tabular.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Found local copy...\n",
      "Loading...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "from tdc.single_pred import Tox\n",
    "import deepchem\n",
    "data = Tox(name=\"LD50_Zhu\")\n",
    "df = data.get_data()\n",
    "splits = data.get_split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import autokeras as ak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6646\n"
     ]
    }
   ],
   "source": [
    "#smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O']\n",
    "\n",
    "featurizer = deepchem.deepchem.feat.RDKitDescriptors()\n",
    "f = featurizer.featurize(df.iloc[:,1].to_list())\n",
    "#print(f[0])\n",
    "\n",
    "total_size = int(f.shape[0])\n",
    "train_size = int(total_size * 0.9)\n",
    "\n",
    "print(train_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 10 Complete [00h 01m 23s]\n",
      "val_loss: 0.7160578966140747\n",
      "\n",
      "Best val_loss So Far: 0.7004590034484863\n",
      "Total elapsed time: 00h 10m 56s\n",
      "INFO:tensorflow:Oracle triggered exit\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/52\n",
      "141/141 [==============================] - 8s 12ms/step - loss: 97.6243 - mean_squared_error: 97.6243\n",
      "Epoch 2/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 4.3045 - mean_squared_error: 4.3045\n",
      "Epoch 3/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 5.2527 - mean_squared_error: 5.2527\n",
      "Epoch 4/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 10.0822 - mean_squared_error: 10.0822\n",
      "Epoch 5/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 56.2699 - mean_squared_error: 56.2699\n",
      "Epoch 6/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 220.0311 - mean_squared_error: 220.0311\n",
      "Epoch 7/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 204.5233 - mean_squared_error: 204.5233A: \n",
      "Epoch 8/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 71.5431 - mean_squared_error: 71.5431\n",
      "Epoch 9/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 11.4573 - mean_squared_error: 11.4573\n",
      "Epoch 10/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 3.2314 - mean_squared_error: 3.2314\n",
      "Epoch 11/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 1.8413 - mean_squared_error: 1.8413\n",
      "Epoch 12/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 1.3311 - mean_squared_error: 1.3311\n",
      "Epoch 13/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 1.2530 - mean_squared_error: 1.2530\n",
      "Epoch 14/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 1.1416 - mean_squared_error: 1.1416\n",
      "Epoch 15/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 1.2884 - mean_squared_error: 1.2884\n",
      "Epoch 16/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 1.4353 - mean_squared_error: 1.4353\n",
      "Epoch 17/52\n",
      "141/141 [==============================] - 2s 12ms/step - loss: 2.4212 - mean_squared_error: 2.4212\n",
      "Epoch 18/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 4.9236 - mean_squared_error: 4.9236\n",
      "Epoch 19/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 15.1282 - mean_squared_error: 15.1282\n",
      "Epoch 20/52\n",
      "141/141 [==============================] - 2s 11ms/step - loss: 48.2325 - mean_squared_error: 48.2325\n",
      "Epoch 21/52\n",
      "141/141 [==============================] - 2s 14ms/step - loss: 105.3258 - mean_squared_error: 105.3258 0s - loss: 119.3112 - mean_squared_error\n",
      "Epoch 22/52\n",
      "141/141 [==============================] - 2s 15ms/step - loss: 123.0236 - mean_squared_error: 123.0236\n",
      "Epoch 23/52\n",
      "141/141 [==============================] - 1s 10ms/step - loss: 29.6562 - mean_squared_error: 29.6562\n",
      "Epoch 24/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 3.9624 - mean_squared_error: 3.9624\n",
      "Epoch 25/52\n",
      "141/141 [==============================] - 1s 7ms/step - loss: 1.0356 - mean_squared_error: 1.0356\n",
      "Epoch 26/52\n",
      "141/141 [==============================] - 1s 7ms/step - loss: 0.8252 - mean_squared_error: 0.8252\n",
      "Epoch 27/52\n",
      "141/141 [==============================] - 1s 7ms/step - loss: 0.7873 - mean_squared_error: 0.7873\n",
      "Epoch 28/52\n",
      "141/141 [==============================] - 1s 7ms/step - loss: 0.7699 - mean_squared_error: 0.7699\n",
      "Epoch 29/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7595 - mean_squared_error: 0.7595\n",
      "Epoch 30/52\n",
      "141/141 [==============================] - 1s 8ms/step - loss: 0.7541 - mean_squared_error: 0.7541\n",
      "Epoch 31/52\n",
      "141/141 [==============================] - 1s 6ms/step - loss: 0.7463 - mean_squared_error: 0.7463\n",
      "Epoch 32/52\n",
      "141/141 [==============================] - 1s 6ms/step - loss: 0.7359 - mean_squared_error: 0.7359\n",
      "Epoch 33/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7297 - mean_squared_error: 0.7297\n",
      "Epoch 34/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7343 - mean_squared_error: 0.7343\n",
      "Epoch 35/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7192 - mean_squared_error: 0.7192\n",
      "Epoch 36/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7228 - mean_squared_error: 0.7228\n",
      "Epoch 37/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.7439 - mean_squared_error: 0.7439\n",
      "Epoch 38/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.8152 - mean_squared_error: 0.8152\n",
      "Epoch 39/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 1.1843 - mean_squared_error: 1.1843\n",
      "Epoch 40/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 6.3810 - mean_squared_error: 6.3810\n",
      "Epoch 41/52\n",
      "141/141 [==============================] - 1s 6ms/step - loss: 38.9287 - mean_squared_error: 38.9287\n",
      "Epoch 42/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 101.8631 - mean_squared_error: 101.8631\n",
      "Epoch 43/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 18.3047 - mean_squared_error: 18.3047\n",
      "Epoch 44/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 2.5978 - mean_squared_error: 2.5978\n",
      "Epoch 45/52\n",
      "141/141 [==============================] - 1s 6ms/step - loss: 0.7812 - mean_squared_error: 0.7812\n",
      "Epoch 46/52\n",
      "141/141 [==============================] - 1s 8ms/step - loss: 0.7059 - mean_squared_error: 0.7059\n",
      "Epoch 47/52\n",
      "141/141 [==============================] - 1s 9ms/step - loss: 0.6742 - mean_squared_error: 0.6742\n",
      "Epoch 48/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.6504 - mean_squared_error: 0.6504\n",
      "Epoch 49/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.6380 - mean_squared_error: 0.6380\n",
      "Epoch 50/52\n",
      "141/141 [==============================] - 1s 8ms/step - loss: 0.6246 - mean_squared_error: 0.6246\n",
      "Epoch 51/52\n",
      "141/141 [==============================] - 1s 8ms/step - loss: 0.6158 - mean_squared_error: 0.6158\n",
      "Epoch 52/52\n",
      "141/141 [==============================] - 1s 5ms/step - loss: 0.6051 - mean_squared_error: 0.6051\n",
      "INFO:tensorflow:Assets written to: .\\structured_data_regressor\\best_model\\assets\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x24b24f92c40>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newData = pd.DataFrame(f)\n",
    "#print(newData)\n",
    "train_data = newData[:train_size]\n",
    "test_data = newData[train_size:]\n",
    "\n",
    "train_data_y = df.iloc[:train_size,2]\n",
    "test_data_y = df.iloc[train_size:total_size,2]\n",
    "\n",
    "reg = ak.StructuredDataRegressor(overwrite=True, max_trials=50, metrics=['mae'])\n",
    "reg.fit(x=train_data, y=train_data_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      " [1.876107  ]\n",
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      " [2.219726  ]\n",
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      " [2.929281  ]\n",
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      " [3.767925  ]\n",
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      " [2.511669  ]\n",
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      " [1.8975279 ]\n",
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      " [2.4572415 ]\n",
      " [2.427338  ]\n",
      " [1.6402252 ]\n",
      " [2.0223594 ]\n",
      " [2.0827239 ]\n",
      " [2.8834646 ]\n",
      " [2.1021025 ]\n",
      " [2.1260564 ]\n",
      " [2.987314  ]\n",
      " [3.1880438 ]\n",
      " [3.1945164 ]\n",
      " [2.3012052 ]\n",
      " [2.8907886 ]\n",
      " [2.177905  ]\n",
      " [3.1681702 ]\n",
      " [2.6727586 ]\n",
      " [2.385788  ]\n",
      " [1.5636845 ]\n",
      " [2.975863  ]\n",
      " [2.8772483 ]\n",
      " [3.3844247 ]\n",
      " [2.384265  ]\n",
      " [2.1124778 ]\n",
      " [2.3577752 ]\n",
      " [3.0227308 ]\n",
      " [4.7960906 ]\n",
      " [2.9758282 ]\n",
      " [2.7859502 ]\n",
      " [2.668057  ]\n",
      " [3.2430842 ]\n",
      " [2.9421992 ]\n",
      " [2.080914  ]\n",
      " [3.2960913 ]\n",
      " [3.0820987 ]\n",
      " [2.5276673 ]\n",
      " [2.3309097 ]\n",
      " [1.6846887 ]\n",
      " [2.61199   ]\n",
      " [1.9187182 ]\n",
      " [2.6291804 ]\n",
      " [3.7772596 ]\n",
      " [2.945617  ]\n",
      " [3.6654167 ]\n",
      " [2.4342196 ]\n",
      " [2.7233326 ]\n",
      " [2.4540625 ]\n",
      " [1.8165138 ]\n",
      " [3.8404744 ]\n",
      " [2.3133762 ]\n",
      " [2.5481293 ]]\n",
      "16/16 [==============================] - 1s 6ms/step - loss: 0.5681 - mean_squared_error: 0.5681\n",
      "[0.5681450366973877, 0.5681450366973877]\n"
     ]
    }
   ],
   "source": [
    "predicted_y = reg.predict(test_data)\n",
    "print(predicted_y)\n",
    "print(reg.evaluate(x=test_data, y=test_data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4500    2.833\n",
      "4501    1.828\n",
      "4502    1.413\n",
      "4503    2.245\n",
      "4504    1.560\n",
      "        ...  \n",
      "4995    2.045\n",
      "4996    1.329\n",
      "4997    5.213\n",
      "4998    2.975\n",
      "4999    2.062\n",
      "Name: Y, Length: 500, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(test_data_y)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
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  "kernelspec": {
   "display_name": "Python 3.9.7 64-bit ('deepchem-test': conda)",
   "language": "python",
   "name": "python3"
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