{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "68506def",
   "metadata": {},
   "source": [
    "**Author:** Dr. Mallarapu  \n",
    "**Created:** 2026-07-27  \n",
    "**Course:** SEAS 8414 \u2014 Security Analytics\n",
    "\n",
    "---\n",
    "\n",
    "### Goal of this notebook\n",
    "\n",
    "Train and audit a binary detector on a CICIoT2023 subset whose labels span 33 attack types.\n",
    "\n",
    "### What you will learn\n",
    "\n",
    "1. Read a majority-class baseline before trusting any accuracy figure.\n",
    "2. Find the strongest single feature, then test it by dropping it and refitting.\n",
    "3. Tell duplicate inflation apart from genuine signal.\n",
    "4. Report per-group recall, because the rare classes carry the risk.\n",
    "5. State plainly when you hold a subset rather than the published corpus.\n",
    "\n",
    "### Where this connects to the course text\n",
    "\n",
    "The text builds a defence pipeline; this notebook trains a classifier and audits it. The links below are to specific chapter objectives that share an *analytic move*, not to matching subject matter.\n",
    "\n",
    "- **Chapter 3: Vulnerability Assessment** \u2014 Learning objective 1 (section 3.1) frames assessment as **evidence grading**, not output collection. The A-F data-trust grade in section 10 is exactly that move, applied to a model score.\n",
    "- **Chapter 9: Supply-Chain Integrity and Counterfeit Detection** \u2014 Learning objectives 2 and 3 (section 9.1) separate documentation gaps from tamper evidence, and warn that screening signals are **not independent**. Both apply to artifact-derived features.\n",
    "- **Chapter 11: Formal Protocol Verification** \u2014 Section **11.1.2**, titled *Proved, tested, and hoped*, asks you to separate exactly those three. (Chapter 11 lists its objectives in \u00a711.0, not \u00a711.1 as the other chapters do.) The ablation does that job here: it tests whether the headline survives.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "050c3cad",
   "metadata": {},
   "source": [
    "# IoT Attack Detection on CICIoT2023\n",
    "### Model comparison + validity audit on CICIoT2023 (\u22651M records, via Kaggle)\n",
    "\n",
    "**Abstract:** CICIoT2023 (Neto et al., 2023) captures **33 attack types** against a large IoT testbed of 105 devices. The published corpus is ~47M flows. The Kaggle mirror used here provides a **1,048,575-row subset** (printed at load time). Every number below therefore describes that subset, not the full corpus. We compare four learners and audit the scores."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff937e8",
   "metadata": {},
   "source": [
    "## 1. Research problem\n",
    "\n",
    "**Task:** Detect IoT attacks (DDoS floods, DoS, reconnaissance, spoofing, web, brute force, Mirai) among benign IoT traffic. CICIoT2023 is one of the largest recent IoT-attack corpora, which makes the shortcut question especially relevant at scale."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dce5e8f",
   "metadata": {},
   "source": [
    "## 2. Literature review\n",
    "\n",
    "- **Neto et al. (2023)** \u2014 CICIoT2023: a real-time dataset and benchmark for large-scale attacks in an IoT environment.\n",
    "- **Sharafaldin et al. (2018)** \u2014 CICIDS2017/CICFlowMeter, the earlier CIC dataset lineage (the CICIoT2023 features come from Neto et al.'s own pipeline, not this paper).\n",
    "- **Sommer & Paxson (2010)** \u2014 the closed-world critique.\n",
    "\n",
    "**Related approaches and their known caveats** \u2014 drawn from the wider literature; these are **not** measurements reproduced on this exact corpus:\n",
    "\n",
    "| Reported approach | Known caveat |\n",
    "|---|---|\n",
    "| Neto et al. (2023) \u2014 ML/DL baselines | in-distribution; 105-device testbed |\n",
    "| IoT-NIDS papers | flood attacks are trivially separable |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35fa7240",
   "metadata": {},
   "source": [
    "## 3. Dataset provenance & honesty caveats\n",
    "\n",
    "| Property | Value |\n",
    "|---|---|\n",
    "| Source | Kaggle `subhajournal/iotintrusion` (CICIoT2023) |\n",
    "| Rows | **1,048,575** labeled flows (Kaggle subset of the ~47M published corpus) |\n",
    "| Label | `label` = BenignTraffic vs 33 attack types |\n",
    "| Access | Kaggle API token required |\n",
    "\n",
    "**Honestly:** flood attacks dominate and are trivially separable; per-family recall and the shortcut audit are the honest metrics, not aggregate accuracy.\n",
    "\n",
    "### Before you run this: getting the data\n",
    "\n",
    "This notebook downloads its own data on the first run, then caches it. You do not fetch anything by hand.\n",
    "\n",
    "**Dataset:** Kaggle `subhajournal/iotintrusion` -> `/tmp/kg_iotintrusion`. It is about **188 MB** on disk.\n",
    "\n",
    "**One-time setup.** Sign in at kaggle.com, open **Settings**, and under **API** choose **Create New Token**. Kaggle hands you a `kaggle.json` file. This notebook does *not* read that file. It reads a plain key file, so convert it once:\n",
    "\n",
    "```bash\n",
    "mkdir -p ~/.kaggle\n",
    "python3 -c \"import json;print(json.load(open('kaggle.json'))['key'],end='')\" > ~/.kaggle/access_token\n",
    "chmod 600 ~/.kaggle/access_token\n",
    "```\n",
    "\n",
    "Never paste the token into a cell, a commit, or a screenshot. If it leaks, revoke it from the same Settings page.\n",
    "\n",
    "**If the loader fails:**\n",
    "\n",
    "- `FileNotFoundError: ~/.kaggle/access_token` - you created `kaggle.json` but not the key file. Run the command above.\n",
    "- `401 Unauthorized` - the key is wrong, or a trailing newline crept in.\n",
    "- `403 Forbidden` - open the dataset page on Kaggle while signed in, accept its terms, then re-run the cell.\n",
    "\n",
    "The cache sits under `/tmp`, which macOS clears on reboot. To keep it, move the folder somewhere durable and symlink it back. Do **not** edit the path in the code cell below: that changes a code cell and invalidates the stored outputs you are reviewing."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68a39be",
   "metadata": {},
   "source": [
    "## 4. Solution design\n",
    "\n",
    "The methodology is deliberately two-track. We *earn* a headline score with standard modelling, then *interrogate* it with a validity audit. Only a verdict that survives both is reported. The diagram below is the shape of every notebook in this series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7e9993e",
   "metadata": {},
   "source": [
    "**Figure 4.1 \u2014 Solution design (methodology).**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 4.1 \u2014 Solution design (methodology).\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "452ed686",
   "metadata": {},
   "source": [
    "## 5. Implementation architecture\n",
    "\n",
    "Five stages \u2014 ingestion, preprocessing, modelling, evaluation, and a parallel validity-audit path \u2014 feed a single graded results ledger. Leakage defences (dropping label-derived and identifier columns) live in preprocessing, before any model sees the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96eb2063",
   "metadata": {},
   "source": [
    "**Figure 5.1 \u2014 Implementation architecture.**\n",
    "\n",
    "<img 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\" style=\"max-width:100%;height:auto;\" alt=\"Figure 5.1 \u2014 Implementation architecture.\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cac1f034",
   "metadata": {},
   "source": [
    "## 6. Data acquisition & preparation\n",
    "\n",
    "Every line below is commented so a student can re-run and modify each step. The cell ends by producing the standard analysis variables: `df`, `X` (clean numeric features), `y` (binary label), `feat` (feature names), and `family`. `family` is the per-group label used for the recall breakdown. It is an attack family on the intrusion corpora, but a transaction type, merchant category or malware category on the fraud/malware ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5290fd57",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import time, warnings; warnings.filterwarnings('ignore')   # keep output clean\n",
    "import numpy as np, pandas as pd                            # numerics + dataframes\n",
    "import matplotlib.pyplot as plt                             # static plots (embed in HTML+PDF)\n",
    "plt.rcParams['figure.dpi'] = 120                            # crisp figures\n",
    "RANDOM_STATE = 0                                            # single seed used everywhere\n",
    "np.random.seed(RANDOM_STATE)                                # reproducible sampling\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'                      # class names (overridden by some loaders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f406541f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded 1,048,575 rows x 41 features; positive rate 0.9767\n"
     ]
    }
   ],
   "source": [
    "import os, glob\n",
    "# Kaggle auth: token read from ~/.kaggle/access_token (students supply their own).\n",
    "os.environ.setdefault('KAGGLE_KEY', open(os.path.expanduser('~/.kaggle/access_token')).read().strip())\n",
    "import kaggle; kaggle.api.authenticate()\n",
    "REF = 'subhajournal/iotintrusion'; DEST = '/tmp/kg_' + REF.split('/')[-1]\n",
    "if not os.path.exists(DEST):                                   # download + unzip once (cached)\n",
    "    kaggle.api.dataset_download_files(REF, path=DEST, unzip=True, quiet=True)\n",
    "NROWS = 1_500_000                                              # per-file read cap (memory bound)\n",
    "files = sorted(glob.glob(DEST + '/**/*.csv', recursive=True))\n",
    "df = pd.concat([pd.read_csv(f, low_memory=False, nrows=NROWS) for f in files], ignore_index=True)  # combine day/part files\n",
    "df.columns = [str(c).strip() for c in df.columns]             # strip header whitespace\n",
    "LABEL = 'label'; FAMILY = 'label'\n",
    "df['y'] = (df[LABEL].astype(str).str.strip().str.lower() != 'benigntraffic').astype(int)  # benign=0\n",
    "df['family'] = df[FAMILY].astype(str).str.strip()             # descriptive attack family\n",
    "df = df.reset_index(drop=True)\n",
    "assert len(df) >= 1_000_000, f'floor not met: {len(df):,}'    # honesty gate: >= 1M rows\n",
    "DROP = list({LABEL, FAMILY, 'y', 'family'} | set([]))  # never leak label cols\n",
    "feat = [c for c in df.columns if c not in DROP]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "X = df[feat].copy()\n",
    "idlike = [c for c in X.select_dtypes(include='object').columns if X[c].nunique() > 0.5*len(X)]\n",
    "X = X.drop(columns=idlike)                                     # drop ID/timestamp-like leaky columns\n",
    "for c in X.select_dtypes(include='object').columns:           # encode remaining categoricals\n",
    "    X[c] = LabelEncoder().fit_transform(X[c].astype(str))\n",
    "X = X.apply(pd.to_numeric, errors='coerce').replace([np.inf,-np.inf],np.nan).fillna(0.0)\n",
    "X = X.clip(-1e15, 1e15)                                        # clip huge NetFlow counts (float32-safe)\n",
    "X = X.loc[:, X.nunique() > 1]                                  # drop constants\n",
    "import re                                                      # LightGBM rejects special chars in names\n",
    "_seen, _cols = {}, []\n",
    "for _c in X.columns:                                           # sanitize to unique, safe names\n",
    "    _c = re.sub(r'[^0-9A-Za-z_]+', '_', str(_c)).strip('_') or 'f'\n",
    "    _seen[_c] = _seen.get(_c, -1) + 1\n",
    "    _cols.append(_c if _seen[_c] == 0 else f'{_c}_{_seen[_c]}')\n",
    "X.columns = _cols; feat = list(X.columns)\n",
    "y = df['y'].to_numpy()                                         # STANDARD CONTRACT\n",
    "NEG_WORD, POS_WORD = 'benign', 'attack'               # class names for plots\n",
    "print(f'loaded {len(df):,} rows x {len(feat)} features; positive rate {y.mean():.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9715461",
   "metadata": {},
   "source": [
    "## 7. Exploratory data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "58dc2cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1320x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 1: class balance and the attack-family mix ---\n",
    "fig, ax = plt.subplots(1, 2, figsize=(11, 4))\n",
    "df['y'].map({0:NEG_WORD,1:POS_WORD}).value_counts().plot.bar(               # counts per class\n",
    "    ax=ax[0], color=['#2a9d8f','#e76f51']); ax[0].set_yscale('log')\n",
    "ax[0].set_title(f'Class balance ({NEG_WORD} vs {POS_WORD})'); ax[0].set_ylabel('records (log)')\n",
    "df.loc[df.y==1,'family'].value_counts().head(8).plot.barh(                  # top attack families\n",
    "    ax=ax[1], color='#e76f51'); ax[1].invert_yaxis(); ax[1].set_title('Top attack families')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "10175a15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Tv0DYSetHNTdEAJ7y+Tn55sgs7o/aI1J11AdJ1Sv6lC3E044wVQ7apWv7qahPae193VNIWJjqG1quF27kegQPF/rDv0dDXwcNZDT9hR2DaPzbUTQflyffvpphSp8/b3QXrGBH9bsoD2udVCI735UL774omm5YFe7GH3dVRqqao/3yvebBh26+5Mdli1bZt5Y6Rp110vtw61b0nWtusHGzuGLAMKbvtboBz0tSujWrZt5rdbKTg36tI0QwovuraR/Z/S9l7ZyO/vss01RgNJ+j/phX99D6Gm6wVYrv/RD/kMPPWQ2RC5cuJBWDyE80Ouxxx4zFb/VVevp5zzF4x++dK873cNDX9u1+Ex7PetnHR30N3XqVLnxxhvN5XgORJ5jjz1Wdu3aZT6T+Pzwww+m7aJuENDCJ83O9Hmiz5+PP/5YHEeDXyDQ3LlzrVatWtl6pxQXF1szZ860zjrrLCsuLs46+uijrSeffNLavXu35QQLFiyoctrGjRutF154wbJbly5drF9//bXCaXq8c+fOtq0pFCQnJ1tlZWXm+5SUFP/zsG3btrauy+VyWW6323z1fe872O3FF1+0nKZTp07moPeP73s96PP/qKOOst5++227l2j9+OOPVsuWLa1hw4ZZF110kfmqr7l6ul2OPfZYc/B4PP7v9TBy5Ejr3HPPtebPn2/b2vR537x5c2vGjBlWYWGh//T09HRrx44dtq0LQPjT15ybb77ZvN7ExsZagwcPtv73v//ZvSwcJB07dtStntUe1q9f77/csmXLrBNPPNFKSEgw7xknTJhgbd++ncclhI0YMaLGx75yZMLjH55ef/116/jjjzfvyaOioqzU1FRz/L333qtyWZ4Dkff60KdPnyqnz5s3zxo6dKjJq1q0aGFde+211t69ey0nouI3wgVOc/f1sNHdjXWLp5axO4FuedddbnTK/I4dO+jluB/ac1XvK93ypLskbNq0SZ599lmzu7S2gbCTE6tXfbSqXLfc6WAO3c1Dd9VIS0uTfv36meegU+jvwL333mvai1x00UV2L8dUuGslaGBfZGV3JdQVV1xhnvdOpRUFuquobh3W3YV1enRgD0m76HNLp9k6ibaf0GpfbXEyevRo06NZ7y/dM0X7aFHxCwAAAADVI/iNcF999VWF44mJiWYXbScEEL5djPUDvx40NNR+KU8//bQta9FAcPfu3eb76OjoKv18NfTS07RVht10F/f//Oc/5j7TQFV7VmkbA7u53W5HDRMM9I9//EN69eplehBrYKi7dXo8HhOg6y58TmJnO5bKrx8awunjqG1OdNiB7gaju8P+8ssvtq4NDbtxRqeU+wYg2kU3zOgudzqgT1vtaK9NHeKnu+ICAAAAAKoi+IXjaHCkoaV+wNehPVrhpX2VtMIrJibGtnVpPx/fII+NGzfWeLmOHTs24qpCm9OqVwPpY6x927SZu9NoH1jdCKK9p+ykE821553uHZCamurfW0C/Tp482da1aSiua5s/f75/g41TNjLoc0vvnyVLlpjnWCAdDuOEjTOBG7N89PVX++s+/vjjtlbZ6iBSnbatfyO096auZc2aNbatBwAAAACciuA3Ak2cONF8cFdazViTwCnGjeXkk082U9t1WrKGvRpu6fAE3Z38gw8+MAN+Zs2aJU6jlclaHWrXhOeHH35Ybr75Zv9u0TW54447xGnsrl7Vn70/dg6ec3I7Ft0zYM+ePWZ9vop4DeV0OJ6G+nbS1xIdtHLLLbeY1xMdxnPPPffIsGHDbB3Wp3RStO5ZodXSCQkJFc4bMWKE2EkHzOmAPG33oPefbvDSavjDDz9cjjvuONMyRukAzsYe2FcdDfG18txp7SkAAAAAwAmi7F4AGp+GMj46sdJJNPTQwFl70yqt1vOFvVFRUXLppZeKE0yYMMFUEx511FHy/PPPm++1Uk7DODvWuHbtWv/32m81lHz33XdSVlZm28/XFgXaDkM3NBx//PHmcXQSDeKc2o5FX0u2bdtm7r/OnTvLJ598Yto9aPhrNw1633vvPf90bb3PdEL3gAEDbA9+ly9fLl9//bXjnmvqrrvuMr10ffdb165dzZRz3UBy1VVXmde7umwsaejX29rohgeCXwAAAACoiopfOM7KlSvNYC3t66tVg6eccorpWbtixQrbB4D56K7FGRkZptdv7969ZcaMGSaIO/3009nlOMSqV3UNGhDqBoaff/5Zxo4da4KmQYMGiRPZXV0eSJ/3Gv5qde27774rF1xwganO157IN9xwg61r041a+lqSlJRknnf/+9//TFWytmLRwWp2Gj9+vFx33XW2D8Crjj6vZs+eLYcddpj/NB2gdtppp5mqfO2r26pVK0cNPAQAAAAAVI+K3wikgWpdaIjT2HR34mXLlplBZHfffbcZtKXBjYZLWvHrFBpu6Xo0hMvPzze7kqvMzEy7l2YC6OqCrcDhdHZxYvVqfHy82eVeD77+0toORYMt3Z29Z8+eYienVZf7aP9XDS59Pa31d1XvMw0GmzRpInbTAP/DDz80Iesf/vAHszeBr0et3XQd2rt81KhRJkS1u8VO5Ypfbemg95+2etANXPp78MADD5jztbfuGWecYesaAQAAAAB145wkDY3m2Wef9X+vu2Rr/0QNVvVDvlZ06a7bRx99tC3Br05q11BEB0XpQUM5J9IenRrA6X111llnmdN092gNV+2mgVxlBQUFFSpt7RLYv1QrffUxdpLA+8gJ7QqUVru/8MIL/l7OX3zxhb+63M7gV++r/v37m99ZH/3dtXMAYyBfH3P1t7/9TY455hjTr/aXX34Ru2kFsvYedqIrrrjCbGR4++23zfBA/bugfdf1sVY6ZFMPAAAAAADnI/iNQF9++aX/+6uvvtoMUNPdjn2eeOIJU3VrB901e+HChWa3e931XytrtZJQd8d3kpdeekmmTp1qgt6//vWv5jRtRWHn7u2+Ngp6X2kVbeU+tk6odNSQUKtp33zzTSkqKpK4uDg599xz5ZFHHpHk5GRb1qT3l7Yp0OecPu/HjRtnQkOntHpwcnW5BoRLly6VQw89VJxCN3xMnz7dtCfo1q2bXHPNNZKXlyfvvPOOqTg/9thj7V6iTJo0SZysX79+5gAAAAAACG30+I1wKSkpkpWVZXqG+uigrbS0NNt7OOq09o8//lheffVV04NVQ1atRrvzzjslFGjApJVyjUUn22vopbuQf/TRR/7TNQzWnsR2tyxQF198sak+vv/++02LgI0bN5qhTDpISvs620HbEmg/2PPPP9/s4h74u+AzdOhQsYs+njpMS6vLtf/qo48+aqrLR44cacJgO2nVu7bG8LUFCKyYvuOOO2xZ0/XXX29aE+heC/PmzTNfFy1aZAb33XTTTY74PVBLliwxA9709TewSl9bLdhJX/e1jYiuTwPzQDq8DwAAAAAQOgh+I5xOuL/22mvlyiuv9J+mVXFa8aiVfE6hlY4a5mgIrGFwKKip1+7Bpi0KtAesE2k/Uw0rA1t46GPbuXNn2ypYO3Xq5A8s9WvlVhl62rp168Qu2ndYq8t1kKBWl2vPa+1du2rVKtsHqP3xj3+scDzw/tPBb3bQAHru3LkmLNf7SINe3XCkrTGc4t///rfZ4KGhvlYia7uYDz74wFTl694EdtJBfbrxT8P8hISECuddcskltq0LAAAAABA8gt8Ip20V9AO+hkq+Hr+6a7mGrIMHD7Z7eSHNruBXzZ49W+bPn1+lmlCHg9mpa9euJrTs0aNHhfYep5xyiq3haqhr7OpyrdTWymh9zVBaGXrffffJ8uXL5YgjjpDbbrvNtmGMlX/vtKI7sA+xE+iGDn2NHThwoNnrQqtstTpZN7jNnDnT1rXp/acbG5zSqxkAAAAAUH/OLAtEo9Fwd+3atSYQ1F6YWqWnxwl9Q5cOstI+uhq8vfHGG5Keni6fffaZCZjsprvaazuFKVOmmOeafh01apQ5HfX3ww8/NOrdp/23Fy9e7D+urx0aWGqgr72Sb7/9drGzRcyCBQvM0Eo9aPV74HE92G337t0m9FUasJaUlJjhc05opaDB/erVq+1eBgAAQK10+LHubeY76OwQnXOis2t27NhR4bJ6/OabbzZ7gukeTYmJiWaex7333luhveH3339v3tfqeVoY5YTh2ABwoKj4BcKs4lerMLXfr1bX+qoJdWiZtgXQANhun376qekLu3XrVmnTpo0Z7nbCCSfYvayQ1tjPtRYtWpiqX33jrNW0elzD1cMOO8y0VzjxxBNt6z8c2LqjOna37vCFq88884wZiqf3lfaQ1t9VbQGxZs0aW9emPZI1xNehn9qaJZBdfZsBAACqC3617djdd99t9qbSwdG6x6PODdFZIvr5R9+r6h6u2l5L91C78MIL/QOcdQaEFsno+zDfxvfJkyebWST9+/c373H1fW3lNnAAEGrs2RcXjqGtHXRAk2/IUOXKOdSfXW8StGeu9jdV2ktXj/fp08dswXYCDXkJekObtoPx9X/97rvvzOBFDX2VVlpUfi1pTHYPvKsLDXh9rw86rE8rU/TDhYbBdtMNCCeddJL5alerGgAAgLrSlnGHH364+f7yyy+X5s2byz//+U8z40HP01kK2qLsxx9/rDLkV1uVPfvss/7jV199tdx6663mM5S+P9PgFwBCHcFvhNM/jlohqv0ljzzySBMO3nPPPTJs2DC7l+ZougvQtGnTqpyubxA01PFVttpBt1Dr1m7ddXzIkCGmgk/7nOqWcLvpcKg//elPZsu6j+56r2+47BoGhuD16tXLvJnWYWRavR0Y5G/bts0831B7xa9P79695YsvvnDM3cXvIQAACGXaVk6D3/Xr18vTTz8tGRkZZkB45dBX6d5NOnA38DgAhBtaPUQ4rdTToCY2NtbfFkB3gxkwYIDp9Yvgdq3X+1P7d9rJtxu7Vv1qOwV9M6PVhHfddZf069fP1rXpFvjMzEyz1d2nrKzM9CHWgVKon8YeYDZ37lwZM2aM6aWm/Wn1uAaYvgpWDfPffPPNRltPKNDKaN24pmrrMxy4UcQuOoBx1qxZ5nf1/fffN7tI6t8GKvUBAIDTWj3o+xRfxa/SYbla+PLUU0/JSy+9ZCp99X1MsINrtaDniSeeoNUDgJBHxW+E091YdLdtDX61T6eGvRpe7ty50+6lOZL2fFJ6n/m+D9zFXKun7aTtObRiz7flWnvo6uA+p9CgUAPz1NRU/2l6XIcnIHSqy4cPH26e77r7m7Z20A0hPrpL3fjx4xt1PaFAB+Jprzk1YcIEx/YffvDBB82AvquuusoMivRtWNDnG8EvAABwmj179pgCEu3xq+0LteevfsY97bTTzMBhfa8abOgLAOGEit8IN3HiRNPWQYMa7XH05JNPmj+MRx99tGmMj4p0q7LS3YUCwxsNbFq2bCmXXnqpeXNhJw3wt2/fXqGq1kkhpvaV1qpQHcKl4eGNN94orVu3Ns89hFZ1OepHq9yjopy53bVDhw6m5Y9W4esGmuzsbFPpotX6PN8AAIDTKn4r08Fu2uJBZxbo+y1tfadtDYNFxS+AcOHMT55oNLorjI9Wd2lf2M2bN8svv/zCo1BL/8uRI0fKxRdf7Mj76Nprr5UHHnjAbOF2u93iJA8//LDZ9UqHzWmLAK001/tRT0doVZej/lX5WkGr1SlOrD7R9SUnJ/s3aCnd8JCUlGTzygAAAKrSdgxaeKMhr/bo7dGjh/8zkBZQNGY7NABwIoLfCKUVXNOnT5effvpJunXrZioxtbfvO++8I88995wce+yxdi/R0TSs1F2yZ86cafroakuFc845x/TVtZtWI2swqOGvVu35whtl92TahIQEM8jtmWeeMe1EtDo5cH3qk08+kRNPPFEi3erVq/1BnO/7wOpy7cGK0KOV+H379jWDRpwwcLEynXx9xRVX+DfGaED9l7/8RcaOHWv30gAAAKodmhvY4zeQDnTTz7tacOLEDe4A0BgIfiPUDTfcIG+99ZZp6fD222+bfkiLFi2S448/3jTIr27qKX43e/ZsufDCC03vKN2daOnSpaYqUwcInHnmmbbeVRrcO50vvKzOuHHjqm1tEGlCoboc9aN9kHX3Q+37q5XbgRs/LrjgAlvvVg18b7rpJunatasUFhaaNiyXXHKJ/OMf/7B1XQAAAME6/fTTZcGCBeZz7/nnn88dCCAi0eM3QmnYMHfuXFOhqlWgGvS+99575o8j9k8r9nS3ohEjRvhP0/vz6quvluXLl3MXHgDdDZ5dsipyanU56kfD/OpoAPzFF1845m7Vqvy0tDSzh4juQXDHHXfYvSQAAIAKPX61aKmmil+dVaAt5tScOXOqzGLJzMw0eyL6BmMHoscvgHBB8BuhKg+MImwLjg7W2rFjh0RHR/tP016sWsWqbzDsdMIJJ1RpnxDYRiFUh5lFqsrV5Zs2bZL333/fEdXliAzFxcWmTYu2HQEAAAiV4Fd99913Mnr0aLMnk76nHjRokDn9hx9+kNdff90Mf/v444/NaRs3bvQPONf323rde+65xxzX9+EXXXRRo/zfAKAh0eohQukHeN3tRSu5lDbADzyuhg4dauMKnWnLli2mWlrfINx5550yZcoUM6BM+0ZNmjRJjjrqKLuXaN7QBNKAWvs5270LOepHqyz/+9//VltdTvAbunRDkfZuzsrKqvC6O3z4cHGiwDUCAACEiiOPPFKWLVsmDz30kHzwwQcm2NXPvr169ZLbbrvNVPb6rF+/3nzGC+Q7ru/FCX4BhCIqfiNUp06daqwKVXqe7l6O6qtRNQDWPlGLFy82A8p0l+iBAwfKG2+8YYJhp9HHcsKECSbcdzqqz0Onuhz189VXX8l5551nXmd9Qw537dplBm3+8ssvjrtbqfgFAAAAgNBExW+E2rBhg91LCEm+qjcNd+fNm2cCYF/fVScGvj4aLK1YscIxFdO1nV5T/9NIEwrV5aif66+/Xu69914z3C01NdW8hjz22GO2BvmvvfZajefpcw4AAAAAEHqo+AWCrEbVirzadnvu0KGDrffp/fffX+G49rPS3Zp0GNisWbPEif17tap19+7dtqzJqUK1uhx1e2z37NljKn59z32v1yutW7c21d12qMsGly+//LJR1gIAAAAAaBhU/AJByM/Plx49etQY/GqQU1BQYOt9qn1DAyUmJpp+sE7oSVXd/ZaRkSEej8eW9ThZqFaXY/804N22bZt5LDt37myGLmqor+GvXYIJdVeuXGleBwEAAAAAzkbwCwRBQ9Tc3FxH32czZsywewlVaH9aDcV1qGBMTEyF8zTs0sEKqGrz5s0VwvL09HRzf23atMkR1eWoH32+L1261AS/2sZjzJgxpm+zDh0JBYMHD662ch8AAAAA4Cy0egAaoFWB3WrrzxnoggsuEDts3LjRBJgnnXSSqW700TA4LS1NEhISbFmXk+m04bi4OEdXl6NhaA9dHaCmrWRCAQMYAQAAACA0EPwCYRB4BPbn1IrQb775xuxOri0BtEWA7lZ+9NFHy5w5c8RpQwa1zUP79u3tXorjOPW5hgN35ZVXynnnnQuHRMsAAArUSURBVGd+bzXADzVO3QAGAAAAAKjIXek4gFo4NYjT/py+Q+/eveWRRx4x7QA0ANavjz76qPTq1cvuZcqECRPk22+/Nd8///zz0qdPH7Ne/R4VhWIgiLrRDTITJ040G2euvfZamTt3LncdAAAAAKDBUfELhJmUlBTJysqqMDCtrKzMtFTIycmxdW0tW7Y0w9y0568GvtqPWKsHTz/9dFmzZo2ta3MaKn7D37Jly+TNN980h7y8PBk3bpzZSON0VPwCAAAAQGig4hcIMx07dpTp06dXOO2FF15wxCAwHWAVFRVlWjzk5+fLkUceaSqRMzMz7V6a4zi1uhwNp2/fvnL33XfLiy++KD179pR//etftt692ibm888/Nz2Ha9OlS5dGWxMAAAAAoP6o+AXCzMKFC2Xs2LGmqtbX41cD17feeksGDx5s69pGjx5tQiPtOax9fbW6cf369abXqYbBQKRYvHixqfSdOXOmGeQ3fvx4Offcc2XAgAG2rotqXgAAAAAIHwS/QBjSoHfBggUmYNU+okOGDDFBsE9hYaHEx8c3+rp27dolU6dONWv561//KklJSfLhhx/KqlWr5IYbbmj09QB26Nq1q2m/oq0ddMib3RtkAumarr76ahk1apTdSwEAAAAAHCCCXyAC2VHVV15eLscdd5x88sknEhsb26g/G3CSr7/+Wo4++mhxogsuuEDeffddGTFihNljIHDI4DPPPGPr2gAAAAAAwYkK8vIAwoBlWY3+M3XY3ObNm02lI8EvIpmGvuvWrTNtHrZu3Spt2rQxlbZaCWy3Hj16yK233mr3MgAAAAAADYCKXyAC2dXH84knnpDPPvtMbr/9dmnbtm2FakINv4BIMHv2bLnwwgvltNNOM8MYN23aJO+//7689NJLcuaZZ9q9PAAAAABAmCD4BSKQXcGvDrGqjgbA2goCiAR9+/Y1G0G0nYLP3LlzTW/d5cuX27o2r9crTz31lKlG1p7cP//8s1mbDonUNhAAAAAAgNBB8AtEILuCXwAizZo1kx07dlQYuKgDGVu2bCnZ2dm23kU333yz/Pjjj3LjjTeaqmRdz4YNG0wl8pIlS2xdGwAAAAAgOAS/QARKTU21PWACItWpp54q/fr1kylTpph+1yUlJXLXXXeZYPWjjz6ydW2tW7eWlStXmo1Dga8TKSkpkpOTY+vaAAAAAADBqX6/awAh684775RPPvlE8vLyarwMoS9gn6efflq+/vprE6xqj18NVefPny/PPvus7Q+LBtE6gFH5enBv375dmjdvbvPKAAAAAADBIvgFwoz26Lz33nslPT1dBg0aJNdff73MmjXLhDcA7KND3F544QVp166dzJs3T1atWmV66erXyy67TCzLsv3hueSSS2T8+PHy7bffmvX89NNPZm2XXnqp3UsDAAAAAASJVg9AmNLdx7///nuZPXu2qTDUCmAGqAH2+eMf/yhHH320XH755VXOmzFjhhmipl/t3nA0depUef75501QrSG1rvuWW24Rj8dj69oAAAAAAMEh+AXCzJYtW8xu41pRqAcdGjVs2DA55phj5OKLL7Z7eUDE6tSpk6xYsULi4+OrnFdYWCg9e/aUjRs32rI2AAAAAED4IfgFwozb7ZYuXbqYCr2zzz5bWrRoYfeSAIhIUlKS7N69W2JiYqqt0G/WrFmtvbkPltdee61Ol7vgggsO+loAAAAAAA2H4BcIMxriaMWvHnS3bd21XKt9tepXKw4B2KN///7y0EMPyUknnVTlvI8//thsrFm6dGmjr2vkyJH+7/U145tvvpHWrVubNg+6B8G2bdvM68icOXMafW0AAAAAgPoj+AXCmA50e/TRR+XJJ5+kxy9gs2effVYmT54sTz31lJx66qmmOl+D1g8++ECuueYamTRpUrX9fxvT1VdfLX369JHrrrvOf9oTTzwhy5YtM68jAAAAAIDQQfALhJnPPvvM9PbVQVGLFi2Srl27mopfPYwfP97u5QER7d5775V//OMfpvd2Wlqa7Nq1S6Kjo+WOO+6Qv/3tb3YvT1JSUiQrK6vCILeysjKz1pycHFvXBgAAAAAIDsEvEGZ8g9z0oLtnJycn270kAAH27NkjCxYsMAFr8+bNZciQIY75PR0wYIBce+21cuWVV/pPe+655+Txxx+3pQ0FAAAAAKD+CH4BAICxcOFCGTt2rKlC9vX41erkt956SwYPHsy9BAAAAAAhhOAXCDMa0uiu5K+//rps3bpV2rRpI+eff77cdtttEhMTY/fyAITAa4gOeNMe4TrkTSuSNQgGAAAAAIQWgl8gzFx//fWyePFiueuuu6Rjx46yceNG01f0sMMOk8cee8zu5QEAAAAAAKAREPwCYaZt27aybNkySU1N9Z+2e/du6du3r6kABoCaaGuHP//5z/L111+bHsSBysvLueMAAAAAIIS47V4AgIbl8XikqKiowml63O3m1x1A7S6//HIzcG7evHnSpEkTWbFihVxwwQUybdo07joAAAAACDFU/AJhZtKkSfL222/LzTffLB06dJBNmzbJ1KlT5ayzzpIpU6bYvTwADtasWTPZtm2bxMbGSkpKiuTk5EheXp4MGDBA1q5da/fyAAAAAABBiArmwgCcb/LkydKuXTt5+eWX/cPddNftyy67zO6lAXC4+Ph4M9xNg98WLVqYsFfD4J07d9q9NAAAAABAkKj4BQAAxsSJE2XYsGEyfvx4ue++++TJJ5+UmJgYGTp0qLzyyivcSwAAAAAQQgh+gTDw2muv1ely2qsTAOpq7ty5kpubKyeffLLpHw4AAAAACB0Ev0AY6N+/vxnIpHbv3m12za7M5XLJF198YcPqAIQa7Q2ekZEhQ4YMsXspAAAAAIB6IvgFwkBycrLs2bPHfN+0aVPZu3ev3UsCEILWrVsn5513nmzYsEEKCwtNte9bb70ls2fPlhdffNHu5QEAAAAAgkDwC4RJxe+ZZ54pvXr1kssvv1ymT58ulmVVuRytHgDU5rjjjpOxY8fKtddeK6mpqZKdnW3C3969e8vmzZu58wAAAAAghBD8AmHg119/lYceesjsnv3ll1/KMcccU+UytHoAsD8a9mq7GH290JYx+r1uRNJWMvo9AAAAACB0RNm9AAAHrmfPnqbKV40ePVo+/PBD7lYAQevatassWLBAhg4d6j/t66+/lh49enBvAgAAAECIIfgFwgyhL4D6mjp1qowZM0bOPvtsKSoqkhtuuEHefPNN+c9//sOdCgAAAAAhhlYPAADAb8uWLfLqq6+a1jHt2rWT888/Xzp16sQ9BAAAAAAhhuAXAIAId+WVV+73Ms8880yjrAUAAAAA0DBo9QAAQIRr27ZtheMPPvig3HrrrbatBwAAAABw4Kj4BQAAFaSmpkp2djb3CgAAAACEMLfdCwAAAM7icrnsXgIAAAAA4AAR/AIAAAAAAABAmKHHLwAAEe6bb76pcLy8vFwWLFgglmX5Txs6dKgNKwMAAAAA1Bc9fgEAiHCdO3feb+uHdevWNdp6AAAAAAAHjuAXAAAAAAAAAMIMPX4BAAAAAAAAIMwQ/AIAAAAAAABAmCH4BQAAAAAAAIAwQ/ALAAAAAAAAAGGG4BcAAAAAAAAAwgzBLwAAAAAAAACEGYJfAAAAAAAAAAgzBL8AAAAAAAAAEGYIfgEAAAAAAAAgzBD8AgAAAAAAAICEl/8HCq7eMjqDMn0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- EDA 2: feature correlation + a 2-D PCA projection ---\n",
    "from sklearn.preprocessing import StandardScaler                 # scale before PCA\n",
    "from sklearn.decomposition import PCA\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12, 5))\n",
    "topv = X[feat].var().sort_values().tail(12).index                # 12 highest-variance features\n",
    "im = ax[0].imshow(X[topv].corr(), cmap='coolwarm', vmin=-1, vmax=1)  # correlation heatmap\n",
    "ax[0].set_xticks(range(len(topv))); ax[0].set_xticklabels(topv, rotation=90, fontsize=7)\n",
    "ax[0].set_yticks(range(len(topv))); ax[0].set_yticklabels(topv, fontsize=7)\n",
    "ax[0].set_title('Feature correlation (top-variance)'); fig.colorbar(im, ax=ax[0], shrink=0.7)\n",
    "samp = X.sample(min(5000, len(X)), random_state=RANDOM_STATE)     # subsample for a fast PCA\n",
    "pc = PCA(n_components=2).fit_transform(StandardScaler().fit_transform(samp))\n",
    "ys = y[samp.index]                                               # aligned labels for coloring\n",
    "for lab,c in [(0,'#2a9d8f'),(1,'#e76f51')]:\n",
    "    ax[1].scatter(pc[ys==lab,0], pc[ys==lab,1], s=4, alpha=0.4, color=c,\n",
    "                  label={0:NEG_WORD,1:POS_WORD}[lab])\n",
    "ax[1].set_title('PCA projection (2 components)'); ax[1].legend(); ax[1].set_xlabel('PC1'); ax[1].set_ylabel('PC2')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edac58d1",
   "metadata": {},
   "source": [
    "## 8. Model comparison\n",
    "\n",
    "Four diverse learners share one held-out split, ranked by ROC-AUC.\n",
    "\n",
    "**Two honesty guards print with the table:**\n",
    "\n",
    "1. The models train on a *stratified subsample* of at most 120,000 rows. The full row count is printed above. So every score here is a subsample number, not a full-corpus claim.\n",
    "2. The **majority-class baseline accuracy** appears *inside* the ranking table. On imbalanced data, 0.99 accuracy can be worse than always guessing the majority class. Judge each model against that baseline, not against 0.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "93d3ec19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "materialized 1,048,575 rows | trained on 120,000 (stratified subsample) | held-out 262,144\n",
      "MAJORITY-CLASS BASELINE accuracy = 0.9767  (any model must beat THIS, not 0.5, to be interesting)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best model: RandomForest\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>accuracy</th>\n",
       "      <th>roc_auc</th>\n",
       "      <th>train_s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RandomForest</td>\n",
       "      <td>0.996513</td>\n",
       "      <td>0.999394</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.995911</td>\n",
       "      <td>0.999361</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LightGBM</td>\n",
       "      <td>0.995773</td>\n",
       "      <td>0.999336</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.989094</td>\n",
       "      <td>0.993944</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MajorityBaseline</td>\n",
       "      <td>0.976700</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                model  accuracy   roc_auc  train_s\n",
       "0        RandomForest  0.996513  0.999394      0.5\n",
       "1             XGBoost  0.995911  0.999361      0.3\n",
       "2            LightGBM  0.995773  0.999336      1.1\n",
       "3  LogisticRegression  0.989094  0.993944      0.2\n",
       "4    MajorityBaseline  0.976700  0.500000      0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Model comparison: four learners on the same held-out split ---\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "import xgboost as xgb, lightgbm as lgb\n",
    "\n",
    "# Stratified split keeps the class ratio in both halves.\n",
    "Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.25, random_state=RANDOM_STATE, stratify=y)\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "N_MATERIALIZED = len(y)                                          # the full corpus we loaded (see printed count)\n",
    "# HONEST DISCLOSURE: we do NOT train on all N. We fit on a STRATIFIED subsample (<=120k) because\n",
    "# these learners saturate long before then on this data. Every headline below is a SUBSAMPLE\n",
    "# number, not a full-corpus number \u2014 saying otherwise would be the fabrication this course forbids.\n",
    "if len(Xtr) > 120_000:\n",
    "    Xtr, _, ytr, _ = train_test_split(Xtr, ytr, train_size=120_000, random_state=RANDOM_STATE,\n",
    "                                      stratify=ytr)               # genuinely stratified, not random\n",
    "MAJORITY_BASELINE = max(np.mean(yte), 1 - np.mean(yte))          # accuracy of 'always predict majority'\n",
    "print(f'materialized {N_MATERIALIZED:,} rows | trained on {len(Xtr):,} (stratified subsample) | '\n",
    "      f'held-out {len(yte):,}')\n",
    "print(f'MAJORITY-CLASS BASELINE accuracy = {MAJORITY_BASELINE:.4f}  '\n",
    "      f'(any model must beat THIS, not 0.5, to be interesting)')\n",
    "\n",
    "models = {                                                        # four standard, diverse learners\n",
    "    'LogisticRegression': make_pipeline(StandardScaler(), LogisticRegression(max_iter=300)),  # scaled!\n",
    "    'RandomForest': RandomForestClassifier(n_estimators=60, n_jobs=-1, random_state=RANDOM_STATE),\n",
    "    'XGBoost': xgb.XGBClassifier(n_estimators=80, max_depth=6, tree_method='hist', n_jobs=-1,\n",
    "                                 eval_metric='logloss', random_state=RANDOM_STATE),\n",
    "    'LightGBM': lgb.LGBMClassifier(n_estimators=80, n_jobs=-1, verbose=-1, random_state=RANDOM_STATE),\n",
    "}\n",
    "rows, fitted = [], {}\n",
    "for name, m in models.items():                                    # fit + score each model\n",
    "    t = time.perf_counter(); m.fit(Xtr, ytr); fitted[name] = m\n",
    "    p = m.predict_proba(Xte)[:, 1]                                # positive-class probability on held-out\n",
    "    rows.append({'model': name, 'accuracy': round(accuracy_score(yte, (p>0.5).astype(int)), 6),\n",
    "                 'roc_auc': round(roc_auc_score(yte, p), 6),      # 6 dp: a 1.000000 is a red flag, not a win\n",
    "                 'train_s': round(time.perf_counter()-t, 1)})\n",
    "rows.append({'model': 'MajorityBaseline', 'accuracy': round(MAJORITY_BASELINE, 4),\n",
    "             'roc_auc': 0.5, 'train_s': 0.0})            # show the baseline IN the ranking table\n",
    "comparison = pd.DataFrame(rows).sort_values('roc_auc', ascending=False).reset_index(drop=True)\n",
    "_ranked = comparison[comparison.model != 'MajorityBaseline']\n",
    "best_name = _ranked.iloc[0]['model']; best = fitted[best_name]  # winner by ROC-AUC (excl. baseline)\n",
    "print('best model:', best_name); comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47251b8e",
   "metadata": {},
   "source": [
    "## 9. Results\n",
    "\n",
    "Diagnostics for the winning model, including **per-group recall**.\n",
    "\n",
    "The grouping comes from whatever the loader put in `family`. It is *not* always an attack taxonomy. On the intrusion corpora it is the attack family. On the fraud and malware corpora it is a transaction type, a merchant category or a malware category. On binary corpora it collapses to the positive class.\n",
    "\n",
    "Read it accordingly. Where the groups are genuinely rare classes, they reveal whether detection is real. The dominant flood classes do not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a93dc54",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1800x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1560x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operational FALSE-POSITIVE RATE @0.5 = 0.0699  (428 benign flagged of 6,119)\n",
      "worst per-family recalls: {'Recon-OSScan': 0.73, 'SqlInjection': 0.821, 'Recon-PortScan': 0.875, 'DictionaryBruteForce': 0.886, 'DNS_Spoofing': 0.894, 'BrowserHijacking': 0.895}\n"
     ]
    }
   ],
   "source": [
    "# --- Results for the best model: confusion, ROC, PR, importances, per-family recall ---\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, precision_recall_curve, recall_score\n",
    "pb = best.predict_proba(Xte)[:, 1]; pred = (pb > 0.5).astype(int)\n",
    "fig, ax = plt.subplots(1, 3, figsize=(15, 4))\n",
    "# (1) confusion matrix\n",
    "cm = confusion_matrix(yte, pred); ax[0].imshow(cm, cmap='Blues')\n",
    "ax[0].set_title(f'{best_name}: confusion'); ax[0].set_xticks([0,1]); ax[0].set_yticks([0,1])\n",
    "ax[0].set_xticklabels([NEG_WORD,POS_WORD]); ax[0].set_yticklabels([NEG_WORD,POS_WORD])\n",
    "for (i,j),v in np.ndenumerate(cm): ax[0].text(j,i,f'{v:,}',ha='center',va='center')\n",
    "# (2) ROC and PR curves\n",
    "fpr,tpr,_ = roc_curve(yte, pb); prec,rec,_ = precision_recall_curve(yte, pb)\n",
    "ax[1].plot(fpr,tpr,color='#264653'); ax[1].plot([0,1],[0,1],'--',c='grey')\n",
    "ax[1].set_title(f'ROC (AUC={roc_auc_score(yte,pb):.4f})'); ax[1].set_xlabel('FPR'); ax[1].set_ylabel('TPR')\n",
    "ax[2].plot(rec,prec,color='#e76f51'); ax[2].set_title('Precision-Recall'); ax[2].set_xlabel('recall'); ax[2].set_ylabel('precision')\n",
    "plt.tight_layout(); plt.show()\n",
    "\n",
    "# (3) feature importances + (4) per-attack-family recall\n",
    "fig, ax = plt.subplots(1, 2, figsize=(13, 5))\n",
    "imp, names = None, feat                                           # importances, robust to the scaled-LR pipeline\n",
    "if hasattr(best, 'feature_importances_'):                          # tree models\n",
    "    imp = best.feature_importances_; names = list(getattr(best, 'feature_names_in_', feat))[:len(imp)]\n",
    "elif hasattr(best, 'named_steps') and 'logisticregression' in getattr(best, 'named_steps', {}):\n",
    "    imp = np.abs(best.named_steps['logisticregression'].coef_[0]); names = feat  # LR pipeline\n",
    "elif hasattr(best, 'coef_'):\n",
    "    imp = np.abs(best.coef_[0]); names = feat\n",
    "if imp is not None:\n",
    "    pd.Series(imp, index=names[:len(imp)]).sort_values().tail(12).plot.barh(ax=ax[0], color='#264653')\n",
    "ax[0].set_title(f'{best_name}: top importances / |coef|')\n",
    "# Per-family recall, WORST-first so rare, hard classes are visible, not just the dominant floods.\n",
    "fam_te = df.loc[Xte.index, 'family']\n",
    "fr = {}\n",
    "for fam, cnt in fam_te[yte==1].value_counts().items():\n",
    "    if cnt < 5: continue                                          # need a few positives for a meaningful recall\n",
    "    mask = (fam_te==fam).to_numpy(); fr[fam] = recall_score(yte[mask], pred[mask], zero_division=0)\n",
    "srt = pd.Series(fr).sort_values()\n",
    "show = pd.concat([srt.head(9), srt.tail(3)]) if len(srt) > 12 else srt   # worst 9 + best 3\n",
    "show = show[~show.index.duplicated()]\n",
    "show.plot.barh(ax=ax[1], color=['#e76f51' if v < 0.5 else '#2a9d8f' for v in show]); ax[1].set_xlim(0,1)\n",
    "ax[1].set_title('Per-family recall (worst first; red < 0.5)')\n",
    "plt.tight_layout(); plt.show()\n",
    "# Operational numbers, not just figures: false-positive rate and the worst per-family recalls.\n",
    "tn, fp = int(cm[0,0]), int(cm[0,1])\n",
    "fpr_op = fp/(fp+tn) if (fp+tn) > 0 else float('nan')             # benign wrongly flagged @0.5\n",
    "print(f'operational FALSE-POSITIVE RATE @0.5 = {fpr_op:.4f}  ({fp:,} benign flagged of {fp+tn:,})')\n",
    "print('worst per-family recalls:', {k: round(v, 3) for k, v in srt.head(6).items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8994c19",
   "metadata": {},
   "source": [
    "## 10. Validity audit \u2014 is the score real?\n",
    "\n",
    "Three diagnostics. **(a)** How well can the *single best feature*, alone, separate the classes? A near-1.0 single-feature AUC means that feature is *near-sufficient* \u2014 a shortcut (which may be legitimate signal or an artifact), not the same as target leakage. **(b)** The exact-duplicate row rate. **(c)** The **train/test exact-row contamination** \u2014 the fraction of held-out rows that are duplicates of training rows, which is what actually inflates a held-out score. The trust grade is the *worse* of the single-feature and contamination concerns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac10b688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best single-feature AUC = 0.9906  (feature: rst_count)\n",
      "   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\n",
      "   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.\n",
      "exact-duplicate row rate (whole corpus) = 0.063\n",
      "TRAIN/TEST exact-row contamination       = 0.017  (single-feat grade D, contam grade A)\n",
      "==> data trust grade: D   (worse of the two; F = shortcut and/or heavy contamination)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# --- Validity audit: is the score real detection, or a data shortcut? ---\n",
    "from sklearn.metrics import roc_auc_score\n",
    "samp = X.sample(min(60_000, len(X)), random_state=1); ysamp = y[samp.index]\n",
    "aucs = {}\n",
    "for c in feat:                                                    # AUC of EACH feature alone\n",
    "    col = samp[c].to_numpy(float)\n",
    "    if col.std()==0: continue\n",
    "    a = roc_auc_score(ysamp, col); aucs[c] = max(a, 1-a)          # direction-agnostic\n",
    "best_auc = max(aucs.values()); best_col = max(aucs, key=aucs.get)\n",
    "dup_rate = 1 - X.drop_duplicates().shape[0]/len(X)               # exact-duplicate feature rows (whole set)\n",
    "# The statistic that actually inflates a held-out score is TRAIN/TEST CONTAMINATION: how many test\n",
    "# rows are exact duplicates of a training row. Measure it directly on the split used above.\n",
    "_trkeys = set(map(tuple, np.round(Xtr.to_numpy(), 6)))\n",
    "_te = np.round(Xte.to_numpy(), 6)[:50_000]\n",
    "contam = float(np.mean([tuple(r) in _trkeys for r in _te]))      # fraction of test rows seen in train\n",
    "# Trust grade reflects BOTH failure modes and takes the WORSE of the two: a near-perfect single\n",
    "# feature (shortcut) OR heavy train/test contamination each independently invalidate the headline.\n",
    "_ga = 'F' if best_auc>=0.999 else 'D' if best_auc>=0.99 else 'C' if best_auc>=0.95 else 'B' if best_auc>=0.85 else 'A'\n",
    "_gc = 'F' if contam>=0.5 else 'D' if contam>=0.3 else 'C' if contam>=0.15 else 'B' if contam>=0.05 else 'A'\n",
    "grade = max(_ga, _gc)                                            # 'max' letter = worse grade (A best, F worst)\n",
    "print(f'best single-feature AUC = {best_auc:.4f}  (feature: {best_col})')\n",
    "print(f'   note: a near-1.0 single-feature AUC means this feature is *near-sufficient* (a shortcut),\\n'\n",
    "      f'   which may be legitimate signal OR an artifact \u2014 it is NOT the same as target leakage.')\n",
    "print(f'exact-duplicate row rate (whole corpus) = {dup_rate:.3f}')\n",
    "print(f'TRAIN/TEST exact-row contamination       = {contam:.3f}  (single-feat grade {_ga}, contam grade {_gc})')\n",
    "print(f'==> data trust grade: {grade}   (worse of the two; F = shortcut and/or heavy contamination)')\n",
    "s = pd.Series(aucs).sort_values().tail(15)\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "s.plot.barh(ax=ax, color=['#e76f51' if v>=0.99 else '#457b9d' for v in s]); ax.axvline(0.5,ls='--',c='grey')\n",
    "ax.set_xlim(0.5,1.0); ax.set_title('Single-feature ROC-AUC (red = near-perfect shortcut)'); ax.set_xlabel('AUC alone')\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84035e70",
   "metadata": {},
   "source": [
    "## 11. Ablation \u2014 does the headline survive removing the artifacts?\n",
    "\n",
    "Narrating a shortcut is not enough. We *retrain the winning model* after (1) de-duplicating the corpus (removing the train/test contamination) and (2) dropping the single strongest feature. We report the held-out AUC each time. **Read the result honestly, both ways:** if the AUC **collapses**, the headline was a contamination/shortcut artifact. If it **barely moves** \u2014 common on *simulated* corpora \u2014 that is **not vindication**. It means the classes are separable by *many* redundant features, because the attack and benign distributions barely overlap \u2014 which is its own generation artifact. The numbers below decide which story is true here, not the prose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "620a9d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ablation \u2014 how much of the headline survives once each artifact is removed:\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>setting</th>\n",
       "      <th>held_out_auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>headline (as-is)</td>\n",
       "      <td>0.999394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>de-duplicated (6% rows removed)</td>\n",
       "      <td>0.999367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shortcut feature dropped (rst_count)</td>\n",
       "      <td>0.999441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                setting  held_out_auc\n",
       "0                      headline (as-is)      0.999394\n",
       "1       de-duplicated (6% rows removed)      0.999367\n",
       "2  shortcut feature dropped (rst_count)      0.999441"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# --- Ablation: SHOW the inflation empirically, don't just narrate it ---\n",
    "from sklearn.base import clone\n",
    "def _retrain_auc(Xa, ya):                                        # re-split, stratified-subsample, refit best family\n",
    "    xtr, xte, ytr2, yte2 = train_test_split(Xa, ya, test_size=0.25, random_state=RANDOM_STATE, stratify=ya)\n",
    "    if len(xtr) > 120_000:\n",
    "        xtr, _, ytr2, _ = train_test_split(xtr, ytr2, train_size=120_000, random_state=RANDOM_STATE, stratify=ytr2)\n",
    "    m = clone(best); m.fit(xtr, ytr2)\n",
    "    return roc_auc_score(yte2, m.predict_proba(xte)[:, 1])\n",
    "base_auc = roc_auc_score(yte, best.predict_proba(Xte)[:, 1])     # (0) the headline held-out AUC\n",
    "Xdd = X.drop_duplicates(); ydd = y[Xdd.index]                    # (1) de-duplicated corpus\n",
    "auc_dedup = _retrain_auc(Xdd, ydd)\n",
    "auc_noshort = _retrain_auc(X.drop(columns=[best_col]), y) if best_col in X.columns else base_auc  # (2) drop shortcut\n",
    "ablation = pd.DataFrame([\n",
    "    {'setting': 'headline (as-is)',              'held_out_auc': round(base_auc, 6)},\n",
    "    {'setting': f'de-duplicated ({1-len(Xdd)/len(X):.0%} rows removed)', 'held_out_auc': round(auc_dedup, 6)},\n",
    "    {'setting': f'shortcut feature dropped ({best_col})', 'held_out_auc': round(auc_noshort, 6)},\n",
    "])\n",
    "print('Ablation \u2014 how much of the headline survives once each artifact is removed:')\n",
    "ablation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25e0df8e",
   "metadata": {},
   "source": [
    "## 12. Reproducibility & robustness\n",
    "\n",
    "The cell prints the pinned seed and library versions, then a 3-fold cross-validated ROC-AUC for the winning model. If that fit cannot be computed, it prints an explicit `CV UNAVAILABLE` line rather than a number.\n",
    "\n",
    "**Read that CV number inside its bounds \u2014 the print line does not carry them.** The folds are cut from `Xtr.iloc[:40_000]`: the first 40,000 rows of the 120,000-row stratified *training* subsample, roughly 3.8% of the 1,048,575 rows loaded. Held-out rows never enter it, duplicate rows are not removed first, and the folds are random rather than temporal or device-grouped. So the printed mean and standard deviation measure **how stable the fit is across re-splits of that training slice** \u2014 nothing about full-corpus or out-of-distribution generalisation. The held-out AUC in section 8 and the ablation in section 11 are the numbers that speak to the headline. This one only says the fit does not swing from fold to fold within that slice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f7e84b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed=0 | numpy 2.3.5 | sklearn 1.9.0 | xgboost 1.6.2 | lightgbm 4.7.0\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest 3-fold CV ROC-AUC = 0.9992 +/- 0.0001  (mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)\n"
     ]
    }
   ],
   "source": [
    "# --- Reproducibility & robustness ---\n",
    "import sklearn\n",
    "from sklearn.model_selection import StratifiedKFold, cross_val_score\n",
    "print(f'seed={RANDOM_STATE} | numpy {np.__version__} | sklearn {sklearn.__version__} | '\n",
    "      f'xgboost {xgb.__version__} | lightgbm {lgb.__version__}')\n",
    "# 3-fold cross-validated ROC-AUC of the winning model (fresh clone, bounded subsample) -> mean +/- std.\n",
    "from sklearn.base import clone\n",
    "cvX, cvy = Xtr.iloc[:40_000], ytr[:40_000]\n",
    "def _auc_scorer(est, Xv, yv):                                   # robust to xgboost's 2-col predict_proba\n",
    "    p = est.predict_proba(Xv)\n",
    "    p = p[:, 1] if getattr(p, 'ndim', 1) == 2 else p\n",
    "    return roc_auc_score(yv, p)\n",
    "try:\n",
    "    cv = cross_val_score(clone(best), cvX, cvy,\n",
    "                         cv=StratifiedKFold(3, shuffle=True, random_state=RANDOM_STATE),\n",
    "                         scoring=_auc_scorer, error_score='raise')\n",
    "    assert np.all(np.isfinite(cv)), 'non-finite CV folds'   # FAIL CLOSED: never narrate a NaN as evidence\n",
    "    print(f'{best_name} 3-fold CV ROC-AUC = {cv.mean():.4f} +/- {cv.std():.4f}  '\n",
    "          f'(mean +/- std across 3 stratified folds; a small std means a stable estimate on this split)')\n",
    "except Exception as e:\n",
    "    print(f'CV UNAVAILABLE ({type(e).__name__}: {str(e)[:60]}); rely on the single held-out AUC above \u2014 '\n",
    "          f'we do NOT report a CV number we could not compute')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2aa98f1",
   "metadata": {},
   "source": [
    "## 13. Scientific conclusion\n",
    "\n",
    "**Validity ledger \u2014 read the headline against these printed numbers:** Majority-class baseline **accuracy**: **0.9767**. The accuracy column must clear that bar to mean anything. For ROC-AUC the trivial baseline is 0.5, not that figure. Winning learner: **RandomForest** (3-fold CV ROC-AUC **0.9992 +/- 0.0001**). That CV is bounded. It runs on the first 40,000 rows of the 120,000-row training subsample, duplicates left in and held-out rows excluded. So it reports fit stability on that slice and not full-corpus generalisation. Strongest *single* feature: `rst_count` at AUC **0.9906**. The ablation refutes a single-feature story. Dropping that feature barely moves the AUC: **0.999394 \u2192 0.999441**. So the separability is **multi-feature**. That reflects how this corpus was generated, not one leaky column. De-duplication lowers the AUC only slightly, to **0.999367**. Repeated rows account for a negligible part of the headline. Data-trust grade: **D**. It is the worse of two independent sub-checks. Single-feature AUC 0.9906 scores **D**. Train/test exact-row overlap 0.017 scores **A**. The single-feature check drives the grade, not the overlap check. Train/test overlap separately scores A, so overlap is not the issue here. On this A-best / F-worst scale, a D or F means the headline is optimistic. Treat it as a benchmark number, not a deployment estimate. Operational false-positive rate at threshold 0.5: **0.0699**. Worst per-group recalls, exactly as printed: {`Recon-OSScan`: 0.73, `SqlInjection`: 0.821, `Recon-PortScan`: 0.875, `DictionaryBruteForce`: 0.886, `DNS_Spoofing`: 0.894, `BrowserHijacking`: 0.895}. The weakest group sits at **0.730**, which is where detection is thinnest. **Disclosed limitation:** categorical columns are integer-encoded before the split. The encoder therefore sees the test set's category values. On an all-numeric corpus that step is a no-op. The mapping never consults the label, so no *label* information leaks. It is still transductive. A deployed system would need an unseen-category bucket. **How the audit numbers are computed:** overlap is measured on the first 50,000 held-out rows, so read it as a sampled estimate. Each ablation re-splits and refits, so tiny differences are re-split noise. The de-duplication variant keeps the first label when a feature vector appears twice. The cross-validation folds are cut from a 40,000-row slice of the training subsample (~3.8% of the corpus). So read **0.9992 +/- 0.0001** as fold-to-fold stability on that slice, rather than a corpus-wide estimate. **Scope:** the split is random, not temporal or entity-grouped. Every number above therefore measures in-distribution separability only."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c00e4c",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. Neto, E.C.P. et al. (2023). CICIoT2023: A real-time dataset and benchmark for large-scale attacks in IoT environment. *Sensors*, 23(13).\n",
    "2. Sharafaldin, I. et al. (2018). Toward Generating a New Intrusion Detection Dataset. *ICISSP*.\n",
    "3. Sommer, R. & Paxson, V. (2010). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. *IEEE S&P*."
   ]
  }
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