vdss_ensemble_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n"
     ]
    }
   ],
   "source": [
    "from tdc.single_pred import Tox\n",
    "import deepchem\n",
    "import xgboost\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.EState import Fingerprinter\n",
    "from rdkit.Chem.EState import EState\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import autokeras as ak\n",
    "\n",
    "from tdc.benchmark_group import admet_group\n",
    "group = admet_group(path = 'data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "generating training, validation splits...\n",
      "100%|██████████| 728/728 [00:00<00:00, 1195.39it/s]\n",
      "generating training, validation splits...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions for run #\n",
      "1\n",
      "[-5.771012  -5.046923  -4.6453915 -4.920735  -5.0959873]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 728/728 [00:00<00:00, 1309.13it/s]\n",
      "generating training, validation splits...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions for run #\n",
      "2\n",
      "[-5.600657  -5.3811393 -4.7251935 -5.0375338 -4.929484 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 728/728 [00:00<00:00, 1560.73it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:5 out of the last 5 calls to <function KerasModel._compute_model at 0x000001AE1D90D940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "WARNING:tensorflow:6 out of the last 6 calls to <function KerasModel._compute_model at 0x000001AE1D90D940> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "generating training, validation splits...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions for run #\n",
      "3\n",
      "[-5.7573266 -5.13537   -4.807997  -4.929074  -4.974685 ]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 728/728 [00:00<00:00, 1351.20it/s]\n",
      "generating training, validation splits...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions for run #\n",
      "4\n",
      "[-5.829679  -4.932092  -4.635282  -4.8561287 -4.9756303]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 728/728 [00:00<00:00, 1462.16it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predictions for run #\n",
      "5\n",
      "[-5.628306  -5.0700135 -4.8060203 -4.9077096 -5.0281405]\n",
      "Prediction List:\n",
      "[{'caco2_wang': array([-5.771012 , -5.046923 , -4.6453915, -4.920735 , -5.0959873,\n",
      "       -5.237889 , -4.5879326, -4.552511 , -4.5664997, -4.575129 ,\n",
      "       -5.6537967, -5.5742555, -5.5554657, -5.0212235, -4.983809 ,\n",
      "       -4.916327 , -6.1348686, -5.73414  , -5.5837593, -5.8737144,\n",
      "       -5.448843 , -5.448843 , -6.1043663, -4.60896  , -5.4166527,\n",
      "       -5.1214275, -5.971646 , -5.4688015, -5.4680004, -6.7522545,\n",
      "       -6.7266536, -6.848917 , -4.6554017, -6.7282276, -6.3253207,\n",
      "       -5.5226784, -6.3253207, -5.246919 , -5.2127233, -5.4008446,\n",
      "       -5.2327175, -5.1732697, -5.2602215, -5.341051 , -4.7905774,\n",
      "       -4.7905774, -4.7905774, -4.7905774, -5.03299  , -4.887938 ,\n",
      "       -4.7500954, -5.03299  , -4.8879385, -4.7500954, -4.7500954,\n",
      "       -4.8879385, -5.10747  , -5.0639095, -5.073127 , -5.10747  ,\n",
      "       -5.073127 , -5.0639095, -5.0639095, -5.0731273, -5.0639095,\n",
      "       -5.073127 , -5.073127 , -5.10747  , -5.0639095, -5.039208 ,\n",
      "       -4.8118544, -4.8118544, -4.775413 , -4.8118544, -5.039208 ,\n",
      "       -4.775413 , -4.8118544, -4.817995 , -4.817995 , -4.817995 ,\n",
      "       -4.817995 , -4.817995 , -4.7905774, -4.887938 , -5.03299  ,\n",
      "       -5.03299  , -5.039208 , -4.8118544, -4.169684 , -5.8460526,\n",
      "       -4.743767 , -4.839334 , -6.5011444, -6.0718637, -6.0643888,\n",
      "       -5.0751786, -4.532429 , -5.1919637, -5.36651  , -5.283536 ,\n",
      "       -5.1767735, -4.283734 , -4.387953 , -4.407246 , -4.939409 ,\n",
      "       -4.20836  , -4.766941 , -4.7225966, -6.484936 , -4.6798186,\n",
      "       -5.212351 , -4.8593755, -4.839705 , -4.932178 , -5.1979976,\n",
      "       -4.8906283, -5.10878  , -5.5176034, -5.652529 , -5.261359 ,\n",
      "       -6.533446 , -6.533446 , -4.811918 , -4.735695 , -4.8085437,\n",
      "       -5.251133 , -6.8162003, -4.7372622, -4.5083   , -4.4600863,\n",
      "       -4.6077914, -5.4236617, -5.339454 , -5.167286 , -5.438309 ,\n",
      "       -5.2687974, -5.2327037, -5.1735682, -5.145659 , -5.119824 ,\n",
      "       -5.665586 , -5.6363525, -6.8993926, -4.4088354, -4.674206 ,\n",
      "       -5.8247714, -4.856852 , -4.750462 , -4.9093966, -4.7890925,\n",
      "       -5.370791 , -5.3855863, -5.799787 , -5.5965924, -5.3182054,\n",
      "       -5.8600554, -4.7800484, -5.95372  , -6.122789 , -4.695612 ,\n",
      "       -5.1628637, -4.570678 , -4.4976   , -4.8951125, -4.9638734,\n",
      "       -4.934661 , -5.203176 , -6.0232635, -4.617266 , -5.370182 ,\n",
      "       -5.2911487, -4.9657536, -5.1065764, -4.584864 , -4.584864 ,\n",
      "       -5.171597 , -5.7969947, -4.619859 , -4.617371 , -6.1651273,\n",
      "       -6.315292 , -4.7440577], dtype=float32)}, {'caco2_wang': array([-5.600657 , -5.3811393, -4.7251935, -5.0375338, -4.929484 ,\n",
      "       -5.137591 , -4.4952254, -4.457388 , -4.448448 , -4.4716134,\n",
      "       -5.460697 , -5.4906297, -4.9665856, -4.9186864, -5.116644 ,\n",
      "       -4.9988246, -6.083334 , -5.8127275, -5.7084136, -5.64576  ,\n",
      "       -5.4839416, -5.4839416, -5.7968445, -4.5674086, -5.216383 ,\n",
      "       -4.82391  , -5.819657 , -5.4430604, -5.421255 , -6.705417 ,\n",
      "       -6.638773 , -6.7833953, -4.226686 , -6.7699647, -6.4060135,\n",
      "       -5.685286 , -6.4060135, -5.087589 , -5.017054 , -5.085045 ,\n",
      "       -4.8609967, -4.8208175, -4.8994274, -5.0400715, -4.6614776,\n",
      "       -4.6614776, -4.6614776, -4.6614776, -4.8805633, -4.747534 ,\n",
      "       -4.628712 , -4.8805633, -4.747534 , -4.628712 , -4.628712 ,\n",
      "       -4.747534 , -4.8368716, -4.8546963, -4.853723 , -4.8368716,\n",
      "       -4.853723 , -4.854696 , -4.854696 , -4.853723 , -4.8546963,\n",
      "       -4.853723 , -4.853723 , -4.8368716, -4.8546963, -4.7478185,\n",
      "       -4.7012076, -4.7012076, -4.6674733, -4.7012076, -4.747818 ,\n",
      "       -4.6674733, -4.7012076, -4.5794053, -4.579406 , -4.5794053,\n",
      "       -4.579406 , -4.5794053, -4.6614776, -4.747534 , -4.8805633,\n",
      "       -4.8805633, -4.7478185, -4.7012076, -4.2306166, -5.8567123,\n",
      "       -4.8404007, -4.983269 , -6.6578774, -5.961909 , -5.943622 ,\n",
      "       -4.8709536, -4.0733204, -5.1129336, -5.317019 , -5.0372705,\n",
      "       -5.177255 , -4.4248967, -4.3287635, -4.2027645, -4.784693 ,\n",
      "       -4.4314237, -4.7948813, -4.7025924, -6.3103633, -4.634252 ,\n",
      "       -5.3764057, -4.8633075, -4.8782616, -4.906323 , -4.996826 ,\n",
      "       -4.679477 , -5.193496 , -5.483368 , -5.5885434, -5.334625 ,\n",
      "       -6.2770844, -6.2770844, -4.9604187, -5.100752 , -4.3310566,\n",
      "       -5.21643  , -6.737957 , -4.775987 , -4.65456  , -4.5888257,\n",
      "       -4.928451 , -5.3997817, -5.3822656, -5.203092 , -5.3135796,\n",
      "       -5.3217463, -5.199981 , -5.1238747, -5.083811 , -4.99583  ,\n",
      "       -5.583467 , -5.872395 , -6.892142 , -4.3341937, -4.804498 ,\n",
      "       -5.88956  , -5.056993 , -4.9586034, -4.759356 , -5.0344944,\n",
      "       -5.432835 , -5.538265 , -5.861361 , -5.249319 , -5.4168334,\n",
      "       -5.801136 , -5.2177734, -5.878264 , -5.802883 , -4.771353 ,\n",
      "       -5.1183763, -4.660879 , -4.6907787, -4.77449  , -4.7512426,\n",
      "       -4.775422 , -4.815695 , -5.924347 , -4.6377697, -5.719338 ,\n",
      "       -5.6951985, -4.9941597, -4.9946465, -4.4162087, -4.4162087,\n",
      "       -5.253041 , -5.9006286, -4.6091366, -4.648596 , -6.311796 ,\n",
      "       -6.356191 , -4.7352815], dtype=float32)}, {'caco2_wang': array([-5.7573266, -5.13537  , -4.807997 , -4.929074 , -4.974685 ,\n",
      "       -5.0047803, -4.6011143, -4.453317 , -4.4523396, -4.5374   ,\n",
      "       -5.49987  , -5.66102  , -5.476686 , -5.098333 , -5.080527 ,\n",
      "       -5.0084577, -5.972294 , -6.1222343, -5.7651997, -5.7802124,\n",
      "       -5.5131955, -5.5131955, -5.9906316, -4.611073 , -5.475443 ,\n",
      "       -5.073639 , -5.956968 , -5.536864 , -5.4737573, -6.8051515,\n",
      "       -6.8755307, -6.822607 , -4.451388 , -6.6459317, -6.3218255,\n",
      "       -5.398338 , -6.3218255, -5.1738067, -5.0606055, -5.2612576,\n",
      "       -5.0177574, -4.8815455, -5.036983 , -5.1629634, -4.9874306,\n",
      "       -4.9874306, -4.9874306, -4.9874306, -4.999231 , -4.8487473,\n",
      "       -4.7075443, -4.999231 , -4.8487473, -4.7075443, -4.7075443,\n",
      "       -4.8487473, -5.085373 , -5.1833854, -5.1851296, -5.085373 ,\n",
      "       -5.1851296, -5.1833854, -5.1833854, -5.1851296, -5.183386 ,\n",
      "       -5.1851296, -5.1851296, -5.085373 , -5.183386 , -5.131601 ,\n",
      "       -5.1392217, -5.1392217, -5.106928 , -5.1392217, -5.131601 ,\n",
      "       -5.106928 , -5.1392217, -4.971103 , -4.971103 , -4.971103 ,\n",
      "       -4.971103 , -4.971103 , -4.9874306, -4.8487473, -4.999231 ,\n",
      "       -4.999231 , -5.131601 , -5.1392217, -4.130204 , -6.1290803,\n",
      "       -5.2479625, -4.8670464, -6.5090885, -5.740248 , -5.755794 ,\n",
      "       -4.8996654, -4.407899 , -5.0851197, -5.327566 , -5.244148 ,\n",
      "       -5.025508 , -4.51124  , -4.4321647, -4.4706655, -4.9587474,\n",
      "       -4.531752 , -4.648915 , -5.017814 , -6.352234 , -4.8126864,\n",
      "       -5.1847396, -4.862145 , -4.7506237, -4.9196987, -5.041966 ,\n",
      "       -4.7988896, -5.2622924, -5.339765 , -5.508368 , -5.583036 ,\n",
      "       -6.3881974, -6.3881974, -5.003309 , -4.930144 , -4.662336 ,\n",
      "       -5.1507926, -6.7054696, -4.773928 , -4.576591 , -4.5337157,\n",
      "       -4.868812 , -5.178828 , -5.2031474, -5.419356 , -5.3621407,\n",
      "       -5.416037 , -5.374291 , -5.2598815, -5.137777 , -5.0132375,\n",
      "       -5.776594 , -5.7783475, -6.853185 , -4.297719 , -4.7038646,\n",
      "       -5.925121 , -4.8783364, -4.886229 , -5.053201 , -5.12786  ,\n",
      "       -5.5525146, -5.4439716, -5.6878934, -5.4709277, -5.282211 ,\n",
      "       -5.7402053, -5.2490745, -5.7735696, -5.7581763, -4.7701774,\n",
      "       -4.974753 , -4.736523 , -4.555386 , -5.1342144, -5.028767 ,\n",
      "       -4.9254456, -5.0704627, -5.790979 , -4.780258 , -5.5714035,\n",
      "       -5.471955 , -4.973776 , -5.1196737, -4.5688467, -4.5688467,\n",
      "       -5.269855 , -5.6938367, -4.802833 , -4.709955 , -6.1916347,\n",
      "       -6.173637 , -4.807625 ], dtype=float32)}, {'caco2_wang': array([-5.829679 , -4.932092 , -4.635282 , -4.8561287, -4.9756303,\n",
      "       -5.070421 , -4.6048546, -4.5164056, -4.526396 , -4.6850815,\n",
      "       -5.521099 , -5.869558 , -5.1075454, -4.923315 , -4.9378715,\n",
      "       -4.907722 , -5.935341 , -5.902619 , -5.825052 , -5.589144 ,\n",
      "       -5.570952 , -5.570952 , -6.0188613, -4.8431945, -5.4531274,\n",
      "       -4.7554774, -5.7447343, -5.514314 , -5.4427123, -6.7656465,\n",
      "       -6.6841125, -6.7155666, -4.542058 , -6.634289 , -6.290329 ,\n",
      "       -5.6731834, -6.290329 , -5.111708 , -5.0627494, -5.1664333,\n",
      "       -5.0249515, -5.0081   , -5.1361585, -5.2618327, -4.9881134,\n",
      "       -4.9881134, -4.9881134, -4.9881134, -5.0564313, -4.960295 ,\n",
      "       -4.794411 , -5.0564313, -4.960295 , -4.794411 , -4.794411 ,\n",
      "       -4.960295 , -5.243042 , -5.242611 , -5.2362666, -5.243042 ,\n",
      "       -5.2362666, -5.242611 , -5.242611 , -5.2362666, -5.242611 ,\n",
      "       -5.2362666, -5.2362666, -5.243042 , -5.242611 , -5.2158723,\n",
      "       -4.993089 , -4.993089 , -4.9252324, -4.993089 , -5.2158723,\n",
      "       -4.9252324, -4.993089 , -4.848204 , -4.848204 , -4.848204 ,\n",
      "       -4.848204 , -4.848204 , -4.9881134, -4.960295 , -5.0564313,\n",
      "       -5.0564313, -5.2158723, -4.993089 , -4.3034987, -6.065558 ,\n",
      "       -5.0231104, -5.0443263, -6.7714763, -5.972056 , -5.9352875,\n",
      "       -5.0311427, -4.4255643, -5.0001807, -5.366045 , -5.231083 ,\n",
      "       -5.0693   , -4.6598797, -4.6419444, -4.46744  , -5.1082973,\n",
      "       -4.5731883, -4.7519646, -4.6740994, -6.243988 , -4.660044 ,\n",
      "       -5.065247 , -5.064055 , -5.055603 , -4.8690467, -5.100163 ,\n",
      "       -4.764672 , -5.3956046, -5.4347577, -5.6673565, -5.2956676,\n",
      "       -6.5644536, -6.5644536, -5.4006305, -4.7599387, -4.777082 ,\n",
      "       -5.161085 , -6.741098 , -4.72271  , -4.557695 , -4.367809 ,\n",
      "       -5.028682 , -5.4152794, -5.331698 , -5.1626134, -5.2489495,\n",
      "       -5.287208 , -5.2183537, -5.1445575, -4.9926276, -4.881977 ,\n",
      "       -5.7242446, -5.573603 , -6.8721485, -4.3477483, -4.8053317,\n",
      "       -5.8191833, -4.8882957, -4.7694855, -4.851198 , -4.9179873,\n",
      "       -5.41498  , -5.4558697, -5.8414936, -5.365789 , -5.221086 ,\n",
      "       -5.770642 , -5.1509476, -5.787737 , -5.765013 , -4.746876 ,\n",
      "       -5.150347 , -4.544813 , -4.498017 , -4.841253 , -5.0569463,\n",
      "       -5.0243382, -5.181851 , -5.96252  , -4.7138896, -5.622438 ,\n",
      "       -5.627754 , -5.109737 , -5.027533 , -4.51434  , -4.51434  ,\n",
      "       -5.152317 , -5.8101964, -4.7932396, -4.6312613, -6.2751946,\n",
      "       -6.341646 , -4.928208 ], dtype=float32)}, {'caco2_wang': array([-5.628306 , -5.0700135, -4.8060203, -4.9077096, -5.0281405,\n",
      "       -5.1160326, -4.7003655, -4.645813 , -4.6342072, -4.66746  ,\n",
      "       -5.49436  , -5.665879 , -5.185826 , -4.968078 , -4.963281 ,\n",
      "       -4.925488 , -6.0012946, -6.0057697, -5.845083 , -5.92123  ,\n",
      "       -5.4785647, -5.4785647, -5.994442 , -4.756321 , -5.3835626,\n",
      "       -4.5486984, -5.8616853, -5.5354614, -5.288626 , -6.7744727,\n",
      "       -6.714375 , -6.7550607, -4.088616 , -6.5150685, -6.0767007,\n",
      "       -5.577469 , -6.0767007, -5.149548 , -5.106221 , -5.142906 ,\n",
      "       -5.123945 , -5.043043 , -5.128023 , -5.260677 , -5.0751863,\n",
      "       -5.0751863, -5.0751863, -5.0751863, -5.196158 , -5.1429057,\n",
      "       -4.9974217, -5.1961575, -5.1429057, -4.9974217, -4.9974217,\n",
      "       -5.1429057, -5.3134027, -5.3464446, -5.352001 , -5.3134027,\n",
      "       -5.352001 , -5.3464446, -5.3464446, -5.352001 , -5.3464446,\n",
      "       -5.352001 , -5.352001 , -5.313402 , -5.3464446, -5.215492 ,\n",
      "       -5.1621175, -5.1621175, -5.1273346, -5.1621175, -5.215492 ,\n",
      "       -5.1273346, -5.1621175, -5.056948 , -5.056948 , -5.056948 ,\n",
      "       -5.056948 , -5.056948 , -5.0751863, -5.1429057, -5.196158 ,\n",
      "       -5.196158 , -5.215492 , -5.1621175, -4.4128914, -5.9502945,\n",
      "       -4.961986 , -4.9303966, -6.514443 , -5.9515953, -5.9804177,\n",
      "       -4.8907733, -4.2700686, -4.9101176, -5.106148 , -5.011643 ,\n",
      "       -4.86136  , -4.5593867, -4.556287 , -4.3500466, -4.8982434,\n",
      "       -4.429546 , -4.713654 , -4.708538 , -6.415737 , -4.5818276,\n",
      "       -5.198696 , -4.8149967, -5.110009 , -4.914353 , -5.0319457,\n",
      "       -4.750749 , -5.119683 , -5.528666 , -5.616266 , -5.3663597,\n",
      "       -6.1391945, -6.1391945, -5.1813793, -4.7497535, -4.8749633,\n",
      "       -5.1567154, -6.735251 , -4.6446157, -4.5454125, -4.441026 ,\n",
      "       -4.9100947, -5.409044 , -5.352457 , -5.133142 , -5.264436 ,\n",
      "       -5.137753 , -5.068745 , -4.9458613, -4.9618473, -5.120331 ,\n",
      "       -5.6496787, -5.7616963, -6.721335 , -4.3424373, -4.936245 ,\n",
      "       -5.7860436, -4.919562 , -4.9502397, -4.7834105, -4.85887  ,\n",
      "       -5.609895 , -5.6579533, -6.0152946, -5.217724 , -5.3027987,\n",
      "       -5.680078 , -5.1126523, -5.650331 , -5.7413373, -4.633413 ,\n",
      "       -5.0438538, -4.665066 , -4.6280417, -4.900148 , -4.946823 ,\n",
      "       -5.0032167, -4.984532 , -6.0391145, -4.641065 , -5.831198 ,\n",
      "       -5.7989974, -4.956062 , -4.9672146, -4.5334344, -4.5334344,\n",
      "       -5.206225 , -5.681801 , -4.809612 , -4.668732 , -6.2024975,\n",
      "       -6.151879 , -4.8081455], dtype=float32)}]\n"
     ]
    }
   ],
   "source": [
    "predictions_list = []\n",
    "metric = deepchem.deepchem.metrics.Metric(deepchem.deepchem.metrics.mean_absolute_error)\n",
    "ecfpFeat = deepchem.deepchem.feat.CircularFingerprint(radius=4)\n",
    "convMolFeat = deepchem.deepchem.feat.ConvMolFeaturizer()\n",
    "xgb_reg_ecfp = xgboost.XGBRegressor()\n",
    "xgb_reg_estate = xgboost.XGBRegressor()\n",
    "\n",
    "for seed in [1, 2, 3, 4, 5]:\n",
    "    benchmark = group.get('Caco2_Wang') \n",
    "    \n",
    "    predictions = {}\n",
    "    name = benchmark['name']\n",
    "    train_val, test = benchmark['train_val'], benchmark['test']\n",
    "    train, valid = group.get_train_valid_split(benchmark = name, split_type = 'default', seed = seed)  \n",
    "\n",
    "    #trainMol = train.iloc[:,1].map(lambda x: Chem.MolFromSmiles(x))\n",
    "    #validMol = valid.iloc[:,1].map(lambda x: Chem.MolFromSmiles(x))\n",
    "    testMol = test.iloc[:,1].map(lambda x: Chem.MolFromSmiles(x))\n",
    "    train_valMol = train_val.iloc[:,1].map(lambda x: Chem.MolFromSmiles(x))\n",
    "\n",
    "    #featurize training, valid, and test data for xgboost\n",
    "    #trainFeat = np.stack(np.array(trainMol.map(lambda x: Fingerprinter.FingerprintMol(x)[1])))\n",
    "    #validFeat = np.stack(np.array(validMol.map(lambda x: Fingerprinter.FingerprintMol(x)[1])))\n",
    "    testFeat = np.stack(np.array(testMol.map(lambda x: Fingerprinter.FingerprintMol(x)[1])))\n",
    "    train_valFeat = np.stack(np.array(train_valMol.map(lambda x: Fingerprinter.FingerprintMol(x)[1])))\n",
    "\n",
    "    #featurize training, valid, and test data for xgboost\n",
    "    #ecfp_f_train = ecfpFeat.featurize(train.iloc[:,1].to_list())\n",
    "    #ecfp_f_valid = ecfpFeat.featurize(valid.iloc[:,1].to_list())\n",
    "    ecfp_f_test = ecfpFeat.featurize(test.iloc[:,1].to_list())\n",
    "    train_val_f = ecfpFeat.featurize(train_val.iloc[:,1].to_list())\n",
    "\n",
    "    #featurize training, valid, and test data for the GCN\n",
    "    cv_f_train = convMolFeat.featurize(train.iloc[:,1].to_list())\n",
    "    cv_f_valid = convMolFeat.featurize(valid.iloc[:,1].to_list())\n",
    "    cv_f_test = convMolFeat.featurize(test.iloc[:,1].to_list())\n",
    "\n",
    "    #convert training and validation data into a deepchem dataset for the gcn\n",
    "    gcn_train_data = deepchem.deepchem.data.NumpyDataset(X=cv_f_train, y=np.array(train.iloc[:,2]), ids=np.array(train.iloc[:,1].to_list()))\n",
    "    gcn_valid_data = deepchem.deepchem.data.NumpyDataset(X=cv_f_valid, y=np.array(valid.iloc[:,2]), ids=np.array(valid.iloc[:,1].to_list()))\n",
    "\n",
    "    #fit data on GCN\n",
    "    reg = deepchem.deepchem.models.GraphConvModel(\n",
    "        n_tasks=1, \n",
    "        dropout=.0005,\n",
    "        dense_layer_size=1063,\n",
    "        graph_conv_layers=[128, 128, 128],\n",
    "        mode=\"regression\",)\n",
    "    callback = deepchem.deepchem.models.ValidationCallback(gcn_valid_data, 1000, metric)\n",
    "    reg.fit(gcn_train_data, nb_epoch=100, callbacks=callback)\n",
    "\n",
    "    #predict values on gcn and reshape array (reshape value is 20% of dataset size)\n",
    "    gcn_pred = reg.predict_on_batch(X=np.array(cv_f_test)).reshape(182,)\n",
    "\n",
    "    #fit xgboost model and store np ndarray in xgb_pred\n",
    "    xgb_reg_estate.fit(X=train_valFeat, y=train_val.iloc[:,2], eval_metric=\"mae\", verbose=True)\n",
    "    xgb_reg_ecfp.fit(X=train_val_f, y=train_val.iloc[:,2], eval_metric=\"mae\")\n",
    "    pred_estate = xgb_reg_estate.predict(X=testFeat)\n",
    "    pred_ecfp = xgb_reg_ecfp.predict(X=ecfp_f_test)\n",
    "    \n",
    "    #pred_ak = akReg.predict(rdkit_f_test)\n",
    "\n",
    "    # store test predictions in y_pred_test\n",
    "    y_pred_test = np.mean([ pred_estate, pred_ecfp, gcn_pred ], axis=0)\n",
    "\n",
    "    print(\"predictions for run #\")\n",
    "    print(seed)\n",
    "    print(y_pred_test[0:5])\n",
    "        \n",
    "    predictions[name] = y_pred_test\n",
    "    predictions_list.append(predictions)\n",
    "\n",
    "print(\"Prediction List:\")\n",
    "print(predictions_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'caco2_wang': {'mae': 0.397}}\n",
      "{'caco2_wang': {'mae': 0.437}}\n",
      "{'caco2_wang': {'mae': 0.389}}\n",
      "{'caco2_wang': {'mae': 0.362}}\n",
      "{'caco2_wang': {'mae': 0.344}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'caco2_wang': [0.386, 0.032]}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in predictions_list:\n",
    "    print(group.evaluate(i))\n",
    "\n",
    "group.evaluate_many(predictions_list)"
   ]
  }
 ],
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