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  {
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   "id": "1f325e4e",
   "metadata": {},
   "source": [
    "# Module 3 — Lab 3: A Random Forest Security Pipeline\n",
    "\n",
    "**Topics:** Scikit-learn Pipeline, imputation, Random Forest, fit, predict\n",
    "\n",
    "> **Solution version:** This notebook contains the complete solution. Run the cells in order and compare the results with your Lab notebook.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ba9f17",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "After completing this lab, you will be able to:\n",
    "\n",
    "- create one reusable preprocessing-and-model pipeline;\n",
    "- train a Random Forest binary classifier;\n",
    "- generate predictions for unseen data;\n",
    "- explain why tree models do not require feature standardization.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5dc04708",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "DATA_PATH = Path(\"data/cicids2017_binary_sample.csv\")\n",
    "RANDOM_STATE = 42\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3746fff2",
   "metadata": {},
   "source": [
    "## 1. Prepare the binary classification data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96d91cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(DATA_PATH)\n",
    "df.columns = df.columns.str.strip()\n",
    "df = df.drop_duplicates().replace([np.inf, -np.inf], np.nan)\n",
    "\n",
    "X = df.drop(columns=\"Label\").select_dtypes(include=\"number\")\n",
    "y = df[\"Label\"].astype(str).str.strip().str.upper().ne(\"BENIGN\").astype(int)\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.2,\n",
    "    random_state=RANDOM_STATE,\n",
    "    stratify=y,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69eb8c19",
   "metadata": {},
   "source": [
    "## 2. Build one pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0dbae88b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
    "    (\"model\", RandomForestClassifier(\n",
    "        n_estimators=100,\n",
    "        max_depth=12,\n",
    "        random_state=RANDOM_STATE,\n",
    "        n_jobs=-1,\n",
    "    )),\n",
    "])\n",
    "\n",
    "print(pipeline)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea59d402",
   "metadata": {},
   "source": [
    "## Why there is no `StandardScaler`\n",
    "\n",
    "Random Forest makes decisions by comparing feature values with thresholds. Scaling a column does not change the ordering of its values, so standardization is not required for this model family. The imputer remains inside the pipeline because the model needs finite values.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f9a97a",
   "metadata": {},
   "source": [
    "## 3. Train and predict\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6194b17",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.fit(X_train, y_train)\n",
    "y_pred = pipeline.predict(X_test)\n",
    "\n",
    "print(\"Predictions:\", y_pred[:20])\n",
    "print(\"Number of test samples:\", len(y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ad6c87",
   "metadata": {},
   "source": [
    "## 4. Predict one flow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7eca199f",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_flow = X_test.iloc[[0]]\n",
    "one_prediction = int(pipeline.predict(one_flow)[0])\n",
    "\n",
    "print(\"Prediction:\", one_prediction)\n",
    "print(\"Meaning:\", \"ATTACK\" if one_prediction == 1 else \"BENIGN\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90d72108",
   "metadata": {},
   "source": [
    "## Self-check\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98f48da9",
   "metadata": {},
   "outputs": [],
   "source": [
    "assert list(pipeline.named_steps) == [\"imputer\", \"model\"]\n",
    "assert pipeline.named_steps[\"model\"].n_estimators == 100\n",
    "assert len(y_pred) == len(y_test)\n",
    "assert set(np.unique(y_pred)).issubset({0, 1})\n",
    "assert one_prediction in {0, 1}\n",
    "print(\"✓ Lab 3 completed\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6100563d",
   "metadata": {},
   "source": [
    "## Reflection questions\n",
    "\n",
    "1. What problem does the imputer solve inside the pipeline?\n",
    "2. Why is a pipeline safer than manually transforming train and test data in separate cells?\n",
    "3. What does `random_state=42` contribute to the experiment?\n"
   ]
  },
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   "id": "aeab0fb6-54bf-4e88-87bc-400d658d67b7",
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   "source": []
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