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  {
   "cell_type": "markdown",
   "id": "cell-001",
   "metadata": {},
   "source": [
    "# Module 5 — Lab 1: Auditing a Malware Feature Dataset\n",
    "\n",
    "**Topics:** provenance, schema, imbalance, missing values, leakage\n",
    "\n",
    "> **Lab version:** Complete every `TODO`, run the cells in order, and use the self-check before opening the Solution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-002",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "After completing this lab, you will be able to:\n",
    "\n",
    "- load a prepared table of static and dynamic malware features safely;\n",
    "- distinguish identifiers, provenance fields, features, and the binary label;\n",
    "- audit class imbalance, source composition, missing values, infinite values, and duplicates;\n",
    "- identify columns that must not enter a predictive model;\n",
    "- document dataset limitations before modeling.\n",
    "\n",
    "> **Safety boundary:** This practical uses numeric feature tables only. Do not download, open, or execute malware samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-003",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "DATA_PATH = Path(\"data/malware_features.csv\")\n",
    "\n",
    "if not DATA_PATH.exists():\n",
    "    raise FileNotFoundError(\n",
    "        \"Missing data/malware_features.csv. Follow data/README.md, place the three \"\n",
    "        \"official UCI CSV files under data/raw/, and run: python data/prepare_dataset.py\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-004",
   "metadata": {},
   "source": [
    "## 1. Load the prepared table\n",
    "\n",
    "The preparation utility adds two non-feature columns: `source` records provenance and `label` uses `0 = benign`, `1 = malware`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 1: load data/malware_features.csv into df.\n",
    "df = None\n",
    "\n",
    "# TODO 2: verify that source and label exist, then create feature_cols.\n",
    "required_columns = {\"source\", \"label\"}\n",
    "feature_cols = None\n",
    "\n",
    "print(\"Dataset shape:\", None)\n",
    "print(\"Number of candidate features:\", None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-006",
   "metadata": {},
   "source": [
    "## 2. Audit labels and provenance\n",
    "\n",
    "`source` is useful for detecting collection bias, but it must never be used as a predictor. In this dataset, collection source and class are closely related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO 3: calculate class counts and class proportions.\n",
    "class_counts = None\n",
    "class_proportions = None\n",
    "\n",
    "# TODO 4: create a source-by-label cross-tabulation with margins.\n",
    "source_by_label = None\n",
    "\n",
    "display(pd.DataFrame({\"count\": class_counts, \"proportion\": class_proportions}))\n",
    "display(source_by_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-008",
   "metadata": {},
   "source": [
    "## 3. Audit data quality and leakage risks\n",
    "\n",
    "Missing values will be imputed inside a training pipeline in later labs. Imputing before the split would allow test-set statistics to influence training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-009",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_features = df[feature_cols].apply(pd.to_numeric, errors=\"coerce\")\n",
    "\n",
    "# TODO 5: count infinite numeric values and exact duplicate rows.\n",
    "infinite_count = None\n",
    "duplicate_count = None\n",
    "\n",
    "# TODO 6: calculate per-feature missing rates and find constant features.\n",
    "missing_summary = None\n",
    "constant_features = None\n",
    "\n",
    "# TODO 7: find feature names containing possible label, source, filename, or hash tokens.\n",
    "suspicious_tokens = (\"label\", \"class\", \"target\", \"source\", \"filename\", \"hash\", \"md5\", \"sha\")\n",
    "suspicious_columns = None\n",
    "\n",
    "print(\"Exact duplicate rows:\", duplicate_count)\n",
    "print(\"Infinite numeric values:\", infinite_count)\n",
    "print(\"Constant candidate features:\", len(constant_features))\n",
    "print(\"Suspicious feature names:\", suspicious_columns[:20])\n",
    "display(missing_summary.head(10).to_frame())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-010",
   "metadata": {},
   "source": [
    "## 4. Write the dataset risk statement\n",
    "\n",
    "Record three facts before modeling:\n",
    "\n",
    "1. The positive class is the majority class.\n",
    "2. Benign and malware rows come from different source collections.\n",
    "3. A random split can measure interpolation within this historical table, but not performance on future malware campaigns."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-011",
   "metadata": {},
   "source": [
    "## Self-check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cell-012",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert {\"source\", \"label\"}.issubset(df.columns)\n",
    "assert len(feature_cols) > 0\n",
    "assert set(df[\"label\"].dropna().unique()) == {0, 1}\n",
    "assert set(feature_cols).isdisjoint({\"source\", \"label\"})\n",
    "assert class_counts.loc[1] > class_counts.loc[0]\n",
    "assert len(missing_summary) == len(feature_cols)\n",
    "assert infinite_count == 0\n",
    "print(\"✓ Lab 1 completed\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cell-013",
   "metadata": {},
   "source": [
    "## Reflection questions\n",
    "\n",
    "1. Why must `source` be excluded from the model?\n",
    "2. Why should missing-value imputation happen after the train-test split?\n",
    "3. Why is a random split not sufficient evidence for production deployment?"
   ]
  }
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