{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DA4QCzEoJU-U",
        "outputId": "d143f122-422b-415f-c742-4b4c5b479802"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All packages installed successfully!\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "BLOCK 1: INSTALLATION & SETUP\n",
        "==============================\n",
        "Install required packages for the classification pipeline\n",
        "\"\"\"\n",
        "\n",
        "# Install required packages\n",
        "!pip install -q scikit-learn pandas numpy matplotlib seaborn wandb nltk pyarrow fastparquet\n",
        "\n",
        "# Download NLTK data\n",
        "import nltk\n",
        "nltk.download('stopwords', quiet=True)\n",
        "nltk.download('wordnet', quiet=True)\n",
        "\n",
        "print(\"All packages installed successfully!\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 2: IMPORT LIBRARIES\n",
        "=========================\n",
        "Import all necessary libraries for the classification pipeline\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "import json\n",
        "import re\n",
        "import pickle\n",
        "import time\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from pathlib import Path\n",
        "from typing import List, Dict, Tuple\n",
        "\n",
        "# Scikit-learn imports\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import (\n",
        "    classification_report,\n",
        "    confusion_matrix,\n",
        "    accuracy_score,\n",
        "    f1_score,\n",
        "    precision_recall_fscore_support\n",
        ")\n",
        "\n",
        "# NLP Processing\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Sparse matrix operations\n",
        "from scipy.sparse import hstack\n",
        "\n",
        "# Experiment tracking\n",
        "import wandb\n",
        "\n",
        "# Set random seed for reproducibility\n",
        "RANDOM_STATE = 42\n",
        "np.random.seed(RANDOM_STATE)\n",
        "\n",
        "print(\"All libraries imported successfully!\")\n",
        "print(f\"Random seed set to: {RANDOM_STATE}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkl6hPSodP--",
        "outputId": "94de3d1a-cea1-413c-f3f9-e0e96e0893d5"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "All libraries imported successfully!\n",
            "Random seed set to: 42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "\"\"\"\n",
        "BLOCK 3: UNZIP DATASET & SET DATA PATH\n",
        "=====================================\n",
        "Unzips dataset.zip, normalizes folder structure,\n",
        "and sets DATA_PATH for training/testing.\n",
        "\"\"\"\n",
        "\n",
        "ZIP_PATH = 'dataset.zip'\n",
        "TEMP_EXTRACT_DIR = './_extracted'\n",
        "FINAL_DATA_PATH = './math'\n",
        "SOURCE_DIR = os.path.join(TEMP_EXTRACT_DIR, 'MATH')\n",
        "\n",
        "# Step 1: Unzip dataset if needed\n",
        "if not os.path.exists(FINAL_DATA_PATH):\n",
        "    if not os.path.exists(TEMP_EXTRACT_DIR):\n",
        "        with zipfile.ZipFile(ZIP_PATH, 'r') as zip_ref:\n",
        "            zip_ref.extractall(TEMP_EXTRACT_DIR)\n",
        "        print(\" Dataset unzipped\")\n",
        "    else:\n",
        "        print(\" Dataset already extracted\")\n",
        "\n",
        "    # Step 2: Move MATH → math\n",
        "    if os.path.exists(SOURCE_DIR):\n",
        "        shutil.move(SOURCE_DIR, FINAL_DATA_PATH)\n",
        "        print(\" Dataset moved to ./math/\")\n",
        "    else:\n",
        "        raise FileNotFoundError(\" MATH folder not found inside dataset.zip\")\n",
        "\n",
        "    # Step 3: Cleanup temp files\n",
        "    shutil.rmtree(TEMP_EXTRACT_DIR)\n",
        "    print(\"🧹 Temporary extraction folder removed\")\n",
        "else:\n",
        "    print(\"ℹ./math/ already exists, skipping unzip\")\n",
        "\n",
        "# Step 4: Set DATA_PATH\n",
        "DATA_PATH = FINAL_DATA_PATH\n",
        "\n",
        "# Step 5: Sanity checks\n",
        "assert os.path.isdir(os.path.join(DATA_PATH, 'train')), \" train/ folder missing\"\n",
        "assert os.path.isdir(os.path.join(DATA_PATH, 'test')), \" test/ folder missing\"\n",
        "\n",
        "print(\"\\n🎯 Data path set successfully!\")\n",
        "print(f\"📁 DATA_PATH: {DATA_PATH}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7fd_0KDdQu_",
        "outputId": "048569f8-e578-4557-b59d-788bfb407480"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ℹ./math/ already exists, skipping unzip\n",
            "\n",
            "🎯 Data path set successfully!\n",
            "📁 DATA_PATH: ./math\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 4: CONVERT JSON TO PARQUET\n",
        "=================================\n",
        "Convert JSON files to Parquet format for faster loading\n",
        "\"\"\"\n",
        "\n",
        "def json_to_parquet(data_path: str):\n",
        "    \"\"\"Convert all JSON files to Parquet format\"\"\"\n",
        "    data_path = Path(data_path)\n",
        "\n",
        "    train_path = data_path / 'train'\n",
        "    test_path = data_path / 'test'\n",
        "\n",
        "    if not train_path.exists():\n",
        "        print(f\"ERROR: Train path {train_path} does not exist\")\n",
        "        return\n",
        "\n",
        "    topics = [d.name for d in train_path.iterdir()\n",
        "             if d.is_dir() and not d.name.startswith('.')]\n",
        "    topics = sorted(topics)\n",
        "\n",
        "    print(f\"Discovered {len(topics)} topics: {topics}\")\n",
        "    print(\"\\nConverting JSON to Parquet...\")\n",
        "\n",
        "    for split in ['train', 'test']:\n",
        "        split_path = data_path / split\n",
        "        data = []\n",
        "\n",
        "        print(f\"\\nProcessing {split} data...\")\n",
        "        for topic in topics:\n",
        "            topic_path = split_path / topic\n",
        "\n",
        "            if not topic_path.exists():\n",
        "                print(f\"  WARNING: {topic} directory not found in {split}\")\n",
        "                continue\n",
        "\n",
        "            json_files = list(topic_path.glob('*.json'))\n",
        "            print(f\"  {topic}: {len(json_files)} files\")\n",
        "\n",
        "            for json_file in json_files:\n",
        "                try:\n",
        "                    with open(json_file, 'r', encoding='utf-8') as f:\n",
        "                        problem_data = json.load(f)\n",
        "                        problem_data['topic'] = topic\n",
        "                        problem_data['split'] = split\n",
        "                        data.append(problem_data)\n",
        "                except Exception as e:\n",
        "                    print(f\"  ERROR loading {json_file}: {e}\")\n",
        "\n",
        "        df = pd.DataFrame(data)\n",
        "        parquet_file = data_path / f'{split}.parquet'\n",
        "        df.to_parquet(parquet_file, engine='pyarrow', compression='snappy')\n",
        "\n",
        "        print(f\"  Saved {len(df)} samples to {parquet_file}\")\n",
        "\n",
        "    print(\"\\nConversion complete!\")\n",
        "\n",
        "json_to_parquet(DATA_PATH)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NjcM8tWsip1l",
        "outputId": "e1ddd60d-18be-4547-aaed-5f321d976aaf"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Discovered 7 topics: ['algebra', 'counting_and_probability', 'geometry', 'intermediate_algebra', 'number_theory', 'prealgebra', 'precalculus']\n",
            "\n",
            "Converting JSON to Parquet...\n",
            "\n",
            "Processing train data...\n",
            "  algebra: 1744 files\n",
            "  counting_and_probability: 771 files\n",
            "  geometry: 870 files\n",
            "  intermediate_algebra: 1295 files\n",
            "  number_theory: 869 files\n",
            "  prealgebra: 1205 files\n",
            "  precalculus: 746 files\n",
            "  Saved 7500 samples to math/train.parquet\n",
            "\n",
            "Processing test data...\n",
            "  algebra: 1187 files\n",
            "  counting_and_probability: 474 files\n",
            "  geometry: 479 files\n",
            "  intermediate_algebra: 903 files\n",
            "  number_theory: 540 files\n",
            "  prealgebra: 871 files\n",
            "  precalculus: 546 files\n",
            "  Saved 5000 samples to math/test.parquet\n",
            "\n",
            "Conversion complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 5: DATASET LOADER CLASS\n",
        "==============================\n",
        "Load dataset from Parquet files\n",
        "\"\"\"\n",
        "\n",
        "class MathDatasetLoader:\n",
        "    \"\"\"Load math question dataset from Parquet files\"\"\"\n",
        "\n",
        "    def __init__(self, data_path: str):\n",
        "        self.data_path = Path(data_path)\n",
        "        self.train_file = self.data_path / 'train.parquet'\n",
        "        self.test_file = self.data_path / 'test.parquet'\n",
        "\n",
        "    def load_all_data(self) -> Tuple[pd.DataFrame, pd.DataFrame]:\n",
        "        \"\"\"Load both train and test datasets from Parquet files\"\"\"\n",
        "        print(\"=\"*80)\n",
        "        print(\"LOADING DATASET FROM PARQUET FILES\")\n",
        "        print(\"=\"*80)\n",
        "\n",
        "        print(f\"\\nLoading training data from {self.train_file}\")\n",
        "        train_df = pd.read_parquet(self.train_file)\n",
        "        print(f\"Training samples loaded: {len(train_df)}\")\n",
        "\n",
        "        print(f\"\\nLoading test data from {self.test_file}\")\n",
        "        test_df = pd.read_parquet(self.test_file)\n",
        "        print(f\"Test samples loaded: {len(test_df)}\")\n",
        "\n",
        "        return train_df, test_df\n",
        "\n",
        "    def explore_dataset(self, train_df: pd.DataFrame, test_df: pd.DataFrame):\n",
        "        \"\"\"Display dataset statistics\"\"\"\n",
        "        print(\"\\n\" + \"=\"*80)\n",
        "        print(\"DATASET STATISTICS\")\n",
        "        print(\"=\"*80)\n",
        "\n",
        "        print(f\"\\nTraining set shape: {train_df.shape}\")\n",
        "        print(f\"Test set shape: {test_df.shape}\")\n",
        "\n",
        "        print(\"\\n--- Training Set Class Distribution ---\")\n",
        "        train_dist = train_df['topic'].value_counts()\n",
        "        print(train_dist)\n",
        "\n",
        "        print(\"\\n--- Test Set Class Distribution ---\")\n",
        "        test_dist = test_df['topic'].value_counts()\n",
        "        print(test_dist)\n",
        "\n",
        "        # Visualizations\n",
        "        fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "        train_dist.plot(kind='bar', ax=axes[0], color='steelblue')\n",
        "        axes[0].set_title('Training Set Distribution', fontsize=14, fontweight='bold')\n",
        "        axes[0].set_xlabel('Topic')\n",
        "        axes[0].set_ylabel('Count')\n",
        "        axes[0].tick_params(axis='x', rotation=45)\n",
        "        axes[0].grid(axis='y', alpha=0.3)\n",
        "\n",
        "        test_dist.plot(kind='bar', ax=axes[1], color='coral')\n",
        "        axes[1].set_title('Test Set Distribution', fontsize=14, fontweight='bold')\n",
        "        axes[1].set_xlabel('Topic')\n",
        "        axes[1].set_ylabel('Count')\n",
        "        axes[1].tick_params(axis='x', rotation=45)\n",
        "        axes[1].grid(axis='y', alpha=0.3)\n",
        "\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "        return train_dist, test_dist\n",
        "\n",
        "print(\"MathDatasetLoader class defined!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7NUJv4TitJq",
        "outputId": "2e1d7f99-8eab-4c1d-ed35-3fb5642452c6"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MathDatasetLoader class defined!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 6: FEATURE EXTRACTION CLASS\n",
        "==================================\n",
        "Extract features from mathematical text\n",
        "\"\"\"\n",
        "\n",
        "class MathFeatureExtractor:\n",
        "    \"\"\"Extract features from math problems\"\"\"\n",
        "\n",
        "    def __init__(self):\n",
        "        self.lemmatizer = WordNetLemmatizer()\n",
        "        self.stop_words = set(stopwords.words('english'))\n",
        "\n",
        "    def clean_latex(self, text: str) -> str:\n",
        "        \"\"\"Remove or simplify LaTeX commands\"\"\"\n",
        "        text = re.sub(r'\\\\[a-zA-Z]+\\{([^}]*)\\}', r'\\1', text)\n",
        "        text = re.sub(r'\\\\[a-zA-Z]+', ' ', text)\n",
        "        text = re.sub(r'[\\{\\}\\$\\\\]', ' ', text)\n",
        "        return text\n",
        "\n",
        "    def extract_math_symbols(self, text: str) -> Dict[str, int]:\n",
        "        \"\"\"Extract mathematical symbols as binary features\"\"\"\n",
        "        symbols = {\n",
        "            'has_fraction': int('frac' in text or '/' in text),\n",
        "            'has_sqrt': int('sqrt' in text or '√' in text),\n",
        "            'has_exponent': int('^' in text or 'pow' in text),\n",
        "            'has_integral': int('int' in text or '∫' in text),\n",
        "            'has_derivative': int(\"'\" in text or 'prime' in text),\n",
        "            'has_summation': int('sum' in text or '∑' in text),\n",
        "            'has_pi': int('pi' in text or 'π' in text),\n",
        "            'has_trigonometric': int(any(t in text.lower() for t in ['sin', 'cos', 'tan'])),\n",
        "            'has_inequality': int(any(s in text for s in ['<', '>', 'leq', 'geq', '≤', '≥'])),\n",
        "            'has_absolute': int('abs' in text or '|' in text),\n",
        "        }\n",
        "        return symbols\n",
        "\n",
        "    def extract_numeric_features(self, text: str) -> Dict[str, float]:\n",
        "        \"\"\"Extract numeric features from text\"\"\"\n",
        "        numbers = re.findall(r'-?\\d+\\.?\\d*', text)\n",
        "        return {\n",
        "            'num_count': len(numbers),\n",
        "            'has_large_numbers': int(any(float(n) > 100 for n in numbers if n)),\n",
        "            'has_decimals': int(any('.' in n for n in numbers)),\n",
        "            'has_negatives': int(any(n.startswith('-') for n in numbers)),\n",
        "            'avg_number': np.mean([float(n) for n in numbers]) if numbers else 0,\n",
        "        }\n",
        "\n",
        "    def preprocess_text(self, text: str) -> str:\n",
        "        \"\"\"Clean and preprocess text\"\"\"\n",
        "        text = self.clean_latex(text)\n",
        "        text = text.lower()\n",
        "        text = re.sub(r'[^a-zA-Z0-9\\s]', ' ', text)\n",
        "        words = text.split()\n",
        "        words = [self.lemmatizer.lemmatize(w) for w in words\n",
        "                if w not in self.stop_words and len(w) > 2]\n",
        "        return ' '.join(words)\n",
        "\n",
        "print(\"MathFeatureExtractor class defined!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8rMhYurivUS",
        "outputId": "cc430067-3c8a-4d1d-9a13-b41e2e2a0f33"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MathFeatureExtractor class defined!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 7: CLASSIFIER CLASS\n",
        "=========================\n",
        "Train and evaluate classification models\n",
        "\"\"\"\n",
        "\n",
        "class MathQuestionClassifier:\n",
        "    \"\"\"Train and evaluate classification models\"\"\"\n",
        "\n",
        "    def __init__(self, use_wandb: bool = False):\n",
        "        self.feature_extractor = MathFeatureExtractor()\n",
        "        self.label_encoder = LabelEncoder()\n",
        "        self.best_model = None\n",
        "        self.vectorizer = None\n",
        "        self.scaler = None\n",
        "        self.use_wandb = use_wandb\n",
        "\n",
        "        self.vectorizer_config = {\n",
        "            'max_features': 5000,\n",
        "            'ngram_range': (1, 3),\n",
        "            'min_df': 2,\n",
        "            'max_df': 0.95,\n",
        "            'sublinear_tf': True\n",
        "        }\n",
        "\n",
        "    def prepare_features(self, df: pd.DataFrame) -> Tuple[pd.DataFrame, np.ndarray]:\n",
        "        \"\"\"Prepare features WITHOUT vectorization\"\"\"\n",
        "        print(\"\\n\" + \"=\"*80)\n",
        "        print(\"FEATURE ENGINEERING\")\n",
        "        print(\"=\"*80)\n",
        "\n",
        "        print(\"\\nPreprocessing text...\")\n",
        "        df['processed_text'] = df['problem'].apply(\n",
        "            self.feature_extractor.preprocess_text\n",
        "        )\n",
        "\n",
        "        print(\"Extracting mathematical symbol features...\")\n",
        "        math_symbols = df['problem'].apply(\n",
        "            self.feature_extractor.extract_math_symbols\n",
        "        )\n",
        "\n",
        "        print(\"Extracting numeric features...\")\n",
        "        numeric_features = df['problem'].apply(\n",
        "            self.feature_extractor.extract_numeric_features\n",
        "        )\n",
        "\n",
        "        symbol_df = pd.DataFrame(list(math_symbols))\n",
        "        numeric_df = pd.DataFrame(list(numeric_features))\n",
        "\n",
        "        additional_features = np.hstack([\n",
        "            symbol_df.values,\n",
        "            numeric_df.values\n",
        "        ])\n",
        "\n",
        "        for i, col in enumerate(list(symbol_df.columns) + list(numeric_df.columns)):\n",
        "            df[f'feature_{col}'] = additional_features[:, i]\n",
        "\n",
        "        y = self.label_encoder.fit_transform(df['topic'])\n",
        "\n",
        "        print(f\"\\nProcessed {len(df)} samples\")\n",
        "        print(f\"Number of classes: {len(np.unique(y))}\")\n",
        "\n",
        "        return df, y\n",
        "\n",
        "    def vectorize_features(self, train_df: pd.DataFrame, test_df: pd.DataFrame = None):\n",
        "        \"\"\"Vectorize features with proper train/test separation\"\"\"\n",
        "        print(\"\\n--- TF-IDF Vectorization ---\")\n",
        "\n",
        "        self.vectorizer = TfidfVectorizer(**self.vectorizer_config)\n",
        "\n",
        "        X_train_text = self.vectorizer.fit_transform(train_df['processed_text'])\n",
        "        print(f\"Training TF-IDF shape: {X_train_text.shape}\")\n",
        "        print(f\"Vocabulary size: {len(self.vectorizer.get_feature_names_out())}\")\n",
        "\n",
        "        feature_cols = [c for c in train_df.columns if c.startswith('feature_')]\n",
        "        X_train_additional = train_df[feature_cols].values\n",
        "\n",
        "        self.scaler = MinMaxScaler()\n",
        "        X_train_additional_scaled = self.scaler.fit_transform(X_train_additional)\n",
        "\n",
        "        X_train = hstack([X_train_text, X_train_additional_scaled])\n",
        "\n",
        "        if test_df is not None:\n",
        "            X_test_text = self.vectorizer.transform(test_df['processed_text'])\n",
        "            X_test_additional = test_df[feature_cols].values\n",
        "            X_test_additional_scaled = self.scaler.transform(X_test_additional)\n",
        "            X_test = hstack([X_test_text, X_test_additional_scaled])\n",
        "            print(f\"Test TF-IDF shape: {X_test_text.shape}\")\n",
        "            return X_train, X_test\n",
        "\n",
        "        return X_train\n",
        "\n",
        "    def train_models(self, X_train, y_train, X_test, y_test, dataset_info: dict = None):\n",
        "        \"\"\"Train and compare models\"\"\"\n",
        "        print(\"\\n\" + \"=\"*80)\n",
        "        print(\"MODEL TRAINING\")\n",
        "        print(\"=\"*80)\n",
        "\n",
        "        n_samples = X_train.shape[0]\n",
        "        n_features = X_train.shape[1]\n",
        "        n_classes = len(np.unique(y_train))\n",
        "\n",
        "        print(f\"\\nDataset Statistics:\")\n",
        "        print(f\"   Training samples: {n_samples}\")\n",
        "        print(f\"   Features: {n_features}\")\n",
        "        print(f\"   Classes: {n_classes}\")\n",
        "\n",
        "        models = {\n",
        "            'Naive Bayes': MultinomialNB(alpha=0.1, fit_prior=True),\n",
        "            'Logistic Regression': LogisticRegression(\n",
        "                max_iter=1000, C=1.0, solver='saga',\n",
        "                random_state=RANDOM_STATE, class_weight='balanced', n_jobs=-1\n",
        "            ),\n",
        "            'SVM': SVC(\n",
        "                kernel='linear', C=1.0, random_state=RANDOM_STATE,\n",
        "                class_weight='balanced', probability=True\n",
        "            ),\n",
        "            'Random Forest': RandomForestClassifier(\n",
        "                n_estimators=200, max_depth=30, min_samples_split=5,\n",
        "                min_samples_leaf=2, max_features='sqrt',\n",
        "                random_state=RANDOM_STATE, class_weight='balanced',\n",
        "                n_jobs=-1, bootstrap=True\n",
        "            ),\n",
        "            'Gradient Boosting': GradientBoostingClassifier(\n",
        "                n_estimators=100, learning_rate=0.1, max_depth=7,\n",
        "                min_samples_split=5, min_samples_leaf=2,\n",
        "                subsample=0.8, max_features='sqrt',\n",
        "                random_state=RANDOM_STATE\n",
        "            )\n",
        "        }\n",
        "\n",
        "        results = {}\n",
        "\n",
        "        for name, model in models.items():\n",
        "            print(f\"\\nTraining {name}...\")\n",
        "\n",
        "            if self.use_wandb:\n",
        "                run = wandb.init(project=\"math-question-classifier\", name=name, reinit=True)\n",
        "                wandb.config.update({\n",
        "                    \"model_name\": name,\n",
        "                    \"train_size\": X_train.shape[0],\n",
        "                    \"n_features\": X_train.shape[1],\n",
        "                    \"n_classes\": n_classes\n",
        "                })\n",
        "\n",
        "            start_time = time.time()\n",
        "            model.fit(X_train, y_train)\n",
        "            training_time = time.time() - start_time\n",
        "\n",
        "            y_pred = model.predict(X_test)\n",
        "\n",
        "            accuracy = accuracy_score(y_test, y_pred)\n",
        "            f1 = f1_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "            results[name] = {\n",
        "                'model': model,\n",
        "                'accuracy': accuracy,\n",
        "                'f1_score': f1,\n",
        "                'training_time': training_time,\n",
        "                'predictions': y_pred\n",
        "            }\n",
        "\n",
        "            print(f\"   Accuracy: {accuracy:.4f}\")\n",
        "            print(f\"   F1 Score: {f1:.4f}\")\n",
        "            print(f\"   Training Time: {training_time:.2f}s\")\n",
        "\n",
        "            if self.use_wandb:\n",
        "                wandb.log({'accuracy': accuracy, 'f1_score': f1, 'training_time': training_time})\n",
        "                wandb.finish()\n",
        "\n",
        "        best_name = max(results, key=lambda x: results[x]['f1_score'])\n",
        "        self.best_model = results[best_name]['model']\n",
        "\n",
        "        print(f\"\\n{'='*80}\")\n",
        "        print(f\"BEST MODEL: {best_name}\")\n",
        "        print(f\"F1 Score: {results[best_name]['f1_score']:.4f}\")\n",
        "        print(f\"{'='*80}\")\n",
        "\n",
        "        return results, best_name\n",
        "\n",
        "    def evaluate_model(self, model, X_test, y_test, model_name: str):\n",
        "        \"\"\"Detailed evaluation\"\"\"\n",
        "        y_pred = model.predict(X_test)\n",
        "\n",
        "        print(f\"\\n{'='*80}\")\n",
        "        print(f\"EVALUATION: {model_name}\")\n",
        "        print(f\"{'='*80}\")\n",
        "\n",
        "        print(\"\\n--- Classification Report ---\")\n",
        "        print(classification_report(\n",
        "            y_test, y_pred,\n",
        "            target_names=self.label_encoder.classes_,\n",
        "            digits=4\n",
        "        ))\n",
        "\n",
        "        cm = confusion_matrix(y_test, y_pred)\n",
        "        plt.figure(figsize=(12, 10))\n",
        "        sns.heatmap(\n",
        "            cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=self.label_encoder.classes_,\n",
        "            yticklabels=self.label_encoder.classes_,\n",
        "            cbar_kws={'label': 'Count'}\n",
        "        )\n",
        "        plt.title(f'Confusion Matrix - {model_name}', fontsize=16, fontweight='bold')\n",
        "        plt.ylabel('True Label', fontsize=12)\n",
        "        plt.xlabel('Predicted Label', fontsize=12)\n",
        "        plt.xticks(rotation=45, ha='right')\n",
        "        plt.yticks(rotation=0)\n",
        "        plt.tight_layout()\n",
        "        plt.show()\n",
        "\n",
        "        precision, recall, f1, support = precision_recall_fscore_support(\n",
        "            y_test, y_pred, labels=np.unique(y_test)\n",
        "        )\n",
        "\n",
        "        metrics_df = pd.DataFrame({\n",
        "            'Class': self.label_encoder.classes_,\n",
        "            'Precision': precision,\n",
        "            'Recall': recall,\n",
        "            'F1-Score': f1,\n",
        "            'Support': support\n",
        "        })\n",
        "\n",
        "        print(\"\\n--- Per-Class Metrics ---\")\n",
        "        print(metrics_df.to_string(index=False))\n",
        "\n",
        "        return metrics_df\n",
        "\n",
        "print(\"MathQuestionClassifier class defined!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FqK4p0cJiw9v",
        "outputId": "c1c73ce9-db23-4994-f1bc-adfe9fd3cea4"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MathQuestionClassifier class defined!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 8: LOAD DATASET\n",
        "=====================\n",
        "Load pre-split train and test datasets\n",
        "\"\"\"\n",
        "\n",
        "loader = MathDatasetLoader(DATA_PATH)\n",
        "train_df, test_df = loader.load_all_data()\n",
        "\n",
        "if len(train_df) == 0 or len(test_df) == 0:\n",
        "    print(\"\\nERROR: No data loaded!\")\n",
        "else:\n",
        "    train_dist, test_dist = loader.explore_dataset(train_df, test_df)\n",
        "\n",
        "    print(f\"\\nDataset loaded successfully!\")\n",
        "    print(f\"   Training samples: {len(train_df)}\")\n",
        "    print(f\"   Test samples: {len(test_df)}\")\n",
        "    print(f\"   Number of topics: {train_df['topic'].nunique()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "19ajdOGQiyWH",
        "outputId": "886d0a8b-144b-4445-a186-07ce89b62805"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "LOADING DATASET FROM PARQUET FILES\n",
            "================================================================================\n",
            "\n",
            "Loading training data from math/train.parquet\n",
            "Training samples loaded: 7500\n",
            "\n",
            "Loading test data from math/test.parquet\n",
            "Test samples loaded: 5000\n",
            "\n",
            "================================================================================\n",
            "DATASET STATISTICS\n",
            "================================================================================\n",
            "\n",
            "Training set shape: (7500, 6)\n",
            "Test set shape: (5000, 6)\n",
            "\n",
            "--- Training Set Class Distribution ---\n",
            "topic\n",
            "algebra                     1744\n",
            "intermediate_algebra        1295\n",
            "prealgebra                  1205\n",
            "geometry                     870\n",
            "number_theory                869\n",
            "counting_and_probability     771\n",
            "precalculus                  746\n",
            "Name: count, dtype: int64\n",
            "\n",
            "--- Test Set Class Distribution ---\n",
            "topic\n",
            "algebra                     1187\n",
            "intermediate_algebra         903\n",
            "prealgebra                   871\n",
            "precalculus                  546\n",
            "number_theory                540\n",
            "geometry                     479\n",
            "counting_and_probability     474\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Dataset loaded successfully!\n",
            "   Training samples: 7500\n",
            "   Test samples: 5000\n",
            "   Number of topics: 7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 9: PREPARE FEATURES\n",
        "=========================\n",
        "Extract features from train and test datasets\n",
        "\"\"\"\n",
        "\n",
        "classifier = MathQuestionClassifier(use_wandb=False)\n",
        "\n",
        "print(\"\\nProcessing TRAINING set...\")\n",
        "train_df_features, y_train = classifier.prepare_features(train_df)\n",
        "\n",
        "print(\"\\nProcessing TEST set...\")\n",
        "test_df_features, y_test = classifier.prepare_features(test_df)\n",
        "\n",
        "print(f\"\\nFeature preparation complete!\")\n",
        "print(f\"   Training samples: {len(train_df_features)}\")\n",
        "print(f\"   Test samples: {len(test_df_features)}\")\n",
        "print(f\"   Number of classes: {len(np.unique(y_train))}\")\n",
        "print(f\"   Class labels: {classifier.label_encoder.classes_}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_nY91u-zwt6",
        "outputId": "f1494849-1a29-48c1-9e61-80038eb5e731"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Processing TRAINING set...\n",
            "\n",
            "================================================================================\n",
            "FEATURE ENGINEERING\n",
            "================================================================================\n",
            "\n",
            "Preprocessing text...\n",
            "Extracting mathematical symbol features...\n",
            "Extracting numeric features...\n",
            "\n",
            "Processed 7500 samples\n",
            "Number of classes: 7\n",
            "\n",
            "Processing TEST set...\n",
            "\n",
            "================================================================================\n",
            "FEATURE ENGINEERING\n",
            "================================================================================\n",
            "\n",
            "Preprocessing text...\n",
            "Extracting mathematical symbol features...\n",
            "Extracting numeric features...\n",
            "\n",
            "Processed 5000 samples\n",
            "Number of classes: 7\n",
            "\n",
            "Feature preparation complete!\n",
            "   Training samples: 7500\n",
            "   Test samples: 5000\n",
            "   Number of classes: 7\n",
            "   Class labels: ['algebra' 'counting_and_probability' 'geometry' 'intermediate_algebra'\n",
            " 'number_theory' 'prealgebra' 'precalculus']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 10: VECTORIZE FEATURES\n",
        "============================\n",
        "Create TF-IDF features\n",
        "\"\"\"\n",
        "\n",
        "X_train, X_test = classifier.vectorize_features(train_df_features, test_df_features)\n",
        "\n",
        "# Convert to CSR format for efficient operations\n",
        "X_train = X_train.tocsr()\n",
        "X_test = X_test.tocsr()\n",
        "\n",
        "print(f\"\\nVectorization complete!\")\n",
        "print(f\"   Training features: {X_train.shape}\")\n",
        "print(f\"   Test features: {X_test.shape}\")\n",
        "print(f\"   Format: CSR (Compressed Sparse Row)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pwK-NA8CzynS",
        "outputId": "941c54c4-42ac-45a9-e6f8-63336664d085"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- TF-IDF Vectorization ---\n",
            "Training TF-IDF shape: (7500, 5000)\n",
            "Vocabulary size: 5000\n",
            "Test TF-IDF shape: (5000, 5000)\n",
            "\n",
            "Vectorization complete!\n",
            "   Training features: (7500, 5015)\n",
            "   Test features: (5000, 5015)\n",
            "   Format: CSR (Compressed Sparse Row)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 11: TRAIN MODELS\n",
        "======================\n",
        "Train and compare classification models\n",
        "\"\"\"\n",
        "\n",
        "dataset_info = {\n",
        "    'total_samples': len(train_df) + len(test_df),\n",
        "    'train_samples': len(train_df),\n",
        "    'test_samples': len(test_df),\n",
        "    'n_topics': len(train_df['topic'].unique()),\n",
        "    'topics': list(train_df['topic'].unique())\n",
        "}\n",
        "\n",
        "results, best_name = classifier.train_models(\n",
        "    X_train, y_train, X_test, y_test, dataset_info\n",
        ")\n",
        "\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"MODEL COMPARISON\")\n",
        "print(\"=\"*80)\n",
        "\n",
        "comparison_df = pd.DataFrame({\n",
        "    'Model': list(results.keys()),\n",
        "    'Accuracy': [results[m]['accuracy'] for m in results],\n",
        "    'F1-Score': [results[m]['f1_score'] for m in results],\n",
        "    'Training Time (s)': [results[m]['training_time'] for m in results]\n",
        "}).sort_values('F1-Score', ascending=False)\n",
        "\n",
        "print(\"\\n\" + comparison_df.to_string(index=False))\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
        "\n",
        "axes[0].barh(comparison_df['Model'], comparison_df['F1-Score'], color='steelblue')\n",
        "axes[0].set_xlabel('F1-Score', fontsize=12)\n",
        "axes[0].set_title('Model Performance', fontsize=14, fontweight='bold')\n",
        "axes[0].set_xlim([0.6, 1.0])\n",
        "axes[0].grid(axis='x', alpha=0.3)\n",
        "\n",
        "axes[1].barh(comparison_df['Model'], comparison_df['Training Time (s)'], color='coral')\n",
        "axes[1].set_xlabel('Training Time (seconds)', fontsize=12)\n",
        "axes[1].set_title('Training Time', fontsize=14, fontweight='bold')\n",
        "axes[1].grid(axis='x', alpha=0.3)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(f\"\\nBest Model: {best_name}\")\n",
        "print(f\"   F1-Score: {results[best_name]['f1_score']:.4f}\")\n",
        "print(f\"   Accuracy: {results[best_name]['accuracy']:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "epZcARrdz0mM",
        "outputId": "f8b74eef-539b-4212-e364-7f6ccc512c9a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================================================================================\n",
            "MODEL TRAINING\n",
            "================================================================================\n",
            "\n",
            "Dataset Statistics:\n",
            "   Training samples: 7500\n",
            "   Features: 5015\n",
            "   Classes: 7\n",
            "\n",
            "Training Naive Bayes...\n",
            "   Accuracy: 0.6588\n",
            "   F1 Score: 0.6491\n",
            "   Training Time: 0.01s\n",
            "\n",
            "Training Logistic Regression...\n",
            "   Accuracy: 0.6930\n",
            "   F1 Score: 0.6892\n",
            "   Training Time: 15.84s\n",
            "\n",
            "Training SVM...\n",
            "   Accuracy: 0.7056\n",
            "   F1 Score: 0.7028\n",
            "   Training Time: 49.11s\n",
            "\n",
            "Training Random Forest...\n",
            "   Accuracy: 0.6500\n",
            "   F1 Score: 0.6430\n",
            "   Training Time: 3.19s\n",
            "\n",
            "Training Gradient Boosting...\n",
            "   Accuracy: 0.7044\n",
            "   F1 Score: 0.7040\n",
            "   Training Time: 5.12s\n",
            "\n",
            "================================================================================\n",
            "BEST MODEL: Gradient Boosting\n",
            "F1 Score: 0.7040\n",
            "================================================================================\n",
            "\n",
            "================================================================================\n",
            "MODEL COMPARISON\n",
            "================================================================================\n",
            "\n",
            "              Model  Accuracy  F1-Score  Training Time (s)\n",
            "  Gradient Boosting    0.7044  0.703957           5.117404\n",
            "                SVM    0.7056  0.702767          49.113254\n",
            "Logistic Regression    0.6930  0.689196          15.838561\n",
            "        Naive Bayes    0.6588  0.649144           0.014010\n",
            "      Random Forest    0.6500  0.642970           3.193031\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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HjyawBfDA4ExbAAAAAAAAALAQ7mkLAAAAAAAAABZCaAsAAAAAAAAAFkJoCwAAAAAAAAAWQmgLAAAAAAAAABZCaAsAAAAAAAAAFkJoCwAAAAAAAAAWQmgLAAAAAAAAABZCaAsAAAAAAAAAFkJoCwAAAAAAAAAW8v8Auo/vv9iNeFkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Best Model: Gradient Boosting\n",
            "   F1-Score: 0.7040\n",
            "   Accuracy: 0.7044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 12: DETAILED EVALUATION\n",
        "==============================\n",
        "Evaluate best model with detailed metrics\n",
        "\"\"\"\n",
        "\n",
        "if 'results' not in locals() or 'best_name' not in locals():\n",
        "    print(\"ERROR: Run Block 11 first!\")\n",
        "else:\n",
        "    best_model = results[best_name]['model']\n",
        "\n",
        "    metrics_df = classifier.evaluate_model(\n",
        "        best_model, X_test, y_test, best_name\n",
        "    )\n",
        "\n",
        "    if hasattr(best_model, 'feature_importances_'):\n",
        "        print(\"\\n\" + \"=\"*80)\n",
        "        print(\"FEATURE IMPORTANCE\")\n",
        "        print(\"=\"*80)\n",
        "\n",
        "        feature_names = list(classifier.vectorizer.get_feature_names_out())\n",
        "        manual_features = [c.replace('feature_', '') for c in train_df_features.columns\n",
        "                          if c.startswith('feature_')]\n",
        "        feature_names.extend(manual_features)\n",
        "\n",
        "        importances = best_model.feature_importances_\n",
        "        indices = np.argsort(importances)[-20:]\n",
        "\n",
        "        plt.figure(figsize=(10, 8))\n",
        "        plt.barh(range(len(indices)), importances[indices], color='forestgreen')\n",
        "        plt.yticks(range(len(indices)), [feature_names[i] for i in indices])\n",
        "        plt.xlabel('Importance Score', fontsize=12)\n",
        "        plt.title('Top 20 Features', fontsize=14, fontweight='bold')\n",
        "        plt.grid(axis='x', alpha=0.3)\n",
        "        plt.tight_layout()\n",
        "        plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4ZQ1iFg6z4-f",
        "outputId": "3d77198d-4fd4-4000-cb0f-aeea47441b9e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================================================================================\n",
            "EVALUATION: Gradient Boosting\n",
            "================================================================================\n",
            "\n",
            "--- Classification Report ---\n",
            "                          precision    recall  f1-score   support\n",
            "\n",
            "                 algebra     0.6452    0.7767    0.7049      1187\n",
            "counting_and_probability     0.8049    0.6962    0.7466       474\n",
            "                geometry     0.6940    0.7432    0.7177       479\n",
            "    intermediate_algebra     0.7828    0.7542    0.7682       903\n",
            "           number_theory     0.7347    0.7537    0.7441       540\n",
            "              prealgebra     0.5560    0.4960    0.5243       871\n",
            "             precalculus     0.8814    0.7216    0.7936       546\n",
            "\n",
            "                accuracy                         0.7044      5000\n",
            "               macro avg     0.7284    0.7059    0.7142      5000\n",
            "            weighted avg     0.7098    0.7044    0.7040      5000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "--- Per-Class Metrics ---\n",
            "                   Class  Precision   Recall  F1-Score  Support\n",
            "                 algebra   0.645206 0.776748  0.704893     1187\n",
            "counting_and_probability   0.804878 0.696203  0.746606      474\n",
            "                geometry   0.693957 0.743215  0.717742      479\n",
            "    intermediate_algebra   0.782759 0.754153  0.768190      903\n",
            "           number_theory   0.734657 0.753704  0.744059      540\n",
            "              prealgebra   0.555985 0.495982  0.524272      871\n",
            "             precalculus   0.881432 0.721612  0.793555      546\n",
            "\n",
            "================================================================================\n",
            "FEATURE IMPORTANCE\n",
            "================================================================================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "BLOCK 13: COMPLETE TEST SET ANALYSIS\n",
        "====================================\n",
        "Comprehensive evaluation on entire test set\n",
        "\"\"\"\n",
        "\n",
        "if 'results' not in locals() or 'best_name' not in locals():\n",
        "    print(\"ERROR: Run Block 11 first!\")\n",
        "else:\n",
        "    print(\"=\"*80)\n",
        "    print(\"COMPLETE TEST SET EVALUATION\")\n",
        "    print(\"=\"*80)\n",
        "\n",
        "    best_model = results[best_name]['model']\n",
        "\n",
        "    print(f\"\\nEvaluating on {len(y_test)} test samples...\")\n",
        "    y_pred_full = best_model.predict(X_test)\n",
        "\n",
        "    has_proba = hasattr(best_model, 'predict_proba')\n",
        "    if has_proba:\n",
        "        y_pred_proba = best_model.predict_proba(X_test)\n",
        "\n",
        "    overall_accuracy = accuracy_score(y_test, y_pred_full)\n",
        "    overall_f1 = f1_score(y_test, y_pred_full, average='weighted')\n",
        "\n",
        "    print(f\"\\n{'='*80}\")\n",
        "    print(f\"OVERALL PERFORMANCE\")\n",
        "    print(f\"{'='*80}\")\n",
        "    print(f\"Test Accuracy: {overall_accuracy:.4f} ({overall_accuracy*100:.2f}%)\")\n",
        "    print(f\"Test F1-Score: {overall_f1:.4f}\")\n",
        "    print(f\"Correct: {np.sum(y_pred_full == y_test)}/{len(y_test)}\")\n",
        "    print(f\"Incorrect: {np.sum(y_pred_full != y_test)}/{len(y_test)}\")\n",
        "\n",
        "    print(f\"\\n{'='*80}\")\n",
        "    print(f\"PER-CLASS PERFORMANCE\")\n",
        "    print(f\"{'='*80}\")\n",
        "\n",
        "    class_report = classification_report(\n",
        "        y_test, y_pred_full,\n",
        "        target_names=classifier.label_encoder.classes_,\n",
        "        output_dict=True\n",
        "    )\n",
        "\n",
        "    class_metrics = []\n",
        "    for topic in classifier.label_encoder.classes_:\n",
        "        metrics = class_report[topic]\n",
        "        class_metrics.append({\n",
        "            'Topic': topic,\n",
        "            'Precision': metrics['precision'],\n",
        "            'Recall': metrics['recall'],\n",
        "            'F1-Score': metrics['f1-score'],\n",
        "            'Support': int(metrics['support'])\n",
        "        })\n",
        "\n",
        "    metrics_df = pd.DataFrame(class_metrics)\n",
        "    print(\"\\n\" + metrics_df.to_string(index=False))\n",
        "\n",
        "    print(f\"\\n{'='*80}\")\n",
        "    print(f\"CONFUSION ANALYSIS\")\n",
        "    print(f\"{'='*80}\")\n",
        "\n",
        "    cm = confusion_matrix(y_test, y_pred_full)\n",
        "\n",
        "    confusion_pairs = []\n",
        "    for i in range(len(classifier.label_encoder.classes_)):\n",
        "        for j in range(len(classifier.label_encoder.classes_)):\n",
        "            if i != j and cm[i, j] > 0:\n",
        "                confusion_pairs.append({\n",
        "                    'True': classifier.label_encoder.classes_[i],\n",
        "                    'Predicted': classifier.label_encoder.classes_[j],\n",
        "                    'Count': cm[i, j]\n",
        "                })\n",
        "\n",
        "    confusion_df = pd.DataFrame(confusion_pairs).sort_values('Count', ascending=False)\n",
        "\n",
        "    if len(confusion_df) > 0:\n",
        "        print(\"\\nTop Confusion Pairs:\")\n",
        "        print(confusion_df.head(10).to_string(index=False))\n",
        "\n",
        "    # Comprehensive Visualizations\n",
        "    fig = plt.figure(figsize=(20, 12))\n",
        "\n",
        "    # 1. Confusion Matrix\n",
        "    ax1 = plt.subplot(2, 3, 1)\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='YlOrRd',\n",
        "                xticklabels=classifier.label_encoder.classes_,\n",
        "                yticklabels=classifier.label_encoder.classes_,\n",
        "                cbar_kws={'label': 'Count'}, ax=ax1)\n",
        "    ax1.set_title('Confusion Matrix', fontsize=14, fontweight='bold')\n",
        "    ax1.set_ylabel('True Label')\n",
        "    ax1.set_xlabel('Predicted Label')\n",
        "    plt.setp(ax1.get_xticklabels(), rotation=45, ha='right')\n",
        "\n",
        "    # 2. Normalized Confusion Matrix\n",
        "    ax2 = plt.subplot(2, 3, 2)\n",
        "    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    sns.heatmap(cm_normalized, annot=True, fmt='.2f', cmap='Blues',\n",
        "                xticklabels=classifier.label_encoder.classes_,\n",
        "                yticklabels=classifier.label_encoder.classes_,\n",
        "                cbar_kws={'label': 'Proportion'}, vmin=0, vmax=1, ax=ax2)\n",
        "    ax2.set_title('Normalized Confusion Matrix', fontsize=14, fontweight='bold')\n",
        "    ax2.set_ylabel('True Label')\n",
        "    ax2.set_xlabel('Predicted Label')\n",
        "    plt.setp(ax2.get_xticklabels(), rotation=45, ha='right')\n",
        "\n",
        "    # 3. F1-Scores by Topic\n",
        "    ax3 = plt.subplot(2, 3, 3)\n",
        "    f1_scores = metrics_df['F1-Score'].values\n",
        "    topics = metrics_df['Topic'].values\n",
        "    colors = plt.cm.viridis(np.linspace(0, 1, len(topics)))\n",
        "    bars = ax3.barh(topics, f1_scores, color=colors)\n",
        "    ax3.set_xlabel('F1-Score')\n",
        "    ax3.set_title('F1-Score by Topic', fontsize=14, fontweight='bold')\n",
        "    ax3.set_xlim([0, 1])\n",
        "    ax3.grid(axis='x', alpha=0.3)\n",
        "    for i, (bar, score) in enumerate(zip(bars, f1_scores)):\n",
        "        ax3.text(score + 0.01, i, f'{score:.3f}', va='center', fontsize=9)\n",
        "\n",
        "    # 4. Precision vs Recall\n",
        "    ax4 = plt.subplot(2, 3, 4)\n",
        "    precision_scores = metrics_df['Precision'].values\n",
        "    recall_scores = metrics_df['Recall'].values\n",
        "    ax4.scatter(recall_scores, precision_scores, s=metrics_df['Support']*5,\n",
        "                c=range(len(topics)), cmap='tab10', alpha=0.6,\n",
        "                edgecolors='black', linewidth=1.5)\n",
        "    for i, topic in enumerate(topics):\n",
        "        ax4.annotate(topic, (recall_scores[i], precision_scores[i]),\n",
        "                    xytext=(5, 5), textcoords='offset points', fontsize=8,\n",
        "                    bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.3))\n",
        "    ax4.set_xlabel('Recall')\n",
        "    ax4.set_ylabel('Precision')\n",
        "    ax4.set_title('Precision vs Recall', fontsize=14, fontweight='bold')\n",
        "    ax4.set_xlim([0, 1.05])\n",
        "    ax4.set_ylim([0, 1.05])\n",
        "    ax4.grid(True, alpha=0.3)\n",
        "    ax4.plot([0, 1], [0, 1], 'k--', alpha=0.3)\n",
        "\n",
        "    # 5. Test Set Distribution\n",
        "    ax5 = plt.subplot(2, 3, 5)\n",
        "    support_counts = metrics_df['Support'].values\n",
        "    bars = ax5.bar(topics, support_counts, color='steelblue', alpha=0.7)\n",
        "    ax5.set_ylabel('Samples')\n",
        "    ax5.set_title('Test Set Distribution', fontsize=14, fontweight='bold')\n",
        "    ax5.tick_params(axis='x', rotation=45)\n",
        "    ax5.grid(axis='y', alpha=0.3)\n",
        "    for bar, count in zip(bars, support_counts):\n",
        "        ax5.text(bar.get_x() + bar.get_width()/2., bar.get_height(),\n",
        "                f'{int(count)}', ha='center', va='bottom', fontsize=9)\n",
        "\n",
        "    # 6. Confidence Distribution\n",
        "    ax6 = plt.subplot(2, 3, 6)\n",
        "    if has_proba:\n",
        "        max_probabilities = np.max(y_pred_proba, axis=1)\n",
        "        correct_mask = y_pred_full == y_test\n",
        "        ax6.hist(max_probabilities[correct_mask], bins=30, alpha=0.6,\n",
        "                label='Correct', color='green', edgecolor='black')\n",
        "        ax6.hist(max_probabilities[~correct_mask], bins=30, alpha=0.6,\n",
        "                label='Incorrect', color='red', edgecolor='black')\n",
        "        ax6.set_xlabel('Confidence')\n",
        "        ax6.set_ylabel('Frequency')\n",
        "        ax6.set_title('Prediction Confidence', fontsize=14, fontweight='bold')\n",
        "        ax6.legend()\n",
        "        ax6.grid(axis='y', alpha=0.3)\n",
        "        avg_conf_correct = np.mean(max_probabilities[correct_mask])\n",
        "        avg_conf_incorrect = np.mean(max_probabilities[~correct_mask])\n",
        "        ax6.axvline(avg_conf_correct, color='green', linestyle='--', linewidth=2)\n",
        "        ax6.axvline(avg_conf_incorrect, color='red', linestyle='--', linewidth=2)\n",
        "    else:\n",
        "        ax6.text(0.5, 0.5, f'{best_name}\\nNo probability support',\n",
        "                ha='center', va='center', fontsize=12, transform=ax6.transAxes)\n",
        "        ax6.axis('off')\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "    # Error Analysis\n",
        "    print(f\"\\n{'='*80}\")\n",
        "    print(f\"ERROR ANALYSIS\")\n",
        "    print(f\"{'='*80}\")\n",
        "\n",
        "    misclassified_indices = np.where(y_pred_full != y_test)[0]\n",
        "    print(f\"\\nTotal Misclassifications: {len(misclassified_indices)}\")\n",
        "\n",
        "    # Summary\n",
        "    print(f\"\\n{'='*80}\")\n",
        "    print(f\"SUMMARY\")\n",
        "    print(f\"{'='*80}\")\n",
        "    print(f\"Model: {best_name}\")\n",
        "    print(f\"Accuracy: {overall_accuracy:.4f} ({overall_accuracy*100:.2f}%)\")\n",
        "    print(f\"F1-Score: {overall_f1:.4f}\")\n",
        "    print(f\"Best Topic: {metrics_df.loc[metrics_df['F1-Score'].idxmax(), 'Topic']} (F1: {metrics_df['F1-Score'].max():.4f})\")\n",
        "    print(f\"Worst Topic: {metrics_df.loc[metrics_df['F1-Score'].idxmin(), 'Topic']} (F1: {metrics_df['F1-Score'].min():.4f})\")\n",
        "    if has_proba:\n",
        "        print(f\"Avg Confidence: {np.mean(np.max(y_pred_proba, axis=1)):.4f}\")\n",
        "    print(f\"{'='*80}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-HpiUJ2tz7NP",
        "outputId": "5b0a6654-1c56-4918-c889-4d4ab097af6b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================================\n",
            "COMPLETE TEST SET EVALUATION\n",
            "================================================================================\n",
            "\n",
            "Evaluating on 5000 test samples...\n",
            "\n",
            "================================================================================\n",
            "OVERALL PERFORMANCE\n",
            "================================================================================\n",
            "Test Accuracy: 0.7044 (70.44%)\n",
            "Test F1-Score: 0.7040\n",
            "Correct: 3522/5000\n",
            "Incorrect: 1478/5000\n",
            "\n",
            "================================================================================\n",
            "PER-CLASS PERFORMANCE\n",
            "================================================================================\n",
            "\n",
            "                   Topic  Precision   Recall  F1-Score  Support\n",
            "                 algebra   0.645206 0.776748  0.704893     1187\n",
            "counting_and_probability   0.804878 0.696203  0.746606      474\n",
            "                geometry   0.693957 0.743215  0.717742      479\n",
            "    intermediate_algebra   0.782759 0.754153  0.768190      903\n",
            "           number_theory   0.734657 0.753704  0.744059      540\n",
            "              prealgebra   0.555985 0.495982  0.524272      871\n",
            "             precalculus   0.881432 0.721612  0.793555      546\n",
            "\n",
            "================================================================================\n",
            "CONFUSION ANALYSIS\n",
            "================================================================================\n",
            "\n",
            "Top Confusion Pairs:\n",
            "                    True            Predicted  Count\n",
            "              prealgebra              algebra    208\n",
            "    intermediate_algebra              algebra    162\n",
            "                 algebra           prealgebra    118\n",
            "                 algebra intermediate_algebra     95\n",
            "counting_and_probability           prealgebra     83\n",
            "              prealgebra        number_theory     77\n",
            "              prealgebra             geometry     75\n",
            "           number_theory           prealgebra     65\n",
            "                geometry           prealgebra     63\n",
            "             precalculus intermediate_algebra     62\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "================================================================================\n",
            "ERROR ANALYSIS\n",
            "================================================================================\n",
            "\n",
            "Total Misclassifications: 1478\n",
            "\n",
            "================================================================================\n",
            "SUMMARY\n",
            "================================================================================\n",
            "Model: Gradient Boosting\n",
            "Accuracy: 0.7044 (70.44%)\n",
            "F1-Score: 0.7040\n",
            "Best Topic: precalculus (F1: 0.7936)\n",
            "Worst Topic: prealgebra (F1: 0.5243)\n",
            "Avg Confidence: 0.5968\n",
            "================================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "model_data = {\n",
        "    'model': classifier.best_model,\n",
        "    'vectorizer': classifier.vectorizer,\n",
        "    'scaler': classifier.scaler,\n",
        "    'label_encoder': classifier.label_encoder\n",
        "}\n",
        "with open('model.pkl', 'wb') as f:\n",
        "    pickle.dump(model_data, f)"
      ],
      "metadata": {
        "id": "ABOjOLpgwyVF"
      },
      "execution_count": 67,
      "outputs": []
    }
  ]
}