code/Farhanul_Thouship/logistic_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e1d74938",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5f92a69e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sadness</td>\n",
       "      <td>im feeling rather rotten im ambitious right</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sadness</td>\n",
       "      <td>im updating blog feel shitty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>sadness</td>\n",
       "      <td>never make separate ever want feel like ashamed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>joy</td>\n",
       "      <td>left bouquet red yellow tulip arm feeling slig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sadness</td>\n",
       "      <td>feeling little vain one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sentiment                                            content\n",
       "0   sadness        im feeling rather rotten im ambitious right\n",
       "1   sadness                       im updating blog feel shitty\n",
       "2   sadness    never make separate ever want feel like ashamed\n",
       "3       joy  left bouquet red yellow tulip arm feeling slig...\n",
       "4   sadness                            feeling little vain one"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Read data\n",
    "file_path = \"../../dataset/train_val_test_preprocessed.csv\"\n",
    "data = pd.read_csv(file_path)\n",
    "data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fb545499",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.dropna(subset=['content'])\n",
    "\n",
    "# Label encode the target variable\n",
    "le = LabelEncoder()\n",
    "data['encoded_sentiment'] = le.fit_transform(data['sentiment'])\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data['content'], data['encoded_sentiment'], test_size=0.1, random_state=42\n",
    ")\n",
    "\n",
    "# TF-IDF vectorization\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "10cbc4be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "# Initialize and train a Logistic Regression classifier\n",
    "logreg = LogisticRegression(random_state=42)\n",
    "logreg.fit(X_train_tfidf, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "logreg_pred = logreg.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e9fb77a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.81      0.84       266\n",
      "           1       0.90      0.81      0.85       254\n",
      "           2       0.86      0.96      0.91       683\n",
      "           3       0.91      0.69      0.78       156\n",
      "           4       0.89      0.93      0.91       572\n",
      "           5       0.92      0.55      0.69        64\n",
      "\n",
      "    accuracy                           0.88      1995\n",
      "   macro avg       0.89      0.79      0.83      1995\n",
      "weighted avg       0.88      0.88      0.88      1995\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print classification report\n",
    "print(classification_report(y_test, logreg_pred, zero_division='warn'))\n",
    "\n",
    "# Generate and display confusion matrix\n",
    "confusion_m = confusion_matrix(y_test, logreg_pred)\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(confusion_m, annot=True, fmt='d', cmap='Blues', cbar=False)\n",
    "plt.xlabel('Predicted Labels')\n",
    "plt.ylabel('True Labels')\n",
    "plt.title('Confusion Matrix - Logistic Regression')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "485522f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Logistic Regression\n",
      "Accuracy: 0.8792\n",
      "Precision: 0.8813\n",
      "Recall: 0.8792\n",
      "F1: 0.8759\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, logreg_pred)\n",
    "precision = precision_score(y_test, logreg_pred, average='weighted', zero_division=1)\n",
    "recall = recall_score(y_test, logreg_pred, average='weighted')\n",
    "f1 = f1_score(y_test, logreg_pred, average='weighted')\n",
    "\n",
    "print(\"Name: Logistic Regression\")\n",
    "print(\"Accuracy: {:.4f}\".format(accuracy))\n",
    "print(\"Precision: {:.4f}\".format(precision))\n",
    "print(\"Recall: {:.4f}\".format(recall))\n",
    "print(\"F1: {:.4f}\".format(f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80f8e26f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51b95259",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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