hmm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- sarthak gupta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from hmmlearn import hmm\n",
    "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "data = pd.read_csv(\"data/processed_data_full.csv\")\n",
    "\n",
    "# Extract columns\n",
    "file_names = data.iloc[:, 0]  # First column: file names\n",
    "features = data.iloc[:, 1:26].values  # Middle columns: features\n",
    "labels = data.iloc[:, -1].values  # Last column: labels\n",
    "\n",
    "# Encode labels\n",
    "label_encoder = LabelEncoder()\n",
    "encoded_labels = label_encoder.fit_transform(labels)  # Encode labels into numbers\n",
    "\n",
    "# Train-Test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, encoded_labels, test_size=0.3, random_state=42, stratify=encoded_labels)\n",
    "\n",
    "# Normalize features (after splitting to avoid data leakage)\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Grid Search...\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=1, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 7/9 models trained.\n",
      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 93.25%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=1, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 73.47%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=1, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 7/9 models trained.\n",
      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 67.88%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=2, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 55.87%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=2, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 50.64%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=2, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Model is not converging.  Current: -427250.8497587363 is not greater than -427250.8494382065. Delta is -0.00032052979804575443\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 48.04%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=3, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 44.91%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=3, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 41.77%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=3, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 42.00%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=4, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 40.82%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=4, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
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      "  Model Accuracy: 41.80%\n",
      "\n",
      "Training models with: hmm_type=GMMHMM, n_components=4, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 40.95%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=1, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 93.25%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=1, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 73.47%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=1, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 67.88%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=2, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 55.87%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=2, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
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      "  Model Accuracy: 50.64%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=2, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
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      "  Model Accuracy: 48.04%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=3, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 44.91%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=3, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
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      "  Model Accuracy: 41.77%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=3, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 42.00%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=4, covariance_type=full\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
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      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 44.41%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=4, covariance_type=diag\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
      "  Progress: 4/9 models trained.\n",
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      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 41.80%\n",
      "\n",
      "Training models with: hmm_type=GaussianHMM, n_components=4, covariance_type=spherical\n",
      "  Progress: 1/9 models trained.\n",
      "  Progress: 2/9 models trained.\n",
      "  Progress: 3/9 models trained.\n",
      "  Progress: 4/9 models trained.\n",
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      "  Progress: 8/9 models trained.\n",
      "  Progress: 9/9 models trained.\n",
      "  Model Accuracy: 40.95%\n",
      "\n",
      "\n",
      "Grid Search Completed!\n",
      "Best Accuracy: 93.25%\n",
      "Best Parameters: {'hmm_type': 'GMMHMM', 'n_components': 1, 'covariance_type': 'full'}\n",
      "\n",
      "Grid Search Results:\n",
      "       hmm_type  n_components covariance_type  accuracy\n",
      "0        GMMHMM             1            full  0.932491\n",
      "1        GMMHMM             1            diag  0.734693\n",
      "2        GMMHMM             1       spherical  0.678823\n",
      "3        GMMHMM             2            full  0.558681\n",
      "4        GMMHMM             2            diag  0.506442\n",
      "5        GMMHMM             2       spherical  0.480440\n",
      "6        GMMHMM             3            full  0.449138\n",
      "7        GMMHMM             3            diag  0.417718\n",
      "8        GMMHMM             3       spherical  0.420032\n",
      "9        GMMHMM             4            full  0.408202\n",
      "10       GMMHMM             4            diag  0.418026\n",
      "11       GMMHMM             4       spherical  0.409549\n",
      "12  GaussianHMM             1            full  0.932491\n",
      "13  GaussianHMM             1            diag  0.734693\n",
      "14  GaussianHMM             1       spherical  0.678823\n",
      "15  GaussianHMM             2            full  0.558681\n",
      "16  GaussianHMM             2            diag  0.506427\n",
      "17  GaussianHMM             2       spherical  0.480425\n",
      "18  GaussianHMM             3            full  0.449138\n",
      "19  GaussianHMM             3            diag  0.417718\n",
      "20  GaussianHMM             3       spherical  0.419958\n",
      "21  GaussianHMM             4            full  0.444145\n",
      "22  GaussianHMM             4            diag  0.418026\n",
      "23  GaussianHMM             4       spherical  0.409534\n"
     ]
    }
   ],
   "source": [
    "# Define grid search parameters\n",
    "n_components_range = [1,2,3,4]  # Number of hidden states\n",
    "hmm_types = ['GMMHMM', 'GaussianHMM'] \n",
    "covariance_types = ['full', 'diag', 'spherical']  # Covariance types (only for Gaussian and GMM HMMs)\n",
    "n_iter = 100  # Fixed number of iterations\n",
    "\n",
    "# Initialize variables to store the best model and accuracies\n",
    "best_accuracy = 0\n",
    "best_params = {}\n",
    "results = []\n",
    "\n",
    "# Define function for predicting language\n",
    "def predict_language(models, test_features):\n",
    "    max_log_likelihood = float('-inf')\n",
    "    predicted_language = None\n",
    "    for lang, model in models.items():\n",
    "        try:\n",
    "            log_likelihood = model.score(test_features)\n",
    "            if log_likelihood > max_log_likelihood:\n",
    "                max_log_likelihood = log_likelihood\n",
    "                predicted_language = lang\n",
    "        except:\n",
    "            pass  # Handle any scoring errors gracefully\n",
    "    return predicted_language\n",
    "\n",
    "# Perform grid search\n",
    "print(\"Starting Grid Search...\\n\")\n",
    "for hmm_type in hmm_types:\n",
    "    for n_components in n_components_range:\n",
    "        for covariance_type in covariance_types:\n",
    "            print(f\"Training models with: hmm_type={hmm_type}, \"\n",
    "                  f\"n_components={n_components}, covariance_type={covariance_type}\")\n",
    "            \n",
    "            # Initialize models dictionary for current configuration\n",
    "            models = {}\n",
    "            progress = 0\n",
    "            \n",
    "            # Train HMM models for each language\n",
    "            for lang in np.unique(y_train):\n",
    "                # Get features corresponding to the current language\n",
    "                lang_features = X_train[y_train == lang]\n",
    "\n",
    "                # Create HMM model\n",
    "                if hmm_type == 'GMMHMM':\n",
    "                    model = hmm.GMMHMM(n_components=n_components, \n",
    "                                            covariance_type=covariance_type, \n",
    "                                            n_iter=n_iter, \n",
    "                                            random_state=42)\n",
    "                    \n",
    "                elif hmm_type == 'GaussianHMM':\n",
    "                    model = hmm.GaussianHMM(n_components=n_components, \n",
    "                                               covariance_type=covariance_type, \n",
    "                                               n_iter=n_iter, \n",
    "                                               random_state=42)\n",
    "                \n",
    "                elif hmm_type == 'MultinomialHMM':\n",
    "                    model = hmm.MultinomialHMM(n_components=n_components, \n",
    "                                               n_iter=n_iter, \n",
    "                                               random_state=42)\n",
    "                elif hmm_type == 'PoissonHMM':\n",
    "                    model = hmm.PoissonHMM(n_components=n_components, \n",
    "                                           n_iter=n_iter, \n",
    "                                           random_state=42)\n",
    "                \n",
    "                # Train the model\n",
    "                model.fit(lang_features)\n",
    "                models[lang] = model\n",
    "                progress += 1\n",
    "                print(f\"  Progress: {progress}/{len(np.unique(y_train))} models trained.\")\n",
    "            \n",
    "            # Evaluate on test data\n",
    "            correct = 0\n",
    "            for i in range(len(X_test)):\n",
    "                predicted_lang = predict_language(models, [X_test[i]])\n",
    "                if predicted_lang == y_test[i]:\n",
    "                    correct += 1\n",
    "\n",
    "            accuracy = correct / len(X_test)\n",
    "            print(f\"  Model Accuracy: {accuracy * 100:.2f}%\\n\")\n",
    "            \n",
    "            # Store results\n",
    "            results.append({\n",
    "                'hmm_type': hmm_type,\n",
    "                'n_components': n_components,\n",
    "                'covariance_type': covariance_type,\n",
    "                'accuracy': accuracy\n",
    "            })\n",
    "            \n",
    "            # Update best model if current is better\n",
    "            if accuracy > best_accuracy:\n",
    "                best_accuracy = accuracy\n",
    "                best_params = {\n",
    "                    'hmm_type': hmm_type,\n",
    "                    'n_components': n_components,\n",
    "                    'covariance_type': covariance_type\n",
    "                }\n",
    "\n",
    "# Display final results\n",
    "print(\"\\nGrid Search Completed!\")\n",
    "print(f\"Best Accuracy: {best_accuracy * 100:.2f}%\")\n",
    "print(f\"Best Parameters: {best_params}\")\n",
    "\n",
    "# Convert results to DataFrame and display as a table\n",
    "results_df = pd.DataFrame(results)\n",
    "print(\"\\nGrid Search Results:\")\n",
    "print(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class GaussianHMM_Collective:\n",
    "    def __init__(self, n_components=1, covariance_type='full', n_iter=100):\n",
    "        self.n_components = n_components\n",
    "        self.covariance_type = covariance_type\n",
    "        self.n_iter = n_iter\n",
    "        self.models = {}\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        for lang in np.unique(y):\n",
    "            lang_features = X[y == lang]\n",
    "            model = hmm.GaussianHMM(n_components=self.n_components, \n",
    "                                covariance_type=self.covariance_type, \n",
    "                                n_iter=self.n_iter, \n",
    "                                random_state=42)\n",
    "            model.fit(lang_features)\n",
    "            self.models[lang] = model\n",
    "    \n",
    "    def predict(self, X):\n",
    "        predictions = []\n",
    "        for i in range(len(X)):\n",
    "            max_log_likelihood = float('-inf')\n",
    "            predicted_language = None\n",
    "            for lang, model in self.models.items():\n",
    "                try:\n",
    "                    log_likelihood = model.score([X[i]])\n",
    "                    if log_likelihood > max_log_likelihood:\n",
    "                        max_log_likelihood = log_likelihood\n",
    "                        predicted_language = lang\n",
    "                except:\n",
    "                    pass\n",
    "            predictions.append(predicted_language)\n",
    "        return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "gaussian_hmm = GaussianHMM_Collective(n_components=1, covariance_type='full', n_iter=100)\n",
    "gaussian_hmm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 93.25%\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Bengali       0.95      0.94      0.94      8164\n",
      "    Gujarati       0.90      0.93      0.92      7868\n",
      "       Hindi       0.96      0.98      0.97      7636\n",
      "     Kannada       0.98      0.88      0.92      6488\n",
      "   Malayalam       0.90      0.97      0.93      7200\n",
      "     Marathi       0.94      0.90      0.92      7426\n",
      "       Tamil       0.95      0.97      0.96      7258\n",
      "      Telugu       0.97      0.96      0.97      7096\n",
      "        Urdu       0.88      0.87      0.87      9166\n",
      "\n",
      "    accuracy                           0.93     68302\n",
      "   macro avg       0.94      0.93      0.93     68302\n",
      "weighted avg       0.93      0.93      0.93     68302\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = gaussian_hmm.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "\n",
    "# Display classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred, target_names=label_encoder.classes_))\n",
    "\n",
    "# Display confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix (GaussianHMM)')\n",
    "plt.xticks(np.arange(len(label_encoder.classes_)), label_encoder.classes_, rotation=45)\n",
    "plt.yticks(np.arange(len(label_encoder.classes_)), label_encoder.classes_, rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/gaussian_hmm.pkl', 'wb') as file:\n",
    "    pickle.dump(gaussian_hmm, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"data/data_train.csv\")\n",
    "test = pd.read_csv(\"data/data_test.csv\")\n",
    "\n",
    "X_train = train.drop('class', axis=1)\n",
    "y_train = train['class']\n",
    "\n",
    "X_test = test.drop('class', axis=1)\n",
    "y_test = test['class']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "lbe = LabelEncoder()\n",
    "y_train = lbe.fit_transform(y_train)\n",
    "y_test = lbe.transform(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "full_gaussian_hmm = GaussianHMM_Collective(n_components=1, covariance_type='full', n_iter=100)\n",
    "full_gaussian_hmm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.97%\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Bengali       0.94      0.97      0.95      8164\n",
      "    Gujarati       0.97      0.95      0.96      7868\n",
      "       Hindi       0.96      0.99      0.97      7636\n",
      "     Kannada       0.97      0.90      0.93      6488\n",
      "   Malayalam       0.93      0.96      0.95      7200\n",
      "     Marathi       0.94      0.93      0.93      7426\n",
      "       Tamil       0.97      0.98      0.97      7258\n",
      "      Telugu       0.98      0.98      0.98      7096\n",
      "        Urdu       0.91      0.90      0.90      9166\n",
      "\n",
      "    accuracy                           0.95     68302\n",
      "   macro avg       0.95      0.95      0.95     68302\n",
      "weighted avg       0.95      0.95      0.95     68302\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "full_y_pred = full_gaussian_hmm.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, full_y_pred)\n",
    "print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
    "\n",
    "# Display classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, full_y_pred, target_names=lbe.classes_))\n",
    "\n",
    "# Display confusion matrix\n",
    "cm = confusion_matrix(y_test, full_y_pred)\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix (Full GaussianHMM)')\n",
    "plt.xticks(np.arange(len(lbe.classes_)), lbe.classes_, rotation=45)\n",
    "plt.yticks(np.arange(len(lbe.classes_)), lbe.classes_, rotation=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/full_gaussian_hmm.pkl', 'wb') as file:\n",
    "    pickle.dump(full_gaussian_hmm, file)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}