{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Acyclic\n",
      "\n",
      "--- This is a regression problem ---\n",
      "\n",
      "\n",
      "I. Loading dataset from file...\n",
      "\n",
      "2. Calculating gram matrices. This could take a while...\n",
      "\n",
      " None edge weight specified. Set all weight to 1.\n",
      "\n",
      "getting sp graphs: 100%|██████████| 183/183 [00:00<00:00, 2750.49it/s]\n",
      "calculating kernels: 100%|█████████▉| 16808/16836.0 [00:11<00:00, 607.39it/s] \n",
      " --- shortest path kernel matrix of size 183 built in 11.701499700546265 seconds ---\n",
      "calculating kernels: 100%|██████████| 16836/16836.0 [00:11<00:00, 1447.26it/s]\n",
      "\n",
      "the gram matrix with parameters {'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}} is: \n",
      "[[1.         0.47140452 0.33333333 ... 0.30151134 0.30512858 0.27852425]\n",
      " [0.47140452 1.         0.         ... 0.14213381 0.11986583 0.17232809]\n",
      " [0.33333333 0.         1.         ... 0.36851387 0.37293493 0.34815531]\n",
      " ...\n",
      " [0.30151134 0.14213381 0.36851387 ... 1.         0.96429344 0.95175317]\n",
      " [0.30512858 0.11986583 0.37293493 ... 0.96429344 1.         0.96671243]\n",
      " [0.27852425 0.17232809 0.34815531 ... 0.95175317 0.96671243 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1 gram matrices are calculated, 0 of which are ignored.\n",
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/1230 [00:00<01:26, 14.18it/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": [
      "                                                                          \n",
      "4. Getting final performance...\n",
      "best_params_out:  [{'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}]\n",
      "best_params_in:  [{'alpha': 0.01}]\n",
      "\n",
      "best_val_perf:  10.66283832911368\n",
      "best_val_std:  0.5408278153570373\n",
      "final_performance:  [10.315559722243599]\n",
      "final_confidence:  [2.384096453432681]\n",
      "train_performance: [7.431503564719363]\n",
      "train_std:  [0.22208257392321618]\n",
      "\n",
      "time to calculate gram matrix with different hyper-params: 11.70±nans\n",
      "time to calculate best gram matrix: 11.70±nans\n",
      "\n",
      "params                                                                                                                                                                                                                                                                                            train_perf    valid_perf            test_perf              gram_matrix_time\n",
      "------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------  ------------  --------------------  -------------------  ------------------\n",
      "{'alpha': '1.00e-10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     324939.95±1779702.52  162506.31±890024.17                11.7\n",
      "{'alpha': '3.16e-10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     103389.05±566218.31   51709.44±283164.71                 11.7\n",
      "{'alpha': '1.00e-09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     32773.42±179440.54    16394.79±89738.41                  11.7\n",
      "{'alpha': '3.16e-09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     10371.32±56739.21     5191.57±28375.88                   11.7\n",
      "{'alpha': '1.00e-08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     3287.91±17941.77      1649.18±8973.41                    11.7\n",
      "{'alpha': '3.16e-08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     1047.95±5673.01       528.99±2837.84                     11.7\n",
      "{'alpha': '1.00e-07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     339.61±1793.28        174.75±897.61                      11.7\n",
      "{'alpha': '3.16e-07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.82     115.62±566.41         62.73±284.06                       11.7\n",
      "{'alpha': '1.00e-06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.81     44.78±178.46          27.30±90.09                        11.7\n",
      "{'alpha': '3.16e-06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.60±0.81     22.37±55.86           16.10±28.87                        11.7\n",
      "{'alpha': '1.00e-05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.59±0.78     15.26±17.31           12.54±9.86                         11.7\n",
      "{'alpha': '3.16e-05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.57±0.72     12.94±5.71            11.36±4.52                         11.7\n",
      "{'alpha': '1.00e-04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.52±0.58     12.03±2.70            10.88±3.23                         11.7\n",
      "{'alpha': '3.16e-04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.43±0.41     11.49±1.53            10.55±2.70                         11.7\n",
      "{'alpha': '1.00e-03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.33±0.31     11.06±0.82            10.26±2.40                         11.7\n",
      "{'alpha': '3.16e-03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.28±0.27     10.76±0.60            10.15±2.28                         11.7\n",
      "{'alpha': '1.00e-02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  7.43±0.22     10.66±0.54            10.32±2.38                         11.7\n",
      "{'alpha': '3.16e-02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  8.11±0.20     10.86±0.47            10.89±2.61                         11.7\n",
      "{'alpha': '1.00e-01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  9.76±0.21     11.90±0.42            12.20±2.84                         11.7\n",
      "{'alpha': '3.16e-01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  12.88±0.23    14.53±0.39            14.79±2.97                         11.7\n",
      "{'alpha': '1.00e+00', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  17.64±0.24    18.83±0.32            19.02±3.17                         11.7\n",
      "{'alpha': '3.16e+00', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  32.86±17.99   33.24±17.71           33.25±17.11                        11.7\n",
      "{'alpha': '1.00e+01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  34.19±0.37    34.39±0.43            34.96±5.17                         11.7\n",
      "{'alpha': '3.16e+01', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  49.15±0.43    48.90±0.56            49.48±7.14                         11.7\n",
      "{'alpha': '1.00e+02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  76.17±0.53    75.76±0.64            76.22±8.54                         11.7\n",
      "{'alpha': '3.16e+02', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  108.04±0.74   107.66±0.79           108.09±8.81                        11.7\n",
      "{'alpha': '1.00e+03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  128.28±0.90   127.91±0.93           128.37±8.78                        11.7\n",
      "{'alpha': '3.16e+03', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  136.82±0.97   136.44±0.99           136.92±8.75                        11.7\n",
      "{'alpha': '1.00e+04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  139.80±0.99   139.43±1.01           139.91±8.74                        11.7\n",
      "{'alpha': '3.16e+04', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  140.78±1.00   140.40±1.02           140.89±8.73                        11.7\n",
      "{'alpha': '1.00e+05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.09±1.00   140.72±1.02           141.20±8.73                        11.7\n",
      "{'alpha': '3.16e+05', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.19±1.00   140.81±1.02           141.30±8.73                        11.7\n",
      "{'alpha': '1.00e+06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.22±1.00   140.85±1.02           141.33±8.73                        11.7\n",
      "{'alpha': '3.16e+06', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.23±1.00   140.86±1.02           141.34±8.73                        11.7\n",
      "{'alpha': '1.00e+07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.23±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '3.16e+07', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '1.00e+08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '3.16e+08', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '1.00e+09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '3.16e+09', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n",
      "{'alpha': '1.00e+10', 'node_kernels': {'symb': <function deltakernel at 0x7f7e8eb76730>, 'nsymb': <function rbf_kernel at 0x7f7e8a6e0f28>, 'mix': functools.partial(<function kernelsum at 0x7f7e8eb76840>, <function deltakernel at 0x7f7e8eb76730>, <function rbf_kernel at 0x7f7e8a6e0f28>)}}  141.24±1.00   140.86±1.02           141.35±8.73                        11.7\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculate performance: 100%|██████████| 1230/1230 [00:40<00:00, 30.18it/s]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  keepdims=keepdims)\n",
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "%matplotlib inline\n",
    "import functools\n",
    "from libs import *\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "from pygraph.utils.kernels import deltakernel, kernelsum\n",
    "from sklearn.metrics.pairwise import rbf_kernel\n",
    "\n",
    "dslist = [   \n",
    "    {'name': 'Acyclic', 'dataset': '../datasets/acyclic/dataset_bps.ds', 'task': 'regression'}, # node symb\n",
    "#     {'name': 'COIL-DEL', 'dataset': '../datasets/COIL-DEL/COIL-DEL_A.txt'}, # edge symb, node nsymb\n",
    "#     {'name': 'PAH', 'dataset': '../datasets/PAH/dataset.ds',}, # unlabeled\n",
    "#     {'name': 'Mutagenicity', 'dataset': '../datasets/Mutagenicity/Mutagenicity_A.txt'}, # node/edge symb\n",
    "#     {'name': 'MAO', 'dataset': '../datasets/MAO/dataset.ds',}, # node/edge symb\n",
    "#     {'name': 'MUTAG', 'dataset': '../datasets/MUTAG/MUTAG.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}, # node/edge symb\n",
    "#     {'name': 'Alkane', 'dataset': '../datasets/Alkane/dataset.ds', 'task': 'regression', \n",
    "#         'dataset_y': '../datasets/Alkane/dataset_boiling_point_names.txt',}, # contains single node graph, node symb\n",
    "#     {'name': 'BZR', 'dataset': '../datasets/BZR_txt/BZR_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'COX2', 'dataset': '../datasets/COX2_txt/COX2_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'ENZYMES', 'dataset': '../datasets/ENZYMES_txt/ENZYMES_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'DHFR', 'dataset': '../datasets/DHFR_txt/DHFR_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'SYNTHETIC', 'dataset': '../datasets/SYNTHETIC_txt/SYNTHETIC_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'MSRC9', 'dataset': '../datasets/MSRC_9_txt/MSRC_9_A.txt'}, # node symb\n",
    "#     {'name': 'MSRC21', 'dataset': '../datasets/MSRC_21_txt/MSRC_21_A.txt'}, # node symb\n",
    "#     {'name': 'FIRSTMM_DB', 'dataset': '../datasets/FIRSTMM_DB/FIRSTMM_DB_A.txt'}, # node symb/nsymb ,edge nsymb\n",
    "\n",
    "#     {'name': 'PROTEINS', 'dataset': '../datasets/PROTEINS_txt/PROTEINS_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'PROTEINS_full', 'dataset': '../datasets/PROTEINS_full_txt/PROTEINS_full_A_sparse.txt'}, # node symb/nsymb\n",
    "#     {'name': 'D&D', 'dataset': '../datasets/D&D/DD.mat',\n",
    "#      'extra_params': {'am_sp_al_nl_el': [0, 1, 2, 1, -1]}}, # node symb\n",
    "#     {'name': 'AIDS', 'dataset': '../datasets/AIDS/AIDS_A.txt'}, # node symb/nsymb, edge symb\n",
    "#     {'name': 'NCI1', 'dataset': '../datasets/NCI1/NCI1.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
    "#     {'name': 'NCI109', 'dataset': '../datasets/NCI109/NCI109.mat',\n",
    "#         'extra_params': {'am_sp_al_nl_el': [1, 1, 2, 0, -1]}}, # node symb\n",
    "#     {'name': 'NCI-HIV', 'dataset': '../datasets/NCI-HIV/AIDO99SD.sdf',\n",
    "#         'dataset_y': '../datasets/NCI-HIV/aids_conc_may04.txt',}, # node/edge symb\n",
    "    \n",
    "#     # not working below\n",
    "#     {'name': 'PTC_FM', 'dataset': '../datasets/PTC/Train/FM.ds',},\n",
    "#     {'name': 'PTC_FR', 'dataset': '../datasets/PTC/Train/FR.ds',},\n",
    "#     {'name': 'PTC_MM', 'dataset': '../datasets/PTC/Train/MM.ds',},\n",
    "#     {'name': 'PTC_MR', 'dataset': '../datasets/PTC/Train/MR.ds',},\n",
    "]\n",
    "estimator = spkernel\n",
    "mixkernel = functools.partial(kernelsum, deltakernel, rbf_kernel)\n",
    "param_grid_precomputed = {'node_kernels': [{'symb': deltakernel, 'nsymb': rbf_kernel, 'mix': mixkernel}]}\n",
    "param_grid = [{'C': np.logspace(-10, 10, num = 41, base = 10)}, \n",
    "              {'alpha': np.logspace(-10, 10, num = 41, base = 10)}]\n",
    "\n",
    "for 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",
    "        ds_name=ds['name'])\n",
    "    \n",
    "#     %lprun -f spkernel \\\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",
      "--- shortest path kernel matrix of size 183 built in 13.54222846031189 seconds ---\n",
      "\n",
      "gram matrix with parameters {} is: \n",
      "[[1.         0.23570226 1.         ... 0.07784989 0.07784989 0.07784989]\n",
      " [0.23570226 1.         0.23570226 ... 0.         0.         0.16514456]\n",
      " [1.         0.23570226 1.         ... 0.07784989 0.07784989 0.07784989]\n",
      " ...\n",
      " [0.07784989 0.         0.07784989 ... 1.         0.38181818 0.12727273]\n",
      " [0.07784989 0.         0.07784989 ... 0.38181818 1.         0.12727273]\n",
      " [0.07784989 0.16514456 0.07784989 ... 0.12727273 0.12727273 1.        ]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faafef48390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3. Fitting and predicting using nested cross validation. This could really take a while...\n",
      "calculate performance:   0%|          | 2/1230 [00:00<01:34, 12.98it/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": [
      "                                                                          \n",
      "4. Getting final performances...\n",
      "\n",
      "best_params_out:  [{}]\n",
      "best_params_in:  [{'alpha': 1.0}]\n",
      "\n",
      "best_val_perf:  38.310091123281964\n",
      "best_val_std:  0.921980968599538\n",
      "final_performance:  39.40204478386939\n",
      "final_confidence:  6.323960893696847\n",
      "train_performance:  28.772353915665963\n",
      "train_std:  0.6079886263011134\n",
      "\n",
      "time to calculate gram matrix with different hyperpapams: 13.54±nan\n",
      "time to calculate best gram matrix:  13.54222846031189 s\n",
      "\n",
      "params                 train_perf      valid_perf      test_perf         gram_matrix_time\n",
      "---------------------  --------------  --------------  --------------  ------------------\n",
      "{'alpha': '1.00e-01'}  38.29±21.43     90.94±85.41     95.89±109.61                 13.54\n",
      "{'alpha': '1.12e-01'}  41.08±38.73     87.86±80.28     123.11±281.23                13.54\n",
      "{'alpha': '1.26e-01'}  32.82±6.74      65.09±20.13     71.11±39.78                  13.54\n",
      "{'alpha': '1.41e-01'}  30.31±2.59      57.03±8.94      59.35±14.38                  13.54\n",
      "{'alpha': '1.58e-01'}  29.75±2.56      54.54±9.19      56.87±14.53                  13.54\n",
      "{'alpha': '1.78e-01'}  29.95±7.00      57.65±46.21     56.04±27.59                  13.54\n",
      "{'alpha': '2.00e-01'}  28.41±1.44      48.10±5.22      49.80±8.33                   13.54\n",
      "{'alpha': '2.24e-01'}  28.07±1.22      46.08±2.85      47.97±7.38                   13.54\n",
      "{'alpha': '2.51e-01'}  27.83±1.10      44.68±2.22      46.57±6.89                   13.54\n",
      "{'alpha': '2.82e-01'}  27.65±1.00      43.54±1.90      45.39±6.54                   13.54\n",
      "{'alpha': '3.16e-01'}  27.51±0.92      42.57±1.68      44.36±6.28                   13.54\n",
      "{'alpha': '3.55e-01'}  27.42±0.85      41.74±1.53      43.45±6.09                   13.54\n",
      "{'alpha': '3.98e-01'}  27.38±0.80      41.01±1.40      42.66±5.97                   13.54\n",
      "{'alpha': '4.47e-01'}  27.37±0.75      40.38±1.30      41.96±5.91                   13.54\n",
      "{'alpha': '5.01e-01'}  27.41±0.72      39.84±1.22      41.35±5.89                   13.54\n",
      "{'alpha': '5.62e-01'}  27.50±0.69      39.39±1.15      40.82±5.91                   13.54\n",
      "{'alpha': '6.31e-01'}  27.64±0.66      39.02±1.09      40.38±5.96                   13.54\n",
      "{'alpha': '7.08e-01'}  27.83±0.64      38.72±1.04      40.02±6.03                   13.54\n",
      "{'alpha': '7.94e-01'}  28.08±0.63      38.51±0.99      39.73±6.11                   13.54\n",
      "{'alpha': '8.91e-01'}  28.39±0.62      38.37±0.95      39.53±6.21                   13.54\n",
      "{'alpha': '1.00e+00'}  28.77±0.61      38.31±0.92      39.40±6.32                   13.54\n",
      "{'alpha': '1.12e+00'}  29.22±0.60      38.33±0.89      39.36±6.44                   13.54\n",
      "{'alpha': '1.26e+00'}  29.74±0.60      38.44±0.87      39.40±6.56                   13.54\n",
      "{'alpha': '1.41e+00'}  30.34±0.59      38.63±0.85      39.53±6.68                   13.54\n",
      "{'alpha': '1.58e+00'}  32.30±4.94      40.59±6.49      41.14±9.07                   13.54\n",
      "{'alpha': '1.78e+00'}  65.08±118.93    70.87±110.34    70.05±105.01                 13.54\n",
      "{'alpha': '2.00e+00'}  61.14±47.95     63.56±40.28     63.69±32.83                  13.54\n",
      "{'alpha': '2.24e+00'}  517.26±2507.14  514.17±2482.82  385.18±1762.10               13.54\n",
      "{'alpha': '2.51e+00'}  38.51±2.50      43.44±2.15      44.18±8.19                   13.54\n",
      "{'alpha': '2.82e+00'}  37.61±0.88      42.78±0.93      43.42±7.80                   13.54\n",
      "{'alpha': '3.16e+00'}  38.17±0.62      43.21±0.76      43.79±7.82                   13.54\n",
      "{'alpha': '3.55e+00'}  39.19±0.57      44.01±0.73      44.54±7.90                   13.54\n",
      "{'alpha': '3.98e+00'}  40.46±0.56      45.03±0.73      45.51±8.00                   13.54\n",
      "{'alpha': '4.47e+00'}  41.92±0.56      46.23±0.74      46.66±8.13                   13.54\n",
      "{'alpha': '5.01e+00'}  43.55±0.56      47.59±0.75      47.98±8.26                   13.54\n",
      "{'alpha': '5.62e+00'}  45.34±0.57      49.12±0.76      49.46±8.40                   13.54\n",
      "{'alpha': '6.31e+00'}  47.28±0.57      50.81±0.77      51.11±8.54                   13.54\n",
      "{'alpha': '7.08e+00'}  49.37±0.57      52.66±0.78      52.92±8.69                   13.54\n",
      "{'alpha': '7.94e+00'}  51.60±0.57      54.67±0.79      54.89±8.84                   13.54\n",
      "{'alpha': '8.91e+00'}  53.99±0.57      56.84±0.79      57.02±8.99                   13.54\n",
      "{'alpha': '1.00e+01'}  56.52±0.57      59.17±0.79      59.32±9.13                   13.54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:135: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
      "  keepdims=keepdims)\n",
      "/usr/local/lib/python3.5/dist-packages/numpy/core/_methods.py:127: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "calculate performance: 100%|██████████| 1230/1230 [01:20<00:00, 19.86it/s]"
     ]
    }
   ],
   "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.spKernel import spkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "estimator = spkernel\n",
    "param_grid_precomputed = {}\n",
    "param_grid = {'alpha': np.logspace(-1, 1, 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": 1,
   "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",
      "--- shortest path kernel matrix of size 185 built in 13.3865065574646 seconds ---\n",
      "[[ 3.  1.  3. ...  1.  1.  1.]\n",
      " [ 1.  6.  1. ...  0.  0.  3.]\n",
      " [ 3.  1.  3. ...  1.  1.  1.]\n",
      " ...\n",
      " [ 1.  0.  1. ... 55. 21.  7.]\n",
      " [ 1.  0.  1. ... 21. 55.  7.]\n",
      " [ 1.  3.  1. ...  7.  7. 55.]]\n",
      "\n",
      " Starting calculate accuracy/rmse...\n",
      "calculate performance:  94%|█████████▎| 936/1000 [00:01<00:00, 757.54it/s]\n",
      " Mean performance on train set: 28.360361\n",
      "With standard deviation: 1.357183\n",
      "\n",
      " Mean performance on test set: 35.191954\n",
      "With standard deviation: 4.495767\n",
      "calculate performance: 100%|██████████| 1000/1000 [00:01<00:00, 771.22it/s]\n",
      "\n",
      "\n",
      "  rmse_test    std_test    rmse_train    std_train    k_time\n",
      "-----------  ----------  ------------  -----------  --------\n",
      "     35.192     4.49577       28.3604      1.35718   13.3865\n"
     ]
    }
   ],
   "source": [
    "%load_ext line_profiler\n",
    "\n",
    "import sys\n",
    "sys.path.insert(0, \"../\")\n",
    "from pygraph.utils.utils import kernel_train_test\n",
    "from pygraph.kernels.spKernel import spkernel\n",
    "\n",
    "datafile = '../../../../datasets/acyclic/Acyclic/dataset_bps.ds'\n",
    "kernel_file_path = 'kernelmatrices_path_acyclic/'\n",
    "\n",
    "kernel_para = dict(edge_weight = 'atom')\n",
    "\n",
    "kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)\n",
    "\n",
    "# %lprun -f spkernel \\\n",
    "#     kernel_train_test(datafile, kernel_file_path, spkernel, kernel_para, normalize = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results\n",
    "\n",
    "# with y normalization\n",
    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "-----------  ----------  ------------  -----------  --------\n",
    "    35.6337     5.23183       32.3805      3.92531   14.9301\n",
    "\n",
    "# without y normalization\n",
    "  RMSE_test    std_test    RMSE_train    std_train    k_time\n",
    "-----------  ----------  ------------  -----------  --------\n",
    "     35.192     4.49577       28.3604      1.35718   14.5768"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "- This script take as input a kernel matrix\n",
      "and returns the classification or regression performance\n",
      "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
      "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
      "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
      "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
      "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
      "correspond to the average of the performances on the test sets. \n",
      "\n",
      "@references\n",
      "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
      "\n",
      "\n",
      " Loading dataset from file...\n",
      "[ -23.7   14.    37.3  109.7   10.8   39.    42.    66.6  135.   148.5\n",
      "   40.    34.6   32.    63.    53.5   67.    64.4   84.7   95.5   92.\n",
      "   84.4  154.   156.   166.   183.    70.3   63.6   52.5   59.    59.5\n",
      "   55.2   88.    83.   104.5  102.    92.   107.4  123.2  112.5  118.5\n",
      "  101.5  173.7  165.5  181.    99.5   92.3   90.1   80.2   82.    91.2\n",
      "   91.5   81.2   93.    69.    86.3   82.   103.   103.5   96.   112.   104.\n",
      "  132.5  123.5  120.3  145.   144.2  142.8  132.   134.2  137.   139.\n",
      "  133.6  120.4  120.   137.   195.8  177.2  181.   185.9  175.7  186.   211.\n",
      "  125.   118.   117.1  107.   102.5  112.    97.4   91.5   87.6  106.5\n",
      "  101.    99.3   90.   137.   114.   126.   124.   140.5  157.5  146.   145.\n",
      "  141.   171.   166.   155.   145.   159.   138.   142.   159.   163.5\n",
      "  229.5  142.   125.   132.   130.5  125.   122.   121.   122.2  112.   106.\n",
      "  114.5  151.   128.5  109.5  126.   147.   158.   147.   165.   188.9\n",
      "  170.   178.   148.5  165.   177.   167.   195.   226.   215.   201.   205.\n",
      "  151.5  165.5  157.   139.   163.   153.5  139.   162.   173.   159.5\n",
      "  159.5  155.5  141.   126.   164.   163.   166.5  146.   165.   159.   195.\n",
      "  218.   250.   235.   186.5  156.5  162.   162.   170.2  173.2  186.8\n",
      "  173.   187.   174.   188.5  199.   228.   215.   216.   240. ]\n",
      "\n",
      " Loading the matrix from file...\n",
      "[[  3.   1.   3. ...,   1.   1.   1.]\n",
      " [  1.   6.   1. ...,   0.   0.   3.]\n",
      " [  3.   1.   3. ...,   1.   1.   1.]\n",
      " ..., \n",
      " [  1.   0.   1. ...,  55.  21.   7.]\n",
      " [  1.   0.   1. ...,  21.  55.   7.]\n",
      " [  1.   3.   1. ...,   7.   7.  55.]]\n",
      "\n",
      " --- This is a regression problem ---\n",
      "\n",
      " Starting split 10...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 40.422382\n",
      "The corresponding performance on test set is: 47.424532\n",
      "\n",
      " Starting split 11...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 33.084913\n",
      "The corresponding performance on test set is: 35.493699\n",
      "\n",
      " Starting split 12...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 31.306710\n",
      "The corresponding performance on test set is: 33.173366\n",
      "\n",
      " Starting split 13...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 43.500424\n",
      "The corresponding performance on test set is: 32.633129\n",
      "\n",
      " Starting split 14...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 10 with parameter alpha = 1.000000\n",
      "The best performance on the validation set is: 53.561752\n",
      "The corresponding performance on test set is: 42.883548\n",
      "\n",
      " Starting split 15...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 40.444773\n",
      "The corresponding performance on test set is: 32.713040\n",
      "\n",
      " Starting split 16...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 11 with parameter alpha = 10.000000\n",
      "The best performance on the validation set is: 37.046818\n",
      "The corresponding performance on test set is: 37.337851\n",
      "\n",
      " Starting split 17...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 39.907628\n",
      "The corresponding performance on test set is: 38.889064\n",
      "\n",
      " Starting split 18...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 12 with parameter alpha = 100.000000\n",
      "The best performance on the validation set is: 29.879950\n",
      "The corresponding performance on test set is: 27.652558\n",
      "\n",
      " Starting split 19...\n",
      "\n",
      " Normalizing output y...\n",
      "The best performance is for trial 11 with parameter alpha = 10.000000\n",
      "The best performance on the validation set is: 44.911892\n",
      "The corresponding performance on test set is: 35.804454\n",
      "\n",
      " Mean performance on val set: 39.406724\n",
      "With standard deviation: 6.720820\n",
      "\n",
      " Mean performance on test set: 36.400524\n",
      "With standard deviation: 5.352940\n"
     ]
    }
   ],
   "source": [
    "# Author: Elisabetta Ghisu\n",
    "\n",
    "\"\"\"\n",
    "- This script take as input a kernel matrix\n",
    "and returns the classification or regression performance\n",
    "- The kernel matrix can be calculated using any of the graph kernels approaches\n",
    "- The criteria used for prediction are SVM for classification and kernel Ridge regression for regression\n",
    "- For predition we divide the data in training, validation and test. For each split, we first train on the train data, \n",
    "then evaluate the performance on the validation. We choose the optimal parameters for the validation set and finally\n",
    "provide the corresponding performance on the test set. If more than one split is performed, the final results \n",
    "correspond to the average of the performances on the test sets. \n",
    "\n",
    "@references\n",
    "    https://github.com/eghisu/GraphKernels/blob/master/GraphKernelsCollection/python_scripts/compute_perf_gk.py\n",
    "\"\"\"\n",
    "\n",
    "print(__doc__)\n",
    "\n",
    "import sys\n",
    "import pathlib\n",
    "sys.path.insert(0, \"../\")\n",
    "from tabulate import tabulate\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.kernel_ridge import KernelRidge # 0.17\n",
    "from sklearn.metrics import accuracy_score, mean_squared_error\n",
    "from sklearn import svm\n",
    "\n",
    "from pygraph.kernels.spkernel import spkernel\n",
    "from pygraph.utils.graphfiles import loadDataset\n",
    "\n",
    "print('\\n Loading dataset from file...')\n",
    "dataset, y = loadDataset(\"../../../../datasets/acyclic/Acyclic/dataset_bps.ds\")\n",
    "y = np.array(y)\n",
    "print(y)\n",
    "\n",
    "kernel_file_path = 'kernelmatrix.ds'\n",
    "path = pathlib.Path(kernel_file_path)\n",
    "if path.is_file():\n",
    "    print('\\n Loading the matrix from file...')\n",
    "    Kmatrix = np.loadtxt(kernel_file_path)\n",
    "    print(Kmatrix)\n",
    "else:\n",
    "    print('\\n Calculating kernel matrix, this could take a while...')\n",
    "    #@Q: is it appropriate to use bond type between atoms as the edge weight to calculate shortest path????????\n",
    "    Kmatrix, run_time = spkernel(dataset, edge_weight = 'bond_type')\n",
    "    print(Kmatrix)\n",
    "    print('Saving kernel matrix to file...')\n",
    "    np.savetxt(kernel_file_path, Kmatrix)\n",
    "\n",
    "# setup the parameters\n",
    "model_type = 'regression' # Regression or classification problem\n",
    "print('\\n --- This is a %s problem ---' % model_type)\n",
    "\n",
    "datasize = len(dataset)\n",
    "trials = 21 # Trials for hyperparameters random search\n",
    "splits = 10 # Number of splits of the data\n",
    "alpha_grid = np.logspace(-10, 10, num = trials, base = 10) # corresponds to (2*C)^-1 in other linear models such as LogisticRegression\n",
    "C_grid = np.logspace(-10, 10, num = trials, base = 10)\n",
    "random.seed(20) # Set the seed for uniform parameter distribution\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "-  Here starts the main program\n",
    "-  First we permute the data, then for each split we evaluate corresponding performances\n",
    "-  In the end, the performances are averaged over the test sets\n",
    "\"\"\"\n",
    "\n",
    "# Initialize the performance of the best parameter trial on validation with the corresponding performance on test\n",
    "val_split = []\n",
    "test_split = []\n",
    "\n",
    "# For each split of the data\n",
    "for j in range(10, 10 + splits):\n",
    "    print('\\n Starting split %d...' % j)\n",
    "\n",
    "    # Set the random set for data permutation\n",
    "    random_state = int(j)\n",
    "    np.random.seed(random_state)\n",
    "    idx_perm = np.random.permutation(datasize)\n",
    "#     print(idx_perm)\n",
    "    \n",
    "    # Permute the data\n",
    "    y_perm = y[idx_perm] # targets permutation\n",
    "#     print(y_perm)\n",
    "    Kmatrix_perm = Kmatrix[:, idx_perm] # inputs permutation\n",
    "#     print(Kmatrix_perm)\n",
    "    Kmatrix_perm = Kmatrix_perm[idx_perm, :] # inputs permutation\n",
    "    \n",
    "    # Set the training, validation and test\n",
    "    # Note: the percentage can be set up by the user\n",
    "    num_train_val = int((datasize * 90) / 100)         # 90% (of entire dataset) for training and validation\n",
    "    num_test = datasize - num_train_val              # 10% (of entire dataset) for test\n",
    "    num_train = int((num_train_val * 90) / 100) # 90% (of train + val) for training\n",
    "    num_val = num_train_val - num_train # 10% (of train + val) for validation\n",
    "    \n",
    "    # Split the kernel matrix\n",
    "    Kmatrix_train = Kmatrix_perm[0:num_train, 0:num_train]\n",
    "    Kmatrix_val = Kmatrix_perm[num_train:(num_train + num_val), 0:num_train]\n",
    "    Kmatrix_test = Kmatrix_perm[(num_train + num_val):datasize, 0:num_train]\n",
    "\n",
    "    # Split the targets\n",
    "    y_train = y_perm[0:num_train]\n",
    "\n",
    "    # Normalization step (for real valued targets only)\n",
    "    print('\\n Normalizing output y...')\n",
    "    if model_type == 'regression':\n",
    "        y_train_mean = np.mean(y_train)\n",
    "        y_train_std = np.std(y_train)\n",
    "        y_train = (y_train - y_train_mean) / float(y_train_std)\n",
    "#         print(y)\n",
    "        \n",
    "    y_val = y_perm[num_train:(num_train + num_val)]\n",
    "    y_test = y_perm[(num_train + num_val):datasize]\n",
    "    \n",
    "    # Record the performance for each parameter trial respectively on validation and test set\n",
    "    perf_all_val = []\n",
    "    perf_all_test = []\n",
    "    \n",
    "    # For each parameter trial\n",
    "    for i in range(trials):\n",
    "        # For regression use the Kernel Ridge method\n",
    "        if model_type == 'regression':\n",
    "#             print('\\n Starting experiment for trial %d and parameter alpha = %3f\\n ' % (i, alpha_grid[i]))\n",
    "\n",
    "            # Fit the kernel ridge model\n",
    "            KR = KernelRidge(kernel = 'precomputed', alpha = alpha_grid[i])\n",
    "#             KR = svm.SVR(kernel = 'precomputed', C = C_grid[i])\n",
    "            KR.fit(Kmatrix_train, y_train)\n",
    "\n",
    "            # predict on the validation and test set\n",
    "            y_pred = KR.predict(Kmatrix_val)\n",
    "            y_pred_test = KR.predict(Kmatrix_test)\n",
    "#             print(y_pred)\n",
    "\n",
    "            # adjust prediction: needed because the training targets have been normalizaed\n",
    "            y_pred = y_pred * float(y_train_std) + y_train_mean\n",
    "#             print(y_pred)\n",
    "            y_pred_test = y_pred_test * float(y_train_std) + y_train_mean\n",
    "#             print(y_pred_test)\n",
    "\n",
    "            # root mean squared error on validation\n",
    "            rmse = np.sqrt(mean_squared_error(y_val, y_pred))\n",
    "            perf_all_val.append(rmse)\n",
    "\n",
    "            # root mean squared error in test \n",
    "            rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))\n",
    "            perf_all_test.append(rmse_test)\n",
    "\n",
    "#             print('The performance on the validation set is: %3f' % rmse)\n",
    "#             print('The performance on the test set is: %3f' % rmse_test)\n",
    "            \n",
    "    # --- FIND THE OPTIMAL PARAMETERS --- #\n",
    "    # For regression: minimise the mean squared error\n",
    "    if model_type == 'regression':\n",
    "\n",
    "        # get optimal parameter on validation (argmin mean squared error)\n",
    "        min_idx = np.argmin(perf_all_test)\n",
    "        alpha_opt = alpha_grid[min_idx]\n",
    "\n",
    "        # performance corresponding to optimal parameter on val\n",
    "        perf_val_opt = perf_all_val[min_idx]\n",
    "\n",
    "        # corresponding performance on test for the same parameter\n",
    "        perf_test_opt = perf_all_test[min_idx]\n",
    "\n",
    "        print('The best performance is for trial %d with parameter alpha = %3f' % (min_idx, alpha_opt))\n",
    "        print('The best performance on the validation set is: %3f' % perf_val_opt)\n",
    "        print('The corresponding performance on test set is: %3f' % perf_test_opt)\n",
    "\n",
    "    # append the best performance on validation\n",
    "    # at the current split\n",
    "    val_split.append(perf_val_opt)\n",
    "\n",
    "    # append the correponding performance on the test set\n",
    "    test_split.append(perf_test_opt)\n",
    "\n",
    "# average the results\n",
    "# mean of the validation performances over the splits\n",
    "val_mean = np.mean(np.asarray(val_split))\n",
    "# std deviation of validation over the splits\n",
    "val_std = np.std(np.asarray(val_split))\n",
    "\n",
    "# mean of the test performances over the splits\n",
    "test_mean = np.mean(np.asarray(test_split))\n",
    "# std deviation of the test oer the splits\n",
    "test_std = np.std(np.asarray(test_split))\n",
    "\n",
    "print('\\n Mean performance on val set: %3f' % val_mean)\n",
    "print('With standard deviation: %3f' % val_std)\n",
    "print('\\n Mean performance on test set: %3f' % test_mean)\n",
    "print('With standard deviation: %3f' % test_std)"
   ]
  }
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