3.model_building.ipynb

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   "source": [
    "# <font color = green>In this notebook we'll build a model based on RdKit fingerprints</font>\n",
    "* Dataset is still unbalanced.\n",
    "* First we'll build a model on imbalanced dataset, then we'll balance the dataset and build the model again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfe8977b-1c3f-47fa-b952-55a31b789f8a",
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      "displayName": "ANKIT SINGH dharmvir singh",
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    },
    "id": "dfe8977b-1c3f-47fa-b952-55a31b789f8a"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random\n",
    "import pickle\n",
    "import sklearn.ensemble\n",
    "from sklearn.metrics import roc_auc_score, mean_squared_error, mean_absolute_error\n",
    "\n",
    "from rdkit import Chem, DataStructs\n",
    "import rdkit.Chem as rkc\n",
    "import rdkit.Chem.AllChem as rkac\n",
    "import rdkit.Chem.Scaffolds.MurckoScaffold as mrks\n",
    "from rdkit.Chem import AllChem, MACCSkeys\n",
    "from rdkit.Avalon import pyAvalonTools\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13c48390-9f82-4058-9c24-48fd1651007f",
   "metadata": {
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    "outputId": "70c96402-7545-470f-e3d3-0ff359c0b4c7"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C has no label.\n",
      " Volume Serial Number is A29E-1842\n",
      "\n",
      " Directory of C:\\Users\\Ankit\\Desktop\\Project_work\n",
      "\n",
      "13-05-2024  22:57    <DIR>          .\n",
      "13-05-2024  19:24    <DIR>          ..\n",
      "13-05-2024  19:24    <DIR>          .ipynb_checkpoints\n",
      "13-05-2024  22:52         4,551,921 1_uM_Muski.ipynb\n",
      "13-05-2024  22:57         6,619,774 3_Final_dataset_notebook.ipynb\n",
      "01-05-2024  00:47           255,907 4_model_building.ipynb\n",
      "01-05-2024  16:15             7,732 active_molecules.csv\n",
      "13-05-2024  22:38            10,805 clean_df_uM.csv\n",
      "01-05-2024  01:11            90,678 combined_predictions.csv\n",
      "30-04-2024  22:43               262 enc.pickle\n",
      "30-04-2024  22:50             4,374 Final_dataframe_encoded.csv\n",
      "13-05-2024  22:40             5,968 final_dataset_encoded.csv\n",
      "30-04-2024  23:40            22,470 knn_model_25.pkl\n",
      "01-05-2024  01:03            30,228 knn_predictions.csv\n",
      "13-05-2024  22:43               124 mannwhitneyu_LogP.csv\n",
      "13-05-2024  22:43               120 mannwhitneyu_MW.csv\n",
      "13-05-2024  22:43               131 mannwhitneyu_NumHAcceptors.csv\n",
      "13-05-2024  22:43               129 mannwhitneyu_NumHDonors.csv\n",
      "13-05-2024  22:43               125 mannwhitneyu_pIC50.csv\n",
      "13-05-2024  19:24    <DIR>          Models\n",
      "01-05-2024  18:13               446 mols_2_doc.csv\n",
      "01-05-2024  00:46           427,319 pca.pkl\n",
      "13-05-2024  22:41            14,338 plot_activity_class.pdf\n",
      "13-05-2024  22:43            13,932 plot_MW_vs_LogP.pdf\n",
      "30-04-2024  19:56            71,142 Raw_data.csv\n",
      "30-04-2024  23:40            50,665 rf_model_25.pkl\n",
      "01-05-2024  01:03            30,228 rf_predictions.csv\n",
      "01-05-2024  00:00            49,614 scaler.pkl\n",
      "01-05-2024  18:22           166,611 screening.ipynb\n",
      "13-05-2024  19:24    <DIR>          screening_data\n",
      "30-04-2024  23:40             6,027 svm_model_25.pkl\n",
      "01-05-2024  01:03            30,228 svm_predictions.csv\n",
      "01-05-2024  17:40               392 tani_sim_mol.csv\n",
      "13-05-2024  22:40             5,827 tb_final_df.csv\n",
      "30-04-2024  23:40            77,424 xgb_model_25.pkl\n",
      "01-05-2024  01:03            30,228 xgb_predictions.csv\n",
      "              31 File(s)     12,575,169 bytes\n",
      "               5 Dir(s)  195,870,060,544 bytes free\n"
     ]
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   "source": [
    "ls"
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  {
   "cell_type": "code",
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   "id": "cdd6646e-8ae4-4e84-8d6b-dc12c7ab7b08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "executionInfo": {
     "elapsed": 1141,
     "status": "ok",
     "timestamp": 1712865247219,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
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    "outputId": "62576e8e-287a-47a5-af40-ed50016fd6c3",
    "scrolled": true
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molecule_chembl_id</th>\n",
       "      <th>activity_class</th>\n",
       "      <th>preprocessed_smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>5</th>\n",
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       "      <th>6</th>\n",
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       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4I)c3)c2C1</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CHEMBL258966</td>\n",
       "      <td>0</td>\n",
       "      <td>CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(Cl)cc4)c3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CHEMBL409940</td>\n",
       "      <td>0</td>\n",
       "      <td>CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4Br)c3)c2C1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CHEMBL408197</td>\n",
       "      <td>0</td>\n",
       "      <td>CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4)c3)c2C1</td>\n",
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       "      <th>12</th>\n",
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       "      <td>0</td>\n",
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       "      <th>13</th>\n",
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       "      <td>1</td>\n",
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       "      <th>15</th>\n",
       "      <td>CHEMBL1797249</td>\n",
       "      <td>1</td>\n",
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       "      <th>16</th>\n",
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       "      <td>1</td>\n",
       "      <td>CC(C)(C)OC(=O)N1CCc2[nH]nc(-c3ccccc3)c2C1</td>\n",
       "    </tr>\n",
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       "      <th>17</th>\n",
       "      <td>CHEMBL2435683</td>\n",
       "      <td>1</td>\n",
       "      <td>CC(=O)c1ccc(S(=O)(=O)N2CCc3c(c(-c4ccccc4)nn3C(...</td>\n",
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       "      <th>18</th>\n",
       "      <td>CHEMBL2435681</td>\n",
       "      <td>1</td>\n",
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       "      <th>19</th>\n",
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       "      <td>CC(=O)N1CCc2c(sc(NC(=O)c3cccs3)c2C(N)=O)C1</td>\n",
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       "      <th>23</th>\n",
       "      <td>CHEMBL44884</td>\n",
       "      <td>1</td>\n",
       "      <td>CCC(CO)NCCNC(CC)CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CHEMBL3121933</td>\n",
       "      <td>1</td>\n",
       "      <td>COc1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CHEMBL3121932</td>\n",
       "      <td>1</td>\n",
       "      <td>CC(=O)c1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CHEMBL3121931</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <th>27</th>\n",
       "      <td>CHEMBL3121930</td>\n",
       "      <td>1</td>\n",
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       "      <td>CHEMBL3121928</td>\n",
       "      <td>1</td>\n",
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       "      <td>CHEMBL3121927</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <td>CHEMBL3121926</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <td>CHEMBL3121925</td>\n",
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       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <th>33</th>\n",
       "      <td>CHEMBL3121924</td>\n",
       "      <td>1</td>\n",
       "      <td>COc1ccc(NC(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=O)[...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CHEMBL3121950</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <td>CHEMBL3121949</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "      <th>36</th>\n",
       "      <td>CHEMBL3121948</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...</td>\n",
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       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>CHEMBL3121947</td>\n",
       "      <td>1</td>\n",
       "      <td>COc1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>CHEMBL3121946</td>\n",
       "      <td>1</td>\n",
       "      <td>CC(=O)c1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>CHEMBL3121944</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>CHEMBL3121943</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CHEMBL3121942</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>CHEMBL3121939</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
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       "      <th>43</th>\n",
       "      <td>CHEMBL3121938</td>\n",
       "      <td>1</td>\n",
       "      <td>COc1ccc(NC(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=O)[...</td>\n",
       "    </tr>\n",
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       "      <th>44</th>\n",
       "      <td>CHEMBL3121937</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
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       "      <th>45</th>\n",
       "      <td>CHEMBL3121935</td>\n",
       "      <td>1</td>\n",
       "      <td>NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>CHEMBL3212161</td>\n",
       "      <td>1</td>\n",
       "      <td>Cc1nc2ccccn2c1C(=O)N/N=C/c1ccc([N+](=O)[O-])cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>CHEMBL3310312</td>\n",
       "      <td>1</td>\n",
       "      <td>Cc1nc2ccccn2c1C(=O)Nc1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CHEMBL3310314</td>\n",
       "      <td>1</td>\n",
       "      <td>Cc1nc2ccccn2c1C(=O)Nc1ccccn1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>CHEMBL3310318</td>\n",
       "      <td>1</td>\n",
       "      <td>CCOc1ccc(NC(=O)c2c(C)nc3ccccn23)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>CHEMBL3764865</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sc3ccccc3n2c1C(=O)NNC(=O)c1ccc(Oc2ccccc2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>CHEMBL3765314</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sccn2c1C(=O)NNC(=O)c1cccc2ccccc12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>CHEMBL3764614</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sccn2c1C(=O)N/N=C/c1ccc(C(F)(F)F)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>CHEMBL3763241</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sccn2c1C(=O)N/N=C/c1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>CHEMBL3764280</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sccn2c1C(=O)Nc1ccc(Br)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>CHEMBL3765736</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sccn2c1C(=O)Nc1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>CHEMBL3765483</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sc3ccccc3n2c1C(=O)Nc1ccc(Br)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>CHEMBL3764694</td>\n",
       "      <td>0</td>\n",
       "      <td>Cc1nc2sc3ccccc3n2c1C(=O)NCc1ccccc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>CHEMBL3818314</td>\n",
       "      <td>0</td>\n",
       "      <td>COc1ccc2c(c1)cc(C(=O)NS(=O)(=O)Cc1ccc(C(F)(F)F...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   molecule_chembl_id  activity_class  \\\n",
       "0        CHEMBL412059               0   \n",
       "1        CHEMBL438202               0   \n",
       "2        CHEMBL261734               0   \n",
       "3        CHEMBL410040               0   \n",
       "4        CHEMBL409299               0   \n",
       "5        CHEMBL261486               0   \n",
       "6        CHEMBL261487               0   \n",
       "7        CHEMBL428787               0   \n",
       "8        CHEMBL260779               0   \n",
       "9        CHEMBL258966               0   \n",
       "10       CHEMBL409940               0   \n",
       "11       CHEMBL408197               0   \n",
       "12       CHEMBL259507               0   \n",
       "13       CHEMBL260609               1   \n",
       "14      CHEMBL1439099               1   \n",
       "15      CHEMBL1797249               1   \n",
       "16      CHEMBL2435653               1   \n",
       "17      CHEMBL2435683               1   \n",
       "18      CHEMBL2435681               1   \n",
       "19      CHEMBL2435692               1   \n",
       "20      CHEMBL2435688               1   \n",
       "21      CHEMBL2435686               1   \n",
       "22      CHEMBL3121934               1   \n",
       "23        CHEMBL44884               1   \n",
       "24      CHEMBL3121933               1   \n",
       "25      CHEMBL3121932               1   \n",
       "26      CHEMBL3121931               1   \n",
       "27      CHEMBL3121930               1   \n",
       "28      CHEMBL3121929               1   \n",
       "29      CHEMBL3121928               1   \n",
       "30      CHEMBL3121927               1   \n",
       "31      CHEMBL3121926               1   \n",
       "32      CHEMBL3121925               1   \n",
       "33      CHEMBL3121924               1   \n",
       "34      CHEMBL3121950               1   \n",
       "35      CHEMBL3121949               1   \n",
       "36      CHEMBL3121948               1   \n",
       "37      CHEMBL3121947               1   \n",
       "38      CHEMBL3121946               1   \n",
       "39      CHEMBL3121944               1   \n",
       "40      CHEMBL3121943               1   \n",
       "41      CHEMBL3121942               1   \n",
       "42      CHEMBL3121939               1   \n",
       "43      CHEMBL3121938               1   \n",
       "44      CHEMBL3121937               1   \n",
       "45      CHEMBL3121935               1   \n",
       "46      CHEMBL3212161               1   \n",
       "47      CHEMBL3310312               1   \n",
       "48      CHEMBL3310314               1   \n",
       "49      CHEMBL3310318               1   \n",
       "50      CHEMBL3764865               0   \n",
       "51      CHEMBL3765314               0   \n",
       "52      CHEMBL3764614               0   \n",
       "53      CHEMBL3763241               0   \n",
       "54      CHEMBL3764280               0   \n",
       "55      CHEMBL3765736               0   \n",
       "56      CHEMBL3765483               0   \n",
       "57      CHEMBL3764694               0   \n",
       "58      CHEMBL3818314               0   \n",
       "\n",
       "                                  preprocessed_smiles  \n",
       "0    Cc1ccccc1Cn1cc(NC(=O)c2noc3c2CC(C(C)(C)C)CC3)cn1  \n",
       "1   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(C(=O)NCCc...  \n",
       "2   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(C(=O)NCc5...  \n",
       "3   CC(C)(C)OC(=O)c1ccc(Cn2cc(NC(=O)c3noc4c3CC(C(C...  \n",
       "4   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(C(=O)O)cc...  \n",
       "5   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(F)cc4F)c3...  \n",
       "6   COC(=O)c1ccc(Cn2cc(NC(=O)c3noc4c3CC(C(C)(C)C)C...  \n",
       "7   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc5ccccc5c4)...  \n",
       "8    CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4I)c3)c2C1  \n",
       "9   CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(Cl)cc4)c3...  \n",
       "10  CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4Br)c3)c2C1  \n",
       "11    CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccccc4)c3)c2C1  \n",
       "12  CC(C)(C)C1CCc2onc(C(=O)Nc3cnn(Cc4ccc(F)cc4)c3)...  \n",
       "13            CC(C)(C)C1CCc2onc(C(=O)Nc3cn[nH]c3)c2C1  \n",
       "14  CCN(CC)CCOC(=O)C(Cc1cccc2ccccc12)CC1CCCO1.O=C(...  \n",
       "15             COC(=O)c1cc2c(OCc3ccccc3)cc(N)cc2[nH]1  \n",
       "16          CC(C)(C)OC(=O)N1CCc2[nH]nc(-c3ccccc3)c2C1  \n",
       "17  CC(=O)c1ccc(S(=O)(=O)N2CCc3c(c(-c4ccccc4)nn3C(...  \n",
       "18  O=C(c1ccccc1)n1nc(-c2ccccc2)c2c1CCN(S(=O)(=O)c...  \n",
       "19  O=C(Nc1cccc2ccccc12)N1CCc2c(c(-c3ccccc3)nn2C(=...  \n",
       "20  O=C(Nc1ccc([N+](=O)[O-])cc1)N1CCc2c(c(-c3ccccc...  \n",
       "21  O=C(Nc1ccc(Br)cc1)N1CCc2c(c(-c3ccccc3)nn2C(=O)...  \n",
       "22         CC(=O)N1CCc2c(sc(NC(=O)c3cccs3)c2C(N)=O)C1  \n",
       "23                                 CCC(CO)NCCNC(CC)CO  \n",
       "24  COc1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=...  \n",
       "25  CC(=O)c1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N...  \n",
       "26  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "27  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "28  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "29  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "30  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "31  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "32  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "33  COc1ccc(NC(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=O)[...  \n",
       "34  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "35  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "36  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])o2)sc2c1CCN...  \n",
       "37  COc1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=...  \n",
       "38  CC(=O)c1ccc(S(=O)(=O)N2CCc3c(sc(NC(=O)c4ccc([N...  \n",
       "39  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "40  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "41  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "42  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "43  COc1ccc(NC(=O)N2CCc3c(sc(NC(=O)c4ccc([N+](=O)[...  \n",
       "44  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "45  NC(=O)c1c(NC(=O)c2ccc([N+](=O)[O-])s2)sc2c1CCN...  \n",
       "46    Cc1nc2ccccn2c1C(=O)N/N=C/c1ccc([N+](=O)[O-])cc1  \n",
       "47                       Cc1nc2ccccn2c1C(=O)Nc1ccccc1  \n",
       "48                       Cc1nc2ccccn2c1C(=O)Nc1ccccn1  \n",
       "49                CCOc1ccc(NC(=O)c2c(C)nc3ccccn23)cc1  \n",
       "50  Cc1nc2sc3ccccc3n2c1C(=O)NNC(=O)c1ccc(Oc2ccccc2...  \n",
       "51            Cc1nc2sccn2c1C(=O)NNC(=O)c1cccc2ccccc12  \n",
       "52         Cc1nc2sccn2c1C(=O)N/N=C/c1ccc(C(F)(F)F)cc1  \n",
       "53                   Cc1nc2sccn2c1C(=O)N/N=C/c1ccccc1  \n",
       "54                    Cc1nc2sccn2c1C(=O)Nc1ccc(Br)cc1  \n",
       "55                        Cc1nc2sccn2c1C(=O)Nc1ccccc1  \n",
       "56              Cc1nc2sc3ccccc3n2c1C(=O)Nc1ccc(Br)cc1  \n",
       "57                 Cc1nc2sc3ccccc3n2c1C(=O)NCc1ccccc1  \n",
       "58  COc1ccc2c(c1)cc(C(=O)NS(=O)(=O)Cc1ccc(C(F)(F)F...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data/Final_dataframe_encoded.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3b5c8219-2996-48b7-87d6-007a509e6ed2",
   "metadata": {
    "executionInfo": {
     "elapsed": 738,
     "status": "ok",
     "timestamp": 1712865253534,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "3b5c8219-2996-48b7-87d6-007a509e6ed2"
   },
   "outputs": [],
   "source": [
    "X = df['preprocessed_smiles']\n",
    "\n",
    "y = df['activity_class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4237558-a438-4e98-a575-5f5d156a7993",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1712865254064,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "d4237558-a438-4e98-a575-5f5d156a7993"
   },
   "outputs": [],
   "source": [
    "# divide data into train and test set\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "89998857-9936-4fed-b8ab-c4e05972403e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1712865255743,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "89998857-9936-4fed-b8ab-c4e05972403e",
    "outputId": "2e09c7b5-01c9-4517-de58-f58240407b45"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "47\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(X_train.size)\n",
    "print(X_test.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f65081-b812-4f34-a523-7a6cdd43180d",
   "metadata": {
    "id": "12f65081-b812-4f34-a523-7a6cdd43180d"
   },
   "source": [
    "## Creating Circular Fingerprints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "55677f0e-e451-4085-a385-92bcdaf0c78c",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1712865261664,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "55677f0e-e451-4085-a385-92bcdaf0c78c"
   },
   "outputs": [],
   "source": [
    "# convers a list of SMILEs to a list of mols\n",
    "def smi_to_mols(smi_list):\n",
    "    mols_list = [Chem.MolFromSmiles(smile) for smile in smi_list]\n",
    "    return mols_list\n",
    "\n",
    "\n",
    "# returns morgan fingerprints as a 2D array for a list of SMILE strings\n",
    "def get_morgan_fingerprints(smi_list, size, radius, useFeatures, useCounts=True):\n",
    "        mols = smi_to_mols(smi_list)\n",
    "        fps = [AllChem.GetMorganFingerprint(mol, radius, useCounts=useCounts, useFeatures=useFeatures) for mol in mols]\n",
    "        fps_arr = np.zeros((len(fps), size), np.int32)\n",
    "        for i, fp in enumerate(fps):\n",
    "            for index, value in fp.GetNonzeroElements().items():\n",
    "                n_index = index % size\n",
    "                fps_arr[i, n_index] += int(value)\n",
    "        return fps_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "32d33066-61a5-4dfe-b861-9a825e12342e",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1712865263960,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "32d33066-61a5-4dfe-b861-9a825e12342e"
   },
   "outputs": [],
   "source": [
    "# conver series to a list\n",
    "X_train_list = X_train.tolist()\n",
    "X_test_list = X_test.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f19efa1-439f-4297-8e94-bd94b8ce1fa1",
   "metadata": {
    "executionInfo": {
     "elapsed": 5913,
     "status": "ok",
     "timestamp": 1712865271852,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "6f19efa1-439f-4297-8e94-bd94b8ce1fa1"
   },
   "outputs": [],
   "source": [
    "# get fingerprints for training and test data\n",
    "X_train_arr = get_morgan_fingerprints(X_train_list, size=2048, radius=2, useFeatures=True, useCounts=True)\n",
    "X_test_arr = get_morgan_fingerprints(X_test_list, size=2048, radius=2, useFeatures=True, useCounts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a4b3e85-294c-4cdc-852a-975d47b859ac",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1712865271853,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "0a4b3e85-294c-4cdc-852a-975d47b859ac",
    "outputId": "96ad7aa9-e794-4be5-f5dc-d16da7af7cc5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  1,  2, ...,  0,  0,  0],\n",
       "       [ 3,  0,  3, ...,  0,  0,  0],\n",
       "       [10,  1,  1, ...,  0,  0,  0],\n",
       "       ...,\n",
       "       [10,  1,  1, ...,  0,  0,  0],\n",
       "       [ 8,  2,  3, ...,  0,  0,  0],\n",
       "       [ 3,  1,  1, ...,  0,  0,  0]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "56fac2ea-62a3-40be-8e4b-5669b247986e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1712865273885,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "56fac2ea-62a3-40be-8e4b-5669b247986e",
    "outputId": "1375c739-b758-4244-c913-591b74126ea8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 2048)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of dimensions\n",
    "X_train_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "cec059c9-4f99-4d0f-a1e2-dd67609eb082",
   "metadata": {},
   "outputs": [],
   "source": [
    "g = pd.DataFrame(X_train_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "c07dd726-e5bf-4068-9db0-c8d4e1947de5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>47 rows × 2048 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    0     1     2     3     4     5     6     7     8     9     ...  2038  \\\n",
       "0      4     1     2     0    13     0     2     0     0     0  ...     0   \n",
       "1      3     0     3     0    14     1     0     0     0     0  ...     0   \n",
       "2     10     1     1     0    13     0     3     0     1     0  ...     0   \n",
       "3      6     0     3     0    21     0     2     0     1     0  ...     0   \n",
       "4      7     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "5      7     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "6      6     1     4     0    21     0     2     0     0     0  ...     0   \n",
       "7      7     2     5     0    14     0     2     0     0     0  ...     0   \n",
       "8      2     1     1     0    12     0     2     0     1     0  ...     0   \n",
       "9     11     1     1     0    13     0     3     0     0     0  ...     0   \n",
       "10     9     1     1     0     7     1     2     0     0     0  ...     0   \n",
       "11     8     3     5     0    14     0     2     0     0     0  ...     0   \n",
       "12    15     1     3     0    13     0     3     0     0     0  ...     0   \n",
       "13     9     0     2     0     9     1     1     0     0     0  ...     0   \n",
       "14     7     2     8     0    14     0     2     0     0     0  ...     0   \n",
       "15     4     1     2     0    12     0     2     0     3     0  ...     0   \n",
       "16     6     1     2     0    21     0     2     0     1     0  ...     0   \n",
       "17     6     2     8     0    14     0     2     0     0     0  ...     0   \n",
       "18     2     1     1     0    12     0     2     0     0     0  ...     0   \n",
       "19    10     0     0     2     0     0     0     0     0     0  ...     0   \n",
       "20     2     1     1     0    13     0     2     0     0     0  ...     0   \n",
       "21     6     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "22     2     1     1     0    12     0     3     0     0     0  ...     0   \n",
       "23     9     2     7     0    14     0     2     0     0     0  ...     0   \n",
       "24     7     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "25    12     2     2     0    19     0     3     0     0     0  ...     0   \n",
       "26    10     1     1     0    13     0     3     0     1     0  ...     0   \n",
       "27     7     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "28     7     3     5     0    14     0     2     0     1     0  ...     0   \n",
       "29    10     2     2     0    13     0     3     0     0     0  ...     0   \n",
       "30     6     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "31    10     1     1     0    13     0     3     0     1     0  ...     0   \n",
       "32     9     2     7     0    14     0     2     0     0     0  ...     0   \n",
       "33    10     1     1     0    17     0     3     0     0     0  ...     0   \n",
       "34     7     3     7     0    14     0     2     0     0     0  ...     0   \n",
       "35     8     3     6     0    14     0     2     0     0     0  ...     0   \n",
       "36    10     1     1     0    13     0     3     0     2     0  ...     0   \n",
       "37     3     2     3     0    22     0     2     0     0     0  ...     0   \n",
       "38    13     2     2     0    19     0     3     0     0     0  ...     0   \n",
       "39     6     2     6     0    14     0     2     0     1     0  ...     0   \n",
       "40     8     0     4     0    21     0     2     0     0     0  ...     0   \n",
       "41     6     2     5     0    14     0     1     0     3     0  ...     0   \n",
       "42     3     1     2     0    12     0     2     0     0     0  ...     0   \n",
       "43     6     1     2     0    25     0     2     0     0     0  ...     0   \n",
       "44    10     1     1     0    13     0     3     0     0     0  ...     0   \n",
       "45     8     2     3     0     8     0     2     0     0     0  ...     0   \n",
       "46     3     1     1     0    16     0     2     0     0     0  ...     0   \n",
       "\n",
       "    2039  2040  2041  2042  2043  2044  2045  2046  2047  \n",
       "0      1     3     0     0     0     0     0     0     0  \n",
       "1      0     1     0     0     0     0     0     0     0  \n",
       "2      3     1     0     0     0     0     0     0     0  \n",
       "3      0     3     0     0     0     2     0     0     0  \n",
       "4      0     4     0     0     0     0     0     0     0  \n",
       "5      0     4     0     0     0     0     0     0     0  \n",
       "6      0     2     0     0     0     2     0     0     0  \n",
       "7      0     3     0     0     0     0     0     0     0  \n",
       "8      1     1     0     0     0     0     0     0     0  \n",
       "9      3     1     0     0     0     0     0     0     0  \n",
       "10     2     1     0     0     0     0     0     0     0  \n",
       "11     0     3     0     0     0     0     0     0     0  \n",
       "12     3     2     0     0     0     0     0     0     0  \n",
       "13     0     1     0     0     0     2     0     0     0  \n",
       "14     0     4     0     0     0     0     0     0     0  \n",
       "15     1     1     0     0     0     0     0     0     0  \n",
       "16     0     2     0     0     0     2     0     0     0  \n",
       "17     0     3     0     0     0     0     0     0     0  \n",
       "18     1     1     0     0     0     0     0     0     0  \n",
       "19     0     0     2     0     0     0     0     0     0  \n",
       "20     1     1     0     0     0     0     0     0     0  \n",
       "21     0     3     0     0     0     0     0     0     0  \n",
       "22     2     1     0     0     0     0     0     0     0  \n",
       "23     0     5     0     0     0     0     0     0     0  \n",
       "24     0     4     0     0     0     0     0     0     0  \n",
       "25     3     2     0     0     0     0     0     0     0  \n",
       "26     3     1     0     0     0     0     0     0     0  \n",
       "27     0     4     0     0     0     0     0     0     0  \n",
       "28     0     3     0     0     0     0     0     0     0  \n",
       "29     3     1     0     0     0     0     0     0     0  \n",
       "30     0     3     0     0     0     0     0     0     0  \n",
       "31     3     1     0     0     0     0     0     0     0  \n",
       "32     0     5     0     0     0     0     0     0     0  \n",
       "33     3     1     0     0     0     0     0     0     0  \n",
       "34     0     3     0     0     0     0     0     0     0  \n",
       "35     0     5     0     0     0     0     0     0     0  \n",
       "36     3     1     0     0     0     0     0     0     0  \n",
       "37     0     6     0     0     0     0     0     0     0  \n",
       "38     3     2     0     0     0     0     0     0     0  \n",
       "39     0     3     0     0     0     0     0     0     0  \n",
       "40     0     4     0     0     0     2     0     0     0  \n",
       "41     0     4     0     0     0     0     0     0     0  \n",
       "42     1     1     0     0     0     0     0     0     0  \n",
       "43     0     2     0     0     0     2     0     0     0  \n",
       "44     3     1     0     0     0     0     0     0     0  \n",
       "45     1     3     0     0     0     0     0     0     0  \n",
       "46     0     1     0     0     0     0     0     0     0  \n",
       "\n",
       "[47 rows x 2048 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78254c42-6731-418c-a884-53b34308d9a5",
   "metadata": {
    "id": "78254c42-6731-418c-a884-53b34308d9a5"
   },
   "source": [
    "## Dimensionality Reduction with PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "03922b4c-abf3-4066-b555-a70e822170e4",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5021,
     "status": "ok",
     "timestamp": 1712865280914,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "03922b4c-abf3-4066-b555-a70e822170e4",
    "outputId": "878e1010-d597-4ad5-81ee-4e5adb0f62cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: tqdm in c:\\programdata\\anaconda3\\lib\\site-packages (4.65.0)\n",
      "Requirement already satisfied: colorama in c:\\programdata\\anaconda3\\lib\\site-packages (from tqdm) (0.4.6)\n"
     ]
    }
   ],
   "source": [
    "! pip install tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cadc98e1-0484-4c27-8286-90a93598ce73",
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1712865282912,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "cadc98e1-0484-4c27-8286-90a93598ce73"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2aeeb1b0-0366-4e0f-995c-c655989f0fb3",
   "metadata": {
    "executionInfo": {
     "elapsed": 641,
     "status": "ok",
     "timestamp": 1712865286060,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "2aeeb1b0-0366-4e0f-995c-c655989f0fb3"
   },
   "outputs": [],
   "source": [
    "# apply scaling to train and test data\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train_arr)\n",
    "X_test = sc.transform(X_test_arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "91d345dd-2fd1-4a8f-bdff-9b079c5ac521",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('scaler.pkl', 'wb') as file:\n",
    "    pickle.dump(sc, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "a552679a-db42-4db9-b463-e60a39128d6e",
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1712865663892,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "a552679a-db42-4db9-b463-e60a39128d6e"
   },
   "outputs": [],
   "source": [
    "# calculates the amount of variation explained by a each number of components\n",
    "def get_component_performance(fps_arr):\n",
    "    result = []\n",
    "    for n in tqdm(range(2,48)):\n",
    "        pca = PCA(n_components = n)\n",
    "        cmpts = pca.fit_transform(fps_arr)\n",
    "        variance = np.sum(pca.explained_variance_ratio_)\n",
    "        result.append([n, variance])\n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9YreSViQzQdc",
   "metadata": {
    "id": "9YreSViQzQdc"
   },
   "source": [
    "for parallel computation, will use all the available resources except for gpu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "qpQW-CAWv0GQ",
   "metadata": {
    "executionInfo": {
     "elapsed": 546,
     "status": "ok",
     "timestamp": 1712867043309,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "qpQW-CAWv0GQ"
   },
   "outputs": [],
   "source": [
    "# from joblib import Parallel, delayed\n",
    "\n",
    "# def compute_variance(n, fps_arr):\n",
    "#     pca = PCA(n_components=n)\n",
    "#     cmpts = pca.fit_transform(fps_arr)\n",
    "#     variance = np.sum(pca.explained_variance_ratio_)\n",
    "#     return [n, variance]\n",
    "\n",
    "# def get_component_performance(fps_arr):\n",
    "#     result = Parallel(n_jobs=-1)(delayed(compute_variance)(n, fps_arr) for n in tqdm(range(2, 2000)))\n",
    "#     return result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rN-nbntGzX9W",
   "metadata": {
    "id": "rN-nbntGzX9W"
   },
   "source": [
    "this will exploit all the resources, it will use all the cores, cpus, and gpus. It may heat up the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "e9ywMRGzwxH1",
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1712866990683,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "e9ywMRGzwxH1"
   },
   "outputs": [],
   "source": [
    "# import cudf\n",
    "# from cuml import PCA\n",
    "\n",
    "# def get_component_performance(fps_arr):\n",
    "#     result = []\n",
    "#     for n in tqdm(range(2, 802)):\n",
    "#         pca = PCA(n_components=n)\n",
    "#         cmpts = pca.fit_transform(cudf.DataFrame(fps_arr))\n",
    "#         variance = pca.explained_variance_ratio_.sum()\n",
    "#         result.append([n, variance])\n",
    "#     return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "067e1243-6506-4cb8-aa05-81192cab35d8",
   "metadata": {
    "id": "067e1243-6506-4cb8-aa05-81192cab35d8"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 46/46 [00:00<00:00, 254.27it/s]\n"
     ]
    }
   ],
   "source": [
    "results = get_component_performance(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "094bc5f3-e989-408d-b9ad-2f6db7165263",
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "aborted",
     "timestamp": 1712865520313,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "094bc5f3-e989-408d-b9ad-2f6db7165263"
   },
   "outputs": [],
   "source": [
    "results_df = pd.DataFrame(results, columns = ['Components','Variance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "ac75d44e-ffaf-4918-ba3c-9bfa6754a034",
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "aborted",
     "timestamp": 1712865520314,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "ac75d44e-ffaf-4918-ba3c-9bfa6754a034",
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Components</th>\n",
       "      <th>Variance</th>\n",
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       "      <td>9</td>\n",
       "      <td>0.688037</td>\n",
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       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>0.721422</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
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       "      <td>14</td>\n",
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       "      <th>16</th>\n",
       "      <td>18</td>\n",
       "      <td>0.900558</td>\n",
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       "      <th>17</th>\n",
       "      <td>19</td>\n",
       "      <td>0.915208</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20</td>\n",
       "      <td>0.928433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>21</td>\n",
       "      <td>0.940792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>22</td>\n",
       "      <td>0.951249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>23</td>\n",
       "      <td>0.960408</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>24</td>\n",
       "      <td>0.968252</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>25</td>\n",
       "      <td>0.975520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>0.982269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27</td>\n",
       "      <td>0.987556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>28</td>\n",
       "      <td>0.991490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>29</td>\n",
       "      <td>0.994741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>30</td>\n",
       "      <td>0.997527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31</td>\n",
       "      <td>0.999254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>32</td>\n",
       "      <td>0.999989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>33</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>34</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>35</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>36</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>37</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>38</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>39</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>40</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>42</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>43</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>44</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>45</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>46</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>47</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Components  Variance\n",
       "0            2  0.252991\n",
       "1            3  0.334864\n",
       "2            4  0.413744\n",
       "3            5  0.486857\n",
       "4            6  0.541989\n",
       "5            7  0.594526\n",
       "6            8  0.641774\n",
       "7            9  0.688037\n",
       "8           10  0.721422\n",
       "9           11  0.750221\n",
       "10          12  0.777702\n",
       "11          13  0.801972\n",
       "12          14  0.824877\n",
       "13          15  0.846369\n",
       "14          16  0.866993\n",
       "15          17  0.884145\n",
       "16          18  0.900558\n",
       "17          19  0.915208\n",
       "18          20  0.928433\n",
       "19          21  0.940792\n",
       "20          22  0.951249\n",
       "21          23  0.960408\n",
       "22          24  0.968252\n",
       "23          25  0.975520\n",
       "24          26  0.982269\n",
       "25          27  0.987556\n",
       "26          28  0.991490\n",
       "27          29  0.994741\n",
       "28          30  0.997527\n",
       "29          31  0.999254\n",
       "30          32  0.999989\n",
       "31          33  1.000000\n",
       "32          34  1.000000\n",
       "33          35  1.000000\n",
       "34          36  1.000000\n",
       "35          37  1.000000\n",
       "36          38  1.000000\n",
       "37          39  1.000000\n",
       "38          40  1.000000\n",
       "39          41  1.000000\n",
       "40          42  1.000000\n",
       "41          43  1.000000\n",
       "42          44  1.000000\n",
       "43          45  1.000000\n",
       "44          46  1.000000\n",
       "45          47  1.000000"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "d072b989-a554-4474-8057-4abbe74ad87d",
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "aborted",
     "timestamp": 1712865520315,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "d072b989-a554-4474-8057-4abbe74ad87d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.lineplot(data = results_df, x = 'Components', y = 'Variance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6bdadd2-5149-4459-988c-08b0d388036a",
   "metadata": {
    "executionInfo": {
     "elapsed": 9564,
     "status": "ok",
     "timestamp": 1712873801447,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "c6bdadd2-5149-4459-988c-08b0d388036a"
   },
   "outputs": [],
   "source": [
    "# fit the PCA using train data\n",
    "pca = PCA(n_components = 25)\n",
    "train_cmpts = pca.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0418a240-d8b9-45b3-b381-7b2192f72237",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open('pca.pkl', 'wb') as file:\n",
    "    pickle.dump(pca, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5CRz85if3FxA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 790,
     "status": "ok",
     "timestamp": 1712873839276,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "5CRz85if3FxA",
    "outputId": "0fab19ce-216c-43db-8131-5bec7cb937ee",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(47, 25)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_cmpts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "65d848ec-7dfd-4867-b2c8-bea84168349a",
   "metadata": {
    "executionInfo": {
     "elapsed": 692,
     "status": "ok",
     "timestamp": 1712873839964,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "65d848ec-7dfd-4867-b2c8-bea84168349a"
   },
   "outputs": [],
   "source": [
    "# transform the test data\n",
    "test_cmpts = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "349302f1-b2cd-46c5-ba20-b89d5aeec690",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 44,
     "status": "ok",
     "timestamp": 1712873839964,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "349302f1-b2cd-46c5-ba20-b89d5aeec690",
    "outputId": "10f4eb9e-9ddf-4466-b09b-fc02b6f25062"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(47, 25)\n",
      "(12, 25)\n"
     ]
    }
   ],
   "source": [
    "print(train_cmpts.shape)\n",
    "print(test_cmpts.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00df6898-8334-456c-bcef-88a996823299",
   "metadata": {
    "id": "00df6898-8334-456c-bcef-88a996823299"
   },
   "source": [
    "# Model building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "8c2bdf26-028e-4776-a9a5-154d36410999",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: xgboost in c:\\users\\acer\\appdata\\roaming\\python\\python311\\site-packages (2.0.3)\n",
      "Requirement already satisfied: numpy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.26.4)\n",
      "Requirement already satisfied: scipy in c:\\programdata\\anaconda3\\lib\\site-packages (from xgboost) (1.11.4)\n"
     ]
    }
   ],
   "source": [
    "! pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "fbf0721e-5607-4e5e-b90a-ff8cafcd8fcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: scikit-learn in c:\\programdata\\anaconda3\\lib\\site-packages (1.2.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn) (2.2.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "c1963176-c57e-4eaa-9c90-d402b6cef526",
   "metadata": {
    "executionInfo": {
     "elapsed": 36,
     "status": "ok",
     "timestamp": 1712873839965,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "c1963176-c57e-4eaa-9c90-d402b6cef526"
   },
   "outputs": [],
   "source": [
    "# Importing useful libraries\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics  import roc_auc_score,accuracy_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "783b88ae-23f3-4a33-8bf2-bc005eef934b",
   "metadata": {},
   "source": [
    "# <font color = red>**XGBoost Classifier**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "38b6015f-acaa-4ab0-a532-d5ce8fa8994e",
   "metadata": {
    "executionInfo": {
     "elapsed": 35,
     "status": "ok",
     "timestamp": 1712873839971,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "38b6015f-acaa-4ab0-a532-d5ce8fa8994e"
   },
   "outputs": [],
   "source": [
    "def xgboost(train_x,train_y):\n",
    "\n",
    "     xgb = XGBClassifier()\n",
    "\n",
    "     # initializing with different combination of parameters\n",
    "     param_grid_xgboost = {'tree_method': ['auto'],\n",
    "                  \"n_estimators\": [10, 100, 120],\n",
    "                  'booster' : ['dart', 'gbtree'],\n",
    "#                   \"criterion\": ['gini', 'entropy'],\n",
    "                  \"max_depth\": [2,3],\n",
    "#                   \"max_features\": ['auto', 'log2'],\n",
    "                  'objective': ['binary:logistic'],\n",
    "#                   'lambda' : range(1e-4,10),\n",
    "                  'alpha' : (1e-4,1),\n",
    "                  'colsample_bytree' : (.1, .5),\n",
    "                  'subsample' : (.1, 1)\n",
    "\n",
    "\n",
    "\n",
    "             }\n",
    "\n",
    "     # object for Grid Search cv\n",
    "     grid= GridSearchCV(xgb, param_grid_xgboost, n_jobs= -1, verbose=3, cv=5,)\n",
    "\n",
    "     # finding the best parameters\n",
    "     grid.fit(train_x, train_y)\n",
    "\n",
    "     # getting best parameters\n",
    "     booster = grid.best_params_['booster']\n",
    "     tree_method = grid.best_params_['tree_method']\n",
    "     n_estimators = grid.best_params_['n_estimators']\n",
    "#      criterion = grid.best_params_['criterion']\n",
    "     max_depth = grid.best_params_['max_depth']\n",
    "#      max_features = grid.best_params_['max_features']\n",
    "     objective = grid.best_params_['objective']\n",
    "#      lambda = grid.best_params_['lambda']\n",
    "     alpha = grid.best_params_['alpha']\n",
    "     colsample_bytree = grid.best_params_['colsample_bytree']\n",
    "     subsample = grid.best_params_['colsample_bytree']\n",
    "\n",
    "\n",
    "     # creating a new model with the best parameters\n",
    "     xgb = XGBClassifier(booster=booster, tree_method=tree_method, n_estimators=n_estimators,\n",
    "                                max_depth=max_depth, objective=objective, alpha=alpha,\n",
    "                                colsample_bytree=colsample_bytree, subsample=subsample)\n",
    "\n",
    "     # training the mew model\n",
    "     xgb.fit(train_x, train_y)\n",
    "     print('Best parameters for XGBoost: {}'.format (grid.best_params_))\n",
    "\n",
    "\n",
    "     return xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f5b262da-fcd9-4560-a9c1-427397f05799",
   "metadata": {
    "executionInfo": {
     "elapsed": 34,
     "status": "ok",
     "timestamp": 1712873839971,
     "user": {
      "displayName": "ANKIT SINGH dharmvir singh",
      "userId": "08327006042311051948"
     },
     "user_tz": -330
    },
    "id": "f5b262da-fcd9-4560-a9c1-427397f05799"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n",
      "Best parameters for XGBoost: {'alpha': 0.0001, 'booster': 'dart', 'colsample_bytree': 0.5, 'max_depth': 2, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1, 'tree_method': 'auto'}\n"
     ]
    }
   ],
   "source": [
    "xgb_classifier = xgboost(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "rr6mpC5Q_hI8",
   "metadata": {
    "id": "rr6mpC5Q_hI8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(alpha=0.0001, base_score=None, booster=&#x27;dart&#x27;, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(alpha=0.0001, base_score=None, booster='dart', callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=0.5, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "              gamma=None, grow_policy=None, importance_type=None,\n",
       "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "              max_delta_step=None, max_depth=2, max_leaves=None,\n",
       "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
       "              num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "Qe5OqWjCOpCp",
   "metadata": {
    "id": "Qe5OqWjCOpCp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'objective': 'binary:logistic',\n",
       " 'base_score': None,\n",
       " 'booster': 'dart',\n",
       " 'callbacks': None,\n",
       " 'colsample_bylevel': None,\n",
       " 'colsample_bynode': None,\n",
       " 'colsample_bytree': 0.5,\n",
       " 'device': None,\n",
       " 'early_stopping_rounds': None,\n",
       " 'enable_categorical': False,\n",
       " 'eval_metric': None,\n",
       " 'feature_types': None,\n",
       " 'gamma': None,\n",
       " 'grow_policy': None,\n",
       " 'importance_type': None,\n",
       " 'interaction_constraints': None,\n",
       " 'learning_rate': None,\n",
       " 'max_bin': None,\n",
       " 'max_cat_threshold': None,\n",
       " 'max_cat_to_onehot': None,\n",
       " 'max_delta_step': None,\n",
       " 'max_depth': 2,\n",
       " 'max_leaves': None,\n",
       " 'min_child_weight': None,\n",
       " 'missing': nan,\n",
       " 'monotone_constraints': None,\n",
       " 'multi_strategy': None,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'num_parallel_tree': None,\n",
       " 'random_state': None,\n",
       " 'reg_alpha': None,\n",
       " 'reg_lambda': None,\n",
       " 'sampling_method': None,\n",
       " 'scale_pos_weight': None,\n",
       " 'subsample': 0.5,\n",
       " 'tree_method': 'auto',\n",
       " 'validate_parameters': None,\n",
       " 'verbosity': None,\n",
       " 'alpha': 0.0001}"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "m0EMeurK4y6O",
   "metadata": {
    "id": "m0EMeurK4y6O"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.score(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "fM4Iemzu5dGC",
   "metadata": {
    "id": "fM4Iemzu5dGC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_classifier.score(test_cmpts, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5da8d3bc-f666-42cd-bbed-621d37c21319",
   "metadata": {
    "id": "wPwy5li46eo_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['xgb_model_25.pkl']"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the trained model to a file\n",
    "import joblib\n",
    "joblib.dump(xgb_classifier, 'xgb_model_25.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "E31z01HN8X03",
   "metadata": {
    "id": "E31z01HN8X03"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have test data 'test_x' and corresponding labels 'test_y'\n",
    "predictions = xgb_classifier.predict(test_cmpts)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "JUR2qo4U7VLA",
   "metadata": {
    "id": "JUR2qo4U7VLA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[4 0]\n",
      " [0 8]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, predictions)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "m-UzzjvZ8Z73",
   "metadata": {
    "id": "m-UzzjvZ8Z73"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         4\n",
      "           1       1.00      1.00      1.00         8\n",
      "\n",
      "    accuracy                           1.00        12\n",
      "   macro avg       1.00      1.00      1.00        12\n",
      "weighted avg       1.00      1.00      1.00        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "7mnRmSKu9xWa",
   "metadata": {
    "id": "7mnRmSKu9xWa"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "   y_test, predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "cliR5Hab-N3v",
   "metadata": {
    "id": "cliR5Hab-N3v"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "XN5BtjNE-R-5",
   "metadata": {
    "id": "XN5BtjNE-R-5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_class\n",
       "1    8\n",
       "0    4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "kF5Dblfu8ce-",
   "metadata": {
    "id": "kF5Dblfu8ce-"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have binary classification with probabilities\n",
    "probs = xgb_classifier.predict_proba(test_cmpts)[:, 1]\n",
    "fpr, tpr, _ = roc_curve(y_test, probs)\n",
    "auc = roc_auc_score(y_test, probs)\n",
    "\n",
    "plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd66c44b-eb96-480e-8510-3125090e8c96",
   "metadata": {},
   "source": [
    "# <font color = red>**KNN Classifier**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "1a01fd1a-4136-48d2-a073-833946ac1a99",
   "metadata": {},
   "outputs": [],
   "source": [
    "def KNN(train_x, train_y):\n",
    "\n",
    "    knn = KNeighborsClassifier()\n",
    "\n",
    "    # Providing different combination of parameters\n",
    "    param_grid_knn = {\n",
    "        'algorithm' : ['ball_tree', 'kd_tree', 'brute'],\n",
    "        'leaf_size' : [10,17,24,28,30,35],\n",
    "        'n_neighbors':[4,5,8,10,11],\n",
    "        'p':[1,2]\n",
    "\n",
    "    }\n",
    "\n",
    "\n",
    "    # Object for Grid Search cv\n",
    "    grid = GridSearchCV(knn, param_grid_knn, n_jobs= -1, cv=10, verbose=3)\n",
    "\n",
    "    # finding the best parameters\n",
    "    grid.fit(train_x, train_y)\n",
    "\n",
    "    # getting best parameters\n",
    "    algorithm =grid.best_params_['algorithm']\n",
    "    leaf_size = grid.best_params_['leaf_size']\n",
    "    n_neighbors = grid.best_params_['n_neighbors']\n",
    "    p  = grid.best_params_['p']\n",
    "\n",
    "    # creating a new model with the best parameters\n",
    "    knn = KNeighborsClassifier(algorithm=algorithm, leaf_size=leaf_size, n_neighbors=n_neighbors, p=p, n_jobs=-1)\n",
    "\n",
    "    # training the mew model\n",
    "    knn.fit(train_x, train_y)\n",
    "    print('Best Parameters for KNN: {}'.format(grid.best_params_))\n",
    "    return knn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "b0967e37-5f97-4f79-afab-8c75bff759de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 180 candidates, totalling 1800 fits\n",
      "Best Parameters for KNN: {'algorithm': 'ball_tree', 'leaf_size': 10, 'n_neighbors': 11, 'p': 1}\n"
     ]
    }
   ],
   "source": [
    "knn_classifier = KNN(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "5081c241-ab51-469d-bada-87884137aee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9148936170212766"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier.score(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "d080b41b-fb2e-42a0-bd21-34b9fd2a6fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_classifier.score(test_cmpts, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "4debf109-d18d-4d00-86fa-d3b506b91e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn_model_25.pkl']"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the trained model to a file\n",
    "import joblib\n",
    "joblib.dump(knn_classifier, 'knn_model_25.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "55ce6383-8e7c-4cac-b22d-fecdd637fa68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have test data 'test_x' and corresponding labels 'test_y'\n",
    "predictions = knn_classifier.predict(test_cmpts)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "79616dde-6758-4189-ad59-a31a07008005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[4 0]\n",
      " [1 7]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, predictions)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "75e366cf-36c4-49d4-9cf7-86bb10f63645",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      1.00      0.89         4\n",
      "           1       1.00      0.88      0.93         8\n",
      "\n",
      "    accuracy                           0.92        12\n",
      "   macro avg       0.90      0.94      0.91        12\n",
      "weighted avg       0.93      0.92      0.92        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "1cd7943e-c56c-438d-8a3a-4ceb7bc5fb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "   y_test, predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f23def1-63d8-4bdb-b543-9ff03e21a123",
   "metadata": {},
   "outputs": [],
   "source": [
    "import joblib"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e88e7e-522e-43f5-97fd-520ccd0b0459",
   "metadata": {},
   "source": [
    "# <font color = red>**Random Forest Classifier**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "1bf122bb-dd93-418c-a0d2-cd5862a513c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def random_forest(train_x,train_y):\n",
    "\n",
    "    rfc = RandomForestClassifier()\n",
    "\n",
    "    # Providing different combination of parameters\n",
    "    param_grid = {\"n_estimators\": [10, 20, 30,50, 70, 100, 120],\n",
    "                  \"criterion\": ['gini', 'entropy'],\n",
    "                  \"max_depth\": range(2, 4, 1),\n",
    "                  \"max_features\": ['sqrt', 'log2'],\n",
    "                  \"ccp_alpha\" : (1e-4, 10)\n",
    "\n",
    "\n",
    "\n",
    "                 }\n",
    "\n",
    "    # object for GridSearchCV\n",
    "    grid = GridSearchCV(rfc, param_grid=param_grid, n_jobs= -1, verbose=3, cv=10)\n",
    "\n",
    "    #finding the best parameters\n",
    "    grid.fit(train_x, train_y)\n",
    "\n",
    "    #getting best parameters\n",
    "    criterion = grid.best_params_['criterion']\n",
    "    max_depth = grid.best_params_['max_depth']\n",
    "    ccp_alpha = grid.best_params_['ccp_alpha']\n",
    "    max_features = grid.best_params_['max_features']\n",
    "    n_estimators = grid.best_params_['n_estimators']\n",
    "\n",
    "\n",
    "    #create new model with best parameters\n",
    "    rfc = RandomForestClassifier(n_estimators=n_estimators, criterion=criterion,\n",
    "                                      max_depth=max_depth, ccp_alpha=ccp_alpha, max_features=max_features)\n",
    "    # train new model\n",
    "    rfc.fit(train_x, train_y)\n",
    "\n",
    "    print('Best parameters for random forest are: {}'.format(grid.best_params_))\n",
    "\n",
    "    return rfc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "1ea82386-dd83-407a-892a-71daadb50632",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 112 candidates, totalling 1120 fits\n",
      "Best parameters for random forest are: {'ccp_alpha': 0.0001, 'criterion': 'gini', 'max_depth': 3, 'max_features': 'sqrt', 'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "rfc_classifier = random_forest(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "106fad08-0bef-47c8-8814-ca578b13570f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rf_model_25.pkl']"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the trained model to a file\n",
    "import joblib\n",
    "joblib.dump(rfc_classifier, 'rf_model_25.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "01672d81-a19d-49a5-8c20-7ca223c2aca3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have test data 'test_x' and corresponding labels 'test_y'\n",
    "predictions = rfc_classifier.predict(test_cmpts)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "952f1fac-8807-4d91-8e1d-2a0e825cea05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[3 1]\n",
      " [1 7]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, predictions)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "38604901-c504-4800-9711-63f4c315e00d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.75      0.75         4\n",
      "           1       0.88      0.88      0.88         8\n",
      "\n",
      "    accuracy                           0.83        12\n",
      "   macro avg       0.81      0.81      0.81        12\n",
      "weighted avg       0.83      0.83      0.83        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "8b6a2ad2-dc8d-401f-a13c-16205f7273f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "   y_test, predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d81953fb-3ba6-479a-aba1-0338338772e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c09c6867-2903-45bf-8bec-a6afeb181764",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ba7bf0-c378-4d42-8971-a3f27aa6bcf9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "73c699eb-9b0c-47a8-870a-5142f18717ae",
   "metadata": {},
   "source": [
    "# **SUPPORT VECTOR CLASSIFIER**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "cd16e353-5154-45c0-a61f-4b3aec813ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "def support_vector(train_x, train_y):\n",
    "\n",
    "    svc = SVC()\n",
    "\n",
    "    # Providing different combination of parameters\n",
    "    param_grid_svc = {\n",
    "        'C': [0.1, 1, 10, 100], \n",
    "        'gamma': [0.001, 0.01, 0.1, 1], \n",
    "        'kernel': ['linear', 'rbf']\n",
    "    }\n",
    "\n",
    "\n",
    "    # Object for Grid Search cv\n",
    "    grid = GridSearchCV(svc, param_grid_svc, n_jobs= -1, cv=10, verbose=3)\n",
    "\n",
    "    # finding the best parameters\n",
    "    grid.fit(train_x, train_y)\n",
    "\n",
    "    # getting best parameters\n",
    "    C =grid.best_params_['C']\n",
    "    gamma = grid.best_params_['gamma']\n",
    "    kernel = grid.best_params_['kernel']\n",
    "\n",
    "    # creating a new model with the best parameters\n",
    "    svc = SVC(C=C, gamma = gamma, kernel = kernel)\n",
    "\n",
    "    # training the mew model\n",
    "    svc.fit(train_x, train_y)\n",
    "    print('Best Parameters for SVC: {}'.format(grid.best_params_))\n",
    "    return svc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "cfb32527-babf-4605-b2ab-e3e1c5a4c22b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 32 candidates, totalling 320 fits\n",
      "Best Parameters for SVC: {'C': 1, 'gamma': 0.001, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "svc_classifier = support_vector(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "761e53e9-88ff-40ae-87c5-82a5ba65f1c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9787234042553191"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier.score(train_cmpts, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "958f1920-804f-4db9-8dd1-f25410758927",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9166666666666666"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_classifier.score(test_cmpts, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "28a59553-9d4c-4452-a345-60198f124a44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Assuming you have test data 'test_x' and corresponding labels 'test_y'\n",
    "predictions = svc_classifier.predict(test_cmpts)\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "827837e1-fd03-4340-82a5-43ed46853cd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[4 0]\n",
      " [1 7]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test, predictions)\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "9cb65adb-cfbd-48bc-86ef-4808fe4297d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      1.00      0.89         4\n",
      "           1       1.00      0.88      0.93         8\n",
      "\n",
      "    accuracy                           0.92        12\n",
      "   macro avg       0.90      0.94      0.91        12\n",
      "weighted avg       0.93      0.92      0.92        12\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "470be60a-b2fd-4d85-8228-1a06c7de739b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ConfusionMatrixDisplay.from_predictions(\n",
    "   y_test, predictions)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "0360fb6c-fb83-4cdc-bc26-049b431f9742",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "activity_class\n",
       "1    8\n",
       "0    4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "70b28520-2e1c-4b89-ad6b-99b6729a8eee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['svm_model_25.pkl']"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the trained model to a file\n",
    "import joblib\n",
    "joblib.dump(svc_classifier, 'svm_model_25.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ddd6a9b-dbad-4ed5-a613-3b5004414acd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e10ba5f3-c996-4139-90ab-68ab0fd1e5e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}