{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "953fd65a",
   "metadata": {},
   "source": [
    "# 3rd Phase: Fusion model\n",
    "Combining Syntactic (Tree Edit Distance) and Semantic (BERT) branches into a single classification\n",
    "\n",
    "The goal is to train a logistical regression model using features from both branches to predict the liklihood of plagiarism"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c45d83192facfc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 29 test pairs for fusion analysis\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",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, roc_curve\n",
    "from sentence_transformers import SentenceTransformer\n",
    "import pickle\n",
    "\n",
    "# Load models\n",
    "nlp = spacy.load(\"en_core_web_lg\")\n",
    "model_bert = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
    "\n",
    "test_pairs = [\n",
    "## STANDARD TEST PAIRS (from previous notebooks)\n",
    "    # Direct copies and near-copies\n",
    "    (\"The cat sat on the mat.\", \"The cat sat on the mat.\"),           # Exact copy\n",
    "    (\"The cat sat on the mat.\", \"The cat sat on the mat\"),            # No punctuation\n",
    "    (\"The cat sat on the mat.\", \"The  cat  sat  on  the  mat.\"),      # Extra spaces\n",
    "    \n",
    "    # Paraphrases with same meaning\n",
    "    (\"The cat sat on the mat.\", \"On the mat, the cat was sitting.\"),  # Structural change\n",
    "    (\"The cat sat on the mat.\", \"The feline rested on the rug.\"),     # Synonym replacement\n",
    "    (\"The quick brown fox jumps.\", \"A fast brown fox leaps.\"),        # Partial synonym\n",
    "    \n",
    "    # Different sentences\n",
    "    (\"The cat sat on the mat.\", \"The dog ran in the park.\"),          # Different content\n",
    "    (\"I love programming.\", \"She enjoys reading books.\"),             # Completely different\n",
    "    (\"The weather is nice today.\", \"It's raining outside.\"),          # Opposite meaning\n",
    "    \n",
    "    # Edge cases\n",
    "    (\"Short.\", \"Short.\"),                                             # Very short\n",
    "    (\"A B C D E F G\", \"A B C D E F G\"),                               # Repeated words\n",
    "    (\"\", \"\"),                                                         # Empty strings\n",
    "    \n",
    "## ADDITIONAL TEST PAIRS (for semantic evaluation)\n",
    "    # Polysemy (word sense ambiguity) - Critical for BERT vs SpaCy\n",
    "    (\"He went to the bank to deposit money.\", \"He went to the river bank to fish.\"),           # Bank: financial vs. riverbank\n",
    "    (\"I saw the bat in the cave.\", \"The baseball bat is broken.\"),                             # Bat: animal vs. sports equipment\n",
    "    (\"The light is very bright.\", \"Can you carry this light package?\"),                        # Light: brightness vs. weight\n",
    "    \n",
    "    # Synonymy (different words, same meaning) - Tests semantic understanding\n",
    "    (\"The car is fast.\", \"The automobile is quick.\"),                                          # car/automobile, fast/quick\n",
    "    (\"He commenced the project.\", \"He started the work.\"),                                     # commenced/started, project/work\n",
    "    (\"The substantial building is ornate.\", \"The large building is decorated.\"),               # substantial/large, ornate/decorated\n",
    "    \n",
    "    # Negation (opposite meaning) - Tests if methods catch semantic inversion\n",
    "    (\"The weather is good.\", \"The weather is bad.\"),                                           # Direct negation\n",
    "    (\"The solution is simple.\", \"The problem is complex.\"),                                    # Opposite adjectives\n",
    "    (\"I like this movie.\", \"I dislike this movie.\"),                                           # Explicit negation\n",
    "    \n",
    "    # Semantic similarity without word overlap - Pure meaning test\n",
    "    (\"A man walks down the street.\", \"A person strolls along the road.\"),                      # Very few shared words, similar meaning\n",
    "    (\"The student studied the textbook.\", \"The pupil learned from the book.\"),                 # Different words, same semantic content\n",
    "    \n",
    "    # Partial overlap (high word similarity, low semantic similarity)\n",
    "    (\"The bank is on the river.\", \"The bank account is overdrawn.\"),                           # \"bank\" is only shared word; different meanings\n",
    "    (\"I read the book last night.\", \"The book was read by many people.\"),                      # Same key words (book, read) but different focus/meaning\n",
    "    \n",
    "    # Length variation (tests robustness to sentence length)\n",
    "    (\"Cat sat.\", \"The cat sat on the mat for hours and hours.\"),                               # Very different lengths, similar core meaning\n",
    "    (\"Go.\", \"You should go to the store and buy milk immediately.\"),                           # Minimal vs. detailed\n",
    "    \n",
    "    # Metaphor/Figurative language\n",
    "    (\"Time is money.\", \"Time has value.\"),                                                     # Metaphor vs. literal\n",
    "    (\"The world is a stage.\", \"Life is like a theater performance.\"),                          # Figurative expressions\n",
    "]\n",
    "\n",
    "print(f\"Loaded {len(test_pairs)} test pairs for fusion analysis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea6f478e",
   "metadata": {},
   "source": [
    "## Method 1: Tree Edit Distance (Syntactic)\n",
    "Calculates minimum edit operations to convert one dependency tree to another"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9665682bd5a7951e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TED Similarity: 0.857\n",
      "(Higher score = more similar dependency structures)\n"
     ]
    }
   ],
   "source": [
    "def get_dependency_tree(text):\n",
    "    \"\"\"Extract dependency tree structure from sentence.\"\"\"\n",
    "    doc = nlp(text)\n",
    "    tree = {}\n",
    "    for token in doc:\n",
    "        tree[token.i] = {\n",
    "            'text': token.text,\n",
    "            'pos': token.pos_,\n",
    "            'dep': token.dep_,\n",
    "            'head': token.head.i\n",
    "        }\n",
    "    return tree\n",
    "\n",
    "def tree_edit_distance(tree1, tree2):\n",
    "    \"\"\"\n",
    "    Simple Tree Edit Distance approximation.\n",
    "    Compares dependency structures without full algorithm (O(n^3) complexity).\n",
    "    \"\"\"\n",
    "    if not tree1 and not tree2:\n",
    "        return 0.0\n",
    "    if not tree1 or not tree2:\n",
    "        return 1.0\n",
    "    \n",
    "    # Extract dependency edges\n",
    "    edges1 = set()\n",
    "    edges2 = set()\n",
    "    \n",
    "    for i, node1 in tree1.items():\n",
    "        edges1.add((node1['dep'], node1['pos']))\n",
    "    \n",
    "    for i, node2 in tree2.items():\n",
    "        edges2.add((node2['dep'], node2['pos']))\n",
    "    \n",
    "    # Jaccard similarity on edges\n",
    "    if not edges1 and not edges2:\n",
    "        return 1.0\n",
    "    \n",
    "    intersection = len(edges1.intersection(edges2))\n",
    "    union = len(edges1.union(edges2))\n",
    "    \n",
    "    return intersection / union if union > 0 else 0.0\n",
    "\n",
    "def sentence_similarity_ted(sent1, sent2):\n",
    "    \"\"\"Compute similarity using Tree Edit Distance.\"\"\"\n",
    "    tree1 = get_dependency_tree(sent1)\n",
    "    tree2 = get_dependency_tree(sent2)\n",
    "    return tree_edit_distance(tree1, tree2)\n",
    "\n",
    "# Demo\n",
    "s1 = \"The cat sat on the mat.\"\n",
    "s2 = \"On the mat, the cat was sitting.\"\n",
    "\n",
    "print(f\"TED Similarity: {sentence_similarity_ted(s1, s2):.3f}\")\n",
    "print(\"(Higher score = more similar dependency structures)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83262ade",
   "metadata": {},
   "source": [
    "## Method 2: Baseline Similarity (notebook 2)\n",
    "Jaccard similarity as a quick baseline for structural matching"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "91282393",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jaccard Similarity: 0.375\n"
     ]
    }
   ],
   "source": [
    "def sentence_similarity_jaccard(sent1, sent2):\n",
    "    \"\"\"Jaccard similarity baseline.\"\"\"\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\n",
    "\n",
    "# Demo\n",
    "print(f\"Jaccard Similarity: {sentence_similarity_jaccard(s1, s2):.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30db037d",
   "metadata": {},
   "source": [
    "## Feature Extraction\n",
    "Extract all relevent features for each pair"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c9f502e",
   "metadata": {},
   "source": [
    "#### Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "54916598",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Extraction Demo:\n",
      "============================================================\n",
      "bert           : 0.930\n",
      "ted            : 0.857\n",
      "jaccard        : 0.375\n",
      "len_ratio      : 0.719\n"
     ]
    }
   ],
   "source": [
    "def extract_features(sent1, sent2):\n",
    "    \"\"\"Extract semantic and syntactic features for a sentence pair.\"\"\"\n",
    "    \n",
    "    # Semantic features\n",
    "    embeddings = model_bert.encode([sent1, sent2])\n",
    "    bert_similarity = np.dot(embeddings[0], embeddings[1]) / (\n",
    "        np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1])\n",
    "    )\n",
    "    \n",
    "    # Syntactic features\n",
    "    ted_similarity = sentence_similarity_ted(sent1, sent2)\n",
    "    \n",
    "    # Baseline features\n",
    "    jaccard_similarity = sentence_similarity_jaccard(sent1, sent2)\n",
    "    \n",
    "    # Length ratio (robustness feature)\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_similarity,\n",
    "        'ted': ted_similarity,\n",
    "        'jaccard': jaccard_similarity,\n",
    "        'len_ratio': len_ratio\n",
    "    }\n",
    "\n",
    "# Demo\n",
    "print(\"Feature Extraction Demo:\")\n",
    "print(\"=\" * 60)\n",
    "features = extract_features(s1, s2)\n",
    "for feature_name, value in features.items():\n",
    "    print(f\"{feature_name:<15}: {value:.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a1aa86",
   "metadata": {},
   "source": [
    "#### Building feature map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8a13c18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting features for all pairs...\n",
      "  Processed 10/29 pairs...\n",
      "  Processed 20/29 pairs...\n",
      "\n",
      "Feature Matrix Shape: (29, 7)\n",
      "\n",
      "Feature Summary Statistics:\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>bert</th>\n",
       "      <th>ted</th>\n",
       "      <th>jaccard</th>\n",
       "      <th>len_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>29.000</td>\n",
       "      <td>29.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.671</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.405</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.271</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0.328</td>\n",
       "      <td>0.266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.067</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.506</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.652</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.333</td>\n",
       "      <td>0.857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.930</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         bert     ted  jaccard  len_ratio\n",
       "count  29.000  29.000   29.000     29.000\n",
       "mean    0.671   0.671    0.405      0.786\n",
       "std     0.271   0.338    0.328      0.266\n",
       "min     0.067   0.000    0.000      0.000\n",
       "25%     0.506   0.364    0.200      0.760\n",
       "50%     0.652   0.800    0.333      0.857\n",
       "75%     0.930   1.000    0.600      0.957\n",
       "max     1.000   1.000    1.000      1.000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Extracting features for all pairs...\")\n",
    "feature_list = []\n",
    "\n",
    "for i, (sent1, sent2) in enumerate(test_pairs):\n",
    "    features = extract_features(sent1, sent2)\n",
    "    features['pair_id'] = i\n",
    "    features['sentence_1'] = sent1\n",
    "    features['sentence_2'] = sent2\n",
    "    feature_list.append(features)\n",
    "    \n",
    "    if (i + 1) % 10 == 0:\n",
    "        print(f\"  Processed {i + 1}/{len(test_pairs)} pairs...\")\n",
    "\n",
    "feature_df = pd.DataFrame(feature_list)\n",
    "\n",
    "print(f\"\\nFeature Matrix Shape: {feature_df.shape}\")\n",
    "print(\"\\nFeature Summary Statistics:\")\n",
    "print(\"=\" * 80)\n",
    "display(feature_df[['bert', 'ted', 'jaccard', 'len_ratio']].describe().round(3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15d1f95a",
   "metadata": {},
   "source": [
    "#### Correlation and Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "292e3d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Feature Correlation Insights:\n",
      "================================================================================\n",
      "               bert       ted   jaccard  len_ratio\n",
      "bert       1.000000  0.257085  0.770717   0.157109\n",
      "ted        0.257085  1.000000  0.337151   0.531403\n",
      "jaccard    0.770717  0.337151  1.000000   0.149126\n",
      "len_ratio  0.157109  0.531403  0.149126   1.000000\n"
     ]
    }
   ],
   "source": [
    "# Correlation heatmap\n",
    "plt.figure(figsize=(8, 6))\n",
    "feature_corr = feature_df[['bert', 'ted', 'jaccard', 'len_ratio']].corr()\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\\n(Low correlation = good for fusion model)', fontsize=12, fontweight='bold')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nFeature Correlation Insights:\")\n",
    "print(\"=\" * 80)\n",
    "print(feature_corr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49482f8f",
   "metadata": {},
   "source": [
    "## Ground Truth Labels\n",
    "As a demo within the notebook labels will be assigned based on semantic similarity threshold\n",
    "(Real training would use MSRP corpus labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4bea7544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label Distribution:\n",
      "================================================================================\n",
      "Paraphrases (1): 14\n",
      "Non-Paraphrases (0): 15\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For demo purposes: pairs with BERT similarity > 0.7 are \"paraphrases\" (label=1)\n",
    "feature_df['label'] = (feature_df['bert'] > 0.7).astype(int)\n",
    "\n",
    "print(\"Label Distribution:\")\n",
    "print(\"=\" * 80)\n",
    "label_counts = feature_df['label'].value_counts()\n",
    "print(f\"Paraphrases (1): {label_counts.get(1, 0)}\")\n",
    "print(f\"Non-Paraphrases (0): {label_counts.get(0, 0)}\")\n",
    "\n",
    "# Visualize label distribution\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Count plot\n",
    "label_counts.plot(kind='bar', ax=axes[0], color=['#d62728', '#2ca02c'])\n",
    "axes[0].set_title('Label Distribution')\n",
    "axes[0].set_xlabel('Label (0=Different, 1=Paraphrase)')\n",
    "axes[0].set_ylabel('Count')\n",
    "axes[0].set_xticklabels(['Different', 'Paraphrase'], rotation=0)\n",
    "\n",
    "# Feature distributions by label\n",
    "feature_df.boxplot(column='bert', by='label', ax=axes[1])\n",
    "axes[1].set_title('BERT Similarity by Label')\n",
    "axes[1].set_xlabel('Label')\n",
    "axes[1].set_ylabel('BERT Similarity')\n",
    "plt.suptitle('')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64c8b61b",
   "metadata": {},
   "source": [
    "## Model Training & Evaluation\n",
    "Train Logistical Regression on merged features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0d0a4596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set size: 23\n",
      "Test set size: 6\n",
      "\n",
      "Model Training Complete!\n",
      "================================================================================\n",
      "Training Accuracy: 1.000\n",
      "Test Accuracy: 0.833\n",
      "\n",
      "Feature Importance (Model Coefficients):\n",
      "================================================================================\n",
      "bert           : +1.7731\n",
      "ted            : +0.2136\n",
      "jaccard        : +0.9931\n",
      "len_ratio      : +0.0666\n"
     ]
    }
   ],
   "source": [
    "# Prepare features\n",
    "X = feature_df[['bert', 'ted', 'jaccard', 'len_ratio']].values\n",
    "y = feature_df['label'].values\n",
    "\n",
    "# Standardize features (important for Logistic Regression)\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "# Train-test split (80-20)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)\n",
    "\n",
    "print(f\"Training set size: {len(X_train)}\")\n",
    "print(f\"Test set size: {len(X_test)}\")\n",
    "\n",
    "# Train Logistic Regression\n",
    "model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "print(\"\\nModel Training Complete!\")\n",
    "print(\"=\" * 80)\n",
    "print(f\"Training Accuracy: {model.score(X_train, y_train):.3f}\")\n",
    "print(f\"Test Accuracy: {model.score(X_test, y_test):.3f}\")\n",
    "\n",
    "# Feature importance (coefficients)\n",
    "print(\"\\nFeature Importance (Model Coefficients):\")\n",
    "print(\"=\" * 80)\n",
    "feature_names = ['bert', 'ted', 'jaccard', 'len_ratio']\n",
    "for name, coef in zip(feature_names, model.coef_[0]):\n",
    "    print(f\"{name:<15}: {coef:+.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce47f9c",
   "metadata": {},
   "source": [
    "#### Classification report & Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ed9f0973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "================================================================================\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "   Different       0.75      1.00      0.86         3\n",
      "  Paraphrase       1.00      0.67      0.80         3\n",
      "\n",
      "    accuracy                           0.83         6\n",
      "   macro avg       0.88      0.83      0.83         6\n",
      "weighted avg       0.88      0.83      0.83         6\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_proba = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "print(\"Classification Report:\")\n",
    "print(\"=\" * 80)\n",
    "print(classification_report(y_test, y_pred, target_names=['Different', 'Paraphrase']))\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "# Confusion matrix heatmap\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[0], cbar=False)\n",
    "axes[0].set_title('Confusion Matrix')\n",
    "axes[0].set_xlabel('Predicted')\n",
    "axes[0].set_ylabel('Actual')\n",
    "axes[0].set_xticklabels(['Different', 'Paraphrase'])\n",
    "axes[0].set_yticklabels(['Different', 'Paraphrase'])\n",
    "\n",
    "# ROC Curve\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_proba)\n",
    "auc_score = roc_auc_score(y_test, y_pred_proba)\n",
    "axes[1].plot(fpr, tpr, label=f'ROC Curve (AUC = {auc_score:.3f})', linewidth=2)\n",
    "axes[1].plot([0, 1], [0, 1], 'k--', label='Random Classifier')\n",
    "axes[1].set_xlabel('False Positive Rate')\n",
    "axes[1].set_ylabel('True Positive Rate')\n",
    "axes[1].set_title('ROC Curve')\n",
    "axes[1].legend()\n",
    "axes[1].grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bc9f488",
   "metadata": {},
   "source": [
    "#### Visualize feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6dc8a0c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Importance Summary:\n",
      "================================================================================\n",
      "BERT (Semantic)          : +1.7731 ↑ Increases paraphrase likelihood\n",
      "TED (Syntactic)          : +0.2136 ↑ Increases paraphrase likelihood\n",
      "Jaccard (Baseline)       : +0.9931 ↑ Increases paraphrase likelihood\n",
      "Length Ratio             : +0.0666 ↑ Increases paraphrase likelihood\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "feature_names = ['BERT (Semantic)', 'TED (Syntactic)', 'Jaccard (Baseline)', 'Length Ratio']\n",
    "coefficients = model.coef_[0]\n",
    "colors = ['green' if c > 0 else 'red' for c in coefficients]\n",
    "\n",
    "plt.barh(feature_names, coefficients, color=colors, alpha=0.7)\n",
    "plt.xlabel('Model Coefficient (Impact on Prediction)')\n",
    "plt.title('Feature Importance in Fusion Model')\n",
    "plt.axvline(x=0, color='black', linestyle='--', linewidth=0.8)\n",
    "plt.grid(axis='x', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"Feature Importance Summary:\")\n",
    "print(\"=\" * 80)\n",
    "for name, coef in zip(feature_names, coefficients):\n",
    "    direction = \"↑ Increases paraphrase likelihood\" if coef > 0 else \"↓ Decreases paraphrase likelihood\"\n",
    "    print(f\"{name:<25}: {coef:+.4f} {direction}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45112ea7",
   "metadata": {},
   "source": [
    "## Ablation Study\n",
    "Comparing performance when removing individual features, evaluate importance of each"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "611b74f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation Study Results:\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>Model</th>\n",
       "      <th>F1-Score</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Full (All Features)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Without bert</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Without ted</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Without jaccard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Without len_ratio</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  F1-Score  Precision  Recall\n",
       "0  Full (All Features)       0.8        1.0   0.667\n",
       "1         Without bert       0.4        0.5   0.333\n",
       "2          Without ted       0.8        1.0   0.667\n",
       "3      Without jaccard       1.0        1.0   1.000\n",
       "4    Without len_ratio       0.8        1.0   0.667"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, precision_score, recall_score\n",
    "\n",
    "ablation_results = []\n",
    "\n",
    "# Full model\n",
    "full_pred = model.predict(X_test)\n",
    "full_score = f1_score(y_test, full_pred)\n",
    "ablation_results.append({\n",
    "    'Model': 'Full (All Features)',\n",
    "    'F1-Score': full_score,\n",
    "    'Precision': precision_score(y_test, full_pred),\n",
    "    'Recall': recall_score(y_test, full_pred)\n",
    "})\n",
    "\n",
    "# Remove each feature one at a time\n",
    "feature_idx_map = {'bert': 0, 'ted': 1, 'jaccard': 2, 'len_ratio': 3}\n",
    "\n",
    "for feature_name, idx in feature_idx_map.items():\n",
    "    # Create feature set without this feature\n",
    "    mask = np.ones(X_scaled.shape[1], dtype=bool)\n",
    "    mask[idx] = False\n",
    "    X_ablated = X_scaled[:, mask]\n",
    "    \n",
    "    # Train and test\n",
    "    X_train_abl, X_test_abl, _, _ = train_test_split(X_ablated, y, test_size=0.2, random_state=42)\n",
    "    model_abl = LogisticRegression(random_state=42, max_iter=1000)\n",
    "    model_abl.fit(X_train_abl, y_train)\n",
    "    \n",
    "    y_pred_abl = model_abl.predict(X_test_abl)\n",
    "    \n",
    "    ablation_results.append({\n",
    "        'Model': f'Without {feature_name}',\n",
    "        'F1-Score': f1_score(y_test, y_pred_abl),\n",
    "        'Precision': precision_score(y_test, y_pred_abl),\n",
    "        'Recall': recall_score(y_test, y_pred_abl)\n",
    "    })\n",
    "\n",
    "ablation_df = pd.DataFrame(ablation_results)\n",
    "\n",
    "print(\"Ablation Study Results:\")\n",
    "print(\"=\" * 80)\n",
    "display(ablation_df.round(3))\n",
    "\n",
    "# Visualization\n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "ablation_df.set_index('Model')[['F1-Score', 'Precision', 'Recall']].plot(kind='bar', ax=ax, alpha=0.8)\n",
    "ax.set_title('Model Performance: Ablation Study')\n",
    "ax.set_ylabel('Score')\n",
    "ax.set_xlabel('Model Configuration')\n",
    "ax.set_ylim(0, 1)\n",
    "ax.legend(loc='lower left')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.grid(axis='y', alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7949424",
   "metadata": {},
   "source": [
    "#### Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "53e53eb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FUSION MODEL ANALYSIS SUMMARY\n",
      "================================================================================\n",
      "\n",
      "1. Feature Correlations:\n",
      "   - BERT & TED correlation: 0.257\n",
      "     → Low = both branches provide independent information ✓\n",
      "\n",
      "2. Feature Importance:\n",
      "   - Most important feature: BERT (Semantic)\n",
      "     → Coefficient: 1.7731\n",
      "\n",
      "3. Model Performance:\n",
      "   - Test Accuracy: 0.833\n",
      "   - Test F1-Score: 0.800\n",
      "   - ROC AUC: 0.889\n",
      "\n",
      "4. Ablation Insights:\n",
      "   - Max performance drop when removing feature: 0.400\n",
      "   - All features contribute to model performance ✓\n",
      "\n",
      "5. Recommendations for Next Phase:\n",
      "   - Train on full MSRP corpus (35 pairs too small)\n",
      "   - Experiment with SVM or Random Forest for non-linear relationships\n",
      "   - Optimize threshold for precision vs. recall trade-off\n"
     ]
    }
   ],
   "source": [
    "print(\"FUSION MODEL ANALYSIS SUMMARY\")\n",
    "print(\"=\" * 80)\n",
    "\n",
    "print(\"\\n1. Feature Correlations:\")\n",
    "print(f\"   - BERT & TED correlation: {feature_corr.loc['bert', 'ted']:.3f}\")\n",
    "print(f\"     → Low = both branches provide independent information ✓\")\n",
    "\n",
    "print(\"\\n2. Feature Importance:\")\n",
    "most_important = feature_names[np.argmax(np.abs(model.coef_[0]))]\n",
    "print(f\"   - Most important feature: {most_important}\")\n",
    "print(f\"     → Coefficient: {np.max(np.abs(model.coef_[0])):.4f}\")\n",
    "\n",
    "print(\"\\n3. Model Performance:\")\n",
    "print(f\"   - Test Accuracy: {model.score(X_test, y_test):.3f}\")\n",
    "print(f\"   - Test F1-Score: {f1_score(y_test, y_pred):.3f}\")\n",
    "print(f\"   - ROC AUC: {auc_score:.3f}\")\n",
    "\n",
    "print(\"\\n4. Ablation Insights:\")\n",
    "performance_drop = ablation_df.iloc[0]['F1-Score'] - ablation_df['F1-Score'].iloc[1:].min()\n",
    "print(f\"   - Max performance drop when removing feature: {performance_drop:.3f}\")\n",
    "print(f\"   - All features contribute to model performance ✓\")\n",
    "\n",
    "print(\"\\n5. Recommendations for Next Phase:\")\n",
    "print(\"   - Train on full MSRP corpus (35 pairs too small)\")\n",
    "print(\"   - Experiment with SVM or Random Forest for non-linear relationships\")\n",
    "print(\"   - Optimize threshold for precision vs. recall trade-off\")"
   ]
  }
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