SasiBonuA04.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datasets import load_dataset\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_en_hi = load_dataset(\"open_subtitles\", \"en-hi\")\n",
    "dataset_da_ru = load_dataset(\"open_subtitles\", \"da-ru\")\n",
    "data_en_hi = dataset_en_hi[\"train\"]\n",
    "data_da_ru = dataset_da_ru[\"train\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': ['0', '1'],\n",
       " 'meta': [{'year': 1948,\n",
       "   'imdbId': 40522,\n",
       "   'subtitleId': {'en': 4180294, 'hi': 4239106},\n",
       "   'sentenceIds': {'en': [1], 'hi': [1]}},\n",
       "  {'year': 1948,\n",
       "   'imdbId': 40522,\n",
       "   'subtitleId': {'en': 4180294, 'hi': 4239106},\n",
       "   'sentenceIds': {'en': [2], 'hi': [2]}}],\n",
       " 'translation': [{'en': 'THE BICYCLE THIEF', 'hi': 'साइकिल चोर'},\n",
       "  {'en': 'Ricci?', 'hi': 'रिच्ची?'}]}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_en_hi[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_en_hi = data_en_hi[:len(data_en_hi)//4]  # Get the first half of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "english_subtitles = [item['en'] for item in data_en_hi['translation']]\n",
    "hindi_translations = [item['hi'] for item in data_en_hi['translation']]\n",
    "merged_subtitles = english_subtitles + hindi_translations\n",
    "labels = [0] * len(english_subtitles) + [1] * len(hindi_translations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert text data into numerical features using CountVectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "X = vectorizer.fit_transform(merged_subtitles)\n",
    "y = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9756\n",
      "Confusion Matrix:\n",
      "[[17835   660]\n",
      " [  246 18466]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=1, kernel='linear')\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluate model performance (e.g., accuracy)\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_l1 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9726\n",
      "Confusion Matrix:\n",
      "[[17776   719]\n",
      " [  301 18411]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=1, kernel='rbf') \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_r1 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_r1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9321\n",
      "Confusion Matrix:\n",
      "[[16087  2408]\n",
      " [  117 18595]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=1, kernel='poly', degree=3)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_p1 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_p1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9746\n",
      "Confusion Matrix:\n",
      "[[17923   572]\n",
      " [  374 18338]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = SVC(C=2, kernel='rbf')  \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_r2 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_r2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9402\n",
      "Confusion Matrix:\n",
      "[[16395  2100]\n",
      " [  126 18586]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=2, kernel='poly', degree=3) \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_p2 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_p2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9746\n",
      "Confusion Matrix:\n",
      "[[17797   698]\n",
      " [  247 18465]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=2, kernel='linear') \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_l2 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9732\n",
      "Confusion Matrix:\n",
      "[[17909   586]\n",
      " [  410 18302]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = SVC(C=5, kernel='rbf')   \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_r5 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_r5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9482\n",
      "Confusion Matrix:\n",
      "[[16713  1782]\n",
      " [  146 18566]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=5, kernel='poly', degree=3)   \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_p5 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_p5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy on testing data: 0.9740\n",
      "Confusion Matrix:\n",
      "[[17777   718]\n",
      " [  251 18461]]\n"
     ]
    }
   ],
   "source": [
    "model = SVC(C=5, kernel='linear')   \n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    " \n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Model accuracy on testing data: {accuracy:.4f}\")\n",
    "\n",
    "conf_matrix_l5 = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(conf_matrix_l5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 18 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "confusion_matrices = [ conf_matrix_l1, conf_matrix_l2, conf_matrix_l5, conf_matrix_r1, conf_matrix_r2, conf_matrix_r5, conf_matrix_p1, conf_matrix_p2, conf_matrix_p5]\n",
    "\n",
    "fig, axes = plt.subplots(3, 3, figsize=(15, 15))\n",
    "\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i, cm in enumerate(confusion_matrices):\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[i])\n",
    "    axes[i].set_title(f'Confusion Matrix {i+1}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}