20200612- AKT model2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [08:44:31] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from rdkit import Chem as ch\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.feature_selection import SelectFromModel, RFECV, SelectKBest\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import pickle as pkl\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>SPLIT DATA FUNCTION</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def split_data(X_new):\n",
    "    X_final, validate = np.split(X_new.sample(frac=1), [int(.8*len(X_new))])\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_final.drop(columns=['pChemBL'],inplace=False), X_final['pChemBL'], test_size=0.2, random_state=42)\n",
    "    X_validate = validate.drop(columns='pChemBL',inplace=False).copy()\n",
    "    y_validate = validate['pChemBL']\n",
    "    return X_train, X_test, X_validate, y_train, y_test, y_validate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>TRAIN RFR FUNCTION</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate):\n",
    "    model2 = RandomForestRegressor(n_estimators= 150)\n",
    "    model2.fit(X_train,y_train)\n",
    "    r_square = model2.score(X_train,y_train)\n",
    "    rmse = np.sqrt(mean_squared_error(model2.predict(X_train),y_train))\n",
    "    print(\"Training set results:\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "    q_square = model2.score(X_test,y_test)\n",
    "    rmse = np.sqrt(mean_squared_error(model2.predict(X_test),y_test))\n",
    "    print(\"Test set results\\nRMSE = {}\\t Q^2 = {}\".format(rmse,q_square))\n",
    "    q_square = model2.score(X_validate,y_validate)\n",
    "    rmse = np.sqrt(mean_squared_error(y_validate,model2.predict(X_validate)))\n",
    "    print(\"Validation set results\\nRMSE = {}\\t Q^2 = {}\".format(rmse,q_square))    \n",
    "    return model2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>AKT MODEL-2</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nAcid</th>\n",
       "      <th>ALogP</th>\n",
       "      <th>ALogp2</th>\n",
       "      <th>AMR</th>\n",
       "      <th>apol</th>\n",
       "      <th>naAromAtom</th>\n",
       "      <th>nAromBond</th>\n",
       "      <th>nAtom</th>\n",
       "      <th>nHeavyAtom</th>\n",
       "      <th>nH</th>\n",
       "      <th>...</th>\n",
       "      <th>WTPT-1</th>\n",
       "      <th>WTPT-2</th>\n",
       "      <th>WTPT-3</th>\n",
       "      <th>WTPT-4</th>\n",
       "      <th>WTPT-5</th>\n",
       "      <th>WPATH</th>\n",
       "      <th>WPOL</th>\n",
       "      <th>XLogP</th>\n",
       "      <th>Zagreb</th>\n",
       "      <th>pChemBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.7109</td>\n",
       "      <td>0.505379</td>\n",
       "      <td>70.4932</td>\n",
       "      <td>73.378583</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>70.514766</td>\n",
       "      <td>2.073964</td>\n",
       "      <td>27.938805</td>\n",
       "      <td>11.267053</td>\n",
       "      <td>16.671753</td>\n",
       "      <td>3404</td>\n",
       "      <td>59</td>\n",
       "      <td>2.188</td>\n",
       "      <td>184</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.4578</td>\n",
       "      <td>2.125181</td>\n",
       "      <td>44.0418</td>\n",
       "      <td>64.304653</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>65.167083</td>\n",
       "      <td>2.102164</td>\n",
       "      <td>17.955201</td>\n",
       "      <td>2.565237</td>\n",
       "      <td>15.389964</td>\n",
       "      <td>2593</td>\n",
       "      <td>55</td>\n",
       "      <td>6.575</td>\n",
       "      <td>180</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.005595</td>\n",
       "      <td>27.2874</td>\n",
       "      <td>69.268860</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>76.232497</td>\n",
       "      <td>2.060338</td>\n",
       "      <td>31.795843</td>\n",
       "      <td>2.556014</td>\n",
       "      <td>22.098211</td>\n",
       "      <td>4979</td>\n",
       "      <td>56</td>\n",
       "      <td>5.589</td>\n",
       "      <td>198</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-10.0458</td>\n",
       "      <td>100.918098</td>\n",
       "      <td>183.2952</td>\n",
       "      <td>142.418303</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>135</td>\n",
       "      <td>64</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>125.580825</td>\n",
       "      <td>1.962200</td>\n",
       "      <td>61.611922</td>\n",
       "      <td>20.276064</td>\n",
       "      <td>41.335857</td>\n",
       "      <td>21440</td>\n",
       "      <td>92</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>302</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.7897</td>\n",
       "      <td>3.203026</td>\n",
       "      <td>32.3564</td>\n",
       "      <td>58.685481</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>62.402934</td>\n",
       "      <td>2.080098</td>\n",
       "      <td>16.872064</td>\n",
       "      <td>13.655728</td>\n",
       "      <td>3.216336</td>\n",
       "      <td>2319</td>\n",
       "      <td>52</td>\n",
       "      <td>4.844</td>\n",
       "      <td>168</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1209 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nAcid    ALogP      ALogp2       AMR        apol  naAromAtom  nAromBond  \\\n",
       "0      0  -0.7109    0.505379   70.4932   73.378583          16         18   \n",
       "1      0  -1.4578    2.125181   44.0418   64.304653          18         20   \n",
       "2      0   0.0748    0.005595   27.2874   69.268860          27         29   \n",
       "3      0 -10.0458  100.918098  183.2952  142.418303           6          6   \n",
       "4      0  -1.7897    3.203026   32.3564   58.685481          21         23   \n",
       "\n",
       "   nAtom  nHeavyAtom  nH  ...      WTPT-1    WTPT-2     WTPT-3     WTPT-4  \\\n",
       "0     65          34  31  ...   70.514766  2.073964  27.938805  11.267053   \n",
       "1     52          31  21  ...   65.167083  2.102164  17.955201   2.565237   \n",
       "2     57          37  20  ...   76.232497  2.060338  31.795843   2.556014   \n",
       "3    135          64  71  ...  125.580825  1.962200  61.611922  20.276064   \n",
       "4     47          30  17  ...   62.402934  2.080098  16.872064  13.655728   \n",
       "\n",
       "      WTPT-5  WPATH  WPOL  XLogP  Zagreb  pChemBL  \n",
       "0  16.671753   3404    59  2.188     184     6.82  \n",
       "1  15.389964   2593    55  6.575     180     7.64  \n",
       "2  22.098211   4979    56  5.589     198     4.30  \n",
       "3  41.335857  21440    92 -0.194     302     6.03  \n",
       "4   3.216336   2319    52  4.844     168     6.42  \n",
       "\n",
       "[5 rows x 1209 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= pd.read_csv('no_zeros_no_chembl.csv')\n",
    "#X.drop(columns=['ChemBLID'],inplace=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1209, 1209)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = X.corr()\n",
    "correlation_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([345., 271., 217., 159., 114.,  69.,  19.,   7.,   5.,   2.]),\n",
       " array([6.86000008e-05, 3.71605334e-02, 7.42524668e-02, 1.11344400e-01,\n",
       "        1.48436334e-01, 1.85528267e-01, 2.22620200e-01, 2.59712134e-01,\n",
       "        2.96804067e-01, 3.33896001e-01, 3.70987934e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Absolute value of Correlation with pChemBL')\n",
    "plt.hist((correlation_matrix.iloc[:1208,1208]).abs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "757\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "757"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_cols_index = []\n",
    "for cols in range(1208):\n",
    "    if correlation_matrix.abs().iloc[cols,1208] < 0.1:\n",
    "        bad_cols_index.append(cols)\n",
    "print(len(bad_cols_index))\n",
    "\n",
    "bad_cols2 = []\n",
    "cols = X.columns\n",
    "for index in bad_cols_index:\n",
    "    bad_cols2.append(cols[index])\n",
    "len(bad_cols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(columns = bad_cols2, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2861, 452)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([345., 271., 217., 159., 114.,  69.,  19.,   7.,   5.,   2.]),\n",
       " array([6.86000008e-05, 3.71605334e-02, 7.42524668e-02, 1.11344400e-01,\n",
       "        1.48436334e-01, 1.85528267e-01, 2.22620200e-01, 2.59712134e-01,\n",
       "        2.96804067e-01, 3.33896001e-01, 3.70987934e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAbyklEQVR4nO3de5RcZZ3u8e/DLcg1gTQMuUijE46GORIkAo6KDDByGwmuQQUvREQDM+E4znBchss64ghOnIWijAwYBAmjXHJANAPoMdzCMArYYAyEyBBCICEhaRLCXcaE3/ljv012uqu6qruquiuvz2etWr3rrX351a6qp956d/UuRQRmZpaXrYa7ADMzaz6Hu5lZhhzuZmYZcribmWXI4W5mliGHu5lZhhzuAyTpakkXNHmdn5F0bzPX2QqS7pb0uWHY7t9IWi3pZUm7D/X2a5HUKSkkbTPI5c+R9P1m19Ws7Q/181PSMklHDtX2cuVwryIF2fOSRgx3LWVbyhtBs0jaFvgW8KGI2Cki1laYZztJ50t6XNIrKRyuktQ51PXWIukwSSvKbRHx9YgY8jfNSttv9I2qHpJ2kfRtSU+nN+wl6froVm2zSh099/XldFkt6V/Tc65nni32jcbhXkEKhQ8AARw/rMXYnsD2wKJ+5rmR4nH6BLArsD/wIHDEQDdWKdRaGXR/bCRtB9wB7AccDewC/DmwFjhomMoaGRE7Af8TeC8wfZjqaCqHe2WnAPcBVwNTK9w+WtI8SS9Jmi9pbwAVLpa0RtILkhZK+rN0266SrpHULekpSedJ6rP/K/WceoZDJL0TuBx4b+pprE+3j5B0UeoJrZZ0uaS3VFj3CEnre2pKbR2SXpO0h6RRkm5JNT6fpsdV2kGpp/zDanWn+3ulpFWSnpF0gaStq6xrROq5rUyXb6e2fYHH0mzrJd1ZYdkjgb8EpkTEryNiQ0S8EBGXRsSVaZ4xkuZKWpd6iZ/vdT9ulPRDSS8Cn6nStpWkGZKekLRW0hxJu1W5P6dKWpyeH0slnZ7adwR+Bowp9RbHVNiXx0talB6ru9Pj3nPbMkn/Oz23XpB0g6Ttq9TxlKQD0/Sn0uMzMV3/nKSfVHgs7ynt75clvbe0vovS8+JJScdU2mapxrMlPZrm/0GpxlOAtwIfiYhHI+KNiFgTEV+LiNtKq5lU7T5K+itJC9L++aWkd/Xa9pfSsq+k5+Cekn6WHo/bJY2qVHdErAHmAROr3bcticO9slOAH6XLUZL27HX7J4GvAaOBBWk+gA8BhwL7AiOBj1P0SAD+haJX+Tbgg2kbpw6kqIhYDJwB/CoNUYxMN30jbXMS8KfAWOD/VFj+deDHwMml5o8B89MTeyvgB8DeFC/A14DvDqTGktnAhlTPART7ptrQw7nAIan+/Sl6cOdFxH9R9PCg6F0dXmHZI4EHImJ5P7VcB6wAxgAnAl+XVO7VT6Ho/Y9k02PZu+0LwAkUj90Y4Hng0irbWwP8FUWv9FTgYknvjohXgGOAlenx2ykiVpYXTG9o1wFfBDqA24B/V9Hj7fExil7vPsC7gM9UqWM+cFiaPhRYmurvuT6/wjKHpr8jU32/StcPpnijHQ38M3ClJFXZLhSvkaOAt1M8N89L7UcCP4+Il/tZFqrcR0nvBq4CTgd2B74HzNXmw6d/TfGGvy/wYYo31HNS7VtRPJZ9SBqTar6vRm1bBId7L5LeTxFucyLiQeAJio/7ZbdGxD0pLM+l6EmPB/4A7Ay8A1BELI6IVanH+nHg7Ih4KSKWAd8EPt2EegV8Hvj7iFgXES8BXwdOqrLItWwe7p9IbUTE2oi4KSJeTeu5kE1hMJCa9qQIsS9GxCvpjePifmr6JPCPqQfXDXyV+vfN7sCqfmoZD7wf+HJE/D4iFgDf77X+X0XET1Iv8rUqbacD50bEivS4nw+cqApDNhFxa0Q8EYX5wC8ohvnq8XGK59e8iPgDcBHwFoqhix6XRMTKiFgH/DvFm2Il89n0+H0A+KfS9Q9SOdyreSoiroiIjRRv3HtRDJlV892IWJ5qvJBNz7l+H6+Savfx88D3IuL+iNgYEbOB1yk6Bz3+JSJWR8QzwH8A90fEb9LjdjNFZ6PsORWfgp8BXqF4U9/iOdz7mgr8IiKeS9evpe/QzJu9xNQDWQeMiYg7KXq6lwKrJc2StAtFj2E74KnSOp6i6GE3qgPYAXgwfUxdD/w8tVdyJ/AWSQerGE6aRPGER9IOkr6XPs6/SPERfWS14ZR+7A1sC6wq1fQ9YI8q84+h774ZU+e21lIETTVjgJ43vfL6y/u+Uq+/d9vewM2l+7MY2EiFgJN0jKT70jDQeuBYiudAPTbbFxHxRqqlXO+zpelXgZ2qrGs+8AFJfwJsDdwAvE/FMaVdKT511uvNbUbEq2my2nZh8/1XfjxrPV59tsfm93Fv4KyexyHt3/Fs/nxZXZp+rcL13nWPTp+CdwD+k+L1s8VzuJeoGKf+GPBBSc9Kehb4e2B/SfuXZh1fWmYnYDdgJUBEXBIRB1IMJ+wLfAl4jqJXv3dpHW+l6Cn09kr6u0Op7U9K071P4/kcxRN2v4gYmS67pgNEfaSwmEPRk/oEcEsp+M4C/gdwcETswqaP6JU+fr/ST43LKXpTo0s17RIR+1HZSvrum5VV5u3tduAgVTk2kNazm6Sde62/vO8rnRq1d9ty4JjS/RkZEdun3uGb0vDATRQ97j1TaNzGpn1Y6zSsm+2L9MlsPJWfK/2KiCUUwfgF4J70OD8LTAPuTc+FPosNdDtVjC9Nlx/P2ymGOncc5HqXAxf2ehx2iIjrGikWIH1Cu5rik/iQfnOnFRzumzuBojc2kaJHOwl4J8VHu1NK8x0r6f1pHPRrFB/7lkt6T+oRb0sRfr8HNqaPsnOACyXtnHrM/wD8kF7SsMQzwKckbS3psxTjlj1WA+N6xmDTC/QKinHdPQAkjZV0VD/381qKj/+fTNM9dqZ4o1iv4mDhV/pZxwLgUElvlbQrcHbpPqyiGIr4poqvvW0l6e2Sqg3xXAecp+Lg7miK4wV99k0lEXE7xUGwmyUdKGmbtI/PkPTZNBb/S+CfJG2fDr6dxqax9XpdTvH49Rw875A0pcJ82wEjgG5gQzrw+KHS7auB3dM+q2QOcJykI9Lz6CyKN8pfDrDeHvOBM9k0BHN3r+u9dQNvUBwbasR0SePS8+gcik8NAP9GEdA3SXpHem7sruK79sfWsd4rgDPS60ySdpR0XK8370FJb8yfpngDLH/ldtv03Om5bBHfnnK4b24q8IOIeDoinu25UAy1fLL0oF5LEXzrgAMpQhKKA2hXUBxse4riCXJRuu1/UQT+UuDetI6rqtTxeYoe/1qKTwDlF/adFF8LfFZSz9DRl4ElwH1pOOV2ih54RRFxf6plDMXBph7fphjffY7ioFLVj6cRMY/iBbuQ4muHt/Sa5RSKoHuUYn/cSPWP4xcAXWldDwMPpbZ6nUjRO74BeAF4BJhMsR+g+JTSSdF7vBn4Sqp/IL4DzAV+Iekliv1zcO+ZUu/4CxQh/TzFp6O5pdt/R/FmtjQNK4zptfxjwKcoDsA/R3FA8MMR8d8DrLfHfIo37XuqXO9d/6sUY+T/meo7pNJ8dbiW4g1+abpckNb/OsVB1d9RvCm/CDxAMWx1f62VRkQXxevjuxT7dwnVDyjXa72klyneeN8LHB+x2Q9d3EbR6em5nN/g9oaEwj/WYWZNJGkZ8Ln0qcqGiXvuZmYZcribmWXIwzJmZhlyz93MLENt8ZWe0aNHR2dn53CXYWa2RXnwwQefi4iK/7DYFuHe2dlJV1fXcJdhZrZFkfRUtds8LGNmliGHu5lZhhzuZmYZqhnu6VwKD0j6rYofEPhqar9axUn7F6TLpNQuSZeo+FGEhSrOv2xmZkOongOqrwOHR8TL6URG90rqOR/JlyKi97mPjwEmpMvBwGVUOAeHmZm1Ts2ee/rBgZ5fTdk2Xfr7z6cpwDVpufsozgdez/mbzcysSeoac0+nnl1A8fNh89JZBaE4BepCFb8b2vMzV2PZ/ET9K6jwoxSSpknqktTV3d3dwF0wM7Pe6gr39HNWk4BxFD+M8GcU5+9+B/Aeih+r+HKavdIPO/Tp6UfErIiYHBGTOzqq/WiQmZkNxoC+LRMR6ylO9n90RKxKQy+vU/yo8kFpthVs/iss46j/V3XMzKwJah5QldQB/CEi1qefoTsS+IakvaL48WdR/ILRI2mRucCZkq6nOJD6QvplnpbonHFrq1Zd07KZxw3bts3M+lPPt2X2Amar+JHkrYA5EXGLpDtT8IviJ9fOSPPfRvGDwD2/33hq88s2M7P+1Az3iFgIHFCh/fAq8wcwvfHSzMxssPwfqmZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGaoZ7pK2l/SApN9KWiTpq6l9H0n3S3pc0g2StkvtI9L1Jen2ztbeBTMz662envvrwOERsT8wCTha0iHAN4CLI2IC8DxwWpr/NOD5iPhT4OI0n5mZDaGa4R6Fl9PVbdMlgMOBG1P7bOCEND0lXSfdfoQkNa1iMzOrqa4xd0lbS1oArAHmAU8A6yNiQ5plBTA2TY8FlgOk218Adq+wzmmSuiR1dXd3N3YvzMxsM3WFe0RsjIhJwDjgIOCdlWZLfyv10qNPQ8SsiJgcEZM7OjrqrdfMzOowoG/LRMR64G7gEGCkpG3STeOAlWl6BTAeIN2+K7CuGcWamVl96vm2TIekkWn6LcCRwGLgLuDENNtU4Kdpem66Trr9zojo03M3M7PW2ab2LOwFzJa0NcWbwZyIuEXSo8D1ki4AfgNcmea/Evg3SUsoeuwntaBuMzPrR81wj4iFwAEV2pdSjL/3bv898NGmVGdmZoPi/1A1M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDNVz4jCronPGrcOy3WUzjxuW7ZrZlsM9dzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDNUMd0njJd0labGkRZL+LrWfL+kZSQvS5djSMmdLWiLpMUlHtfIOmJlZX/WcfmADcFZEPCRpZ+BBSfPSbRdHxEXlmSVNBE4C9gPGALdL2jciNjazcDMzq65mzz0iVkXEQ2n6JWAxMLafRaYA10fE6xHxJLAEOKgZxZqZWX0GNOYuqRM4ALg/NZ0paaGkqySNSm1jgeWlxVZQ4c1A0jRJXZK6uru7B1y4mZlVV3e4S9oJuAn4YkS8CFwGvB2YBKwCvtkza4XFo09DxKyImBwRkzs6OgZcuJmZVVdXuEvaliLYfxQRPwaIiNURsTEi3gCuYNPQywpgfGnxccDK5pVsZma11PNtGQFXAosj4lul9r1Ks30EeCRNzwVOkjRC0j7ABOCB5pVsZma11PNtmfcBnwYelrQgtZ0DnCxpEsWQyzLgdICIWCRpDvAoxTdtpvubMmZmQ6tmuEfEvVQeR7+tn2UuBC5soC4zM2uA/0PVzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczswzVcz53azOdM24dtm0vm3ncsG3bzOrnnruZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGaoZ7pLGS7pL0mJJiyT9XWrfTdI8SY+nv6NSuyRdImmJpIWS3t3qO2FmZpurp+e+ATgrIt4JHAJMlzQRmAHcERETgDvSdYBjgAnpMg24rOlVm5lZv2qGe0SsioiH0vRLwGJgLDAFmJ1mmw2ckKanANdE4T5gpKS9ml65mZlVNaAxd0mdwAHA/cCeEbEKijcAYI8021hgeWmxFamt97qmSeqS1NXd3T3wys3MrKq6w13STsBNwBcj4sX+Zq3QFn0aImZFxOSImNzR0VFvGWZmVoe6wl3SthTB/qOI+HFqXt0z3JL+rkntK4DxpcXHASubU66ZmdWjnm/LCLgSWBwR3yrdNBeYmqanAj8ttZ+SvjVzCPBCz/CNmZkNjXrOCvk+4NPAw5IWpLZzgJnAHEmnAU8DH0233QYcCywBXgVObWrFZmZWU81wj4h7qTyODnBEhfkDmN5gXWZm1gD/h6qZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGXK4m5llyOFuZpYhh7uZWYYc7mZmGarnN1TN3tQ549Zh2e6ymccNy3bNtlTuuZuZZcjhbmaWoZrhLukqSWskPVJqO1/SM5IWpMuxpdvOlrRE0mOSjmpV4WZmVl09PfergaMrtF8cEZPS5TYASROBk4D90jL/KmnrZhVrZmb1qRnuEXEPsK7O9U0Bro+I1yPiSWAJcFAD9ZmZ2SA0MuZ+pqSFadhmVGobCywvzbMitfUhaZqkLkld3d3dDZRhZma9DTbcLwPeDkwCVgHfTO2qMG9UWkFEzIqIyRExuaOjY5BlmJlZJYMK94hYHREbI+IN4Ao2Db2sAMaXZh0HrGysRDMzG6hBhbukvUpXPwL0fJNmLnCSpBGS9gEmAA80VqKZmQ1Uzf9QlXQdcBgwWtIK4CvAYZImUQy5LANOB4iIRZLmAI8CG4DpEbGxNaWbmVk1NcM9Ik6u0HxlP/NfCFzYSFFmZtYY/4eqmVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZcjhbmaWIYe7mVmGHO5mZhlyuJuZZajm+dzN2kHnjFuHbdvLZh43bNs2Gyz33M3MMuRwNzPLkMPdzCxDDnczsww53M3MMlQz3CVdJWmNpEdKbbtJmifp8fR3VGqXpEskLZG0UNK7W1m8mZlVVk/P/Wrg6F5tM4A7ImICcEe6DnAMMCFdpgGXNadMMzMbiJrhHhH3AOt6NU8BZqfp2cAJpfZronAfMFLSXs0q1szM6jPYMfc9I2IVQPq7R2ofCywvzbcitfUhaZqkLkld3d3dgyzDzMwqafYBVVVoi0ozRsSsiJgcEZM7OjqaXIaZ2R+3wYb76p7hlvR3TWpfAYwvzTcOWDn48szMbDAGG+5zgalpeirw01L7KelbM4cAL/QM35iZ2dCpeeIwSdcBhwGjJa0AvgLMBOZIOg14Gvhomv024FhgCfAqcGoLajYzsxpqhntEnFzlpiMqzBvA9EaLMjOzxvg/VM3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczswzVPHGY2R+7zhm3Dst2l808bli2a3lwz93MLEMOdzOzDDnczcwy5HA3M8uQw93MLEMOdzOzDDnczcwy5HA3M8tQQ//EJGkZ8BKwEdgQEZMl7QbcAHQCy4CPRcTzjZVpZmYD0Yye+19ExKSImJyuzwDuiIgJwB3pupmZDaFWDMtMAWan6dnACS3YhpmZ9aPRcA/gF5IelDQtte0ZEasA0t89Ki0oaZqkLkld3d3dDZZhZmZljZ447H0RsVLSHsA8Sb+rd8GImAXMApg8eXI0WIeZmZU01HOPiJXp7xrgZuAgYLWkvQDS3zWNFmlmZgMz6HCXtKOknXumgQ8BjwBzgalptqnATxst0szMBqaRYZk9gZsl9azn2oj4uaRfA3MknQY8DXy08TLNzGwgBh3uEbEU2L9C+1rgiEaKMjOzxvg/VM3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkMPdzCxDDnczsww1+mMdZtYinTNuHZbtLpt53LBs15rLPXczsww53M3MMuRwNzPLkMPdzCxDDnczsww53M3MMuRwNzPLkL/nbmabGa7v14O/Y99MLQt3SUcD3wG2Br4fETNbtS0zy4P/cat5WjIsI2lr4FLgGGAicLKkia3YlpmZ9dWqnvtBwJKIWAog6XpgCvBoi7ZnZjZoOQ5FtSrcxwLLS9dXAAeXZ5A0DZiWrr4s6bFBbms08Nwglx0qrrE5XGPzbAl1/lHUqG80tP29q93QqnBXhbbY7ErELGBWwxuSuiJicqPraSXX2ByusXm2hDpdY2Na9VXIFcD40vVxwMoWbcvMzHppVbj/GpggaR9J2wEnAXNbtC0zM+ulJcMyEbFB0pnA/6P4KuRVEbGoFduiCUM7Q8A1NodrbJ4toU7X2ABFRO25zMxsi+LTD5iZZcjhbmaWobYOd0lHS3pM0hJJMyrcPkLSDen2+yV1lm47O7U/JumodqtRUqek1yQtSJfLh7HGQyU9JGmDpBN73TZV0uPpMrVNa9xY2o8tO3BfR43/IOlRSQsl3SFp79Jt7bIf+6uxXfbjGZIeTnXcW/7v9jZ6XVescShf1zVFRFteKA7EPgG8DdgO+C0wsdc8fwtcnqZPAm5I0xPT/COAfdJ6tm6zGjuBR9pkP3YC7wKuAU4ste8GLE1/R6XpUe1UY7rt5TbZj38B7JCm/6b0WLfTfqxYY5vtx11K08cDP0/T7fS6rlbjkLyu67m0c8/9zVMYRMR/Az2nMCibAsxO0zcCR0hSar8+Il6PiCeBJWl97VTjUKlZY0Qsi4iFwBu9lj0KmBcR6yLieWAecHSb1ThU6qnxroh4NV29j+L/O6C99mO1GodKPTW+WLq6I5v+AbJtXtf91Ng22jncK53CYGy1eSJiA/ACsHudyw53jQD7SPqNpPmSPtCC+uqtsRXLDkSj29leUpek+ySd0NzS3jTQGk8DfjbIZQerkRqhjfajpOmSngD+GfjCQJYd5hphaF7XNbXz+dxrnsKgn3nqWbYZGqlxFfDWiFgr6UDgJ5L269UjGKoaW7HsQDS6nbdGxEpJbwPulPRwRDzRpNp61F2jpE8Bk4EPDnTZBjVSI7TRfoyIS4FLJX0COA+YWu+yTdBIjUP1uq6pnXvu9ZzC4M15JG0D7Aqsq3PZYa0xfbRcCxARD1KM8e07TDW2YtmBaGg7EbEy/V0K3A0c0MzikrpqlHQkcC5wfES8PpBlh7nGttqPJdcDPZ8i2mo/lrxZ4xC+rmsb7kH/aheKTxVLKQ6c9BzU2K/XPNPZ/GDlnDS9H5sfeFlKaw68NFJjR09NFAdungF2G44aS/NeTd8Dqk9SHAQclabbrcZRwIg0PRp4nF4Hv4bwsT6A4sU8oVd72+zHfmpsp/04oTT9YaArTbfT67pajUPyuq7rfgzHRgewk48F/is9Gc9Nbf9I0eMA2B74vxQHVh4A3lZa9ty03GPAMe1WI/DXwKL0xHkI+PAw1vgeit7KK8BaYFFp2c+m2pcAp7ZbjcCfAw+n/fgwcNow1ng7sBpYkC5z23A/Vqyxzfbjd9JrYwFwF6VgbaPXdcUah/J1Xevi0w+YmWWoncfczcxskBzuZmYZcribmWXI4W5mliGHu5lZhhzuZmYZcribmWXo/wO/DrzDtr6rVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation_matrix = X.corr()\n",
    "rows, cols = correlation_matrix.shape\n",
    "plt.title('Absolute value of Correlation with pChemBL')\n",
    "plt.hist((correlation_matrix.iloc[:(rows-1),rows-1]).abs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1191"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bad_cols_index = []\n",
    "for cols in range(rows-1):\n",
    "    if correlation_matrix.abs().iloc[cols,rows-1] < 0.25:\n",
    "        bad_cols_index.append(cols)\n",
    "print(len(bad_cols_index))\n",
    "\n",
    "bad_cols2 = []\n",
    "cols = X.columns\n",
    "for index in bad_cols_index:\n",
    "    bad_cols2.append(cols[index])\n",
    "len(bad_cols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATSC4c</th>\n",
       "      <th>ATSC2v</th>\n",
       "      <th>SCH-7</th>\n",
       "      <th>ASP-7</th>\n",
       "      <th>hmin</th>\n",
       "      <th>ETA_BetaP_s</th>\n",
       "      <th>IC1</th>\n",
       "      <th>IC2</th>\n",
       "      <th>SIC2</th>\n",
       "      <th>BIC2</th>\n",
       "      <th>ZMIC1</th>\n",
       "      <th>ZMIC2</th>\n",
       "      <th>MDEO-11</th>\n",
       "      <th>nHeteroRing</th>\n",
       "      <th>n6HeteroRing</th>\n",
       "      <th>nT6HeteroRing</th>\n",
       "      <th>LipinskiFailures</th>\n",
       "      <th>pChemBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.183691</td>\n",
       "      <td>808.584430</td>\n",
       "      <td>0.639198</td>\n",
       "      <td>0.027205</td>\n",
       "      <td>-0.189456</td>\n",
       "      <td>0.69853</td>\n",
       "      <td>3.498205</td>\n",
       "      <td>4.793532</td>\n",
       "      <td>0.795955</td>\n",
       "      <td>0.762645</td>\n",
       "      <td>51.535290</td>\n",
       "      <td>33.179219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.052198</td>\n",
       "      <td>-125.913748</td>\n",
       "      <td>1.013922</td>\n",
       "      <td>0.025619</td>\n",
       "      <td>-0.114131</td>\n",
       "      <td>0.65323</td>\n",
       "      <td>3.560855</td>\n",
       "      <td>4.402672</td>\n",
       "      <td>0.772339</td>\n",
       "      <td>0.718301</td>\n",
       "      <td>58.106835</td>\n",
       "      <td>36.787814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.177672</td>\n",
       "      <td>-435.726504</td>\n",
       "      <td>0.622175</td>\n",
       "      <td>0.031451</td>\n",
       "      <td>-0.014566</td>\n",
       "      <td>0.68243</td>\n",
       "      <td>3.677764</td>\n",
       "      <td>4.767468</td>\n",
       "      <td>0.817342</td>\n",
       "      <td>0.761894</td>\n",
       "      <td>65.634530</td>\n",
       "      <td>39.004854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.568588</td>\n",
       "      <td>-1158.676211</td>\n",
       "      <td>0.405022</td>\n",
       "      <td>0.033239</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>0.63281</td>\n",
       "      <td>3.492463</td>\n",
       "      <td>4.900736</td>\n",
       "      <td>0.692506</td>\n",
       "      <td>0.679765</td>\n",
       "      <td>108.296299</td>\n",
       "      <td>67.713621</td>\n",
       "      <td>2.886374</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.464528</td>\n",
       "      <td>150.179266</td>\n",
       "      <td>1.160056</td>\n",
       "      <td>0.025954</td>\n",
       "      <td>-0.117708</td>\n",
       "      <td>0.64167</td>\n",
       "      <td>3.468889</td>\n",
       "      <td>4.475247</td>\n",
       "      <td>0.805685</td>\n",
       "      <td>0.744481</td>\n",
       "      <td>61.609585</td>\n",
       "      <td>34.166117</td>\n",
       "      <td>0.655185</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ATSC4c       ATSC2v     SCH-7     ASP-7      hmin  ETA_BetaP_s       IC1  \\\n",
       "0  0.183691   808.584430  0.639198  0.027205 -0.189456      0.69853  3.498205   \n",
       "1  0.052198  -125.913748  1.013922  0.025619 -0.114131      0.65323  3.560855   \n",
       "2 -0.177672  -435.726504  0.622175  0.031451 -0.014566      0.68243  3.677764   \n",
       "3 -1.568588 -1158.676211  0.405022  0.033239 -0.177539      0.63281  3.492463   \n",
       "4  0.464528   150.179266  1.160056  0.025954 -0.117708      0.64167  3.468889   \n",
       "\n",
       "        IC2      SIC2      BIC2       ZMIC1      ZMIC2   MDEO-11  nHeteroRing  \\\n",
       "0  4.793532  0.795955  0.762645   51.535290  33.179219  0.000000            4   \n",
       "1  4.402672  0.772339  0.718301   58.106835  36.787814  0.000000            3   \n",
       "2  4.767468  0.817342  0.761894   65.634530  39.004854  0.000000            3   \n",
       "3  4.900736  0.692506  0.679765  108.296299  67.713621  2.886374            1   \n",
       "4  4.475247  0.805685  0.744481   61.609585  34.166117  0.655185            2   \n",
       "\n",
       "   n6HeteroRing  nT6HeteroRing  LipinskiFailures  pChemBL  \n",
       "0             4              4                 0     6.82  \n",
       "1             2              2                 1     7.64  \n",
       "2             2              2                 1     4.30  \n",
       "3             0              0                 3     6.03  \n",
       "4             0              0                 0     6.42  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_try = X.drop(columns=bad_cols2,inplace=False).copy()\n",
    "X_try.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_lol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set results\n",
      "RMSE = 0.9657719395874582\t R^2 = 0.2707232396332827\n"
     ]
    }
   ],
   "source": [
    "model1 = LinearRegression()\n",
    "model1.fit(X_train,y_train)\n",
    "r_square = model1.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(model1.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "rr = Ridge(alpha=0.001)\n",
    "rr1 = Ridge(alpha=1)\n",
    "rr2 = Ridge(alpha=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1000, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rr.fit(X_train,y_train)\n",
    "rr1.fit(X_train,y_train)\n",
    "rr2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set results\n",
      "RMSE = 0.9655870349071923\t R^2 = 0.2710024645143161\n",
      "Test set results\n",
      "RMSE = 0.9669370921556286\t R^2 = 0.2689625107786966\n",
      "Test set results\n",
      "RMSE = 1.0286100605699529\t R^2 = 0.172734822006677\n"
     ]
    }
   ],
   "source": [
    "r_square = rr.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(rr.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "r_square = rr1.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(rr1.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "r_square = rr2.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(rr2.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "l = Lasso(alpha=0.001)\n",
    "l1 = Lasso(alpha=0.1)\n",
    "l2 = Lasso(alpha=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.fit(X_train,y_train)\n",
    "l1.fit(X_train,y_train)\n",
    "l2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set results\n",
      "RMSE = 0.9664722122175726\t R^2 = 0.2696652720460432\n",
      "Test set results\n",
      "RMSE = 1.0458835268612656\t R^2 = 0.1447169722531192\n",
      "Test set results\n",
      "RMSE = 0.9735882297659069\t R^2 = 0.25887094730607263\n"
     ]
    }
   ],
   "source": [
    "r_square = l.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(l.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "r_square = l1.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(l1.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "r_square = l2.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(l2.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=150, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "model2 = RandomForestRegressor(n_estimators= 150)\n",
    "model2.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.3014686431847232\t R^2 = 0.9329120097126015\n",
      "Test set results\n",
      "RMSE = 0.7847904425853084\t Q^2 = 0.5184397631297106\n",
      "Validation set results\n",
      "RMSE = 0.7960686191878112\t Q^2 = 0.553739539653053\n"
     ]
    }
   ],
   "source": [
    "r_square = model2.score(X_train,y_train)\n",
    "rmse = np.sqrt(mean_squared_error(model2.predict(X_train),y_train))\n",
    "print(\"Training set results:\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))\n",
    "q_square = model2.score(X_test,y_test)\n",
    "rmse = np.sqrt(mean_squared_error(model2.predict(X_test),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t Q^2 = {}\".format(rmse,q_square))\n",
    "q_square = model2.score(X_validate,y_validate)\n",
    "rmse = np.sqrt(mean_squared_error(y_validate,model2.predict(X_validate)))\n",
    "print(\"Validation set results\\nRMSE = {}\\t Q^2 = {}\".format(rmse,q_square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATASET 1\n",
      "Training set results:\n",
      "RMSE = 0.3013015244582267\t R^2 = 0.9338124991486569\n",
      "Test set results\n",
      "RMSE = 0.7924468215760563\t Q^2 = 0.5183661839782061\n",
      "Validation set results\n",
      "RMSE = 0.765770334115545\t Q^2 = 0.5680658032462265\n",
      "\n",
      "DATASET 2\n",
      "Training set results:\n",
      "RMSE = 0.2983003916996915\t R^2 = 0.9354876941705035\n",
      "Test set results\n",
      "RMSE = 0.726900194299771\t Q^2 = 0.5712444020795474\n",
      "Validation set results\n",
      "RMSE = 0.7713985827138343\t Q^2 = 0.5712511138991998\n",
      "\n",
      "DATASET 3\n",
      "Training set results:\n",
      "RMSE = 0.2947474000597755\t R^2 = 0.9339368298167487\n",
      "Test set results\n",
      "RMSE = 0.772550057200154\t Q^2 = 0.5801616987660878\n",
      "Validation set results\n",
      "RMSE = 0.7997484510858857\t Q^2 = 0.5489120351355846\n",
      "\n",
      "DATASET 4\n",
      "Training set results:\n",
      "RMSE = 0.29567767099839787\t R^2 = 0.9358886810200453\n",
      "Test set results\n",
      "RMSE = 0.7590063817669721\t Q^2 = 0.5824451119464238\n",
      "Validation set results\n",
      "RMSE = 0.7752016861511418\t Q^2 = 0.5456693117870706\n",
      "\n",
      "DATASET 5\n",
      "Training set results:\n",
      "RMSE = 0.3019129441159326\t R^2 = 0.9327152336201596\n",
      "Test set results\n",
      "RMSE = 0.7616153486246097\t Q^2 = 0.5959014747198224\n",
      "Validation set results\n",
      "RMSE = 0.7637411557509403\t Q^2 = 0.5485966984262335\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "for i in range(5):\n",
    "    print(\"\\nDATASET {}\".format(i+1))\n",
    "    X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_try)\n",
    "    models.append(train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate))\n",
    "    train = X_train.copy()\n",
    "    train['pChemBL'] = y_train\n",
    "    train.to_csv('./data/train'+str(i)+'.csv')\n",
    "    test = X_test.copy()\n",
    "    test['pChemBL'] = y_test\n",
    "    test.to_csv('./data/test'+str(i)+'.csv')\n",
    "    validation = X_validate.copy()\n",
    "    validation['pChemBL'] = y_validate\n",
    "    validation.to_csv('./data/validation'+str(i)+'.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Summary</h3>\n",
    "Best R^2 (train)      = DATASET 4(everything differs only in 10^-3)<br>\n",
    "Best Q^2 (test)       = DATASET 5<br>\n",
    "Best Q^2 (validation) = DATASET 2<br>\n",
    "Overall Best Data split -> <b>DATASET 2</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    plt.figure(figsize=(20,20))\n",
    "    plt.subplot(5,3,3*i+1)\n",
    "    train = pd.read_csv('./data/train'+str(i)+'.csv')\n",
    "    \n",
    "    test = pd.read_csv('./data/test'+str(i)+'.csv')\n",
    "    validate = pd.read_csv('./data/validation'+str(i)+'.csv')\n",
    "    plt.title('TRAIN')\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.hist(train['pChemBL'])\n",
    "    plt.subplot(5,3,3*i+2)\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.title('TEST')\n",
    "    plt.hist(test['pChemBL'],color='green')\n",
    "    plt.subplot(5,3,3*i+3)\n",
    "    plt.title('VALIDATE')\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.hist(validate['pChemBL'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    bins = [4,6,7,8,10]\n",
    "    plt.figure(figsize=(20,20))\n",
    "    plt.subplot(5,3,3*i+1)\n",
    "    train = pd.read_csv('./data/train'+str(i)+'.csv')\n",
    "    test = pd.read_csv('./data/test'+str(i)+'.csv')\n",
    "    validate = pd.read_csv('./data/validation'+str(i)+'.csv')\n",
    "    plt.title('TRAIN')\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.hist(train['pChemBL'],bins=bins)\n",
    "    plt.subplot(5,3,3*i+2)\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.title('TEST')\n",
    "    plt.hist(test['pChemBL'],color='green',bins=bins)\n",
    "    plt.subplot(5,3,3*i+3)\n",
    "    plt.title('VALIDATE')\n",
    "    plt.xlabel('pChemBL')\n",
    "    plt.hist(validate['pChemBL'],color='red',bins=bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Random Forest Regressor with normalisation</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATASET 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.281202157725047\t R^2 = 0.9423484985195044\n",
      "Test set results\n",
      "RMSE = 0.7417899402134879\t Q^2 = 0.5779745956797784\n",
      "Validation set results\n",
      "RMSE = 0.7258734029409973\t Q^2 = 0.6119012173867547\n",
      "\n",
      "DATASET 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.27763963301033506\t R^2 = 0.9441146672668734\n",
      "Test set results\n",
      "RMSE = 0.6932167244248182\t Q^2 = 0.6100595422223682\n",
      "Validation set results\n",
      "RMSE = 0.7335086064538805\t Q^2 = 0.6123357440351114\n",
      "\n",
      "DATASET 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.27820869723828534\t R^2 = 0.9411426303096134\n",
      "Test set results\n",
      "RMSE = 0.7350648110178425\t Q^2 = 0.6199155837891641\n",
      "Validation set results\n",
      "RMSE = 0.7660100304793359\t Q^2 = 0.5861686974907196\n",
      "\n",
      "DATASET 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.28027270673989113\t R^2 = 0.942395120835752\n",
      "Test set results\n",
      "RMSE = 0.732196507113153\t Q^2 = 0.6114221664031111\n",
      "Validation set results\n",
      "RMSE = 0.739923824095135\t Q^2 = 0.5860797466049559\n",
      "\n",
      "DATASET 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  from ipykernel import kernelapp as app\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:20: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.27964618016626125\t R^2 = 0.9422740531337913\n",
      "Test set results\n",
      "RMSE = 0.7193416107386147\t Q^2 = 0.6395157818331578\n",
      "Validation set results\n",
      "RMSE = 0.7321152025772548\t Q^2 = 0.5852072151347352\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "for i in range(5):\n",
    "    print(\"\\nDATASET {}\".format(i+1))\n",
    "    train = pd.read_csv('./data/train'+str(i)+'.csv')\n",
    "    X_train = train.drop(columns='pChemBL',inplace=False)\n",
    "    y_train = train['pChemBL']\n",
    "    scaler = StandardScaler(copy=True)\n",
    "    scaler.fit(X_train)\n",
    "    X = scaler.transform(X_train)#.drop(columns=['ChemBLID'],inplace=False))\n",
    "    X_train = pd.DataFrame(data=X,columns=X_train.columns)\n",
    "    test = pd.read_csv('./data/test'+str(i)+'.csv')\n",
    "    X_test = test.drop(columns='pChemBL',inplace=False)\n",
    "    y_test = test['pChemBL']\n",
    "    X = scaler.transform(X_test)\n",
    "    X_test = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "    validate = pd.read_csv('./data/validation'+str(i)+'.csv')\n",
    "    X_validate = validate.drop(columns='pChemBL',inplace=False)\n",
    "    y_validate = validate['pChemBL']\n",
    "    X = scaler.transform(X_validate)\n",
    "    X_validate = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "    model = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Summary:</b> Scaling reduces RMSE as well as increases Q^2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29483860745933277"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ARDRegression\n",
    "ARD = ARDRegression(normalize = True)\n",
    "ARD.fit(X_train,y_train)\n",
    "ARD.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.29393488903750853"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ARDRegression\n",
    "ARD = ARDRegression()\n",
    "ARD.fit(X_train,y_train)\n",
    "ARD.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('./data/train'+str(1)+'.csv')\n",
    "test = pd.read_csv('./data/test'+str(1)+'.csv')\n",
    "validate = pd.read_csv('./data/validation'+str(1)+'.csv')\n",
    "frames = [train, test, validate]\n",
    "result = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE =0.9178856979208991\n",
      "Q^2 = 0.3163442996795902\n"
     ]
    }
   ],
   "source": [
    "linear_model = LinearRegression()\n",
    "linear_model.fit(train.drop(columns='pChemBL',inplace=False),train['pChemBL'])\n",
    "q_ = linear_model.score(test.drop(columns='pChemBL',inplace=False),test['pChemBL'])\n",
    "rmse = np.sqrt(mean_squared_error(linear_model.predict(test.drop(columns='pChemBL',inplace=False)),test['pChemBL']))\n",
    "print(\"RMSE ={}\\nQ^2 = {}\".format(rmse,q_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  3.,   2.,  25.,  53.,  81., 145., 128.,  92.,  42.,   2.]),\n",
       " array([ 2.61798296,  3.35618466,  4.09438637,  4.83258807,  5.57078977,\n",
       "         6.30899148,  7.04719318,  7.78539489,  8.52359659,  9.2617983 ,\n",
       "        10.        ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(10,10))\n",
    "plt.subplot(5,3,1)\n",
    "plt.title('TRAIN')\n",
    "plt.xlabel('pChemBL')\n",
    "plt.hist(train['pChemBL'])\n",
    "plt.subplot(5,3,3*i+2)\n",
    "plt.xlabel('pChemBL')\n",
    "plt.title('TEST')\n",
    "plt.hist(test['pChemBL'],color='green')\n",
    "plt.subplot(5,3,3*i+3)\n",
    "plt.title('VALIDATE')\n",
    "plt.xlabel('pChemBL')\n",
    "plt.hist(validate['pChemBL'],color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_matrix = result.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "plot = sns.heatmap(correlation_matrix,annot=True)\n",
    "plt.savefig('./heatmapwith annot.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATASET 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:625: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
      "  \n",
      "C:\\Users\\HP\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:19: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.29713388849466377\t R^2 = 0.9359912581861661\n",
      "Test set results\n",
      "RMSE = 0.7283122594900334\t Q^2 = 0.5695769959897943\n",
      "Validation set results\n",
      "RMSE = 0.7653218253109645\t Q^2 = 0.5779795183451446\n"
     ]
    }
   ],
   "source": [
    "models = []\n",
    "i = 1\n",
    "print(\"\\nDATASET {}\".format(i+1))\n",
    "train = pd.read_csv('./data/train'+str(i)+'.csv')\n",
    "X_train = train.drop(columns='pChemBL',inplace=False)\n",
    "y_train = train['pChemBL']\n",
    "scaler = StandardScaler(copy=True)\n",
    "scaler.fit(X_train)\n",
    "X = scaler.transform(X_train)#.drop(columns=['ChemBLID'],inplace=False))\n",
    "X_train = pd.DataFrame(data=X,columns=X_train.columns)\n",
    "test = pd.read_csv('./data/test'+str(i)+'.csv')\n",
    "X_test = test.drop(columns='pChemBL',inplace=False)\n",
    "y_test = test['pChemBL']\n",
    "X = scaler.transform(X_test)\n",
    "X_test = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "validate = pd.read_csv('./data/validation'+str(i)+'.csv')\n",
    "X_validate = validate.drop(columns='pChemBL',inplace=False)\n",
    "y_validate = validate['pChemBL']\n",
    "X = scaler.transform(X_validate)\n",
    "X_validate = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "model = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Confusion Matrix</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "tp, tn, fp, fn = 0, 0, 0, 0\n",
    "y = []\n",
    "for val in y_test:\n",
    "    if val<7:\n",
    "        y.append(0)\n",
    "    else:\n",
    "        y.append(1)\n",
    "yp = []\n",
    "for val in y_pred:\n",
    "    if val<7:\n",
    "        yp.append(0)\n",
    "    else:\n",
    "        yp.append(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y)):\n",
    "    v1,v2 = y[i],yp[i]\n",
    "    if v1 and v2:\n",
    "        tp += 1\n",
    "    elif not(v1 or v2):\n",
    "        tn += 1\n",
    "    elif v1 == 1 and v2 ==0:\n",
    "        fn +=1\n",
    "    else:\n",
    "        fp += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "matrix = np.array([[tp,fp],[fn,tn]],dtype='float64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[188.  38.]\n",
      " [ 46. 186.]]\n"
     ]
    }
   ],
   "source": [
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision =  0.831858407079646\n",
      "Recall =  0.8034188034188035\n",
      "F1 score =  0.817391304347826\n"
     ]
    }
   ],
   "source": [
    "p = (tp/(tp+fp))\n",
    "print('Precision = ',p)\n",
    "r = (tp/(tp+fn))\n",
    "print('Recall = ',r)\n",
    "print('F1 score = ',2*(p*r)/(p+r))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Storing the required descriptors</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Descriptor Java Class</th>\n",
       "      <th>Descriptor</th>\n",
       "      <th>Description</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AcidicGroupCountDescriptor</td>\n",
       "      <td>nAcid</td>\n",
       "      <td>Number of acidic groups. The list of acidic gr...</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALOGPDescriptor</td>\n",
       "      <td>ALogP</td>\n",
       "      <td>Ghose-Crippen LogKow</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>APolDescriptor</td>\n",
       "      <td>apol</td>\n",
       "      <td>Sum of the atomic polarizabilities (including ...</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AromaticAtomsCountDescriptor</td>\n",
       "      <td>naAromAtom</td>\n",
       "      <td>Number of aromatic atoms</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AromaticBondsCountDescriptor</td>\n",
       "      <td>nAromBond</td>\n",
       "      <td>Number of aromatic bonds</td>\n",
       "      <td>2D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Descriptor Java Class  Descriptor  \\\n",
       "0    AcidicGroupCountDescriptor       nAcid   \n",
       "1               ALOGPDescriptor       ALogP   \n",
       "4                APolDescriptor        apol   \n",
       "5  AromaticAtomsCountDescriptor  naAromAtom   \n",
       "6  AromaticBondsCountDescriptor   nAromBond   \n",
       "\n",
       "                                         Description Class  \n",
       "0  Number of acidic groups. The list of acidic gr...    2D  \n",
       "1                               Ghose-Crippen LogKow    2D  \n",
       "4  Sum of the atomic polarizabilities (including ...    2D  \n",
       "5                           Number of aromatic atoms    2D  \n",
       "6                           Number of aromatic bonds    2D  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Descriptors.xlsx',sheet_name='Detailed')\n",
    "df.dropna(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_types = np.asarray(df['Descriptor Java Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "required = [\"RingCount\",\"Autocorrelation\",\"ChiChain\",\"PaDELChiPath\",\"ElectrotopologicalStateAtomType\",\"ExtendedTopochemicalAtom\",\"InformationContent\",\"MDE\",\"RuleOfFive\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./fd.xml','w') as f:\n",
    "    f.write('<Root>\\n    <Group name=\"2D\">\\n')\n",
    "    for desc in new:\n",
    "        if desc in required:\n",
    "            f.write('        <Descriptor name=\"'+str(desc)+'\" value=\"true\"/>\\n')\n",
    "        else:\n",
    "            f.write('        <Descriptor name=\"'+str(desc)+'\" value=\"false\"/>\\n')\n",
    "    f.write('    </Group>\\n</Root>')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "new = []\n",
    "for s in all_types:\n",
    "    s = re.sub('Descriptor', '', s) \n",
    "    new.append(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AcidicGroupCount',\n",
       " 'ALOGP',\n",
       " 'APol',\n",
       " 'AromaticAtomsCount',\n",
       " 'AromaticBondsCount',\n",
       " 'AtomCount',\n",
       " 'Autocorrelation',\n",
       " 'BaryszMatrix',\n",
       " 'BasicGroupCount',\n",
       " 'BCUT',\n",
       " 'PaDELBondCount',\n",
       " 'BPol',\n",
       " 'BurdenModifiedEigenvalues',\n",
       " 'PaDELCarbonTypes',\n",
       " 'ChiChain',\n",
       " 'ChiCluster',\n",
       " 'ChiPathCluster',\n",
       " 'PaDELChiPath',\n",
       " 'Constitutional',\n",
       " 'Crippen',\n",
       " 'DetourMatrix',\n",
       " 'EccentricConnectivityIndex',\n",
       " 'ElectrotopologicalStateAtomType',\n",
       " 'ExtendedTopochemicalAtom',\n",
       " 'FMF',\n",
       " 'PaDELFragmentComplexity',\n",
       " 'PaDELHBondAcceptorCount',\n",
       " 'PaDELHBondDonorCount',\n",
       " 'HybridizationRatio',\n",
       " 'InformationContent',\n",
       " 'KappaShapeIndices',\n",
       " 'LargestChain',\n",
       " 'LargestPiSystem',\n",
       " 'LongestAliphaticChain',\n",
       " 'MannholdLogP',\n",
       " 'McGowanVolume',\n",
       " 'MDE',\n",
       " 'MLFER',\n",
       " 'PathCount',\n",
       " 'PetitjeanNumber',\n",
       " 'RingCount',\n",
       " 'PaDELRotatableBondsCount',\n",
       " 'RuleOfFive',\n",
       " 'Topological',\n",
       " 'TopologicalCharge',\n",
       " 'TopologicalDistanceMatrix',\n",
       " 'TPSA',\n",
       " 'VABC',\n",
       " 'VAdjMa',\n",
       " 'WalkCount',\n",
       " 'PaDELWeight',\n",
       " 'PaDELWeightedPath',\n",
       " 'WienerNumbers',\n",
       " 'XLogP',\n",
       " 'ZagrebIndex',\n",
       " 'Autocorrelation3D',\n",
       " 'CPSA',\n",
       " 'GravitationalIndex',\n",
       " 'LengthOverBreadth',\n",
       " 'MomentOfInertia',\n",
       " 'PaDELPetitjeanShapeIndex',\n",
       " 'RDF',\n",
       " 'PaDELWHIM']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_csv('./trial.csv')\n",
    "bad = []\n",
    "if \"SP-7\" in df2.columns:\n",
    "    print(1)\n",
    "else:\n",
    "    print(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Name', 'ATS0m', 'ATS1m', 'ATS2m', 'ATS3m', 'ATS4m', 'ATS5m', 'ATS6m', 'ATS7m', 'ATS8m', 'ATS0v', 'ATS1v', 'ATS2v', 'ATS3v', 'ATS4v', 'ATS5v', 'ATS6v', 'ATS7v', 'ATS8v', 'ATS0e', 'ATS1e', 'ATS2e', 'ATS3e', 'ATS4e', 'ATS5e', 'ATS6e', 'ATS7e', 'ATS8e', 'ATS0p', 'ATS1p', 'ATS2p', 'ATS3p', 'ATS4p', 'ATS5p', 'ATS6p', 'ATS7p', 'ATS8p', 'ATS0i', 'ATS1i', 'ATS2i', 'ATS3i', 'ATS4i', 'ATS5i', 'ATS6i', 'ATS7i', 'ATS8i', 'ATS0s', 'ATS1s', 'ATS2s', 'ATS3s', 'ATS4s', 'ATS5s', 'ATS6s', 'ATS7s', 'ATS8s', 'AATS0m', 'AATS1m', 'AATS2m', 'AATS3m', 'AATS4m', 'AATS5m', 'AATS6m', 'AATS7m', 'AATS8m', 'AATS0v', 'AATS1v', 'AATS2v', 'AATS3v', 'AATS4v', 'AATS5v', 'AATS6v', 'AATS7v', 'AATS8v', 'AATS0e', 'AATS1e', 'AATS2e', 'AATS3e', 'AATS4e', 'AATS5e', 'AATS6e', 'AATS7e', 'AATS8e', 'AATS0p', 'AATS1p', 'AATS2p', 'AATS3p', 'AATS4p', 'AATS5p', 'AATS6p', 'AATS7p', 'AATS8p', 'AATS0i', 'AATS1i', 'AATS2i', 'AATS3i', 'AATS4i', 'AATS5i', 'AATS6i', 'AATS7i', 'AATS8i', 'AATS0s', 'AATS1s', 'AATS2s', 'AATS3s', 'AATS4s', 'AATS5s', 'AATS6s', 'AATS7s', 'AATS8s', 'ATSC0c', 'ATSC1c', 'ATSC2c', 'ATSC3c', 'ATSC5c', 'ATSC6c', 'ATSC7c', 'ATSC8c', 'ATSC0m', 'ATSC1m', 'ATSC2m', 'ATSC3m', 'ATSC4m', 'ATSC5m', 'ATSC6m', 'ATSC7m', 'ATSC8m', 'ATSC0v', 'ATSC1v', 'ATSC3v', 'ATSC4v', 'ATSC5v', 'ATSC6v', 'ATSC7v', 'ATSC8v', 'ATSC0e', 'ATSC1e', 'ATSC2e', 'ATSC3e', 'ATSC4e', 'ATSC5e', 'ATSC6e', 'ATSC7e', 'ATSC8e', 'ATSC0p', 'ATSC1p', 'ATSC2p', 'ATSC3p', 'ATSC4p', 'ATSC5p', 'ATSC6p', 'ATSC7p', 'ATSC8p', 'ATSC0i', 'ATSC1i', 'ATSC2i', 'ATSC3i', 'ATSC4i', 'ATSC5i', 'ATSC6i', 'ATSC7i', 'ATSC8i', 'ATSC0s', 'ATSC1s', 'ATSC2s', 'ATSC3s', 'ATSC4s', 'ATSC5s', 'ATSC6s', 'ATSC7s', 'ATSC8s', 'AATSC0c', 'AATSC1c', 'AATSC2c', 'AATSC3c', 'AATSC4c', 'AATSC5c', 'AATSC6c', 'AATSC7c', 'AATSC8c', 'AATSC0m', 'AATSC1m', 'AATSC2m', 'AATSC3m', 'AATSC4m', 'AATSC5m', 'AATSC6m', 'AATSC7m', 'AATSC8m', 'AATSC0v', 'AATSC1v', 'AATSC2v', 'AATSC3v', 'AATSC4v', 'AATSC5v', 'AATSC6v', 'AATSC7v', 'AATSC8v', 'AATSC0e', 'AATSC1e', 'AATSC2e', 'AATSC3e', 'AATSC4e', 'AATSC5e', 'AATSC6e', 'AATSC7e', 'AATSC8e', 'AATSC0p', 'AATSC1p', 'AATSC2p', 'AATSC3p', 'AATSC4p', 'AATSC5p', 'AATSC6p', 'AATSC7p', 'AATSC8p', 'AATSC0i', 'AATSC1i', 'AATSC2i', 'AATSC3i', 'AATSC4i', 'AATSC5i', 'AATSC6i', 'AATSC7i', 'AATSC8i', 'AATSC0s', 'AATSC1s', 'AATSC2s', 'AATSC3s', 'AATSC4s', 'AATSC5s', 'AATSC6s', 'AATSC7s', 'AATSC8s', 'MATS1c', 'MATS2c', 'MATS3c', 'MATS4c', 'MATS5c', 'MATS6c', 'MATS7c', 'MATS8c', 'MATS1m', 'MATS2m', 'MATS3m', 'MATS4m', 'MATS5m', 'MATS6m', 'MATS7m', 'MATS8m', 'MATS1v', 'MATS2v', 'MATS3v', 'MATS4v', 'MATS5v', 'MATS6v', 'MATS7v', 'MATS8v', 'MATS1e', 'MATS2e', 'MATS3e', 'MATS4e', 'MATS5e', 'MATS6e', 'MATS7e', 'MATS8e', 'MATS1p', 'MATS2p', 'MATS3p', 'MATS4p', 'MATS5p', 'MATS6p', 'MATS7p', 'MATS8p', 'MATS1i', 'MATS2i', 'MATS3i', 'MATS4i', 'MATS5i', 'MATS6i', 'MATS7i', 'MATS8i', 'MATS1s', 'MATS2s', 'MATS3s', 'MATS4s', 'MATS5s', 'MATS6s', 'MATS7s', 'MATS8s', 'GATS1c', 'GATS2c', 'GATS3c', 'GATS4c', 'GATS5c', 'GATS6c', 'GATS7c', 'GATS8c', 'GATS1m', 'GATS2m', 'GATS3m', 'GATS4m', 'GATS5m', 'GATS6m', 'GATS7m', 'GATS8m', 'GATS1v', 'GATS2v', 'GATS3v', 'GATS4v', 'GATS5v', 'GATS6v', 'GATS7v', 'GATS8v', 'GATS1e', 'GATS2e', 'GATS3e', 'GATS4e', 'GATS5e', 'GATS6e', 'GATS7e', 'GATS8e', 'GATS1p', 'GATS2p', 'GATS3p', 'GATS4p', 'GATS5p', 'GATS6p', 'GATS7p', 'GATS8p', 'GATS1i', 'GATS2i', 'GATS3i', 'GATS4i', 'GATS5i', 'GATS6i', 'GATS7i', 'GATS8i', 'GATS1s', 'GATS2s', 'GATS3s', 'GATS4s', 'GATS5s', 'GATS6s', 'GATS7s', 'GATS8s', 'SCH-3', 'SCH-4', 'SCH-5', 'SCH-6', 'VCH-3', 'VCH-4', 'VCH-5', 'VCH-6', 'VCH-7', 'SP-0', 'SP-1', 'SP-2', 'SP-3', 'SP-4', 'SP-5', 'SP-6', 'SP-7', 'ASP-0', 'ASP-1', 'ASP-2', 'ASP-3', 'ASP-4', 'ASP-5', 'ASP-6', 'VP-0', 'VP-1', 'VP-2', 'VP-3', 'VP-4', 'VP-5', 'VP-6', 'VP-7', 'AVP-0', 'AVP-1', 'AVP-2', 'AVP-3', 'AVP-4', 'AVP-5', 'AVP-6', 'AVP-7', 'nHBd', 'nwHBd', 'nHBa', 'nwHBa', 'nHBint2', 'nHBint3', 'nHBint4', 'nHBint5', 'nHBint6', 'nHBint7', 'nHBint8', 'nHBint9', 'nHBint10', 'nHsOH', 'nHdNH', 'nHsSH', 'nHsNH2', 'nHssNH', 'nHaaNH', 'nHsNH3p', 'nHssNH2p', 'nHsssNHp', 'nHtCH', 'nHdCH2', 'nHdsCH', 'nHaaCH', 'nHCHnX', 'nHCsats', 'nHCsatu', 'nHAvin', 'nHother', 'nHmisc', 'nsLi', 'nssBe', 'nssssBem', 'nsBH2', 'nssBH', 'nsssB', 'nssssBm', 'nsCH3', 'ndCH2', 'nssCH2', 'ntCH', 'ndsCH', 'naaCH', 'nsssCH', 'nddC', 'ntsC', 'ndssC', 'naasC', 'naaaC', 'nssssC', 'nsNH3p', 'nsNH2', 'nssNH2p', 'ndNH', 'nssNH', 'naaNH', 'ntN', 'nsssNHp', 'ndsN', 'naaN', 'nsssN', 'nddsN', 'naasN', 'nssssNp', 'nsOH', 'ndO', 'nssO', 'naaO', 'naOm', 'nsOm', 'nsF', 'nsSiH3', 'nssSiH2', 'nsssSiH', 'nssssSi', 'nsPH2', 'nssPH', 'nsssP', 'ndsssP', 'nddsP', 'nsssssP', 'nsSH', 'ndS', 'nssS', 'naaS', 'ndssS', 'nddssS', 'nssssssS', 'nSm', 'nsCl', 'nsGeH3', 'nssGeH2', 'nsssGeH', 'nssssGe', 'nsAsH2', 'nssAsH', 'nsssAs', 'ndsssAs', 'nddsAs', 'nsssssAs', 'nsSeH', 'ndSe', 'nssSe', 'naaSe', 'ndssSe', 'nssssssSe', 'nddssSe', 'nsBr', 'nsSnH3', 'nssSnH2', 'nsssSnH', 'nssssSn', 'nsI', 'nsPbH3', 'nssPbH2', 'nsssPbH', 'nssssPb', 'SHBd', 'SwHBd', 'SHBa', 'SwHBa', 'SHBint2', 'SHBint3', 'SHBint4', 'SHBint5', 'SHBint6', 'SHBint7', 'SHBint8', 'SHBint9', 'SHBint10', 'SHsOH', 'SHdNH', 'SHsSH', 'SHsNH2', 'SHssNH', 'SHaaNH', 'SHsNH3p', 'SHssNH2p', 'SHsssNHp', 'SHtCH', 'SHdCH2', 'SHdsCH', 'SHaaCH', 'SHCHnX', 'SHCsats', 'SHCsatu', 'SHAvin', 'SHother', 'SHmisc', 'SsLi', 'SssBe', 'SssssBem', 'SsBH2', 'SssBH', 'SsssB', 'SssssBm', 'SsCH3', 'SdCH2', 'SssCH2', 'StCH', 'SdsCH', 'SaaCH', 'SsssCH', 'SddC', 'StsC', 'SdssC', 'SaasC', 'SaaaC', 'SssssC', 'SsNH3p', 'SsNH2', 'SssNH2p', 'SdNH', 'SssNH', 'SaaNH', 'StN', 'SsssNHp', 'SdsN', 'SaaN', 'SsssN', 'SddsN', 'SaasN', 'SssssNp', 'SsOH', 'SdO', 'SssO', 'SaaO', 'SaOm', 'SsOm', 'SsF', 'SsSiH3', 'SssSiH2', 'SsssSiH', 'SssssSi', 'SsPH2', 'SssPH', 'SsssP', 'SdsssP', 'SddsP', 'SsssssP', 'SsSH', 'SdS', 'SssS', 'SaaS', 'SdssS', 'SddssS', 'SssssssS', 'SSm', 'SsCl', 'SsGeH3', 'SssGeH2', 'SsssGeH', 'SssssGe', 'SsAsH2', 'SssAsH', 'SsssAs', 'SdsssAs', 'SddsAs', 'SsssssAs', 'SsSeH', 'SdSe', 'SssSe', 'SaaSe', 'SdssSe', 'SssssssSe', 'SddssSe', 'SsBr', 'SsSnH3', 'SssSnH2', 'SsssSnH', 'SssssSn', 'SsI', 'SsPbH3', 'SssPbH2', 'SsssPbH', 'SssssPb', 'minHBd', 'minwHBd', 'minHBa', 'minwHBa', 'minHBint2', 'minHBint3', 'minHBint4', 'minHBint5', 'minHBint6', 'minHBint7', 'minHBint8', 'minHBint9', 'minHBint10', 'minHsOH', 'minHdNH', 'minHsSH', 'minHsNH2', 'minHssNH', 'minHaaNH', 'minHsNH3p', 'minHssNH2p', 'minHsssNHp', 'minHtCH', 'minHdCH2', 'minHdsCH', 'minHaaCH', 'minHCHnX', 'minHCsats', 'minHCsatu', 'minHAvin', 'minHother', 'minHmisc', 'minsLi', 'minssBe', 'minssssBem', 'minsBH2', 'minssBH', 'minsssB', 'minssssBm', 'minsCH3', 'mindCH2', 'minssCH2', 'mintCH', 'mindsCH', 'minaaCH', 'minsssCH', 'minddC', 'mintsC', 'mindssC', 'minaasC', 'minaaaC', 'minssssC', 'minsNH3p', 'minsNH2', 'minssNH2p', 'mindNH', 'minssNH', 'minaaNH', 'mintN', 'minsssNHp', 'mindsN', 'minaaN', 'minsssN', 'minddsN', 'minaasN', 'minssssNp', 'minsOH', 'mindO', 'minssO', 'minaaO', 'minaOm', 'minsOm', 'minsF', 'minsSiH3', 'minssSiH2', 'minsssSiH', 'minssssSi', 'minsPH2', 'minssPH', 'minsssP', 'mindsssP', 'minddsP', 'minsssssP', 'minsSH', 'mindS', 'minssS', 'minaaS', 'mindssS', 'minddssS', 'minssssssS', 'minSm', 'minsCl', 'minsGeH3', 'minssGeH2', 'minsssGeH', 'minssssGe', 'minsAsH2', 'minssAsH', 'minsssAs', 'mindsssAs', 'minddsAs', 'minsssssAs', 'minsSeH', 'mindSe', 'minssSe', 'minaaSe', 'mindssSe', 'minssssssSe', 'minddssSe', 'minsBr', 'minsSnH3', 'minssSnH2', 'minsssSnH', 'minssssSn', 'minsI', 'minsPbH3', 'minssPbH2', 'minsssPbH', 'minssssPb', 'maxHBd', 'maxwHBd', 'maxHBa', 'maxwHBa', 'maxHBint2', 'maxHBint3', 'maxHBint4', 'maxHBint5', 'maxHBint6', 'maxHBint7', 'maxHBint8', 'maxHBint9', 'maxHBint10', 'maxHsOH', 'maxHdNH', 'maxHsSH', 'maxHsNH2', 'maxHssNH', 'maxHaaNH', 'maxHsNH3p', 'maxHssNH2p', 'maxHsssNHp', 'maxHtCH', 'maxHdCH2', 'maxHdsCH', 'maxHaaCH', 'maxHCHnX', 'maxHCsats', 'maxHCsatu', 'maxHAvin', 'maxHother', 'maxHmisc', 'maxsLi', 'maxssBe', 'maxssssBem', 'maxsBH2', 'maxssBH', 'maxsssB', 'maxssssBm', 'maxsCH3', 'maxdCH2', 'maxssCH2', 'maxtCH', 'maxdsCH', 'maxaaCH', 'maxsssCH', 'maxddC', 'maxtsC', 'maxdssC', 'maxaasC', 'maxaaaC', 'maxssssC', 'maxsNH3p', 'maxsNH2', 'maxssNH2p', 'maxdNH', 'maxssNH', 'maxaaNH', 'maxtN', 'maxsssNHp', 'maxdsN', 'maxaaN', 'maxsssN', 'maxddsN', 'maxaasN', 'maxssssNp', 'maxsOH', 'maxdO', 'maxssO', 'maxaaO', 'maxaOm', 'maxsOm', 'maxsF', 'maxsSiH3', 'maxssSiH2', 'maxsssSiH', 'maxssssSi', 'maxsPH2', 'maxssPH', 'maxsssP', 'maxdsssP', 'maxddsP', 'maxsssssP', 'maxsSH', 'maxdS', 'maxssS', 'maxaaS', 'maxdssS', 'maxddssS', 'maxssssssS', 'maxSm', 'maxsCl', 'maxsGeH3', 'maxssGeH2', 'maxsssGeH', 'maxssssGe', 'maxsAsH2', 'maxssAsH', 'maxsssAs', 'maxdsssAs', 'maxddsAs', 'maxsssssAs', 'maxsSeH', 'maxdSe', 'maxssSe', 'maxaaSe', 'maxdssSe', 'maxssssssSe', 'maxddssSe', 'maxsBr', 'maxsSnH3', 'maxssSnH2', 'maxsssSnH', 'maxssssSn', 'maxsI', 'maxsPbH3', 'maxssPbH2', 'maxsssPbH', 'maxssssPb', 'sumI', 'meanI', 'hmax', 'gmax', 'gmin', 'LipoaffinityIndex', 'MAXDN', 'MAXDP', 'DELS', 'MAXDN2', 'MAXDP2', 'DELS2', 'ETA_Alpha', 'ETA_AlphaP', 'ETA_dAlpha_A', 'ETA_dAlpha_B', 'ETA_Epsilon_1', 'ETA_Epsilon_2', 'ETA_Epsilon_3', 'ETA_Epsilon_4', 'ETA_Epsilon_5', 'ETA_dEpsilon_A', 'ETA_dEpsilon_B', 'ETA_dEpsilon_C', 'ETA_dEpsilon_D', 'ETA_Psi_1', 'ETA_dPsi_A', 'ETA_dPsi_B', 'ETA_Shape_P', 'ETA_Shape_Y', 'ETA_Shape_X', 'ETA_Beta', 'ETA_BetaP', 'ETA_Beta_s', 'ETA_Beta_ns', 'ETA_BetaP_ns', 'ETA_dBeta', 'ETA_dBetaP', 'ETA_Beta_ns_d', 'ETA_BetaP_ns_d', 'ETA_Eta', 'ETA_EtaP', 'ETA_Eta_R', 'ETA_Eta_F', 'ETA_EtaP_F', 'ETA_Eta_L', 'ETA_EtaP_L', 'ETA_Eta_R_L', 'ETA_Eta_F_L', 'ETA_EtaP_F_L', 'ETA_Eta_B', 'ETA_EtaP_B', 'ETA_Eta_B_RC', 'ETA_EtaP_B_RC', 'IC0', 'IC3', 'IC4', 'IC5', 'TIC0', 'TIC1', 'TIC2', 'TIC3', 'TIC4', 'TIC5', 'SIC0', 'SIC1', 'SIC3', 'SIC4', 'SIC5', 'CIC0', 'CIC1', 'CIC2', 'CIC3', 'CIC4', 'CIC5', 'BIC0', 'BIC1', 'BIC3', 'BIC4', 'BIC5', 'MIC0', 'MIC1', 'MIC2', 'MIC3', 'MIC4', 'MIC5', 'ZMIC0', 'ZMIC3', 'ZMIC4', 'ZMIC5', 'MDEC-11', 'MDEC-12', 'MDEC-13', 'MDEC-14', 'MDEC-22', 'MDEC-23', 'MDEC-24', 'MDEC-33', 'MDEC-34', 'MDEC-44', 'MDEO-12', 'MDEO-22', 'MDEN-11', 'MDEN-12', 'MDEN-13', 'MDEN-22', 'MDEN-23', 'MDEN-33', 'nRing', 'n3Ring', 'n4Ring', 'n5Ring', 'n6Ring', 'n7Ring', 'n8Ring', 'n9Ring', 'n10Ring', 'n11Ring', 'n12Ring', 'nG12Ring', 'nFRing', 'nF4Ring', 'nF5Ring', 'nF6Ring', 'nF7Ring', 'nF8Ring', 'nF9Ring', 'nF10Ring', 'nF11Ring', 'nF12Ring', 'nFG12Ring', 'nTRing', 'nT4Ring', 'nT5Ring', 'nT6Ring', 'nT7Ring', 'nT8Ring', 'nT9Ring', 'nT10Ring', 'nT11Ring', 'nT12Ring', 'nTG12Ring', 'n3HeteroRing', 'n4HeteroRing', 'n5HeteroRing', 'n7HeteroRing', 'n8HeteroRing', 'n9HeteroRing', 'n10HeteroRing', 'n11HeteroRing', 'n12HeteroRing', 'nG12HeteroRing', 'nFHeteroRing', 'nF4HeteroRing', 'nF5HeteroRing', 'nF6HeteroRing', 'nF7HeteroRing', 'nF8HeteroRing', 'nF9HeteroRing', 'nF10HeteroRing', 'nF11HeteroRing', 'nF12HeteroRing', 'nFG12HeteroRing', 'nTHeteroRing', 'nT4HeteroRing', 'nT5HeteroRing', 'nT7HeteroRing', 'nT8HeteroRing', 'nT9HeteroRing', 'nT10HeteroRing', 'nT11HeteroRing', 'nT12HeteroRing', 'nTG12HeteroRing']\n"
     ]
    }
   ],
   "source": [
    "for col in df2.columns:\n",
    "    if col not in X_train.columns:\n",
    "        bad.append(col)\n",
    "print(bad)\n",
    "df2.drop(columns=bad,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_try.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "cmd_list = [\"java\", \"-jar\", \"C:\\\\Users\\\\HP\\\\PaDEL-Descriptor\\\\PaDEL-Descriptor.jar\", \"-dir\",\"./mol\", \"-file\",\"./trial.csv\", \"-2d\", \"-descriptortypes\", 'fd.xml','-usefilenameasmolname']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Saving the model</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DATASET 2\n",
      "Training set results:\n",
      "RMSE = 0.29475911942030314\t R^2 = 0.9370103176157272\n",
      "Test set results\n",
      "RMSE = 0.7300750749763446\t Q^2 = 0.5674908726860093\n",
      "Validation set results\n",
      "RMSE = 0.7644826866055227\t Q^2 = 0.5789044614569528\n"
     ]
    }
   ],
   "source": [
    "i = 1\n",
    "print(\"\\nDATASET {}\".format(i+1))\n",
    "train = pd.read_csv('./data/train'+str(i)+'.csv')\n",
    "X_train = train.drop(columns='pChemBL',inplace=False)\n",
    "y_train = train['pChemBL']\n",
    "scaler = StandardScaler(copy=True)\n",
    "scaler.fit(X_train)\n",
    "X = scaler.transform(X_train)#.drop(columns=['ChemBLID'],inplace=False))\n",
    "X_train = pd.DataFrame(data=X,columns=X_train.columns)\n",
    "test = pd.read_csv('./data/test'+str(i)+'.csv')\n",
    "X_test = test.drop(columns='pChemBL',inplace=False)\n",
    "y_test = test['pChemBL']\n",
    "X = scaler.transform(X_test)\n",
    "X_test = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "validate = pd.read_csv('./data/validation'+str(i)+'.csv')\n",
    "X_validate = validate.drop(columns='pChemBL',inplace=False)\n",
    "y_validate = validate['pChemBL']\n",
    "X = scaler.transform(X_validate)\n",
    "X_validate = pd.DataFrame(data=X,columns=X_test.columns)\n",
    "model = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./saved_models/new_scaler.pkl','wb') as f:\n",
    "    pkl.dump(scaler,f)\n",
    "with open('./saved_models/new_RFR.pkl','wb') as f:\n",
    "    pkl.dump(model,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1034\n"
     ]
    }
   ],
   "source": [
    "print(len(bad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./saved_models/drop.txt','wb') as f:\n",
    "    pkl.dump(bad,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1830, 17)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.76038889, 6.83616833, 6.64931111, 6.72137717, 6.82115556,\n",
       "       7.16074   , 7.39086667, 7.00673333, 7.48421984, 6.92455556,\n",
       "       7.30871111, 7.23157778, 6.64908889, 6.67484444, 7.051     ,\n",
       "       7.56206667, 6.92743333, 6.44477778, 7.11447111, 5.8927897 ,\n",
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       "       6.81987778, 7.5248    , 7.49273333, 7.2907    , 6.71488333,\n",
       "       6.6837    , 7.06688889, 6.84569206, 7.51524444, 6.20306667,\n",
       "       7.04193333, 6.76966667, 6.4916    , 6.21536667, 7.02874222,\n",
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       "       6.1818    , 7.32953889, 6.82994667, 6.85104389, 7.24575556,\n",
       "       6.974     , 6.97323333, 6.32305333, 7.15522222, 7.211     ,\n",
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       "       7.22905556, 6.75426889, 6.8864    , 6.1682    , 6.77116667,\n",
       "       7.13653333, 7.42939778, 6.96858333, 7.17543333, 7.39417778,\n",
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       "       5.81320574, 7.80515   , 7.22606667, 6.82976096, 7.22217111,\n",
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       "       5.83054166, 6.64248889, 7.28133333, 7.14804444, 7.04520167,\n",
       "       7.23011667, 6.96993333, 7.52072   , 6.14408333, 6.80551333,\n",
       "       7.50824444, 5.07333889, 6.71001429, 6.1822    , 6.37563333,\n",
       "       6.92611111, 6.66539111, 6.95248889, 5.65265073, 6.67313333,\n",
       "       6.69066667, 7.14461429, 6.58203778, 5.33218018, 6.77191556,\n",
       "       6.83443556, 6.16638333, 6.74958   , 6.87433333, 6.40993333,\n",
       "       6.8248    , 7.72993333, 6.03974038, 6.58953333, 6.43224444,\n",
       "       6.87774444, 8.03137   , 6.1402    , 6.55167222, 7.09073333,\n",
       "       6.55546667, 6.69445778, 6.56375556, 7.1364    , 7.1763    ,\n",
       "       6.77926667, 7.58973778, 6.12272   , 6.71570222, 7.09026222,\n",
       "       6.93366667, 7.53      , 7.8773    , 7.20526667, 6.94637778,\n",
       "       6.56881333, 7.00818   , 5.61067659, 6.81665556, 7.01121111,\n",
       "       7.00673333, 7.02030667, 6.72307222, 7.40351   , 5.31983333,\n",
       "       6.36038667, 7.31006667, 6.9616    , 5.89606667, 7.39782222,\n",
       "       7.01402222, 5.16070208, 6.86302222, 6.86817778, 7.16255   ,\n",
       "       6.44266667, 5.83177371, 6.79199   , 6.79536667, 6.66969151,\n",
       "       6.72352778, 6.38477778, 5.4961375 , 7.01853333, 6.72801667,\n",
       "       6.3558    , 7.10533333, 6.61712222, 5.68940574, 6.41045556,\n",
       "       6.83416667, 6.36526667, 6.4374    , 7.4938    , 6.93966667,\n",
       "       6.77383556, 6.6384    , 7.2048    , 7.36046667, 7.406     ,\n",
       "       7.1346    , 7.06812222, 6.88099667, 6.72296667, 7.03819317,\n",
       "       6.72353333, 7.53044   , 6.96515556, 6.62260317, 7.11543556,\n",
       "       6.23181333, 6.61647222, 7.66546667, 6.87684667, 6.31175111,\n",
       "       6.8984    , 6.51168   , 6.8156    , 5.96088495, 6.81123333,\n",
       "       6.40535   , 5.9966    , 5.45719778, 7.3528    , 6.60013333,\n",
       "       7.21874667, 6.29904667, 6.38392   , 6.62977778, 7.42777778,\n",
       "       6.09618246, 6.91757222, 7.13193889, 6.9894    , 6.75266667,\n",
       "       6.99267222, 6.93006667, 6.94336667, 6.49351556, 6.92242222,\n",
       "       7.1944    , 7.29469778, 6.45566667, 6.51829444, 6.94243889,\n",
       "       6.921     , 6.30848   , 7.1118    , 7.2808    , 7.17456667,\n",
       "       6.56293333, 7.49723333, 6.93006889, 7.20518889, 6.58483333,\n",
       "       5.78573333, 6.80686222, 7.12688889, 6.89876167, 6.46646667,\n",
       "       6.75834889, 6.81952   , 6.58228   , 5.47529849, 7.05766667,\n",
       "       6.65701333, 6.72526667])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pickle\n",
    "X = pd.read_csv('./descriptors.csv')\n",
    "#y = X['pChemBL']\n",
    "#X.drop(columns='pChemBL',inplace=True)\n",
    "#Filling Null Values\n",
    "X.fillna(value=0,inplace=True)\n",
    "#Removing the columns with zero variance in original data\n",
    "with open('./saved_models/drop.txt','rb') as fp:\n",
    "    bad_cols = pickle.load(fp)\n",
    "X_step1 = X.drop(columns=bad_cols,inplace=False)\n",
    "X_step2 = X_step1\n",
    "\n",
    "\n",
    "\n",
    "#Doing StandardScaler() as applied to original data\n",
    "with open('./saved_models/new_scaler.pkl','rb') as fp:\n",
    "    scaler = pickle.load(fp)\n",
    "X2 = scaler.transform(X_step2.astype('float64'))\n",
    "X_step3 = pd.DataFrame(data=X2,columns=X_step2.columns)\n",
    "\n",
    "#X.head()\n",
    "#Dropping columns with low correlation with pIC50\n",
    "\n",
    "# =============================================================================\n",
    "#     X.to_csv('./X.csv',index=False)\n",
    "#     X_step1.to_csv('./X_step1.csv')\n",
    "#     X_step2.to_csv('./X_step2.csv')\n",
    "#     X_step3.to_csv('./X_step3.csv')\n",
    "# =============================================================================\n",
    "\n",
    "\n",
    "\n",
    "#Using the Random forest Predictor\n",
    "with open('./saved_models/new_RFR.pkl','rb') as fp:\n",
    "    pp = pickle.load(fp)\n",
    "pp.predict(X_step3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATSC4c</th>\n",
       "      <th>ATSC2v</th>\n",
       "      <th>SCH-7</th>\n",
       "      <th>ASP-7</th>\n",
       "      <th>hmin</th>\n",
       "      <th>ETA_BetaP_s</th>\n",
       "      <th>IC1</th>\n",
       "      <th>IC2</th>\n",
       "      <th>SIC2</th>\n",
       "      <th>BIC2</th>\n",
       "      <th>ZMIC1</th>\n",
       "      <th>ZMIC2</th>\n",
       "      <th>MDEO-11</th>\n",
       "      <th>nHeteroRing</th>\n",
       "      <th>n6HeteroRing</th>\n",
       "      <th>nT6HeteroRing</th>\n",
       "      <th>LipinskiFailures</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.578280</td>\n",
       "      <td>179.753983</td>\n",
       "      <td>0.369801</td>\n",
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       "      <td>0.68750</td>\n",
       "      <td>3.988302</td>\n",
       "      <td>4.841615</td>\n",
       "      <td>0.871642</td>\n",
       "      <td>0.830054</td>\n",
       "      <td>38.680776</td>\n",
       "      <td>28.512776</td>\n",
       "      <td>0.388067</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.115642</td>\n",
       "      <td>252.455141</td>\n",
       "      <td>0.621971</td>\n",
       "      <td>0.028386</td>\n",
       "      <td>0.072471</td>\n",
       "      <td>0.67188</td>\n",
       "      <td>4.396648</td>\n",
       "      <td>5.030633</td>\n",
       "      <td>0.916017</td>\n",
       "      <td>0.848231</td>\n",
       "      <td>42.295311</td>\n",
       "      <td>34.671329</td>\n",
       "      <td>0.512993</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.097349</td>\n",
       "      <td>325.955735</td>\n",
       "      <td>0.578327</td>\n",
       "      <td>0.029740</td>\n",
       "      <td>-0.174475</td>\n",
       "      <td>0.65152</td>\n",
       "      <td>3.897113</td>\n",
       "      <td>4.760294</td>\n",
       "      <td>0.831068</td>\n",
       "      <td>0.772791</td>\n",
       "      <td>56.380963</td>\n",
       "      <td>38.830028</td>\n",
       "      <td>0.650698</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.464415</td>\n",
       "      <td>-30.113074</td>\n",
       "      <td>0.700831</td>\n",
       "      <td>0.026403</td>\n",
       "      <td>0.029772</td>\n",
       "      <td>0.68333</td>\n",
       "      <td>4.016156</td>\n",
       "      <td>4.604828</td>\n",
       "      <td>0.838483</td>\n",
       "      <td>0.771873</td>\n",
       "      <td>46.255943</td>\n",
       "      <td>34.935527</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.134785</td>\n",
       "      <td>349.032190</td>\n",
       "      <td>0.881447</td>\n",
       "      <td>0.028300</td>\n",
       "      <td>-0.053398</td>\n",
       "      <td>0.68519</td>\n",
       "      <td>3.757635</td>\n",
       "      <td>4.808388</td>\n",
       "      <td>0.856391</td>\n",
       "      <td>0.814030</td>\n",
       "      <td>39.266077</td>\n",
       "      <td>27.847084</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ATSC4c      ATSC2v     SCH-7     ASP-7      hmin  ETA_BetaP_s       IC1  \\\n",
       "0  0.578280  179.753983  0.369801  0.033885  0.072784      0.68750  3.988302   \n",
       "1 -0.115642  252.455141  0.621971  0.028386  0.072471      0.67188  4.396648   \n",
       "2 -0.097349  325.955735  0.578327  0.029740 -0.174475      0.65152  3.897113   \n",
       "3  0.464415  -30.113074  0.700831  0.026403  0.029772      0.68333  4.016156   \n",
       "4 -0.134785  349.032190  0.881447  0.028300 -0.053398      0.68519  3.757635   \n",
       "\n",
       "        IC2      SIC2      BIC2      ZMIC1      ZMIC2   MDEO-11  nHeteroRing  \\\n",
       "0  4.841615  0.871642  0.830054  38.680776  28.512776  0.388067            2   \n",
       "1  5.030633  0.916017  0.848231  42.295311  34.671329  0.512993            3   \n",
       "2  4.760294  0.831068  0.772791  56.380963  38.830028  0.650698            2   \n",
       "3  4.604828  0.838483  0.771873  46.255943  34.935527  0.000000            3   \n",
       "4  4.808388  0.856391  0.814030  39.266077  27.847084  0.200000            3   \n",
       "\n",
       "   n6HeteroRing  nT6HeteroRing  LipinskiFailures  \n",
       "0             2              2                 0  \n",
       "1             3              3                 0  \n",
       "2             0              0                 0  \n",
       "3             2              2                 0  \n",
       "4             1              1                 0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.76408889])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_step3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Playing Around</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "with open('./saved_models/new_RFR.pkl','rb') as fp:\n",
    "    pp = pickle.load(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>nAcid</th>\n",
       "      <th>ALogP</th>\n",
       "      <th>ALogp2</th>\n",
       "      <th>AMR</th>\n",
       "      <th>apol</th>\n",
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       "      <th>Zagreb</th>\n",
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       "  </thead>\n",
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       "      <td>-0.7109</td>\n",
       "      <td>0.505379</td>\n",
       "      <td>70.4932</td>\n",
       "      <td>73.378583</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>65</td>\n",
       "      <td>34</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>70.514766</td>\n",
       "      <td>2.073964</td>\n",
       "      <td>27.938805</td>\n",
       "      <td>11.267053</td>\n",
       "      <td>16.671753</td>\n",
       "      <td>3404</td>\n",
       "      <td>59</td>\n",
       "      <td>2.188</td>\n",
       "      <td>184</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.4578</td>\n",
       "      <td>2.125181</td>\n",
       "      <td>44.0418</td>\n",
       "      <td>64.304653</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>65.167083</td>\n",
       "      <td>2.102164</td>\n",
       "      <td>17.955201</td>\n",
       "      <td>2.565237</td>\n",
       "      <td>15.389964</td>\n",
       "      <td>2593</td>\n",
       "      <td>55</td>\n",
       "      <td>6.575</td>\n",
       "      <td>180</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.005595</td>\n",
       "      <td>27.2874</td>\n",
       "      <td>69.268860</td>\n",
       "      <td>27</td>\n",
       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>76.232497</td>\n",
       "      <td>2.060338</td>\n",
       "      <td>31.795843</td>\n",
       "      <td>2.556014</td>\n",
       "      <td>22.098211</td>\n",
       "      <td>4979</td>\n",
       "      <td>56</td>\n",
       "      <td>5.589</td>\n",
       "      <td>198</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-10.0458</td>\n",
       "      <td>100.918098</td>\n",
       "      <td>183.2952</td>\n",
       "      <td>142.418303</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>135</td>\n",
       "      <td>64</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>125.580825</td>\n",
       "      <td>1.962200</td>\n",
       "      <td>61.611922</td>\n",
       "      <td>20.276064</td>\n",
       "      <td>41.335857</td>\n",
       "      <td>21440</td>\n",
       "      <td>92</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>302</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.7897</td>\n",
       "      <td>3.203026</td>\n",
       "      <td>32.3564</td>\n",
       "      <td>58.685481</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>47</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>62.402934</td>\n",
       "      <td>2.080098</td>\n",
       "      <td>16.872064</td>\n",
       "      <td>13.655728</td>\n",
       "      <td>3.216336</td>\n",
       "      <td>2319</td>\n",
       "      <td>52</td>\n",
       "      <td>4.844</td>\n",
       "      <td>168</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1209 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nAcid    ALogP      ALogp2       AMR        apol  naAromAtom  nAromBond  \\\n",
       "0      0  -0.7109    0.505379   70.4932   73.378583          16         18   \n",
       "1      0  -1.4578    2.125181   44.0418   64.304653          18         20   \n",
       "2      0   0.0748    0.005595   27.2874   69.268860          27         29   \n",
       "3      0 -10.0458  100.918098  183.2952  142.418303           6          6   \n",
       "4      0  -1.7897    3.203026   32.3564   58.685481          21         23   \n",
       "\n",
       "   nAtom  nHeavyAtom  nH  ...      WTPT-1    WTPT-2     WTPT-3     WTPT-4  \\\n",
       "0     65          34  31  ...   70.514766  2.073964  27.938805  11.267053   \n",
       "1     52          31  21  ...   65.167083  2.102164  17.955201   2.565237   \n",
       "2     57          37  20  ...   76.232497  2.060338  31.795843   2.556014   \n",
       "3    135          64  71  ...  125.580825  1.962200  61.611922  20.276064   \n",
       "4     47          30  17  ...   62.402934  2.080098  16.872064  13.655728   \n",
       "\n",
       "      WTPT-5  WPATH  WPOL  XLogP  Zagreb  pChemBL  \n",
       "0  16.671753   3404    59  2.188     184     6.82  \n",
       "1  15.389964   2593    55  6.575     180     7.64  \n",
       "2  22.098211   4979    56  5.589     198     4.30  \n",
       "3  41.335857  21440    92 -0.194     302     6.03  \n",
       "4   3.216336   2319    52  4.844     168     6.42  \n",
       "\n",
       "[5 rows x 1209 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= pd.read_csv('no_zeros_no_chembl.csv')\n",
    "#X.drop(columns=['ChemBLID'],inplace=True)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1209, 1209)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = X.corr()\n",
    "correlation_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1191\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1191"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rows, cols = correlation_matrix.shape\n",
    "bad_cols_index = []\n",
    "for cols in range(rows-1):\n",
    "    if correlation_matrix.abs().iloc[cols,rows-1] < 0.25:\n",
    "        bad_cols_index.append(cols)\n",
    "print(len(bad_cols_index))\n",
    "\n",
    "bad_cols2 = []\n",
    "cols = X.columns\n",
    "for index in bad_cols_index:\n",
    "    bad_cols2.append(cols[index])\n",
    "len(bad_cols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nAcid</th>\n",
       "      <th>ALogP</th>\n",
       "      <th>ALogp2</th>\n",
       "      <th>AMR</th>\n",
       "      <th>apol</th>\n",
       "      <th>naAromAtom</th>\n",
       "      <th>nAromBond</th>\n",
       "      <th>nAtom</th>\n",
       "      <th>nHeavyAtom</th>\n",
       "      <th>nH</th>\n",
       "      <th>...</th>\n",
       "      <th>WTPT-1</th>\n",
       "      <th>WTPT-2</th>\n",
       "      <th>WTPT-3</th>\n",
       "      <th>WTPT-4</th>\n",
       "      <th>WTPT-5</th>\n",
       "      <th>WPATH</th>\n",
       "      <th>WPOL</th>\n",
       "      <th>XLogP</th>\n",
       "      <th>Zagreb</th>\n",
       "      <th>pChemBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>16.671753</td>\n",
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       "      <td>184</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>2.125181</td>\n",
       "      <td>44.0418</td>\n",
       "      <td>64.304653</td>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "      <td>31</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>65.167083</td>\n",
       "      <td>2.102164</td>\n",
       "      <td>17.955201</td>\n",
       "      <td>2.565237</td>\n",
       "      <td>15.389964</td>\n",
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       "      <td>55</td>\n",
       "      <td>6.575</td>\n",
       "      <td>180</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>0.005595</td>\n",
       "      <td>27.2874</td>\n",
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       "      <td>29</td>\n",
       "      <td>57</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>76.232497</td>\n",
       "      <td>2.060338</td>\n",
       "      <td>31.795843</td>\n",
       "      <td>2.556014</td>\n",
       "      <td>22.098211</td>\n",
       "      <td>4979</td>\n",
       "      <td>56</td>\n",
       "      <td>5.589</td>\n",
       "      <td>198</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>-10.0458</td>\n",
       "      <td>100.918098</td>\n",
       "      <td>183.2952</td>\n",
       "      <td>142.418303</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
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       "      <td>64</td>\n",
       "      <td>71</td>\n",
       "      <td>...</td>\n",
       "      <td>125.580825</td>\n",
       "      <td>1.962200</td>\n",
       "      <td>61.611922</td>\n",
       "      <td>20.276064</td>\n",
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       "      <td>6.03</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>-1.7897</td>\n",
       "      <td>3.203026</td>\n",
       "      <td>32.3564</td>\n",
       "      <td>58.685481</td>\n",
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       "      <td>23</td>\n",
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       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>62.402934</td>\n",
       "      <td>2.080098</td>\n",
       "      <td>16.872064</td>\n",
       "      <td>13.655728</td>\n",
       "      <td>3.216336</td>\n",
       "      <td>2319</td>\n",
       "      <td>52</td>\n",
       "      <td>4.844</td>\n",
       "      <td>168</td>\n",
       "      <td>6.42</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1209 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   nAcid    ALogP      ALogp2       AMR        apol  naAromAtom  nAromBond  \\\n",
       "0      0  -0.7109    0.505379   70.4932   73.378583          16         18   \n",
       "1      0  -1.4578    2.125181   44.0418   64.304653          18         20   \n",
       "2      0   0.0748    0.005595   27.2874   69.268860          27         29   \n",
       "3      0 -10.0458  100.918098  183.2952  142.418303           6          6   \n",
       "4      0  -1.7897    3.203026   32.3564   58.685481          21         23   \n",
       "\n",
       "   nAtom  nHeavyAtom  nH  ...      WTPT-1    WTPT-2     WTPT-3     WTPT-4  \\\n",
       "0     65          34  31  ...   70.514766  2.073964  27.938805  11.267053   \n",
       "1     52          31  21  ...   65.167083  2.102164  17.955201   2.565237   \n",
       "2     57          37  20  ...   76.232497  2.060338  31.795843   2.556014   \n",
       "3    135          64  71  ...  125.580825  1.962200  61.611922  20.276064   \n",
       "4     47          30  17  ...   62.402934  2.080098  16.872064  13.655728   \n",
       "\n",
       "      WTPT-5  WPATH  WPOL  XLogP  Zagreb  pChemBL  \n",
       "0  16.671753   3404    59  2.188     184     6.82  \n",
       "1  15.389964   2593    55  6.575     180     7.64  \n",
       "2  22.098211   4979    56  5.589     198     4.30  \n",
       "3  41.335857  21440    92 -0.194     302     6.03  \n",
       "4   3.216336   2319    52  4.844     168     6.42  \n",
       "\n",
       "[5 rows x 1209 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>ATSC4c</th>\n",
       "      <th>ATSC2v</th>\n",
       "      <th>SCH-7</th>\n",
       "      <th>ASP-7</th>\n",
       "      <th>hmin</th>\n",
       "      <th>ETA_BetaP_s</th>\n",
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       "      <td>3.560855</td>\n",
       "      <td>4.402672</td>\n",
       "      <td>0.772339</td>\n",
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       "      <td>58.106835</td>\n",
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       "      <td>7.64</td>\n",
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       "      <td>1</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.568588</td>\n",
       "      <td>-1158.676211</td>\n",
       "      <td>0.405022</td>\n",
       "      <td>0.033239</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>0.63281</td>\n",
       "      <td>3.492463</td>\n",
       "      <td>4.900736</td>\n",
       "      <td>0.692506</td>\n",
       "      <td>0.679765</td>\n",
       "      <td>108.296299</td>\n",
       "      <td>67.713621</td>\n",
       "      <td>2.886374</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.464528</td>\n",
       "      <td>150.179266</td>\n",
       "      <td>1.160056</td>\n",
       "      <td>0.025954</td>\n",
       "      <td>-0.117708</td>\n",
       "      <td>0.64167</td>\n",
       "      <td>3.468889</td>\n",
       "      <td>4.475247</td>\n",
       "      <td>0.805685</td>\n",
       "      <td>0.744481</td>\n",
       "      <td>61.609585</td>\n",
       "      <td>34.166117</td>\n",
       "      <td>0.655185</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ATSC4c       ATSC2v     SCH-7     ASP-7      hmin  ETA_BetaP_s       IC1  \\\n",
       "0  0.183691   808.584430  0.639198  0.027205 -0.189456      0.69853  3.498205   \n",
       "1  0.052198  -125.913748  1.013922  0.025619 -0.114131      0.65323  3.560855   \n",
       "2 -0.177672  -435.726504  0.622175  0.031451 -0.014566      0.68243  3.677764   \n",
       "3 -1.568588 -1158.676211  0.405022  0.033239 -0.177539      0.63281  3.492463   \n",
       "4  0.464528   150.179266  1.160056  0.025954 -0.117708      0.64167  3.468889   \n",
       "\n",
       "        IC2      SIC2      BIC2       ZMIC1      ZMIC2   MDEO-11  nHeteroRing  \\\n",
       "0  4.793532  0.795955  0.762645   51.535290  33.179219  0.000000            4   \n",
       "1  4.402672  0.772339  0.718301   58.106835  36.787814  0.000000            3   \n",
       "2  4.767468  0.817342  0.761894   65.634530  39.004854  0.000000            3   \n",
       "3  4.900736  0.692506  0.679765  108.296299  67.713621  2.886374            1   \n",
       "4  4.475247  0.805685  0.744481   61.609585  34.166117  0.655185            2   \n",
       "\n",
       "   n6HeteroRing  nT6HeteroRing  LipinskiFailures  pChemBL  \n",
       "0             4              4                 0     6.82  \n",
       "1             2              2                 1     7.64  \n",
       "2             2              2                 1     4.30  \n",
       "3             0              0                 3     6.03  \n",
       "4             0              0                 0     6.42  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_try = X.drop(columns=bad_cols2,inplace=False).copy()\n",
    "X_try.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_try.to_csv('./X17.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ATSC4c</th>\n",
       "      <th>ATSC2v</th>\n",
       "      <th>SCH-7</th>\n",
       "      <th>ASP-7</th>\n",
       "      <th>hmin</th>\n",
       "      <th>ETA_BetaP_s</th>\n",
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       "      <th>IC2</th>\n",
       "      <th>SIC2</th>\n",
       "      <th>BIC2</th>\n",
       "      <th>ZMIC1</th>\n",
       "      <th>ZMIC2</th>\n",
       "      <th>MDEO-11</th>\n",
       "      <th>nHeteroRing</th>\n",
       "      <th>n6HeteroRing</th>\n",
       "      <th>nT6HeteroRing</th>\n",
       "      <th>LipinskiFailures</th>\n",
       "      <th>pChemBL</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.183691</td>\n",
       "      <td>808.584430</td>\n",
       "      <td>0.639198</td>\n",
       "      <td>0.027205</td>\n",
       "      <td>-0.189456</td>\n",
       "      <td>0.69853</td>\n",
       "      <td>3.498205</td>\n",
       "      <td>4.793532</td>\n",
       "      <td>0.795955</td>\n",
       "      <td>0.762645</td>\n",
       "      <td>51.535290</td>\n",
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       "      <td>6.82</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.052198</td>\n",
       "      <td>-125.913748</td>\n",
       "      <td>1.013922</td>\n",
       "      <td>0.025619</td>\n",
       "      <td>-0.114131</td>\n",
       "      <td>0.65323</td>\n",
       "      <td>3.560855</td>\n",
       "      <td>4.402672</td>\n",
       "      <td>0.772339</td>\n",
       "      <td>0.718301</td>\n",
       "      <td>58.106835</td>\n",
       "      <td>36.787814</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>7.64</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.177672</td>\n",
       "      <td>-435.726504</td>\n",
       "      <td>0.622175</td>\n",
       "      <td>0.031451</td>\n",
       "      <td>-0.014566</td>\n",
       "      <td>0.68243</td>\n",
       "      <td>3.677764</td>\n",
       "      <td>4.767468</td>\n",
       "      <td>0.817342</td>\n",
       "      <td>0.761894</td>\n",
       "      <td>65.634530</td>\n",
       "      <td>39.004854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.568588</td>\n",
       "      <td>-1158.676211</td>\n",
       "      <td>0.405022</td>\n",
       "      <td>0.033239</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>0.63281</td>\n",
       "      <td>3.492463</td>\n",
       "      <td>4.900736</td>\n",
       "      <td>0.692506</td>\n",
       "      <td>0.679765</td>\n",
       "      <td>108.296299</td>\n",
       "      <td>67.713621</td>\n",
       "      <td>2.886374</td>\n",
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       "      <td>3</td>\n",
       "      <td>6.03</td>\n",
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       "      <th>4</th>\n",
       "      <td>0.464528</td>\n",
       "      <td>150.179266</td>\n",
       "      <td>1.160056</td>\n",
       "      <td>0.025954</td>\n",
       "      <td>-0.117708</td>\n",
       "      <td>0.64167</td>\n",
       "      <td>3.468889</td>\n",
       "      <td>4.475247</td>\n",
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       "      <td>0.744481</td>\n",
       "      <td>61.609585</td>\n",
       "      <td>34.166117</td>\n",
       "      <td>0.655185</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.42</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "     ATSC4c       ATSC2v     SCH-7     ASP-7      hmin  ETA_BetaP_s       IC1  \\\n",
       "0  0.183691   808.584430  0.639198  0.027205 -0.189456      0.69853  3.498205   \n",
       "1  0.052198  -125.913748  1.013922  0.025619 -0.114131      0.65323  3.560855   \n",
       "2 -0.177672  -435.726504  0.622175  0.031451 -0.014566      0.68243  3.677764   \n",
       "3 -1.568588 -1158.676211  0.405022  0.033239 -0.177539      0.63281  3.492463   \n",
       "4  0.464528   150.179266  1.160056  0.025954 -0.117708      0.64167  3.468889   \n",
       "\n",
       "        IC2      SIC2      BIC2       ZMIC1      ZMIC2   MDEO-11  nHeteroRing  \\\n",
       "0  4.793532  0.795955  0.762645   51.535290  33.179219  0.000000            4   \n",
       "1  4.402672  0.772339  0.718301   58.106835  36.787814  0.000000            3   \n",
       "2  4.767468  0.817342  0.761894   65.634530  39.004854  0.000000            3   \n",
       "3  4.900736  0.692506  0.679765  108.296299  67.713621  2.886374            1   \n",
       "4  4.475247  0.805685  0.744481   61.609585  34.166117  0.655185            2   \n",
       "\n",
       "   n6HeteroRing  nT6HeteroRing  LipinskiFailures  pChemBL  \n",
       "0             4              4                 0     6.82  \n",
       "1             2              2                 1     7.64  \n",
       "2             2              2                 1     4.30  \n",
       "3             0              0                 3     6.03  \n",
       "4             0              0                 0     6.42  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_try = pd.read_csv('./X17.csv')\n",
    "X_try.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = X_try.corr()\n",
    "plt.figure(figsize=(15,15))\n",
    "sns.heatmap(mat,annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 18)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_try2 = X_try.drop(columns=['ZMIC1','SIC2','IC1','nT6HeteroRing','LipinskiFailures'],inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.29163578305840543\t R^2 = 0.9381236824086415\n",
      "Test set results\n",
      "RMSE = 0.7734585110204392\t Q^2 = 0.5242157674858365\n",
      "Validation set results\n",
      "RMSE = 0.826755742497236\t Q^2 = 0.5060865146067871\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_try2)\n",
    "model=train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(clusters[20])\n",
    "(553, 297, 547)\n",
    "\n",
    "#now display structures from one of the clusters\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "m1 = ms[553]\n",
    "m2 = ms[297]\n",
    "m3 = ms[547]\n",
    "mols=(m1,m2,m3)\n",
    "Draw.MolsToGridImage(mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'chemfp'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-e2cc11aacbf6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mchemfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbitops\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbyte_tanimoto\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcluster_generator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mJarvisPatrick\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfps\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbyte_tanimoto\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mcluster\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcluster_generator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m9\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'chemfp'"
     ]
    }
   ],
   "source": [
    "from chemfp.bitops import byte_tanimoto\n",
    "cluster_generator = JarvisPatrick(fps, byte_tanimoto)\n",
    "cluster = cluster_generator(9, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>K-Means trials</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'X_try' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-2468f4ea53b7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mKMeans\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_clusters\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# fit the model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_try\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'X_try' is not defined"
     ]
    }
   ],
   "source": [
    "from numpy import unique\n",
    "from numpy import where\n",
    "from sklearn.cluster import KMeans\n",
    "# define dataset\n",
    "nc = 5\n",
    "# define the model\n",
    "model = KMeans(n_clusters=nc)\n",
    "# fit the model\n",
    "model.fit(X_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2861\n"
     ]
    }
   ],
   "source": [
    "# assign a cluster to each example\n",
    "yhat = model.predict(X_try)\n",
    "# retrieve unique clusters\n",
    "ylol = pd.Series(data=yhat)\n",
    "print(len(ylol))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1       4\n",
       "2       4\n",
       "3       2\n",
       "4       0\n",
       "       ..\n",
       "2856    3\n",
       "2857    1\n",
       "2858    4\n",
       "2859    4\n",
       "2860    0\n",
       "Length: 2861, dtype: int32"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ylol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "countc = []\n",
    "for i in range(1,(nc)+1):\n",
    "    countc.append(ylol.loc[ylol==i].count())\n",
    "plt.bar(range(1,(nc+1)),countc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Linear on RFR model</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('./saved_models/new_scaler.pkl','rb') as fp:\n",
    "    scaler = pkl.load(fp)\n",
    "X2 = scaler.transform(X_try.drop(columns='pChemBL',inplace=False).astype('float64'))\n",
    "X_step3 = pd.DataFrame(data=X2,columns=X_try.drop(columns='pChemBL',inplace=False).columns)\n",
    "#pp.score(X_step3,X_try['pChemBL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e88735710>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_try['pChemBL'],pp.predict(X_step3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = StandardScaler()\n",
    "s.fit(X_try.drop(columns='pChemBL',inplace=False).astype('float64'))\n",
    "X2 = scaler.transform(X_try.drop(columns='pChemBL',inplace=False).astype('float64'))\n",
    "X_step3 = pd.DataFrame(data=X2,columns=X_try.drop(columns='pChemBL',inplace=False).columns)\n",
    "X_step3['pChemBL'] = X_try['pChemBL']\n",
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_step3)\n",
    "model=train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e8ac36940>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X_try['pChemBL'],pp.predict(X_step3.drop(columns='pChemBL',inplace=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8023075986740092"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_try.drop(columns='pChemBL',inplace=False),X_try['pChemBL'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set results\n",
      "RMSE = 0.47899595634554365\t R^2 = 0.8298877522272652\n"
     ]
    }
   ],
   "source": [
    "model1 = LinearRegression()\n",
    "pred = pp.predict(X_step3)\n",
    "X_train, X_test, y_train, y_test = train_test_split(pred,X_try['pChemBL'],test_size=0.25)\n",
    "model1.fit(X_train.reshape(-1,1),y_train)\n",
    "r_square = model1.score(X_test.reshape(-1,1),y_test)\n",
    "rmse = np.sqrt(mean_squared_error(model1.predict(X_test.reshape(-1, 1)),y_test))\n",
    "print(\"Test set results\\nRMSE = {}\\t R^2 = {}\".format(rmse,r_square))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_pred = model1.predict(pred.reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4889327515203013"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(X_try['pChemBL'],final_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = np.full(X_try.shape[0],X_try['pChemBL'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse_ = mean_squared_error(X_try['pChemBL'],mean)\n",
    "mse = (mean_squared_error(X_try['pChemBL'],final_pred))\n",
    "r2 =  1 - mse/mse_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8239639509105376"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.sqrt(mean_squared_error(X_try['pChemBL'],final_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE = 0.7465435519535069\n",
      "Q^2 = 0.5806446748804595\n"
     ]
    }
   ],
   "source": [
    "pred = model.predict(X_validate)\n",
    "final_pred = model1.predict(pred.reshape(-1,1))\n",
    "mean = np.full(X_validate.shape[0],y_validate.mean())\n",
    "mse_ = mean_squared_error(y_validate,mean)\n",
    "mse = (mean_squared_error(y_validate,final_pred))\n",
    "r2 =  1 - mse/mse_\n",
    "print(\"RMSE = {}\\nQ^2 = {}\".format(np.sqrt(mse),r2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A slight improvement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Implementing t-SNE</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>17 features+pChemBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 2861 samples in 0.003s...\n",
      "[t-SNE] Computed neighbors for 2861 samples in 0.091s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2861 / 2861\n",
      "[t-SNE] Mean sigma: 8.767688\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 48.824619\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.397429\n",
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2861 / 2861\n",
      "[t-SNE] Mean sigma: 8.763419\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 48.882889\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.396825\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2,init='pca',verbose=1)\n",
    "tsne_exact = TSNE(n_components=2,init='pca',method='exact',verbose=1)\n",
    "\n",
    "x_t = tsne.fit_transform(X_try)\n",
    "x_t2 = tsne_exact.fit_transform(X_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2861, 2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e8b21f208>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gm/zTC7EjoFo7++gfDBEy+dlB7g8EuefpHQyGTEyiPzfOn3ddW/zvPuLJrd3RoA8wMGTy7mdXpakoAn9LQzUVZR68knkHrD8Q5KOT/a7PeRN8WkdPDrjuP3z8VFrndHZG52NuoEWzJrluOy96QyETF+Cz+fvJhfDs7zMBWsT9Ag+JL4ButjqG/hoSH1ep0VQUTT2N9T7W3tmSlfbZ1s6+mFqa3VAovqlnXVsXgY9Our5+WYJ/cmdTgbMz2h8I4g8E86pJoGp8efSx2LZnTBoHxA+f9XrPBPhs/n5yoaWhmjKP0G/93j3OnB02zqbA5Ytnub7OHwjy3sHYC3jioyo1uooi8EM4uGQjoKSqkZ34NLZ279bME2EPlhFubeU3La3lsbe6oqkiTIK+hLHU0lDNuPJwP4q91n7X5efw0s5DcfmSQqHCSWzXWO/j5qY5rLWabYZCJuHnH2nTT9XG39rZFzeD3JB/v1dVmoom8GdLY72Psyq9nDg95Pp8R09sxulENV6Iv0iAe1PBgplViBDNNVHmlbxoEnCOcklUaxeR8NXKZjAU7rtorPeFO68fao1eNNZ/K/86d6sqz/wrhEzyNR1WNdelHNW026W5LlkTklKjqSja+LPttub6hM8527qvWOC6vgwAb7p05Lm1lW/c2s2QrYJ8xYLpYx4YIyNx7B21jfU+vnPluTFle/C1vTEJ8uwifRcPvraX/kFrhbTBEE/mWee1PxDk4d98ELMvmKCfJ93jPf32gbj92tSj8oUGfhd3X7eQ+qkTXJ87fnowZjtZgJgxaVzcIvDO5p+q8eV5GRDcRuKsa+viGz9vixnJ0tn7ccJj1FRVhtdLeC92KYZMkujlQmtnH4OOi1c6q7g5f7f24xmXHzIyHFipsaZNPQlcOr+GgMtQPWeQdrulj5hQ4Y1r4nAGFN+ECloaqllva+N/ddfhMe/cjYzEiZT9xKcD/MOvdwGx4/gbpp3Fnt5PXI+xePbk8IgZR1DNdL2EbIvM/rb36afK95Ss+Sry2fW7LOCTybKgSmWL1vgTWJQgODn3b9t/NOExIrXmSBPHg6/tJXiyP+bi8equw+w6eCKmczQfxns31vu45/pFfP7cGv542Vye7zgY8/wjvwknxrvr8nMos3JmeOTMhdEj4buhloZqvI5RMpk0o+SC28CAVPmekjVfNdb7uPcPF7m+L99+dlWatMafgHMd3wjnP+6SOVPY1+c+nNNZ43vx3UM01EyMaep44d1DdB6JrTFnoxNwOOkH3F7rDwT50bMdnB4I8cbuI3F3Ont6P2FdWxermuvYsHoZrZ19+CZU8KNnO+gfCOERwTehgsZ6H58/pzom/1Eh1HqPJgnQ9z23kxfejV3u07mOwy/e3Bf3PkE7d1V+yErgF5FHgOuBw8aYxda+qcAGYC6wD/iaMaZgktkfSjD5yhm0JlYm/gg/ckzsMsBLjvWBAfY75gFcem5NRs08wxlFkyidgv1uJVJ2p0d+08mq5rqYGnNX3yeseaOToZDh3l91sOn33WzZF/trz7darz8QxN8Ve+fWHgjyg03b4/LrrGvr4oHXO52HiGue2x+Mrwx8YX5mv1elsiVbTT3/Dlzr2Hc38LIxZj7wsrVdMBKts+sMWsPtqDzmMsTTuZj7b/cciesw9FuB6IebtqdcDObJrd1pj6JJlE6hpaGaskRTlS2dRz6JKUtkdEzIED23M+h78nBIo1s/RMiEg7xz8Z1EM3UHHc1z1yycEfea3+7t04V8VF7ISuA3xrwOfOTYvQJ41Hr8KLAyG+caLaua65g0Lr427xybf9IxyieV/jQmNg0ZuOPRLdz33E78gSA/3LSdm//td6xr62JtWxe3PpR8JTC3iUOJJE2nYEzKEUf2YOdMfeBm9Rca8q7W65tQkfAzOu3I25Nopi7EXtB+dsuFTCiP/feKTAxTaqzlsnN3hjGmB8D67jrgXURWi0i7iLT39vbmsDjDd9V58UV2TuB6/f3EZXYbEuqV9D7yoycHeOD1Tm76t9+xtq0L++WiP0Wis5uW1lLhFQSo8IprmurIUEQg2ol7z/WLokE5MsQxWRh39kWkukvwCNRVT0z4/HC4DS0dqVRNT/afcVVzHSuXzI57jfNyvq6ti5MDsXt1ApfKF2PeuWuMWQOsAWhqasqrId7zZ1TF7XPW+CZUeOPa8gEumuuj5+incfsvm1/DU9viJ/cMV6oO0pub5iTMAW9v1y/zesAYBkOGLfs+YsHMKhrrffgmVMSlYXCaPL4ipjO4tbOPWZPGJcxdFDLwN0/viJ5jpJKliB6JcK6e8Gxjp0njy9Iua2SmMpwZ9WT3hQz7bpTKllzW+A+JyCwA67v7MJk8FslPI4Rrq9++rCEuwPzplfNd3+sPBDnliCTjyj0ccLkYjESiWmokqK9r62LDW108+Npe1wlG9nb9/qH4jJrprFt87rSJMef8x1/vShj0I4ZCJuOZu878SMnyJaUjcqFzc+zTwZi7inVtXQkv3PZmsVMD8Sk/3Pp3lBoLuazxPwPcDtxnfX86h+fKiUyySoZMuMPP7tRAiLf2Zd655/UkbjJo7ezj9IC1NoEJDxd99f1e1n+rJfq8b0IFFWWe6OsiRODA0U/xB4JpzSZeeWFt9Jj2EUB2thREUZne1lWWeZJuO6VaOMUfCNL7ceLmng1bumKSsyVyo61J7fzZk+k+GjsybPqkcUnLqdRoydZwzvXAFUCNiHQD/4NwwH9cRO4AuoCbs3Gu0ZYq62ey2uanLrW+bBgKwS4r5a/zotTSUI3HMQs1Mnns9d290WGb582oYlv3sbjjrn+ri41bu7l20cyU5diwpYsFM6uiaY0HhuL7BC6aG07SFilPWYI+h+E4d0ZVzAX0XJcmuYh0moVaO/tcL1AR9oCdbIW2XQdPRH8PVyyYHjfW/8okeZ2UGk1ZCfzGmFsTPHV1No6fz5LVNp0zVrNpw5Yudh48wcBgCK9X+HrTHG602vMb631xdxYvW6mTDeE7D2fQh/BzxoQvFE+n0Q/xdvcxbl3zJt+8ZB5Dxj1wzp9RxfeWL+SB1/Zy+Pgpvn5RXVy/wHDvpiY55k4kSwHhrKHba+8RLQ3VVJZ7ODXgPuLKHrBXNdfx1gd9rs09m3f0RI/t1lTWkUbzmVKjYcw7dwuds/ZpN7d6Au/25GY1rUpbLpjBIcPatnBNfe2dLa4BOMHaMgml+/L+IeM6oQnCzTyR5o83rLuNXYc6WDAzXENPdx1e+wUC4CFHJs1kAdVZQ99x4FhcHqRIeoq/fmq7a4e2/fj+QJBn33Fv7rF3/LulfMirkQuqpGngz9BNS2t5vH1/XHs+wCcuOf3P9o3nw2BmHbzTqyqY4tIZGWnScU6aGg6BlKN50j6WdcOTaJKYc59b4LePQPKIcNV50+PSQCdLlLequY6nft8dvTgPhWJH30TsOHAs4c9t3+2WydONc1U1D2TcxKVUtmiStgw11vsStt0edEn78Mmp4U34cnP81GBc+zGEA3a6zQkewkNRnbJZK42kIXabJBbpFxDCTWJundX+QJCfvfQ+pwfCF4jBkOGFdw/FdTw7Zz47OS+Szvf7A0F+2b4/4fvtTUnJhtHam5Xscym8An93wwU6lFPlDa3xZ8F0R+0uotwjOG/4s9Hhm6gt2gOMr0jvV/rZ2smu7fzD4fVAKJT8YhFJ1OYcHeUPBIkuOybxfSH+QJBb17wZXQfXzrlnWUM197+yx7WvwB8IxiTcszc/RbR29rnesUXY2+uTTfayL7LTWO9j/eplPPja3oR5n5QaKxr4s2DR7Ml4PRLXBDFzynj2HI5dqCRV7XSkPEBFuYdvXjKPH27anrLmnmnQh3BHcKrzbN7RQ1ffJ3T0HGf54lkxM4MHrCGggy5NPRu3drsG/YiFM6voOX6Kz549mUd++wEDQ4Zyr7B+9bLo8VsaqsPNTLbjGODFjoPRi09keGt5gvz5EHuHEE4zTcyKaRHORXp2HTwRvTN7uzs8siiTiWZKZYsG/gxF0hcbYyjzCNd/dhZ9n/RHO/oiQwkjkg0bzETD9LP46U2fpbHex4YtXbydhcCeSjp9AW/sPhLtXLUPp7TnxwkR34SSajzUTms4qz3dc/+Q4aebd/LOh8eincb3XB+fF//5joN8cdFMbnu4ldMDIbwe4c5L5/H67l7Xzvj4tRncf4vOMrutr6yBX+UDbePPkL3j0hjD/BlV/OKOZlY119HVF78ylUurRlZMrPBGa8zL8jgfzCO/DY/I2XHgWNyiLXY3Lq2NWeAlXVv2BaN9Av0DIdd1FZbMmRIz0W0wZFjzRie73BZIJ7ZsrZ19CdcYdl4gnOk9kiV4U2o0aY0/Q84lCu2dlM5VqwBmTx4XN6MzG5Y1VPODTdvZ1hXM2RDSbNhz+GPue24nT/i7Y+rMbp2mkXjv9QiXNsQu5pKI/ZghiFvkBsJzC5wT3UIG11uxMq/EJaJLxHnxss/2TTRrWKmxoIE/Q8nSOly7aGbcGPeqceVAdgP/zKpKHny9s2DGiT/fcZABW3t6yMC9v+qISd725NZuBqyoPDBk0gr6Th6BqRPK4/ZFfk9uE92cBhz9DJEZ007OC0TEquY6Dfgq72hTTxY01vv4zpXnxo0o+aJL2oOBUOoc98N18MTpggn6AHVTJ8SV15lq2rmU4XB4rWGiZV4PkxMMv/QHgmztSm++w0ZbUrlEKTrOnjxeh2uqgqGBP4fcslA21Eyksry0P/ZEF6lIjdk5BHM4BJDIGVwWkonMLdi4tds1DXOiY0bMSJBoLfDRyaysDaDUaCjtCJRjzgDnEbjr8nNYe2cLt5Xo7b9HYse7R9iDa7qzY92EO2vD392SxkG4P8EtpYIbj8SO+78iSaK1ZJk7lconGvhzKG725soLom3LP77hAhbOTJxVsliJwAmX5SoN8OBrewFSLgIzeXwZZdbnmqzZzADHHB2ukVE66V5Wrlk4I6YJJ9kELh21owqFdu7mUGT2plvHrz8Q5H3H8MFzp5/FB0c+dp0cVCyGQnD4xGkqXCZMRWa4ploE5sSpQb7yudls238UY0i6+MuHx2I70r1WJ2w6C+JE7tBizp1gMRW3RXqUylca+HMsUT7/8MLkZ7Y9Aj+96bPsOngiYZbIYjG9qpLrFs+MS20cmX+QqvM7ZEi4CtbMqkoO2ppxnAnxls6ZQmO9L+HoHDu3heGday4DfOn8Gdx93cKUx1MqX+S8qUdErhWRXSKyR0TuzvX5CkVLQzXl3jMhzmPNUkq18Hcx6PjwmGvgjoy5v3FpLRUpVtVy4xE4a1zyukz7viD+QDCtz/mR3+2LW7bSrTnnP987FPc6pfJZTgO/iHiB+4HlwPnArSJyfi7PWSga633c3DQnWrs1IRNtEirzFnfXS6I8QS+/dziaK3/9t1o4qzI+e2gyIeM+YcvOcGb5yVT6B0NxI7NWNdfxpfNnxOwbChEzFFWpfJfrCHMxsMcY02mM6QceA1bk+JwF48altVSWx6YrBqLDEHO3fld+GrIufhCeKPWxy3oGqaRqIiu32vjTTV/tdrgrFkyPSSNhSJ6uWal8k+vAfzZgT3Tebe1TnJn1+5dfWhBdgSoylLGIm/iB8OpkbiIB9P5Xdmf9nB6Be7+ymMZ6H4fTHM7pXNYxkpQvpn+G0miiU8Uj1527bpXWmJgmIquB1QB1daU3KsLZ+evM/XPtopk8ve1AXl0Izqr0jqg2HuER6A66j8S55+ntvPVBHx/mIJ+RfaGaRGsoODnvDCLJ3ewSpWtQKl/lOvB3A3Ns27VATK+eMWYNsAagqakpn+LbmHDm/mnt7MuroA9w/qxJbN1/NOniJckYk3gN4MEQaS30PlKR08anWnbnHHIbzscv0QlmAtzcNEfTNaiCkuumni3AfBGZJyIVwC3AMzk+Z8Gz5/6JBJp84RFYeWFma8cmu1xkul7BktrEAV3kzLq36TbN+APBmBE7jfU+frRiMWUewSNQWe6JW9FLqXyX08BvjBkE/gz4NbATeNwY05HLcxabxnoff7ticd4E/zKP0HHgGEMjrO1HuL3b60neoZ3OJ/BO9zFWLpnt3sZowqtvAbSlOQonktvHblVzHRvuWsZf2fpmlCokOSKwPZIAABLKSURBVJ/AZYx5Dngu1+cpZqua61gws4q/enwb+/oSz1IdDYNDhsM5ygaaasZyOucMAVsCQTwJlkdc80YnddUTo6uBpRJJ4+yUaGKeUoWguAeMF5HGeh+rLzsn9QtzLER8/pt882Hw04QXEWPCydTSvXA1aYBXRUgDfwFZMLMqOtt3LBt+2gt4lmpluYfli2elPTN4io7PV0VIA38Bsa/3OpYjfQo1j5BH4J7rF7GquY6vNqbXIfuypmNQRUgDfwFJla44286eMo7bmuv49mUNTJ1YnvoNeWbaWbG19ZA5k/lzUmV63VtDIfcFdZQqZJqds0D4A0E27+hJOdxxJMMhRcJt3041Z1XyyelB1hboylK9H8f3RfyyfT+LZ09mjWMt5GQK9AZHqYQ08BcAfyDIbQ+3cnoghCF5cB9RkDLhZhDn3cTb3cd4O0FCtUI1OGTYvKOHdJc88NjG/itVLLSppwC0dvbRPxgO+h7g0vk1nD8re6t3GQq33X64DOGlH9NJgOqxrZqmVDHRwF8AIvl7vAIV5R7+4prPsKROg9FIVY0v5+rzZqR8nUfCI6mUKjYa+AuAWxbPm5bWkmpE4sols9M6/rcvayBPJgbnnEfCneSv7jqc8rWDIdioHbuqCGkbf4FwzhRtrPex4a7Ps3FrN0dOnCZ4sp8t+2KHHaazvKAAddUT8XiEUIZpGAqBR8IZNwcS/KzO/pMjaaZvVqqQaOAvYPaLwTd+3hb3/P4EqY/tDPDIbzoJlUgjvzHhn9meYRPgc7WTWXz2ZN4/dCLmAloan4oqNdrUUyTc1oJdmm4/gEi0D6HYRTJ03nnpvJj9X7+ojh/fcAGfmRHbpp9u3n6lCokG/iKxqrmOi+bGBvraqROi/QDJYvo1503nnusX8flza6g5a2xSFCwcpU7UmZPG0Vjvo2p8efQzsa+g5czTn27efqUKiTb1FAl/IMjWrtg2/iMnTkeHaSZrsjh+epB7f9VB/2C6o9uzb2ca/RHZcODYKfyBIC0N1VSWe+gfCCECb+8/ij8QJHiyP9rOr0sqqmKlNf4isXFrd1xGyqMn+1OOz/dI+AIxlkF/NBkrv35jvY97rl+EeIQhAy+8e4hb17yJb0IFleVnhs7qkoqqGGmNv0i4NeWcTiOYr/5CA3uPfJL9AuUpj+fM+rjBk/3RpHcAA0OG4Mn+mKUvdfKWKkYZ1fhF5GYR6RCRkIg0OZ77vojsEZFdIvLlzIqpUnFri55XM5GKBD22UyeU85MbLuDu6xbmrAMzH/uKrzpvejSY+xwpl73Woun2pS+VKkaZ1vh3ADcCD9p3isj5hNfXXQTMBl4Skc8YY4YyPJ9KwK0tuu+TftavXsbGrd3ssYYpGqDMKzx0+0XRwJarDsx8HAp55YLp0cfOz+xrumi6KhEZBX5jzE4Akbi63QrgMWPMaeADEdkDXAy8mcn5VGItDdVxidYWzZoUM9Z/XVsXm3f0sHzxrJgAl88dmG7J4zIRScsM8TX+xTqCR5WIXLXxnw202ra7rX1xRGQ1sBqgrq4uR8UpDeLIr1w1/kwO/fue28maNzoxBrbs+4gFM6uiwf/EpwOjXtZ0NdRMZG/vJ1m7e7DPxHVe8DoOFFcmUqUSSdnGLyIvicgOl68Vyd7mss/1f9cYs8YY02SMaZo2bVq65VYOrZ19cbNvIzXadW1dPPB6JyFr1urpgRCtnX3R1z37zoHRLOqwTJ1YgdcjCOHaf6b9Bq/uOhxdUct5wXvsrS5dbUuVhJQ1fmPMNSM4bjcwx7ZdC+RvdCkCvgkVcVfWSI12846emP0iREe2+ANBuo+eSvs8VeO8nDg1Ol01HoGtXUGGQuELlttiMcM1FDLR4ZwvvRebqG3INtRTqWKWq3H8zwC3iEiliMwD5gNv5ehcivhmizLbsEVnOofVX2iIBjd7zT8VAWZMGp9ZQYchZMIZMrPZSey1fS5uVxIdt69KQabDOW8QkW5gGfD/ROTXAMaYDuBx4F3geeA7OqInt1oaqhlX7sFDOOj/aMXiaHBfZa2bO7d6At++rIG7r1sY875EQz6dyss8zKuZOOyyleVREqCbbSN3rlkYm5N/5ZLZWttXJSHTUT2bgE0Jnvsx8ONMjq/SF8nZ7zbxyB8I8vPffsDAkOHnv/2ALy6aGX2+sd7HvV9ZzA82bY87pgBfPH8GhnCyshutJQhffPfQsMq2dM4U3tqXH23n9qGrkXw9kXr/xDQXYFeq0OlfehFx5uyPePC1vdH88wNDhp9u3snj3/589PlEi5KcN7OKNX/UFLf/4rm+YQXyzr78mRlsH7nT0lBNmVein80v2/dz49JarfWroqe5ekrAoeOxnbdv7QvGjF7pTJCy4cIEAfB7yxcOa8WuIyfyZ56As1V/cCg2ZcNw+jyUKlQa+EvA1y+Knx/xpG1JwakTyuOe9xDOW++msd7HVz6X3rKO+cTrif2ZHnhtb8yFwBA/qUupYqSBvwS45eq3B7xzZ8Tnwq8+qyJhk8e6ti6e2pbd0bmj0f3rHMRz+Hj8MNZ8nsWsVLZo4C8Rdy9fGF2UpcxR83Wr2fd+3M+6ti7XYznnBRSKkIm903HeCXk9OpxTlQYN/CXE4/GEZ8B64n/tbjXuRAF+0axJ2SuTgFfC6ZJHw/uHziz4sqq5jpVLzjRZeV0+F6WKkf6ll4jWzj4GBkMYYHAwNmXDg4627gi3dXwhNgeQnVfgsvk1wypXyMAtF9fxLccauLkSPHkmTYM/EORXb59pshocCmnnrioJOpyzRNhTOoSI7cR0jvoBOH9WFaua3ZPmOTtAF86s4sJ6HzdZQyFX/utv2NadfsKz2VPGs23/0bRfnwn7BLSNW7uxDepB0KYeVRq0xl8igif7o0MwPRLbiek26ue9nhMJE5btcGSxvLDex09uuIDGeh/r2rqGFfQ9Vt4gt47WXLDn43c2Ll29cIaO4VclQQN/iWhpqKaizFpLtix2LdlVzXVc7DLqJ1Gzhz21sXN7uB2/Tdaks2WjVNO2X7RuXFpLRVm436OizMNdl58zKmVQaqxpU0+JSJbSAcKTsr724JvRNWjLvZKw2aPGsVSjfXv54lm8sfvIsMuXaBJZtu2xde421vtY/y1dX1eVHg38JSRRSocIsXXxfvOSeQlfe9PSWp5o38/AkKHcKzHDQSP9Ahu2dNE/GGLnwROux4iILAjf0XM87Z8jE84F6FN9JkoVIw38Cgh3dNpj4kO/iU3mZtdY72P96mUJa8qrmutY1VzH/a/sYefBXUnPG+lfGF/uzfyHSIOzP8MfCGqNX5UcDfwKiO/oDNkWLHGTTk15OOkPrjlvOnsOf5z260fCI7Bg5plZyv5AkFsfamVgMER5mYf132rR4K9KgnbuKiA2XTGEc+hnOrQxnfQHm3f04A8E+fc392V0rnSETGyH9ZNbu+m35jb0D4ZiZvUqVcwyXYjlH0TkPRF5R0Q2icgU23PfF5E9IrJLRL6ceVFVLtmHewqxC5aMVEtDdcocPMsXz6K1s49TA6EUr8xczOpbxGfqzOZKX0rls0xr/C8Ci40xnwXeB74PICLnA7cAi4Brgf8jIqPTiKtGxD7cs7LckzAz53A01vviksNBeKWrL8yv4Sc3XMCq5rq07iwmVnj50vkzmDRu5K2Tf2tblQzCndQV3vBC7hWOTmqlilmmK3C9YNtsBb5qPV4BPGaMOQ18ICJ7gIuBNzM5n8qdVMM9R+rcGVVxi7ZMrCzjZ7dcGHPu+qkTCHx0MuFx6qZOYM0fNbGurct1tbBUzp4yLm4mcqpOaqWKVTY7d78JbLAen034QhDRbe1TeSwXQxsXO/oOAA47JoABXDq/hkCCbKAAS61ydThmDYcXgK/k4PH4Y9r1HDuFPxCM+/l0OKcqRSmbekTkJRHZ4fK1wvaaHwKDwNrILpdDuTahishqEWkXkfbe3t6R/Awqj7l18E53TACD+M5lpxuX1uIPBPll+/6Y/eVlHj5bOyXBu85wpmRWqpSlrPEbY65J9ryI3A5cD1xtTHSpi25gju1ltYDryh3GmDXAGoCmpibtXysykb6DfmuSgEeILtpu56zJ24lVjWjt7GMwdOZPRIB7/3ARAC+ksQC8/nEpFZbpqJ5rge8BXzHG2BtonwFuEZFKEZkHzAfeyuRcqjA11vu4bvHM6HbIwC6X2bxuzT8RYg3DbGmoxiNnbiaF8B1FuqtmaeetUmGZjur5V6AKeFFEtonIAwDGmA7gceBd4HngO8aYoQzPpQqQPxDkmbdjb/bcErk52wbrp06gzCN4gIpyT7Tz9UcrFsftP/HpQNzxnM6dfpa25StlyXRUz7lJnvsx8ONMjq8KX2tnX9xat24LvDgTv10yv4abltbGjbhZ1VzHgplVMft/9KuOlOVYPDt7q4YpVeg0ZYPKqZaGairLPZy2JmitWDLbdYEXt8RviUbcOPfPmDQOONNHELl7sF9vnn2nh28sm6u1fqXQlA0qxxrrfdxz/SK81rTg5zsOui7wEhlT/9++vID1q5cNK0Dfdfk5lHvDxy/3Cj++4QJuba7DvoxvJPeQUkpr/GoUBE/2EzIGAwxY6/2mU5NPV2O9j8ccE7HWtXVhGwCEx6PLKioVoTV+lXP2dBDljtW/csXZgbxo9mRt5lHKojV+lXO5SgcR4Q8Eue3hVvoHQ1SUeVh7Z0vcSmBu6worVao08KtRkcvUCK2dffQPhgiZM01J37kyPOBs844eli+e5dqhrFSp0sCvCl6kKSmyoEqkKSmyEphSKpYGflXwct2UpFSx0cCvioJm2VQqfTqqRymlSowGfqWUKjEa+JVSqsRo4FdKqRKjgV8ppUqMBn6llCoxYpzJ0seQiPQCgVE+bQ1wJOWr8kOhlLVQygla1lwolHJC4ZQ1VTnrjTHT0j1YXgX+sSAi7caYprEuRzoKpayFUk7QsuZCoZQTCqes2S6nNvUopVSJ0cCvlFIlRgM/rBnrAgxDoZS1UMoJWtZcKJRyQuGUNavlLPk2fqWUKjVa41dKqRJT9IFfRG4WkQ4RCYlIk+O574vIHhHZJSJftu2/1tq3R0Tutu2fJyJtIrJbRDaISEWOynyviHwoItusr+tGWubRli/lsJVnn4hstz7HdmvfVBF50fo9vigiPmu/iMj/tsr+jogszXHZHhGRwyKyw7Zv2GUTkdut1+8WkdtHsax593cqInNE5BUR2Wn933/X2p93n2uSsub+czXGFPUXsBBYALwKNNn2nw+8DVQC84C9gNf62gs0ABXWa8633vM4cIv1+AHgv+SozPcC/81l/7DLPMqfdV6Uw1GmfUCNY9/fA3dbj+8Gfmo9vg7YDAjQArTluGyXAUuBHSMtGzAV6LS++6zHvlEqa979nQKzgKXW4yrgfas8efe5Jilrzj/Xoq/xG2N2GmN2uTy1AnjMGHPaGPMBsAe42PraY4zpNMb0A48BK0REgKuAJ6z3PwqszP1PMPIyj3LZyKNypLKC8O8PYn+PK4D/MGGtwBQRmZWrQhhjXgc+yrBsXwZeNMZ8ZIwJAi8C145SWRMZs79TY0yPMWar9fgEsBM4mzz8XJOUNZGsfa5FH/iTOBvYb9vutvYl2l8NHDXGDDr258qfWbeej0RuS0dQ5tGWL+WwM8ALIuIXkdXWvhnGmB4I//MB0639+VD+4ZZtrMuct3+nIjIXuBBoI88/V0dZIcefa1EEfhF5SUR2uHwlu+qJyz4zgv0jkqLM/wacAywBeoB/GmGZR1u+lMPuEmPMUmA58B0RuSzJa/Ox/BH5+LvP279TETkL2Aj8hTHmeLKXJijTWJY1559rUSy9aIy5ZgRv6wbm2LZrgQPWY7f9RwjfBpZZtX7764ct3TKLyEPAsyMs82hLVr4xYYw5YH0/LCKbCN8WHxKRWcaYHuu2/rD18nwo/3DL1g1c4dj/6iiUE2PMocjjfPo7FZFywoF0rTHmSWt3Xn6ubmUdlc81m50V+fxFfOfuImI7SjoJd5KUWY/ncaajZJH1nl8S27n7pzkq6yzb4/9KuF1vRGUe5c84L8phK89EoMr2+HeE22n/gdiOvr+3Hv8BsR19b41CGecS22E6rLIR7nz8gHAHpM96PHWUypp3f6fW5/MfwM8c+/Puc01S1px/rmPyDzmaX8ANhK+Up4FDwK9tz/2QcG/4LmC5bf91hHvY9wI/tO1vAN4i3KnyS6AyR2X+BbAdeAd4xvGHMKwyj8HnnRflsP2+3ra+OiLlIdxf8zKw2/o+1dovwP1W2bdjqyjkqHzrCd/KD1h/o3eMpGzAN62/yT3An4xiWfPu7xS4lHAzxzvANuvrunz8XJOUNeefq87cVUqpElMUnbtKKaXSp4FfKaVKjAZ+pZQqMRr4lVKqxGjgV0qpEqOBXymlSowGfqWUKjEa+JVSqsT8fxbuLMVuO031AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_t[:,0],x_t[:,1],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e8b3b2908>]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_t2[:,0],x_t2[:,1],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 31 nearest neighbors...\n",
      "[t-SNE] Indexed 2861 samples in 0.002s...\n",
      "[t-SNE] Computed neighbors for 2861 samples in 0.033s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2861 / 2861\n",
      "[t-SNE] Mean sigma: 4.905116\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 46.470039\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.386734\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25e8b41f898>]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2,init='pca',verbose=1,perplexity=10)\n",
    "x_t = tsne.fit_transform(X_try)\n",
    "plt.plot(x_t[:,0],x_t[:,1],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tsne = TSNE(n_components=3,init='pca',verbose=1)\n",
    "x_t = tsne.fit_transform(X_try)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=x_t,columns=['1','2','3'])\n",
    "df.to_csv('tSNE_3D.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>17 features without pChemBL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 2861 samples in 0.009s...\n",
      "[t-SNE] Computed neighbors for 2861 samples in 0.083s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2861 / 2861\n",
      "[t-SNE] Mean sigma: 8.755023\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 48.927467\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.381227\n",
      "Shape of x_t (2861, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x202d277e518>]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2,init='pca',verbose=1)\n",
    "x_t = tsne.fit_transform(X_try.drop(columns='pChemBL',inplace=False))\n",
    "print('Shape of x_t',x_t.shape)\n",
    "plt.plot(x_t[:,0],x_t[:,1],'.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>With some more features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "757\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "757"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv('no_zeros_no_chembl.csv')\n",
    "correlation_matrix = X.corr()\n",
    "bad_cols_index = []\n",
    "for cols in range(1208):\n",
    "    if correlation_matrix.abs().iloc[cols,1208] < 0.1:\n",
    "        bad_cols_index.append(cols)\n",
    "print(len(bad_cols_index))\n",
    "\n",
    "bad_cols2 = []\n",
    "cols = X.columns\n",
    "for index in bad_cols_index:\n",
    "    bad_cols2.append(cols[index])\n",
    "len(bad_cols2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X.drop(columns=bad_cols2,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = X['pChemBL']\n",
    "X.drop(columns='pChemBL',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Mutual Info Regression</h3>(for feature selection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "X_new = SelectKBest(mutual_info_regression, k=50).fit_transform(X, y)\n",
    "\n",
    "cols = []\n",
    "for i in range(50):\n",
    "    cols.append('comp'+str(i+1))\n",
    "X_red = pd.DataFrame(data=X_new, columns=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([17., 11.,  8.,  5.,  2.,  1.,  2.,  3.,  0.,  1.]),\n",
       " array([0.10101353, 0.12449249, 0.14797145, 0.17145042, 0.19492938,\n",
       "        0.21840835, 0.24188731, 0.26536627, 0.28884524, 0.3123242 ,\n",
       "        0.33580316]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_red['pChemBL'] = y\n",
    "corr_matrix = X_red.corr()\n",
    "plt.title('Absolute value of Correlation with pChemBL')\n",
    "plt.hist((corr_matrix.iloc[:len(cols),len(cols)]).abs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Indexed 2861 samples in 0.039s...\n",
      "[t-SNE] Computed neighbors for 2861 samples in 0.346s...\n",
      "[t-SNE] Computed conditional probabilities for sample 1000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2000 / 2861\n",
      "[t-SNE] Computed conditional probabilities for sample 2861 / 2861\n",
      "[t-SNE] Mean sigma: 6.107326\n",
      "[t-SNE] KL divergence after 250 iterations with early exaggeration: 51.743423\n",
      "[t-SNE] KL divergence after 1000 iterations: 0.541202\n",
      "Shape of x_t (2861, 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x202d2b50a20>]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2,init='pca',verbose=1)\n",
    "x_t = tsne.fit_transform(X_red)\n",
    "print('Shape of x_t',x_t.shape)\n",
    "plt.plot(x_t[:,0],x_t[:,1],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.2640578932983205\t R^2 = 0.9509766910448381\n",
      "Test set results\n",
      "RMSE = 0.6594638081557663\t Q^2 = 0.6714872397300438\n",
      "Validation set results\n",
      "RMSE = 0.6725619696869898\t Q^2 = 0.6157031300553855\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_red)\n",
    "RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comp1</th>\n",
       "      <th>comp2</th>\n",
       "      <th>comp3</th>\n",
       "      <th>comp4</th>\n",
       "      <th>comp5</th>\n",
       "      <th>comp6</th>\n",
       "      <th>comp7</th>\n",
       "      <th>comp8</th>\n",
       "      <th>comp9</th>\n",
       "      <th>comp10</th>\n",
       "      <th>...</th>\n",
       "      <th>comp42</th>\n",
       "      <th>comp43</th>\n",
       "      <th>comp44</th>\n",
       "      <th>comp45</th>\n",
       "      <th>comp46</th>\n",
       "      <th>comp47</th>\n",
       "      <th>comp48</th>\n",
       "      <th>comp49</th>\n",
       "      <th>comp50</th>\n",
       "      <th>pChemBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.351928</td>\n",
       "      <td>0.225817</td>\n",
       "      <td>-1.302195</td>\n",
       "      <td>0.570100</td>\n",
       "      <td>0.382146</td>\n",
       "      <td>0.326812</td>\n",
       "      <td>0.656777</td>\n",
       "      <td>0.522070</td>\n",
       "      <td>0.447225</td>\n",
       "      <td>0.726585</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.378044</td>\n",
       "      <td>1.085501</td>\n",
       "      <td>0.233193</td>\n",
       "      <td>0.653632</td>\n",
       "      <td>0.732004</td>\n",
       "      <td>0.834264</td>\n",
       "      <td>0.824194</td>\n",
       "      <td>1.173686</td>\n",
       "      <td>-0.209875</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.351928</td>\n",
       "      <td>-0.138740</td>\n",
       "      <td>1.765971</td>\n",
       "      <td>0.807637</td>\n",
       "      <td>-0.350463</td>\n",
       "      <td>-1.058278</td>\n",
       "      <td>-1.000160</td>\n",
       "      <td>0.548928</td>\n",
       "      <td>-0.212760</td>\n",
       "      <td>-0.579420</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.378044</td>\n",
       "      <td>-0.014287</td>\n",
       "      <td>-0.727830</td>\n",
       "      <td>0.980360</td>\n",
       "      <td>1.031408</td>\n",
       "      <td>1.067285</td>\n",
       "      <td>-0.088705</td>\n",
       "      <td>-0.719429</td>\n",
       "      <td>-0.398305</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.523607</td>\n",
       "      <td>-0.776044</td>\n",
       "      <td>0.199628</td>\n",
       "      <td>0.703677</td>\n",
       "      <td>-0.238897</td>\n",
       "      <td>-0.210956</td>\n",
       "      <td>0.148180</td>\n",
       "      <td>1.007079</td>\n",
       "      <td>0.585002</td>\n",
       "      <td>0.549196</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.378044</td>\n",
       "      <td>0.203337</td>\n",
       "      <td>-0.727830</td>\n",
       "      <td>0.489264</td>\n",
       "      <td>0.483153</td>\n",
       "      <td>0.507164</td>\n",
       "      <td>-0.088705</td>\n",
       "      <td>-0.721436</td>\n",
       "      <td>0.587844</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.025748</td>\n",
       "      <td>-4.632288</td>\n",
       "      <td>2.721977</td>\n",
       "      <td>1.278363</td>\n",
       "      <td>1.865578</td>\n",
       "      <td>2.334750</td>\n",
       "      <td>2.646201</td>\n",
       "      <td>1.211694</td>\n",
       "      <td>2.389423</td>\n",
       "      <td>2.791551</td>\n",
       "      <td>...</td>\n",
       "      <td>4.759578</td>\n",
       "      <td>-0.325660</td>\n",
       "      <td>-0.727830</td>\n",
       "      <td>-1.935419</td>\n",
       "      <td>-1.865746</td>\n",
       "      <td>-1.971017</td>\n",
       "      <td>-1.914504</td>\n",
       "      <td>3.133633</td>\n",
       "      <td>3.415884</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-2.102998</td>\n",
       "      <td>1.004422</td>\n",
       "      <td>1.686057</td>\n",
       "      <td>-0.259085</td>\n",
       "      <td>-0.568936</td>\n",
       "      <td>-0.417660</td>\n",
       "      <td>-1.195183</td>\n",
       "      <td>-0.385867</td>\n",
       "      <td>-0.399293</td>\n",
       "      <td>-1.012185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.788157</td>\n",
       "      <td>-1.421030</td>\n",
       "      <td>-0.727830</td>\n",
       "      <td>0.948702</td>\n",
       "      <td>0.917908</td>\n",
       "      <td>0.884662</td>\n",
       "      <td>-1.001605</td>\n",
       "      <td>1.693352</td>\n",
       "      <td>-2.187896</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      comp1     comp2     comp3     comp4     comp5     comp6     comp7  \\\n",
       "0 -0.351928  0.225817 -1.302195  0.570100  0.382146  0.326812  0.656777   \n",
       "1 -0.351928 -0.138740  1.765971  0.807637 -0.350463 -1.058278 -1.000160   \n",
       "2  0.523607 -0.776044  0.199628  0.703677 -0.238897 -0.210956  0.148180   \n",
       "3  4.025748 -4.632288  2.721977  1.278363  1.865578  2.334750  2.646201   \n",
       "4 -2.102998  1.004422  1.686057 -0.259085 -0.568936 -0.417660 -1.195183   \n",
       "\n",
       "      comp8     comp9    comp10  ...    comp42    comp43    comp44    comp45  \\\n",
       "0  0.522070  0.447225  0.726585  ... -0.378044  1.085501  0.233193  0.653632   \n",
       "1  0.548928 -0.212760 -0.579420  ... -0.378044 -0.014287 -0.727830  0.980360   \n",
       "2  1.007079  0.585002  0.549196  ... -0.378044  0.203337 -0.727830  0.489264   \n",
       "3  1.211694  2.389423  2.791551  ...  4.759578 -0.325660 -0.727830 -1.935419   \n",
       "4 -0.385867 -0.399293 -1.012185  ...  0.788157 -1.421030 -0.727830  0.948702   \n",
       "\n",
       "     comp46    comp47    comp48    comp49    comp50  pChemBL  \n",
       "0  0.732004  0.834264  0.824194  1.173686 -0.209875     6.82  \n",
       "1  1.031408  1.067285 -0.088705 -0.719429 -0.398305     7.64  \n",
       "2  0.483153  0.507164 -0.088705 -0.721436  0.587844     4.30  \n",
       "3 -1.865746 -1.971017 -1.914504  3.133633  3.415884     6.03  \n",
       "4  0.917908  0.884662 -1.001605  1.693352 -2.187896     6.42  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale = StandardScaler()\n",
    "anon = X_red.drop(columns='pChemBL',inplace=False)\n",
    "scale.fit(anon)\n",
    "a = scale.transform(anon)\n",
    "anon = pd.DataFrame(data=a,columns = anon.columns)\n",
    "anon['pChemBL'] = X_red['pChemBL']\n",
    "anon.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.27289866209770247\t R^2 = 0.9441413471318102\n",
      "Test set results\n",
      "RMSE = 0.6698488549617897\t Q^2 = 0.6634389367667869\n",
      "Validation set results\n",
      "RMSE = 0.6735866354140885\t Q^2 = 0.6876581119589311\n"
     ]
    }
   ],
   "source": [
    "scale = StandardScaler()\n",
    "anon = X_red.drop(columns='pChemBL',inplace=False)\n",
    "scale.fit(anon)\n",
    "a = scale.transform(anon)\n",
    "anon = pd.DataFrame(data=a,columns = anon.columns)\n",
    "anon['pChemBL'] = X_try['pChemBL']\n",
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(anon)\n",
    "RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Butina Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import DataStructs\n",
    "from rdkit.ML.Cluster import Butina\n",
    "\n",
    "#Define clustering setup\n",
    "def ClusterFps(fps,cutoff=0.2):\n",
    "    # first generate the distance matrix:\n",
    "    dists = []\n",
    "    nfps = len(fps)\n",
    "    for i in range(1,nfps):\n",
    "        sims = DataStructs.BulkTanimotoSimilarity(fps[i],fps[:i])\n",
    "        dists.extend([1-x for x in sims])\n",
    "\n",
    "    # now cluster the data:\n",
    "    cs = Butina.ClusterData(dists,nfps,cutoff,isDistData=True)\n",
    "    return cs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sno</th>\n",
       "      <th>ChEMBL_ID</th>\n",
       "      <th>Smiles</th>\n",
       "      <th>Standard_Value</th>\n",
       "      <th>Standard_Units</th>\n",
       "      <th>pChEMBL_Value</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CHEMBL3403664</td>\n",
       "      <td>C[C@H](Nc1ncnc(N)c1C#N)c1nc2ccc(F)cc2c(S(C)(=O...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>nM</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CHEMBL3899716</td>\n",
       "      <td>Cc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Br)CC3...</td>\n",
       "      <td>0.10</td>\n",
       "      <td>nM</td>\n",
       "      <td>10.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CHEMBL3966806</td>\n",
       "      <td>CC(C)c1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Br...</td>\n",
       "      <td>0.15</td>\n",
       "      <td>nM</td>\n",
       "      <td>9.82</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>CHEMBL379300</td>\n",
       "      <td>Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4c[nH]c5ccc...</td>\n",
       "      <td>0.16</td>\n",
       "      <td>nM</td>\n",
       "      <td>9.80</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>CHEMBL573326</td>\n",
       "      <td>N[C@H](COc1cncc(-c2ccc3c(c2)C(c2ccco2)C(=O)N3)...</td>\n",
       "      <td>0.17</td>\n",
       "      <td>nM</td>\n",
       "      <td>9.77</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sno      ChEMBL_ID                                             Smiles  \\\n",
       "0    1  CHEMBL3403664  C[C@H](Nc1ncnc(N)c1C#N)c1nc2ccc(F)cc2c(S(C)(=O...   \n",
       "1    2  CHEMBL3899716  Cc1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Br)CC3...   \n",
       "2    3  CHEMBL3966806  CC(C)c1cc(-c2cn(CCN3CCC3)c(C3CCN(c4ncnc(N)c4Br...   \n",
       "3    4   CHEMBL379300  Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4c[nH]c5ccc...   \n",
       "4    5   CHEMBL573326  N[C@H](COc1cncc(-c2ccc3c(c2)C(c2ccco2)C(=O)N3)...   \n",
       "\n",
       "   Standard_Value Standard_Units  pChEMBL_Value  Class  \n",
       "0            0.10             nM          10.00      1  \n",
       "1            0.10             nM          10.00      1  \n",
       "2            0.15             nM           9.82      1  \n",
       "3            0.16             nM           9.80      1  \n",
       "4            0.17             nM           9.77      1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('C:\\\\Users\\\\HP\\\\AZC_Internship\\\\Data\\\\ChemBL\\\\AKT-binding_simplified.xlsx',sheet_name='Step3')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2861"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = []\n",
    "for row in df.iterrows():\n",
    "    mol.append(Chem.MolFromSmiles(row[1]['Smiles']))\n",
    "len(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps = [AllChem.GetMorganFingerprintAsBitVect(x,2,1024) for x in mol]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc = []\n",
    "for i in range(20):\n",
    "    clusters=ClusterFps(fps,cutoff=i/20)\n",
    "    nc.append(len(clusters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x202daf9ebe0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(range(1,21))\n",
    "plt.plot(range(1,21),nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x202c44e2ba8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(np.asarray(range(10,21))/20)\n",
    "plt.scatter(np.asarray(range(10,21))/20,nc[9:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters=ClusterFps(fps,cutoff=0.7)\n",
    "\n",
    "c = np.asarray(clusters)\n",
    "l = []\n",
    "for arr in c:\n",
    "    l.append(len(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 120 artists>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Vv5mbbXvPMntDiS59NQ2CwAAgLkNv2EJAAAA20e8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADSwbrxV1SVVdVdVfb6qjlbVu6vqhdO8y6vqvqp6oqruqarLZrdbcx4AAACbs5GRt/On6/3TJG9P8h1J3lpV5ya5Pclzk9yQ5Lwkt1XVOSebt/W7AAAAsPPt2cB13jfG+LPLL6rqFUm+KcmVWUTZa8YYt1TVC5K8LskVSb7+JPPes6V7AAAAsAusO/I2xnhy+XlV7U/yB5McSHLRNPnB6fKB6fLideYBAACwSRt+w5KqelGSdyY5lOSHTnSV6XJsZl5VXVdVB6vq4JEjRza6OQAAALvKhuKtqv54kvcmeSrJt48xPp3k/mn2BdPl+dPl/evMe4Yxxq1jjP1jjP179+7d5OYDAADsDuv+zVtVXZjk7ix+XfLHkry0ql6a5NeSHE5yfVUdTXJtFqNydyd5zknmAQAAsEkbGXl7YZK9Sc5J8hNJfinJL40xHk9ydZJHktycRaxdPcY4drJ5W78LAAAAO9+6I29jjLuz8jdrq+cdSHLpZucBAACwORt+wxIAAAC2j3gDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANLBuvFXVG6vqs1U1quqO2fRD07Tlx72zeZdX1X1V9URV3VNVl52pHQAAANgNNjry9strTD+Q5Pumjx9Nkqo6N8ntSZ6b5IYk5yW5rarOOb1NBQAA2L32rHeFMcarqmpfkledYPb9Se4cYxydTbsyi2B7zRjjlqp6QZLXJbkiyXtOd4MBAAB2o9P9m7drkjxcVYer6tpp2kXT5YPT5QPT5cUnWkBVXVdVB6vq4JEjR05zcwAAAHam04m3tyR5eZIfSPJkkjdX1UUnuF5Nl+NECxlj3DrG2D/G2L93797T2BwAAICda91fm1zLGOPHl59X1TcneXWSb8ziVymT5ILp8vzp8v4AAABwStaNt6q6KsmLpy8vrKpXJnl/kjckede0jGuSPJbkI0m+kORwkuur6miSa5McSnL3Fm87AADArrGRX5v8kSQ3TZ+/JItfl/yLSc5J8vpp3ieSfNcY41NjjMeTXJ3kkSQ3ZxFyV48xjm3xtgMAAOwaG3m3ySvWmPUTJ7nNgSSXnuI2AQAAsMrpvtskAAAAzwLxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEADe7Z7Azg77LvxzuOmHbrpqm3YEgAA4ESMvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAAN7NnuDaCvfTfeedy0QzddtQ1bAgAAO5+RNwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABvZs9wbsVPtuvPO4aYduumobtuTs4n4BAIBTY+QNAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANLBuvFXVG6vqs1U1quqO2fTLq+q+qnqiqu6pqss2Mg8AAIDN2+jI2y/Pv6iqc5PcnuS5SW5Icl6S26rqnJPN27KtBgAA2GXWjbcxxquS/OSqyVdmEWW3jDFuSfK2JBcluWKdeQAAAJyCU/2bt4umywenywemy4vXmXecqrquqg5W1cEjR46c4uYAAADsbFv1hiU1XY5NzssY49Yxxv4xxv69e/du0eYAAADsLHtO8Xb3T5cXTJfnz6Z//UnmAQAAcArWjbequirJi6cvL6yqVyZ5f5LDSa6vqqNJrk1yKMndSZ5zknkAAACcgo382uSPJLlp+vwlSd6S5E8muTrJI0luziLWrh5jHBtjPL7WvC3edgAAgF1j3ZG3McYVJ5l96Rq3ObDWPAAAADZvq96wBAAAgDNIvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANLBnuzcATtW+G+88btqhm67ahi0BAIAzz8gbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAAN+D9vrMv/UwMAgO1n5A0AAKAB8QYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA2INwAAgAbEGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANCAeAMAAGhgz3ZvALC19t1453HTDt101TZsCQAAW8nIGwAAQAPiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEADe7Z7A4CdY9+Nd55w+qGbrnqWtwQAYOcx8gYAANCAeAMAAGjgtOOtqg5V1Zh93DtNv7yq7quqJ6rqnqq67PQ3FwAAYHfaqr95O5DkZ6bPv1hV5ya5PcljSW5I8tokt1XVJWOMY1u0TgAAgF1jq+Lt/iR3jjGOJklVfVeS85K8ZoxxS1W9IMnrklyR5D1btE4AAIBdY6vi7Zokf6OqjiT5R0meN01/cLp8YLq8OKviraquS3JdknzDN3zDFm0O7AwnevfGQzddteZ0AAB2rq14w5K3JHl5kh9I8mSSNyepVddZfj1W33iMcesYY/8YY//evXu3YHMAAAB2ntMeeRtj/Pjy86r65iSvzspI2wXT5fnT5f2nuz4AAIDd6LTiraouTfKGJO+alnVNFm9S8ltJDie5vqqOJrk2yaEkd5/O+gAAAHar0/21yc8lOSfJ65PclOQTSb5rjPGpJFcneSTJzVmE3NXeaRIAAODUnNbI2xjj00m+c415B5JcejrLBwAAYGGr3m0SYMt5V00AgBVb8W6TAAAAnGHiDQAAoAHxBgAA0IB4AwAAaEC8AQAANCDeAAAAGhBvAAAADYg3AACABsQbAABAA+INAACgAfEGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAAN7NnuDWDn2XfjnSecfuimq57lLWHuRI+LxwQAoA8jbwAAAA0YeQPYRkZEAYCNMvIGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANeLdJgLOQd6EEAFYz8gYAANCAeAMAAGhAvAEAADQg3gAAABoQbwAAAA14t8kdyjvVwc7U8bndcZsB4Gxk5A0AAKABI28AO5yRLwDYGYy8AQAANCDeAAAAGhBvAAAADfibN84K/iYHdh/PewDYHCNvAAAADRh5A+CsY1QOAI5n5A0AAKABI28A7EpG9wDoxsgbAABAA+INAACgAfEGAADQgL95Y8fxdyxbx30JAHD2MPIGAADQgHgDAABoQLwBAAA0IN4AAAAaEG8AAAANiDcAAIAGxBsAAEAD4g0AAKAB8QYAANDAnu3eADgb7LvxzuOmHbrpqm3YEgAAODEjbwAAAA0YeYOTMCIHAMDZwsgbAABAA0beYIsZrQMA4Eww8gYAANCAkTc4CxitA852zlMA28/IGwAAQANG3gDY0YwYAbBTGHkDAABowMgb0I6RFNgYzxWAncXIGwAAQANG3hrzE9XNc58BANCVkTcAAIAGjLwBsCWMbAPAmWXkDQAAoAEjbwBwmow6AvBsMPIGAADQgJE3eJb4yTycvu1+Hm33+gHY3Yy8AQAANGDkDdgVjJgAAN0ZeQMAAGjAyBuwrbZyRMzoGruVY39z3F9AV0beAAAAGjDyBkB7RlI40xxjwNnAyBsAAEADRt6ATdvtP4He7fvP9jjRcZec2rF3smXt5uP7VPZ9N99fwLPPyBsAAEADRt5gl9vKn+bDTmAkZefwWAI7jZE3AACABoy8nUXW+gnhbv/J4W7ff6Av5y8AtpKRNwAAgAaMvAHPim4jEFu5vd32HbbKs/HujWfr8+tsfkfP7V4/cOqMvAEAADRg5A1OkZ9csppjAjbG/1PbOjvpftlJ+wJnipE3AACABoy8AbvaTvpJ77OxLzvp/oKzzWbedXo5b7PL2mrb+U7ZJ1uHcxU71Rkdeauqy6vqvqp6oqruqarLzjYMLqoAABwuSURBVOT6AAAAdqozNvJWVecmuT3JY0luSPLaJLdV1SVjjGNnar2cnJ9EAUtn87vh7QY76X97dtxmjvdsHJPb/feOp7KPm73NqZxbz/T6LavnufVEzuTI25VJzktyyxjjliRvS3JRkivO4DoBAAB2pBpjnJkFV706yb9K8ooxxi9W1XVJ3pzkujHGW2bXuy7JddOXL0rysTOyQVvn+Uk+twXTn63bWJbHy7Ke/WVt9/otq9f6Lctjb1k91m9ZvdZ/Kss6W/zRMcbeE84ZY5yRjySvTjKSfP/09d+Zvn7lmVrns/GR5OBWTH+2bmNZHi/L8thb1tm9fsvy2FtWj/VbVq/1n8qyOnycyV+bvH+6vGC6PH/VdAAAADboTP6rgHclOZzk+qo6muTaJIeS3H0G1wkAALAjnbGRtzHG40muTvJIkpuzCLmrR/93mrx1i6Y/W7exrO1b1nav37K2b1nbvX7L6rV+y9q+ZW33+i2r1/otq9f6T2VZZ70z9oYlAAAAbJ0z+k+6AQAA2BriDQAAoIPtfrvLDh9J3pjks1n8q4M7ZtMvSXJXks8nOZrk3UleOJv//mn6o0kOJvnW2bxzs/ifdiPJm2bTD03Tlh/3TtP/QJKfT/JQFn9HeCDJD6667vJjX5Ifnpb1RBbv8PlDs3X8rST/N8lT0/zV+/WOJF+Zpn8pyWWz++HR2XrumN0PDyR5epp+ZHk/TPfBk7PbvG+N+/Wr90OSh09yH/yv2Xo+P00/2f1wIMmx6etHl/dDkhuTPDZNf2q63nKb/+p0H4/ptv89yQvX2s8k35Hki/PtSvJt07LunZa/XNYHZ8taHjvL2/38NH25XcuPj07LuizJZ2bX/+K0rB9dY/+vSPK7s/1/LMk/nx0Dj8+268OZjs8kvzO7zZeTXDtNn6/7oSTfOtuPr8yW9ZHZspb343JZf3/Vc2O5vF9d4/h/dLasg6vW8+Gs/xxYHt9PJ/nkbFl/K8nHZ+t/+zT98mn7l9N/5QTH6nKf3rTW43iyfZw9///PbHvftMb+H5kd+78we1wePMm+/5vpNj+c5BOz9b93tu9fWXWb5XPs06umf2y2/8dW3yYnP16fWDX9d2f7/4k11v/UqumPTdM/uWr6IyfZ/+X6v7Bq+gPT9L83PS7L5/5/nz32/3Paz6eyOPYOTPN+NsefLy5J8luz/XwqyQdWPfZPnWBZxz2Wa+3j7Pq/lJXz6LGsf/7/R1k5Tp9O8nvTst62xm3+4SanX5LFuXgrlrUvi/+1tJnbvHWN6X86yS+eZD2/lJVj/9EkPzZ77D81u78ezMprxXGvIdP+r3VufX8W59anp49PzZa11nNyrWP1D2TxmrG8/uPT+n/wJPv4H7LyPPpKknfMnveHp2Udy+LN486f5r0hK8fxw0leNk3/L7NlHc7iXcOX57xHZss6MFvW/Lz6pSTfvc4599CqfXh4tqzjnqvr7PsP55nH0nLb78jK9z1P5JnfkxyYnZd+czb9WFb+1dU7Ztv91dtk8b3CfFlfzMr3EZ9ftR3XzpY1fx0bWbyWXJ4TnGOn2/zaqvXfM03/wBr3xffkma+9j2XlGJyfZ56Y3df3rl5HFt9zvDHPfL347Oz7vqOrlrXc99WvFw9l5bX33lXz3jSbvno/7s3x33cuz78/uMa+7zvBvtyf5PU5+XF7eZL7pv24J9P3vWfrx7ZvQIeP6eC5eToI5pFzRZL3Jvn703VGkrtm838yyd/M4oX0qST/ZzbvDVl8M/vVg3eafmha5l+bPpYn0V+dDrZ/leSVSX4uyUWz6/316aD7TJI/Ni3341mc/B6Yvr4wyf7pwD0wreerJ7dpPedO2/WlrDzpP57knGkf50+wO2b3wwPTE2M5767ZffDuJP91mv7Iqvv1gzn+SfzwbJvvWXUfPD1NG0k+OU2/KIt3N31XVk6I8/thvi8jyVXTch7K4kS6vA/umvb/C9N1fyUrJ8C71trPLF4wHkry72f3zxenbXtHkv+d5J1ZOZksl/XeJL+RlRPjA9P0x7P4xmC5Px+elnVg+vo9WbyoLpf115J8NItj4jeyciK9Zvr8c6u2ebn/DyT5d9P+P51FTCwf/yNZvGCMLF6czknyP6bHcsyuv9yP31w9b9rme7L4hvE/L5c1Oy7uyMoL3xdnx/8DSX4mi2+0js2W9bvTsn9rmv6lLB77O6fr/0pWTu7Lx/6hJP82K8fAx7PyHPhkVr5huX/a989Mt1lO/1JWjv2bZ8tfvuCe8HE82T7Onv/zF/15vH0iK2H9zlXH/nIdH80zn//vnC3rlixeWEcW30zMI2r52D+e5Pem5R1M8rJp/49lcey/LYvnwadn+7/8oco90zpflpMfr1/K4thfPo4PzPb/oay8gP9GVp7jT03zbp4+/vE0fXmuvCPJm6fbLPf/SBYh8ZXptt852//HsnheLL+Rump2X/zmdJtPzh77L0/3z9FpuW+f1v97WXn+fnha5hXTdebnpE/PHvsPZeUb+M8k+bm1Hsu19nF2/eXz6jeS/HZWzv8fmKb/p2l5j2bl2F+eK5ZhsHzs/1eSfz1df3mufPF03x1O8vasHOcvyonPrX8+i0j5udm85X5ekhM/Jy+ZHq//kJXj+8tJnpPFc/6j02P+piyeG8tlnWgfvy0rx8gts21b7vtnp21b7vvhJN+dlWPiHbP76MLpcVyeKx/KynP8z+TEryH/cHb9u1Yta3n/fWHapq+sWtbq5+SfmS17fqxemJXz+UNZnKuXx8j3zfbzX8+W9Yrp8ivTfb/cj+/JyvH7e9P0Y0l+Pcm3TNMfz+I1bPm4/Knp8wdn9++vZ3HcL4/t5bynp3n7p68/luQ/Tp8/kjXOudPxvYzm5XH55KplrX6u/uWsHMc/P9v2+XH/u6vu49/KymvH8jy2jLwvZ3GOe15Wvqlf3u4rSX7/dJ358r6c5Oumx/ipPPMHcXdlcT5ZHqvL6U9Ot5l/f7X8+NksjvdjWRwfY1rfldOynlq1nIenZT06W8fy9eQzWTmel8fr8rFfnv/mYffr0+NwX1aO1Yenx/bjSX5qtryRlXi7Iovz5P+b7/vse7vDs+nz7wnuzYl/aH9vnvm6u3yNeWMWx//y+svz70VZvF4ems37TBbnk/kPlubfTy5/MLv6uF2e/+9P8ndn+37OdvfHml2y3RvQ5SOLmh95Zrx97arrfD7J4dnXlcV/cP9T0xP2f0/TXzI9qX5kfvBO8w5lcQJ77mzaxdP1fiHJ157ogEryvdN13pDFC+7yhPWiLL45ezzJ3iT/YJr3iul2y286lt8ofNdsOSMrJ6U/t+p++Op9sbwfZvOeXN4Ps/vgr0zz5vH2kqy8iM+fxIeS3LZqHfP74JKseixOsG3z+2H506mHpvX9k1X3wX+bvj4y2/8fmea9PisvuCfczyS/b9V2/P/2rj1Yq6qK/6RGbPqnKZ2ybBIdswGVGrtjJT4SEnKwKRMSs7IgsTRJSimF8pEoBj4mkjQabGSkB05pKpMoSYql+dYcfJQ4QsDlJSBcvnvv953+WOt31zrr7v2h0x94m71mznz37HP2Y+313nudfVsAWpybwANNrbN3gg92ankfD0B5KsUD7tneCT7YAXHIPB/QAPThr2N7xtGa+P9Yx0yDMFJp+TGYM7bSzQnpTKO00pV/GLZK1ggywEWPLYH/P+TmbKXD//cQZ2NH6GNfyG4qjbKXgaMgjiGNOmWg2819BeArMIPty8n7Y/Sebc3N0XE3OJLv/WomeX+Nzu101wdx73U4zg1y5B2LuQ7/JoCrXX3SfoPO88MQfn0PjPb/gPAeeZ/40wAu8ToQCX51tFwEMfJR/7VgCzcel16Ic7l/Qv/9C8C7EfSf9vMXfWemlhH/9ZDVY+rAa7X8QR3733QspD1l7CfE3fX/B9R10qHu/b0D7qyzJOCepGUOR1deQXaU9kk8WwhxdKj3RurfnRAdQNyj3mPmx7WO9hfAdEhO789Ef9vHFfiZOZkMMkGn+9Fo91C3ZUkcQ9+ztXwF+tu+f+n9dQCu0r9X6jM6sF93uD0A4Ocw/fAJpG3Ip0I/1K37uTE8AdHpXVqHbUWZZHnk1aPcuB6EBCePhrYeAHCea2uc/r0Voq8Y7F3m2voyzOa1YMHfrTqfdMYX6O8UmA1sAXgvnB8B4fsefTZDyydD9DcDIZZHnfseWIDNnSnKZE5WZ7i+Pa/6+fq+zhWDFupstul3Pynn5I/79JdBNOdnpnu/gsnTBajLxxaYPLG/VqjzS5geriB6uNK5WIz6uM7Wv5/V3xdCWxwXg+Dlrr7fmet2dS51ZVH/kxf79H/Aj8Ebbf8Ih6P3f32dXtT1f81euTrM/OhM6H9e0e8b4Z5F/V9B/IF+OhB1vqX+j37fyD0de+SuPT6AgXIhEbyF51wlWuzK3hUEegTkO8NHIAbn+ATzroIJeifk/+ON1fvnldl2AZgV+l8KUZQH6v00104TwFe1nEw6D8BHYIrzAX0+Ve9pEJ7W32+GeUgJkX+2ODEHFYAVWs55uCkKcZiDRmYOKmgqUKb/1DxUkN2oOAdc1bnX4c90iSv0/rF2eLpnbHt1An+u+C0OfDDZ4xPwryCKJ+LfyPRPI3B3Bv9H3BhjCtVEhz8vGv9vBly6AYzI8HrfM0gQxvIWgG8p7o+Ffm7L4L4DIjdjw/sVgIUZHjs1gzvb4o7kk+7vCsA1+nsnTDaJ+yDYriodn7k5OuZwdO+vhe1K+LZiquF9AE7Wv7nK28ePrr1VqKfgDEJ9RbSC6AjSfluYm8tgtG+5d7zsr3bvd8KcrUgzOuQ+PacF4Eo33q2uzk7XVm+oswBp2vsAcpUr3+jaiqlo8yGZCxVE5n3wfI37+3k3jiWufwYCTQCzUJdJvk8H5Uuh74bWSdIyh2OmvDfRP+dyXqbOneiv97gwM9bR/nQIn7C9lN4/MGP7+p4hIZOoy8qjWv7rBA/RoTwwh2Pon072iIzcsy0foPuLtF8U6v0qZ0NyujWDexOiZ5Iy2YZXibvf9Wi16X+qlt8d2rox4L4ItrtTwdJMl0L4goE4My8WOrpUAI507THQoN6aEfpuhfKoc48M+G1y/d+GtKyyrXmwjB629Zsw7wzQuVDB9326XwXR8cxe+LX+Ut9xkem8UIe7lKejLh+Pob8d7Q11VqGuU150tPY0vRmWIszdqlZoi+MiTnc6Otf42dHtD6GtI8OY1wM4y82Nx299kD8fPHn/9whX3o26/+tTm1PBW4X+/q+n66xM/we68nmhjvf9It9SB9Dv68N9T8ce2ZhkTw9goFxoE7xBovw1kC1Xv2L8dkh6yXcgq2/LlBk7lbGZ4nALgP20zsUQQ3cGRHn0QhxeKpNTYCtDo7TOwSqEd+n9fvru45Adr8chjtgBAAZDVrLI1BT4+7VuLnibFOah31wAOEHLd3Ie3Bz8WJ9xBZbzMMa1d4uO/WKHc1diDia7OqNc/8fCKRc3D1yx2qrzcFCYAyqw41B3Yg6FGbnvtcNTy8fA0mSGB/wvd/js7/A/GWa4Fzv8z4M5eBH/c2E7luNd/6P8uBz+/4QYxYbSO+LP1IqHHf4zIXxLgz1JcTkDpgyXub6HQhznTdreMi0fDHEymKa02eF+FsxQrnO4j4esgnLuH4Kk/dC4zYcFKqN0XF+FpbwsQ10GfgRzmlZA+MenLPK6R3+nwWSzgqTFToSl2TwB49cpGTqyPOI4DcKDm2C7EjTEUyDOxlRYKlgLki5Fg3qtq/NFHRe/t4vjakJWnBmwdAP4aIb2vbDV9OuUzpz/Hygtf+rwp24agjS/DoE4WZc5/FuO9j4NdYercw/Eub8B5vBwAWWTjo28f4aOa47Dn+PqgMj7vyGGmfTmjiYv9jHHla2GpDHx/hL9pWOxMZSvg/E9eZI7IEyRI2+w3UhLzm3E8Vz3zkaYPo7jarlxcSeqGxIwMwj/Muq05zUMJvff1ba4Gz8ppVuD7Vunz+4Nti/KJGl/Ekznep1/KuoLCENgch9xpO37tN53Jmwf7UQPxPZ9APXFEeqQn7m5fxwme10QO52yISyPuvUAAO+D6KlXXfk2SMCdksnjkOZVymOvPnsKJi9+XC03ruGwbyyvgKXUHYV6oOOvH4V78vGlqPMLA6hhEL3uF6foR0T9wmsK0jp3hPaf+n4yjouyOjkzruN0Tl5PtLU81NkZnk9CPnjj7lUM3rj4djrMJyDdyMfcxW2FOpRJLnCtcs+/5dpqwgIN6uM+n20349oE41vSNef7eB1A/UT5mYT2wRtTeJuo+0MHBzy8Dviae+b93xthchb9X15RB4zX+4brez9Y2uZrMB/4IKT5dhZpqfX7cN/TsUc2JtnTAxgoFzLBG8RpXQfZhh/Spj63sq9GnRF5zU/UoYI/R38XBcaarPdsc6zek5n5MTYdlnF6Pwii5IdB0i4q2E4Nt86v1N/dpk26eeAK2n1t5q+CpNNc0m4e3Ptcmeqbg9DWZNcHHaNHwjzQmVnJedA5+AJEETCt5R0O/9mw7588/kk8Ic47Dzo4JsMjdMja4o86TzG9gvjfBXMWPQ8MhRmtiQH/7doW+Yn4kwe4a8tnFYALtY1Vev+FQBf2v28Y7xAYr+8b5oFzmTts4JYM/1ewlBjKAPmSq82UATqdORloJ4NbMuV3t6HX42+y/JZMebs66zLlD7YZVw6X+xK0p/PD3SLSnjuM06IOdLQZjTS/jg60ZPDsv3GNV6zD3RnyC2nPbyuvj/rPjYu4kP4PhraWKP5Mp7rYjWMR6mlmDB7v0N+nQ3mX4n6R3k+GrGSzLZ/G5HcZ/MXAO+LoZWUR6gtX7N+n8VawNN2ntS06pTc62nPXvYG63rvf9VXB9F5Ntwa9xlTpsYF+USZ3a/vCO6PdPEYcqfeYacDDQvrkPrQ1zj2bq7R/Se/9Qs30QHuuyEcbwvKUbvVj8LSPu268OD85XmVbnvbsn/abeoULAksDH81R2n8UsgD8EOz71gmu7jBIoFhBvrcapL/k3S4Iv5D2/wFwovbDZ16/UA+RJ+O1IPgkN8P48mikZfVcfX8BTHa6YAHBDG3L93m31jlf719A/ZMNnza5TH9j2uSVqI+dweVs1OV6JPr7UQwWr0J6HnhdiLp/U8Gyk4g/d+DuQPtxeb71cjDIzVXD0e1NpU06HcB52tjG5+M7bXUAbBdtPfr7v7yof6kDuLjpvykfjzoutP/nI8230faVtMn/hwvykSdP9HsKshJxCOSD4k7Y9wunAThN64yGGMiJEAerR5lmKGSF8VTYbtQSiJE6HPKR77chqykbIEL/fmXYTsgW9t+1z8MgueCdEAMySPtmGstK7Z+rWsMh30pdD/kGidvrHq/DIQqXK8U9Oo5DdR4WuDqvQhyWY1A/gOAViKMwWefgOpgR6dF+hkN2d7wz+4wK0cOwbX2eiMmTgF6DpVNQUR0C2WFk8EJcOvSeKzBU1p/TcfnUsTuVfvsovk3YoRidEAP3wQye/mPwJbAP6UdD0jjYT1PrnwZZqduqZZybJ5X2DdjH4dv0/v2wkzabkBW7JiS3n3xYKe0maB/+cIbFsI90P6bz+1eYoeJBBCdDlNkGGH/0Qgz4Pah/6L/V0b6lfSzW99dBeON5CM/c7/o5CxJE3AD7VqmhtP87ZIX4VljKZqfO5WadjzvcfJ+l7W+Dpdesc7R/TZ/7AwkOgwTBc2Grqi9DnLjXIMqeH9vv1LbOhvEqg6nlkN2FFB3nZHA8GrK7MRv1tMkV2tajkACIqU9NiMy/ov3QEW9BDNRwLd8AC+SWwIzRGlj6UwXhi4UQh4eOJT96P0HHuFHH1tR+DoDw5XOuze0QPjkRaX69GMKjC2EHWlSQb4WmKp7kr25t60yd299A+Ig0Hq/tbkf9tMBPQ3i5AXEOpsB05pmOVrfCVu0/o/htc3RpQXhivY5lO0SOdsH07Msw/cJvVye4dm+DnT44BaJfXoXtKnbr7xEZWn4pg+MJEL3Xq8/pWHNc/AB/E8wujHDv3AzbSRkH0UsTYc7kHLUZ+yj+TW2LmSRvQ1q3HgOzfa/rvE9wti8lk0Mh/LcVtpBDXv0TZKd/u46Bdm+vDI6HAXinlvfCvgP0tm+bm8fhsAM41qJ+quBwmHO5TmnPXSIGNtGG+GDf69Ypjq4bYTvRvq0okxchzaujYOl0a2ELmb6tXu2HNp7pXg2lAXcax0GCmXtgmTsVRE/x4IsNMD29FXLww+2w9LwKcoAKad+C2IM/u7Y+C9mN/BVMt/L765TOJe2XQXQFefUXEN5LyeoRkN1Sygv7Ju03a/8+NfIpyGcR02G8z7ltQHTskTC7z3q9EB2zJbTXA9G3PJSm5cpvgvg+DdR3AZswfb3dlVWQIJzfU/pD4RoQHdDr+uDvKaj7apXS0PPz9lDncxAZesrVeRbCa5fD+KVb6bYW4hMsde93Qw5RmgA7uZHly7WtGagf4sKxjIHYFr9QuRqyu/ZzWGDKz4N2QVIdPY60NydBgiyOuQui70bBdvA4LuqbzUjzLfXfyzqWPv23p+OPErz9b8Hb/YEJK4hzcHyivNI6HSoUXco4fwHQEdplfX7rtT/EadsI+/cCPIVtGGQlfJcKBbd3+RH39ND2VGW+XdBTJ7Wc3+7EAxM8Xk9kylPzwGep8gtgR2+/0fZ+iPoRu/79Yeh/ShOfPZcpfylR/o1MOWkX0xB4HZ8pzx1N3aE0eDNtnZIq13Hl5jnXVq58EOrfkDSUFh16/ds92w4JKjpgwZS/ciuJHZDVPb+6uUXLvGzQuP0Owv8rYM5LL8TIcFz+aO6dEEPcAVvR7oKTM8hKsj9e/QV938sA+ekGrXMsxFmkbPy2jQ64t80cJ3HMyH8FcWq8/MfTJin/xOfPUf7RX59QB7DOcsWdMtlSPB+DONz7ax+c/52wf+3wUALH2cjwK8RIe2dnG+yoauLJuWdwPhzmFFYQfrtI3/+ro8kuAFdpW2e7d/t0ptZ5DsYvDdi3gCtdH1tg3xAdC+GRll6vwPRs6kjudrz/LCz4a4a2+tFyNzgOgx193YI4VGyLpx72oG4XZsF4qBviyJPv2e/fAAx2/HgJTO6eAPBxLU/p1iTuzvblZJL8yp0/z/dMJVsDtwvbBkem3S5N2D4GW5tRt31rHO23ApjiaM+02BbEoWS9lK3YHe15kmdT+zynjUwmedXRfpWj/XrXFg996UHdxt8O4yOeOknaE/cuiLM8WJ+96J5tAPBJLU+lIOZwHwwJkP0Jh6shCzZJneto73X+7W5c/WRVy1fBdNTPoHwMSVmM3w37q0f7z/k+L2bKUz5RBdv1idelbh7eSFv3Avi8m4dYJ5VWmmvrTAg/p+bh60j7Y/NRP9GR1x+R9/vuypTPR//vvf3YUv3MR9q3W9Km/zOR9klyuOTShgc7HfAMRDb79N9b9dpLB12gQIECBQoUKFCgQIECBd7CMGhPD6BAgQIFChQoUKBAgQIFCuweSvBWoECBAgUKFChQoECBAgMASvBWoECBAgUKFChQoECBAgMASvBWoECBAgUKFChQoECBAgMASvBWoECBAgUKFChQoECBAgMASvBWoECBAgUKFChQoECBAgMASvBWoECBAgUKFChQoECBAgMA/gtcagkw4izWlgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,15))\n",
    "plt.xticks(range(1,len(clusters)+1))\n",
    "plt.bar(range(1,len(clusters)+1),l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2321, 261, 392, 579, 680, 712, 732, 762, 818, 835, 889, 1040, 1042, 1119, 1133, 1164, 1348, 1447, 1456, 1529, 1627, 1702, 1716, 1725, 1750, 1778, 1849, 1896, 1991, 2040, 2145, 2146, 2244, 2254, 2386)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x200 at 0x202C45601D0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#show one of the clusters\n",
    "print(clusters[20])\n",
    "#(553, 297, 547)\n",
    "\n",
    "#now display structures from one of the clusters\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "\n",
    "m1 = mol[553]\n",
    "m2 = mol[297]\n",
    "m3 = mol[547]\n",
    "mols=(m1,m2,m3)\n",
    "Draw.MolsToGridImage(mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "444"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clusters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Feature Selection</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.26733787339248866\t R^2 = 0.9479117561270232\n",
      "Test set results\n",
      "RMSE = 0.6896876396250171\t Q^2 = 0.6388231438867185\n",
      "Validation set results\n",
      "RMSE = 0.6950496221708883\t Q^2 = 0.6405874030635628\n",
      "Training set results:\n",
      "RMSE = 0.27277202380565596\t R^2 = 0.9461701525272005\n",
      "Test set results\n",
      "RMSE = 0.6611262258419099\t Q^2 = 0.6611384171900576\n",
      "Validation set results\n",
      "RMSE = 0.6834249212476704\t Q^2 = 0.6499614877160751\n",
      "Training set results:\n",
      "RMSE = 0.2693904069134315\t R^2 = 0.9469766150174291\n",
      "Test set results\n",
      "RMSE = 0.714555936940678\t Q^2 = 0.6252680400859787\n",
      "Validation set results\n",
      "RMSE = 0.7640861594009303\t Q^2 = 0.5548729826769982\n",
      "Training set results:\n",
      "RMSE = 0.2632325822927935\t R^2 = 0.9470783918685622\n",
      "Test set results\n",
      "RMSE = 0.7212686075543397\t Q^2 = 0.6395663955689602\n",
      "Validation set results\n",
      "RMSE = 0.7382844920000958\t Q^2 = 0.6200882485234493\n",
      "Training set results:\n",
      "RMSE = 0.2646183416962703\t R^2 = 0.9488812219515873\n",
      "Test set results\n",
      "RMSE = 0.7150654963193709\t Q^2 = 0.6330476786059596\n",
      "Validation set results\n",
      "RMSE = 0.7339654711260806\t Q^2 = 0.5826095394589661\n",
      "Training set results:\n",
      "RMSE = 0.27234772567002824\t R^2 = 0.9455862492952052\n",
      "Test set results\n",
      "RMSE = 0.7206425703134666\t Q^2 = 0.5934376791346039\n",
      "Validation set results\n",
      "RMSE = 0.7400726400574612\t Q^2 = 0.6104188750533031\n",
      "Training set results:\n",
      "RMSE = 0.2699388207763588\t R^2 = 0.9457078119884829\n",
      "Test set results\n",
      "RMSE = 0.6992834735664069\t Q^2 = 0.6494881375100237\n",
      "Validation set results\n",
      "RMSE = 0.6797932120860687\t Q^2 = 0.6643812962710025\n",
      "Training set results:\n",
      "RMSE = 0.2721320801372518\t R^2 = 0.9433615139987438\n",
      "Test set results\n",
      "RMSE = 0.6740537170963768\t Q^2 = 0.6780730280610678\n",
      "Validation set results\n",
      "RMSE = 0.7765772948313839\t Q^2 = 0.5902968967140734\n",
      "Training set results:\n",
      "RMSE = 0.2676356229781021\t R^2 = 0.9466738008406648\n",
      "Test set results\n",
      "RMSE = 0.711709923279766\t Q^2 = 0.637414137100359\n",
      "Validation set results\n",
      "RMSE = 0.6820635566186773\t Q^2 = 0.6574506673534797\n",
      "Training set results:\n",
      "RMSE = 0.27431546329910295\t R^2 = 0.9450178125672468\n",
      "Test set results\n",
      "RMSE = 0.741434362436034\t Q^2 = 0.577093943039666\n",
      "Validation set results\n",
      "RMSE = 0.694373444501396\t Q^2 = 0.647725581184792\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "\n",
    "for i in range(10,101,10): \n",
    "    X_new = SelectKBest(mutual_info_regression, k=i).fit_transform(X, y)\n",
    "    cols = []\n",
    "    for j in range(i):\n",
    "        cols.append('comp'+str(j+1))\n",
    "    X_red = pd.DataFrame(data=X_new, columns=cols)\n",
    "    X_red['pChemBL'] = y\n",
    "    X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_red)\n",
    "    RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 10, 20, 30, 40, 50]\n"
     ]
    }
   ],
   "source": [
    "print([i for i in range(0,51,10)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "25 features(from 452) with SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2861, 25)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "reg = ExtraTreesRegressor(n_estimators=150)\n",
    "reg = reg.fit(X, y)\n",
    "model = SelectFromModel(reg, prefit=True,max_features=25)\n",
    "X_new = model.transform(X)\n",
    "X_new.shape               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.2917748287379361\t R^2 = 0.9353128704432097\n",
      "Test set results\n",
      "RMSE = 0.7231279051647945\t Q^2 = 0.6332444131479319\n",
      "Validation set results\n",
      "RMSE = 0.7141039799257112\t Q^2 = 0.6430208161103215\n"
     ]
    }
   ],
   "source": [
    "cols = []\n",
    "for i in range(X_new.shape[1]):\n",
    "    cols.append('comp'+str(i+1))\n",
    "X_new = pd.DataFrame(data=X_new,columns=cols)\n",
    "X_new['pChemBL'] = y\n",
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_new)\n",
    "model = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>RFECV 452"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.27578056281743857\t R^2 = 0.9427413001587287\n",
      "Test set results\n",
      "RMSE = 0.7044532192594573\t Q^2 = 0.6390706145187659\n",
      "Validation set results\n",
      "RMSE = 0.7003509714774089\t Q^2 = 0.6585269884225984\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFECV\n",
    "reg = RandomForestRegressor()\n",
    "select = RFECV(reg,step =50,n_jobs=-1,min_features_to_select= 50,verbose=1)\n",
    "select.fit(X,y)\n",
    "X_new = select.transform(X)\n",
    "cols = []\n",
    "for i in range(X_new.shape[1]):\n",
    "    cols.append('comp'+str(i+1))\n",
    "X_red = pd.DataFrame(data=X_new, columns=cols)\n",
    "X_red['pChemBL'] = y\n",
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_red)\n",
    "RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2861, 452)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_red.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>RFECV452+SKB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, mutual_info_regression, RFECV\n",
    "#X_red.drop(columns='pChemBL',inplace=True)\n",
    "selector = SelectKBest(mutual_info_regression, k=50)\n",
    "X_new = selector.fit_transform(X_red, y)\n",
    "cols = model.get_support(indices=True)\n",
    "gc = []\n",
    "all_columns = X.columns\n",
    "cols = selector.get_support(indices=True)\n",
    "for i in range(len(all_columns)):\n",
    "    if i in cols:\n",
    "        gc.append(all_columns[i])\n",
    "X_red1 = pd.DataFrame(data=X_new, columns=gc)\n",
    "X_red1['pChemBL'] = y\n",
    "X_red1.to_csv('./data/RFEandSKB.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2861, 451)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_red.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_red1)\n",
    "RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> ExtraTrees + SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.2735181700236474\t R^2 = 0.9451619774615067\n",
      "Test set results\n",
      "RMSE = 0.7119150691432141\t Q^2 = 0.6507601078202636\n",
      "Validation set results\n",
      "RMSE = 0.6681760434719178\t Q^2 = 0.6439455262898088\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "reg = ExtraTreesRegressor(n_estimators=150)\n",
    "reg = reg.fit(X_red, y)\n",
    "model = SelectFromModel(reg, prefit=True)\n",
    "X_new = model.transform(X_red)\n",
    "X_new.shape               \n",
    "cols = []\n",
    "for i in range(X_new.shape[1]):\n",
    "    cols.append('comp'+str(i+1))\n",
    "X_new = pd.DataFrame(data=X_new,columns=cols)\n",
    "X_new['pChemBL'] = y\n",
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_new)\n",
    "model = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1830, 65)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['comp1', 'comp2', 'comp3', 'comp4', 'comp5', 'comp6', 'comp7', 'comp8',\n",
       "       'comp9', 'comp10',\n",
       "       ...\n",
       "       'comp442', 'comp443', 'comp444', 'comp445', 'comp446', 'comp447',\n",
       "       'comp448', 'comp449', 'comp450', 'comp451'],\n",
       "      dtype='object', length=451)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_red.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "reg = ExtraTreesRegressor(n_estimators=150)\n",
    "reg = reg.fit(X_red, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel\n",
    "model = SelectFromModel(reg, prefit=True)\n",
    "X_new = model.transform(X_red)\n",
    "X_new.shape               \n",
    "cols = X.columns\n",
    "gc = []\n",
    "for i in range(len(X.columns)):\n",
    "    if i in model.get_support(indices=True):\n",
    "        gc.append(X.columns[i])\n",
    "X_new = pd.DataFrame(data=X_new,columns=gc)\n",
    "X_new['pChemBL'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set results:\n",
      "RMSE = 0.26620189658995014\t R^2 = 0.9466377526514312\n",
      "Test set results\n",
      "RMSE = 0.6997386217377103\t Q^2 = 0.6386748895352093\n",
      "Validation set results\n",
      "RMSE = 0.6915037896285295\t Q^2 = 0.6709559035888681\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, X_validate, y_train, y_test, y_validate = split_data(X_new)\n",
    "RFR = train_RFR(X_train, X_test, X_validate, y_train, y_test, y_validate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nN</th>\n",
       "      <th>ATSC3c</th>\n",
       "      <th>AATSC2v</th>\n",
       "      <th>AATSC2p</th>\n",
       "      <th>AATSC3i</th>\n",
       "      <th>AATSC8s</th>\n",
       "      <th>MATS2c</th>\n",
       "      <th>MATS2v</th>\n",
       "      <th>MATS2p</th>\n",
       "      <th>MATS3i</th>\n",
       "      <th>...</th>\n",
       "      <th>topoRadius</th>\n",
       "      <th>topoDiameter</th>\n",
       "      <th>GGI5</th>\n",
       "      <th>VE1_D</th>\n",
       "      <th>VE2_D</th>\n",
       "      <th>MWC8</th>\n",
       "      <th>MWC9</th>\n",
       "      <th>MWC10</th>\n",
       "      <th>XLogP</th>\n",
       "      <th>pChemBL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.145569</td>\n",
       "      <td>6.520842</td>\n",
       "      <td>0.033282</td>\n",
       "      <td>-0.192045</td>\n",
       "      <td>0.005802</td>\n",
       "      <td>-0.043265</td>\n",
       "      <td>0.133123</td>\n",
       "      <td>0.151351</td>\n",
       "      <td>-0.129099</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.298611</td>\n",
       "      <td>0.179965</td>\n",
       "      <td>0.005293</td>\n",
       "      <td>10.570162</td>\n",
       "      <td>11.469569</td>\n",
       "      <td>12.376806</td>\n",
       "      <td>2.188</td>\n",
       "      <td>6.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>-0.052259</td>\n",
       "      <td>-1.311602</td>\n",
       "      <td>-0.006195</td>\n",
       "      <td>-0.119898</td>\n",
       "      <td>-0.028528</td>\n",
       "      <td>0.117528</td>\n",
       "      <td>-0.026376</td>\n",
       "      <td>-0.027534</td>\n",
       "      <td>-0.072937</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.350833</td>\n",
       "      <td>0.207667</td>\n",
       "      <td>0.006699</td>\n",
       "      <td>10.698763</td>\n",
       "      <td>11.619904</td>\n",
       "      <td>12.547891</td>\n",
       "      <td>6.575</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.078087</td>\n",
       "      <td>-4.401278</td>\n",
       "      <td>-0.004569</td>\n",
       "      <td>-0.280655</td>\n",
       "      <td>-0.112216</td>\n",
       "      <td>0.142885</td>\n",
       "      <td>-0.097913</td>\n",
       "      <td>-0.020252</td>\n",
       "      <td>-0.099931</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1.187500</td>\n",
       "      <td>0.111470</td>\n",
       "      <td>0.003013</td>\n",
       "      <td>10.503450</td>\n",
       "      <td>11.374020</td>\n",
       "      <td>12.259002</td>\n",
       "      <td>5.589</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.0</td>\n",
       "      <td>0.515464</td>\n",
       "      <td>-4.768215</td>\n",
       "      <td>-0.028557</td>\n",
       "      <td>0.088748</td>\n",
       "      <td>0.336153</td>\n",
       "      <td>0.157322</td>\n",
       "      <td>-0.103900</td>\n",
       "      <td>-0.144668</td>\n",
       "      <td>0.063773</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2.475833</td>\n",
       "      <td>0.107781</td>\n",
       "      <td>0.001684</td>\n",
       "      <td>10.727553</td>\n",
       "      <td>11.575609</td>\n",
       "      <td>12.430979</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>6.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.234616</td>\n",
       "      <td>1.831454</td>\n",
       "      <td>-0.000231</td>\n",
       "      <td>-0.152061</td>\n",
       "      <td>0.027539</td>\n",
       "      <td>-0.058844</td>\n",
       "      <td>0.038389</td>\n",
       "      <td>-0.000993</td>\n",
       "      <td>-0.105832</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.174722</td>\n",
       "      <td>0.074599</td>\n",
       "      <td>0.002487</td>\n",
       "      <td>10.612139</td>\n",
       "      <td>11.533502</td>\n",
       "      <td>12.463822</td>\n",
       "      <td>4.844</td>\n",
       "      <td>6.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 133 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     nN    ATSC3c   AATSC2v   AATSC2p   AATSC3i   AATSC8s    MATS2c    MATS2v  \\\n",
       "0   5.0 -0.145569  6.520842  0.033282 -0.192045  0.005802 -0.043265  0.133123   \n",
       "1   5.0 -0.052259 -1.311602 -0.006195 -0.119898 -0.028528  0.117528 -0.026376   \n",
       "2   7.0  0.078087 -4.401278 -0.004569 -0.280655 -0.112216  0.142885 -0.097913   \n",
       "3  15.0  0.515464 -4.768215 -0.028557  0.088748  0.336153  0.157322 -0.103900   \n",
       "4   1.0 -0.234616  1.831454 -0.000231 -0.152061  0.027539 -0.058844  0.038389   \n",
       "\n",
       "     MATS2p    MATS3i  ...  topoRadius  topoDiameter      GGI5     VE1_D  \\\n",
       "0  0.151351 -0.129099  ...         7.0          14.0  1.298611  0.179965   \n",
       "1 -0.027534 -0.072937  ...         7.0          14.0  1.350833  0.207667   \n",
       "2 -0.020252 -0.099931  ...        10.0          19.0  1.187500  0.111470   \n",
       "3 -0.144668  0.063773  ...        14.0          28.0  2.475833  0.107781   \n",
       "4 -0.000993 -0.105832  ...         6.0          12.0  1.174722  0.074599   \n",
       "\n",
       "      VE2_D       MWC8       MWC9      MWC10  XLogP  pChemBL  \n",
       "0  0.005293  10.570162  11.469569  12.376806  2.188     6.82  \n",
       "1  0.006699  10.698763  11.619904  12.547891  6.575     7.64  \n",
       "2  0.003013  10.503450  11.374020  12.259002  5.589     4.30  \n",
       "3  0.001684  10.727553  11.575609  12.430979 -0.194     6.03  \n",
       "4  0.002487  10.612139  11.533502  12.463822  4.844     6.42  \n",
       "\n",
       "[5 rows x 133 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_new.to_csv('./data/SFM452.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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