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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1a45cbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dca90192",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NaN Values per Column:\n",
      "file       0\n",
      "cwe        0\n",
      "label    400\n",
      "code       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data_path = \"C:\\\\Users\\\\MartyNattakit\\\\Desktop\\\\CodeSentinel2025\\\\datasets\\\\cleaned\\\\cwe_top5_sampled_with_juliet_none.csv\"\n",
    "df = pd.read_csv(data_path)\n",
    "\n",
    "# Check for NaN values\n",
    "print(\"NaN Values per Column:\")\n",
    "print(df.isna().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a24bf6fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clean data and map labels\n",
    "label_map = {\n",
    "    'none': 0,\n",
    "    'cwe121': 1,\n",
    "    'cwe78': 2,\n",
    "    'cwe190': 3,\n",
    "    'cwe191': 4,\n",
    "    'cwe122': 5\n",
    "}\n",
    "df['label'] = df['cwe'].astype(str).str.strip().str.lower().map(label_map).fillna(0)\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fccabacb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Duplicate Analysis:\n",
      "Total samples: 2400\n",
      "Unique samples: 1730\n",
      "Number of duplicate code snippets: 121\n"
     ]
    }
   ],
   "source": [
    "duplicate_counts = df['code'].value_counts()\n",
    "print(\"\\nDuplicate Analysis:\")\n",
    "print(f\"Total samples: {len(df)}\")\n",
    "print(f\"Unique samples: {len(df['code'].unique())}\")\n",
    "print(f\"Number of duplicate code snippets: {sum(duplicate_counts > 1)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e9a980a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_clean = df.dropna(subset=['label'])\n",
    "df_clean = df_clean.drop_duplicates(subset=['code'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "be23c261",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualization function\n",
    "def visualize_data_quality(df, df_clean):\n",
    "    plt.style.use('default')\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(16, 13))\n",
    "    \n",
    "    # 1. Original vs Clean Distribution\n",
    "    df_counts = df['cwe'].value_counts().sort_index()\n",
    "    df_clean_counts = df_clean['cwe'].value_counts().sort_index()\n",
    "    x = np.arange(len(df_counts.index))\n",
    "    width = 0.35\n",
    "    \n",
    "    axes[0,0].bar(x - width/2, df_counts, width, label='Original', color='skyblue')\n",
    "    axes[0,0].bar(x + width/2, df_clean_counts, width, label='Clean', color='lightgreen')\n",
    "    axes[0,0].set_xticks(x)\n",
    "    axes[0,0].set_xticklabels(df_counts.index, rotation=45)\n",
    "    axes[0,0].set_title('CWE Distribution: Original vs Clean', pad=20)\n",
    "    axes[0,0].legend()\n",
    "    axes[0,0].grid(True, alpha=0.3)\n",
    "    \n",
    "    # 2. Duplicate Analysis by CWE\n",
    "    duplicate_mask = df.duplicated(subset=['code'], keep=False)\n",
    "    duplicates = df[duplicate_mask]\n",
    "    dup_counts = duplicates['cwe'].value_counts()\n",
    "    \n",
    "    colors = plt.cm.Pastel1(np.linspace(0, 1, len(dup_counts)))\n",
    "    axes[0,1].pie(dup_counts, labels=dup_counts.index, autopct='%1.1f%%', \n",
    "                  colors=colors, startangle=90)\n",
    "    axes[0,1].set_title('Distribution of Duplicates by CWE', pad=20)\n",
    "    \n",
    "    # 3. Cross-CWE Heatmap\n",
    "    cross_cwe_matrix = pd.crosstab(\n",
    "        duplicates['cwe'], \n",
    "        duplicates['cwe'],\n",
    "        values=duplicates['code'],\n",
    "        aggfunc='count'\n",
    "    ).fillna(0)\n",
    "    \n",
    "    im = axes[1,0].imshow(cross_cwe_matrix, cmap='YlOrRd')\n",
    "    axes[1,0].set_xticks(np.arange(len(cross_cwe_matrix.columns)))\n",
    "    axes[1,0].set_yticks(np.arange(len(cross_cwe_matrix.index)))\n",
    "    axes[1,0].set_xticklabels(cross_cwe_matrix.columns, rotation=45)\n",
    "    axes[1,0].set_yticklabels(cross_cwe_matrix.index)\n",
    "    plt.colorbar(im, ax=axes[1,0])\n",
    "    axes[1,0].set_title('Cross-CWE Duplicate Patterns', pad=20)\n",
    "    \n",
    "    # 4. Data Cleaning Impact\n",
    "    cleaning_impact = pd.DataFrame({\n",
    "        'Original': df_counts,\n",
    "        'Clean': df_clean_counts,\n",
    "        'Reduction %': ((df_counts - df_clean_counts) / df_counts * 100)\n",
    "    })\n",
    "    \n",
    "    bars = axes[1,1].bar(cleaning_impact.index, cleaning_impact['Reduction %'],\n",
    "                        color='lightcoral')\n",
    "    axes[1,1].set_title('Data Reduction % by CWE', pad=20)\n",
    "    axes[1,1].set_ylabel('Reduction %')\n",
    "    axes[1,1].set_xticklabels(cleaning_impact.index, rotation=45)\n",
    "    axes[1,1].grid(True, alpha=0.3)\n",
    "    \n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        axes[1,1].text(bar.get_x() + bar.get_width()/2., height,\n",
    "                      f'{height:.1f}%', ha='center', va='bottom')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return cleaning_impact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f35b483",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\MartyNattakit\\AppData\\Local\\Temp\\ipykernel_5976\\2336444108.py:57: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[1,1].set_xticklabels(cleaning_impact.index, rotation=45)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Cleaning Impact Statistics:\n",
      "-------------------------\n",
      "        Original  Clean  Reduction %\n",
      "cwe                                 \n",
      "CWE121       400    333        16.75\n",
      "CWE122       400    330        17.50\n",
      "CWE190       400    351        12.25\n",
      "CWE191       400    311        22.25\n",
      "CWE78        400    400         0.00\n",
      "none         400      5        98.75\n"
     ]
    }
   ],
   "source": [
    "impact_stats = visualize_data_quality(df, df_clean)\n",
    "print(\"\\nCleaning Impact Statistics:\")\n",
    "print(\"-------------------------\")\n",
    "print(impact_stats.round(2))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}