{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CYB008 Baseline Classifier — Inference Example\n",
    "\n",
    "End-to-end demo: load the trained XGBoost and PyTorch MLP models from the Hugging Face repo and predict the **SOC alert triage outcome** from a per-alert record.\n",
    "\n",
    "**Models predict one of 5 outcome classes:** `auto_resolved_soar`, `duplicate_merged`, `false_positive_closed`, `true_positive_remediated`, `true_positive_escalated`.\n",
    "\n",
    "**This is a baseline reference model**, not a production SOC triage system. See the model card and **especially `leakage_diagnostic.json`** for the structural-leakage findings (three columns were dropped as oracles)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Install dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --quiet xgboost torch safetensors pandas numpy huggingface_hub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Download model artifacts from Hugging Face"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "\n",
    "REPO_ID = \"xpertsystems/cyb008-baseline-classifier\"\n",
    "\n",
    "files = {}\n",
    "for name in [\"model_xgb.json\", \"model_mlp.safetensors\",\n",
    "             \"feature_engineering.py\", \"feature_meta.json\",\n",
    "             \"feature_scaler.json\"]:\n",
    "    files[name] = hf_hub_download(repo_id=REPO_ID, filename=name)\n",
    "    print(f\"  downloaded: {name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "fe_dir = os.path.dirname(files[\"feature_engineering.py\"])\n",
    "if fe_dir not in sys.path:\n",
    "    sys.path.insert(0, fe_dir)\n",
    "\n",
    "from feature_engineering import transform_single, load_meta, INT_TO_LABEL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Load models and metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import xgboost as xgb\n",
    "from safetensors.torch import load_file\n",
    "\n",
    "meta = load_meta(files[\"feature_meta.json\"])\n",
    "with open(files[\"feature_scaler.json\"]) as f:\n",
    "    scaler = json.load(f)\n",
    "\n",
    "N_FEATURES = len(meta[\"feature_names\"])\n",
    "N_CLASSES = len(meta[\"int_to_label\"])\n",
    "print(f\"feature count: {N_FEATURES}\")\n",
    "print(f\"class count:   {N_CLASSES}\")\n",
    "print(f\"label classes: {list(meta['int_to_label'].values())}\")\n",
    "print(f\"\\noracle columns excluded (do not pass these to the model):\")\n",
    "for c in meta.get(\"oracle_excluded\", []):\n",
    "    print(f\"  - {c}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_model = xgb.XGBClassifier()\n",
    "xgb_model.load_model(files[\"model_xgb.json\"])\n",
    "\n",
    "# MLP architecture (must match training)\n",
    "class TriageMLP(nn.Module):\n",
    "    def __init__(self, n_features, n_classes=5, hidden1=128, hidden2=64, dropout=0.3):\n",
    "        super().__init__()\n",
    "        self.net = nn.Sequential(\n",
    "            nn.Linear(n_features, hidden1),\n",
    "            nn.BatchNorm1d(hidden1),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden1, hidden2),\n",
    "            nn.BatchNorm1d(hidden2),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(dropout),\n",
    "            nn.Linear(hidden2, n_classes),\n",
    "        )\n",
    "    def forward(self, x):\n",
    "        return self.net(x)\n",
    "\n",
    "mlp_model = TriageMLP(N_FEATURES, n_classes=N_CLASSES)\n",
    "mlp_model.load_state_dict(load_file(files[\"model_mlp.safetensors\"]))\n",
    "mlp_model.eval()\n",
    "print(\"models loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Prediction helper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "MU = np.array(scaler[\"mean\"], dtype=np.float32)\n",
    "SD = np.array(scaler[\"std\"],  dtype=np.float32)\n",
    "\n",
    "def predict_triage_outcome(record: dict) -> dict:\n",
    "    \"\"\"Predict the resolution outcome for one SOC alert record.\n",
    "\n",
    "    Note: do NOT include alert_lifecycle_phase, automation_resolved,\n",
    "    or escalation_flag in the record. These were structural oracles\n",
    "    in the training data and are excluded from the feature set.\n",
    "    \"\"\"\n",
    "    X = transform_single(record, meta)\n",
    "\n",
    "    xgb_proba = xgb_model.predict_proba(X)[0]\n",
    "    xgb_label = INT_TO_LABEL[int(np.argmax(xgb_proba))]\n",
    "\n",
    "    Xs = ((X - MU) / SD).astype(np.float32)\n",
    "    with torch.no_grad():\n",
    "        logits = mlp_model(torch.tensor(Xs))\n",
    "        mlp_proba = torch.softmax(logits, dim=1).numpy()[0]\n",
    "    mlp_label = INT_TO_LABEL[int(np.argmax(mlp_proba))]\n",
    "\n",
    "    return {\n",
    "        \"xgboost\": {\n",
    "            \"label\": xgb_label,\n",
    "            \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(xgb_proba)},\n",
    "        },\n",
    "        \"mlp\": {\n",
    "            \"label\": mlp_label,\n",
    "            \"probabilities\": {INT_TO_LABEL[i]: float(p) for i, p in enumerate(mlp_proba)},\n",
    "        },\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Run on an example record\n",
    "\n",
    "Real high-severity ITDR identity-anomaly alert assigned to an L3 threat hunter, who escalated it to a true-positive incident. Both models should predict `true_positive_escalated` or the adjacent `true_positive_remediated`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Real alert from the sample dataset (true outcome: true_positive_escalated)\n",
    "example_record = {\n",
    "    \"alert_severity\": \"high_severity\",\n",
    "    \"alert_source\": \"itdr_identity_anomaly\",\n",
    "    \"mitre_tactic\": \"initial_access\",\n",
    "    \"analyst_tier\": \"L3_threat_hunter\",\n",
    "    \"siem_platform\": \"logrhythm_axon\",\n",
    "    \"raw_score\": 0.2683,\n",
    "    \"enriched_score\": 0.343,\n",
    "    \"time_in_phase_minutes\": 429.26,\n",
    "    \"queue_depth_at_ingestion\": 0,\n",
    "    \"soar_playbook_triggered\": 0,\n",
    "    \"sla_breached_flag\": 1,\n",
    "    \"mttd_minutes\": 177.47,\n",
    "    \"mttr_minutes\": 429.26,\n",
    "    \"fatigue_score_at_alert\": 0.3805,\n",
    "}\n",
    "\n",
    "result = predict_triage_outcome(example_record)\n",
    "\n",
    "print(f\"XGBoost  ->  {result['xgboost']['label']}\")\n",
    "for lbl, p in sorted(result['xgboost']['probabilities'].items(), key=lambda x: -x[1]):\n",
    "    print(f\"    P({lbl:30s}) = {p:.4f}\")\n",
    "\n",
    "print(f\"\\nMLP      ->  {result['mlp']['label']}\")\n",
    "for lbl, p in sorted(result['mlp']['probabilities'].items(), key=lambda x: -x[1]):\n",
    "    print(f\"    P({lbl:30s}) = {p:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Honest confusion between TP-remediated and TP-escalated\n",
    "\n",
    "The two `true_positive_*` outcomes look behaviourally similar in the data — both involve genuine threats. They differ by whether the alert was closed by the original analyst (remediated) or passed to a higher tier (escalated). When the trained models confuse these two classes on individual alerts, that's honest learning — not a defect.\n",
    "\n",
    "In a production triage workflow, the better operational metric is **TP vs FP** (recall on true positives, regardless of remediated/escalated). The published baseline achieves ROC-AUC 0.955 on the full 5-class task, which substantially exceeds practical thresholds for downstream binary TP-vs-FP decisions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Batch prediction on the sample dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from huggingface_hub import snapshot_download\n",
    "import pandas as pd\n",
    "\n",
    "ds_path = snapshot_download(repo_id=\"xpertsystems/cyb008-sample\", repo_type=\"dataset\")\n",
    "alerts = pd.read_csv(f\"{ds_path}/soc_alerts.csv\")\n",
    "\n",
    "# Score the first 500 alerts\n",
    "sample = alerts.head(500).copy()\n",
    "preds = [predict_triage_outcome(row.to_dict())[\"xgboost\"][\"label\"] for _, row in sample.iterrows()]\n",
    "sample[\"xgb_pred\"] = preds\n",
    "\n",
    "ct = pd.crosstab(sample[\"resolution_outcome\"], sample[\"xgb_pred\"],\n",
    "                 rownames=[\"true\"], colnames=[\"pred\"])\n",
    "print(\"Confusion on first 500 sample alerts (XGBoost):\")\n",
    "print(ct)\n",
    "acc = (sample[\"resolution_outcome\"] == sample[\"xgb_pred\"]).mean()\n",
    "print(f\"\\nbatch accuracy on first 500 alerts (in-distribution): {acc:.4f}\")\n",
    "print(\"\\nNote: this includes training-set alerts. See validation_results.json\\n\"\n",
    "      \"for proper held-out test metrics.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Important reading: the leakage diagnostic\n",
    "\n",
    "Before using CYB008 sample data to train your own triage model, read **`leakage_diagnostic.json`** in this repo. The CYB008 sample has three columns (`alert_lifecycle_phase`, `automation_resolved`, `escalation_flag`) that structurally encode the resolution_outcome label. With these columns present, a plain XGBoost achieves 100% accuracy that does not reflect real learning. The published baseline excludes them; the diagnostic file shows the cumulative ablation.\n",
    "\n",
    "The diagnostic also documents that **mitre_tactic prediction is unlearnable on this sample** (acc 0.08 vs majority 0.14). The README lists this as a top suggested use case, but the per-tactic feature distributions are too similar to learn from."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Next steps\n",
    "\n",
    "- See `validation_results.json` for held-out test metrics (1,380 alerts).\n",
    "- See `multi_seed_results.json` for the across-10-seeds picture (accuracy 0.777 ± 0.007, ROC-AUC 0.955 ± 0.003).\n",
    "- See `ablation_results.json` for per-feature-group contribution. Alert severity carries the dominant signal (−25 pp accuracy when removed); the SOAR-playbook-triggered indicator is second (−15 pp).\n",
    "- See **`leakage_diagnostic.json`** for the full structural-leakage and unlearnable-target audit.\n",
    "- For the full ~335k-row CYB008 dataset and commercial licensing, contact **pradeep@xpertsystems.ai**."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python",
   "version": "3.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}