676007e+01\n",
      "   5.81629373e+01 8.77217243e+01 1.13717840e+02 1.27010834e+02\n",
      "   1.32057094e+02]\n",
      "  [5.89199398e+09 1.95781583e+09 8.09611176e+08 2.18108858e+08\n",
      "   3.69133145e+07 8.91313873e+06 2.81911259e+06 1.88361729e+06\n",
      "   1.94204384e+05 1.35716796e+04 1.01390059e+03 2.21479686e+02\n",
      "   7.09922558e+01 6.61898839e+01 1.01654130e+02 1.09179268e+02\n",
      "   1.23638178e+02 1.30155346e+02 1.32533356e+02 1.33323894e+02\n",
      "   1.33578009e+02]]\n",
      "\n",
      " [[1.17791920e+01 1.17791898e+01 1.17791830e+01 1.17791614e+01\n",
      "   1.17790934e+01 1.17788781e+01 1.17781985e+01 1.17760583e+01\n",
      "   1.17693813e+01 1.17491622e+01 1.16938120e+01 1.15940756e+01\n",
      "   1.17816347e+01 1.39168442e+01 1.96644716e+01 2.67492097e+01\n",
      "   3.43837442e+01 5.12289757e+01 8.31181756e+01 1.12302385e+02\n",
      "   1.27436547e+02]\n",
      "  [7.71537542e+00 7.71535264e+00 7.71528072e+00 7.71505524e+00\n",
      "   7.71436206e+00 7.71236557e+00 7.70793665e+00 7.71069331e+00\n",
      "   7.83513473e+00 8.58149501e+00 1.02957564e+01 1.19846526e+01\n",
      "   1.30140218e+01 1.42518056e+01 1.78006583e+01 2.37519188e+01\n",
      "   3.13381322e+01 4.65747889e+01 7.64367130e+01 1.07220263e+02\n",
      "   1.24621390e+02]\n",
      "  [2.86456979e+02 2.86468848e+02 2.86519322e+02 2.86792686e+02\n",
      "   2.88435856e+02 2.96826949e+02 5.48198626e+02 5.16469079e+03\n",
      "   9.96789282e+01 3.48532952e+01 2.22901159e+01 1.94323473e+01\n",
      "   2.01735014e+01 2.25218536e+01 2.66221693e+01 3.38344478e+01\n",
      "   5.20468926e+01 8.14709252e+01 1.07401165e+02 1.20641793e+02\n",
      "   1.25663953e+02]\n",
      "  [2.70616839e+06 2.67294009e+06 2.57802640e+06 2.34577037e+06\n",
      "   1.74580056e+06 1.11275317e+06 4.87121208e+05 1.06354715e+05\n",
      "   2.95130780e+04 1.03229488e+04 1.47608541e+05 1.35634290e+03\n",
      "   2.10707547e+02 7.20165997e+01 7.61419105e+01 1.00915322e+02\n",
      "   1.20092103e+02 1.29270102e+02 1.32699279e+02 1.33848599e+02\n",
      "   1.34219025e+02]]]\n",
      "best_val_perf:  7.3308521751859255\n",
      "best_params_index:  (array([1]), array([8]))\n",
      "best_params_out:  [{'height': 1, 'base_kernel': 'subtree'}]\n",
      "best_params_in:  [{'alpha': 0.1}]\n",
      "best_val_std:  0.3650376434508593\n",
      "final_performance:  8.919096949439876\n",
      "final_confidence:  1.8547728660540608\n",
      "train_performance:  5.734950013600905\n",
      "train_std:  0.31351301797621395\n",
      "run_time:  [0.36398959159851074, 0.779775857925415, 1.3914790153503418, 1.8359944820404053]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ACEkGCzN73MyumtnLpdvOmtmPzOxXxf9nitvNzL5pZpfN7CUz+9AyGw/g+ER6Fk9IunvutkckPe/ud0p6vvhdku6RdGfx75KkR4+mmQBWLRks3P3Hkv40d/N9kp4sfn5S0mdKt3/HJ34i6bSZ3XZUjQWwOm3HLM67++vFz3+QdL74+XZJvy897rXiNgDXuM4DnO7ukhpny5jZJTN7wcxeGIze6doMAEvWNli8MT29KP6/Wtx+RdL7So97b3HbAnd/zN0vuvvFzfxEy2YAOC5t072flfSgpK8V/3+/dPvDZvaUpI9KerN0ulLPXfb2u1JepLTmmbL+aCbF2/NsfznD+VTuqtRuzw5SiSuXN0wsQ1iX1l2XDj3/HG3TsaPbtU0nT23XdEnBBQ1SuJscg+dqvbzg/j5S21ugg1yRcl6Vit1EZPu8eN5yavm7e8G5AdN9VKSlW4OM+2SwMLPvSbpL0i1m9pqkL2sSJJ42sy9I+q2k+4uHPyfpXkmXJb0j6fPxpgBYZ8lg4e6fq7nrkxWPdUkPdW0UgPVDBieAEIIFgBCCBYAQggWAEIIFgBCCBYAQggWAEIIFgJC1qe4tSconscs3F5s1X+07pZx+XVepu426quBH+RwRy64efhyaVBJvVDW8rUD18FSF8GWZpnlHKoHXqaoGbg3mgK5HsMgy+c1/IxtMXggb7Gl4/oQ8P/gC5v2xsr3Jgb1zcvZTVvWhS5Xs758+/Mtd90GOzp3on2n34Y4GgWUtBTA813HHTcr1N5zrceLWvzZszKw8P/yLPhy2+zrccesfW2031bN0AKqaB/Lhs79r9Dz98eLxvZTHJwNxGgIghGABIIRgASCEYAEghGABIIRgASCEYAEghGABIIRgASCEYAEghGABIIRgASCEYAEghGABIIRgASBkPepZSBpv95QPDmopeG4ab5brWah039y2FQVdUmuZpgqvVEz9lxSvZ9F6LdJokZe9JRXbaVKPokKT4jDJtUfnpOpRpGz1Fou/lLWtZxGpR3GYmzf6ycdU1bPYqChmc5iqehZN0LMAEEKwABBCsAAQQrAAEEKwABBCsAAQQrAAELIeeRbuyt/alfeK2JVlyvvjmdyKcc/21xGZXxOkKqdiXFpzpCqnIpUvUXd/Xf7E/HO0XtcjmD/ROo8jtV3D3IeF/TfZ3hrkdOTeOg9iKrX9dCGfQ5tRketRlQPRRGT7jXySU1HO6Xh7b6vR82xVfCizBu8BPQsAIQQLACEECwAhBAsAIQQLACEECwAhBAsAIQQLACHrkZQ1lU1i13h7sVnzxXBSyklSVcVx2qormtOxrkjzdiy7uM5xaFBop0lRnbYixXVSBXSWZZqMFSmUU6eqWI6JpCwAR4xgASCEYAEghGABIGQtBjg9M412tpXtTmbFZbt7Gr5nW54dDGhmA5cVk+b6O7aw/bx8ePjAzWAn0aaagcxode/hmXYDch4c9LPBcqp7b597t9P2kZmbU8NhosT6nDtu/WPT5sxIVeFuO3v0Y7f8ptV2U1uWnqL89mhxhuknd15p9Dy7vnh8T2XxAVN6FgBCCBYAQggWAEIIFgBCksHCzB43s6tm9nLptq+Y2RUze7H4d2/pvi+Z2WUz+6WZfXpZDQdwvCI9iyck3V1x+zfc/ULx7zlJMrMPSHpA0geLbb5lZs2GvAGspWSwcPcfS/pTcH/3SXrK3fvu/htJlyV9pEP7AKyJLmMWD5vZS8Vpypnittsl/b70mNeK2wBc49oGi0cl3SHpgqTXJX296Q7M7JKZvWBmLwz33mnZDADHpVWwcPc33H3k7mNJ39bBqcYVSe8rPfS9xW1V+3jM3S+6+8WN3ok2zQBwjFoFCzO7rfTrZyVNr5Q8K+kBM9sys/dLulPST7s1EcA6SM4NMbPvSbpL0i1m9pqkL0u6y8wuSHJJr0r6oiS5+ytm9rSkX0jak/SQu6+mWgiAI5UMFu7+uYqb//2Qx39V0le7NArA+iGDE0AIwQJAyFrUs5CZxhv5fj0LqahxsTlbz6J8X9l4c3GXeaLuRF29iv191hS2zYbBVc4bFKOd0QvWgxgsJzG2ST2KKlsb8eXjm9azSNWjSEkVu21bzyJSj+IwJ/Pd5GOq6llsW7C4SqGqnkUT9CwAhBAsAIQQLACEECwAhBAsAIQQLACEECwAhBAsAISsRVKWjV29v/TlvUns8l6mbOCziVi9g2Ss+QWEqhKwxqV8n6pFiFKLBdUlX9WtXj6f5NV6EaBoslXLnKxUsljTRKku2+cNEteybNw6aWoqtf1Gnp7zWJUYVpUw1URk++kK6OUEsD+Pm5V2qEriYhV1AEduLXoWU9OexXhj8a/TfPp3cl+l3kRVOnhbdWnidenhy7L0dPJj0CS1vEkaeVuRdPJUyviyTHsUkdTwOlU9i8zoWQA4YgQLACFrcRrimWnv5Jay4WQQJxuONLx5a2YwMRu6bDTpMg1Ozsa4qlOD1ADm4FSi+5VV32/BWad2dhB63MLT1jzvvNFwOXH+9nNvdtp+M4sXRms6YPnhs79r2pwZG4m2vb3XbqCy6Wrm8yKzR6sGM//5RLNTkqEvfrZuajAOT88CQAjBAkAIwQJACMECQAjBAkAIwQJACMECQAjBAkAIwQJACMECQAjBAkDIWswNkSbzQabTyj3PZCOXlVL5Pbf9uSLzM4mrZhaX55VUzR2x1BSGUU3xm7raLnNzOrxm+5RRcDtrOUU9NfdkNO729+PdBtvnDaaob2Yj9cfdPq6p7bey9DT4qvklXVf6imw/nT9SnkdSNdfjMBu2+OE1xT+n9CwAhBAsAIQQLACEECwAhBAsAISYNxxRXUojzP5H0l8l/XHVbTlmt4hjvhGs8zH/vbvfGnngWgQLSTKzF9z94qrbcZw45hvD9XLMnIYACCFYAAhZp2Dx2KobsAIc843hujjmtRmzALDe1qlnAWCNESwAhBAsAIQQLACEECwAhPw/LvSDOWuiMpAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc5984c8b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc59827eb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {