Task_1_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Task 1 Classification\n",
    "\n",
    "## PLAN\n",
    "\n",
    "Binary classification for healthy vs irregular heartbeats\n",
    "\n",
    "\n",
    "## ANALYZE\n",
    "\n",
    "### 2.1 Import, Load and Examine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Analysis\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Modeling\n",
    "from sklearn.ensemble import GradientBoostingClassifier  # GBM\n",
    "from sklearn.ensemble import RandomForestClassifier  # Random Forest\n",
    "from sklearn.tree import DecisionTreeClassifier  # Decision Trees\n",
    "from sklearn.svm import SVC # SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier  # K-Nearest Neighbors (KNN)\n",
    "from xgboost import XGBClassifier  # XGBoost\n",
    "from lightgbm import LGBMClassifier  # LightGBM\n",
    "\n",
    "from scipy.stats import randint, uniform\n",
    "\n",
    "# Evaluate\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV, train_test_split, RandomizedSearchCV\n",
    "from sklearn.metrics import roc_auc_score, roc_curve, accuracy_score, recall_score, f1_score, \\\n",
    "precision_score, confusion_matrix, classification_report, ConfusionMatrixDisplay\n",
    "\n",
    "# Save model\n",
    "import pickle\n",
    "\n",
    "# Ignore warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "pd.set_option(\"display.max_rows\", 100)\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_abnormal = pd.read_csv(\"ecg_dataset/ptbdb_abnormal.csv\", header=None)\n",
    "df_normal = pd.read_csv(\"ecg_dataset/ptbdb_normal.csv\", header=None)\n",
    "df = pd.concat([df_abnormal, df_normal], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
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   "outputs": [
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       "      <td>0.794681</td>\n",
       "      <td>0.375387</td>\n",
       "      <td>0.116883</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.171923</td>\n",
       "      <td>0.283859</td>\n",
       "      <td>0.293754</td>\n",
       "      <td>0.325912</td>\n",
       "      <td>0.345083</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.909029</td>\n",
       "      <td>0.791482</td>\n",
       "      <td>0.423169</td>\n",
       "      <td>0.186712</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007836</td>\n",
       "      <td>0.063032</td>\n",
       "      <td>0.077002</td>\n",
       "      <td>0.074957</td>\n",
       "      <td>0.077342</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.478893</td>\n",
       "      <td>0.056760</td>\n",
       "      <td>0.064176</td>\n",
       "      <td>0.081289</td>\n",
       "      <td>0.072732</td>\n",
       "      <td>0.055619</td>\n",
       "      <td>0.048774</td>\n",
       "      <td>0.054478</td>\n",
       "      <td>0.041643</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.867238</td>\n",
       "      <td>0.201360</td>\n",
       "      <td>0.099349</td>\n",
       "      <td>0.141336</td>\n",
       "      <td>0.120934</td>\n",
       "      <td>0.108516</td>\n",
       "      <td>0.096393</td>\n",
       "      <td>0.093436</td>\n",
       "      <td>0.100828</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4041</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929586</td>\n",
       "      <td>0.503550</td>\n",
       "      <td>0.293491</td>\n",
       "      <td>0.169822</td>\n",
       "      <td>0.159763</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.125444</td>\n",
       "      <td>0.137278</td>\n",
       "      <td>0.111243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4042</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.637579</td>\n",
       "      <td>0.154837</td>\n",
       "      <td>0.061983</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>0.083374</td>\n",
       "      <td>0.065143</td>\n",
       "      <td>0.057365</td>\n",
       "      <td>0.060039</td>\n",
       "      <td>0.060039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4043</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872574</td>\n",
       "      <td>0.465071</td>\n",
       "      <td>0.232212</td>\n",
       "      <td>0.126132</td>\n",
       "      <td>0.155886</td>\n",
       "      <td>0.174644</td>\n",
       "      <td>0.190168</td>\n",
       "      <td>0.203752</td>\n",
       "      <td>0.168823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4044</th>\n",
       "      <td>0.915983</td>\n",
       "      <td>0.529373</td>\n",
       "      <td>0.288406</td>\n",
       "      <td>0.144536</td>\n",
       "      <td>0.171802</td>\n",
       "      <td>0.180669</td>\n",
       "      <td>0.177566</td>\n",
       "      <td>0.176458</td>\n",
       "      <td>0.178896</td>\n",
       "      <td>0.180448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4045</th>\n",
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       "      <td>0.390567</td>\n",
       "      <td>0.191146</td>\n",
       "      <td>0.033513</td>\n",
       "      <td>0.055854</td>\n",
       "      <td>0.129086</td>\n",
       "      <td>0.147290</td>\n",
       "      <td>0.155151</td>\n",
       "      <td>0.165494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>4046 rows × 188 columns</p>\n",
       "</div>"
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      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "0     1.000000  0.900324  0.358590  0.051459  0.046596  0.126823  0.133306   \n",
       "1     1.000000  0.794681  0.375387  0.116883  0.000000  0.171923  0.283859   \n",
       "2     0.909029  0.791482  0.423169  0.186712  0.000000  0.007836  0.063032   \n",
       "3     1.000000  0.478893  0.056760  0.064176  0.081289  0.072732  0.055619   \n",
       "4     1.000000  0.867238  0.201360  0.099349  0.141336  0.120934  0.108516   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4041  1.000000  0.929586  0.503550  0.293491  0.169822  0.159763  0.130769   \n",
       "4042  1.000000  0.637579  0.154837  0.061983  0.088965  0.083374  0.065143   \n",
       "4043  1.000000  0.872574  0.465071  0.232212  0.126132  0.155886  0.174644   \n",
       "4044  0.915983  0.529373  0.288406  0.144536  0.171802  0.180669  0.177566   \n",
       "4045  1.000000  0.838643  0.390567  0.191146  0.033513  0.055854  0.129086   \n",
       "\n",
       "           7         8         9    ...  178  179  180  181  182  183  184  \\\n",
       "0     0.119125  0.110616  0.113047  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1     0.293754  0.325912  0.345083  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2     0.077002  0.074957  0.077342  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3     0.048774  0.054478  0.041643  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4     0.096393  0.093436  0.100828  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...        ...       ...       ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4041  0.125444  0.137278  0.111243  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4042  0.057365  0.060039  0.060039  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4043  0.190168  0.203752  0.168823  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4044  0.176458  0.178896  0.180448  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4045  0.147290  0.155151  0.165494  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "      185  186  187  \n",
       "0     0.0  0.0  0.0  \n",
       "1     0.0  0.0  0.0  \n",
       "2     0.0  0.0  0.0  \n",
       "3     0.0  0.0  0.0  \n",
       "4     0.0  0.0  0.0  \n",
       "...   ...  ...  ...  \n",
       "4041  0.0  0.0  0.0  \n",
       "4042  0.0  0.0  0.0  \n",
       "4043  0.0  0.0  0.0  \n",
       "4044  0.0  0.0  0.0  \n",
       "4045  0.0  0.0  0.0  \n",
       "\n",
       "[4046 rows x 188 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4041</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.929586</td>\n",
       "      <td>0.503550</td>\n",
       "      <td>0.293491</td>\n",
       "      <td>0.169822</td>\n",
       "      <td>0.159763</td>\n",
       "      <td>0.130769</td>\n",
       "      <td>0.125444</td>\n",
       "      <td>0.137278</td>\n",
       "      <td>0.111243</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4042</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.637579</td>\n",
       "      <td>0.154837</td>\n",
       "      <td>0.061983</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>0.083374</td>\n",
       "      <td>0.065143</td>\n",
       "      <td>0.057365</td>\n",
       "      <td>0.060039</td>\n",
       "      <td>0.060039</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4043</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.872574</td>\n",
       "      <td>0.465071</td>\n",
       "      <td>0.232212</td>\n",
       "      <td>0.126132</td>\n",
       "      <td>0.155886</td>\n",
       "      <td>0.174644</td>\n",
       "      <td>0.190168</td>\n",
       "      <td>0.203752</td>\n",
       "      <td>0.168823</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4044</th>\n",
       "      <td>0.915983</td>\n",
       "      <td>0.529373</td>\n",
       "      <td>0.288406</td>\n",
       "      <td>0.144536</td>\n",
       "      <td>0.171802</td>\n",
       "      <td>0.180669</td>\n",
       "      <td>0.177566</td>\n",
       "      <td>0.176458</td>\n",
       "      <td>0.178896</td>\n",
       "      <td>0.180448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4045</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.838643</td>\n",
       "      <td>0.390567</td>\n",
       "      <td>0.191146</td>\n",
       "      <td>0.033513</td>\n",
       "      <td>0.055854</td>\n",
       "      <td>0.129086</td>\n",
       "      <td>0.147290</td>\n",
       "      <td>0.155151</td>\n",
       "      <td>0.165494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14552 rows × 188 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0         1         2         3         4         5         6    \\\n",
       "0     0.932233  0.869679  0.886186  0.929626  0.908775  0.933970  0.801043   \n",
       "1     1.000000  0.606941  0.384181  0.254237  0.223567  0.276836  0.253430   \n",
       "2     1.000000  0.951613  0.923963  0.853303  0.791859  0.734255  0.672043   \n",
       "3     0.977819  0.899261  0.230129  0.032348  0.142329  0.223660  0.328096   \n",
       "4     0.935618  0.801661  0.805815  1.000000  0.722741  0.480789  0.454829   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "4041  1.000000  0.929586  0.503550  0.293491  0.169822  0.159763  0.130769   \n",
       "4042  1.000000  0.637579  0.154837  0.061983  0.088965  0.083374  0.065143   \n",
       "4043  1.000000  0.872574  0.465071  0.232212  0.126132  0.155886  0.174644   \n",
       "4044  0.915983  0.529373  0.288406  0.144536  0.171802  0.180669  0.177566   \n",
       "4045  1.000000  0.838643  0.390567  0.191146  0.033513  0.055854  0.129086   \n",
       "\n",
       "           7         8         9    ...  178  179  180  181  182  183  184  \\\n",
       "0     0.749783  0.687229  0.635100  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "1     0.184826  0.153349  0.121872  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "2     0.685100  0.670507  0.667435  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "3     0.367837  0.381701  0.389094  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4     0.319834  0.266874  0.308411  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "...        ...       ...       ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "4041  0.125444  0.137278  0.111243  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4042  0.057365  0.060039  0.060039  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4043  0.190168  0.203752  0.168823  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4044  0.176458  0.178896  0.180448  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "4045  0.147290  0.155151  0.165494  ...  0.0  0.0  0.0  0.0  0.0  0.0  0.0   \n",
       "\n",
       "      185  186  187  \n",
       "0     0.0  0.0  1.0  \n",
       "1     0.0  0.0  1.0  \n",
       "2     0.0  0.0  1.0  \n",
       "3     0.0  0.0  1.0  \n",
       "4     0.0  0.0  1.0  \n",
       "...   ...  ...  ...  \n",
       "4041  0.0  0.0  0.0  \n",
       "4042  0.0  0.0  0.0  \n",
       "4043  0.0  0.0  0.0  \n",
       "4044  0.0  0.0  0.0  \n",
       "4045  0.0  0.0  0.0  \n",
       "\n",
       "[14552 rows x 188 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10506 entries, 0 to 10505\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 15.1 MB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4046 entries, 0 to 4045\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 5.8 MB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 14552 entries, 0 to 4045\n",
      "Columns: 188 entries, 0 to 187\n",
      "dtypes: float64(188)\n",
      "memory usage: 21.0 MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(df_abnormal.info())\n",
    "print()\n",
    "print(df_normal.info())\n",
    "print()\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.int64(0), np.int64(0))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check missing values\n",
    "df_abnormal.isna().sum().sum(), df_normal.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.int64(6), np.int64(1))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check duplicated\n",
    "df_abnormal.duplicated().sum(), df_normal.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1400x1000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Randomly select a few heartbeats from both normal and abnormal datasets and viz them\n",
    "sample_normal = df_normal.sample(5)\n",
    "sample_abnormal = df_abnormal.sample(5)\n",
    "\n",
    "plt.figure(figsize=(14, 10))\n",
    "\n",
    "# Plotting normal heartbeats\n",
    "for i, row in enumerate(sample_normal.iterrows()):\n",
    "    plt.subplot(2, 5, i+1)  \n",
    "    plt.plot(row[1], color='blue')\n",
    "    plt.title('Normal Heartbeat ' + str(i+1))\n",
    "    plt.ylim(0, 1.5)\n",
    "    plt.tight_layout()\n",
    "\n",
    "# Plotting abnormal heartbeats\n",
    "for i, row in enumerate(sample_abnormal.iterrows()):\n",
    "    plt.subplot(2, 5, i+6) \n",
    "    plt.plot(row[1], color='red')\n",
    "    plt.title('Abnormal Heartbeat ' + str(i+1))\n",
    "    plt.ylim(0, 1.5)\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONSTRUCT\n",
    "\n",
    "### 3.1 Data Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build model\n",
    "\n",
    "# set x and y\n",
    "x = df.drop(187, axis=1)\n",
    "y = df[187].values\n",
    "\n",
    "# Split the data into training and test sets\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Explore Distinct Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Number of positive: 7389, number of negative: 2797\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.005779 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 42043\n",
      "[LightGBM] [Info] Number of data points in the train set: 10186, number of used features: 184\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.725407 -> initscore=0.971445\n",
      "[LightGBM] [Info] Start training from score 0.971445\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'score_y_pred_gbm': 0.9326614750343564,\n",
       " 'score_y_pred_rf': 0.9688502061383417,\n",
       " 'score_y_pred_dc': 0.9099862574438846,\n",
       " 'score_y_pred_svm': 0.9015116811726981,\n",
       " 'score_y_pred_knn': 0.9264773247824095,\n",
       " 'score_y_pred_xgb': 0.9821346770499313,\n",
       " 'score_y_pred_lgbm': 0.978240952817224}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the models\n",
    "models = {\n",
    "    \"gbm\": GradientBoostingClassifier(random_state=1),\n",
    "    \"rf\": RandomForestClassifier(random_state=1),\n",
    "    \"dc\": DecisionTreeClassifier(random_state=1),\n",
    "    \"svm\": SVC(random_state=1),\n",
    "    \"knn\": KNeighborsClassifier(),\n",
    "    \"xgb\": XGBClassifier(random_state=1),\n",
    "    \"lgbm\": LGBMClassifier(random_state=1)\n",
    "}\n",
    "\n",
    "# Loop through the models dictionary\n",
    "for name, model in models.items():\n",
    "    model.fit(x_train, y_train)\n",
    "\n",
    "# Start Predict\n",
    "predictions = {}  \n",
    "for name, model in models.items():\n",
    "    predictions[f\"y_pred_{name}\"] = model.predict(x_test)\n",
    "\n",
    "# Accuracy Score\n",
    "scores = {}\n",
    "for name, y_pred in predictions.items():\n",
    "    scores[f\"score_{name}\"] = models[name[7:]].score(x_test, y_test)\n",
    "\n",
    "scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tuned XGBoost Parameters: {'colsample_bytree': np.float64(0.5783896143871318), 'learning_rate': np.float64(0.40813680276128117), 'max_depth': 7, 'min_child_weight': 2, 'n_estimators': 121, 'subsample': np.float64(0.7715949980443677)}\n",
      "Best score is 0.975357890665697\n"
     ]
    }
   ],
   "source": [
    "# Hyperparameter tuning for XGBoost\n",
    "param_dist = {\n",
    "    'n_estimators': randint(50, 200),\n",
    "    'learning_rate': uniform(0, 1),\n",
    "    'subsample': uniform(0, 1),\n",
    "    'max_depth': randint(1, 20),\n",
    "    'colsample_bytree': uniform(0, 1),\n",
    "    'min_child_weight': randint(0, 10)\n",
    "}\n",
    "\n",
    "xgb = XGBClassifier(random_state=1)\n",
    "\n",
    "xgb_cv = RandomizedSearchCV(xgb, param_distributions=param_dist, n_iter=25, scoring='accuracy', cv=4, random_state=1)\n",
    "xgb_cv.fit(x_train, y_train)\n",
    "\n",
    "# Print the best parameters and the corresponding score\n",
    "print(\"Tuned XGBoost Parameters: {}\".format(xgb_cv.best_params_))\n",
    "print(\"Best score is {}\".format(xgb_cv.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model is XBGoost. However, the score is even lower when we perform hyperparameter turning, so let's keep default options.\n",
    "\n",
    "### 3.4 Evaluate Model\n",
    "\n",
    "#### 3.4.1 Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "cm = confusion_matrix(y_test, predictions[\"y_pred_xgb\"])\n",
    "cmd = ConfusionMatrixDisplay(cm, display_labels=[\"Class 0\", \"Class 1\"])\n",
    "cmd.plot(cmap=\"Blues\")\n",
    "cmd.ax_.set(title=\"XGBoost Confusion Matrix\", xlabel=\"Predicted\", ylabel=\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 3.4.2 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metrics for gbm:\n",
      "  f1: 0.953348143446525\n",
      "  precision: 0.9431711145996861\n",
      "  recall: 0.9637471928136028\n",
      "  accuracy: 0.9326614750343564\n",
      "Metrics for rf:\n",
      "  f1: 0.9784126984126984\n",
      "  precision: 0.9682689286836318\n",
      "  recall: 0.9887712544112929\n",
      "  accuracy: 0.9688502061383417\n",
      "Metrics for dc:\n",
      "  f1: 0.9364797155325683\n",
      "  precision: 0.9436482084690554\n",
      "  recall: 0.9294193134424126\n",
      "  accuracy: 0.9099862574438846\n",
      "Metrics for svm:\n",
      "  f1: 0.9325172630257376\n",
      "  precision: 0.9127496159754225\n",
      "  recall: 0.9531600898299647\n",
      "  accuracy: 0.9015116811726981\n",
      "Metrics for knn:\n",
      "  f1: 0.9479656346247366\n",
      "  precision: 0.9580602883355177\n",
      "  recall: 0.9380814886108437\n",
      "  accuracy: 0.9264773247824095\n",
      "Metrics for xgb:\n",
      "  f1: 0.9875279820914614\n",
      "  precision: 0.9843799808734459\n",
      "  recall: 0.9906961822264998\n",
      "  accuracy: 0.9821346770499313\n",
      "Metrics for lgbm:\n",
      "  f1: 0.9848315503752195\n",
      "  precision: 0.9802924348378894\n",
      "  recall: 0.9894128970163619\n",
      "  accuracy: 0.978240952817224\n"
     ]
    }
   ],
   "source": [
    "# Metrics dictionary to store the scores\n",
    "metrics = {}\n",
    "\n",
    "# Calculate F1, Precision, Recall, AUC, and Accuracy\n",
    "for name, model in models.items():\n",
    "    y_pred = predictions[f\"y_pred_{name}\"]\n",
    "    y_true = y_test  \n",
    "    metrics[name] = {\n",
    "        'f1': f1_score(y_true, y_pred, average='binary'),\n",
    "        'precision': precision_score(y_true, y_pred, average='binary'),\n",
    "        'recall': recall_score(y_true, y_pred, average='binary'),\n",
    "        'accuracy': accuracy_score(y_true, y_pred)\n",
    "    }\n",
    "    if f\"y_proba_{name}\" in predictions:\n",
    "        y_proba = predictions[f\"y_proba_{name}\"]\n",
    "        metrics[name]['auc'] = roc_auc_score(y_true, y_proba)\n",
    "\n",
    "# Print the calculated metrics for each model\n",
    "for name, scores in metrics.items():\n",
    "    print(f\"Metrics for {name}:\")\n",
    "    for metric, value in scores.items():\n",
    "        print(f\"  {metric}: {value}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save model\n",
    "with open('model_t1.pkl', 'wb') as file:\n",
    "    pickle.dump(models['xgb'], file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONSLUSION\n",
    "\n",
    "We finished classification on the dataset and determined that the best model is XGBoost. Next, we will apply this model used for prediction in task 2."
   ]
  }
 ],
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