{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa82a715",
   "metadata": {},
   "source": [
    "# Phase 4: Final Evaluation\n",
    "Comprehensive assessment of plagiarism detection models on real MSRP corpus.\n",
    "\n",
    "**Goal:** Evaluate fusion model and baseline methods on held-out test set with proper metrics and analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23c0dbf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading MSRP corpus from pickle files...\n",
      "Train file: /home/henry/code/plagiarism-detector/data/processed/msr_train.pkl\n",
      "Test file: /home/henry/code/plagiarism-detector/data/processed/msr_test.pkl\n",
      "\n",
      "✓ Loaded 4076 training pairs\n",
      "✓ Loaded 1725 test pairs\n",
      "\n",
      "Train data structure: <class 'pandas.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import (\n",
    "    classification_report, confusion_matrix, roc_auc_score, roc_curve,\n",
    "    precision_recall_curve, f1_score, precision_score, recall_score,\n",
    "    accuracy_score\n",
    ")\n",
    "from scipy.stats import mannwhitneyu\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "# Load models\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "model_bert = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
    "\n",
    "# Define paths\n",
    "data_path = Path(\"/home/henry/code/plagiarism-detector/data/processed\")\n",
    "train_file = data_path / \"msr_train.pkl\"\n",
    "test_file = data_path / \"msr_test.pkl\"\n",
    "\n",
    "print(\"Loading MSRP corpus from pickle files...\")\n",
    "print(f\"Train file: {train_file}\")\n",
    "print(f\"Test file: {test_file}\")\n",
    "\n",
    "# Load data\n",
    "with open(train_file, 'rb') as f:\n",
    "    train_data = pickle.load(f)\n",
    "    \n",
    "with open(test_file, 'rb') as f:\n",
    "    test_data = pickle.load(f)\n",
    "\n",
    "print(f\"\\n✓ Loaded {len(train_data)} training pairs\")\n",
    "print(f\"✓ Loaded {len(test_data)} test pairs\")\n",
    "\n",
    "# Inspect structure\n",
    "print(f\"\\nTrain data structure: {type(train_data)}\")\n",
    "if isinstance(train_data, list) and len(train_data) > 0:\n",
    "    print(f\"First sample: {train_data[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37970853",
   "metadata": {},
   "source": [
    "## Data Preparation\n",
    "Extract sentences and labels from pickle files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fef76fa2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Preparation Summary:\n",
      "================================================================================\n",
      "Training pairs: 4076\n",
      "Test pairs: 1725\n",
      "\n",
      "Training label distribution:\n",
      "  Class 0: 1323 (32.5%)\n",
      "  Class 1: 2753 (67.5%)\n",
      "\n",
      "Test label distribution:\n",
      "  Class 0: 578 (33.5%)\n",
      "  Class 1: 1147 (66.5%)\n",
      "\n",
      "✓ Header row removed where necessary and data validation passed\n"
     ]
    }
   ],
   "source": [
    "def prepare_data_from_pickle(data):\n",
    "    \"\"\"\n",
    "    Convert processed MSRP pickle content to a standard format.\n",
    "\n",
    "    Supported inputs:\n",
    "    - pandas DataFrame with sentence and label columns\n",
    "    - list/tuple rows in the form (sent1, sent2, label)\n",
    "    - either format may include a first row containing column headings\n",
    "    \"\"\"\n",
    "    def row_looks_like_header(values):\n",
    "        joined = \" \".join(str(value).strip().lower() for value in values)\n",
    "        header_tokens = [\"quality\", \"label\", \"#1 string\", \"#2 string\", \"string\", \"sentence\"]\n",
    "        return any(token in joined for token in header_tokens)\n",
    "\n",
    "    if isinstance(data, pd.DataFrame):\n",
    "        df = data.copy()\n",
    "\n",
    "        # If the first row contains headings rather than data, promote it to columns.\n",
    "        if len(df) > 0 and row_looks_like_header(df.iloc[0].tolist()):\n",
    "            promoted_columns = [str(value).strip() for value in df.iloc[0].tolist()]\n",
    "            df = df.iloc[1:].copy()\n",
    "            df.columns = promoted_columns\n",
    "\n",
    "        normalized_columns = {str(col).strip().lower(): col for col in df.columns}\n",
    "\n",
    "        label_col = None\n",
    "        for candidate in [\"quality\", \"label\", \"class\", \"target\"]:\n",
    "            if candidate in normalized_columns:\n",
    "                label_col = normalized_columns[candidate]\n",
    "                break\n",
    "\n",
    "        sent1_col = None\n",
    "        for candidate in [\"#1 string\", \"sentence1\", \"sent1\", \"text1\", \"string1\"]:\n",
    "            if candidate in normalized_columns:\n",
    "                sent1_col = normalized_columns[candidate]\n",
    "                break\n",
    "\n",
    "        sent2_col = None\n",
    "        for candidate in [\"#2 string\", \"sentence2\", \"sent2\", \"text2\", \"string2\"]:\n",
    "            if candidate in normalized_columns:\n",
    "                sent2_col = normalized_columns[candidate]\n",
    "                break\n",
    "\n",
    "        if label_col is None or sent1_col is None or sent2_col is None:\n",
    "            raise ValueError(\n",
    "                f\"Could not identify required columns. Found columns: {list(df.columns)}\"\n",
    "            )\n",
    "\n",
    "        df = df[[sent1_col, sent2_col, label_col]].dropna().copy()\n",
    "        df[label_col] = pd.to_numeric(df[label_col], errors=\"coerce\")\n",
    "        df = df.dropna(subset=[label_col])\n",
    "\n",
    "        sentence_pairs = list(zip(df[sent1_col].astype(str), df[sent2_col].astype(str)))\n",
    "        labels = df[label_col].astype(int).to_numpy()\n",
    "        return sentence_pairs, labels\n",
    "\n",
    "    sentence_pairs = []\n",
    "    labels = []\n",
    "    rows = list(data)\n",
    "\n",
    "    if rows and isinstance(rows[0], (tuple, list)) and row_looks_like_header(rows[0]):\n",
    "        rows = rows[1:]\n",
    "\n",
    "    for item in rows:\n",
    "        if isinstance(item, (tuple, list)) and len(item) >= 3:\n",
    "            sent1, sent2, label = item[0], item[1], item[2]\n",
    "            sentence_pairs.append((str(sent1), str(sent2)))\n",
    "            labels.append(int(label))\n",
    "        else:\n",
    "            print(f\"Warning: Unexpected data format: {item}\")\n",
    "\n",
    "    return sentence_pairs, np.array(labels)\n",
    "\n",
    "# Prepare data\n",
    "train_pairs, train_labels = prepare_data_from_pickle(train_data)\n",
    "test_pairs, test_labels = prepare_data_from_pickle(test_data)\n",
    "\n",
    "print(\"Data Preparation Summary:\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"Training pairs: {len(train_pairs)}\")\n",
    "print(f\"Test pairs: {len(test_pairs)}\")\n",
    "print(f\"\\nTraining label distribution:\")\n",
    "unique, counts = np.unique(train_labels, return_counts=True)\n",
    "for label, count in zip(unique, counts):\n",
    "    percent = 100 * count / len(train_labels)\n",
    "    print(f\"  Class {label}: {count} ({percent:.1f}%)\")\n",
    "\n",
    "print(f\"\\nTest label distribution:\")\n",
    "unique, counts = np.unique(test_labels, return_counts=True)\n",
    "for label, count in zip(unique, counts):\n",
    "    percent = 100 * count / len(test_labels)\n",
    "    print(f\"  Class {label}: {count} ({percent:.1f}%)\")\n",
    "\n",
    "print(\"\\n✓ Header row removed where necessary and data validation passed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "958bd0eb",
   "metadata": {},
   "source": [
    "## Feature Extraction Pipeline\n",
    "Extract BERT, TED, Jaccard, and length ratio for all pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6749dabb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting features for training set...\n",
      "  Processed 500/4076 training pairs...\n",
      "  Processed 1000/4076 training pairs...\n",
      "  Processed 1500/4076 training pairs...\n",
      "  Processed 2000/4076 training pairs...\n",
      "  Processed 2500/4076 training pairs...\n",
      "  Processed 3000/4076 training pairs...\n",
      "  Processed 3500/4076 training pairs...\n",
      "  Processed 4000/4076 training pairs...\n",
      "✓ Extracted features for 4076 training pairs\n",
      "\n",
      "Extracting features for test set...\n",
      "  Processed 500/1725 test pairs...\n",
      "  Processed 1000/1725 test pairs...\n",
      "  Processed 1500/1725 test pairs...\n",
      "✓ Extracted features for 1725 test pairs\n"
     ]
    }
   ],
   "source": [
    "def build_tree_from_dependencies(text):\n",
    "    \"\"\"Build hierarchical tree from dependency parse.\"\"\"\n",
    "    doc = nlp(text)\n",
    "    tree = {}\n",
    "    root_token = None\n",
    "    \n",
    "    for token in doc:\n",
    "        tree[token.i] = {\n",
    "            'text': token.text,\n",
    "            'pos': token.pos_,\n",
    "            'dep': token.dep_,\n",
    "            'head_id': token.head.i,\n",
    "            'children': []\n",
    "        }\n",
    "    \n",
    "    for token in doc:\n",
    "        if token.head.i != token.i:\n",
    "            tree[token.head.i]['children'].append(token.i)\n",
    "        else:\n",
    "            root_token = token.i\n",
    "    \n",
    "    return tree, root_token\n",
    "\n",
    "def get_nodes_postorder(node_id, tree):\n",
    "    \"\"\"Get nodes in postorder traversal.\"\"\"\n",
    "    nodes = []\n",
    "    if node_id in tree:\n",
    "        for child_id in tree[node_id]['children']:\n",
    "            nodes.extend(get_nodes_postorder(child_id, tree))\n",
    "        nodes.append(node_id)\n",
    "    return nodes\n",
    "\n",
    "def node_label(node_id, tree):\n",
    "    \"\"\"Get (POS, DEP) label for a node.\"\"\"\n",
    "    if node_id in tree:\n",
    "        return (tree[node_id]['pos'], tree[node_id]['dep'])\n",
    "    return ('', '')\n",
    "\n",
    "def nodes_match(node_id1, tree1, node_id2, tree2):\n",
    "    \"\"\"Check if nodes match.\"\"\"\n",
    "    return node_label(node_id1, tree1) == node_label(node_id2, tree2)\n",
    "\n",
    "def tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2):\n",
    "    \"\"\"Zhang-Shasha Tree Edit Distance.\"\"\"\n",
    "    nodes1 = get_nodes_postorder(root1, tree1)\n",
    "    nodes2 = get_nodes_postorder(root2, tree2)\n",
    "    \n",
    "    m, n = len(nodes1), len(nodes2)\n",
    "    dp = [[0] * (n + 1) for _ in range(m + 1)]\n",
    "    \n",
    "    for i in range(m + 1):\n",
    "        dp[i][0] = i\n",
    "    for j in range(n + 1):\n",
    "        dp[0][j] = j\n",
    "    \n",
    "    for i in range(1, m + 1):\n",
    "        for j in range(1, n + 1):\n",
    "            node1, node2 = nodes1[i - 1], nodes2[j - 1]\n",
    "            \n",
    "            if nodes_match(node1, tree1, node2, tree2):\n",
    "                cost = dp[i - 1][j - 1]\n",
    "            else:\n",
    "                cost = min(\n",
    "                    dp[i - 1][j] + 1,\n",
    "                    dp[i][j - 1] + 1,\n",
    "                    dp[i - 1][j - 1] + 1\n",
    "                )\n",
    "            dp[i][j] = cost\n",
    "    \n",
    "    max_size = max(m, n)\n",
    "    return 1.0 - (dp[m][n] / max_size) if max_size > 0 else 1.0\n",
    "\n",
    "def sentence_similarity_ted(sent1, sent2):\n",
    "    \"\"\"TED similarity wrapper.\"\"\"\n",
    "    if not sent1.strip() or not sent2.strip():\n",
    "        return 0.0\n",
    "    try:\n",
    "        tree1, root1 = build_tree_from_dependencies(sent1)\n",
    "        tree2, root2 = build_tree_from_dependencies(sent2)\n",
    "        return tree_edit_distance_zhang_shasha(tree1, tree2, root1, root2)\n",
    "    except:\n",
    "        return 0.0\n",
    "\n",
    "def sentence_similarity_jaccard(sent1, sent2):\n",
    "    \"\"\"Jaccard similarity.\"\"\"\n",
    "    words1 = set(sent1.lower().split())\n",
    "    words2 = set(sent2.lower().split())\n",
    "    \n",
    "    if not words1 and not words2:\n",
    "        return 1.0\n",
    "    if not words1 or not words2:\n",
    "        return 0.0\n",
    "    \n",
    "    intersection = len(words1.intersection(words2))\n",
    "    union = len(words1.union(words2))\n",
    "    return intersection / union if union > 0 else 0.0\n",
    "\n",
    "def extract_features(sent1, sent2):\n",
    "    \"\"\"Extract all features for a sentence pair.\"\"\"\n",
    "    # BERT\n",
    "    embeddings = model_bert.encode([sent1, sent2])\n",
    "    bert_sim = np.dot(embeddings[0], embeddings[1]) / (\n",
    "        np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])\n",
    "    )\n",
    "    \n",
    "    # TED\n",
    "    ted_sim = sentence_similarity_ted(sent1, sent2)\n",
    "    \n",
    "    # Jaccard\n",
    "    jaccard_sim = sentence_similarity_jaccard(sent1, sent2)\n",
    "    \n",
    "    # Length ratio\n",
    "    len_ratio = min(len(sent1), len(sent2)) / max(len(sent1), len(sent2)) if max(len(sent1), len(sent2)) > 0 else 0.0\n",
    "    \n",
    "    return {\n",
    "        'bert': bert_sim,\n",
    "        'ted': ted_sim,\n",
    "        'jaccard': jaccard_sim,\n",
    "        'len_ratio': len_ratio\n",
    "    }\n",
    "\n",
    "print(\"Extracting features for training set...\")\n",
    "train_features = []\n",
    "for i, (sent1, sent2) in enumerate(train_pairs):\n",
    "    features = extract_features(sent1, sent2)\n",
    "    features['label'] = train_labels[i]\n",
    "    train_features.append(features)\n",
    "    \n",
    "    if (i + 1) % 500 == 0:\n",
    "        print(f\"  Processed {i + 1}/{len(train_pairs)} training pairs...\")\n",
    "\n",
    "train_df = pd.DataFrame(train_features)\n",
    "print(f\"✓ Extracted features for {len(train_df)} training pairs\")\n",
    "\n",
    "print(\"\\nExtracting features for test set...\")\n",
    "test_features = []\n",
    "for i, (sent1, sent2) in enumerate(test_pairs):\n",
    "    features = extract_features(sent1, sent2)\n",
    "    features['label'] = test_labels[i]\n",
    "    test_features.append(features)\n",
    "    \n",
    "    if (i + 1) % 500 == 0:\n",
    "        print(f\"  Processed {i + 1}/{len(test_pairs)} test pairs...\")\n",
    "\n",
    "test_df = pd.DataFrame(test_features)\n",
    "print(f\"✓ Extracted features for {len(test_df)} test pairs\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d85b9414",
   "metadata": {},
   "source": [
    "## Feature Statistics & Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "934bbab5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Summary Statistics:\n",
      "================================================================================\n",
      "           bert       ted   jaccard  len_ratio\n",
      "count  4076.000  4076.000  4076.000   4076.000\n",
      "mean      0.794     0.541     0.452      0.847\n",
      "std       0.141     0.170     0.159      0.102\n",
      "min       0.238     0.000     0.048      0.494\n",
      "25%       0.710     0.421     0.325      0.772\n",
      "50%       0.818     0.545     0.444      0.860\n",
      "75%       0.907     0.667     0.577      0.934\n",
      "max       0.997     1.000     0.917      1.000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Feature Summary Statistics:\")\n",
    "print(\"=\" * 80)\n",
    "print(train_df[['bert', 'ted', 'jaccard', 'len_ratio']].describe().round(3))\n",
    "\n",
    "# Feature correlation\n",
    "feature_corr = train_df[['bert', 'ted', 'jaccard', 'len_ratio']].corr()\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(feature_corr, annot=True, cmap='coolwarm', center=0, vmin=-1, vmax=1, \n",
    "            square=True, fmt='.3f', cbar_kws={'label': 'Correlation'})\n",
    "plt.title('Feature Correlation Matrix (Training Set)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c9f622",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "Train Logistic Regression on full training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e01bb875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Logistic Regression Model...\n",
      "================================================================================\n",
      "Training set: 4076 pairs\n",
      "Test set: 1725 pairs\n",
      "\n",
      "Training Performance:\n",
      "  Accuracy: 0.7159\n",
      "  F1-Score: 0.7712\n",
      "\n",
      "Feature Coefficients:\n",
      "  BERT (Semantic)               : +0.6518\n",
      "  TED (Syntactic)               : +0.2077\n",
      "  Jaccard (Baseline)            : +0.5407\n",
      "  Length Ratio                  : +0.4087\n"
     ]
    }
   ],
   "source": [
    "# Prepare training data\n",
    "X_train = train_df[['bert', 'ted', 'jaccard', 'len_ratio']].values\n",
    "y_train = train_df['label'].values\n",
    "\n",
    "X_test = test_df[['bert', 'ted', 'jaccard', 'len_ratio']].values\n",
    "y_test = test_df['label'].values\n",
    "\n",
    "# Standardize\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(\"Training Logistic Regression Model...\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"Training set: {len(X_train)} pairs\")\n",
    "print(f\"Test set: {len(X_test)} pairs\")\n",
    "\n",
    "# Train model\n",
    "model = LogisticRegression(random_state=42, max_iter=1000, class_weight='balanced')\n",
    "model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Training performance\n",
    "train_pred = model.predict(X_train_scaled)\n",
    "train_acc = accuracy_score(y_train, train_pred)\n",
    "train_f1 = f1_score(y_train, train_pred)\n",
    "\n",
    "print(f\"\\nTraining Performance:\")\n",
    "print(f\"  Accuracy: {train_acc:.4f}\")\n",
    "print(f\"  F1-Score: {train_f1:.4f}\")\n",
    "\n",
    "# Feature importance\n",
    "print(f\"\\nFeature Coefficients:\")\n",
    "feature_names = ['BERT (Semantic)', 'TED (Syntactic)', 'Jaccard (Baseline)', 'Length Ratio']\n",
    "for name, coef in zip(feature_names, model.coef_[0]):\n",
    "    print(f\"  {name:<30}: {coef:+.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "475fc77e",
   "metadata": {},
   "source": [
    "## Test Set Evaluation\n",
    "Comprehensive evaluation on held-out test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0d48f22",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Evaluation:\n",
      "================================================================================\n",
      "                precision    recall  f1-score   support\n",
      "\n",
      "Non-Paraphrase       0.53      0.72      0.61       578\n",
      "    Paraphrase       0.83      0.68      0.75      1147\n",
      "\n",
      "      accuracy                           0.70      1725\n",
      "     macro avg       0.68      0.70      0.68      1725\n",
      "  weighted avg       0.73      0.70      0.70      1725\n",
      "\n",
      "Summary Metrics:\n",
      "  Accuracy:  0.6957\n",
      "  Precision: 0.8294\n",
      "  Recall:    0.6827\n",
      "  F1-Score:  0.7489\n",
      "  ROC AUC:   0.7895\n"
     ]
    }
   ],
   "source": [
    "# Predictions\n",
    "y_pred = model.predict(X_test_scaled)\n",
    "y_pred_proba = model.predict_proba(X_test_scaled)[:, 1]\n",
    "\n",
    "print(\"Test Set Evaluation:\")\n",
    "print(\"=\" * 80)\n",
    "print(classification_report(y_test, y_pred, target_names=['Non-Paraphrase', 'Paraphrase']))\n",
    "\n",
    "# Metrics\n",
    "test_acc = accuracy_score(y_test, y_pred)\n",
    "test_f1 = f1_score(y_test, y_pred)\n",
    "test_prec = precision_score(y_test, y_pred)\n",
    "test_recall = recall_score(y_test, y_pred)\n",
    "test_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "\n",
    "print(f\"Summary Metrics:\")\n",
    "print(f\"  Accuracy:  {test_acc:.4f}\")\n",
    "print(f\"  Precision: {test_prec:.4f}\")\n",
    "print(f\"  Recall:    {test_recall:.4f}\")\n",
    "print(f\"  F1-Score:  {test_f1:.4f}\")\n",
    "print(f\"  ROC AUC:   {test_auc:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3d6326",
   "metadata": {},
   "source": [
    "## Visualizations: Confusion Matrix & ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d3a5bdac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Confusion Matrix Breakdown:\n",
      "  True Negatives (TN):  417\n",
      "  False Positives (FP): 161\n",
      "  False Negatives (FN): 364\n",
      "  True Positives (TP):  783\n",
      "\n",
      "  Specificity (TN/(TN+FP)): 0.7215\n",
      "  Sensitivity (TP/(TP+FN)): 0.6827\n"
     ]
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[0], cbar=False)\n",
    "axes[0].set_title('Confusion Matrix (Test Set)')\n",
    "axes[0].set_xlabel('Predicted')\n",
    "axes[0].set_ylabel('Actual')\n",
    "axes[0].set_xticklabels(['Non-Para', 'Paraphrase'])\n",
    "axes[0].set_yticklabels(['Non-Para', 'Paraphrase'])\n",
    "\n",
    "# ROC Curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "axes[1].plot(fpr, tpr, label=f'ROC (AUC={test_auc:.3f})', linewidth=2.5)\n",
    "axes[1].plot([0, 1], [0, 1], 'k--', label='Random Classifier', linewidth=1)\n",
    "axes[1].set_xlabel('False Positive Rate')\n",
    "axes[1].set_ylabel('True Positive Rate')\n",
    "axes[1].set_title('ROC Curve (Test Set)')\n",
    "axes[1].legend()\n",
    "axes[1].grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Detailed metrics from confusion matrix\n",
    "tn, fp, fn, tp = cm.ravel()\n",
    "print(\"\\nConfusion Matrix Breakdown:\")\n",
    "print(f\"  True Negatives (TN):  {tn}\")\n",
    "print(f\"  False Positives (FP): {fp}\")\n",
    "print(f\"  False Negatives (FN): {fn}\")\n",
    "print(f\"  True Positives (TP):  {tp}\")\n",
    "print(f\"\\n  Specificity (TN/(TN+FP)): {tn/(tn+fp):.4f}\")\n",
    "print(f\"  Sensitivity (TP/(TP+FN)): {tp/(tp+fn):.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e163ea",
   "metadata": {},
   "source": [
    "## Baseline Comparison\n",
    "Compare fusion model against individual features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8296f4bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Method Comparison:\n",
      "====================================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BERT Only</td>\n",
       "      <td>0.6933</td>\n",
       "      <td>0.6875</td>\n",
       "      <td>0.9878</td>\n",
       "      <td>0.8107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TED Only</td>\n",
       "      <td>0.6325</td>\n",
       "      <td>0.7791</td>\n",
       "      <td>0.6242</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>JACCARD Only</td>\n",
       "      <td>0.5930</td>\n",
       "      <td>0.8606</td>\n",
       "      <td>0.4629</td>\n",
       "      <td>0.6020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LEN_RATIO Only</td>\n",
       "      <td>0.6649</td>\n",
       "      <td>0.6649</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>0.7987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FUSION (All Features)</td>\n",
       "      <td>0.6957</td>\n",
       "      <td>0.8294</td>\n",
       "      <td>0.6827</td>\n",
       "      <td>0.7489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Method  Accuracy  Precision  Recall  F1-Score\n",
       "0              BERT Only    0.6933     0.6875  0.9878    0.8107\n",
       "1               TED Only    0.6325     0.7791  0.6242    0.6931\n",
       "2           JACCARD Only    0.5930     0.8606  0.4629    0.6020\n",
       "3         LEN_RATIO Only    0.6649     0.6649  1.0000    0.7987\n",
       "4  FUSION (All Features)    0.6957     0.8294  0.6827    0.7489"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Train baseline models (single feature only)\n",
    "baseline_results = []\n",
    "\n",
    "for feature_name in ['bert', 'ted', 'jaccard', 'len_ratio']:\n",
    "    X_baseline = test_df[[feature_name]].values\n",
    "    \n",
    "    # Simple threshold at 0.5\n",
    "    y_pred_baseline = (X_baseline.flatten() > 0.5).astype(int)\n",
    "    \n",
    "    acc = accuracy_score(y_test, y_pred_baseline)\n",
    "    f1 = f1_score(y_test, y_pred_baseline)\n",
    "    prec = precision_score(y_test, y_pred_baseline)\n",
    "    recall = recall_score(y_test, y_pred_baseline)\n",
    "    \n",
    "    baseline_results.append({\n",
    "        'Method': f'{feature_name.upper()} Only',\n",
    "        'Accuracy': acc,\n",
    "        'Precision': prec,\n",
    "        'Recall': recall,\n",
    "        'F1-Score': f1\n",
    "    })\n",
    "\n",
    "# Add fusion model\n",
    "baseline_results.append({\n",
    "    'Method': 'FUSION (All Features)',\n",
    "    'Accuracy': test_acc,\n",
    "    'Precision': test_prec,\n",
    "    'Recall': test_recall,\n",
    "    'F1-Score': test_f1\n",
    "})\n",
    "\n",
    "comparison_df = pd.DataFrame(baseline_results)\n",
    "\n",
    "print(\"Method Comparison:\")\n",
    "print(\"=\" * 100)\n",
    "display(comparison_df.round(4))\n",
    "\n",
    "# Visualization\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "comparison_df.set_index('Method')[['Accuracy', 'Precision', 'Recall', 'F1-Score']].plot(\n",
    "    kind='bar', ax=ax, alpha=0.8, width=0.8\n",
    ")\n",
    "ax.set_title('Fusion Model vs Baseline Methods (Test Set)')\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_ylim([0, 1])\n",
    "ax.legend(loc='lower right')\n",
    "ax.grid(axis='y', alpha=0.3)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c7609f4",
   "metadata": {},
   "source": [
    "## ROC Comparison: Fusion vs Individual Methods\n",
    "Compare ROC curves and AUC scores to test whether the fusion model separates paraphrases better than any single feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "03aa8f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC Comparison:\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Fusion Model</td>\n",
       "      <td>0.7895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jaccard Only</td>\n",
       "      <td>0.7423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BERT Only</td>\n",
       "      <td>0.7362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TED Only</td>\n",
       "      <td>0.6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Length Ratio Only</td>\n",
       "      <td>0.6334</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Method     AUC\n",
       "0       Fusion Model  0.7895\n",
       "3       Jaccard Only  0.7423\n",
       "1          BERT Only  0.7362\n",
       "2           TED Only  0.6931\n",
       "4  Length Ratio Only  0.6334"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fusion AUC improvement over best individual method: +0.0472\n"
     ]
    }
   ],
   "source": [
    "roc_results = []\n",
    "\n",
    "# Fusion model ROC\n",
    "fusion_fpr, fusion_tpr, fusion_thresholds = roc_curve(y_test, y_pred_proba)\n",
    "fusion_auc = roc_auc_score(y_test, y_pred_proba)\n",
    "roc_results.append({\n",
    "    'Method': 'Fusion Model',\n",
    "    'AUC': fusion_auc\n",
    "})\n",
    "\n",
    "# Individual feature ROC curves\n",
    "individual_scores = {\n",
    "    'BERT Only': test_df['bert'].values,\n",
    "    'TED Only': test_df['ted'].values,\n",
    "    'Jaccard Only': test_df['jaccard'].values,\n",
    "    'Length Ratio Only': test_df['len_ratio'].values\n",
    "}\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(fusion_fpr, fusion_tpr, linewidth=3, color='black', label=f'Fusion Model (AUC = {fusion_auc:.3f})')\n",
    "\n",
    "colors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red']\n",
    "\n",
    "for (method_name, scores), color in zip(individual_scores.items(), colors):\n",
    "    method_fpr, method_tpr, _ = roc_curve(y_test, scores)\n",
    "    method_auc = roc_auc_score(y_test, scores)\n",
    "    roc_results.append({\n",
    "        'Method': method_name,\n",
    "        'AUC': method_auc\n",
    "    })\n",
    "    plt.plot(method_fpr, method_tpr, linewidth=2, color=color, label=f'{method_name} (AUC = {method_auc:.3f})')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', alpha=0.5, label='Random Classifier')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve Comparison: Fusion vs Individual Methods')\n",
    "plt.legend(loc='lower right')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "roc_comparison_df = pd.DataFrame(roc_results).sort_values('AUC', ascending=False)\n",
    "print('ROC AUC Comparison:')\n",
    "print('=' * 80)\n",
    "display(roc_comparison_df.round(4))\n",
    "\n",
    "best_individual_roc = roc_comparison_df[roc_comparison_df['Method'] != 'Fusion Model'].iloc[0]\n",
    "print(f\"Fusion AUC improvement over best individual method: {fusion_auc - best_individual_roc['AUC']:+.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5cc1d4d",
   "metadata": {},
   "source": [
    "## Statistical Analysis: Mann-Whitney U Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3d142fab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney U Test: Feature Discrimination on Test Set\n",
      "==========================================================================================\n",
      "Feature    U-stat     p-value    Effect   Median(Para)    Median(Non-Para)\n",
      "------------------------------------------------------------------------------------------\n",
      "JACCARD    492116     0.0000     -0.485 ✓✓✓  0.500           0.333\n",
      "BERT       488077     0.0000     -0.472 ✓✓✓  0.849           0.729\n",
      "TED        459506     0.0000     -0.386 ✓✓✓  0.576           0.450\n",
      "LEN_RATIO  419908     0.0000     -0.267 ✓✓✓  0.877           0.811\n"
     ]
    }
   ],
   "source": [
    "def mann_whitney_test(df, feature_col):\n",
    "    \"\"\"Mann-Whitney U test on test set.\"\"\"\n",
    "    group0 = df[df['label'] == 0][feature_col].dropna().values\n",
    "    group1 = df[df['label'] == 1][feature_col].dropna().values\n",
    "    \n",
    "    if len(group0) < 2 or len(group1) < 2:\n",
    "        return None\n",
    "    \n",
    "    u_stat, p_val = mannwhitneyu(group1, group0, alternative='two-sided')\n",
    "    n1, n0 = len(group1), len(group0)\n",
    "    rbc = 1 - (2 * u_stat) / (n1 * n0)\n",
    "    \n",
    "    return {\n",
    "        'feature': feature_col.upper(),\n",
    "        'U_statistic': u_stat,\n",
    "        'p_value': p_val,\n",
    "        'effect_size': rbc,\n",
    "        'median_paraphrase': np.median(group1),\n",
    "        'median_non_paraphrase': np.median(group0)\n",
    "    }\n",
    "\n",
    "print(\"Mann-Whitney U Test: Feature Discrimination on Test Set\")\n",
    "print(\"=\" * 90)\n",
    "\n",
    "test_results = []\n",
    "for feature in ['bert', 'ted', 'jaccard', 'len_ratio']:\n",
    "    result = mann_whitney_test(test_df, feature)\n",
    "    if result:\n",
    "        test_results.append(result)\n",
    "\n",
    "# FDR correction\n",
    "p_values = [r['p_value'] for r in test_results]\n",
    "reject, p_corrected, _, _ = multipletests(p_values, alpha=0.05, method='fdr_bh')\n",
    "\n",
    "for i, result in enumerate(test_results):\n",
    "    result['p_corrected'] = p_corrected[i]\n",
    "    result['significant'] = reject[i]\n",
    "\n",
    "results_mw = pd.DataFrame(test_results).sort_values('effect_size', ascending=False, key=abs)\n",
    "\n",
    "print(f\"{'Feature':<10} {'U-stat':<10} {'p-value':<10} {'Effect':<8} {'Median(Para)':<15} {'Median(Non-Para)'}\")\n",
    "print(\"-\" * 90)\n",
    "\n",
    "for _, row in results_mw.iterrows():\n",
    "    sig = \"✓✓✓\" if row['p_corrected'] < 0.001 else (\"✓✓\" if row['p_corrected'] < 0.01 else (\"✓\" if row['significant'] else \"✗\"))\n",
    "    print(f\"{row['feature']:<10} {row['U_statistic']:<10.0f} {row['p_value']:<10.4f} {row['effect_size']:>+.3f} {sig:<4} \"\n",
    "          f\"{row['median_paraphrase']:<15.3f} {row['median_non_paraphrase']:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f80bc3ab",
   "metadata": {},
   "source": [
    "## Error Analysis\n",
    "Examine false positives and false negatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "df1e9d6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error Analysis:\n",
      "================================================================================\n",
      "\n",
      "False Positives: 161 (predicted paraphrase, actually different)\n",
      "False Negatives: 364 (predicted different, actually paraphrase)\n",
      "\n",
      "False Positive Feature Patterns:\n",
      "  Mean BERT: 0.857\n",
      "  Mean TED:  0.563\n",
      "  Mean Jaccard: 0.491\n",
      "  Confidence: 0.659\n",
      "\n",
      "False Negative Feature Patterns:\n",
      "  Mean BERT: 0.720\n",
      "  Mean TED:  0.433\n",
      "  Mean Jaccard: 0.336\n",
      "  Confidence: 0.667\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add predictions to test dataframe\n",
    "test_analysis = test_df.copy()\n",
    "test_analysis['pred'] = y_pred\n",
    "test_analysis['pred_proba'] = y_pred_proba\n",
    "test_analysis['correct'] = (test_analysis['pred'] == test_analysis['label'])\n",
    "\n",
    "# False positives: predicted paraphrase but actually non-paraphrase\n",
    "false_positives = test_analysis[(test_analysis['pred'] == 1) & (test_analysis['label'] == 0)]\n",
    "\n",
    "# False negatives: predicted non-paraphrase but actually paraphrase\n",
    "false_negatives = test_analysis[(test_analysis['pred'] == 0) & (test_analysis['label'] == 1)]\n",
    "\n",
    "print(\"Error Analysis:\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"\\nFalse Positives: {len(false_positives)} (predicted paraphrase, actually different)\")\n",
    "print(f\"False Negatives: {len(false_negatives)} (predicted different, actually paraphrase)\")\n",
    "\n",
    "print(f\"\\nFalse Positive Feature Patterns:\")\n",
    "if len(false_positives) > 0:\n",
    "    print(f\"  Mean BERT: {false_positives['bert'].mean():.3f}\")\n",
    "    print(f\"  Mean TED:  {false_positives['ted'].mean():.3f}\")\n",
    "    print(f\"  Mean Jaccard: {false_positives['jaccard'].mean():.3f}\")\n",
    "    print(f\"  Confidence: {false_positives['pred_proba'].mean():.3f}\")\n",
    "\n",
    "print(f\"\\nFalse Negative Feature Patterns:\")\n",
    "if len(false_negatives) > 0:\n",
    "    print(f\"  Mean BERT: {false_negatives['bert'].mean():.3f}\")\n",
    "    print(f\"  Mean TED:  {false_negatives['ted'].mean():.3f}\")\n",
    "    print(f\"  Mean Jaccard: {false_negatives['jaccard'].mean():.3f}\")\n",
    "    print(f\"  Confidence: {(1 - false_negatives['pred_proba']).mean():.3f}\")\n",
    "\n",
    "# Visualization\n",
    "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
    "\n",
    "# Feature distributions for correct vs incorrect\n",
    "for ax, feature in zip(axes, ['bert', 'ted']):\n",
    "    correct = test_analysis[test_analysis['correct']][feature]\n",
    "    incorrect = test_analysis[~test_analysis['correct']][feature]\n",
    "    \n",
    "    ax.hist(correct, bins=20, alpha=0.6, label=f'Correct (n={len(correct)})', color='green', edgecolor='black')\n",
    "    ax.hist(incorrect, bins=20, alpha=0.6, label=f'Incorrect (n={len(incorrect)})', color='red', edgecolor='black')\n",
    "    ax.set_xlabel(f'{feature.upper()} Score')\n",
    "    ax.set_ylabel('Frequency')\n",
    "    ax.set_title(f'{feature.upper()} Distribution: Correct vs Incorrect Predictions')\n",
    "    ax.legend()\n",
    "    ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc695605",
   "metadata": {},
   "source": [
    "## Precision-Recall Trade-off\n",
    "Analyze decision threshold optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f327e1e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold Optimization:\n",
      "================================================================================\n",
      "\n",
      "High Precision Threshold (≥95%): 0.784\n",
      "  Precision: 0.950, Recall: 0.315\n",
      "\n",
      "Balanced F1 Threshold: 0.267\n",
      "  Precision: 0.760, Recall: 0.919, F1: 0.832\n",
      "\n",
      "High Recall Threshold (≥90%): 0.017\n",
      "  Precision: 0.665, Recall: 1.000\n"
     ]
    }
   ],
   "source": [
    "# Precision-Recall curve\n",
    "precision_vals, recall_vals, thresholds = precision_recall_curve(y_test, y_pred_proba)\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(recall_vals, precision_vals, linewidth=2.5, label='Precision-Recall Curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Trade-off (Test Set)')\n",
    "plt.axvline(test_recall, color='red', linestyle='--', alpha=0.5, label=f'Current Threshold (Recall={test_recall:.3f})')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Find optimal thresholds for different use cases\n",
    "print(\"Threshold Optimization:\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "threshold_high_prec = None\n",
    "threshold_high_recall = None\n",
    "\n",
    "# High precision (minimize false positives)\n",
    "high_prec_mask = precision_vals[:-1] >= 0.95\n",
    "if np.any(high_prec_mask):\n",
    "    high_prec_idx = int(np.argmax(high_prec_mask))\n",
    "    threshold_high_prec = thresholds[high_prec_idx]\n",
    "    print(f\"\\nHigh Precision Threshold (≥95%): {threshold_high_prec:.3f}\")\n",
    "    print(f\"  Precision: {precision_vals[high_prec_idx]:.3f}, Recall: {recall_vals[high_prec_idx]:.3f}\")\n",
    "else:\n",
    "    print(\"\\nHigh Precision Threshold (≥95%): not available on this test set\")\n",
    "\n",
    "# Balanced (F1-score maximization)\n",
    "f1_scores = 2 * (precision_vals[:-1] * recall_vals[:-1]) / (precision_vals[:-1] + recall_vals[:-1] + 1e-10)\n",
    "f1_idx = int(np.argmax(f1_scores))\n",
    "threshold_balanced = thresholds[f1_idx]\n",
    "print(f\"\\nBalanced F1 Threshold: {threshold_balanced:.3f}\")\n",
    "print(f\"  Precision: {precision_vals[f1_idx]:.3f}, Recall: {recall_vals[f1_idx]:.3f}, F1: {f1_scores[f1_idx]:.3f}\")\n",
    "\n",
    "# High recall (minimize false negatives)\n",
    "high_recall_mask = recall_vals[:-1] >= 0.90\n",
    "if np.any(high_recall_mask):\n",
    "    high_recall_idx = int(np.argmax(high_recall_mask))\n",
    "    threshold_high_recall = thresholds[high_recall_idx]\n",
    "    print(f\"\\nHigh Recall Threshold (≥90%): {threshold_high_recall:.3f}\")\n",
    "    print(f\"  Precision: {precision_vals[high_recall_idx]:.3f}, Recall: {recall_vals[high_recall_idx]:.3f}\")\n",
    "else:\n",
    "    print(\"\\nHigh Recall Threshold (≥90%): not available on this test set\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4fbbe63",
   "metadata": {},
   "source": [
    "## Final Summary\n",
    "Summarize model behaviour, strongest features, and practical deployment implications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c625358d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "====================================================================================================\n",
      "FINAL EVALUATION SUMMARY\n",
      "====================================================================================================\n",
      "\n",
      "1. DATASET OVERVIEW\n",
      "   - Training pairs used: 4076\n",
      "   - Test pairs used: 1725\n",
      "   - Any heading row stored in the pickle files was removed before evaluation.\n",
      "\n",
      "2. FUSION MODEL PERFORMANCE ON THE REAL TEST SET\n",
      "   - Accuracy:  0.6957\n",
      "   - Precision: 0.8294\n",
      "   - Recall:    0.6827\n",
      "   - F1-score:  0.7489\n",
      "   - ROC AUC:   0.7895\n",
      "\n",
      "3. FEATURE BEHAVIOUR\n",
      "   - Strongest separating feature by Mann-Whitney U: JACCARD\n",
      "   - Effect size: -0.485 | Corrected p-value: 1.654e-60\n",
      "   - BERT/TED correlation: 0.379\n",
      "   - Low-to-moderate correlation suggests semantic and syntactic features contribute different information.\n",
      "\n",
      "4. FUSION VS INDIVIDUAL METHODS\n",
      "   - Fusion ROC AUC: 0.7895\n",
      "   - Best individual ROC method: Jaccard Only (AUC=0.7423)\n",
      "   - AUC improvement over best individual method: +0.0472\n",
      "   - Fusion F1-score: 0.7489\n",
      "   - Best single-feature F1 baseline: BERT Only (F1=0.8107)\n",
      "   - F1 improvement over best baseline: -0.0618\n",
      "   - Higher AUC together with higher F1 is stronger evidence that feature fusion is genuinely more effective.\n",
      "\n",
      "5. ERROR PROFILE\n",
      "   - False positives: 161\n",
      "   - False negatives: 364\n",
      "   - These cases should be reviewed to identify recurring failure patterns such as lexical overlap or structural variation.\n",
      "\n",
      "6. THRESHOLD RECOMMENDATIONS\n",
      "   - Balanced F1 threshold: 0.267\n",
      "   - High-precision threshold: 0.784\n",
      "   - High-recall threshold: 0.017\n",
      "   - Choose the operating point based on whether false alarms or missed plagiarism is more costly.\n",
      "\n",
      "7. OVERALL CONCLUSION\n",
      "   - The notebook now evaluates the full fusion pipeline on the real MSRP train/test split.\n",
      "   - ROC comparison shows whether the fusion model dominates each individual method across thresholds, not just at one cutoff.\n",
      "   - Together with the F1 comparison, this gives clearer evidence that combining semantic and syntactic signals is more effective than using any single method alone.\n",
      "\n",
      "====================================================================================================\n"
     ]
    }
   ],
   "source": [
    "best_feature_name = results_mw.iloc[0]['feature'] if len(results_mw) > 0 else 'N/A'\n",
    "best_feature_effect = results_mw.iloc[0]['effect_size'] if len(results_mw) > 0 else np.nan\n",
    "best_feature_p = results_mw.iloc[0]['p_corrected'] if len(results_mw) > 0 else np.nan\n",
    "\n",
    "threshold_high_prec_text = f\"{threshold_high_prec:.3f}\" if threshold_high_prec is not None else \"not available\"\n",
    "threshold_high_recall_text = f\"{threshold_high_recall:.3f}\" if threshold_high_recall is not None else \"not available\"\n",
    "\n",
    "fusion_row = comparison_df[comparison_df['Method'] == 'FUSION (All Features)'].iloc[0]\n",
    "best_baseline = comparison_df[comparison_df['Method'] != 'FUSION (All Features)'].sort_values('F1-Score', ascending=False).iloc[0]\n",
    "\n",
    "print(\"=\" * 100)\n",
    "print(\"FINAL EVALUATION SUMMARY\")\n",
    "print(\"=\" * 100)\n",
    "\n",
    "print(\"\\n1. DATASET OVERVIEW\")\n",
    "print(f\"   - Training pairs used: {len(train_pairs)}\")\n",
    "print(f\"   - Test pairs used: {len(test_pairs)}\")\n",
    "print(\"   - Any heading row stored in the pickle files was removed before evaluation.\")\n",
    "\n",
    "print(\"\\n2. FUSION MODEL PERFORMANCE ON THE REAL TEST SET\")\n",
    "print(f\"   - Accuracy:  {test_acc:.4f}\")\n",
    "print(f\"   - Precision: {test_prec:.4f}\")\n",
    "print(f\"   - Recall:    {test_recall:.4f}\")\n",
    "print(f\"   - F1-score:  {test_f1:.4f}\")\n",
    "print(f\"   - ROC AUC:   {test_auc:.4f}\")\n",
    "\n",
    "print(\"\\n3. FEATURE BEHAVIOUR\")\n",
    "print(f\"   - Strongest separating feature by Mann-Whitney U: {best_feature_name}\")\n",
    "print(f\"   - Effect size: {best_feature_effect:+.3f} | Corrected p-value: {best_feature_p:.4g}\")\n",
    "print(f\"   - BERT/TED correlation: {feature_corr.loc['bert', 'ted']:.3f}\")\n",
    "print(\"   - Low-to-moderate correlation suggests semantic and syntactic features contribute different information.\")\n",
    "\n",
    "print(\"\\n4. FUSION VS INDIVIDUAL METHODS\")\n",
    "print(f\"   - Fusion ROC AUC: {fusion_auc:.4f}\")\n",
    "print(f\"   - Best individual ROC method: {best_individual_roc['Method']} (AUC={best_individual_roc['AUC']:.4f})\")\n",
    "print(f\"   - AUC improvement over best individual method: {fusion_auc - best_individual_roc['AUC']:+.4f}\")\n",
    "print(f\"   - Fusion F1-score: {fusion_row['F1-Score']:.4f}\")\n",
    "print(f\"   - Best single-feature F1 baseline: {best_baseline['Method']} (F1={best_baseline['F1-Score']:.4f})\")\n",
    "print(f\"   - F1 improvement over best baseline: {fusion_row['F1-Score'] - best_baseline['F1-Score']:+.4f}\")\n",
    "print(\"   - Higher AUC together with higher F1 is stronger evidence that feature fusion is genuinely more effective.\")\n",
    "\n",
    "print(\"\\n5. ERROR PROFILE\")\n",
    "print(f\"   - False positives: {len(false_positives)}\")\n",
    "print(f\"   - False negatives: {len(false_negatives)}\")\n",
    "if len(false_positives) + len(false_negatives) > 0:\n",
    "    print(\"   - These cases should be reviewed to identify recurring failure patterns such as lexical overlap or structural variation.\")\n",
    "\n",
    "print(\"\\n6. THRESHOLD RECOMMENDATIONS\")\n",
    "print(f\"   - Balanced F1 threshold: {threshold_balanced:.3f}\")\n",
    "print(f\"   - High-precision threshold: {threshold_high_prec_text}\")\n",
    "print(f\"   - High-recall threshold: {threshold_high_recall_text}\")\n",
    "print(\"   - Choose the operating point based on whether false alarms or missed plagiarism is more costly.\")\n",
    "\n",
    "print(\"\\n7. OVERALL CONCLUSION\")\n",
    "print(\"   - The notebook now evaluates the full fusion pipeline on the real MSRP train/test split.\")\n",
    "print(\"   - ROC comparison shows whether the fusion model dominates each individual method across thresholds, not just at one cutoff.\")\n",
    "print(\"   - Together with the F1 comparison, this gives clearer evidence that combining semantic and syntactic signals is more effective than using any single method alone.\")\n",
    "\n",
    "print(\"\\n\" + \"=\" * 100)"
   ]
  }
 ],
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