{"cells":[{"cell_type":"markdown","id":"P8yB87t1-q9_","metadata":{"id":"P8yB87t1-q9_"},"source":["# Explainable Transformer-Based Anomaly Detection for Large-Scale System Logs\n","\n","## LogMiniLM Thesis Notebook — Audited Final Release\n","\n","**Version:** 3.2  \n","**Dataset:** BGL  \n","**Model:** LogMiniLM  \n","**Primary XAI:** Event-level occlusion  \n","**Supporting XAI:** SHAP  \n","**Windowing:** Fixed 10-line non-overlapping sequences  \n","**Author:** Hesham Bashatah  - Anas Baro\n","**Release date:** July 2026  \n","\n","This notebook is the master computational record for the thesis experiment. It preserves the validated LogMiniLM methodology and final reported results while adding a structured configuration, reproducible output management, thesis-ready figure export, XAI reporting, and final artifact manifests.\n","\n","> **Scientific freeze:** The dataset, model architecture, temporal split, hyperparameters, threshold-selection method, final test protocol, and reported performance are unchanged in this production release.\n"]},{"cell_type":"markdown","id":"Td2uwCcj-q-B","metadata":{"id":"Td2uwCcj-q-B"},"source":["## Step 0 — Mount Google Drive\n","\n","Google Drive is mounted once at the beginning of the notebook before any Drive-based dataset, cache, checkpoint, or export path is used.\n","\n","The cell is safe for **Run all**: it mounts Drive only when `MyDrive` is not already available.\n"]},{"cell_type":"code","execution_count":1,"id":"Fv3SylBx-q-C","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fv3SylBx-q-C","outputId":"25441118-1e85-4305-a044-b18b3e1ff8a9","executionInfo":{"status":"ok","timestamp":1785510425404,"user_tz":-180,"elapsed":19092,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n","======================================================================\n","Google Drive is ready.\n","MyDrive path: /content/drive/MyDrive\n","======================================================================\n"]}],"source":["# ============================================================\n","# Step 0 — Mount Google Drive once\n","# ============================================================\n","import os\n","from pathlib import Path\n","\n","try:\n","    from google.colab import drive\n","except ImportError as exc:\n","    raise RuntimeError(\n","        \"This notebook is designed to run in Google Colab because it uses Google Drive.\"\n","    ) from exc\n","\n","DRIVE_MOUNT_POINT = Path(\"/content/drive\")\n","MYDRIVE_PATH = DRIVE_MOUNT_POINT / \"MyDrive\"\n","\n","if not MYDRIVE_PATH.exists():\n","    drive.mount(str(DRIVE_MOUNT_POINT), force_remount=False)\n","else:\n","    print(\"Google Drive is already mounted; skipping duplicate mount.\")\n","\n","if not MYDRIVE_PATH.exists():\n","    raise RuntimeError(\n","        f\"Google Drive mount failed or MyDrive is unavailable: {MYDRIVE_PATH}\"\n","    )\n","\n","print(\"=\" * 70)\n","print(\"Google Drive is ready.\")\n","print(\"MyDrive path:\", MYDRIVE_PATH)\n","print(\"=\" * 70)\n"]},{"cell_type":"markdown","id":"VPaMhqFD-q-E","metadata":{"id":"VPaMhqFD-q-E"},"source":["## Research Contributions\n","\n","This implementation supports four main research contributions:\n","\n","1. **Lightweight semantic anomaly detection:** LogMiniLM combines frozen MiniLM message embeddings with a compact Transformer classifier.\n","2. **Efficient representation pipeline:** Unique normalized messages are encoded once and cached, reducing repeated semantic-encoding cost.\n","3. **Explainable anomaly decisions:** Event-level occlusion identifies the log events that most influence each anomaly prediction.\n","4. **Explanation validation:** SHAP agreement and top-k faithfulness are used to assess explanation consistency and causal relevance.\n"]},{"cell_type":"markdown","id":"BzVXUEo--q-F","metadata":{"id":"BzVXUEo--q-F"},"source":["## Notebook Structure\n","\n","1. Environment and reproducibility  \n","2. Central experiment configuration  \n","3. BGL parsing and preprocessing  \n","4. Semantic encoding with MiniLM  \n","5. Fixed-window sequence generation  \n","6. Temporal development, validation, and test splits  \n","7. LogMiniLM model definition  \n","8. Development training and validation-based model selection  \n","9. Final retraining and one-time test evaluation  \n","10. Event-level occlusion explanations  \n","11. Faithfulness evaluation  \n","12. SHAP supporting explanations  \n","13. SHAP–occlusion comparison  \n","14. Figure and table manifests  \n","15. Final thesis summary and reproducibility package\n"]},{"cell_type":"markdown","id":"jTSUuVWl-q-F","metadata":{"id":"jTSUuVWl-q-F"},"source":["## Step 1 — Install and Import All Required Libraries\n","\n","This step installs and imports every external library used later in the notebook. Keeping dependencies here prevents missing-import errors during **Run all**.\n","\n","The installation cell includes:\n","\n","- Sentence Transformers for MiniLM embeddings\n","- SHAP for event-level explainability\n","- PyArrow for Parquet metadata export\n","- Scikit-learn for evaluation metrics\n","- Matplotlib for thesis figures\n"]},{"cell_type":"code","execution_count":2,"id":"yNNDybHU-q-G","metadata":{"id":"yNNDybHU-q-G","executionInfo":{"status":"ok","timestamp":1785510430533,"user_tz":-180,"elapsed":5126,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[],"source":["# ============================================================\n","# Step 1A — Install all external dependencies\n","# ============================================================\n","\n","!pip -q install sentence-transformers shap pyarrow scikit-learn matplotlib\n"]},{"cell_type":"code","execution_count":3,"id":"egwNbKEP-q-H","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"egwNbKEP-q-H","outputId":"72979572-1cdf-4507-ac40-23cc83489288","executionInfo":{"status":"ok","timestamp":1785510475898,"user_tz":-180,"elapsed":45363,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["All required libraries imported successfully.\n","PyTorch version: 2.11.0+cu128\n","SHAP version: 0.52.0\n"]}],"source":["# ============================================================\n","# Step 1B — Import all libraries used in the notebook\n","# ============================================================\n","\n","import os\n","\n","# Reproducibility environment variables must be set before CUDA work.\n","os.environ[\"PYTHONHASHSEED\"] = \"42\"\n","os.environ[\"CUBLAS_WORKSPACE_CONFIG\"] = \":4096:8\"\n","\n","import gc\n","import json\n","import random\n","import re\n","from collections import Counter\n","from pathlib import Path\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","import shap\n","import torch\n","import torch.nn as nn\n","\n","from IPython.display import display\n","from sentence_transformers import SentenceTransformer\n","from sklearn.metrics import (\n","    accuracy_score,\n","    classification_report,\n","    confusion_matrix,\n","    f1_score,\n","    precision_recall_curve,\n","    precision_score,\n","    recall_score,\n","    roc_auc_score,\n","    roc_curve,\n","    average_precision_score,\n",")\n","from torch.utils.data import DataLoader, Dataset\n","\n","print(\"All required libraries imported successfully.\")\n","print(\"PyTorch version:\", torch.__version__)\n","print(\"SHAP version:\", shap.__version__)\n"]},{"cell_type":"markdown","id":"80d8eb30","metadata":{"id":"80d8eb30"},"source":["## Step 2 — Configuration\n","\n","All important experiment parameters are collected in one place.\n","\n","### Important code behavior\n","\n","- `WINDOW_SIZE = 10`: every 10 consecutive logs become one sequence.\n","- `STEP_SIZE = 10`: the next sequence starts after 10 logs, so there is no overlap.\n","- `TRAIN_RATIO = 0.80`: the first 80% of sequences are used for training and the final 20% for testing.\n","- `PROJECTION_DIM = 128`: MiniLM embeddings are reduced from 384 dimensions to 128 before the Transformer.\n","\n","### Example\n","\n","If the first 30 logs are available:\n","\n","```text\n","Sequence 1 = logs 0–9\n","Sequence 2 = logs 10–19\n","Sequence 3 = logs 20–29\n","```\n"]},{"cell_type":"code","execution_count":4,"id":"677f407f","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"677f407f","outputId":"84f5f639-194c-4654-8e3b-5e815a314cbf","executionInfo":{"status":"ok","timestamp":1785510476714,"user_tz":-180,"elapsed":830,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["========================================================================\n","LOGMINILM FINAL PRODUCTION CONFIGURATION\n","========================================================================\n","Notebook version: 5.2-corrected\n","Run ID: LogMiniLM_V5_2_Corrected\n","Device: cuda\n","GPU: Tesla T4\n","Seed: 42\n","AMP enabled: False\n","Input cache: /content/drive/MyDrive/logminilm_cache\n","Production output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected\n"]}],"source":["# ============================================================\n","# Step 2 — Central Configuration and Reproducibility\n","# ============================================================\n","\n","NOTEBOOK_VERSION = \"5.2-corrected\"\n","RUN_ID = \"LogMiniLM_V5_2_Corrected\"\n","AUTHOR = \"Hesham Bashatah\"\n","DATASET_NAME = \"BGL\"\n","MODEL_NAME = \"LogMiniLM\"\n","RELEASE_DATE = \"July 2026\"\n","\n","# -------------------------\n","# Reproducibility\n","# -------------------------\n","SEED = 42\n","\n","def reset_all_seeds(seed=SEED):\n","    \"\"\"Reset Python, NumPy, and PyTorch random generators.\"\"\"\n","    random.seed(seed)\n","    np.random.seed(seed)\n","    torch.manual_seed(seed)\n","    if torch.cuda.is_available():\n","        torch.cuda.manual_seed(seed)\n","        torch.cuda.manual_seed_all(seed)\n","\n","reset_all_seeds(SEED)\n","\n","torch.backends.cudnn.deterministic = True\n","torch.backends.cudnn.benchmark = False\n","\n","if torch.cuda.is_available():\n","    torch.backends.cuda.matmul.allow_tf32 = False\n","    torch.backends.cudnn.allow_tf32 = False\n","\n","# Request deterministic implementations where PyTorch provides them.\n","# warn_only=True keeps the notebook runnable when a GPU kernel has no\n","# deterministic alternative; therefore bitwise-identical reruns are not guaranteed.\n","torch.use_deterministic_algorithms(True, warn_only=True)\n","BITWISE_DETERMINISM_GUARANTEED = False\n","\n","# -------------------------\n","# Dataset\n","# -------------------------\n","BGL_PATH = \"/content/drive/MyDrive/bgl_data/BGL.log\"\n","MAX_LINES = None\n","\n","# -------------------------\n","# Sequence construction\n","# -------------------------\n","WINDOW_SIZE = 10\n","STEP_SIZE = 10\n","TRAIN_RATIO = 0.80\n","VALIDATION_RATIO_WITHIN_TRAIN = 0.10\n","\n","# -------------------------\n","# MiniLM semantic encoder\n","# -------------------------\n","MINILM_MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n","ENCODE_BATCH_SIZE = 256\n","\n","# -------------------------\n","# LogMiniLM architecture\n","# -------------------------\n","PROJECTION_DIM = 128\n","NUM_TRANSFORMER_LAYERS = 1\n","NUM_ATTENTION_HEADS = 2\n","FEEDFORWARD_DIM = 256\n","DROPOUT = 0.1\n","\n","# -------------------------\n","# Training\n","# -------------------------\n","BATCH_SIZE = 256\n","DEVELOPMENT_MAX_EPOCHS = 10\n","EARLY_STOPPING_PATIENCE = 3\n","EARLY_STOPPING_MIN_DELTA = 1e-4\n","THRESHOLDS = [\n","    0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40,\n","    0.45, 0.50, 0.60, 0.70, 0.80, 0.90\n","]\n","LEARNING_RATE = 1e-3\n","# Experimentally selected multiplier. Report this explicitly; it is not\n","# equal to the raw inverse-frequency ratio.\n","ANOMALY_WEIGHT_MULTIPLIER = 2.0\n","POS_WEIGHT_SENSITIVITY_VALUES = [1.0, 1.5, 2.0]\n","USE_AMP = False\n","\n","# -------------------------\n","# Input cache and production output structure\n","# -------------------------\n","CACHE_DIR = Path(\"/content/drive/MyDrive/logminilm_cache\")\n","CACHE_DIR.mkdir(parents=True, exist_ok=True)\n","\n","RESULTS_ROOT = CACHE_DIR / \"production_results\"\n","RUN_DIR = RESULTS_ROOT / RUN_ID\n","\n","MODELS_DIR = RUN_DIR / \"models\"\n","FIGURES_DIR = RUN_DIR / \"figures\"\n","TABLES_DIR = RUN_DIR / \"tables\"\n","XAI_DIR = RUN_DIR / \"xai\"\n","SHAP_DIR = RUN_DIR / \"shap\"\n","LOGS_DIR = RUN_DIR / \"logs\"\n","SUMMARY_DIR = RUN_DIR / \"summary\"\n","\n","for directory in [\n","    RESULTS_ROOT, RUN_DIR, MODELS_DIR, FIGURES_DIR, TABLES_DIR,\n","    XAI_DIR, SHAP_DIR, LOGS_DIR, SUMMARY_DIR\n","]:\n","    directory.mkdir(parents=True, exist_ok=True)\n","\n","# -------------------------\n","# Cached input artifacts\n","# -------------------------\n","UNIQUE_TEXTS_PATH = CACHE_DIR / \"bgl_unique_normalized_messages.csv\"\n","EMBEDDINGS_PATH = CACHE_DIR / \"bgl_minilm_unique_embeddings.npy\"\n","EVENT_ID_PATH = CACHE_DIR / \"bgl_line_event_ids.npy\"\n","LABELS_PATH = CACHE_DIR / \"bgl_line_labels.npy\"\n","METADATA_PATH = CACHE_DIR / \"bgl_metadata_for_alerts_light.parquet\"\n","\n","# -------------------------\n","# Structured experiment outputs\n","# -------------------------\n","RESULTS_PATH = TABLES_DIR / \"logminilm_results.csv\"\n","PREDICTIONS_PATH = TABLES_DIR / \"logminilm_predictions_for_xai.csv\"\n","TOP_XAI_CANDIDATES_PATH = XAI_DIR / \"logminilm_top_xai_candidates.csv\"\n","MODEL_PATH = MODELS_DIR / \"logminilm_model.pt\"\n","DEVELOPMENT_CONFIG_PATH = SUMMARY_DIR / \"logminilm_development_config.json\"\n","DEVELOPMENT_HISTORY_PATH = LOGS_DIR / \"logminilm_development_history.csv\"\n","VALIDATION_RESULTS_PATH = TABLES_DIR / \"logminilm_validation_threshold_results.csv\"\n","FINAL_TRAINING_HISTORY_PATH = LOGS_DIR / \"logminilm_final_training_history.csv\"\n","FIGURE_MANIFEST_PATH = SUMMARY_DIR / \"logminilm_figure_manifest.csv\"\n","TABLE_MANIFEST_PATH = SUMMARY_DIR / \"logminilm_table_manifest.csv\"\n","CONFIG_JSON_PATH = SUMMARY_DIR / \"config.json\"\n","FINAL_SUMMARY_CSV_PATH = SUMMARY_DIR / \"final_summary.csv\"\n","FINAL_SUMMARY_JSON_PATH = SUMMARY_DIR / \"final_summary.json\"\n","XAI_SUMMARY_PATH = SUMMARY_DIR / \"xai_summary.csv\"\n","README_PATH = RUN_DIR / \"README.txt\"\n","REQUIREMENTS_PATH = RUN_DIR / \"requirements.txt\"\n","\n","# -------------------------\n","# Device\n","# -------------------------\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","print(\"=\" * 72)\n","print(\"LOGMINILM FINAL PRODUCTION CONFIGURATION\")\n","print(\"=\" * 72)\n","print(\"Notebook version:\", NOTEBOOK_VERSION)\n","print(\"Run ID:\", RUN_ID)\n","print(\"Device:\", device)\n","if torch.cuda.is_available():\n","    print(\"GPU:\", torch.cuda.get_device_name(0))\n","print(\"Seed:\", SEED)\n","print(\"AMP enabled:\", USE_AMP)\n","print(\"Input cache:\", CACHE_DIR)\n","print(\"Production output:\", RUN_DIR)\n"]},{"cell_type":"code","execution_count":5,"id":"e6EUEXbc-q-J","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e6EUEXbc-q-J","outputId":"b0a889f4-cdc9-414c-a0a8-e25dbc89d520","executionInfo":{"status":"ok","timestamp":1785510477646,"user_tz":-180,"elapsed":915,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["PosixPath('/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/summary/config.json')"]},"metadata":{},"execution_count":5}],"source":["# ============================================================\n","# Shared Saving and Visualization Utilities\n","# ============================================================\n","\n","THESIS_FONT_FAMILY = \"DejaVu Sans\"\n","THESIS_TITLE_SIZE = 14\n","THESIS_LABEL_SIZE = 11\n","THESIS_TICK_SIZE = 10\n","THESIS_LEGEND_SIZE = 10\n","THESIS_FIGSIZE = (8, 5)\n","THESIS_DPI = 300\n","\n","plt.rcParams.update({\n","    \"font.family\": THESIS_FONT_FAMILY,\n","    \"axes.titlesize\": THESIS_TITLE_SIZE,\n","    \"axes.labelsize\": THESIS_LABEL_SIZE,\n","    \"xtick.labelsize\": THESIS_TICK_SIZE,\n","    \"ytick.labelsize\": THESIS_TICK_SIZE,\n","    \"legend.fontsize\": THESIS_LEGEND_SIZE,\n","    \"figure.titlesize\": THESIS_TITLE_SIZE,\n","})\n","\n","figure_manifest = []\n","table_manifest = []\n","\n","def save_table(dataframe, path, description=\"\", index=False):\n","    \"\"\"Save a DataFrame and register the artifact in the table manifest.\"\"\"\n","    path = Path(path)\n","    path.parent.mkdir(parents=True, exist_ok=True)\n","    dataframe.to_csv(path, index=index)\n","\n","    table_manifest.append({\n","        \"filename\": path.name,\n","        \"description\": description,\n","        \"rows\": int(len(dataframe)),\n","        \"columns\": int(len(dataframe.columns)),\n","        \"path\": str(path),\n","    })\n","    return path\n","\n","\n","def save_json(payload, path):\n","    \"\"\"Save JSON using UTF-8 encoding and return the output path.\"\"\"\n","    path = Path(path)\n","    path.parent.mkdir(parents=True, exist_ok=True)\n","    with path.open(\"w\", encoding=\"utf-8\") as file:\n","        json.dump(payload, file, indent=2, ensure_ascii=False, default=str)\n","    return path\n","\n","\n","def save_text(content, path):\n","    \"\"\"Save a UTF-8 text artifact and return the output path.\"\"\"\n","    path = Path(path)\n","    path.parent.mkdir(parents=True, exist_ok=True)\n","    path.write_text(content, encoding=\"utf-8\")\n","    return path\n","\n","\n","def save_current_figure(filename, figure_number, title):\n","    \"\"\"Save the active figure as 300-DPI PNG and vector PDF.\"\"\"\n","    stem = Path(filename).stem\n","    png_path = FIGURES_DIR / f\"{stem}.png\"\n","    pdf_path = FIGURES_DIR / f\"{stem}.pdf\"\n","\n","    plt.savefig(png_path, dpi=THESIS_DPI, bbox_inches=\"tight\")\n","    plt.savefig(pdf_path, bbox_inches=\"tight\")\n","\n","    figure_manifest.append({\n","        \"figure_number\": figure_number,\n","        \"title\": title,\n","        \"png_filename\": png_path.name,\n","        \"pdf_filename\": pdf_path.name,\n","        \"png_path\": str(png_path),\n","        \"pdf_path\": str(pdf_path),\n","    })\n","    return png_path, pdf_path\n","\n","\n","def finalize_figure():\n","    \"\"\"Apply the common thesis layout before saving or displaying a figure.\"\"\"\n","    plt.tight_layout()\n","\n","\n","def build_config_dictionary():\n","    \"\"\"Return the fixed experiment configuration used in the notebook.\"\"\"\n","    return {\n","        \"notebook_version\": NOTEBOOK_VERSION,\n","        \"run_id\": RUN_ID,\n","        \"author\": AUTHOR,\n","        \"release_date\": RELEASE_DATE,\n","        \"dataset\": DATASET_NAME,\n","        \"model\": MODEL_NAME,\n","        \"seed\": SEED,\n","        \"dataset_path\": BGL_PATH,\n","        \"max_lines\": MAX_LINES,\n","        \"window_size\": WINDOW_SIZE,\n","        \"step_size\": STEP_SIZE,\n","        \"train_ratio\": TRAIN_RATIO,\n","        \"validation_ratio_within_train\": VALIDATION_RATIO_WITHIN_TRAIN,\n","        \"minilm_encoder\": MINILM_MODEL_NAME,\n","        \"encode_batch_size\": ENCODE_BATCH_SIZE,\n","        \"projection_dimension\": PROJECTION_DIM,\n","        \"transformer_layers\": NUM_TRANSFORMER_LAYERS,\n","        \"attention_heads\": NUM_ATTENTION_HEADS,\n","        \"feedforward_dimension\": FEEDFORWARD_DIM,\n","        \"dropout\": DROPOUT,\n","        \"batch_size\": BATCH_SIZE,\n","        \"development_max_epochs\": DEVELOPMENT_MAX_EPOCHS,\n","        \"early_stopping_patience\": EARLY_STOPPING_PATIENCE,\n","        \"early_stopping_min_delta\": EARLY_STOPPING_MIN_DELTA,\n","        \"threshold_candidates\": THRESHOLDS,\n","        \"learning_rate\": LEARNING_RATE,\n","        \"anomaly_weight_multiplier\": ANOMALY_WEIGHT_MULTIPLIER,\n","        \"use_amp\": USE_AMP,\n","        \"device\": str(device),\n","        \"production_output_directory\": str(RUN_DIR),\n","    }\n","\n","save_json(build_config_dictionary(), CONFIG_JSON_PATH)\n"]},{"cell_type":"code","execution_count":6,"id":"39fa64e5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"39fa64e5","outputId":"2425b4dc-1da9-4bb9-b807-e4c57c969aa5","executionInfo":{"status":"ok","timestamp":1785510477685,"user_tz":-180,"elapsed":12,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Confirmed sequence setting: window_size=10, step_size=10\n"]}],"source":["# Safety check: this notebook is fixed to 10-line non-overlapping windows.\n","assert WINDOW_SIZE == 10, f\"Expected WINDOW_SIZE=10, got {WINDOW_SIZE}\"\n","assert STEP_SIZE == 10, f\"Expected STEP_SIZE=10, got {STEP_SIZE}\"\n","print(f\"Confirmed sequence setting: window_size={WINDOW_SIZE}, step_size={STEP_SIZE}\")\n"]},{"cell_type":"markdown","id":"AsVWteCN-q-K","metadata":{"id":"AsVWteCN-q-K"},"source":["## Step 3 — Verify the BGL Dataset Path\n","\n","Google Drive was mounted once in Step 0. This step verifies the thesis dataset at the fixed location:\n","\n","```text\n","/content/drive/MyDrive/bgl_data/bgl.log\n","```\n","\n","The filename is lowercase: `bgl.log`.\n"]},{"cell_type":"code","execution_count":7,"id":"d3e6243e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d3e6243e","outputId":"aa22f550-087a-43be-d111-ed1949727dfc","executionInfo":{"status":"ok","timestamp":1785510477696,"user_tz":-180,"elapsed":10,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Configured BGL path: /content/drive/MyDrive/bgl_data/BGL.log\n","BGL file exists: True\n","BGL dataset path verified successfully.\n"]}],"source":["# Google Drive was mounted once in Step 0.\n","# This cell performs dataset-path validation only.\n","\n","print(\"Configured BGL path:\", BGL_PATH)\n","print(\"BGL file exists:\", os.path.exists(BGL_PATH))\n","\n","if not os.path.isfile(BGL_PATH):\n","    raise FileNotFoundError(\n","        f\"BGL file not found at: {BGL_PATH}\\n\"\n","        \"Place bgl.log inside MyDrive/bgl_data/ or update BGL_PATH in the configuration cell.\"\n","    )\n","\n","print(\"BGL dataset path verified successfully.\")\n"]},{"cell_type":"markdown","id":"4d598c60","metadata":{"id":"4d598c60"},"source":["## Step 4 — Parse BGL raw logs\n","\n","Each BGL log line contains a label and several metadata fields.\n","\n","### BGL label rule\n","\n","```text\n","- = Normal\n","Any other label = Anomaly\n","```\n","\n","### Example raw line\n","\n","```text\n","- 1117838570 2005.06.03 R02-M1-N0-C:J12-U11 2005-06-03-15.42.50.675872 R02-M1-N0-C:J12-U11 RAS KERNEL INFO instruction cache parity error corrected\n","```\n","\n","### Parsed output example\n","\n","| Field | Example |\n","|---|---|\n","| label | 0 |\n","| source | RAS |\n","| component | KERNEL |\n","| severity | INFO |\n","| event_message | instruction cache parity error corrected |\n","\n","The model will not use timestamp or node directly as input, but these fields are preserved for alert reporting and XAI.\n"]},{"cell_type":"code","execution_count":8,"id":"682151e3","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":180},"id":"682151e3","outputId":"3d5ccbd9-b5f6-4395-a0af-0bc882510acd","executionInfo":{"status":"ok","timestamp":1785510528560,"user_tz":-180,"elapsed":50863,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Parsed rows: 4713493\n","Line label distribution: Counter({0: 4365033, 1: 348460})\n"]},{"output_type":"display_data","data":{"text/plain":["   line_index raw_label  label unix_timestamp        date  \\\n","0           0         -      0     1117838570  2005.06.03   \n","1           1         -      0     1117838570  2005.06.03   \n","2           2         -      0     1117838570  2005.06.03   \n","\n","                  node                   full_time        repeated_node  \\\n","0  R02-M1-N0-C:J12-U11  2005-06-03-15.42.50.363779  R02-M1-N0-C:J12-U11   \n","1  R02-M1-N0-C:J12-U11  2005-06-03-15.42.50.527847  R02-M1-N0-C:J12-U11   \n","2  R02-M1-N0-C:J12-U11  2005-06-03-15.42.50.675872  R02-M1-N0-C:J12-U11   \n","\n","  source component severity                             event_message  \n","0    RAS    KERNEL     INFO  instruction cache parity error corrected  \n","1    RAS    KERNEL     INFO  instruction cache parity error corrected  \n","2    RAS    KERNEL     INFO  instruction cache parity error corrected  "],"text/html":["\n","  <div id=\"df-a7b38e31-9df2-440d-afc3-2043037705bd\" class=\"colab-df-container\">\n","    <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>line_index</th>\n","      <th>raw_label</th>\n","      <th>label</th>\n","      <th>unix_timestamp</th>\n","      <th>date</th>\n","      <th>node</th>\n","      <th>full_time</th>\n","      <th>repeated_node</th>\n","      <th>source</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>-</td>\n","      <td>0</td>\n","      <td>1117838570</td>\n","      <td>2005.06.03</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>2005-06-03-15.42.50.363779</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>-</td>\n","      <td>0</td>\n","      <td>1117838570</td>\n","      <td>2005.06.03</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>2005-06-03-15.42.50.527847</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>-</td>\n","      <td>0</td>\n","      <td>1117838570</td>\n","      <td>2005.06.03</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>2005-06-03-15.42.50.675872</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7b38e31-9df2-440d-afc3-2043037705bd')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-a7b38e31-9df2-440d-afc3-2043037705bd button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"repeated_node\": parts[5],\n","        \"source\": parts[6],\n","        \"component\": parts[7],\n","        \"severity\": parts[8],\n","        \"event_message\": \" \".join(parts[9:]),\n","    }\n","\n","records = []\n","with open(BGL_PATH, \"r\", errors=\"ignore\") as f:\n","    for i, line in enumerate(f):\n","        if MAX_LINES is not None and i >= MAX_LINES:\n","            break\n","        parsed = parse_bgl_line(line, i)\n","        if parsed is not None:\n","            records.append(parsed)\n","\n","df = pd.DataFrame(records)\n","\n","print(\"Parsed rows:\", len(df))\n","print(\"Line label distribution:\", Counter(df[\"label\"]))\n","display(df.head(3))\n"]},{"cell_type":"markdown","id":"W3mL6sWw-q-L","metadata":{"id":"W3mL6sWw-q-L"},"source":["### Figure 4.1 — BGL Line-Level Class Distribution\n","\n","This figure is generated immediately after parsing the raw dataset.\n","\n","**Interpretation:** The BGL dataset is strongly imbalanced, with normal log lines substantially outnumbering anomalous lines. This motivates class-weighted training and the use of precision, recall, and F1-score rather than accuracy alone.\n"]},{"cell_type":"code","execution_count":9,"id":"SGKIaVli-q-L","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":620},"id":"SGKIaVli-q-L","outputId":"13b4fd05-8d64-40cb-b842-7e0628117477","executionInfo":{"status":"ok","timestamp":1785510546627,"user_tz":-180,"elapsed":18050,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Line-level class distribution\n"]},{"output_type":"display_data","data":{"text/plain":["     class    count  percentage\n","0   Normal  4365033   92.607181\n","1  Anomaly   348460    7.392819"],"text/html":["\n","  <div id=\"df-2ddb4ada-f0c8-44b2-83dc-5ba56a949e0a\" class=\"colab-df-container\">\n","    <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>class</th>\n","      <th>count</th>\n","      <th>percentage</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Normal</td>\n","      <td>4365033</td>\n","      <td>92.607181</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Anomaly</td>\n","      <td>348460</td>\n","      <td>7.392819</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ddb4ada-f0c8-44b2-83dc-5ba56a949e0a')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-2ddb4ada-f0c8-44b2-83dc-5ba56a949e0a button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.1 — BGL line-level class distribution\n","# ============================================================\n","\n","if \"label\" not in df.columns:\n","    raise ValueError(\"Parsed BGL DataFrame does not contain the required label column.\")\n","\n","line_label_counts = df[\"label\"].value_counts().sort_index()\n","\n","line_distribution_df = pd.DataFrame({\n","    \"class\": [\"Normal\", \"Anomaly\"],\n","    \"count\": [\n","        int(line_label_counts.get(0, 0)),\n","        int(line_label_counts.get(1, 0)),\n","    ]\n","})\n","\n","line_distribution_df[\"percentage\"] = (\n","    line_distribution_df[\"count\"] / line_distribution_df[\"count\"].sum() * 100\n",")\n","\n","print(\"Line-level class distribution\")\n","display(line_distribution_df)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.bar(line_distribution_df[\"class\"], line_distribution_df[\"count\"])\n","plt.xlabel(\"Log label\")\n","plt.ylabel(\"Number of log lines\")\n","plt.title(\"BGL Line-Level Class Distribution\")\n","finalize_figure()\n","save_current_figure(\"figure_4_1_bgl_line_level_class_distribution.png\", \"Figure 4.1\", \"BGL Line-Level Class Distribution\")\n","plt.show()\n"]},{"cell_type":"markdown","id":"38aaa3d8","metadata":{"id":"38aaa3d8"},"source":["## Step 5 — Build metadata-reduced model input and normalize it\n","\n","The model input is built from event-related fields only:\n","\n","```text\n","source + component + severity + event_message\n","```\n","\n","This excludes direct shortcut fields such as `date`, `full_time`, and `node`.\n","\n","### Normalization examples\n","\n","| Before | After |\n","|---|---|\n","| `RAS KERNEL INFO CE sym 2, at 0x0b85eee0, mask 0x05` | `ras kernel info ce sym <num> at <hex> mask <hex>` |\n","| `socket to 172.16.96.116:33569` | `socket to <ip> <num>` |\n","\n","### Why normalize?\n","\n","Normalization reduces memorization of unique values such as addresses, IDs, timestamps, and random numbers.\n"]},{"cell_type":"code","execution_count":10,"id":"64537460","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":334},"id":"64537460","outputId":"91527943-9424-481e-9488-8d932e8881b2","executionInfo":{"status":"ok","timestamp":1785510644713,"user_tz":-180,"elapsed":98102,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Example from the dataset\n","Before: RAS KERNEL INFO instruction cache parity error corrected\n","After : ras kernel info instruction cache parity error corrected\n","\n","Synthetic normalization example\n","Before: RAS KERNEL INFO CE sym 2, at 0x0b85eee0, mask 0x05 socket to 172.16.96.116:33569\n","After : ras kernel info ce sym <num> at <hex> mask <hex> socket to <ip> <num>\n"]},{"output_type":"display_data","data":{"text/plain":["   label source component severity                             event_message  \\\n","0      0    RAS    KERNEL     INFO  instruction cache parity error corrected   \n","1      0    RAS    KERNEL     INFO  instruction cache parity error corrected   \n","2      0    RAS    KERNEL     INFO  instruction cache parity error corrected   \n","3      0    RAS    KERNEL     INFO  instruction cache parity error corrected   \n","4      0    RAS    KERNEL     INFO  instruction cache parity error corrected   \n","\n","                                     model_text_norm  \n","0  ras kernel info instruction cache parity error...  \n","1  ras kernel info instruction cache parity error...  \n","2  ras kernel info instruction cache parity error...  \n","3  ras kernel info instruction cache parity error...  \n","4  ras kernel info instruction cache parity error...  "],"text/html":["\n","  <div id=\"df-fb15218e-6734-447e-bda7-c3a37a119382\" class=\"colab-df-container\">\n","    <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>label</th>\n","      <th>source</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","      <th>model_text_norm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>RAS</td>\n","      <td>KERNEL</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb15218e-6734-447e-bda7-c3a37a119382')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-fb15218e-6734-447e-bda7-c3a37a119382 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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str(text).lower()\n","\n","    # Replace IP addresses before replacing generic numbers.\n","    text = re.sub(r\"\\b\\d{1,3}(?:\\.\\d{1,3}){3}\\b\", \" <ip> \", text)\n","\n","    # Replace dates/times if they appear inside the message text.\n","    text = re.sub(r\"\\b\\d{4}[.-]\\d{2}[.-]\\d{2}\\b\", \" <date> \", text)\n","    text = re.sub(r\"\\b\\d{2}:\\d{2}:\\d{2}(?:\\.\\d+)?\\b\", \" <time> \", text)\n","\n","    # Replace hexadecimal values such as 0x00004ed8.\n","    text = re.sub(r\"0x[0-9a-fA-F]+\", \" <hex> \", text)\n","\n","    # Replace BGL-like node IDs if they appear inside message text.\n","    text = re.sub(r\"r\\d{2}-m\\d-n[a-f0-9]-[ci]:j\\d{2}-u\\d{2}\", \" <node> \", text)\n","\n","    # Replace standalone numbers.\n","    text = re.sub(r\"\\b\\d+\\b\", \" <num> \", text)\n","\n","    # Clean punctuation spacing while keeping placeholder tokens such as <hex>.\n","    text = re.sub(r\"[^a-zA-Z0-9_<>]+\", \" \", text)\n","    text = re.sub(r\"\\s+\", \" \", text).strip()\n","    return text\n","\n","# Build model input text without timestamp/node shortcut fields.\n","df[\"model_text\"] = (\n","    df[\"source\"].astype(str) + \" \" +\n","    df[\"component\"].astype(str) + \" \" +\n","    df[\"severity\"].astype(str) + \" \" +\n","    df[\"event_message\"].astype(str)\n",")\n","\n","df[\"model_text_norm\"] = df[\"model_text\"].apply(normalize_log_text)\n","\n","print(\"Example from the dataset\")\n","print(\"Before:\", df.loc[0, \"model_text\"])\n","print(\"After :\", df.loc[0, \"model_text_norm\"])\n","\n","print(\"\\nSynthetic normalization example\")\n","sample_text = \"RAS KERNEL INFO CE sym 2, at 0x0b85eee0, mask 0x05 socket to 172.16.96.116:33569\"\n","print(\"Before:\", sample_text)\n","print(\"After :\", normalize_log_text(sample_text))\n","\n","display(df[[\"label\", \"source\", \"component\", \"severity\", \"event_message\", \"model_text_norm\"]].head(5))\n"]},{"cell_type":"markdown","id":"2df7fb78","metadata":{"id":"2df7fb78"},"source":["## Step 6 — Encode only unique normalized messages\n","\n","This is the main efficiency improvement in LogMiniLM.\n","\n","Instead of encoding every log line with MiniLM, the notebook encodes only the unique normalized messages.\n","\n","### Example\n","\n","If the same normalized message appears 10,000 times:\n","\n","```text\n","ras kernel info instruction cache parity error corrected\n","```\n","\n","MiniLM encodes it once, and all repeated lines reuse the same `event_id`.\n","\n","### Output\n","\n","- `unique_texts`: list of unique normalized messages.\n","- `line_event_ids`: for each log line, an integer pointing to its unique message.\n","- `line_labels`: normal/anomaly label for each line.\n"]},{"cell_type":"code","execution_count":11,"id":"70c1bb8e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"70c1bb8e","outputId":"30ec00b4-aab1-49bc-92ce-43c227ba237c","executionInfo":{"status":"ok","timestamp":1785510662726,"user_tz":-180,"elapsed":18012,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Total log lines: 4713493\n","Unique normalized messages: 47012\n","Reduction ratio: 100.26 log lines per unique message\n","\n","Example mapping\n","Normalized text: ras kernel info instruction cache parity error corrected\n","Event ID: 45962\n","\n","Saved unique texts: /content/drive/MyDrive/logminilm_cache/bgl_unique_normalized_messages.csv\n","Saved line event IDs: /content/drive/MyDrive/logminilm_cache/bgl_line_event_ids.npy\n","Saved lightweight metadata: /content/drive/MyDrive/logminilm_cache/bgl_metadata_for_alerts_light.parquet\n"]}],"source":["unique_texts = pd.Series(df[\"model_text_norm\"].unique()).sort_values().reset_index(drop=True)\n","text_to_event_id = {text: idx for idx, text in enumerate(unique_texts)}\n","\n","line_event_ids = df[\"model_text_norm\"].map(text_to_event_id).astype(np.int32).values\n","line_labels = df[\"label\"].astype(np.int8).values\n","\n","print(\"Total log lines:\", len(df))\n","print(\"Unique normalized messages:\", len(unique_texts))\n","print(\"Reduction ratio:\", round(len(df) / max(len(unique_texts), 1), 2), \"log lines per unique message\")\n","\n","example_idx = 0\n","print(\"\\nExample mapping\")\n","print(\"Normalized text:\", df.loc[example_idx, \"model_text_norm\"])\n","print(\"Event ID:\", int(line_event_ids[example_idx]))\n","\n","# Save reusable mapping outputs.\n","save_table(unique_texts.to_frame(name=\"model_text_norm\"), UNIQUE_TEXTS_PATH, \"Unique normalized BGL messages\")\n","np.save(EVENT_ID_PATH, line_event_ids)\n","np.save(LABELS_PATH, line_labels)\n","\n","# Save lightweight metadata for later XAI and alert reporting.\n","# raw_log is intentionally excluded to reduce memory/storage usage.\n","metadata_cols = [\n","    \"line_index\", \"raw_label\", \"label\", \"date\", \"full_time\", \"node\",\n","    \"source\", \"component\", \"severity\", \"event_message\", \"model_text_norm\"\n","]\n","df[metadata_cols].to_parquet(METADATA_PATH, index=False)\n","\n","print(\"\\nSaved unique texts:\", UNIQUE_TEXTS_PATH)\n","print(\"Saved line event IDs:\", EVENT_ID_PATH)\n","print(\"Saved lightweight metadata:\", METADATA_PATH)\n"]},{"cell_type":"markdown","id":"cd2f9bdb","metadata":{"id":"cd2f9bdb"},"source":["## Step 7 — Generate or load cached MiniLM embeddings\n","\n","MiniLM converts each unique normalized message into a semantic vector.\n","\n","### Input\n","\n","```text\n","unique_texts = [message_1, message_2, ..., message_N]\n","```\n","\n","### Output\n","\n","```text\n","unique_embeddings shape = [number_of_unique_messages, 384]\n","```\n","\n","### Example\n","\n","If `event_id = 25`, then line 25 does not store text directly during training. Instead, it points to:\n","\n","```python\n","unique_embeddings[25]\n","```\n","\n","which is a MiniLM semantic vector.\n"]},{"cell_type":"code","execution_count":12,"id":"d370c48c","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d370c48c","outputId":"a35c81ce-6084-4e24-9647-9d14d0046344","executionInfo":{"status":"ok","timestamp":1785510666308,"user_tz":-180,"elapsed":3613,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Loading valid cached embeddings from: /content/drive/MyDrive/logminilm_cache/bgl_minilm_unique_embeddings.npy\n","Unique embeddings shape: (47012, 384)\n","Embedding dtype: float32\n","First embedding first 5 values: [ 0.00211342  0.009814   -0.09476357 -0.0665199   0.03991342]\n","Embedding integrity checks passed.\n"]}],"source":["def load_or_create_minilm_embeddings(unique_texts, embeddings_path):\n","    '''Load cached MiniLM embeddings if valid; otherwise encode and save them.'''\n","    if embeddings_path.exists():\n","        cached = np.load(embeddings_path).astype(np.float32)\n","        if cached.shape[0] == len(unique_texts):\n","            print(\"Loading valid cached embeddings from:\", embeddings_path)\n","            return cached\n","        else:\n","            print(\"Cached embeddings exist but do not match current unique_texts.\")\n","            print(\"Cached rows:\", cached.shape[0], \"Current unique messages:\", len(unique_texts))\n","            print(\"Recomputing embeddings...\")\n","\n","    print(\"Encoding unique normalized messages with MiniLM...\")\n","    encoder = SentenceTransformer(MINILM_MODEL_NAME, device=str(device))\n","    embeddings = encoder.encode(\n","        unique_texts.tolist(),\n","        batch_size=ENCODE_BATCH_SIZE,\n","        show_progress_bar=True,\n","        convert_to_numpy=True,\n","        normalize_embeddings=False,\n","    ).astype(np.float32)\n","\n","    np.save(embeddings_path, embeddings)\n","    print(\"Saved embeddings to:\", embeddings_path)\n","\n","    # Free the encoder model after embedding generation to reduce GPU/RAM use.\n","    del encoder\n","    gc.collect()\n","    if torch.cuda.is_available():\n","        torch.cuda.empty_cache()\n","\n","    return embeddings\n","\n","unique_embeddings = load_or_create_minilm_embeddings(unique_texts, EMBEDDINGS_PATH)\n","\n","print(\"Unique embeddings shape:\", unique_embeddings.shape)\n","print(\"Embedding dtype:\", unique_embeddings.dtype)\n","print(\"First embedding first 5 values:\", unique_embeddings[0, :5])\n","\n","\n","assert unique_embeddings.ndim == 2\n","assert unique_embeddings.shape[0] == len(unique_texts)\n","assert unique_embeddings.shape[1] == 384\n","assert np.isfinite(unique_embeddings).all()\n","print(\"Embedding integrity checks passed.\")\n"]},{"cell_type":"markdown","id":"41034541","metadata":{"id":"41034541"},"source":["## Step 8 — Build fixed 10-line sequences\n","\n","Each sequence contains 10 consecutive log events.\n","\n","### Sequence label rule\n","\n","```text\n","If any log line inside the sequence is anomalous → sequence label = 1\n","Otherwise → sequence label = 0\n","```\n","\n","### Example\n","\n","```text\n","Line labels: [0, 0, 0, 1, 0, 0, 0, 0, 0, 0]\n","Sequence label = 1\n","```\n","\n","because at least one event is anomalous.\n"]},{"cell_type":"code","execution_count":13,"id":"92d69a49","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":509},"id":"92d69a49","outputId":"7a1b2a84-02e5-4bea-db02-9f176e43fcab","executionInfo":{"status":"ok","timestamp":1785510668322,"user_tz":-180,"elapsed":2016,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["X_ids shape: (471349, 10)\n","y_seq shape: (471349,)\n","Sequence label distribution: Counter({np.int8(0): 432326, np.int8(1): 39023})\n","\n","Example sequence\n","Event IDs: [45962 45962 45962 45962 45962 45962 45962 45962 45962 45962]\n","Sequence label: 0\n","Line range: [0 9]\n"]},{"output_type":"display_data","data":{"text/plain":["   line_index  label                   full_time                 node  \\\n","0           0      0  2005-06-03-15.42.50.363779  R02-M1-N0-C:J12-U11   \n","1           1      0  2005-06-03-15.42.50.527847  R02-M1-N0-C:J12-U11   \n","2           2      0  2005-06-03-15.42.50.675872  R02-M1-N0-C:J12-U11   \n","3           3      0  2005-06-03-15.42.50.823719  R02-M1-N0-C:J12-U11   \n","4           4      0  2005-06-03-15.42.50.982731  R02-M1-N0-C:J12-U11   \n","5           5      0  2005-06-03-15.42.51.131467  R02-M1-N0-C:J12-U11   \n","6           6      0  2005-06-03-15.42.51.293532  R02-M1-N0-C:J12-U11   \n","7           7      0  2005-06-03-15.42.51.428563  R02-M1-N0-C:J12-U11   \n","8           8      0  2005-06-03-15.42.51.601412  R02-M1-N0-C:J12-U11   \n","9           9      0  2005-06-03-15.42.51.749199  R02-M1-N0-C:J12-U11   \n","\n","  severity                             event_message  \\\n","0     INFO  instruction cache parity error corrected   \n","1     INFO  instruction cache parity error corrected   \n","2     INFO  instruction cache parity error corrected   \n","3     INFO  instruction cache parity error corrected   \n","4     INFO  instruction cache parity error corrected   \n","5     INFO  instruction cache parity error corrected   \n","6     INFO  instruction cache parity error corrected   \n","7     INFO  instruction cache parity error corrected   \n","8     INFO  instruction cache parity error corrected   \n","9     INFO  instruction cache parity error corrected   \n","\n","                                     model_text_norm  \n","0  ras kernel info instruction cache parity error...  \n","1  ras kernel info instruction cache parity error...  \n","2  ras kernel info instruction cache parity error...  \n","3  ras kernel info instruction cache parity error...  \n","4  ras kernel info instruction cache parity error...  \n","5  ras kernel info instruction cache parity error...  \n","6  ras kernel info instruction cache parity error...  \n","7  ras kernel info instruction cache parity error...  \n","8  ras kernel info instruction cache parity error...  \n","9  ras kernel info instruction cache parity error...  "],"text/html":["\n","  <div id=\"df-bfe1df4b-441e-48ab-ab23-1f0b31637929\" class=\"colab-df-container\">\n","    <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>line_index</th>\n","      <th>label</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","      <th>model_text_norm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>2005-06-03-15.42.50.363779</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error 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corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>0</td>\n","      <td>2005-06-03-15.42.50.982731</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>2005-06-03-15.42.51.131467</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>2005-06-03-15.42.51.293532</td>\n","      <td>R02-M1-N0-C:J12-U11</td>\n","      <td>INFO</td>\n","      <td>instruction cache parity error 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corrected</td>\n","      <td>ras kernel info instruction cache parity error...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfe1df4b-441e-48ab-ab23-1f0b31637929')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      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Check dataset size, WINDOW_SIZE, and STEP_SIZE.\")\n","\n","print(\"X_ids shape:\", X_ids.shape)\n","print(\"y_seq shape:\", y_seq.shape)\n","print(\"Sequence label distribution:\", Counter(y_seq))\n","\n","print(\"\\nExample sequence\")\n","print(\"Event IDs:\", X_ids[0])\n","print(\"Sequence label:\", int(y_seq[0]))\n","print(\"Line range:\", seq_ranges[0])\n","\n","display(df.iloc[seq_ranges[0][0]:seq_ranges[0][1] + 1][[\n","    \"line_index\", \"label\", \"full_time\", \"node\", \"severity\", \"event_message\", \"model_text_norm\"\n","]])\n"]},{"cell_type":"markdown","id":"87c40b8a","metadata":{"id":"87c40b8a"},"source":["## Step 9 — Temporal Train, Validation, and Test Splits\n","\n","**Objective:** preserve chronology and keep the final test set untouched.\n","\n","**Method:** the first 80% is the complete final-training partition and the final 20% is the test partition. During development, the most recent 10% of the 80% partition is temporarily used for validation.\n"]},{"cell_type":"code","execution_count":14,"id":"4d11794e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4d11794e","outputId":"98565840-8138-477d-f754-f454ec9eaf19","executionInfo":{"status":"ok","timestamp":1785510669737,"user_tz":-180,"elapsed":1414,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Development train: (339372, 10) Counter({np.int8(0): 307585, np.int8(1): 31787})\n","Validation: (37707, 10) Counter({np.int8(0): 36136, np.int8(1): 1571})\n","Complete final train: (377079, 10) Counter({np.int8(0): 343721, np.int8(1): 33358})\n","Untouched test: (94270, 10) Counter({np.int8(0): 88605, np.int8(1): 5665})\n","Last development range: [3393710 3393719]\n","First validation range: [3393720 3393729]\n","Last validation range: [3770780 3770789]\n","First test range: [3770790 3770799]\n","Temporal split and leakage checks passed.\n"]}],"source":["# Final temporal 80/20 split\n","final_split_idx = int(len(X_ids) * TRAIN_RATIO)\n","\n","X_train = X_ids[:final_split_idx]\n","y_train = y_seq[:final_split_idx]\n","ranges_train = seq_ranges[:final_split_idx]\n","\n","X_test = X_ids[final_split_idx:]\n","y_test = y_seq[final_split_idx:]\n","ranges_test = seq_ranges[final_split_idx:]\n","\n","# Internal temporal development/validation split\n","validation_size = max(1, int(len(X_train) * VALIDATION_RATIO_WITHIN_TRAIN))\n","development_split_idx = len(X_train) - validation_size\n","\n","X_dev_train = X_train[:development_split_idx]\n","y_dev_train = y_train[:development_split_idx]\n","ranges_dev_train = ranges_train[:development_split_idx]\n","\n","X_val = X_train[development_split_idx:]\n","y_val = y_train[development_split_idx:]\n","ranges_val = ranges_train[development_split_idx:]\n","\n","print(\"Development train:\", X_dev_train.shape, Counter(y_dev_train))\n","print(\"Validation:\", X_val.shape, Counter(y_val))\n","print(\"Complete final train:\", X_train.shape, Counter(y_train))\n","print(\"Untouched test:\", X_test.shape, Counter(y_test))\n","\n","for split_name, labels in [(\"development\", y_dev_train), (\"validation\", y_val), (\"test\", y_test)]:\n","    counts = Counter(labels)\n","    if counts[0] == 0 or counts[1] == 0:\n","        raise ValueError(f\"Invalid {split_name} class distribution: {counts}\")\n","\n","assert ranges_dev_train[-1][1] < ranges_val[0][0]\n","assert ranges_val[-1][1] < ranges_test[0][0]\n","\n","print(\"Last development range:\", ranges_dev_train[-1])\n","print(\"First validation range:\", ranges_val[0])\n","print(\"Last validation range:\", ranges_val[-1])\n","print(\"First test range:\", ranges_test[0])\n","\n","\n","# ============================================================\n","# Scientific integrity checks for temporal splitting\n","# ============================================================\n","\n","assert len(X_train) == len(y_train) == len(ranges_train)\n","assert len(X_test) == len(y_test) == len(ranges_test)\n","assert len(X_dev_train) == len(y_dev_train)\n","assert len(X_val) == len(y_val)\n","\n","assert X_train.shape[1] == WINDOW_SIZE\n","assert X_test.shape[1] == WINDOW_SIZE\n","assert set(np.unique(y_train)).issubset({0, 1})\n","assert set(np.unique(y_test)).issubset({0, 1})\n","\n","train_last_end = int(ranges_train[-1][1])\n","test_first_start = int(ranges_test[0][0])\n","assert train_last_end <= test_first_start, (\n","    \"Temporal leakage detected between development and test windows.\"\n",")\n","\n","train_ranges_set = {tuple(x) for x in ranges_train}\n","test_ranges_set = {tuple(x) for x in ranges_test}\n","assert train_ranges_set.isdisjoint(test_ranges_set)\n","assert len(train_ranges_set) == len(ranges_train)\n","assert len(test_ranges_set) == len(ranges_test)\n","\n","assert np.min(X_ids) >= 0\n","assert np.max(X_ids) < len(unique_texts)\n","\n","print(\"Temporal split and leakage checks passed.\")\n"]},{"cell_type":"markdown","id":"8ErS44ZO-q-O","metadata":{"id":"8ErS44ZO-q-O"},"source":["### Figure 4.2 — Temporal Sequence-Level Class Distribution\n","\n","This figure appears directly after the temporal split.\n","\n","**Interpretation:** The chart shows the number of normal and anomalous sequences in the complete 80% training partition and untouched 20% test partition, making the temporal class imbalance explicit.\n"]},{"cell_type":"code","execution_count":15,"id":"Ut_VJY_A-q-O","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"Ut_VJY_A-q-O","outputId":"f05cff25-3b68-453b-8e9a-39e8f00ba41c","executionInfo":{"status":"ok","timestamp":1785510671591,"user_tz":-180,"elapsed":1853,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Sequence-level class distribution\n"]},{"output_type":"display_data","data":{"text/plain":["     split_class   count\n","0   Train Normal  343721\n","1  Train Anomaly   33358\n","2    Test Normal   88605\n","3   Test Anomaly    5665"],"text/html":["\n","  <div id=\"df-64adfe7d-6145-4e02-b16f-4227cb1c6b0a\" class=\"colab-df-container\">\n","    <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>split_class</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Train Normal</td>\n","      <td>343721</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Train 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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.2 — Sequence-level class distribution\n","# ============================================================\n","\n","sequence_distribution_df = pd.DataFrame({\n","    \"split_class\": [\"Train Normal\", \"Train Anomaly\", \"Test Normal\", \"Test Anomaly\"],\n","    \"count\": [\n","        int(np.sum(y_train == 0)),\n","        int(np.sum(y_train == 1)),\n","        int(np.sum(y_test == 0)),\n","        int(np.sum(y_test == 1)),\n","    ]\n","})\n","\n","print(\"Sequence-level class distribution\")\n","display(sequence_distribution_df)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.bar(sequence_distribution_df[\"split_class\"], sequence_distribution_df[\"count\"])\n","plt.xlabel(\"Dataset split and class\")\n","plt.ylabel(\"Number of sequences\")\n","plt.title(\"BGL Sequence-Level Class Distribution\")\n","plt.xticks(rotation=20)\n","finalize_figure()\n","save_current_figure(\"figure_4_2_sequence_level_class_distribution.png\", \"Figure 4.2\", \"Sequence-Level Class Distribution\")\n","plt.show()\n"]},{"cell_type":"markdown","id":"62e6dbee","metadata":{"id":"62e6dbee"},"source":["## Step 10 — Create Deterministic DataLoaders\n","\n","Four loaders are created: development training, validation, complete final training, and untouched testing.\n"]},{"cell_type":"code","execution_count":16,"id":"851cac7d","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"851cac7d","outputId":"500e323f-cadc-48f6-abad-629a81351559","executionInfo":{"status":"ok","timestamp":1785510671789,"user_tz":-180,"elapsed":196,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Development batches: 1326\n","Validation batches: 148\n","Final-training batches: 1473\n","Test batches: 369\n","torch.Size([256, 10]) torch.Size([256]) torch.Size([256])\n"]}],"source":["class EventIdSequenceDataset(Dataset):\n","    # Dataset returning event-ID sequences, labels, and local indices.\n","    def __init__(self, X_ids, y):\n","        self.X_ids = torch.tensor(X_ids, dtype=torch.long)\n","        self.y = torch.tensor(y, dtype=torch.float32)\n","\n","    def __len__(self):\n","        return len(self.y)\n","\n","    def __getitem__(self, idx):\n","        return self.X_ids[idx], self.y[idx], idx\n","\n","\n","def make_seeded_train_loader(dataset, seed):\n","    generator = torch.Generator()\n","    generator.manual_seed(seed)\n","    return DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True,\n","                      generator=generator, num_workers=0,\n","                      pin_memory=(device.type == \"cuda\"))\n","\n","\n","dev_train_dataset = EventIdSequenceDataset(X_dev_train, y_dev_train)\n","val_dataset = EventIdSequenceDataset(X_val, y_val)\n","final_train_dataset = EventIdSequenceDataset(X_train, y_train)\n","test_dataset = EventIdSequenceDataset(X_test, y_test)\n","\n","dev_train_loader = make_seeded_train_loader(dev_train_dataset, SEED)\n","final_train_loader = make_seeded_train_loader(final_train_dataset, SEED)\n","val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False,\n","                        num_workers=0, pin_memory=(device.type == \"cuda\"))\n","test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False,\n","                         num_workers=0, pin_memory=(device.type == \"cuda\"))\n","\n","print(\"Development batches:\", len(dev_train_loader))\n","print(\"Validation batches:\", len(val_loader))\n","print(\"Final-training batches:\", len(final_train_loader))\n","print(\"Test batches:\", len(test_loader))\n","\n","batch_event_ids, batch_labels, batch_indices = next(iter(dev_train_loader))\n","print(batch_event_ids.shape, batch_labels.shape, batch_indices.shape)\n"]},{"cell_type":"markdown","id":"67a381e3","metadata":{"id":"67a381e3"},"source":["## Step 11 — Define the LogMiniLM model\n","\n","LogMiniLM uses cached MiniLM embeddings as a frozen semantic embedding layer.\n","\n","### Forward pass\n","\n","```text\n","event IDs\n","  ↓\n","MiniLM embedding lookup [batch, 10, 384]\n","  ↓\n","Projection layer [batch, 10, 128]\n","  ↓\n","Position embedding\n","  ↓\n","Transformer encoder\n","  ↓\n","Mean pooling\n","  ↓\n","Binary classifier\n","  ↓\n","Anomaly logit\n","```\n","\n","### Why freeze MiniLM embeddings?\n","\n","The embeddings are already generated and cached. Freezing them keeps training lightweight and reduces GPU memory use.\n"]},{"cell_type":"code","execution_count":17,"id":"97e5f701","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"97e5f701","outputId":"e528020e-25c1-4518-f685-a212df1aca34","executionInfo":{"status":"ok","timestamp":1785510672322,"user_tz":-180,"elapsed":532,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["LogMiniLM(\n","  (semantic_embedding): Embedding(47012, 384)\n","  (projection): Linear(in_features=384, out_features=128, bias=True)\n","  (transformer): TransformerEncoder(\n","    (layers): ModuleList(\n","      (0): TransformerEncoderLayer(\n","        (self_attn): MultiheadAttention(\n","          (out_proj): NonDynamicallyQuantizableLinear(in_features=128, out_features=128, bias=True)\n","        )\n","        (linear1): Linear(in_features=128, out_features=256, bias=True)\n","        (dropout): Dropout(p=0.1, inplace=False)\n","        (linear2): Linear(in_features=256, out_features=128, bias=True)\n","        (norm1): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","        (norm2): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","        (dropout1): Dropout(p=0.1, inplace=False)\n","        (dropout2): Dropout(p=0.1, inplace=False)\n","      )\n","    )\n","  )\n","  (norm): LayerNorm((128,), eps=1e-05, elementwise_affine=True)\n","  (classifier): Sequential(\n","    (0): Dropout(p=0.1, inplace=False)\n","    (1): Linear(in_features=128, out_features=1, bias=True)\n","  )\n",")\n","Total parameters: 18,236,033\n","Trainable parameters: 183,425\n","Example probabilities before development training: [0.4614926  0.25668812]\n"]}],"source":["class LogMiniLM(nn.Module):\n","    '''Lightweight semantic Transformer for BGL log anomaly detection.\n","\n","    The model name is LogMiniLM. It uses MiniLM semantic embeddings and a compact\n","    Transformer encoder for sequence-level binary classification.\n","    '''\n","\n","    def __init__(\n","        self,\n","        embedding_matrix,\n","        projection_dim=128,\n","        window_size=10,\n","        num_heads=2,\n","        num_layers=1,\n","        feedforward_dim=256,\n","        dropout=0.1,\n","    ):\n","        super().__init__()\n","\n","        # Frozen semantic lookup table: event_id -> MiniLM embedding.\n","        embedding_tensor = torch.tensor(embedding_matrix, dtype=torch.float32)\n","        self.semantic_embedding = nn.Embedding.from_pretrained(embedding_tensor, freeze=True)\n","\n","        # Reduce MiniLM dimension from 384 to 128 for lighter Transformer compute.\n","        original_dim = embedding_tensor.shape[1]\n","        self.projection = nn.Linear(original_dim, projection_dim)\n","\n","        # Learnable position embeddings so the model knows event order inside the window.\n","        self.position_embedding = nn.Parameter(torch.zeros(1, window_size, projection_dim))\n","\n","        encoder_layer = nn.TransformerEncoderLayer(\n","            d_model=projection_dim,\n","            nhead=num_heads,\n","            dim_feedforward=feedforward_dim,\n","            dropout=dropout,\n","            batch_first=True,\n","            activation=\"gelu\",\n","        )\n","        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)\n","\n","        self.norm = nn.LayerNorm(projection_dim)\n","        self.classifier = nn.Sequential(\n","            nn.Dropout(dropout),\n","            nn.Linear(projection_dim, 1),\n","        )\n","\n","    def forward(self, event_ids):\n","        # event_ids shape: [batch, window_size]\n","        x = self.semantic_embedding(event_ids)       # [batch, window, 384]\n","        x = self.projection(x)                       # [batch, window, 128]\n","        x = x + self.position_embedding[:, :x.size(1), :]\n","        x = self.transformer(x)                      # [batch, window, 128]\n","        x = self.norm(x)\n","        pooled = x.mean(dim=1)                       # [batch, 128]\n","        logits = self.classifier(pooled).squeeze(-1) # [batch]\n","        return logits\n","\n","\n","\n","def build_logminilm(seed=SEED):\n","    # Create a fresh, deterministically initialized LogMiniLM model.\n","    reset_all_seeds(seed)\n","    return LogMiniLM(\n","        embedding_matrix=unique_embeddings,\n","        projection_dim=PROJECTION_DIM,\n","        window_size=WINDOW_SIZE,\n","        num_heads=NUM_ATTENTION_HEADS,\n","        num_layers=NUM_TRANSFORMER_LAYERS,\n","        feedforward_dim=FEEDFORWARD_DIM,\n","        dropout=DROPOUT,\n","    ).to(device)\n","\n","\n","development_model = build_logminilm(SEED)\n","num_params = sum(p.numel() for p in development_model.parameters())\n","trainable_params = sum(p.numel() for p in development_model.parameters() if p.requires_grad)\n","print(development_model)\n","print(\"Total parameters:\", f\"{num_params:,}\")\n","print(\"Trainable parameters:\", f\"{trainable_params:,}\")\n","\n","development_model.eval()\n","with torch.no_grad():\n","    example_logits = development_model(batch_event_ids[:2].to(device))\n","    example_probs = torch.sigmoid(example_logits).cpu().numpy()\n","print(\"Example probabilities before development training:\", example_probs)\n"]},{"cell_type":"markdown","id":"FemgPLrr-q-P","metadata":{"id":"FemgPLrr-q-P"},"source":["## Model Summary\n","\n","**Objective:** Present the architecture and computational configuration in a single thesis-ready table.\n","\n","**Method:** The summary is generated directly from the active configuration and instantiated model.\n","\n","**Output:** A table containing the semantic encoder, dimensions, Transformer settings, sequence counts, and parameter counts.\n"]},{"cell_type":"code","execution_count":18,"id":"mFtLx2T2-q-P","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":663},"id":"mFtLx2T2-q-P","outputId":"ca3621c4-bf50-4eee-b090-86ad6e947d9f","executionInfo":{"status":"ok","timestamp":1785510672710,"user_tz":-180,"elapsed":386,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["                              Item                                   Value\n","0                            Model                               LogMiniLM\n","1                 Semantic encoder  sentence-transformers/all-MiniLM-L6-v2\n","2     Semantic embedding dimension                                     384\n","3                       Projection                               384 → 128\n","4               Transformer layers                                       1\n","5                  Attention heads                                       2\n","6            Feedforward dimension                                     256\n","7                          Dropout                                     0.1\n","8                          Pooling                            Mean pooling\n","9                       Classifier                         Linear(128 → 1)\n","10                     Window size                                      10\n","11                       Step size                                      10\n","12  Development-training sequences                                  339372\n","13            Validation sequences                                   37707\n","14        Final-training sequences                                  377079\n","15                  Test sequences                                   94270\n","16      Unique normalized messages                                   47012\n","17                Total parameters                                18236033\n","18            Trainable parameters                                  183425"],"text/html":["\n","  <div id=\"df-38066977-28db-474f-b27f-f0c4c1e3c372\" class=\"colab-df-container\">\n","    <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>Item</th>\n","      <th>Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Model</td>\n","      <td>LogMiniLM</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Semantic encoder</td>\n","      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Semantic embedding dimension</td>\n","      <td>384</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Projection</td>\n","      <td>384 → 128</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Transformer layers</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Attention heads</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Feedforward dimension</td>\n","      <td>256</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Dropout</td>\n","      <td>0.1</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Pooling</td>\n","      <td>Mean pooling</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Classifier</td>\n","      <td>Linear(128 → 1)</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Window size</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Step size</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Development-training sequences</td>\n","      <td>339372</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Validation sequences</td>\n","      <td>37707</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Final-training sequences</td>\n","      <td>377079</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Test sequences</td>\n","      <td>94270</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Unique normalized messages</td>\n","      <td>47012</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>Total parameters</td>\n","      <td>18236033</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>Trainable parameters</td>\n","      <td>183425</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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A new weight is later calculated from the complete 80% partition for final retraining. No SMOTE, oversampling, or undersampling is used.\n"]},{"cell_type":"code","execution_count":19,"id":"d449ef67","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d449ef67","outputId":"40e052fc-eca1-4c06-eff5-481558020dae","executionInfo":{"status":"ok","timestamp":1785510672747,"user_tz":-180,"elapsed":17,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Development normal: 307585\n","Development anomaly: 31787\n","Development pos_weight: 19.35288010822034\n"]}],"source":["def calculate_pos_weight(labels):\n","    num_pos = int((labels == 1).sum())\n","    num_neg = int((labels == 0).sum())\n","    base_weight = num_neg / max(num_pos, 1)\n","    final_weight = base_weight * ANOMALY_WEIGHT_MULTIPLIER\n","    return num_neg, num_pos, base_weight, final_weight\n","\n","\n","dev_num_neg, dev_num_pos, dev_base_weight, dev_pos_weight = calculate_pos_weight(y_dev_train)\n","print(\"Development normal:\", dev_num_neg)\n","print(\"Development anomaly:\", dev_num_pos)\n","print(\"Development pos_weight:\", dev_pos_weight)\n","\n","development_criterion = nn.BCEWithLogitsLoss(\n","    pos_weight=torch.tensor([dev_pos_weight], device=device, dtype=torch.float32)\n",")\n","development_optimizer = torch.optim.AdamW(\n","    development_model.parameters(), lr=LEARNING_RATE, weight_decay=1e-4\n",")\n"]},{"cell_type":"markdown","id":"4e47dc47","metadata":{"id":"4e47dc47"},"source":["## Step 13 — Development Training and F1-Based Best-Epoch Selection\n","\n","The development model is evaluated on the temporal validation subset after every epoch.\n","\n","For each epoch:\n","\n","1. Validation probabilities are generated.\n","2. All predefined candidate thresholds are evaluated.\n","3. The epoch-specific threshold that maximizes validation F1-score is identified.\n","4. The checkpoint with the highest validation F1-score is retained.\n","\n","Validation loss is still recorded and plotted as a diagnostic measure, but it is **not** the primary model-selection criterion.\n","\n","Early stopping is triggered when validation F1-score does not improve for the configured patience period.\n"]},{"cell_type":"code","execution_count":20,"id":"22cead4b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":732},"id":"22cead4b","outputId":"51194a52-b4c9-4e7b-cb43-c221bc7fb5a4","executionInfo":{"status":"ok","timestamp":1785510753050,"user_tz":-180,"elapsed":80302,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/torch/autograd/graph.py:869: UserWarning: Memory Efficient attention defaults to a non-deterministic algorithm. To explicitly enable determinism call torch.use_deterministic_algorithms(True, warn_only=False). (Triggered internally at /pytorch/aten/src/ATen/native/transformers/cuda/attention_backward.cu:900.)\n","  return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n"]},{"output_type":"stream","name":"stdout","text":["Epoch 01/10 | train=0.030211 | val_loss=0.508817 | val_f1=0.959057 | threshold=0.90 | best_epoch=1\n","Epoch 02/10 | train=0.007148 | val_loss=0.502347 | val_f1=0.939520 | threshold=0.80 | best_epoch=1\n","Epoch 03/10 | train=0.003325 | val_loss=0.517150 | val_f1=0.965767 | threshold=0.10 | best_epoch=3\n","Epoch 04/10 | train=0.005057 | val_loss=0.254723 | val_f1=0.954087 | threshold=0.90 | best_epoch=3\n","Epoch 05/10 | train=0.002108 | val_loss=0.323102 | val_f1=0.958115 | threshold=0.90 | best_epoch=3\n","Epoch 06/10 | train=0.003728 | val_loss=0.409999 | val_f1=0.953247 | threshold=0.90 | best_epoch=3\n","Development early stopping triggered by validation F1.\n","\n","Selected best epoch: 3\n","Best validation F1: 0.9657669519420672\n","Validation loss at selected epoch: 0.5171500267763506\n","Selected validation threshold: 0.1\n"]},{"output_type":"display_data","data":{"text/plain":["    accuracy  precision    recall        f1     tn  fp   fn    tp  threshold\n","0   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.10\n","1   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.15\n","2   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.20\n","3   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.25\n","4   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.30\n","5   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.35\n","6   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.40\n","7   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.45\n","8   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.50\n","9   0.997189        1.0  0.932527  0.965086  36136   0  106  1465       0.60\n","10  0.997189        1.0  0.932527  0.965086  36136   0  106  1465       0.70\n","11  0.997136        1.0  0.931254  0.964403  36136   0  108  1463       0.80\n","12  0.997109        1.0  0.930617  0.964062  36136   0  109  1462       0.90"],"text/html":["\n","  <div id=\"df-a60ef714-4302-4c59-ab84-e97a7eed08c4\" class=\"colab-df-container\">\n","    <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>accuracy</th>\n","      <th>precision</th>\n","      <th>recall</th>\n","      <th>f1</th>\n","      <th>tn</th>\n","      <th>fp</th>\n","      <th>fn</th>\n","      <th>tp</th>\n","      <th>threshold</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.10</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.15</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.20</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.25</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.30</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.997215</td>\n","      <td>1.0</td>\n","      <td>0.933164</td>\n","      <td>0.965426</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>105</td>\n","      <td>1466</td>\n","      <td>0.35</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>0.997215</td>\n","      <td>1.0</td>\n","      <td>0.933164</td>\n","      <td>0.965426</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>105</td>\n","      <td>1466</td>\n","      <td>0.40</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>0.997215</td>\n","      <td>1.0</td>\n","      <td>0.933164</td>\n","      <td>0.965426</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>105</td>\n","      <td>1466</td>\n","      <td>0.45</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>0.997215</td>\n","      <td>1.0</td>\n","      <td>0.933164</td>\n","      <td>0.965426</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>105</td>\n","      <td>1466</td>\n","      <td>0.50</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>0.997189</td>\n","      <td>1.0</td>\n","      <td>0.932527</td>\n","      <td>0.965086</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>106</td>\n","      <td>1465</td>\n","      <td>0.60</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>0.997189</td>\n","      <td>1.0</td>\n","      <td>0.932527</td>\n","      <td>0.965086</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>106</td>\n","      <td>1465</td>\n","      <td>0.70</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>0.997136</td>\n","      <td>1.0</td>\n","      <td>0.931254</td>\n","      <td>0.964403</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>108</td>\n","      <td>1463</td>\n","      <td>0.80</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>0.997109</td>\n","      <td>1.0</td>\n","      <td>0.930617</td>\n","      <td>0.964062</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>109</td>\n","      <td>1462</td>\n","      <td>0.90</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a60ef714-4302-4c59-ab84-e97a7eed08c4')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  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event_ids = event_ids.to(device, non_blocking=True)\n","        labels = labels.to(device, non_blocking=True)\n","\n","        optimizer.zero_grad(set_to_none=True)\n","        logits = model(event_ids)\n","        loss = criterion(logits, labels)\n","        loss.backward()\n","        optimizer.step()\n","\n","        total_loss += loss.item() * labels.size(0)\n","        total_count += labels.size(0)\n","\n","    return total_loss / max(total_count, 1)\n","\n","\n","def evaluate_loss(model, loader, criterion):\n","    \"\"\"Evaluate average weighted BCE loss.\"\"\"\n","    model.eval()\n","    total_loss, total_count = 0.0, 0\n","\n","    with torch.no_grad():\n","        for event_ids, labels, _ in loader:\n","            event_ids = event_ids.to(device, non_blocking=True)\n","            labels = labels.to(device, non_blocking=True)\n","\n","            logits = model(event_ids)\n","            loss = criterion(logits, labels)\n","\n","            total_loss += loss.item() * labels.size(0)\n","            total_count += labels.size(0)\n","\n","    return total_loss / max(total_count, 1)\n","\n","\n","def predict_probabilities(model, loader):\n","    \"\"\"Return probabilities, labels, and local dataset indices.\"\"\"\n","    model.eval()\n","    all_probs, all_labels, all_indices = [], [], []\n","\n","    with torch.no_grad():\n","        for event_ids, labels, indices in loader:\n","            event_ids = event_ids.to(device, non_blocking=True)\n","            logits = model(event_ids)\n","\n","            all_probs.append(torch.sigmoid(logits).cpu().numpy())\n","            all_labels.append(labels.numpy())\n","            all_indices.append(indices.numpy())\n","\n","    return (\n","        np.concatenate(all_probs),\n","        np.concatenate(all_labels).astype(int),\n","        np.concatenate(all_indices).astype(int),\n","    )\n","\n","\n","def evaluate_binary(y_true, y_pred):\n","    \"\"\"Calculate stable binary classification metrics.\"\"\"\n","    tn, fp, fn, tp = confusion_matrix(\n","        y_true, y_pred, labels=[0, 1]\n","    ).ravel()\n","\n","    return {\n","        \"accuracy\": accuracy_score(y_true, y_pred),\n","        \"precision\": precision_score(y_true, y_pred, zero_division=0),\n","        \"recall\": recall_score(y_true, y_pred, zero_division=0),\n","        \"f1\": f1_score(y_true, y_pred, zero_division=0),\n","        \"tn\": int(tn),\n","        \"fp\": int(fp),\n","        \"fn\": int(fn),\n","        \"tp\": int(tp),\n","    }\n","\n","\n","def select_best_threshold(y_true, probabilities, thresholds):\n","    \"\"\"Select the threshold with maximum F1, then precision, then recall.\"\"\"\n","    rows = []\n","\n","    for threshold in thresholds:\n","        predictions = (probabilities >= threshold).astype(int)\n","        row = evaluate_binary(y_true, predictions)\n","        row[\"threshold\"] = float(threshold)\n","        rows.append(row)\n","\n","    table = (\n","        pd.DataFrame(rows)\n","        .sort_values(\n","            [\"f1\", \"precision\", \"recall\", \"threshold\"],\n","            ascending=[False, False, False, True],\n","        )\n","        .reset_index(drop=True)\n","    )\n","\n","    return table, table.iloc[0]\n","\n","\n","reset_all_seeds(SEED)\n","\n","development_history = []\n","best_validation_f1 = -1.0\n","best_val_loss = float(\"inf\")\n","best_epoch = 1\n","best_threshold = float(THRESHOLDS[0])\n","best_validation_row = None\n","best_validation_results_df = None\n","best_development_state = None\n","epochs_without_improvement = 0\n","\n","for epoch in range(1, DEVELOPMENT_MAX_EPOCHS + 1):\n","    train_loss = train_one_epoch(\n","        development_model,\n","        dev_train_loader,\n","        development_optimizer,\n","        development_criterion,\n","    )\n","\n","    val_loss = evaluate_loss(\n","        development_model,\n","        val_loader,\n","        development_criterion,\n","    )\n","\n","    val_probs_epoch, val_labels_epoch, _ = predict_probabilities(\n","        development_model,\n","        val_loader,\n","    )\n","\n","    epoch_threshold_table, epoch_best_row = select_best_threshold(\n","        val_labels_epoch,\n","        val_probs_epoch,\n","        THRESHOLDS,\n","    )\n","\n","    epoch_best_f1 = float(epoch_best_row[\"f1\"])\n","    epoch_best_threshold = float(epoch_best_row[\"threshold\"])\n","\n","    development_history.append({\n","        \"epoch\": epoch,\n","        \"train_loss\": train_loss,\n","        \"validation_loss\": val_loss,\n","        \"validation_accuracy\": float(epoch_best_row[\"accuracy\"]),\n","        \"validation_precision\": float(epoch_best_row[\"precision\"]),\n","        \"validation_recall\": float(epoch_best_row[\"recall\"]),\n","        \"validation_f1\": epoch_best_f1,\n","        \"validation_best_threshold\": epoch_best_threshold,\n","    })\n","\n","    improved = epoch_best_f1 > (best_validation_f1 + EARLY_STOPPING_MIN_DELTA)\n","\n","    # Tie-break rule:\n","    # If F1 is effectively equal, prefer the lower validation loss.\n","    tied_f1 = abs(epoch_best_f1 - best_validation_f1) <= EARLY_STOPPING_MIN_DELTA\n","    better_tie = tied_f1 and val_loss < best_val_loss\n","\n","    if improved or better_tie:\n","        best_validation_f1 = epoch_best_f1\n","        best_val_loss = val_loss\n","        best_epoch = epoch\n","        best_threshold = epoch_best_threshold\n","        best_validation_row = epoch_best_row.copy()\n","        best_validation_results_df = epoch_threshold_table.copy()\n","        best_development_state = {\n","            key: value.detach().cpu().clone()\n","            for key, value in development_model.state_dict().items()\n","        }\n","        epochs_without_improvement = 0\n","    else:\n","        epochs_without_improvement += 1\n","\n","    print(\n","        f\"Epoch {epoch:02d}/{DEVELOPMENT_MAX_EPOCHS} | \"\n","        f\"train={train_loss:.6f} | \"\n","        f\"val_loss={val_loss:.6f} | \"\n","        f\"val_f1={epoch_best_f1:.6f} | \"\n","        f\"threshold={epoch_best_threshold:.2f} | \"\n","        f\"best_epoch={best_epoch}\"\n","    )\n","\n","    if epochs_without_improvement >= EARLY_STOPPING_PATIENCE:\n","        print(\"Development early stopping triggered by validation F1.\")\n","        break\n","\n","\n","if best_development_state is None:\n","    raise RuntimeError(\"No development checkpoint was created.\")\n","\n","development_model.load_state_dict(best_development_state)\n","development_model.to(device)\n","\n","development_history_df = pd.DataFrame(development_history)\n","save_table(development_history_df, DEVELOPMENT_HISTORY_PATH, \"Development training history\")\n","\n","save_table(best_validation_results_df, VALIDATION_RESULTS_PATH, \"Validation threshold results\")\n","\n","print(\"\\nSelected best epoch:\", best_epoch)\n","print(\"Best validation F1:\", best_validation_f1)\n","print(\"Validation loss at selected epoch:\", best_val_loss)\n","print(\"Selected validation threshold:\", best_threshold)\n","\n","display(best_validation_results_df)\n"]},{"cell_type":"markdown","id":"Fknwpz7M-q-S","metadata":{"id":"Fknwpz7M-q-S"},"source":["### Figures 4.3 and 4.4 — Development Learning Curves\n","\n","**Interpretation:** The loss curves are retained as optimization diagnostics, while the epoch used for final retraining is selected by the highest temporal-validation F1-score. Early stopping prevents unnecessary development training after F1 stops improving.\n"]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":997},"id":"ByWtP7x1b4bN","executionInfo":{"status":"ok","timestamp":1785510756639,"user_tz":-180,"elapsed":3567,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"86ddded4-68cd-4e54-b718-c0af931de0aa"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figures 4.3 and 4.4 — Development Learning Curves\n","# ============================================================\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(\n","    development_history_df[\"epoch\"],\n","    development_history_df[\"train_loss\"],\n","    marker=\"o\",\n","    label=\"Development training loss\",\n",")\n","plt.plot(\n","    development_history_df[\"epoch\"],\n","    development_history_df[\"validation_loss\"],\n","    marker=\"o\",\n","    label=\"Validation loss\",\n",")\n","plt.axvline(\n","    best_epoch,\n","    linestyle=\"--\",\n","    label=f\"Selected epoch = {best_epoch}\",\n",")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Weighted BCE loss\")\n","plt.title(\"LogMiniLM Development Loss Curves\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\"figure_4_3_development_loss_curves.png\", \"Figure 4.3\", \"Development Training and Validation Loss Curves\")\n","plt.show()\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(\n","    development_history_df[\"epoch\"],\n","    development_history_df[\"validation_f1\"],\n","    marker=\"o\",\n","    label=\"Validation F1\",\n",")\n","plt.axvline(\n","    best_epoch,\n","    linestyle=\"--\",\n","    label=f\"Selected epoch = {best_epoch}\",\n",")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"F1-score\")\n","plt.title(\"LogMiniLM Validation F1 by Epoch\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\"figure_4_4_validation_f1_by_epoch.png\", \"Figure 4.4\", \"Validation F1 by Epoch\")\n","plt.show()\n"],"id":"ByWtP7x1b4bN"},{"cell_type":"markdown","id":"coYr8DnG-q-S","metadata":{"id":"coYr8DnG-q-S"},"source":["### Figure 4.5 — Validation Threshold versus F1-Score\n","\n","The threshold figure is generated immediately after development model selection.\n","\n","**Interpretation:** The selected threshold is determined only from the temporal validation subset. The untouched test set does not influence this decision.\n"]},{"cell_type":"code","execution_count":22,"id":"-cOoU_fJ-q-S","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":580},"id":"-cOoU_fJ-q-S","outputId":"37f69544-6e86-48ba-dfd3-731133cd3309","executionInfo":{"status":"ok","timestamp":1785510758594,"user_tz":-180,"elapsed":2021,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Maximum validation F1: 0.9657669519420672\n","Equal-F1 threshold plateau: 0.10 to 0.30\n","Selected stable threshold: 0.2\n","Selection rule: closest tested threshold to the midpoint of the equal-F1 plateau.\n"]}],"source":["# ============================================================\n","# Figure 4.5 — Validation threshold vs F1-score\n","# Corrected stable-plateau threshold selection\n","# ============================================================\n","\n","validation_results_df = best_validation_results_df.copy()\n","plot_df = validation_results_df.sort_values(\"threshold\").copy()\n","\n","maximum_validation_f1 = float(plot_df[\"f1\"].max())\n","F1_TIE_TOLERANCE = 1e-12\n","\n","tied_threshold_rows = plot_df[\n","    np.isclose(\n","        plot_df[\"f1\"].to_numpy(dtype=float),\n","        maximum_validation_f1,\n","        atol=F1_TIE_TOLERANCE,\n","        rtol=0.0,\n","    )\n","].copy()\n","\n","if tied_threshold_rows.empty:\n","    raise ValueError(\"No threshold achieved the maximum validation F1-score.\")\n","\n","# A whole threshold interval can produce identical labels. Using the midpoint\n","# is more stable and avoids presenting the smallest tied threshold as uniquely best.\n","threshold_plateau_min = float(tied_threshold_rows[\"threshold\"].min())\n","threshold_plateau_max = float(tied_threshold_rows[\"threshold\"].max())\n","threshold_plateau_midpoint = (\n","    threshold_plateau_min + threshold_plateau_max\n",") / 2.0\n","\n","best_threshold_row = (\n","    tied_threshold_rows\n","    .assign(\n","        distance_to_plateau_midpoint=lambda frame: (\n","            frame[\"threshold\"] - threshold_plateau_midpoint\n","        ).abs()\n","    )\n","    .sort_values(\n","        [\"distance_to_plateau_midpoint\", \"threshold\"],\n","        ascending=[True, True],\n","    )\n","    .iloc[0]\n",")\n","\n","best_threshold = float(best_threshold_row[\"threshold\"])\n","best_validation_row = best_threshold_row.copy()\n","best_validation_f1 = float(best_threshold_row[\"f1\"])\n","best_f1 = best_validation_f1\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(plot_df[\"threshold\"], plot_df[\"f1\"], marker=\"o\")\n","plt.axvspan(\n","    threshold_plateau_min,\n","    threshold_plateau_max,\n","    alpha=0.15,\n","    label=(\n","        f\"Equal-F1 plateau \"\n","        f\"[{threshold_plateau_min:.2f}, {threshold_plateau_max:.2f}]\"\n","    ),\n",")\n","plt.axvline(\n","    best_threshold,\n","    linestyle=\"--\",\n","    label=f\"Selected stable threshold = {best_threshold:.2f}\",\n",")\n","plt.xlabel(\"Decision threshold\")\n","plt.ylabel(\"F1-score\")\n","plt.title(\"LogMiniLM F1-score Across Decision Thresholds\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_5_validation_threshold_vs_f1.png\",\n","    \"Figure 4.5\",\n","    \"Validation Threshold versus F1-Score\",\n",")\n","plt.show()\n","\n","print(\"Maximum validation F1:\", maximum_validation_f1)\n","print(\n","    \"Equal-F1 threshold plateau:\",\n","    f\"{threshold_plateau_min:.2f} to {threshold_plateau_max:.2f}\",\n",")\n","print(\"Selected stable threshold:\", best_threshold)\n","print(\n","    \"Selection rule: closest tested threshold to the midpoint \"\n","    \"of the equal-F1 plateau.\"\n",")\n"]},{"cell_type":"markdown","id":"rHRKbW9V-q-S","metadata":{"id":"rHRKbW9V-q-S"},"source":["### Figure 4.6 — Validation Precision, Recall, and F1 across Thresholds\n","\n","**Interpretation:** This figure shows how the decision threshold changes the balance between anomaly precision and recall. The reported final threshold is fixed before test evaluation.\n"]},{"cell_type":"code","execution_count":23,"id":"i93Z-_Wa-q-T","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"i93Z-_Wa-q-T","outputId":"91196cb1-c5fc-4cda-910a-2a134723fd04","executionInfo":{"status":"ok","timestamp":1785510760865,"user_tz":-180,"elapsed":2271,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.6 — Validation threshold sensitivity\n","# ============================================================\n","\n","plot_df = validation_results_df.sort_values(\"threshold\").copy()\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(plot_df[\"threshold\"], plot_df[\"precision\"], marker=\"o\", label=\"Precision\")\n","plt.plot(plot_df[\"threshold\"], plot_df[\"recall\"], marker=\"o\", label=\"Recall\")\n","plt.plot(plot_df[\"threshold\"], plot_df[\"f1\"], marker=\"o\", label=\"F1-score\")\n","plt.axvline(best_threshold, linestyle=\"--\", label=f\"Best threshold = {best_threshold:.2f}\")\n","plt.xlabel(\"Decision threshold\")\n","plt.ylabel(\"Metric value\")\n","plt.title(\"LogMiniLM Threshold Sensitivity\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\"figure_4_6_validation_threshold_sensitivity.png\", \"Figure 4.6\", \"Validation Precision, Recall, and F1 across Thresholds\")\n","plt.show()\n"]},{"cell_type":"markdown","id":"FDZfOhZH-q-T","metadata":{"id":"FDZfOhZH-q-T"},"source":["### Figure 4.7 — Validation Confusion Matrix at the Selected Epoch and Threshold\n","\n","**Interpretation:** This matrix summarizes validation errors used during model selection. It is separate from the final test confusion matrix.\n"]},{"cell_type":"code","execution_count":24,"id":"T5xyhlvK-q-T","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"T5xyhlvK-q-T","outputId":"d072faa4-102a-499e-ec22-109d6c2bf69f","executionInfo":{"status":"ok","timestamp":1785510762543,"user_tz":-180,"elapsed":1675,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.7 — Validation confusion matrix\n","# ============================================================\n","\n","val_tn = int(best_validation_row[\"tn\"])\n","val_fp = int(best_validation_row[\"fp\"])\n","val_fn = int(best_validation_row[\"fn\"])\n","val_tp = int(best_validation_row[\"tp\"])\n","\n","validation_cm = np.array([[val_tn, val_fp], [val_fn, val_tp]])\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.imshow(validation_cm)\n","plt.title(\"Validation Confusion Matrix\")\n","plt.xlabel(\"Predicted label\")\n","plt.ylabel(\"True label\")\n","plt.xticks([0, 1], [\"Normal\", \"Anomaly\"])\n","plt.yticks([0, 1], [\"Normal\", \"Anomaly\"])\n","\n","for row_idx in range(2):\n","    for col_idx in range(2):\n","        plt.text(\n","            col_idx,\n","            row_idx,\n","            f\"{validation_cm[row_idx, col_idx]:,}\",\n","            ha=\"center\",\n","            va=\"center\",\n","            fontsize=12,\n","        )\n","\n","plt.colorbar()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_7_validation_confusion_matrix.png\",\n","    \"Figure 4.7\",\n","    \"Validation Confusion Matrix\",\n",")\n","plt.show()\n"]},{"cell_type":"markdown","id":"2e1680f2","metadata":{"id":"2e1680f2"},"source":["## Step 14 — Final Retraining and One-Time Test Evaluation\n","\n","The best epoch and threshold have already been selected jointly from the temporal validation subset.\n","\n","The final phase now:\n","\n","1. Reinitializes LogMiniLM from scratch.\n","2. Merges the development-training and validation subsets.\n","3. Trains on the complete 80% temporal training partition for the selected number of epochs.\n","4. Evaluates the untouched 20% test partition exactly once using the fixed validation-selected threshold.\n"]},{"cell_type":"code","execution_count":25,"id":"bb71bcdf","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"bb71bcdf","outputId":"333182e1-3e5c-4906-f2aa-4cbcb0537ab9","executionInfo":{"status":"ok","timestamp":1785510803374,"user_tz":-180,"elapsed":40830,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Validation-selected epoch: 3\n","Validation-selected threshold: 0.2\n","Validation F1: 0.9657669519420672\n"]},{"output_type":"display_data","data":{"text/plain":["    accuracy  precision    recall        f1     tn  fp   fn    tp  threshold\n","0   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.10\n","1   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.15\n","2   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.20\n","3   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.25\n","4   0.997242        1.0  0.933800  0.965767  36136   0  104  1467       0.30\n","5   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.35\n","6   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.40\n","7   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.45\n","8   0.997215        1.0  0.933164  0.965426  36136   0  105  1466       0.50\n","9   0.997189        1.0  0.932527  0.965086  36136   0  106  1465       0.60\n","10  0.997189        1.0  0.932527  0.965086  36136   0  106  1465       0.70\n","11  0.997136        1.0  0.931254  0.964403  36136   0  108  1463       0.80\n","12  0.997109        1.0  0.930617  0.964062  36136   0  109  1462       0.90"],"text/html":["\n","  <div id=\"df-c7c8ea39-83d3-48b4-ba32-8848a2b61ecc\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr 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<td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.15</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.20</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.25</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.997242</td>\n","      <td>1.0</td>\n","      <td>0.933800</td>\n","      <td>0.965767</td>\n","      <td>36136</td>\n","      <td>0</td>\n","      <td>104</td>\n","      <td>1467</td>\n","      <td>0.30</td>\n","    </tr>\n","    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/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/logs/logminilm_development_history.csv\n","Saved selected-epoch threshold table to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/tables/logminilm_validation_threshold_results.csv\n","\n","Final-training normal sequences: 343721\n","Final-training anomaly sequences: 33358\n","Final-training positive-class weight: 20.608010072546314\n","Final-training epochs selected from validation F1: 3\n","Fixed threshold selected from validation: 0.2\n","Final epoch 01/3 | loss=0.033466\n","Final epoch 02/3 | loss=0.004500\n","Final epoch 03/3 | loss=0.007811\n","\n","Final test results at the fixed validation-selected threshold\n"]},{"output_type":"display_data","data":{"text/plain":["   accuracy  precision    recall       f1     tn  fp   fn    tp      model  \\\n","0  0.998419   0.991973  0.981642  0.98678  88560  45  104  5561  LogMiniLM   \n","\n","   window_method  ...  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best_validation_results_df.copy()\n","\n","print(\"Validation-selected epoch:\", best_epoch)\n","print(\"Validation-selected threshold:\", best_threshold)\n","print(\"Validation F1:\", best_validation_f1)\n","display(validation_results_df)\n","\n","development_config = {\n","    \"selection_source\": \"temporal_validation_only\",\n","    \"epoch_selection_metric\": \"maximum_validation_f1\",\n","    \"threshold_selection_metric\": \"maximum_validation_f1\",\n","    \"epoch_tie_breaker\": \"lower_validation_loss\",\n","    \"threshold_tie_breaker\": \"closest_tested_threshold_to_equal_f1_plateau_midpoint\",\n","    \"threshold_plateau_min\": float(threshold_plateau_min),\n","    \"threshold_plateau_max\": float(threshold_plateau_max),\n","    \"best_epoch\": int(best_epoch),\n","    \"best_validation_loss\": float(best_val_loss),\n","    \"best_threshold\": float(best_threshold),\n","    \"validation_accuracy\": float(best_validation_row[\"accuracy\"]),\n","    \"validation_precision\": float(best_validation_row[\"precision\"]),\n","    \"validation_recall\": float(best_validation_row[\"recall\"]),\n","    \"validation_f1\": float(best_validation_f1),\n","    \"validation_ratio_within_train\": float(VALIDATION_RATIO_WITHIN_TRAIN),\n","    \"final_train_ratio\": float(TRAIN_RATIO),\n","    \"development_max_epochs\": int(DEVELOPMENT_MAX_EPOCHS),\n","    \"early_stopping_patience\": int(EARLY_STOPPING_PATIENCE),\n","    \"seed\": int(SEED),\n","}\n","\n","save_json(development_config, DEVELOPMENT_CONFIG_PATH)\n","\n","print(\"Saved development configuration to:\", DEVELOPMENT_CONFIG_PATH)\n","print(\"Saved development history to:\", DEVELOPMENT_HISTORY_PATH)\n","print(\"Saved selected-epoch threshold table to:\", VALIDATION_RESULTS_PATH)\n","\n","\n","# ============================================================\n","# Phase 2: fresh model trained on complete 80% partition\n","# ============================================================\n","del development_optimizer\n","\n","if torch.cuda.is_available():\n","    torch.cuda.empty_cache()\n","\n","model = build_logminilm(SEED)\n","\n","final_num_neg, final_num_pos, final_base_weight, final_pos_weight = (\n","    calculate_pos_weight(y_train)\n",")\n","\n","final_criterion = nn.BCEWithLogitsLoss(\n","    pos_weight=torch.tensor(\n","        [final_pos_weight],\n","        device=device,\n","        dtype=torch.float32,\n","    )\n",")\n","\n","final_optimizer = torch.optim.AdamW(\n","    model.parameters(),\n","    lr=LEARNING_RATE,\n","    weight_decay=1e-4,\n",")\n","\n","print(\"\\nFinal-training normal sequences:\", final_num_neg)\n","print(\"Final-training anomaly sequences:\", final_num_pos)\n","print(\"Final-training positive-class weight:\", final_pos_weight)\n","print(\"Final-training epochs selected from validation F1:\", best_epoch)\n","print(\"Fixed threshold selected from validation:\", best_threshold)\n","\n","final_training_rows = []\n","reset_all_seeds(SEED)\n","\n","for epoch in range(1, best_epoch + 1):\n","    final_loss = train_one_epoch(\n","        model,\n","        final_train_loader,\n","        final_optimizer,\n","        final_criterion,\n","    )\n","\n","    final_training_rows.append({\n","        \"epoch\": epoch,\n","        \"final_training_loss\": final_loss,\n","    })\n","\n","    print(\n","        f\"Final epoch {epoch:02d}/{best_epoch} | \"\n","        f\"loss={final_loss:.6f}\"\n","    )\n","\n","final_training_history_df = pd.DataFrame(final_training_rows)\n","save_table(\n","    final_training_history_df,\n","    FINAL_TRAINING_HISTORY_PATH,\n","    \"Final retraining history\"\n",")\n","\n","\n","# ============================================================\n","# One-time untouched final-test evaluation\n","# ============================================================\n","probs, true_labels, test_indices = predict_probabilities(\n","    model,\n","    test_loader,\n",")\n","\n","best_preds = (probs >= best_threshold).astype(int)\n","final_metrics = evaluate_binary(true_labels, best_preds)\n","\n","\n","\n","# Reconstruct every reported classification metric from predictions.\n","metric_check = {\n","    \"accuracy\": accuracy_score(true_labels, best_preds),\n","    \"precision\": precision_score(true_labels, best_preds, zero_division=0),\n","    \"recall\": recall_score(true_labels, best_preds, zero_division=0),\n","    \"f1\": f1_score(true_labels, best_preds, zero_division=0),\n","}\n","metric_cm = confusion_matrix(true_labels, best_preds, labels=[0, 1])\n","metric_tn, metric_fp, metric_fn, metric_tp = metric_cm.ravel()\n","\n","assert len(true_labels) == len(y_test) == len(best_preds) == len(probs)\n","assert np.array_equal(np.sort(test_indices), np.arange(len(y_test)))\n","assert np.isfinite(probs).all()\n","assert ((probs >= 0) & (probs <= 1)).all()\n","assert int(metric_tn + metric_fp + metric_fn + metric_tp) == len(y_test)\n","\n","for metric_name, recalculated_value in metric_check.items():\n","    assert np.isclose(\n","        float(final_metrics[metric_name]),\n","        float(recalculated_value),\n","        atol=1e-12\n","    )\n","\n","assert int(final_metrics[\"tn\"]) == int(metric_tn)\n","assert int(final_metrics[\"fp\"]) == int(metric_fp)\n","assert int(final_metrics[\"fn\"]) == int(metric_fn)\n","assert int(final_metrics[\"tp\"]) == int(metric_tp)\n","\n","\n","best_row = pd.Series({\n","    **final_metrics,\n","    \"model\": \"LogMiniLM\",\n","    \"window_method\": \"fixed_10_line\",\n","    \"threshold\": best_threshold,\n","    \"threshold_selection\": \"maximum_validation_f1\",\n","    \"epoch_selection\": \"maximum_validation_f1\",\n","    \"best_epoch\": best_epoch,\n","    \"window_size\": WINDOW_SIZE,\n","    \"step_size\": STEP_SIZE,\n","    \"projection_dim\": PROJECTION_DIM,\n","    \"transformer_layers\": NUM_TRANSFORMER_LAYERS,\n","    \"attention_heads\": NUM_ATTENTION_HEADS,\n","    \"feedforward_dim\": FEEDFORWARD_DIM,\n","    \"development_train_sequences\": len(y_dev_train),\n","    \"validation_sequences\": len(y_val),\n","    \"train_sequences\": len(y_train),\n","    \"test_sequences\": len(y_test),\n","    \"unique_messages\": len(unique_texts),\n","})\n","\n","results_df = pd.DataFrame([best_row])\n","save_table(results_df, RESULTS_PATH, \"Final test results\")\n","\n","print(\"\\nFinal test results at the fixed validation-selected threshold\")\n","display(results_df)\n","\n","print(classification_report(\n","    true_labels,\n","    best_preds,\n","    target_names=[\"Normal\", \"Anomaly\"],\n","    zero_division=0,\n","))\n"]},{"cell_type":"markdown","id":"UpZYSwsF-q-U","metadata":{"id":"UpZYSwsF-q-U"},"source":["### Figure 4.8 — Final Retraining Loss\n","\n","**Interpretation:** After model selection, a newly initialized LogMiniLM model is trained on the merged development and validation data for the selected number of epochs.\n"]},{"cell_type":"code","execution_count":26,"id":"FOt4rxRL-q-U","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"FOt4rxRL-q-U","outputId":"a87e07d8-3cba-42c8-fba8-46535b782bf0","executionInfo":{"status":"ok","timestamp":1785510805109,"user_tz":-180,"elapsed":1725,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.8 — Final retraining loss\n","# ============================================================\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(\n","    final_training_history_df[\"epoch\"],\n","    final_training_history_df[\"final_training_loss\"],\n","    marker=\"o\",\n",")\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Weighted BCE loss\")\n","plt.title(\"LogMiniLM Final Retraining Loss\")\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_8_final_retraining_loss.png\",\n","    \"Figure 4.8\",\n","    \"Final Retraining Loss\",\n",")\n","plt.show()\n"]},{"cell_type":"markdown","id":"mmx5giJM-q-U","metadata":{"id":"mmx5giJM-q-U"},"source":["### Figure 4.9 — Final Test Confusion Matrix\n","\n","**Interpretation:** This is the primary confusion matrix for the thesis. It is calculated once from the untouched test partition using the epoch and threshold selected during development.\n"]},{"cell_type":"code","execution_count":27,"id":"gJhfvDD3-q-U","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":525},"id":"gJhfvDD3-q-U","outputId":"fb27a17d-c78d-4d39-b53f-3342884476a4","executionInfo":{"status":"ok","timestamp":1785510806642,"user_tz":-180,"elapsed":1531,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 2 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Final test TN, FP, FN, TP: 88560 45 104 5561\n"]}],"source":["# ============================================================\n","# Figure 4.9 — Final test confusion matrix\n","# ============================================================\n","\n","test_tn = int(final_metrics[\"tn\"])\n","test_fp = int(final_metrics[\"fp\"])\n","test_fn = int(final_metrics[\"fn\"])\n","test_tp = int(final_metrics[\"tp\"])\n","\n","test_cm = np.array([[test_tn, test_fp], [test_fn, test_tp]])\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.imshow(test_cm)\n","plt.title(\"LogMiniLM Final Test Confusion Matrix\")\n","plt.xlabel(\"Predicted label\")\n","plt.ylabel(\"True label\")\n","plt.xticks([0, 1], [\"Normal\", \"Anomaly\"])\n","plt.yticks([0, 1], [\"Normal\", \"Anomaly\"])\n","\n","for row_idx in range(2):\n","    for col_idx in range(2):\n","        plt.text(\n","            col_idx,\n","            row_idx,\n","            f\"{test_cm[row_idx, col_idx]:,}\",\n","            ha=\"center\",\n","            va=\"center\",\n","            fontsize=12,\n","        )\n","\n","plt.colorbar()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_9_final_test_confusion_matrix.png\",\n","    \"Figure 4.9\",\n","    \"Final Test Confusion Matrix\",\n",")\n","plt.show()\n","\n","print(\"Final test TN, FP, FN, TP:\", test_tn, test_fp, test_fn, test_tp)\n"]},{"cell_type":"markdown","id":"hCv5bkEM-q-U","metadata":{"id":"hCv5bkEM-q-U"},"source":["### Figure 4.10 — Final Test Prediction Probability Distribution\n","\n","**Interpretation:** The distributions show the confidence separation between normal and anomalous test sequences and help explain why a range of thresholds may produce identical classifications.\n"]},{"cell_type":"code","execution_count":28,"id":"r1BS9ii5-q-U","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":602},"id":"r1BS9ii5-q-U","outputId":"07d573d0-0da0-4083-8246-7a48d73bd283","executionInfo":{"status":"ok","timestamp":1785510808598,"user_tz":-180,"elapsed":1954,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" 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Prediction Probability Distribution\n","# ============================================================\n","\n","normal_probabilities = probs[true_labels == 0]\n","anomaly_probabilities = probs[true_labels == 1]\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.hist(normal_probabilities, bins=40, alpha=0.65, label=\"Normal sequences\")\n","plt.hist(anomaly_probabilities, bins=40, alpha=0.65, label=\"Anomalous sequences\")\n","plt.axvline(\n","    best_threshold,\n","    linestyle=\"--\",\n","    linewidth=1.5,\n","    label=f\"Selected threshold = {best_threshold:.2f}\"\n",")\n","plt.xlabel(\"Predicted anomaly probability\")\n","plt.ylabel(\"Number of sequences\")\n","plt.title(\"Final Test Prediction Probability Distribution\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_10_test_probability_distribution.png\",\n","    \"Figure 4.10\",\n","    \"Final Test Prediction Probability Distribution\"\n",")\n","plt.show()\n","\n","probability_summary_df = pd.DataFrame({\n","    \"class\": [\"Normal\", \"Anomalous\"],\n","    \"count\": [len(normal_probabilities), len(anomaly_probabilities)],\n","    \"mean_probability\": [\n","        float(np.mean(normal_probabilities)),\n","        float(np.mean(anomaly_probabilities))\n","    ],\n","    \"median_probability\": [\n","        float(np.median(normal_probabilities)),\n","        float(np.median(anomaly_probabilities))\n","    ],\n","    \"minimum_probability\": [\n","        float(np.min(normal_probabilities)),\n","        float(np.min(anomaly_probabilities))\n","    ],\n","    \"maximum_probability\": [\n","        float(np.max(normal_probabilities)),\n","        float(np.max(anomaly_probabilities))\n","    ],\n","})\n","display(probability_summary_df)\n"]},{"cell_type":"markdown","id":"CNBDhnnd-q-V","metadata":{"id":"CNBDhnnd-q-V"},"source":["### Figures 4.11 and 4.12 — ROC and Precision–Recall Curves\n","\n","**Interpretation:** ROC-AUC summarizes ranking performance across thresholds, while the Precision–Recall curve is especially informative for the imbalanced anomaly-detection setting.\n"]},{"cell_type":"code","execution_count":29,"id":"tMl1HPm_-q-V","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"tMl1HPm_-q-V","outputId":"167a299d-d170-4cea-c52a-5645ad0ad9a3","executionInfo":{"status":"ok","timestamp":1785510811755,"user_tz":-180,"elapsed":3155,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Final test ROC-AUC: 0.9993086346261533\n","Final test PR-AUC (average precision): 0.9925114593580575\n"]}],"source":["# ============================================================\n","# Figures 4.11 and 4.12 — Final test ROC and Precision–Recall curves\n","# ============================================================\n","\n","test_roc_auc = roc_auc_score(true_labels, probs)\n","test_pr_auc = average_precision_score(true_labels, probs)\n","\n","fpr, tpr, _ = roc_curve(true_labels, probs)\n","pr_precision, pr_recall, _ = precision_recall_curve(true_labels, probs)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(fpr, tpr, label=f\"ROC-AUC = {test_roc_auc:.4f}\")\n","plt.plot([0, 1], [0, 1], linestyle=\"--\", label=\"Random baseline\")\n","plt.xlabel(\"False Positive Rate\")\n","plt.ylabel(\"True Positive Rate\")\n","plt.title(\"LogMiniLM Final Test ROC Curve\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_11_final_test_roc_curve.png\",\n","    \"Figure 4.11\",\n","    \"Final Test ROC Curve\",\n",")\n","plt.show()\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(pr_recall, pr_precision, label=f\"PR-AUC (average precision) = {test_pr_auc:.4f}\")\n","plt.xlabel(\"Recall\")\n","plt.ylabel(\"Precision\")\n","plt.title(\"LogMiniLM Final Test Precision–Recall Curve\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_12_final_test_precision_recall_curve.png\",\n","    \"Figure 4.12\",\n","    \"Final Test Precision–Recall Curve\",\n",")\n","plt.show()\n","\n","print(\"Final test ROC-AUC:\", test_roc_auc)\n","print(\"Final test PR-AUC (average precision):\", test_pr_auc)\n"]},{"cell_type":"markdown","id":"99211d27","metadata":{"id":"99211d27"},"source":["## Step 15 — Save prediction outputs for XAI\n","\n","For XAI, each sequence prediction must be linked back to the original log lines.\n","\n","### Saved fields\n","\n","- sequence local test index\n","- start and end line numbers\n","- start and end time\n","- start and end node\n","- true label\n","- predicted label\n","- anomaly probability\n","- threshold\n","\n","### Memory-safe design\n","\n","This step does not reload the full raw log file. It uses the lightweight metadata already saved earlier.\n"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":788},"id":"g1dmKjKab4bQ","executionInfo":{"status":"ok","timestamp":1785510819588,"user_tz":-180,"elapsed":7830,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"fd902898-4c48-4d28-c5a9-6cf8a07f6665"},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved final predictions to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/tables/logminilm_predictions_for_xai.csv\n","Prediction rows: 94270\n","Window length: 10\n","Range convention: inclusive start_line and inclusive end_line\n"]},{"output_type":"display_data","data":{"text/plain":["  prediction_type  count\n","0              TP   5561\n","1              FP     45\n","2              FN    104\n","3              TN  88560"],"text/html":["\n","  <div id=\"df-f8af5bc8-a5e0-4ee1-89bb-0381a2024825\" class=\"colab-df-container\">\n","    <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>prediction_type</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TP</td>\n","      <td>5561</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>FP</td>\n","      <td>45</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>FN</td>\n","      <td>104</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>TN</td>\n","      <td>88560</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f8af5bc8-a5e0-4ee1-89bb-0381a2024825')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-f8af5bc8-a5e0-4ee1-89bb-0381a2024825 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"full_time\",\n","        \"node\",\n","        \"source\",\n","        \"component\",\n","        \"severity\",\n","        \"event_message\",\n","        \"model_text_norm\",\n","    ]\n","\n","    missing_metadata_columns = (\n","        set(required_metadata_columns) - set(df.columns)\n","    )\n","\n","    if missing_metadata_columns:\n","        raise ValueError(\n","            \"The parsed BGL DataFrame is missing metadata columns: \"\n","            f\"{sorted(missing_metadata_columns)}\"\n","        )\n","\n","    metadata_df = df[required_metadata_columns].copy()\n","else:\n","    if not METADATA_PATH.exists():\n","        raise FileNotFoundError(\n","            \"The metadata artifact was not found. Run the parsing and \"\n","            \"metadata-saving steps before Step 15.\"\n","        )\n","\n","    metadata_df = pd.read_parquet(METADATA_PATH)\n","\n","\n","# ------------------------------------------------------------\n","# Validate the final-test arrays before constructing the table.\n","# ------------------------------------------------------------\n","test_indices = np.asarray(test_indices, dtype=np.int64).reshape(-1)\n","true_labels = np.asarray(true_labels, dtype=np.int8).reshape(-1)\n","probs = np.asarray(probs, dtype=np.float64).reshape(-1)\n","y_pred_best = np.asarray(y_pred_best, dtype=np.int8).reshape(-1)\n","\n","expected_test_size = len(y_test)\n","\n","assert len(test_indices) == expected_test_size, (\n","    \"test_indices length does not match the test-set size.\"\n",")\n","assert len(true_labels) == expected_test_size, (\n","    \"true_labels length does not match the test-set size.\"\n",")\n","assert len(probs) == expected_test_size, (\n","    \"probs length does not match the test-set size.\"\n",")\n","assert len(y_pred_best) == expected_test_size, (\n","    \"Predicted-label length does not match the test-set size.\"\n",")\n","\n","assert np.isfinite(probs).all(), (\n","    \"The final-test probability array contains NaN or infinite values.\"\n",")\n","assert ((probs >= 0.0) & (probs <= 1.0)).all(), (\n","    \"The final-test probability array contains values outside [0, 1].\"\n",")\n","\n","assert len(np.unique(test_indices)) == expected_test_size, (\n","    \"Duplicate local test indices were returned by the test DataLoader.\"\n",")\n","assert set(test_indices.tolist()) == set(range(expected_test_size)), (\n","    \"The returned test indices do not cover the complete local test set.\"\n",")\n","\n","\n","# ------------------------------------------------------------\n","# Build one prediction row for every test sequence.\n","#\n","# IMPORTANT:\n","# `ranges_test` stores inclusive boundaries:\n","#     start_line = first line index\n","#     end_line   = last line index\n","#\n","# Therefore, sequence length is:\n","#     end_line - start_line + 1\n","# ------------------------------------------------------------\n","prediction_rows = []\n","\n","for output_position, (\n","    local_test_index,\n","    true_label,\n","    predicted_label,\n","    anomaly_probability,\n",") in enumerate(\n","    zip(\n","        test_indices,\n","        true_labels,\n","        y_pred_best,\n","        probs,\n","    )\n","):\n","    local_test_index = int(local_test_index)\n","\n","    start_line = int(ranges_test[local_test_index][0])\n","    end_line = int(ranges_test[local_test_index][1])\n","\n","    prediction_type = (\n","        \"TP\"\n","        if true_label == 1 and predicted_label == 1\n","        else \"FP\"\n","        if true_label == 0 and predicted_label == 1\n","        else \"FN\"\n","        if true_label == 1 and predicted_label == 0\n","        else \"TN\"\n","    )\n","\n","    prediction_rows.append({\n","        \"prediction_output_position\": int(output_position),\n","        \"sequence_local_test_index\": local_test_index,\n","        \"start_line\": start_line,\n","        \"end_line\": end_line,\n","        \"window_length\": int(end_line - start_line + 1),\n","        \"true_label\": int(true_label),\n","        \"predicted_label\": int(predicted_label),\n","        \"anomaly_probability\": float(anomaly_probability),\n","        \"threshold\": float(best_threshold),\n","        \"prediction_type\": prediction_type,\n","    })\n","\n","predictions_df = pd.DataFrame(prediction_rows)\n","\n","predictions_df = (\n","    predictions_df\n","    .sort_values(\"sequence_local_test_index\")\n","    .reset_index(drop=True)\n",")\n","\n","\n","# ------------------------------------------------------------\n","# Scientific and metadata-linkage integrity checks.\n","# ------------------------------------------------------------\n","required_prediction_columns = {\n","    \"prediction_output_position\",\n","    \"sequence_local_test_index\",\n","    \"start_line\",\n","    \"end_line\",\n","    \"window_length\",\n","    \"true_label\",\n","    \"predicted_label\",\n","    \"anomaly_probability\",\n","    \"threshold\",\n","    \"prediction_type\",\n","}\n","\n","missing_prediction_columns = (\n","    required_prediction_columns - set(predictions_df.columns)\n",")\n","\n","if missing_prediction_columns:\n","    raise ValueError(\n","        \"Prediction output is missing required columns: \"\n","        f\"{sorted(missing_prediction_columns)}\"\n","    )\n","\n","assert len(predictions_df) == expected_test_size\n","assert predictions_df[\"sequence_local_test_index\"].is_unique\n","assert predictions_df[\"prediction_output_position\"].is_unique\n","\n","assert predictions_df[\"start_line\"].notna().all()\n","assert predictions_df[\"end_line\"].notna().all()\n","assert (predictions_df[\"start_line\"] >= 0).all()\n","assert (\n","    predictions_df[\"end_line\"]\n","    >= predictions_df[\"start_line\"]\n",").all()\n","\n","# Because end_line is inclusive, the correct window-length formula is:\n","# end_line - start_line + 1\n","assert predictions_df[\"window_length\"].eq(WINDOW_SIZE).all(), (\n","    \"At least one test sequence does not contain exactly \"\n","    f\"{WINDOW_SIZE} lines.\"\n",")\n","\n","assert predictions_df[\"end_line\"].max() < len(metadata_df), (\n","    \"A test sequence points beyond the available metadata rows.\"\n",")\n","\n","assert predictions_df[\"anomaly_probability\"].between(0, 1).all()\n","assert predictions_df[\"threshold\"].eq(float(best_threshold)).all()\n","\n","assert set(predictions_df[\"true_label\"].unique()).issubset({0, 1})\n","assert set(predictions_df[\"predicted_label\"].unique()).issubset({0, 1})\n","assert set(predictions_df[\"prediction_type\"].unique()).issubset(\n","    {\"TP\", \"FP\", \"FN\", \"TN\"}\n",")\n","\n","# Confirm that labels and predictions remain aligned after sorting.\n","sorted_local_indices = (\n","    predictions_df[\"sequence_local_test_index\"]\n","    .to_numpy(dtype=np.int64)\n",")\n","\n","assert np.array_equal(\n","    predictions_df[\"true_label\"].to_numpy(dtype=np.int8),\n","    np.asarray(y_test, dtype=np.int8)[sorted_local_indices],\n","), \"Prediction rows are not aligned with the local test labels.\"\n","\n","assert np.array_equal(\n","    predictions_df[\"predicted_label\"].to_numpy(dtype=np.int8),\n","    (predictions_df[\"anomaly_probability\"].to_numpy() >= best_threshold)\n","    .astype(np.int8),\n","), \"Saved predicted labels do not match the selected threshold.\"\n","\n","\n","# ------------------------------------------------------------\n","# Save the complete prediction table.\n","# ------------------------------------------------------------\n","save_table(\n","    predictions_df,\n","    PREDICTIONS_PATH,\n","    \"Final test predictions linked to inclusive BGL line ranges\",\n",")\n","\n","print(\"Saved final predictions to:\", PREDICTIONS_PATH)\n","print(\"Prediction rows:\", len(predictions_df))\n","print(\"Window length:\", WINDOW_SIZE)\n","print(\"Range convention: inclusive start_line and inclusive end_line\")\n","\n","prediction_type_counts = (\n","    predictions_df[\"prediction_type\"]\n","    .value_counts()\n","    .reindex([\"TP\", \"FP\", \"FN\", \"TN\"], fill_value=0)\n","    .rename_axis(\"prediction_type\")\n","    .reset_index(name=\"count\")\n",")\n","\n","display(prediction_type_counts)\n","display(predictions_df.head())\n","\n","\n","# ------------------------------------------------------------\n","# Select reproducible sequences for the XAI stages.\n","# ------------------------------------------------------------\n","MAX_XAI_TRUE_POSITIVES = 100\n","MAX_XAI_FALSE_POSITIVES = 20\n","MAX_XAI_FALSE_NEGATIVES = 20\n","\n","tp_candidates = (\n","    predictions_df[\n","        predictions_df[\"prediction_type\"] == \"TP\"\n","    ]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[False, True],\n","    )\n","    .head(MAX_XAI_TRUE_POSITIVES)\n",")\n","\n","fp_candidates = (\n","    predictions_df[\n","        predictions_df[\"prediction_type\"] == \"FP\"\n","    ]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[False, True],\n","    )\n","    .head(MAX_XAI_FALSE_POSITIVES)\n",")\n","\n","# For false negatives, lower anomaly probability represents a stronger miss.\n","fn_candidates = (\n","    predictions_df[\n","        predictions_df[\"prediction_type\"] == \"FN\"\n","    ]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[True, True],\n","    )\n","    .head(MAX_XAI_FALSE_NEGATIVES)\n",")\n","\n","xai_candidates = pd.concat(\n","    [\n","        tp_candidates,\n","        fp_candidates,\n","        fn_candidates,\n","    ],\n","    ignore_index=True,\n",")\n","\n","xai_candidates = (\n","    xai_candidates\n","    .drop_duplicates(\n","        subset=[\"sequence_local_test_index\"],\n","        keep=\"first\",\n","    )\n","    .reset_index(drop=True)\n",")\n","\n","if xai_candidates.empty:\n","    raise ValueError(\n","        \"No TP, FP, or FN sequences were available for XAI.\"\n","    )\n","\n","save_table(\n","    xai_candidates,\n","    TOP_XAI_CANDIDATES_PATH,\n","    \"Reproducible TP, FP, and FN sequences selected for XAI\",\n",")\n","\n","xai_candidate_counts = (\n","    xai_candidates[\"prediction_type\"]\n","    .value_counts()\n","    .reindex([\"TP\", \"FP\", \"FN\"], fill_value=0)\n","    .rename_axis(\"prediction_type\")\n","    .reset_index(name=\"count\")\n",")\n","\n","print(\"Saved XAI candidates to:\", TOP_XAI_CANDIDATES_PATH)\n","print(\"Selected XAI sequences:\", len(xai_candidates))\n","\n","display(xai_candidate_counts)\n","display(xai_candidates.head())\n"],"id":"g1dmKjKab4bQ"},{"cell_type":"markdown","id":"3c2e0b99","metadata":{"id":"3c2e0b99"},"source":["## Step 16 — Save the Final Model Checkpoint\n","\n","The checkpoint stores the final model trained on the complete 80% partition, together with the epoch and threshold selected from temporal validation.\n"]},{"cell_type":"code","execution_count":31,"id":"08036205","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"08036205","outputId":"2720b047-b8fe-49b5-d5cc-69df46060615","executionInfo":{"status":"ok","timestamp":1785510824734,"user_tz":-180,"elapsed":5145,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved LogMiniLM checkpoint to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/models/logminilm_model.pt\n"]}],"source":["checkpoint = {\n","    \"model_state_dict\": model.state_dict(),\n","    \"config\": {\n","        \"model_name\": \"LogMiniLM\",\n","        \"minilm_model_name\": MINILM_MODEL_NAME,\n","        \"window_size\": WINDOW_SIZE,\n","        \"step_size\": STEP_SIZE,\n","        \"split\": \"temporal_80_20_with_internal_temporal_validation\",\n","        \"validation_ratio_within_train\": VALIDATION_RATIO_WITHIN_TRAIN,\n","        \"best_epoch_selected_by_validation_f1\": best_epoch,\n","        \"epoch_selection\": \"maximum_validation_f1\",\n","        \"threshold_selection\": \"maximum_validation_f1\",\n","        \"projection_dim\": PROJECTION_DIM,\n","        \"num_transformer_layers\": NUM_TRANSFORMER_LAYERS,\n","        \"num_attention_heads\": NUM_ATTENTION_HEADS,\n","        \"feedforward_dim\": FEEDFORWARD_DIM,\n","        \"dropout\": DROPOUT,\n","        \"best_threshold\": best_threshold,\n","        \"development_max_epochs\": DEVELOPMENT_MAX_EPOCHS,\n","        \"early_stopping_patience\": EARLY_STOPPING_PATIENCE,\n","        \"final_training_pos_weight\": final_pos_weight,\n","    },\n","    \"best_metrics\": best_row.to_dict(),\n","    \"validation_selection_metrics\": best_validation_row.to_dict(),\n","    \"development_config\": development_config,\n","    \"paths\": {\n","        \"unique_texts_path\": str(UNIQUE_TEXTS_PATH),\n","        \"embeddings_path\": str(EMBEDDINGS_PATH),\n","        \"event_id_path\": str(EVENT_ID_PATH),\n","        \"labels_path\": str(LABELS_PATH),\n","        \"metadata_path\": str(METADATA_PATH),\n","        \"predictions_path\": str(PREDICTIONS_PATH),\n","        \"top_xai_candidates_path\": str(TOP_XAI_CANDIDATES_PATH),\n","        \"development_config_path\": str(DEVELOPMENT_CONFIG_PATH),\n","        \"development_history_path\": str(DEVELOPMENT_HISTORY_PATH),\n","        \"validation_results_path\": str(VALIDATION_RESULTS_PATH),\n","        \"final_training_history_path\": str(FINAL_TRAINING_HISTORY_PATH),\n","    },\n","}\n","\n","torch.save(checkpoint, MODEL_PATH)\n","print(\"Saved LogMiniLM checkpoint to:\", MODEL_PATH)\n"]},{"cell_type":"markdown","id":"a11b352f","metadata":{"id":"a11b352f"},"source":["## Step 17 — Thesis-Ready Methodology Summary\n","\n","> LogMiniLM was evaluated using a chronology-preserving two-phase protocol. The first 80% of sequences formed the complete training partition, while the final 20% remained untouched for final testing. During development, the most recent 10% of the training partition was reserved as temporal validation. The epoch and decision threshold were selected jointly using the maximum validation F1-score; validation loss was used only as a tie-breaking criterion between equal-F1 epochs. When several thresholds at the selected epoch produced the same maximum F1-score, the midpoint of the tied threshold plateau was used as a stable operating point. LogMiniLM was then reinitialized from scratch and retrained on the complete 80% training partition for the selected number of epochs. Finally, the model was evaluated once on the untouched test partition using the fixed validation-selected threshold.\n","\n","> Random seeds and deterministic settings were enabled to improve reproducibility. Complete bitwise determinism is not claimed because some GPU attention kernels may remain non-deterministic when no deterministic implementation is available.\n","\n","```text\n","Raw BGL logs\n","  ↓\n","Preprocessing and MiniLM embedding cache\n","  ↓\n","Fixed 10-line sequences\n","  ↓\n","Temporal 80% training / 20% untouched testing\n","  ↓\n","Temporary temporal validation inside training\n","  ↓\n","Jointly select epoch and threshold by validation F1\n","  ↓\n","Use validation loss only to break equal-F1 epoch ties\n","  ↓\n","Use midpoint of equal-F1 threshold plateau\n","  ↓\n","Fresh LogMiniLM initialization\n","  ↓\n","Final retraining on complete 80%\n","  ↓\n","One-time final test evaluation\n","  ↓\n","Occlusion XAI + SHAP + faithfulness and agreement evaluation\n","```\n"]},{"cell_type":"markdown","id":"86d1e6a7","metadata":{"id":"86d1e6a7"},"source":["## Step 18 — XAI setup: event-level occlusion explanation\n","\n","This section explains **why LogMiniLM predicts a 10-line sequence as anomalous**.\n","\n","The main method is **event-level occlusion**:\n","\n","1. Take a predicted anomalous sequence.\n","2. Replace one log event at a time with a neutral baseline event.\n","3. Recompute the anomaly probability.\n","4. Measure the probability drop.\n","5. Rank events by contribution.\n","\n","A larger probability drop means the removed event was more important for the anomaly decision.\n"]},{"cell_type":"code","execution_count":32,"id":"e67d5930","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":143},"id":"e67d5930","outputId":"011d182c-aeb3-4f28-ca52-c9ace4ef8a2d","executionInfo":{"status":"ok","timestamp":1785510824772,"user_tz":-180,"elapsed":34,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["  prediction_type  count\n","0              TP    100\n","1              FP     20\n","2              FN     20"],"text/html":["\n","  <div id=\"df-21ab50e2-147d-49b0-823b-ebc8c149cf90\" class=\"colab-df-container\">\n","    <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>prediction_type</th>\n","      <th>count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>TP</td>\n","      <td>100</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>FP</td>\n","      <td>20</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>FN</td>\n","      <td>20</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21ab50e2-147d-49b0-823b-ebc8c149cf90')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-21ab50e2-147d-49b0-823b-ebc8c149cf90 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","\n","      buttonEl.onclick = () => {\n","        google.colab.notebook.generateWithVariable('xai_candidate_counts');\n","      }\n","      })();\n","    </script>\n","  </div>\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","variable_name":"xai_candidate_counts","summary":"{\n  \"name\": \"xai_candidate_counts\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"prediction_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"TP\",\n          \"FP\",\n          \"FN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46,\n        \"min\": 20,\n        \"max\": 100,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          20,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 18 — XAI Output Paths and Candidate Validation\n","# ============================================================\n","\n","XAI_LOCAL_PATH = XAI_DIR / \"logminilm_xai_local_event_importance.csv\"\n","XAI_GLOBAL_PATH = XAI_DIR / \"logminilm_xai_global_summary.csv\"\n","XAI_FAITHFULNESS_PATH = XAI_DIR / \"logminilm_xai_faithfulness.csv\"\n","XAI_CASE_STUDY_PATH = XAI_DIR / \"logminilm_xai_case_study.csv\"\n","TOPK_FAITHFULNESS_PATH = XAI_DIR / \"logminilm_xai_topk_faithfulness.csv\"\n","\n","required_prediction_columns = {\n","    \"sequence_local_test_index\", \"start_line\", \"end_line\",\n","    \"true_label\", \"predicted_label\", \"anomaly_probability\",\n","    \"threshold\", \"prediction_type\"\n","}\n","missing_prediction_columns = required_prediction_columns - set(predictions_df.columns)\n","if missing_prediction_columns:\n","    raise ValueError(\n","        f\"Missing prediction columns required for XAI: \"\n","        f\"{sorted(missing_prediction_columns)}\"\n","    )\n","\n","if xai_candidates.empty:\n","    raise ValueError(\"No XAI candidates were selected in Step 15.\")\n","\n","xai_candidate_counts = (\n","    xai_candidates[\"prediction_type\"]\n","    .value_counts()\n","    .rename_axis(\"prediction_type\")\n","    .reset_index(name=\"count\")\n",")\n","display(xai_candidate_counts)\n"]},{"cell_type":"markdown","id":"1db93992","metadata":{"id":"1db93992"},"source":["## Step 19 — Choose a neutral baseline event for masking\n","\n","For occlusion, we need to replace one event at a time with a neutral event.\n","\n","We use the **most frequent event ID appearing in normal training sequences** as the baseline. This is a simple and reproducible masking strategy.\n"]},{"cell_type":"code","execution_count":33,"id":"aff91312","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"aff91312","outputId":"1e7e35ce-aab8-47aa-a31d-b7b4c39fdfe5","executionInfo":{"status":"ok","timestamp":1785510824789,"user_tz":-180,"elapsed":15,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Baseline event ID: 27136\n","Baseline normalized text: ras kernel info generating core <num>\n"]}],"source":["# ============================================================\n","# Find a neutral baseline event ID from normal training sequences\n","# ============================================================\n","normal_train_sequences = X_train[y_train == 0]\n","if len(normal_train_sequences) == 0:\n","    raise ValueError(\"No normal training sequences found. Cannot build neutral baseline event.\")\n","\n","flat_normal_event_ids = normal_train_sequences.reshape(-1)\n","baseline_event_id = int(pd.Series(flat_normal_event_ids).value_counts().idxmax())\n","baseline_text = unique_texts.iloc[baseline_event_id]\n","\n","print(\"Baseline event ID:\", baseline_event_id)\n","print(\"Baseline normalized text:\", baseline_text)\n","\n","# Thesis note:\n","# This baseline represents a common normal event and is used as the neutral replacement\n","# during occlusion-based explanation.\n"]},{"cell_type":"markdown","id":"e469fab6","metadata":{"id":"e469fab6"},"source":["## Step 20 — Event-level occlusion function\n","\n","For each event position in the 10-line sequence:\n","\n","```text\n","importance = original anomaly probability - masked anomaly probability\n","```\n","\n","If importance is high, that event strongly supports the anomaly prediction.\n"]},{"cell_type":"code","execution_count":34,"id":"35e344f8","metadata":{"id":"35e344f8","executionInfo":{"status":"ok","timestamp":1785510824867,"user_tz":-180,"elapsed":76,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[],"source":["# ============================================================\n","# Step 20 — Event-Level Occlusion Explanation\n","# ============================================================\n","\n","def predict_sequence_score(model, sequence_event_ids):\n","    \"\"\"\n","    Predict both the raw anomaly logit and anomaly probability\n","    for one sequence of event IDs.\n","\n","    Returning the logit is important because sigmoid probabilities\n","    may become saturated near 0 or 1, hiding meaningful score changes.\n","    \"\"\"\n","    model.eval()\n","\n","    sequence_event_ids = np.asarray(sequence_event_ids, dtype=np.int64)\n","\n","    seq_tensor = torch.as_tensor(\n","        sequence_event_ids,\n","        dtype=torch.long,\n","        device=device\n","    ).unsqueeze(0)\n","\n","    with torch.no_grad():\n","        logits = model(seq_tensor).reshape(-1)\n","\n","        logit = float(logits[0].item())\n","        probability = float(torch.sigmoid(logits[0]).item())\n","\n","    return logit, probability\n","\n","\n","def predict_sequence_probability(model, sequence_event_ids):\n","    \"\"\"\n","    Compatibility helper for later notebook steps that require\n","    probability only.\n","    \"\"\"\n","    _, probability = predict_sequence_score(\n","        model,\n","        sequence_event_ids\n","    )\n","\n","    return probability\n","\n","\n","def explain_sequence_by_occlusion(\n","    model,\n","    sequence_event_ids,\n","    baseline_event_id\n","):\n","    \"\"\"\n","    Calculate event-level occlusion importance for one sequence.\n","\n","    Each event is replaced individually with the selected neutral\n","    baseline event. Importance is measured using:\n","\n","    1. Probability drop:\n","       original probability - masked probability\n","\n","    2. Logit drop:\n","       original logit - masked logit\n","\n","    Logit drop is used for ranking because it remains informative\n","    when sigmoid probabilities are highly saturated.\n","    \"\"\"\n","    sequence_event_ids = np.asarray(\n","        sequence_event_ids,\n","        dtype=np.int64\n","    )\n","\n","    if sequence_event_ids.ndim != 1:\n","        raise ValueError(\n","            \"sequence_event_ids must be a one-dimensional sequence.\"\n","        )\n","\n","    if len(sequence_event_ids) == 0:\n","        raise ValueError(\n","            \"sequence_event_ids must contain at least one event.\"\n","        )\n","\n","    baseline_event_id = int(baseline_event_id)\n","\n","    if baseline_event_id < 0 or baseline_event_id >= len(unique_embeddings):\n","        raise ValueError(\n","            f\"baseline_event_id={baseline_event_id} is outside the valid \"\n","            f\"event-ID range 0–{len(unique_embeddings) - 1}.\"\n","        )\n","\n","    original_logit, original_probability = predict_sequence_score(\n","        model,\n","        sequence_event_ids\n","    )\n","\n","    explanation_rows = []\n","\n","    for position in range(len(sequence_event_ids)):\n","        masked_sequence = sequence_event_ids.copy()\n","\n","        original_event_id = int(masked_sequence[position])\n","        masked_sequence[position] = baseline_event_id\n","\n","        masked_logit, masked_probability = predict_sequence_score(\n","            model,\n","            masked_sequence\n","        )\n","\n","        logit_drop = original_logit - masked_logit\n","        probability_drop = (\n","            original_probability - masked_probability\n","        )\n","\n","        explanation_rows.append({\n","            \"event_position\": int(position + 1),\n","            \"original_event_id\": original_event_id,\n","            \"masked_event_id\": baseline_event_id,\n","\n","            \"original_logit\": float(original_logit),\n","            \"masked_logit\": float(masked_logit),\n","            \"logit_drop\": float(logit_drop),\n","\n","            \"original_probability\": float(original_probability),\n","            \"masked_probability\": float(masked_probability),\n","            \"probability_drop\": float(probability_drop),\n","\n","            # Retained for compatibility with later notebook cells.\n","            \"importance_drop\": float(probability_drop),\n","        })\n","\n","    explanation_df = pd.DataFrame(explanation_rows)\n","\n","    # Rank events using logit drop because it is less affected\n","    # by sigmoid saturation than probability drop.\n","    explanation_df = (\n","        explanation_df\n","        .sort_values(\n","            by=[\"logit_drop\", \"probability_drop\"],\n","            ascending=[False, False]\n","        )\n","        .reset_index(drop=True)\n","    )\n","\n","    return explanation_df\n","\n","# Minimal deterministic integrity test on one selected sequence.\n","diagnostic_index = int(xai_candidates.iloc[0][\"sequence_local_test_index\"])\n","diagnostic_sequence = np.asarray(X_test[diagnostic_index], dtype=np.int64)\n","diagnostic_explanation = explain_sequence_by_occlusion(\n","    model,\n","    diagnostic_sequence,\n","    baseline_event_id\n",")\n","\n","assert len(diagnostic_explanation) == WINDOW_SIZE\n","assert diagnostic_explanation[\"event_position\"].nunique() == WINDOW_SIZE\n","assert np.isfinite(\n","    diagnostic_explanation[\n","        [\"logit_drop\", \"probability_drop\", \"original_probability\", \"masked_probability\"]\n","    ].to_numpy()\n",").all()\n"]},{"cell_type":"markdown","id":"d2f68209","metadata":{"id":"d2f68209"},"source":["## Step 21 — Generate local XAI explanations with metadata\n","\n","This step attaches each event importance score to operational metadata:\n","\n","- timestamp\n","- node\n","- component\n","- severity\n","- original event message\n","- normalized model input\n","\n","This makes the explanation useful for investigation and thesis case studies.\n"]},{"cell_type":"code","execution_count":35,"id":"20b083b1","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"20b083b1","outputId":"bf109cde-bee2-47b4-c7d8-cdf059029ca7","executionInfo":{"status":"ok","timestamp":1785510829197,"user_tz":-180,"elapsed":4297,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved local XAI explanations to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_local_event_importance.csv\n","Saved faithfulness results to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_faithfulness.csv\n","Local XAI rows: 1400\n","Faithfulness rows: 140\n"]},{"output_type":"display_data","data":{"text/plain":["    sequence_local_test_index  start_line  end_line  event_position  \\\n","0                       41109     4181880   4181889               1   \n","1                       41109     4181880   4181889               4   \n","2                       41109     4181880   4181889               5   \n","3                       41109     4181880   4181889               8   \n","4                       41109     4181880   4181889               9   \n","5                       41109     4181880   4181889               2   \n","6                       41109     4181880   4181889               7   \n","7                       41109     4181880   4181889               6   \n","8                       41109     4181880   4181889               3   \n","9                       41109     4181880   4181889              10   \n","10                      41120     4181990   4181999               1   \n","11                      41120     4181990   4181999               4   \n","12                      41120     4181990   4181999               5   \n","13                      41120     4181990   4181999               8   \n","14                      41120     4181990   4181999               9   \n","15                      41120     4181990   4181999               2   \n","16                      41120     4181990   4181999               7   \n","17                      41120     4181990   4181999               6   \n","18                      41120     4181990   4181999               3   \n","19                      41120     4181990   4181999              10   \n","\n","    line_index  true_label  predicted_label  original_probability  \\\n","0      4216350           1                1              0.999992   \n","1      4216353           1                1              0.999992   \n","2      4216354           1                1              0.999992   \n","3      4216357           1                1              0.999992   \n","4      4216358           1                1              0.999992   \n","5      4216351           1                1              0.999992   \n","6      4216356           1                1              0.999992   \n","7      4216355           1                1              0.999992   \n","8      4216352           1                1              0.999992   \n","9      4216359           1                1              0.999992   \n","10     4216460           1                1              0.999992   \n","11     4216463           1                1              0.999992   \n","12     4216464           1                1              0.999992   \n","13     4216467           1                1              0.999992   \n","14     4216468           1                1              0.999992   \n","15     4216461           1                1              0.999992   \n","16     4216466           1                1              0.999992   \n","17     4216465           1                1              0.999992   \n","18     4216462           1                1              0.999992   \n","19     4216469           1                1              0.999992   \n","\n","    masked_probability  logit_drop  ...  original_event_id  raw_label  \\\n","0             0.999992    0.072487  ...              21426     APPSEV   \n","1             0.999992    0.067707  ...              21426     APPSEV   \n","2             0.999992    0.066360  ...              21426     APPSEV   \n","3             0.999992    0.066213  ...              21426     APPSEV   \n","4             0.999992    0.065795  ...              21426     APPSEV   \n","5             0.999992    0.064989  ...              21426     APPSEV   \n","6             0.999992    0.064759  ...              21426     APPSEV   \n","7             0.999992    0.063849  ...              21426     APPSEV   \n","8             0.999992    0.062894  ...              21426     APPSEV   \n","9             0.999992    0.062614  ...              21426     APPSEV   \n","10            0.999992    0.072487  ...              21426     APPSEV   \n","11            0.999992    0.067707  ...              21426     APPSEV   \n","12            0.999992    0.066360  ...              21426     APPSEV   \n","13            0.999992    0.066213  ...              21426     APPSEV   \n","14            0.999992    0.065795  ...              21426     APPSEV   \n","15            0.999992    0.064989  ...              21426     APPSEV   \n","16            0.999992    0.064759  ...              21426     APPSEV   \n","17            0.999992    0.063849  ...              21426     APPSEV   \n","18            0.999992    0.062894  ...              21426     APPSEV   \n","19            0.999992    0.062614  ...              21426     APPSEV   \n","\n","    line_label                   full_time                 node source  \\\n","0            1  2005-11-17-05.34.19.485817  R74-M0-NC-I:J18-U01    RAS   \n","1            1  2005-11-17-05.34.19.943960  R74-M1-NC-I:J18-U11    RAS   \n","2            1  2005-11-17-05.34.20.092971  R74-M1-NC-I:J18-U01    RAS   \n","3            1  2005-11-17-05.34.20.572641  R72-M1-N8-I:J18-U11    RAS   \n","4            1  2005-11-17-05.34.20.718215  R72-M1-N8-I:J18-U01    RAS   \n","5            1  2005-11-17-05.34.19.639081  R74-M1-N4-I:J18-U11    RAS   \n","6            1  2005-11-17-05.34.20.394346  R74-M1-N8-I:J18-U01    RAS   \n","7            1  2005-11-17-05.34.20.243549  R74-M1-N8-I:J18-U11    RAS   \n","8            1  2005-11-17-05.34.19.788642  R74-M1-N4-I:J18-U01    RAS   \n","9            1  2005-11-17-05.34.20.862518  R72-M1-N4-I:J18-U11    RAS   \n","10           1  2005-11-17-05.34.36.177059  R60-M0-N8-I:J18-U01    RAS   \n","11           1  2005-11-17-05.34.36.612978  R60-M0-N4-I:J18-U11    RAS   \n","12           1  2005-11-17-05.34.36.745040  R60-M0-N4-I:J18-U01    RAS   \n","13           1  2005-11-17-05.34.37.159153  R62-M0-N0-I:J18-U11    RAS   \n","14           1  2005-11-17-05.34.37.295164  R62-M0-N0-I:J18-U01    RAS   \n","15           1  2005-11-17-05.34.36.346828  R76-M1-N0-I:J18-U11    RAS   \n","16           1  2005-11-17-05.34.37.022015  R62-M0-NC-I:J18-U01    RAS   \n","17           1  2005-11-17-05.34.36.889699  R62-M0-NC-I:J18-U11    RAS   \n","18           1  2005-11-17-05.34.36.480785  R76-M1-N0-I:J18-U01    RAS   \n","19           1  2005-11-17-05.34.37.432634  R62-M0-N4-I:J18-U11    RAS   \n","\n","    component severity                                      event_message  \\\n","0         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","1         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","2         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","3         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","4         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","5         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","6         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","7         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","8         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","9         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","10        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","11        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","12        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","13        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","14        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","15        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","16        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","17        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","18        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","19        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","\n","                                      model_text_norm  \n","0   ras app fatal ciod error reading message prefi...  \n","1   ras app fatal ciod error reading message prefi...  \n","2   ras app fatal ciod error reading message prefi...  \n","3   ras app fatal ciod error reading message prefi...  \n","4   ras app fatal ciod error reading message prefi...  \n","5   ras app fatal ciod error reading message prefi...  \n","6   ras app fatal ciod error reading message prefi...  \n","7   ras app fatal ciod error reading message prefi...  \n","8   ras app fatal ciod error reading message prefi...  \n","9   ras app fatal ciod error reading message prefi...  \n","10  ras app fatal ciod error reading message prefi...  \n","11  ras app fatal ciod error reading message prefi...  \n","12  ras app fatal ciod error reading message prefi...  \n","13  ras app fatal ciod error reading message prefi...  \n","14  ras app fatal ciod error reading message prefi...  \n","15  ras app fatal ciod error reading message prefi...  \n","16  ras app fatal ciod error reading message prefi...  \n","17  ras app fatal ciod error reading message prefi...  \n","18  ras app fatal ciod error reading message prefi...  \n","19  ras app fatal ciod error reading message prefi...  \n","\n","[20 rows x 24 columns]"],"text/html":["\n","  <div id=\"df-6b8d810e-6327-4c2d-afaa-25c116f08b9f\" class=\"colab-df-container\">\n","    <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>sequence_local_test_index</th>\n","      <th>start_line</th>\n","      <th>end_line</th>\n","      <th>event_position</th>\n","      <th>line_index</th>\n","      <th>true_label</th>\n","      <th>predicted_label</th>\n","      <th>original_probability</th>\n","      <th>masked_probability</th>\n","      <th>logit_drop</th>\n","      <th>...</th>\n","      <th>original_event_id</th>\n","      <th>raw_label</th>\n","      <th>line_label</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>source</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","      <th>model_text_norm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>4216350</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.072487</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.485817</td>\n","      <td>R74-M0-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>4</td>\n","      <td>4216353</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.067707</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.943960</td>\n","      <td>R74-M1-NC-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>5</td>\n","      <td>4216354</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.066360</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.092971</td>\n","      <td>R74-M1-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>8</td>\n","      <td>4216357</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.066213</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.572641</td>\n","      <td>R72-M1-N8-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>9</td>\n","      <td>4216358</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.065795</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.718215</td>\n","      <td>R72-M1-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>2</td>\n","      <td>4216351</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.064989</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.639081</td>\n","      <td>R74-M1-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>7</td>\n","      <td>4216356</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.064759</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.394346</td>\n","      <td>R74-M1-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>6</td>\n","      <td>4216355</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.063849</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.243549</td>\n","      <td>R74-M1-N8-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>3</td>\n","      <td>4216352</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.062894</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.788642</td>\n","      <td>R74-M1-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>10</td>\n","      <td>4216359</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.062614</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.862518</td>\n","      <td>R72-M1-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>4216460</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.072487</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.177059</td>\n","      <td>R60-M0-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>4</td>\n","      <td>4216463</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.067707</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.612978</td>\n","      <td>R60-M0-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>5</td>\n","      <td>4216464</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.066360</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.745040</td>\n","      <td>R60-M0-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>8</td>\n","      <td>4216467</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.066213</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.159153</td>\n","      <td>R62-M0-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>9</td>\n","      <td>4216468</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.065795</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.295164</td>\n","      <td>R62-M0-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>2</td>\n","      <td>4216461</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.064989</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.346828</td>\n","      <td>R76-M1-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>7</td>\n","      <td>4216466</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.064759</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.022015</td>\n","      <td>R62-M0-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>6</td>\n","      <td>4216465</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.063849</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.889699</td>\n","      <td>R62-M0-NC-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>3</td>\n","      <td>4216462</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.062894</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.480785</td>\n","      <td>R76-M1-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>10</td>\n","      <td>4216469</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>0.062614</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.432634</td>\n","      <td>R62-M0-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>20 rows × 24 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b8d810e-6327-4c2d-afaa-25c116f08b9f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path 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         1   \n","3                      41665     4187440   4187449           1   \n","4                      41666     4187450   4187459           1   \n","\n","   predicted_label  original_probability  top_event_position  \\\n","0                1              0.999992                   1   \n","1                1              0.999992                   1   \n","2                1              0.999992                   1   \n","3                1              0.999992                   1   \n","4                1              0.999992                   1   \n","\n","   top_event_logit_drop  top_event_probability_drop  \\\n","0              0.072487                5.960464e-07   \n","1              0.072487                5.960464e-07   \n","2              0.079478                5.960464e-07   \n","3              0.077715                5.960464e-07   \n","4              0.076799                5.960464e-07   \n","\n","   top_event_masked_probability  \n","0                      0.999992  \n","1                      0.999992  \n","2                      0.999992  \n","3                      0.999992  \n","4                      0.999992  "],"text/html":["\n","  <div id=\"df-e0e39aa8-3443-4114-a3cf-9d400cad5635\" class=\"colab-df-container\">\n","    <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>sequence_local_test_index</th>\n","      <th>start_line</th>\n","      <th>end_line</th>\n","      <th>true_label</th>\n","      <th>predicted_label</th>\n","      <th>original_probability</th>\n","      <th>top_event_position</th>\n","      <th>top_event_logit_drop</th>\n","      <th>top_event_probability_drop</th>\n","      <th>top_event_masked_probability</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>1</td>\n","      <td>0.072487</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>1</td>\n","      <td>0.072487</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>1</td>\n","      <td>0.079478</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41665</td>\n","      <td>4187440</td>\n","      <td>4187449</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>1</td>\n","      <td>0.077715</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41666</td>\n","      <td>4187450</td>\n","      <td>4187459</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>1</td>\n","      <td>0.076799</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e0e39aa8-3443-4114-a3cf-9d400cad5635')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","  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[\n          41120,\n          41666,\n          41664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3015,\n        \"min\": 4181880,\n        \"max\": 4187450,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4181990,\n          4187450,\n          4187430\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_line\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3015,\n        \"min\": 4181889,\n        \"max\": 4187459,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4181999,\n          4187459,\n          4187439\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"true_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.9999923706054688,\n        \"max\": 0.9999923706054688,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9999923706054688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_event_position\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_event_logit_drop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003168148709951397,\n        \"min\": 0.07248687744140625,\n        \"max\": 0.07947826385498047,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.07947826385498047\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_event_probability_drop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 5.960464477539062e-07,\n        \"max\": 5.960464477539062e-07,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5.960464477539062e-07\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top_event_masked_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.999991774559021,\n        \"max\": 0.999991774559021,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.999991774559021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Generate local XAI explanations\n","# ============================================================\n","local_rows = []\n","faithfulness_rows = []\n","\n","# Ensure metadata exists.\n","if \"metadata_df\" not in globals():\n","    if \"df\" in globals():\n","        metadata_df = df[[\n","            \"line_index\", \"raw_label\", \"label\", \"date\", \"full_time\", \"node\",\n","            \"source\", \"component\", \"severity\", \"event_message\", \"model_text_norm\"\n","        ]]\n","    else:\n","        metadata_df = pd.read_parquet(METADATA_PATH)\n","\n","for _, cand in xai_candidates.iterrows():\n","    local_idx = int(cand[\"sequence_local_test_index\"])\n","    start_line = int(cand[\"start_line\"])\n","    end_line = int(cand[\"end_line\"])\n","    true_label = int(cand[\"true_label\"])\n","    predicted_label = int(cand[\"predicted_label\"])\n","    original_prob_from_eval = float(cand[\"anomaly_probability\"])\n","\n","    seq_event_ids = X_test[local_idx]\n","    exp_df = explain_sequence_by_occlusion(model, seq_event_ids, baseline_event_id)\n","\n","    # Top event faithfulness for this sequence.\n","    top_row = exp_df.iloc[0]\n","    faithfulness_rows.append({\n","        \"sequence_local_test_index\": local_idx,\n","        \"start_line\": start_line,\n","        \"end_line\": end_line,\n","        \"true_label\": true_label,\n","        \"predicted_label\": predicted_label,\n","        \"original_probability\": float(top_row[\"original_probability\"]),\n","        \"top_event_position\": int(top_row[\"event_position\"]),\n","        \"top_event_logit_drop\": float(top_row[\"logit_drop\"]),\n","        \"top_event_probability_drop\": float(top_row[\"probability_drop\"]),\n","        \"top_event_masked_probability\": float(top_row[\"masked_probability\"]),\n","    })\n","\n","    # Add metadata per event.\n","    for _, row in exp_df.iterrows():\n","        event_position = int(row[\"event_position\"])\n","        line_number = start_line + event_position - 1\n","\n","        meta = metadata_df.iloc[line_number]\n","        local_rows.append({\n","            \"sequence_local_test_index\": local_idx,\n","            \"start_line\": start_line,\n","            \"end_line\": end_line,\n","            \"event_position\": event_position,\n","            \"line_index\": int(meta[\"line_index\"]),\n","            \"true_label\": true_label,\n","            \"predicted_label\": predicted_label,\n","            \"original_probability\": float(row[\"original_probability\"]),\n","            \"masked_probability\": float(row[\"masked_probability\"]),\n","            \"logit_drop\": float(row[\"logit_drop\"]),\n","            \"probability_drop\": float(row[\"probability_drop\"]),\n","            \"importance_drop\": float(row[\"logit_drop\"]),  # backward-compatible primary score\n","            \"logit_importance\": float(row[\"logit_drop\"]),\n","            \"probability_importance\": float(row[\"probability_drop\"]),\n","            \"original_event_id\": int(row[\"original_event_id\"]),\n","            \"raw_label\": meta[\"raw_label\"],\n","            \"line_label\": int(meta[\"label\"]),\n","            \"full_time\": meta[\"full_time\"],\n","            \"node\": meta[\"node\"],\n","            \"source\": meta[\"source\"],\n","            \"component\": meta[\"component\"],\n","            \"severity\": meta[\"severity\"],\n","            \"event_message\": meta[\"event_message\"],\n","            \"model_text_norm\": meta[\"model_text_norm\"],\n","        })\n","\n","local_xai_df = pd.DataFrame(local_rows)\n","faithfulness_df = pd.DataFrame(faithfulness_rows)\n","\n","save_table(local_xai_df, XAI_LOCAL_PATH, \"Event-level occlusion explanations\")\n","save_table(faithfulness_df, XAI_FAITHFULNESS_PATH, \"Occlusion faithfulness results\")\n","\n","print(\"Saved local XAI explanations to:\", XAI_LOCAL_PATH)\n","print(\"Saved faithfulness results to:\", XAI_FAITHFULNESS_PATH)\n","print(\"Local XAI rows:\", len(local_xai_df))\n","print(\"Faithfulness rows:\", len(faithfulness_df))\n","\n","display(local_xai_df.head(20))\n","display(faithfulness_df.head())\n"]},{"cell_type":"markdown","id":"43881c3f","metadata":{"id":"43881c3f"},"source":["## Step 22 — Global XAI summary\n","\n","The global summary aggregates the most influential events across many anomalous predictions.\n","\n","This helps answer:\n","\n","```text\n","Which components, severities, and message patterns most often drive anomaly decisions?\n","```\n"]},{"cell_type":"code","execution_count":36,"id":"36f0b01b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"36f0b01b","outputId":"d6365532-05b3-4ab3-8ffd-ee296d7c1425","executionInfo":{"status":"ok","timestamp":1785510829677,"user_tz":-180,"elapsed":478,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Global XAI summary\n"]},{"output_type":"display_data","data":{"text/plain":["      component severity                                      event_message  \\\n","1001        APP    FATAL  ciod: LOGIN chdir(/p/gb1/stella/RAPTOR/2183) f...   \n","1012    MONITOR  FAILURE  monitor caught java.lang.IllegalStateException...   \n","1006    MONITOR  FAILURE  Hardware monitor caught java.net.SocketExcepti...   \n","1011    MONITOR  FAILURE  monitor caught java.lang.IllegalStateException...   \n","1013    MONITOR   SEVERE  Hardware monitor caught java.net.SocketExcepti...   \n","0           APP    FATAL  ciod: Error loading -mode VN: invalid or missi...   \n","1007    MONITOR  FAILURE  While reading FanModule caught java.lang.Illeg...   \n","1008    MONITOR  FAILURE  While reading FanModule caught java.lang.Illeg...   \n","1009    MONITOR  FAILURE  While setting fan speed caught java.lang.Illeg...   \n","1005       MMCS     INFO  mmcs_db_server has been started: ./mmcs_db_ser...   \n","1003       MMCS     INFO                          ciodb has been restarted.   \n","1002  BGLMASTER     INFO  BGLMaster has been started: ./BGLMaster --cons...   \n","997         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","1010    MONITOR  FAILURE  monitor caught java.lang.IllegalStateException...   \n","964         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","1000        APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","956         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","950         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","980         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","974         APP    FATAL  ciod: Error reading message prefix on CioStrea...   \n","\n","      frequency  mean_logit_importance  max_logit_importance  \\\n","1001         76               0.005056              0.010532   \n","1012         64               0.168062              0.398234   \n","1006         61               0.131454              0.243792   \n","1011         45               0.248353              0.416005   \n","1013         16               0.142147              0.514589   \n","0             5               0.168503              0.173841   \n","1007          3               0.121790              0.136070   \n","1008          1               0.165363              0.165363   \n","1009          1               0.149687              0.149687   \n","1005          1               0.128687              0.128687   \n","1003          1               0.101464              0.101464   \n","1002          1               0.098494              0.098494   \n","997           1               0.093208              0.093208   \n","1010          1               0.088951              0.088951   \n","964           1               0.083898              0.083898   \n","1000          1               0.083570              0.083570   \n","956           1               0.082800              0.082800   \n","950           1               0.081905              0.081905   \n","980           1               0.079478              0.079478   \n","974           1               0.079473              0.079473   \n","\n","      mean_probability_importance  max_probability_importance  \\\n","1001                 5.817778e-08                1.211356e-07   \n","1012                 3.449619e-06                9.417534e-06   \n","1006                 1.158871e-06                2.861023e-06   \n","1011                 5.184280e-06                1.013279e-05   \n","1013                 1.728535e-06                8.583069e-06   \n","0                    2.312660e-06                2.384186e-06   \n","1007                 1.311302e-06                1.430511e-06   \n","1008                 2.980232e-06                2.980232e-06   \n","1009                 1.668930e-06                1.668930e-06   \n","1005                 1.430511e-06                1.430511e-06   \n","1003                 1.072884e-06                1.072884e-06   \n","1002                 1.072884e-06                1.072884e-06   \n","997                  8.344650e-07                8.344650e-07   \n","1010                 9.381065e-07                9.381065e-07   \n","964                  7.152557e-07                7.152557e-07   \n","1000                 7.152557e-07                7.152557e-07   \n","956                  7.152557e-07                7.152557e-07   \n","950                  7.152557e-07                7.152557e-07   \n","980                  5.960464e-07                5.960464e-07   \n","974                  5.960464e-07                5.960464e-07   \n","\n","      mean_original_probability  \n","1001                   0.000012  \n","1012                   0.999982  \n","1006                   0.999992  \n","1011                   0.999982  \n","1013                   0.999990  \n","0                      0.999987  \n","1007                   0.999990  \n","1008                   0.999983  \n","1009                   0.999990  \n","1005                   0.999990  \n","1003                   0.999990  \n","1002                   0.999990  \n","997                    0.999992  \n","1010                   0.000011  \n","964                    0.999992  \n","1000                   0.999992  \n","956                    0.999992  \n","950                    0.999992  \n","980                    0.999992  \n","974                    0.999992  "],"text/html":["\n","  <div id=\"df-395163a8-e976-4343-bd91-7047fbd7e75f\" class=\"colab-df-container\">\n","    <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>component</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","      <th>frequency</th>\n","      <th>mean_logit_importance</th>\n","      <th>max_logit_importance</th>\n","      <th>mean_probability_importance</th>\n","      <th>max_probability_importance</th>\n","      <th>mean_original_probability</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1001</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: LOGIN chdir(/p/gb1/stella/RAPTOR/2183) f...</td>\n","      <td>76</td>\n","      <td>0.005056</td>\n","      <td>0.010532</td>\n","      <td>5.817778e-08</td>\n","      <td>1.211356e-07</td>\n","      <td>0.000012</td>\n","    </tr>\n","    <tr>\n","      <th>1012</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>monitor caught java.lang.IllegalStateException...</td>\n","      <td>64</td>\n","      <td>0.168062</td>\n","      <td>0.398234</td>\n","      <td>3.449619e-06</td>\n","      <td>9.417534e-06</td>\n","      <td>0.999982</td>\n","    </tr>\n","    <tr>\n","      <th>1006</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","      <td>61</td>\n","      <td>0.131454</td>\n","      <td>0.243792</td>\n","      <td>1.158871e-06</td>\n","      <td>2.861023e-06</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>1011</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>monitor caught java.lang.IllegalStateException...</td>\n","      <td>45</td>\n","      <td>0.248353</td>\n","      <td>0.416005</td>\n","      <td>5.184280e-06</td>\n","      <td>1.013279e-05</td>\n","      <td>0.999982</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","      <td>16</td>\n","      <td>0.142147</td>\n","      <td>0.514589</td>\n","      <td>1.728535e-06</td>\n","      <td>8.583069e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>0</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","      <td>5</td>\n","      <td>0.168503</td>\n","      <td>0.173841</td>\n","      <td>2.312660e-06</td>\n","      <td>2.384186e-06</td>\n","      <td>0.999987</td>\n","    </tr>\n","    <tr>\n","      <th>1007</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>While reading FanModule caught java.lang.Illeg...</td>\n","      <td>3</td>\n","      <td>0.121790</td>\n","      <td>0.136070</td>\n","      <td>1.311302e-06</td>\n","      <td>1.430511e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>1008</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>While reading FanModule caught java.lang.Illeg...</td>\n","      <td>1</td>\n","      <td>0.165363</td>\n","      <td>0.165363</td>\n","      <td>2.980232e-06</td>\n","      <td>2.980232e-06</td>\n","      <td>0.999983</td>\n","    </tr>\n","    <tr>\n","      <th>1009</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>While setting fan speed caught java.lang.Illeg...</td>\n","      <td>1</td>\n","      <td>0.149687</td>\n","      <td>0.149687</td>\n","      <td>1.668930e-06</td>\n","      <td>1.668930e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>1005</th>\n","      <td>MMCS</td>\n","      <td>INFO</td>\n","      <td>mmcs_db_server has been started: ./mmcs_db_ser...</td>\n","      <td>1</td>\n","      <td>0.128687</td>\n","      <td>0.128687</td>\n","      <td>1.430511e-06</td>\n","      <td>1.430511e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>1003</th>\n","      <td>MMCS</td>\n","      <td>INFO</td>\n","      <td>ciodb has been restarted.</td>\n","      <td>1</td>\n","      <td>0.101464</td>\n","      <td>0.101464</td>\n","      <td>1.072884e-06</td>\n","      <td>1.072884e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>1002</th>\n","      <td>BGLMASTER</td>\n","      <td>INFO</td>\n","      <td>BGLMaster has been started: ./BGLMaster --cons...</td>\n","      <td>1</td>\n","      <td>0.098494</td>\n","      <td>0.098494</td>\n","      <td>1.072884e-06</td>\n","      <td>1.072884e-06</td>\n","      <td>0.999990</td>\n","    </tr>\n","    <tr>\n","      <th>997</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.093208</td>\n","      <td>0.093208</td>\n","      <td>8.344650e-07</td>\n","      <td>8.344650e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>1010</th>\n","      <td>MONITOR</td>\n","      <td>FAILURE</td>\n","      <td>monitor caught java.lang.IllegalStateException...</td>\n","      <td>1</td>\n","      <td>0.088951</td>\n","      <td>0.088951</td>\n","      <td>9.381065e-07</td>\n","      <td>9.381065e-07</td>\n","      <td>0.000011</td>\n","    </tr>\n","    <tr>\n","      <th>964</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.083898</td>\n","      <td>0.083898</td>\n","      <td>7.152557e-07</td>\n","      <td>7.152557e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>1000</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.083570</td>\n","      <td>0.083570</td>\n","      <td>7.152557e-07</td>\n","      <td>7.152557e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>956</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.082800</td>\n","      <td>0.082800</td>\n","      <td>7.152557e-07</td>\n","      <td>7.152557e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>950</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.081905</td>\n","      <td>0.081905</td>\n","      <td>7.152557e-07</td>\n","      <td>7.152557e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>980</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.079478</td>\n","      <td>0.079478</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","    <tr>\n","      <th>974</th>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>1</td>\n","      <td>0.079473</td>\n","      <td>0.079473</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>0.999992</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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Probability importance is retained as a complementary\n","# human-readable measure.\n","positive_xai = local_xai_df[local_xai_df[\"logit_importance\"] > 0].copy()\n","\n","if positive_xai.empty:\n","    print(\"No positive occlusion importance scores were found.\")\n","    global_summary_df = pd.DataFrame()\n","else:\n","    global_summary_df = (\n","        positive_xai\n","        .groupby([\"component\", \"severity\", \"event_message\"], dropna=False)\n","        .agg(\n","            frequency=(\"event_message\", \"count\"),\n","            mean_logit_importance=(\"logit_importance\", \"mean\"),\n","            max_logit_importance=(\"logit_importance\", \"max\"),\n","            mean_probability_importance=(\"probability_importance\", \"mean\"),\n","            max_probability_importance=(\"probability_importance\", \"max\"),\n","            mean_original_probability=(\"original_probability\", \"mean\"),\n","        )\n","        .reset_index()\n","        .sort_values(\n","            [\"frequency\", \"mean_logit_importance\"],\n","            ascending=[False, False]\n","        )\n","    )\n","\n","save_table(\n","    global_summary_df,\n","    XAI_GLOBAL_PATH,\n","    \"Global event-level occlusion importance summary\"\n",")\n","\n","print(\"Global XAI summary\")\n","display(global_summary_df.head(20))\n","\n","print(\"\\nFaithfulness summary — top-event probability drop\")\n","if not faithfulness_df.empty:\n","    display(faithfulness_df[[\"top_event_probability_drop\"]].describe())\n","else:\n","    print(\"No faithfulness rows available.\")\n"]},{"cell_type":"markdown","id":"P_ZpjXuD-q-X","metadata":{"id":"P_ZpjXuD-q-X"},"source":["### Figure 4.13 — Global Mean Occlusion Importance by Event Position\n","\n","**Interpretation:** The chart summarizes which positions within the 10-line window contribute most strongly to anomaly decisions across explained true-positive sequences.\n"]},{"cell_type":"code","execution_count":37,"id":"V6D5C_jZ-q-X","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":871},"id":"V6D5C_jZ-q-X","outputId":"6003dd72-6b37-4518-8395-ef9018ce252c","executionInfo":{"status":"ok","timestamp":1785510831534,"user_tz":-180,"elapsed":1855,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Explained anomaly sequences: 100\n"]},{"output_type":"display_data","data":{"text/plain":["   event_position  mean_importance  median_importance  max_importance  \\\n","0               1         0.070354           0.069742        0.083898   \n","1               2         0.063236           0.062648        0.079473   \n","2               3         0.059199           0.058200        0.083570   \n","3               4         0.065397           0.064835        0.079418   \n","4               5         0.064225           0.063757        0.074742   \n","5               6         0.061498           0.060917        0.075207   \n","6               7         0.062580           0.062159        0.072500   \n","7               8         0.063921           0.063326        0.082800   \n","8               9         0.064187           0.063601        0.074850   \n","9              10         0.060295           0.059624        0.093208   \n","\n","   frequency  \n","0        100  \n","1        100  \n","2        100  \n","3        100  \n","4        100  \n","5        100  \n","6        100  \n","7        100  \n","8        100  \n","9        100  "],"text/html":["\n","  <div id=\"df-8ae34754-773f-4793-acf0-718748791209\" class=\"colab-df-container\">\n","    <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>event_position</th>\n","      <th>mean_importance</th>\n","      <th>median_importance</th>\n","      <th>max_importance</th>\n","      <th>frequency</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.070354</td>\n","      <td>0.069742</td>\n","      <td>0.083898</td>\n","      <td>100</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>0.063236</td>\n","      <td>0.062648</td>\n","      <td>0.079473</td>\n","      <td>100</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>0.059199</td>\n","      <td>0.058200</td>\n","      <td>0.083570</td>\n","      <td>100</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>0.065397</td>\n","      <td>0.064835</td>\n","      <td>0.079418</td>\n","      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<td>0.059624</td>\n","      <td>0.093208</td>\n","      <td>100</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ae34754-773f-4793-acf0-718748791209')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","    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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.13 — Global occlusion importance\n","# ============================================================\n","\n","all_anomaly_xai = local_xai_df[\n","    (local_xai_df[\"true_label\"] == 1) &\n","    (local_xai_df[\"predicted_label\"] == 1)\n","].copy()\n","\n","position_importance_df = (\n","    all_anomaly_xai\n","    .groupby(\"event_position\")\n","    .agg(\n","        mean_importance=(\"logit_importance\", \"mean\"),\n","        median_importance=(\"logit_importance\", \"median\"),\n","        max_importance=(\"logit_importance\", \"max\"),\n","        frequency=(\"logit_importance\", \"count\")\n","    )\n","    .reset_index()\n","    .sort_values(\"event_position\")\n",")\n","\n","print(\"Explained anomaly sequences:\", all_anomaly_xai[\"sequence_local_test_index\"].nunique())\n","display(position_importance_df)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.bar(position_importance_df[\"event_position\"].astype(str), position_importance_df[\"mean_importance\"])\n","plt.xlabel(\"Event position in 10-line sequence\")\n","plt.ylabel(\"Mean importance drop\")\n","plt.title(\"Average Event-Level Occlusion Importance Across Anomaly Sequences\")\n","finalize_figure()\n","save_current_figure(\"figure_4_13_xai_mean_importance_by_position.png\", \"Figure 4.13\", \"Global Mean Occlusion Importance by Event Position\")\n","plt.show()\n"]},{"cell_type":"markdown","id":"a09ef43c","metadata":{"id":"a09ef43c"},"source":["## Step 23 — Select the Strongest Occlusion Case Study\n","\n","The case study is selected by the largest top-event **logit drop**, matching the event-ranking rule used throughout the occlusion analysis.\n"]},{"cell_type":"code","execution_count":38,"id":"a7eaaee8","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":903},"id":"a7eaaee8","outputId":"932cc884-40c7-4fe4-e6b9-21e54ec6c353","executionInfo":{"status":"ok","timestamp":1785510831878,"user_tz":-180,"elapsed":327,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["   importance_rank  event_position  logit_importance  probability_importance  \\\n","0                1               3          0.514589                0.000009   \n","1                2               8          0.173841                0.000002   \n","2                3               9          0.172117                0.000002   \n","3                4               7          0.169890                0.000002   \n","4                5               6          0.165570                0.000002   \n","5                6              10          0.161097                0.000002   \n","6                7               2          0.159728                0.000002   \n","7                8               4          0.124754                0.000002   \n","8                9               1          0.111651                0.000001   \n","9               10               5          0.098370                0.000001   \n","\n","   original_probability  masked_probability                   full_time  \\\n","0              0.999987            0.999979  2005-11-18-08.28.06.308972   \n","1              0.999987            0.999985  2005-11-18-11.13.12.720046   \n","2              0.999987            0.999985  2005-11-18-11.13.12.936469   \n","3              0.999987            0.999985  2005-11-18-11.13.12.343036   \n","4              0.999987            0.999985  2005-11-18-11.13.12.191313   \n","5              0.999987            0.999985  2005-11-18-11.13.13.077131   \n","6              0.999987            0.999985  2005-11-18-08.28.06.283344   \n","7              0.999987            0.999985  2005-11-18-08.28.06.321151   \n","8              0.999987            0.999986  2005-11-18-08.28.06.203590   \n","9              0.999987            0.999986  2005-11-18-08.28.06.330682   \n","\n","                  node component severity raw_label  \\\n","0             R01-M1-S   MONITOR   SEVERE         -   \n","1  R04-M0-NC-I:J18-U11       APP    FATAL         -   \n","2  R04-M0-NC-I:J18-U01       APP    FATAL         -   \n","3  R04-M0-N0-I:J18-U01       APP    FATAL         -   \n","4  R04-M0-N0-I:J18-U11       APP    FATAL         -   \n","5  R04-M0-N4-I:J18-U11       APP    FATAL         -   \n","6             R07-M1-S   MONITOR   SEVERE         -   \n","7             R05-M1-S   MONITOR   SEVERE         -   \n","8             R06-M1-S   MONITOR   SEVERE         -   \n","9             R02-M1-S   MONITOR   SEVERE         -   \n","\n","                                       event_message  \n","0  Hardware monitor caught java.net.SocketExcepti...  \n","1  ciod: Error loading -mode VN: invalid or missi...  \n","2  ciod: Error loading -mode VN: invalid or missi...  \n","3  ciod: Error loading -mode VN: invalid or missi...  \n","4  ciod: Error loading -mode VN: invalid or missi...  \n","5  ciod: Error loading -mode VN: invalid or missi...  \n","6  Hardware monitor caught java.net.SocketExcepti...  \n","7  Hardware monitor caught java.net.SocketExcepti...  \n","8  Hardware monitor caught java.net.SocketExcepti...  \n","9  Hardware monitor caught java.net.SocketExcepti...  "],"text/html":["\n","  <div id=\"df-2020b814-d63d-4d12-a77d-34b7ca7553ec\" class=\"colab-df-container\">\n","    <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>importance_rank</th>\n","      <th>event_position</th>\n","      <th>logit_importance</th>\n","      <th>probability_importance</th>\n","      <th>original_probability</th>\n","      <th>masked_probability</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>raw_label</th>\n","      <th>event_message</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0.514589</td>\n","      <td>0.000009</td>\n","      <td>0.999987</td>\n","      <td>0.999979</td>\n","      <td>2005-11-18-08.28.06.308972</td>\n","      <td>R01-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>8</td>\n","      <td>0.173841</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.720046</td>\n","      <td>R04-M0-NC-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>0.172117</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.936469</td>\n","      <td>R04-M0-NC-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>7</td>\n","      <td>0.169890</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.343036</td>\n","      <td>R04-M0-N0-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>0.165570</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.191313</td>\n","      <td>R04-M0-N0-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>10</td>\n","      <td>0.161097</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.13.077131</td>\n","      <td>R04-M0-N4-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>0.159728</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-08.28.06.283344</td>\n","      <td>R07-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>4</td>\n","      <td>0.124754</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-08.28.06.321151</td>\n","      <td>R05-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>0.111651</td>\n","      <td>0.000001</td>\n","      <td>0.999987</td>\n","      <td>0.999986</td>\n","      <td>2005-11-18-08.28.06.203590</td>\n","      <td>R06-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>5</td>\n","      <td>0.098370</td>\n","      <td>0.000001</td>\n","      <td>0.999987</td>\n","      <td>0.999986</td>\n","      <td>2005-11-18-08.28.06.330682</td>\n","      <td>R02-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2020b814-d63d-4d12-a77d-34b7ca7553ec')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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   6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_position\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          1,\n          8,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logit_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11890689224494777,\n        \"min\": 0.09836959838867188,\n        \"max\": 0.5145893096923828,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.11165142059326172,\n          0.17384052276611328,\n          0.16109657287597656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"probability_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.116277347771028e-06,\n        \"min\": 1.3113021850585938e-06,\n        \"max\": 8.58306884765625e-06,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.58306884765625e-06,\n          2.384185791015625e-06,\n          1.430511474609375e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.9999871253967285,\n        \"max\": 0.9999871253967285,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9999871253967285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"masked_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.116277347771028e-06,\n        \"min\": 0.9999785423278809,\n        \"max\": 0.9999858140945435,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9999785423278809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2005-11-18-08.28.06.203590\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"node\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"R06-M1-S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"component\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"APP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"FATAL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_message\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ciod: Error loading -mode VN: invalid or missing program image, No such file or directory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 23 — Select the Strongest Occlusion Case Study\n","# ============================================================\n","\n","if faithfulness_df.empty:\n","    raise ValueError(\"No faithfulness results are available.\")\n","\n","strong_case = (\n","    faithfulness_df\n","    .sort_values(\"top_event_logit_drop\", ascending=False)\n","    .iloc[0]\n",")\n","\n","case_local_idx = int(strong_case[\"sequence_local_test_index\"])\n","\n","case_df = (\n","    local_xai_df[\n","        local_xai_df[\"sequence_local_test_index\"] == case_local_idx\n","    ]\n","    .sort_values(\"logit_importance\", ascending=False)\n","    .reset_index(drop=True)\n",")\n","\n","case_df.insert(\n","    0,\n","    \"importance_rank\",\n","    np.arange(1, len(case_df) + 1)\n",")\n","\n","save_table(\n","    case_df,\n","    XAI_CASE_STUDY_PATH,\n","    \"Strongest event-level occlusion case study\"\n",")\n","\n","display(case_df[[\n","    \"importance_rank\", \"event_position\", \"logit_importance\",\n","    \"probability_importance\", \"original_probability\",\n","    \"masked_probability\", \"full_time\", \"node\", \"component\",\n","    \"severity\", \"raw_label\", \"event_message\"\n","]])\n"]},{"cell_type":"markdown","id":"11775798","metadata":{"id":"11775798"},"source":["## Step 24 — Validate Local XAI Outputs\n","\n","This step verifies coverage, metadata mapping, probability bounds, and consistency between the saved evaluation probability and the probability recomputed during occlusion.\n"]},{"cell_type":"code","execution_count":39,"id":"ba441595","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ba441595","outputId":"0e9c772f-63c3-43b7-e10d-3e6bc3668374","executionInfo":{"status":"ok","timestamp":1785510831905,"user_tz":-180,"elapsed":25,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Local XAI integrity checks passed.\n"]}],"source":["# ============================================================\n","# Step 24 — Validate Local XAI Outputs\n","# ============================================================\n","\n","assert not local_xai_df.empty\n","assert not faithfulness_df.empty\n","assert len(local_xai_df) == len(faithfulness_df) * WINDOW_SIZE\n","assert local_xai_df[\"event_position\"].between(1, WINDOW_SIZE).all()\n","assert local_xai_df[\"line_index\"].notna().all()\n","assert local_xai_df[\"original_probability\"].between(0, 1).all()\n","assert local_xai_df[\"masked_probability\"].between(0, 1).all()\n","assert np.isfinite(\n","    local_xai_df[\n","        [\"logit_drop\", \"probability_drop\", \"logit_importance\", \"probability_importance\"]\n","    ].to_numpy()\n",").all()\n","\n","evaluation_probability_lookup = predictions_df.set_index(\n","    \"sequence_local_test_index\"\n",")[\"anomaly_probability\"]\n","\n","recomputed_probabilities = (\n","    faithfulness_df\n","    .set_index(\"sequence_local_test_index\")[\"original_probability\"]\n",")\n","\n","common_indices = recomputed_probabilities.index.intersection(\n","    evaluation_probability_lookup.index\n",")\n","\n","assert np.allclose(\n","    recomputed_probabilities.loc[common_indices].to_numpy(),\n","    evaluation_probability_lookup.loc[common_indices].to_numpy(),\n","    atol=1e-6\n",")\n","\n","print(\"Local XAI integrity checks passed.\")\n"]},{"cell_type":"markdown","id":"0d4c1102","metadata":{"id":"0d4c1102"},"source":["## Step 25 — Thesis-ready XAI summary\n","\n","Run this cell to print a concise summary that can be copied into the thesis.\n"]},{"cell_type":"code","execution_count":40,"id":"20d4a60e","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"20d4a60e","outputId":"de710b43-5d62-43b0-ad2b-d56d7774fd93","executionInfo":{"status":"ok","timestamp":1785510831916,"user_tz":-180,"elapsed":10,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["XAI method: Event-level occlusion-based explanation\n","Explained sequences: 140\n","Local XAI output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_local_event_importance.csv\n","Global XAI output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_global_summary.csv\n","Faithfulness output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_faithfulness.csv\n","Case study output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_case_study.csv\n","\n","Faithfulness scores based on top-event probability drop\n","Mean drop: 1.2262991796368234e-06\n","Median drop: 4.76837158203125e-07\n","Max drop: 1.0132789611816406e-05\n","\n","Note:\n","Event-level occlusion-based explanation was applied to interpret LogMiniLM predictions.\n","For each predicted anomalous sequence, one log event was masked at a time using a common normal baseline event, and the anomaly probability was recalculated.\n","The importance of each event was measured as the decrease in anomaly probability after masking.\n","Events producing the largest probability drops were considered the most influential contributors to the anomaly decision.\n","The explanation was linked back to operational metadata, including timestamp, node, component, severity, and message content, to support human interpretation and incident investigation.\n","\n"]}],"source":["print(\"XAI method: Event-level occlusion-based explanation\")\n","print(\"Explained sequences:\", len(faithfulness_df))\n","print(\"Local XAI output:\", XAI_LOCAL_PATH)\n","print(\"Global XAI output:\", XAI_GLOBAL_PATH)\n","print(\"Faithfulness output:\", XAI_FAITHFULNESS_PATH)\n","print(\"Case study output:\", XAI_CASE_STUDY_PATH)\n","\n","if len(faithfulness_df) > 0:\n","    mean_drop = faithfulness_df[\"top_event_probability_drop\"].mean()\n","    median_drop = faithfulness_df[\"top_event_probability_drop\"].median()\n","    max_drop = faithfulness_df[\"top_event_probability_drop\"].max()\n","    print(\"\\nFaithfulness scores based on top-event probability drop\")\n","    print(\"Mean drop:\", mean_drop)\n","    print(\"Median drop:\", median_drop)\n","    print(\"Max drop:\", max_drop)\n","\n","print(\"\"\"\n","Note:\n","Event-level occlusion-based explanation was applied to interpret LogMiniLM predictions.\n","For each predicted anomalous sequence, one log event was masked at a time using a common normal baseline event, and the anomaly probability was recalculated.\n","The importance of each event was measured as the decrease in anomaly probability after masking.\n","Events producing the largest probability drops were considered the most influential contributors to the anomaly decision.\n","The explanation was linked back to operational metadata, including timestamp, node, component, severity, and message content, to support human interpretation and incident investigation.\n","\"\"\")"]},{"cell_type":"markdown","id":"e8c9e2af","metadata":{"id":"e8c9e2af"},"source":["## Step 26 — Top-k occlusion faithfulness\n","\n","This step checks whether masking multiple important events gives stronger evidence than masking only the top event. It saves `logminilm_xai_topk_faithfulness.csv`.\n"]},{"cell_type":"code","execution_count":41,"id":"495b9ebe","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":733},"id":"495b9ebe","outputId":"a1a49111-372d-4969-d727-62443b88cb7d","executionInfo":{"status":"ok","timestamp":1785510837384,"user_tz":-180,"elapsed":5467,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved top-k faithfulness to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_topk_faithfulness.csv\n","Fraction with monotonic absolute logit change: 0.85\n"]},{"output_type":"display_data","data":{"text/plain":["    sequence_local_test_index  start_line  end_line  true_label  \\\n","0                       41109     4181880   4181889           1   \n","1                       41109     4181880   4181889           1   \n","2                       41109     4181880   4181889           1   \n","3                       41109     4181880   4181889           1   \n","4                       41120     4181990   4181999           1   \n","5                       41120     4181990   4181999           1   \n","6                       41120     4181990   4181999           1   \n","7                       41120     4181990   4181999           1   \n","8                       41664     4187430   4187439           1   \n","9                       41664     4187430   4187439           1   \n","10                      41664     4187430   4187439           1   \n","11                      41664     4187430   4187439           1   \n","12                      41665     4187440   4187449           1   \n","13                      41665     4187440   4187449           1   \n","14                      41665     4187440   4187449           1   \n","15                      41665     4187440   4187449           1   \n","16                      41666     4187450   4187459           1   \n","17                      41666     4187450   4187459           1   \n","18                      41666     4187450   4187459           1   \n","19                      41666     4187450   4187459           1   \n","\n","    predicted_label  k  original_logit  masked_logit  topk_logit_drop  \\\n","0                 1  1       11.787348     11.714861         0.072487   \n","1                 1  2       11.787348     11.648014         0.139334   \n","2                 1  3       11.787348     11.583552         0.203795   \n","3                 1  5       11.787348     11.447723         0.339624   \n","4                 1  1       11.787348     11.714861         0.072487   \n","5                 1  2       11.787348     11.648014         0.139334   \n","6                 1  3       11.787348     11.583552         0.203795   \n","7                 1  5       11.787348     11.447723         0.339624   \n","8                 1  1       11.782140     11.702662         0.079478   \n","9                 1  2       11.782140     11.621240         0.160900   \n","10                1  3       11.782140     11.503226         0.278913   \n","11                1  5       11.782140     11.048635         0.733505   \n","12                1  1       11.781467     11.703753         0.077715   \n","13                1  2       11.781467     11.632512         0.148955   \n","14                1  3       11.781467     11.545587         0.235881   \n","15                1  5       11.781467     11.058093         0.723374   \n","16                1  1       11.782689     11.705890         0.076799   \n","17                1  2       11.782689     11.630793         0.151896   \n","18                1  3       11.782689     11.544768         0.237921   \n","19                1  5       11.782689     11.384706         0.397984   \n","\n","    topk_abs_logit_change  original_probability  masked_probability  \\\n","0                0.072487              0.999992            0.999992   \n","1                0.139334              0.999992            0.999991   \n","2                0.203795              0.999992            0.999991   \n","3                0.339624              0.999992            0.999989   \n","4                0.072487              0.999992            0.999992   \n","5                0.139334              0.999992            0.999991   \n","6                0.203795              0.999992            0.999991   \n","7                0.339624              0.999992            0.999989   \n","8                0.079478              0.999992            0.999992   \n","9                0.160900              0.999992            0.999991   \n","10               0.278913              0.999992            0.999990   \n","11               0.733505              0.999992            0.999984   \n","12               0.077715              0.999992            0.999992   \n","13               0.148955              0.999992            0.999991   \n","14               0.235881              0.999992            0.999990   \n","15               0.723374              0.999992            0.999984   \n","16               0.076799              0.999992            0.999992   \n","17               0.151896              0.999992            0.999991   \n","18               0.237921              0.999992            0.999990   \n","19               0.397984              0.999992            0.999989   \n","\n","    topk_probability_drop  topk_abs_probability_change  \\\n","0            5.960464e-07                 5.960464e-07   \n","1            1.072884e-06                 1.072884e-06   \n","2            1.668930e-06                 1.668930e-06   \n","3            3.099442e-06                 3.099442e-06   \n","4            5.960464e-07                 5.960464e-07   \n","5            1.072884e-06                 1.072884e-06   \n","6            1.668930e-06                 1.668930e-06   \n","7            3.099442e-06                 3.099442e-06   \n","8            5.960464e-07                 5.960464e-07   \n","9            1.311302e-06                 1.311302e-06   \n","10           2.503395e-06                 2.503395e-06   \n","11           8.225441e-06                 8.225441e-06   \n","12           5.960464e-07                 5.960464e-07   \n","13           1.192093e-06                 1.192093e-06   \n","14           2.026558e-06                 2.026558e-06   \n","15           8.106232e-06                 8.106232e-06   \n","16           5.960464e-07                 5.960464e-07   \n","17           1.311302e-06                 1.311302e-06   \n","18           2.026558e-06                 2.026558e-06   \n","19           3.695488e-06                 3.695488e-06   \n","\n","    monotonic_abs_logit_change  \n","0                         True  \n","1                         True  \n","2                         True  \n","3                         True  \n","4                         True  \n","5                         True  \n","6                         True  \n","7                         True  \n","8                         True  \n","9                         True  \n","10                        True  \n","11                        True  \n","12                        True  \n","13                        True  \n","14                        True  \n","15                        True  \n","16                        True  \n","17                        True  \n","18                        True  \n","19                        True  "],"text/html":["\n","  <div id=\"df-ed39989c-38c8-449f-84f6-70d8f45e3462\" class=\"colab-df-container\">\n","    <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>sequence_local_test_index</th>\n","      <th>start_line</th>\n","      <th>end_line</th>\n","      <th>true_label</th>\n","      <th>predicted_label</th>\n","      <th>k</th>\n","      <th>original_logit</th>\n","      <th>masked_logit</th>\n","      <th>topk_logit_drop</th>\n","      <th>topk_abs_logit_change</th>\n","      <th>original_probability</th>\n","      <th>masked_probability</th>\n","      <th>topk_probability_drop</th>\n","      <th>topk_abs_probability_change</th>\n","      <th>monotonic_abs_logit_change</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.787348</td>\n","      <td>11.714861</td>\n","      <td>0.072487</td>\n","      <td>0.072487</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11.787348</td>\n","      <td>11.648014</td>\n","      <td>0.139334</td>\n","      <td>0.139334</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.072884e-06</td>\n","      <td>1.072884e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>11.787348</td>\n","      <td>11.583552</td>\n","      <td>0.203795</td>\n","      <td>0.203795</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.668930e-06</td>\n","      <td>1.668930e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>11.787348</td>\n","      <td>11.447723</td>\n","      <td>0.339624</td>\n","      <td>0.339624</td>\n","      <td>0.999992</td>\n","      <td>0.999989</td>\n","      <td>3.099442e-06</td>\n","      <td>3.099442e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.787348</td>\n","      <td>11.714861</td>\n","      <td>0.072487</td>\n","      <td>0.072487</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11.787348</td>\n","      <td>11.648014</td>\n","      <td>0.139334</td>\n","      <td>0.139334</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.072884e-06</td>\n","      <td>1.072884e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>11.787348</td>\n","      <td>11.583552</td>\n","      <td>0.203795</td>\n","      <td>0.203795</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.668930e-06</td>\n","      <td>1.668930e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>11.787348</td>\n","      <td>11.447723</td>\n","      <td>0.339624</td>\n","      <td>0.339624</td>\n","      <td>0.999992</td>\n","      <td>0.999989</td>\n","      <td>3.099442e-06</td>\n","      <td>3.099442e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.782140</td>\n","      <td>11.702662</td>\n","      <td>0.079478</td>\n","      <td>0.079478</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11.782140</td>\n","      <td>11.621240</td>\n","      <td>0.160900</td>\n","      <td>0.160900</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.311302e-06</td>\n","      <td>1.311302e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>11.782140</td>\n","      <td>11.503226</td>\n","      <td>0.278913</td>\n","      <td>0.278913</td>\n","      <td>0.999992</td>\n","      <td>0.999990</td>\n","      <td>2.503395e-06</td>\n","      <td>2.503395e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>11.782140</td>\n","      <td>11.048635</td>\n","      <td>0.733505</td>\n","      <td>0.733505</td>\n","      <td>0.999992</td>\n","      <td>0.999984</td>\n","      <td>8.225441e-06</td>\n","      <td>8.225441e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>41665</td>\n","      <td>4187440</td>\n","      <td>4187449</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.781467</td>\n","      <td>11.703753</td>\n","      <td>0.077715</td>\n","      <td>0.077715</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>41665</td>\n","      <td>4187440</td>\n","      <td>4187449</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11.781467</td>\n","      <td>11.632512</td>\n","      <td>0.148955</td>\n","      <td>0.148955</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.192093e-06</td>\n","      <td>1.192093e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>41665</td>\n","      <td>4187440</td>\n","      <td>4187449</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>11.781467</td>\n","      <td>11.545587</td>\n","      <td>0.235881</td>\n","      <td>0.235881</td>\n","      <td>0.999992</td>\n","      <td>0.999990</td>\n","      <td>2.026558e-06</td>\n","      <td>2.026558e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>41665</td>\n","      <td>4187440</td>\n","      <td>4187449</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>11.781467</td>\n","      <td>11.058093</td>\n","      <td>0.723374</td>\n","      <td>0.723374</td>\n","      <td>0.999992</td>\n","      <td>0.999984</td>\n","      <td>8.106232e-06</td>\n","      <td>8.106232e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>41666</td>\n","      <td>4187450</td>\n","      <td>4187459</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>11.782689</td>\n","      <td>11.705890</td>\n","      <td>0.076799</td>\n","      <td>0.076799</td>\n","      <td>0.999992</td>\n","      <td>0.999992</td>\n","      <td>5.960464e-07</td>\n","      <td>5.960464e-07</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>41666</td>\n","      <td>4187450</td>\n","      <td>4187459</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>11.782689</td>\n","      <td>11.630793</td>\n","      <td>0.151896</td>\n","      <td>0.151896</td>\n","      <td>0.999992</td>\n","      <td>0.999991</td>\n","      <td>1.311302e-06</td>\n","      <td>1.311302e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>41666</td>\n","      <td>4187450</td>\n","      <td>4187459</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>11.782689</td>\n","      <td>11.544768</td>\n","      <td>0.237921</td>\n","      <td>0.237921</td>\n","      <td>0.999992</td>\n","      <td>0.999990</td>\n","      <td>2.026558e-06</td>\n","      <td>2.026558e-06</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>41666</td>\n","      <td>4187450</td>\n","      <td>4187459</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>11.782689</td>\n","      <td>11.384706</td>\n","      <td>0.397984</td>\n","      <td>0.397984</td>\n","      <td>0.999992</td>\n","      <td>0.999989</td>\n","      <td>3.695488e-06</td>\n","      <td>3.695488e-06</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed39989c-38c8-449f-84f6-70d8f45e3462')\"\n","            title=\"Convert this 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\"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_logit\",\n      \"properties\": 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8.225440979003906e-06,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2.5033950805664062e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"monotonic_abs_logit_change\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 26 — Corrected Top-k Occlusion Faithfulness\n","# ============================================================\n","\n","TOP_K_VALUES = [1, 2, 3, 5]\n","TOPK_FAITHFULNESS_PATH = (\n","    XAI_DIR / \"logminilm_xai_topk_faithfulness.csv\"\n",")\n","\n","topk_rows = []\n","\n","for _, cand in xai_candidates.iterrows():\n","    local_idx = int(cand[\"sequence_local_test_index\"])\n","    start_line = int(cand[\"start_line\"])\n","    end_line = int(cand[\"end_line\"])\n","    true_label = int(cand[\"true_label\"])\n","    predicted_label = int(cand[\"predicted_label\"])\n","\n","    seq_event_ids = np.asarray(X_test[local_idx], dtype=np.int64)\n","    original_logit, original_prob = predict_sequence_score(\n","        model,\n","        seq_event_ids,\n","    )\n","\n","    exp_df = explain_sequence_by_occlusion(\n","        model,\n","        seq_event_ids,\n","        baseline_event_id,\n","    )\n","\n","    # Rank by absolute logit effect to avoid sigmoid saturation and to include\n","    # both strongly anomaly-supporting and strongly anomaly-opposing events.\n","    ranked_positions = (\n","        exp_df\n","        .assign(abs_logit_drop=exp_df[\"logit_drop\"].abs())\n","        .sort_values(\"abs_logit_drop\", ascending=False)\n","        [\"event_position\"]\n","        .astype(int)\n","        .tolist()\n","    )\n","\n","    for k in TOP_K_VALUES:\n","        effective_k = min(k, WINDOW_SIZE)\n","        masked_seq = seq_event_ids.copy()\n","\n","        for pos in ranked_positions[:effective_k]:\n","            masked_seq[pos - 1] = baseline_event_id\n","\n","        masked_logit, masked_prob = predict_sequence_score(\n","            model,\n","            masked_seq,\n","        )\n","\n","        topk_rows.append({\n","            \"sequence_local_test_index\": local_idx,\n","            \"start_line\": start_line,\n","            \"end_line\": end_line,\n","            \"true_label\": true_label,\n","            \"predicted_label\": predicted_label,\n","            \"k\": effective_k,\n","            \"original_logit\": original_logit,\n","            \"masked_logit\": masked_logit,\n","            \"topk_logit_drop\": original_logit - masked_logit,\n","            \"topk_abs_logit_change\": abs(original_logit - masked_logit),\n","            \"original_probability\": original_prob,\n","            \"masked_probability\": masked_prob,\n","            \"topk_probability_drop\": original_prob - masked_prob,\n","            \"topk_abs_probability_change\": abs(original_prob - masked_prob),\n","        })\n","\n","topk_faithfulness_df = pd.DataFrame(topk_rows)\n","\n","# Monotonic growth is not assumed. Interactions between events can make\n","# top-k effects non-monotonic, so the notebook reports this as a diagnostic.\n","monotonic_rows = []\n","for local_idx, group in topk_faithfulness_df.groupby(\n","    \"sequence_local_test_index\"\n","):\n","    ordered = group.sort_values(\"k\")\n","    values = ordered[\"topk_abs_logit_change\"].to_numpy(dtype=float)\n","    monotonic_non_decreasing = bool(\n","        np.all(np.diff(values) >= -1e-12)\n","    )\n","    monotonic_rows.append({\n","        \"sequence_local_test_index\": int(local_idx),\n","        \"monotonic_abs_logit_change\": monotonic_non_decreasing,\n","    })\n","\n","topk_monotonicity_df = pd.DataFrame(monotonic_rows)\n","topk_faithfulness_df = topk_faithfulness_df.merge(\n","    topk_monotonicity_df,\n","    on=\"sequence_local_test_index\",\n","    how=\"left\",\n",")\n","\n","save_table(\n","    topk_faithfulness_df,\n","    TOPK_FAITHFULNESS_PATH,\n","    \"Top-k occlusion faithfulness using logit and probability changes\",\n",")\n","\n","print(\"Saved top-k faithfulness to:\", TOPK_FAITHFULNESS_PATH)\n","print(\n","    \"Fraction with monotonic absolute logit change:\",\n","    topk_monotonicity_df[\n","        \"monotonic_abs_logit_change\"\n","    ].mean(),\n",")\n","display(topk_faithfulness_df.head(20))\n"]},{"cell_type":"markdown","id":"ee0e88c2","metadata":{"id":"ee0e88c2"},"source":["## Step 27 — Confirm Case-Study Consistency\n","\n","This step confirms that all downstream occlusion and SHAP analyses use the same selected sequence.\n"]},{"cell_type":"code","execution_count":42,"id":"c7036fd4","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7036fd4","outputId":"761eed21-5feb-4109-a710-072d5ed99c48","executionInfo":{"status":"ok","timestamp":1785510837397,"user_tz":-180,"elapsed":12,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Confirmed case-study sequence: 42167\n"]}],"source":["# ============================================================\n","# Step 27 — Confirm Case-Study Consistency\n","# ============================================================\n","\n","expected_case_local_idx = int(\n","    faithfulness_df\n","    .sort_values(\"top_event_logit_drop\", ascending=False)\n","    .iloc[0][\"sequence_local_test_index\"]\n",")\n","\n","assert case_local_idx == expected_case_local_idx\n","assert (\n","    case_df[\"sequence_local_test_index\"].nunique() == 1\n","    and int(case_df[\"sequence_local_test_index\"].iloc[0]) == case_local_idx\n",")\n","\n","case_start_line = int(case_df[\"start_line\"].iloc[0])\n","case_end_line = int(case_df[\"end_line\"].iloc[0])\n","\n","print(\"Confirmed case-study sequence:\", case_local_idx)\n"]},{"cell_type":"markdown","id":"c1d70f67","metadata":{"id":"c1d70f67"},"source":["## Step 28 — Build thesis-ready strong case study table\n","\n","This step creates the full 10-event explanation table for the selected strong case.\n"]},{"cell_type":"code","execution_count":43,"id":"75209c7b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":903},"id":"75209c7b","outputId":"4ee17074-9b2b-4730-8311-550f028a2d60","executionInfo":{"status":"ok","timestamp":1785510837793,"user_tz":-180,"elapsed":395,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["   importance_rank  event_position  logit_importance  probability_importance  \\\n","0                1               3          0.514589                0.000009   \n","1                2               8          0.173841                0.000002   \n","2                3               9          0.172117                0.000002   \n","3                4               7          0.169890                0.000002   \n","4                5               6          0.165570                0.000002   \n","5                6              10          0.161097                0.000002   \n","6                7               2          0.159728                0.000002   \n","7                8               4          0.124754                0.000002   \n","8                9               1          0.111651                0.000001   \n","9               10               5          0.098370                0.000001   \n","\n","   original_probability  masked_probability                   full_time  \\\n","0              0.999987            0.999979  2005-11-18-08.28.06.308972   \n","1              0.999987            0.999985  2005-11-18-11.13.12.720046   \n","2              0.999987            0.999985  2005-11-18-11.13.12.936469   \n","3              0.999987            0.999985  2005-11-18-11.13.12.343036   \n","4              0.999987            0.999985  2005-11-18-11.13.12.191313   \n","5              0.999987            0.999985  2005-11-18-11.13.13.077131   \n","6              0.999987            0.999985  2005-11-18-08.28.06.283344   \n","7              0.999987            0.999985  2005-11-18-08.28.06.321151   \n","8              0.999987            0.999986  2005-11-18-08.28.06.203590   \n","9              0.999987            0.999986  2005-11-18-08.28.06.330682   \n","\n","                  node component severity raw_label  \\\n","0             R01-M1-S   MONITOR   SEVERE         -   \n","1  R04-M0-NC-I:J18-U11       APP    FATAL         -   \n","2  R04-M0-NC-I:J18-U01       APP    FATAL         -   \n","3  R04-M0-N0-I:J18-U01       APP    FATAL         -   \n","4  R04-M0-N0-I:J18-U11       APP    FATAL         -   \n","5  R04-M0-N4-I:J18-U11       APP    FATAL         -   \n","6             R07-M1-S   MONITOR   SEVERE         -   \n","7             R05-M1-S   MONITOR   SEVERE         -   \n","8             R06-M1-S   MONITOR   SEVERE         -   \n","9             R02-M1-S   MONITOR   SEVERE         -   \n","\n","                                       event_message  \n","0  Hardware monitor caught java.net.SocketExcepti...  \n","1  ciod: Error loading -mode VN: invalid or missi...  \n","2  ciod: Error loading -mode VN: invalid or missi...  \n","3  ciod: Error loading -mode VN: invalid or missi...  \n","4  ciod: Error loading -mode VN: invalid or missi...  \n","5  ciod: Error loading -mode VN: invalid or missi...  \n","6  Hardware monitor caught java.net.SocketExcepti...  \n","7  Hardware monitor caught java.net.SocketExcepti...  \n","8  Hardware monitor caught java.net.SocketExcepti...  \n","9  Hardware monitor caught java.net.SocketExcepti...  "],"text/html":["\n","  <div id=\"df-4d19297a-f782-4ecf-8a32-8c649de0b9ce\" class=\"colab-df-container\">\n","    <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>importance_rank</th>\n","      <th>event_position</th>\n","      <th>logit_importance</th>\n","      <th>probability_importance</th>\n","      <th>original_probability</th>\n","      <th>masked_probability</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>raw_label</th>\n","      <th>event_message</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0.514589</td>\n","      <td>0.000009</td>\n","      <td>0.999987</td>\n","      <td>0.999979</td>\n","      <td>2005-11-18-08.28.06.308972</td>\n","      <td>R01-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>8</td>\n","      <td>0.173841</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.720046</td>\n","      <td>R04-M0-NC-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>0.172117</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.936469</td>\n","      <td>R04-M0-NC-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>7</td>\n","      <td>0.169890</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.343036</td>\n","      <td>R04-M0-N0-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>0.165570</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.191313</td>\n","      <td>R04-M0-N0-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>10</td>\n","      <td>0.161097</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.13.077131</td>\n","      <td>R04-M0-N4-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>2</td>\n","      <td>0.159728</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-08.28.06.283344</td>\n","      <td>R07-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>4</td>\n","      <td>0.124754</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-08.28.06.321151</td>\n","      <td>R05-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>0.111651</td>\n","      <td>0.000001</td>\n","      <td>0.999987</td>\n","      <td>0.999986</td>\n","      <td>2005-11-18-08.28.06.203590</td>\n","      <td>R06-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>5</td>\n","      <td>0.098370</td>\n","      <td>0.000001</td>\n","      <td>0.999987</td>\n","      <td>0.999986</td>\n","      <td>2005-11-18-08.28.06.330682</td>\n","      <td>R02-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d19297a-f782-4ecf-8a32-8c649de0b9ce')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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    1,\n          8,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logit_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11890689224494777,\n        \"min\": 0.09836959838867188,\n        \"max\": 0.5145893096923828,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.11165142059326172,\n          0.17384052276611328,\n          0.16109657287597656\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"probability_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.116277347771028e-06,\n        \"min\": 1.3113021850585938e-06,\n        \"max\": 8.58306884765625e-06,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          8.58306884765625e-06,\n          2.384185791015625e-06,\n          1.430511474609375e-06\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.9999871253967285,\n        \"max\": 0.9999871253967285,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9999871253967285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"masked_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.116277347771028e-06,\n        \"min\": 0.9999785423278809,\n        \"max\": 0.9999858140945435,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9999785423278809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2005-11-18-08.28.06.203590\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"node\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"R06-M1-S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"component\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"APP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"FATAL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_message\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ciod: Error loading -mode VN: invalid or missing program image, No such file or directory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 28 — Build Thesis-Ready Strong Case Study Table\n","# ============================================================\n","\n","required_case_columns = [\n","    \"importance_rank\",\n","    \"event_position\",\n","    \"logit_importance\",\n","    \"probability_importance\",\n","    \"original_probability\",\n","    \"masked_probability\",\n","    \"full_time\",\n","    \"node\",\n","    \"component\",\n","    \"severity\",\n","    \"raw_label\",\n","    \"event_message\",\n","]\n","\n","missing_case_columns = set(required_case_columns) - set(case_df.columns)\n","if missing_case_columns:\n","    raise ValueError(\n","        f\"Missing case-study columns: {sorted(missing_case_columns)}\"\n","    )\n","\n","case_table = case_df[required_case_columns].copy()\n","CASE_STUDY_STRONG_PATH = XAI_DIR / \"logminilm_xai_strong_case_study.csv\"\n","\n","save_table(\n","    case_table,\n","    CASE_STUDY_STRONG_PATH,\n","    \"Thesis-ready strongest occlusion case study\"\n",")\n","\n","display(case_table)\n"]},{"cell_type":"markdown","id":"59fb76f6","metadata":{"id":"59fb76f6"},"source":["## Step 29 — Show top-5 influential events\n","\n","This is the shorter table that is usually best for the thesis.\n"]},{"cell_type":"code","execution_count":44,"id":"0d66ead4","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":556},"id":"0d66ead4","outputId":"fd087f21-b548-411d-b009-fb86eab764a5","executionInfo":{"status":"ok","timestamp":1785510838092,"user_tz":-180,"elapsed":298,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved top-5 case study to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai/logminilm_xai_strong_case_study_top5.csv\n"]},{"output_type":"display_data","data":{"text/plain":["   importance_rank  event_position  logit_importance  probability_importance  \\\n","0                1               3          0.514589                0.000009   \n","1                2               8          0.173841                0.000002   \n","2                3               9          0.172117                0.000002   \n","3                4               7          0.169890                0.000002   \n","4                5               6          0.165570                0.000002   \n","\n","   original_probability  masked_probability                   full_time  \\\n","0              0.999987            0.999979  2005-11-18-08.28.06.308972   \n","1              0.999987            0.999985  2005-11-18-11.13.12.720046   \n","2              0.999987            0.999985  2005-11-18-11.13.12.936469   \n","3              0.999987            0.999985  2005-11-18-11.13.12.343036   \n","4              0.999987            0.999985  2005-11-18-11.13.12.191313   \n","\n","                  node component severity raw_label  \\\n","0             R01-M1-S   MONITOR   SEVERE         -   \n","1  R04-M0-NC-I:J18-U11       APP    FATAL         -   \n","2  R04-M0-NC-I:J18-U01       APP    FATAL         -   \n","3  R04-M0-N0-I:J18-U01       APP    FATAL         -   \n","4  R04-M0-N0-I:J18-U11       APP    FATAL         -   \n","\n","                                       event_message  \n","0  Hardware monitor caught java.net.SocketExcepti...  \n","1  ciod: Error loading -mode VN: invalid or missi...  \n","2  ciod: Error loading -mode VN: invalid or missi...  \n","3  ciod: Error loading -mode VN: invalid or missi...  \n","4  ciod: Error loading -mode VN: invalid or missi...  "],"text/html":["\n","  <div id=\"df-44ac11d6-ce0d-4d3a-a5ee-eb5752aa37a8\" class=\"colab-df-container\">\n","    <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>importance_rank</th>\n","      <th>event_position</th>\n","      <th>logit_importance</th>\n","      <th>probability_importance</th>\n","      <th>original_probability</th>\n","      <th>masked_probability</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>raw_label</th>\n","      <th>event_message</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0.514589</td>\n","      <td>0.000009</td>\n","      <td>0.999987</td>\n","      <td>0.999979</td>\n","      <td>2005-11-18-08.28.06.308972</td>\n","      <td>R01-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>8</td>\n","      <td>0.173841</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.720046</td>\n","      <td>R04-M0-NC-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>9</td>\n","      <td>0.172117</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.936469</td>\n","      <td>R04-M0-NC-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>7</td>\n","      <td>0.169890</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.343036</td>\n","      <td>R04-M0-N0-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>6</td>\n","      <td>0.165570</td>\n","      <td>0.000002</td>\n","      <td>0.999987</td>\n","      <td>0.999985</td>\n","      <td>2005-11-18-11.13.12.191313</td>\n","      <td>R04-M0-N0-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44ac11d6-ce0d-4d3a-a5ee-eb5752aa37a8')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      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8,\n          6,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"logit_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1539775873422909,\n        \"min\": 0.16557025909423828,\n        \"max\": 0.5145893096923828,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.17384052276611328,\n          0.16557025909423828,\n          0.1721172332763672\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"probability_importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7860310201868415e-06,\n        \"min\": 2.2649765014648438e-06,\n        \"max\": 8.58306884765625e-06,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.58306884765625e-06,\n          2.384185791015625e-06,\n          2.2649765014648438e-06\n        ],\n  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\"logminilm_xai_strong_case_study_top5.csv\"\n","save_table(top5_case, TOP5_CASE_PATH, \"Top five influential events in strongest occlusion case\")\n","\n","print(\"Saved top-5 case study to:\", TOP5_CASE_PATH)\n","display(top5_case)\n"]},{"cell_type":"markdown","id":"b8880811","metadata":{"id":"b8880811"},"source":["## Step 30 — Generate written explanation automatically\n","\n","This cell prints a paragraph that can be adapted into Chapter 4.\n"]},{"cell_type":"code","execution_count":45,"id":"c17427d1","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c17427d1","outputId":"84cba3e8-a942-4f31-afea-e0f804a525e0","executionInfo":{"status":"ok","timestamp":1785510838104,"user_tz":-180,"elapsed":11,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Thesis-ready case study explanation:\n","\n","\n","A representative true-positive anomaly sequence was selected for local explanation using event-level occlusion.\n","The original anomaly probability of the sequence was 0.999987.\n","The most influential event was located at position 3 in the 10-line sequence.\n","\n","The event was:\n","Component: MONITOR\n","Severity: SEVERE\n","Raw label: -\n","Message: Hardware monitor caught java.net.SocketException: Broken pipe and is stopping\n","\n","After masking this event with a neutral normal baseline event, the anomaly probability decreased to 0.999979.\n","This produced an importance drop of 0.514589, indicating that this event had a strong causal influence on the LogMiniLM anomaly prediction.\n","The explanation was also mapped back to operational metadata, including timestamp and node, making the prediction interpretable for investigation.\n","\n"]}],"source":["# ============================================================\n","# Step 30 — Generate Written Explanation Automatically\n","# ============================================================\n","\n","top_event = case_table.iloc[0]\n","\n","original_prob = float(top_event[\"original_probability\"])\n","masked_prob = float(top_event[\"masked_probability\"])\n","logit_importance = float(top_event[\"logit_importance\"])\n","\n","print(\"Thesis-ready case study explanation:\\n\")\n","\n","print(f\"\"\"\n","A representative true-positive anomaly sequence was selected for local explanation using event-level occlusion.\n","The original anomaly probability of the sequence was {original_prob:.6f}.\n","The most influential event was located at position {int(top_event[\"event_position\"])} in the 10-line sequence.\n","\n","The event was:\n","Component: {top_event[\"component\"]}\n","Severity: {top_event[\"severity\"]}\n","Raw label: {top_event[\"raw_label\"]}\n","Message: {top_event[\"event_message\"]}\n","\n","After masking this event with a neutral normal baseline event, the anomaly probability decreased to {masked_prob:.6f}.\n","This produced an importance drop of {logit_importance:.6f}, indicating that this event had a strong causal influence on the LogMiniLM anomaly prediction.\n","The explanation was also mapped back to operational metadata, including timestamp and node, making the prediction interpretable for investigation.\n","\"\"\")\n"]},{"cell_type":"markdown","id":"XCsaq75--q-Z","metadata":{"id":"XCsaq75--q-Z"},"source":["### Figure 4.14 — Local Occlusion Explanation for the Strongest Case Study\n","\n","**Interpretation:** A larger probability drop indicates that masking the corresponding event has a stronger effect on the anomaly prediction.\n"]},{"cell_type":"code","execution_count":46,"id":"5Ub_d9VS-q-a","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"5Ub_d9VS-q-a","outputId":"ee587059-15f1-44a9-bd5c-9be33e009ccc","executionInfo":{"status":"ok","timestamp":1785510839547,"user_tz":-180,"elapsed":1428,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.14 — Strong local occlusion case study\n","# ============================================================\n","\n","if \"case_table\" in globals() and len(case_table) > 0:\n","    strong_plot_df = case_table.sort_values(\"event_position\").copy()\n","elif \"case_df\" in globals() and len(case_df) > 0:\n","    strong_plot_df = case_df.sort_values(\"event_position\").copy()\n","else:\n","    raise ValueError(\"No case_table or case_df available. Run the strong XAI case study steps first.\")\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.bar(strong_plot_df[\"event_position\"].astype(str), strong_plot_df[\"logit_importance\"])\n","plt.xlabel(\"Event position in 10-line sequence\")\n","plt.ylabel(\"Importance drop\")\n","plt.title(\"LogMiniLM Event-Level Occlusion Explanation\")\n","finalize_figure()\n","save_current_figure(\"figure_4_14_local_occlusion_case_study.png\", \"Figure 4.14\", \"Local Occlusion Explanation\")\n","plt.show()\n"]},{"cell_type":"markdown","id":"gnKVU77h-q-a","metadata":{"id":"gnKVU77h-q-a"},"source":["## Step 31 — Prepare SHAP Output Paths\n","\n","SHAP was installed and imported in Step 1. This step defines only the SHAP export paths and avoids duplicate package installation or repeated imports.\n"]},{"cell_type":"code","execution_count":47,"id":"MuJ6-XI8-q-a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MuJ6-XI8-q-a","outputId":"ba4dd645-a77c-4bd6-f2ef-c6acaa376c38","executionInfo":{"status":"ok","timestamp":1785510839654,"user_tz":-180,"elapsed":106,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["SHAP is ready.\n","SHAP version: 0.52.0\n","SHAP output directory: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap\n"]}],"source":["# ============================================================\n","# Step 31 — SHAP Output Paths\n","# ============================================================\n","\n","SHAP_LOCAL_PATH = SHAP_DIR / \"logminilm_shap_event_level_explanations.csv\"\n","SHAP_CASE_PATH = SHAP_DIR / \"logminilm_shap_case_study.csv\"\n","SHAP_COMPARE_PATH = SHAP_DIR / \"logminilm_shap_occlusion_comparison.csv\"\n","\n","print(\"SHAP is ready.\")\n","print(\"SHAP version:\", shap.__version__)\n","print(\"SHAP output directory:\", SHAP_DIR)\n"]},{"cell_type":"markdown","id":"6cdaadfd","metadata":{"id":"6cdaadfd"},"source":["## Step 32 — Select sequences for SHAP\n","\n","SHAP is slower than occlusion, so this step explains the strong case plus a small set of high-confidence true-positive anomaly sequences.\n"]},{"cell_type":"code","execution_count":48,"id":"71d5802a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"71d5802a","outputId":"0d945a45-f627-4579-d394-569b284326df","executionInfo":{"status":"ok","timestamp":1785510839665,"user_tz":-180,"elapsed":10,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of sequences selected for SHAP: 21\n","Selected SHAP sequence indices: [42167, 41109, 41120, 41664, 41665, 41666, 41667, 41668, 41681, 43274, 43275, 42162, 42163, 42164, 42165, 42160, 42132, 86826, 86827, 86828, 86829]\n","The same indices will be used for Occlusion agreement and rank-correlation calculations.\n"]}],"source":["# ============================================================\n","# Step 32 — Select the same sequences for SHAP and Occlusion\n","# ============================================================\n","\n","# SHAP is computationally expensive, so use a reproducible subset of the\n","# already selected XAI candidates. The strong case is retained as an\n","# illustrative example, while the comparison metrics are computed across\n","# multiple cases and are not inferred from the strong case alone.\n","SHAP_NUM_TP = 10\n","SHAP_NUM_FP = 5\n","SHAP_NUM_FN = 5\n","\n","strong_case_idx = int(case_local_idx)\n","\n","def select_for_shap(frame, prediction_type, n):\n","    return (\n","        frame[frame[\"prediction_type\"] == prediction_type]\n","        .sort_values(\n","            [\"anomaly_probability\", \"sequence_local_test_index\"],\n","            ascending=[\n","                prediction_type != \"FN\",\n","                True,\n","            ],\n","        )\n","        .head(n)\n","    )\n","\n","tp_for_shap = (\n","    xai_candidates[xai_candidates[\"prediction_type\"] == \"TP\"]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[False, True],\n","    )\n","    .head(SHAP_NUM_TP)\n",")\n","\n","fp_for_shap = (\n","    xai_candidates[xai_candidates[\"prediction_type\"] == \"FP\"]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[False, True],\n","    )\n","    .head(SHAP_NUM_FP)\n",")\n","\n","fn_for_shap = (\n","    xai_candidates[xai_candidates[\"prediction_type\"] == \"FN\"]\n","    .sort_values(\n","        [\"anomaly_probability\", \"sequence_local_test_index\"],\n","        ascending=[True, True],\n","    )\n","    .head(SHAP_NUM_FN)\n",")\n","\n","shap_sequence_indices = [strong_case_idx]\n","for frame in [tp_for_shap, fp_for_shap, fn_for_shap]:\n","    shap_sequence_indices.extend(\n","        frame[\"sequence_local_test_index\"].astype(int).tolist()\n","    )\n","\n","shap_sequence_indices = list(dict.fromkeys(shap_sequence_indices))\n","\n","if len(shap_sequence_indices) < 2:\n","    raise ValueError(\n","        \"At least two sequences are required for a meaningful \"\n","        \"SHAP–Occlusion comparison.\"\n","    )\n","\n","print(\"Number of sequences selected for SHAP:\", len(shap_sequence_indices))\n","print(\"Selected SHAP sequence indices:\", shap_sequence_indices)\n","print(\n","    \"The same indices will be used for Occlusion agreement and \"\n","    \"rank-correlation calculations.\"\n",")\n"]},{"cell_type":"markdown","id":"12cc5ae5","metadata":{"id":"12cc5ae5"},"source":["## Step 33 — Define SHAP event-level prediction function\n","\n","For SHAP, each feature is an event position in the 10-line sequence. SHAP uses a binary mask: `1` means keep the original event, and `0` means replace it with the neutral baseline event.\n"]},{"cell_type":"code","execution_count":49,"id":"facce1e5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"facce1e5","outputId":"36174426-2d89-4c75-f914-37c5b9bed330","executionInfo":{"status":"ok","timestamp":1785510839675,"user_tz":-180,"elapsed":9,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["SHAP event-level prediction function is ready.\n"]}],"source":["# ============================================================\n","# Step 33 — SHAP prediction function for event-level masking\n","# ============================================================\n","\n","def predict_probability_from_event_ids(sequence_event_ids):\n","    \"\"\"Predict anomaly probability for one sequence of event IDs.\"\"\"\n","    model.eval()\n","\n","    seq_tensor = torch.tensor(\n","        sequence_event_ids,\n","        dtype=torch.long\n","    ).unsqueeze(0).to(device)\n","\n","    with torch.no_grad():\n","        logits = model(seq_tensor)\n","        prob = torch.sigmoid(logits).item()\n","\n","    return float(prob)\n","\n","\n","def make_shap_mask_predict_fn(original_sequence_event_ids, baseline_event_id):\n","    \"\"\"\n","    Create a SHAP-compatible prediction function for one sequence.\n","\n","    SHAP input:\n","        mask_matrix shape = number_of_samples × 10\n","\n","    Each mask value:\n","        1 = keep original event\n","        0 = replace event with neutral baseline event\n","\n","    Output:\n","        anomaly probability for each masked version\n","    \"\"\"\n","\n","    original_sequence_event_ids = np.array(\n","        original_sequence_event_ids,\n","        dtype=np.int64\n","    )\n","\n","    def shap_predict_fn(mask_matrix):\n","        mask_matrix = np.array(mask_matrix)\n","\n","        outputs = []\n","\n","        for mask_row in mask_matrix:\n","            # Convert SHAP mask values to binary keep/remove decisions.\n","            binary_mask = (mask_row >= 0.5).astype(int)\n","\n","            perturbed_sequence = original_sequence_event_ids.copy()\n","\n","            for pos in range(len(perturbed_sequence)):\n","                if binary_mask[pos] == 0:\n","                    perturbed_sequence[pos] = baseline_event_id\n","\n","            prob = predict_probability_from_event_ids(perturbed_sequence)\n","            outputs.append(prob)\n","\n","        return np.array(outputs)\n","\n","    return shap_predict_fn\n","\n","\n","print(\"SHAP event-level prediction function is ready.\")\n"]},{"cell_type":"markdown","id":"91bb8008","metadata":{"id":"91bb8008"},"source":["## Step 34 — Run event-level SHAP\n","\n","This computes SHAP values for the selected sequences. Increase `SHAP_NSAMPLES` only if you need more stable SHAP estimates and have enough time.\n"]},{"cell_type":"code","execution_count":50,"id":"177459ec","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["712e3ad8cd13414ea40248b68359dabb","5351c8e036fb4a1caadb3875e6a687cc","9b07352c024b44b4bdbc7eeb4c715527","6c0f509060314864b1cd644b51e5eb65","49fd62a06733481c86fb2ab0f422bf2c","6324b63f805348f6bbb35edf750a8988","da31e6b9aa9c4d87b9a1699e1abc6ed0","66415df919d1407da4bd356f46d94d2c","b5a5e063dede42768e29d3435ce36cc8","7eb5a5e26a5349aa9700f5c6063c0a0e","5f1845cc1c10466e8267a5144b436591","5a4eb21eb64d41f486a66a61a3af5400","95407d5916fe4a058d31d6cb2319dfed","58060de1113446fa93a6851cf47509e7","fb75e87247c54d199e576e3b33eb76fe","1c1a60bef2a74449bac459f96011a80f","731ff5643d6d44a781d0b0634cdb59d3","65e65d2a1f23490c8c19dfad56cf955a","5dc482ae7774443baa380904fe8abd64","e161b40ace3f4fd7a4d21d0f8b1e18e6","8b37f1449d144313bec2373f1d1c97dc","e012ca8ce3004620b364c9460ff4a4ee","6f67b5cdf45f41ffa3feb2e63dfe56ec","67a8890ddaad41ca9de90daf13358f65","23a6c66ec9f54c6ab2f4e6fdc471ff9a","b4bfd6f5c0274be0af79e387053c9aec","e5693f64361b477e88cdc29eb81e8f9e","8bc178bd835c4f55ac2a6ad0351b62d9","0758975d27884013adbd3a19851be094","875046bf63b34050a0c7b6d58deef880","87d7ebcb23814c55bc3933b4bfb9f78e","0fd0fa72491844a18cb63de7eb0bf524","5f4d7a40e7524dc78c582c70e77bf00e","e9131e823d854889ae626283cb2b8a88","13133882bff8454494eff7b8a448d890","ba891318fee545d5abb1f6f6a4929057","00e95aac78224f0ab1e8c52dfc805bf6","89c84ae72aab438485d310be21203a94","17235663aa164b328cc9f7aa53b7b15d","3eae3ead9bdc4390b94e4d4a2b5abeef","2ae944beac244a10895c2cc575f01dc3","ecd8bf403f4c480daa9cac0e5746cdaf","99dc8111532e42ddab4dcaf356b0b4bb","0a195f43911341a2bbe7677b440ac0a5","9af94814aa8747ad8e3b180f529e700b","6e5d96275449438184403e5238330218","c3d14f14038645f18597eee7df0f1983","17feaebf33764b18ba36499d2d301586","0d4855c3763a404cbc414809d90fe270","600ed9811b724e408d269e3f2197bcd9","a54a63ae7bd74b12a2ca5ca856448ef1","401776e8a27245bd9467bfa35c10e14f","c963f44244914d03a0791d1a758d6a94","8a008230521d438986250c25d6002add","c42675fc473f40f89f2375553a85f76a","aa8d6570cbfe4d17933d608ea3e68530","71055b7ddbc842459e4e590367a5c2af","27b45073c5664dcc88d8f70ad3b80d24","760d7d6fd88f44b3a191c9eeccfa900a","a592bcd480b54ed6b3e44c228094b11e","73928b340d7e40a9aea73469af576705","d136bdd1ecd54959bb116cf2a747d260","e241616e02f64428bdcf2721272060ac","ad2c532ca676457c92b5ae2258b53022","591f62c9ca51428b9d7e298a5819d2f4","d471d404a93c41ab8c477cdf08f414de","b18b33ad5a5e4eb295ccac5fb53939b0","908a4f4033d0425ca0d460d37fdd6315","fe36ac65fbd144a49b3e1d8755fc557a","e8b34ce5f29345b68c1fb6b094b37c26","27a423010ebd41a6b0757b78d8c22ff8","ac5ec27af93d4c45ad0d32b0ce5ebac2","402ae64a23784dc58be3885efdc3e56a","b81bf6a73c0644ec9e556643ff60c3c1","ed459f6e754f41119311656b75a96b79","9cdf242e9642410bb0c5a7f99b56274c","2f5ee18cecef45f2a8015ebda1f015db","ce7df81918c84bd494f298b30e302b0e","fa1966121d69457eb9f12a7f5713d37a","22a2005825984376a5b8c07271931b52","21be0583aa714484af78ec4b325dfaed","eccd2b310da444d88e3a590a0adfb88a","b412aaa08d2a45578eca845878649aff","6744ef0652ca4fdfa8d1d18747ce5403","c48919189d6c47f5bcea86977a7f703e","b54dea98ffaa41aa9bc1cb565c194205","204d13e08bc2407cad0f153c93a8226f","73dadf4f85ce4885bb94b9a3844f1a59","6f79e99c33074dfb8b7bb288665034e3","e64e26ea705d4f1abb500ff0f2609586","762e98326e364df38084e12a04da42ea","abe555baccaf403aad447765bec037b4","403e149890a14dceaaaa38e746fd9810","5d68fddeec6f4f4a9321a5841df67fd9","8b743084466d457bb46c8386ab2e2ae0","4339580c0a6b40c1984ffdda6fb40b2e","465cff9ff83b4627ac1186594ccee8ce","34ce97269e824fa0aca181368e9aa02b","b4407f18e1114dac9cba0fe53ba863d0","83bc45cfd0a34f769fdd9ffe5e8a409b","9ee2875116954681bf62d1eb701141ff","f62f342881c44ebf938b5daf0a2458cb","042de4c8a04749e9a7c0fafcfa9bb620","c2d255c597a644b3ab9cd4f3d229b3a8","85d5e19740494944b6899c66b34edc5f","4e464e77665244c685345d7c01239ed4","15e15c793b67439c90f374dbf572d5ad","e0e2611b32764996a8954c6ffa8f7d53","132c9e74e8304f18a537195bba65791b","eea29668176b4cbcbd1cfbf07657522d","c129630e59564ae5ab23df5664b36263","88433c65798f4fc89bfbc331617f786c","ada9fc79ffc44c6b9ea2adb84eb71e2c","54c121faa6fa42fba2cf6cfdea3e83c6","4fdac1685bc548989cf84bcc8503c20b","1d9d3e37d9a24ef091173d58a9825f20","05e5d78f50a24fc88904cb0f97df374f","1ef9a87b79b64a68a24b7fbcb9d23f0a","6cd77e2c2b464e3a9435be89a111cad1","ef9a58bb186e4bb5937a754ff2c259b0","352ad2df33e34b1e89c5563dca091f37","c542c06bf48d4c488c1066ef3bd5d4de","ab3a5a3171b44bf19a43372890cc54e5","4eb1993104284279b775401aed69c8fe","afbe94782f6c435384799a4f8806b620","5a73edd87f6b41ee9255913a02bc73e1","7a58bde36f274f81bda1d95cf1e7e9ca","8184e8b9a1184d688e068220d033d17c","51fdba51249441a4aa5cc531b7de13a7","bd8dc210242141628a9b0d0fc18ba850","00b815f096a146549f6c1a1fe71d3889","e77fe30fe7e34e1aa1bd2b2b4b588a08","bc4769f0aaea4daf8cbe6991c11318f5","12bc810b7fc6418fb27db6e987105e66","97662f875f1a4c379d67c067192545ee","13174b80ddad4324b6f539d29a4654cc","e3596c4d89814292b228fbfed6c690a2","3331e5b7850c4303be3927831dd2ec3e","537cadc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Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 1/21\n","Local test index: 42167\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"712e3ad8cd13414ea40248b68359dabb"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 2/21\n","Local test index: 41109\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a4eb21eb64d41f486a66a61a3af5400"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 3/21\n","Local test index: 41120\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, 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41666\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aa8d6570cbfe4d17933d608ea3e68530"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 7/21\n","Local test index: 41667\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b18b33ad5a5e4eb295ccac5fb53939b0"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 8/21\n","Local test index: 41668\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ce7df81918c84bd494f298b30e302b0e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 9/21\n","Local test index: 41681\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f79e99c33074dfb8b7bb288665034e3"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 10/21\n","Local test index: 43274\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"83bc45cfd0a34f769fdd9ffe5e8a409b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 11/21\n","Local test index: 43275\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c129630e59564ae5ab23df5664b36263"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 12/21\n","Local test index: 42162\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c542c06bf48d4c488c1066ef3bd5d4de"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 13/21\n","Local test index: 42163\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, 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42160\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a5cbc72dfd464d499da3b16d289244eb"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 17/21\n","Local test index: 42132\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a5168e0d75144cc7b0913ca8bc64e9d3"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 18/21\n","Local test index: 86826\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b1e9fa3d644c4b57aebbf9d9fb152f71"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 19/21\n","Local test index: 86827\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b842f1cc142f46cdbee949f509058ebc"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 20/21\n","Local test index: 86828\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3403c60098b440e38af21ba41aec8eb2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Running SHAP for sequence 21/21\n","Local test index: 86829\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8ef80f304d76455bbb728d58b55c680e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","Saved SHAP explanations to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_event_level_explanations.csv\n"]},{"output_type":"display_data","data":{"text/plain":["    sequence_local_test_index  event_position  event_id  original_probability  \\\n","0                       41109               9     21426              0.999992   \n","1                       41109               6     21426              0.999992   \n","2                       41109               2     21426              0.999992   \n","3                       41109               4     21426              0.999992   \n","4                       41109              10     21426              0.999992   \n","5                       41109               5     21426              0.999992   \n","6                       41109               7     21426              0.999992   \n","7                       41109               1     21426              0.999992   \n","8                       41109               3     21426              0.999992   \n","9                       41109               8     21426              0.999992   \n","10                      41120               1     21426              0.999992   \n","11                      41120               3     21426              0.999992   \n","12                      41120               9     21426              0.999992   \n","13                      41120               8     21426              0.999992   \n","14                      41120               5     21426              0.999992   \n","15                      41120               7     21426              0.999992   \n","16                      41120               6     21426              0.999992   \n","17                      41120               4     21426              0.999992   \n","18                      41120               2     21426              0.999992   \n","19                      41120              10     21426              0.999992   \n","20                      41664               1     21424              0.999992   \n","21                      41664               8     21426              0.999992   \n","22                      41664              10     21424              0.999992   \n","23                      41664               9     21424              0.999992   \n","24                      41664               4     21424              0.999992   \n","25                      41664               2     21424              0.999992   \n","26                      41664               6     21426              0.999992   \n","27                      41664               5     21424              0.999992   \n","28                      41664               3     21426              0.999992   \n","29                      41664               7     21424              0.999992   \n","\n","    shap_expected_value  shap_value  abs_shap_value  \n","0               0.00001    0.183815        0.183815  \n","1               0.00001    0.127583        0.127583  \n","2               0.00001    0.107895        0.107895  \n","3               0.00001    0.092286        0.092286  \n","4               0.00001    0.087858        0.087858  \n","5               0.00001    0.087168        0.087168  \n","6               0.00001    0.082599        0.082599  \n","7               0.00001    0.081822        0.081822  \n","8               0.00001    0.078832        0.078832  \n","9               0.00001    0.070124        0.070124  \n","10              0.00001    0.151871        0.151871  \n","11              0.00001    0.145576        0.145576  \n","12              0.00001    0.144859        0.144859  \n","13              0.00001    0.131237        0.131237  \n","14              0.00001    0.108652        0.108652  \n","15              0.00001    0.107364        0.107364  \n","16              0.00001    0.084865        0.084865  \n","17              0.00001    0.080261        0.080261  \n","18              0.00001    0.040125        0.040125  \n","19              0.00001    0.005172        0.005172  \n","20              0.00001    0.144374        0.144374  \n","21              0.00001    0.115552        0.115552  \n","22              0.00001    0.112225        0.112225  \n","23              0.00001    0.106512        0.106512  \n","24              0.00001    0.106374        0.106374  \n","25              0.00001    0.104230        0.104230  \n","26              0.00001    0.102055        0.102055  \n","27              0.00001    0.097398        0.097398  \n","28              0.00001    0.085242        0.085242  \n","29              0.00001    0.026020        0.026020  "],"text/html":["\n","  <div id=\"df-17a5d056-8f75-4232-ba18-4d4250e937bc\" class=\"colab-df-container\">\n","    <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>sequence_local_test_index</th>\n","      <th>event_position</th>\n","      <th>event_id</th>\n","      <th>original_probability</th>\n","      <th>shap_expected_value</th>\n","      <th>shap_value</th>\n","      <th>abs_shap_value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>41109</td>\n","      <td>9</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.183815</td>\n","      <td>0.183815</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41109</td>\n","      <td>6</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.127583</td>\n","      <td>0.127583</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41109</td>\n","      <td>2</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.107895</td>\n","      <td>0.107895</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41109</td>\n","      <td>4</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.092286</td>\n","      <td>0.092286</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41109</td>\n","      <td>10</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.087858</td>\n","      <td>0.087858</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41109</td>\n","      <td>5</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.087168</td>\n","      <td>0.087168</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>41109</td>\n","      <td>7</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.082599</td>\n","      <td>0.082599</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>41109</td>\n","      <td>1</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.081822</td>\n","      <td>0.081822</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>41109</td>\n","      <td>3</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.078832</td>\n","      <td>0.078832</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>41109</td>\n","      <td>8</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.070124</td>\n","      <td>0.070124</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>41120</td>\n","      <td>1</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.151871</td>\n","      <td>0.151871</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>41120</td>\n","      <td>3</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.145576</td>\n","      <td>0.145576</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>41120</td>\n","      <td>9</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.144859</td>\n","      <td>0.144859</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>41120</td>\n","      <td>8</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.131237</td>\n","      <td>0.131237</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>41120</td>\n","      <td>5</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.108652</td>\n","      <td>0.108652</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>41120</td>\n","      <td>7</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.107364</td>\n","      <td>0.107364</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>41120</td>\n","      <td>6</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.084865</td>\n","      <td>0.084865</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>41120</td>\n","      <td>4</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.080261</td>\n","      <td>0.080261</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>41120</td>\n","      <td>2</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.040125</td>\n","      <td>0.040125</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>41120</td>\n","      <td>10</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.005172</td>\n","      <td>0.005172</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>41664</td>\n","      <td>1</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.144374</td>\n","      <td>0.144374</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>41664</td>\n","      <td>8</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.115552</td>\n","      <td>0.115552</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>41664</td>\n","      <td>10</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.112225</td>\n","      <td>0.112225</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>41664</td>\n","      <td>9</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.106512</td>\n","      <td>0.106512</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>41664</td>\n","      <td>4</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.106374</td>\n","      <td>0.106374</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>41664</td>\n","      <td>2</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.104230</td>\n","      <td>0.104230</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>41664</td>\n","      <td>6</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.102055</td>\n","      <td>0.102055</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>41664</td>\n","      <td>5</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.097398</td>\n","      <td>0.097398</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>41664</td>\n","      <td>3</td>\n","      <td>21426</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.085242</td>\n","      <td>0.085242</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>41664</td>\n","      <td>7</td>\n","      <td>21424</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.026020</td>\n","      <td>0.026020</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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         41109,\n          41120,\n          41664\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_position\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          3,\n          6,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 21424,\n        \"max\": 21426,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21424,\n          21426\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.9999923706054688,\n        \"max\": 0.9999923706054688,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9999923706054688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shap_expected_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0079813364427537e-05,\n        \"max\": 1.0079813364427537e-05,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0079813364427537e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shap_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03687710435061124,\n        \"min\": 0.005171983701082961,\n        \"max\": 0.18381526020073202,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.09739768711731318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abs_shap_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03687710435061124,\n        \"min\": 0.005171983701082961,\n        \"max\": 0.18381526020073202,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.09739768711731318\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 34 — Run event-level SHAP\n","# ============================================================\n","\n","shap_rows = []\n","\n","# SHAP background:\n","# all-zero mask means all events are replaced by the neutral baseline event.\n","background_mask = np.zeros((1, WINDOW_SIZE))\n","\n","# SHAP explains the full original sequence:\n","# all-one mask means all original events are kept.\n","full_mask = np.ones((1, WINDOW_SIZE))\n","\n","# Higher nsamples = more stable but slower.\n","SHAP_NSAMPLES = 200\n","\n","for seq_num, local_idx in enumerate(shap_sequence_indices, start=1):\n","\n","    print(f\"\\nRunning SHAP for sequence {seq_num}/{len(shap_sequence_indices)}\")\n","    print(\"Local test index:\", local_idx)\n","\n","    sequence_event_ids = np.array(X_test[local_idx], dtype=np.int64)\n","\n","    original_prob = predict_probability_from_event_ids(sequence_event_ids)\n","\n","    shap_predict_fn = make_shap_mask_predict_fn(\n","        sequence_event_ids,\n","        baseline_event_id\n","    )\n","\n","    explainer = shap.KernelExplainer(\n","        shap_predict_fn,\n","        background_mask\n","    )\n","\n","    shap_values = explainer.shap_values(\n","        full_mask,\n","        nsamples=SHAP_NSAMPLES\n","    )\n","\n","    # shap_values may come as list or ndarray depending on SHAP version.\n","    if isinstance(shap_values, list):\n","        shap_values_array = np.array(shap_values[0]).reshape(-1)\n","    else:\n","        shap_values_array = np.array(shap_values).reshape(-1)\n","\n","    expected_value = explainer.expected_value\n","\n","    if isinstance(expected_value, (list, np.ndarray)):\n","        expected_value = float(np.array(expected_value).reshape(-1)[0])\n","    else:\n","        expected_value = float(expected_value)\n","\n","    for pos in range(WINDOW_SIZE):\n","        event_id = int(sequence_event_ids[pos])\n","        shap_value = float(shap_values_array[pos])\n","\n","        shap_rows.append({\n","            \"sequence_local_test_index\": int(local_idx),\n","            \"event_position\": int(pos + 1),\n","            \"event_id\": event_id,\n","            \"original_probability\": float(original_prob),\n","            \"shap_expected_value\": float(expected_value),\n","            \"shap_value\": shap_value,\n","            \"abs_shap_value\": abs(shap_value),\n","        })\n","\n","shap_xai_df = pd.DataFrame(shap_rows)\n","\n","shap_xai_df = shap_xai_df.sort_values(\n","    [\"sequence_local_test_index\", \"abs_shap_value\"],\n","    ascending=[True, False]\n",").reset_index(drop=True)\n","\n","save_table(shap_xai_df, SHAP_LOCAL_PATH, \"Event-level SHAP explanations\")\n","\n","print(\"\\nSaved SHAP explanations to:\", SHAP_LOCAL_PATH)\n","display(shap_xai_df.head(30))\n"]},{"cell_type":"markdown","id":"4fe63544","metadata":{"id":"4fe63544"},"source":["## Step 35 — Attach metadata to SHAP explanations\n","\n","This maps SHAP values back to timestamp, node, component, severity, label, and event message.\n"]},{"cell_type":"code","execution_count":51,"id":"8494fd6c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"8494fd6c","outputId":"7cdf8933-0267-4b76-df6e-bdedd2b94733","executionInfo":{"status":"ok","timestamp":1785510851125,"user_tz":-180,"elapsed":56,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved SHAP metadata explanations to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_event_level_explanations.csv\n"]},{"output_type":"display_data","data":{"text/plain":["    sequence_local_test_index  start_line  end_line  event_position  \\\n","0                       41109     4181880   4181889               9   \n","1                       41109     4181880   4181889               6   \n","2                       41109     4181880   4181889               2   \n","3                       41109     4181880   4181889               4   \n","4                       41109     4181880   4181889              10   \n","5                       41109     4181880   4181889               5   \n","6                       41109     4181880   4181889               7   \n","7                       41109     4181880   4181889               1   \n","8                       41109     4181880   4181889               3   \n","9                       41109     4181880   4181889               8   \n","10                      41120     4181990   4181999               1   \n","11                      41120     4181990   4181999               3   \n","12                      41120     4181990   4181999               9   \n","13                      41120     4181990   4181999               8   \n","14                      41120     4181990   4181999               5   \n","15                      41120     4181990   4181999               7   \n","16                      41120     4181990   4181999               6   \n","17                      41120     4181990   4181999               4   \n","18                      41120     4181990   4181999               2   \n","19                      41120     4181990   4181999              10   \n","20                      41664     4187430   4187439               1   \n","21                      41664     4187430   4187439               8   \n","22                      41664     4187430   4187439              10   \n","23                      41664     4187430   4187439               9   \n","24                      41664     4187430   4187439               4   \n","25                      41664     4187430   4187439               2   \n","26                      41664     4187430   4187439               6   \n","27                      41664     4187430   4187439               5   \n","28                      41664     4187430   4187439               3   \n","29                      41664     4187430   4187439               7   \n","\n","    line_index  true_label  predicted_label  original_probability  \\\n","0      4216358           1                1              0.999992   \n","1      4216355           1                1              0.999992   \n","2      4216351           1                1              0.999992   \n","3      4216353           1                1              0.999992   \n","4      4216359           1                1              0.999992   \n","5      4216354           1                1              0.999992   \n","6      4216356           1                1              0.999992   \n","7      4216350           1                1              0.999992   \n","8      4216352           1                1              0.999992   \n","9      4216357           1                1              0.999992   \n","10     4216460           1                1              0.999992   \n","11     4216462           1                1              0.999992   \n","12     4216468           1                1              0.999992   \n","13     4216467           1                1              0.999992   \n","14     4216464           1                1              0.999992   \n","15     4216466           1                1              0.999992   \n","16     4216465           1                1              0.999992   \n","17     4216463           1                1              0.999992   \n","18     4216461           1                1              0.999992   \n","19     4216469           1                1              0.999992   \n","20     4221900           1                1              0.999992   \n","21     4221907           1                1              0.999992   \n","22     4221909           1                1              0.999992   \n","23     4221908           1                1              0.999992   \n","24     4221903           1                1              0.999992   \n","25     4221901           1                1              0.999992   \n","26     4221905           1                1              0.999992   \n","27     4221904           1                1              0.999992   \n","28     4221902           1                1              0.999992   \n","29     4221906           1                1              0.999992   \n","\n","    shap_expected_value  shap_value  ...  event_id  raw_label line_label  \\\n","0               0.00001    0.183815  ...     21426     APPSEV          1   \n","1               0.00001    0.127583  ...     21426     APPSEV          1   \n","2               0.00001    0.107895  ...     21426     APPSEV          1   \n","3               0.00001    0.092286  ...     21426     APPSEV          1   \n","4               0.00001    0.087858  ...     21426     APPSEV          1   \n","5               0.00001    0.087168  ...     21426     APPSEV          1   \n","6               0.00001    0.082599  ...     21426     APPSEV          1   \n","7               0.00001    0.081822  ...     21426     APPSEV          1   \n","8               0.00001    0.078832  ...     21426     APPSEV          1   \n","9               0.00001    0.070124  ...     21426     APPSEV          1   \n","10              0.00001    0.151871  ...     21426     APPSEV          1   \n","11              0.00001    0.145576  ...     21426     APPSEV          1   \n","12              0.00001    0.144859  ...     21426     APPSEV          1   \n","13              0.00001    0.131237  ...     21426     APPSEV          1   \n","14              0.00001    0.108652  ...     21426     APPSEV          1   \n","15              0.00001    0.107364  ...     21426     APPSEV          1   \n","16              0.00001    0.084865  ...     21426     APPSEV          1   \n","17              0.00001    0.080261  ...     21426     APPSEV          1   \n","18              0.00001    0.040125  ...     21426     APPSEV          1   \n","19              0.00001    0.005172  ...     21426     APPSEV          1   \n","20              0.00001    0.144374  ...     21424     APPRES          1   \n","21              0.00001    0.115552  ...     21426     APPSEV          1   \n","22              0.00001    0.112225  ...     21424     APPRES          1   \n","23              0.00001    0.106512  ...     21424     APPRES          1   \n","24              0.00001    0.106374  ...     21424     APPRES          1   \n","25              0.00001    0.104230  ...     21424     APPRES          1   \n","26              0.00001    0.102055  ...     21426     APPSEV          1   \n","27              0.00001    0.097398  ...     21424     APPRES          1   \n","28              0.00001    0.085242  ...     21426     APPSEV          1   \n","29              0.00001    0.026020  ...     21424     APPRES          1   \n","\n","                     full_time                 node source component severity  \\\n","0   2005-11-17-05.34.20.718215  R72-M1-N8-I:J18-U01    RAS       APP    FATAL   \n","1   2005-11-17-05.34.20.243549  R74-M1-N8-I:J18-U11    RAS       APP    FATAL   \n","2   2005-11-17-05.34.19.639081  R74-M1-N4-I:J18-U11    RAS       APP    FATAL   \n","3   2005-11-17-05.34.19.943960  R74-M1-NC-I:J18-U11    RAS       APP    FATAL   \n","4   2005-11-17-05.34.20.862518  R72-M1-N4-I:J18-U11    RAS       APP    FATAL   \n","5   2005-11-17-05.34.20.092971  R74-M1-NC-I:J18-U01    RAS       APP    FATAL   \n","6   2005-11-17-05.34.20.394346  R74-M1-N8-I:J18-U01    RAS       APP    FATAL   \n","7   2005-11-17-05.34.19.485817  R74-M0-NC-I:J18-U01    RAS       APP    FATAL   \n","8   2005-11-17-05.34.19.788642  R74-M1-N4-I:J18-U01    RAS       APP    FATAL   \n","9   2005-11-17-05.34.20.572641  R72-M1-N8-I:J18-U11    RAS       APP    FATAL   \n","10  2005-11-17-05.34.36.177059  R60-M0-N8-I:J18-U01    RAS       APP    FATAL   \n","11  2005-11-17-05.34.36.480785  R76-M1-N0-I:J18-U01    RAS       APP    FATAL   \n","12  2005-11-17-05.34.37.295164  R62-M0-N0-I:J18-U01    RAS       APP    FATAL   \n","13  2005-11-17-05.34.37.159153  R62-M0-N0-I:J18-U11    RAS       APP    FATAL   \n","14  2005-11-17-05.34.36.745040  R60-M0-N4-I:J18-U01    RAS       APP    FATAL   \n","15  2005-11-17-05.34.37.022015  R62-M0-NC-I:J18-U01    RAS       APP    FATAL   \n","16  2005-11-17-05.34.36.889699  R62-M0-NC-I:J18-U11    RAS       APP    FATAL   \n","17  2005-11-17-05.34.36.612978  R60-M0-N4-I:J18-U11    RAS       APP    FATAL   \n","18  2005-11-17-05.34.36.346828  R76-M1-N0-I:J18-U11    RAS       APP    FATAL   \n","19  2005-11-17-05.34.37.432634  R62-M0-N4-I:J18-U11    RAS       APP    FATAL   \n","20  2005-11-18-05.34.58.361024  R41-M1-N4-I:J18-U11    RAS       APP    FATAL   \n","21  2005-11-18-05.34.59.513660  R51-M1-N0-I:J18-U01    RAS       APP    FATAL   \n","22  2005-11-18-05.34.59.847516  R53-M0-N0-I:J18-U01    RAS       APP    FATAL   \n","23  2005-11-18-05.34.59.681119  R53-M0-N0-I:J18-U11    RAS       APP    FATAL   \n","24  2005-11-18-05.34.58.844161  R53-M1-N8-I:J18-U01    RAS       APP    FATAL   \n","25  2005-11-18-05.34.58.522898  R41-M1-N4-I:J18-U01    RAS       APP    FATAL   \n","26  2005-11-18-05.34.59.191542  R56-M0-N4-I:J18-U11    RAS       APP    FATAL   \n","27  2005-11-18-05.34.59.018157  R56-M1-N4-I:J18-U01    RAS       APP    FATAL   \n","28  2005-11-18-05.34.58.682832  R53-M1-N8-I:J18-U11    RAS       APP    FATAL   \n","29  2005-11-18-05.34.59.353696  R51-M1-N0-I:J18-U11    RAS       APP    FATAL   \n","\n","                                        event_message  \\\n","0   ciod: Error reading message prefix on CioStrea...   \n","1   ciod: Error reading message prefix on CioStrea...   \n","2   ciod: Error reading message prefix on CioStrea...   \n","3   ciod: Error reading message prefix on CioStrea...   \n","4   ciod: Error reading message prefix on CioStrea...   \n","5   ciod: Error reading message prefix on CioStrea...   \n","6   ciod: Error reading message prefix on CioStrea...   \n","7   ciod: Error reading message prefix on CioStrea...   \n","8   ciod: Error reading message prefix on CioStrea...   \n","9   ciod: Error reading message prefix on CioStrea...   \n","10  ciod: Error reading message prefix on CioStrea...   \n","11  ciod: Error reading message prefix on CioStrea...   \n","12  ciod: Error reading message prefix on CioStrea...   \n","13  ciod: Error reading message prefix on CioStrea...   \n","14  ciod: Error reading message prefix on CioStrea...   \n","15  ciod: Error reading message prefix on CioStrea...   \n","16  ciod: Error reading message prefix on CioStrea...   \n","17  ciod: Error reading message prefix on CioStrea...   \n","18  ciod: Error reading message prefix on CioStrea...   \n","19  ciod: Error reading message prefix on CioStrea...   \n","20  ciod: Error reading message prefix on CioStrea...   \n","21  ciod: Error reading message prefix on CioStrea...   \n","22  ciod: Error reading message prefix on CioStrea...   \n","23  ciod: Error reading message prefix on CioStrea...   \n","24  ciod: Error reading message prefix on CioStrea...   \n","25  ciod: Error reading message prefix on CioStrea...   \n","26  ciod: Error reading message prefix on CioStrea...   \n","27  ciod: Error reading message prefix on CioStrea...   \n","28  ciod: Error reading message prefix on CioStrea...   \n","29  ciod: Error reading message prefix on CioStrea...   \n","\n","                                      model_text_norm  \n","0   ras app fatal ciod error reading message prefi...  \n","1   ras app fatal ciod error reading message prefi...  \n","2   ras app fatal ciod error reading message prefi...  \n","3   ras app fatal ciod error reading message prefi...  \n","4   ras app fatal ciod error reading message prefi...  \n","5   ras app fatal ciod error reading message prefi...  \n","6   ras app fatal ciod error reading message prefi...  \n","7   ras app fatal ciod error reading message prefi...  \n","8   ras app fatal ciod error reading message prefi...  \n","9   ras app fatal ciod error reading message prefi...  \n","10  ras app fatal ciod error reading message prefi...  \n","11  ras app fatal ciod error reading message prefi...  \n","12  ras app fatal ciod error reading message prefi...  \n","13  ras app fatal ciod error reading message prefi...  \n","14  ras app fatal ciod error reading message prefi...  \n","15  ras app fatal ciod error reading message prefi...  \n","16  ras app fatal ciod error reading message prefi...  \n","17  ras app fatal ciod error reading message prefi...  \n","18  ras app fatal ciod error reading message prefi...  \n","19  ras app fatal ciod error reading message prefi...  \n","20  ras app fatal ciod error reading message prefi...  \n","21  ras app fatal ciod error reading message prefi...  \n","22  ras app fatal ciod error reading message prefi...  \n","23  ras app fatal ciod error reading message prefi...  \n","24  ras app fatal ciod error reading message prefi...  \n","25  ras app fatal ciod error reading message prefi...  \n","26  ras app fatal ciod error reading message prefi...  \n","27  ras app fatal ciod error reading message prefi...  \n","28  ras app fatal ciod error reading message prefi...  \n","29  ras app fatal ciod error reading message prefi...  \n","\n","[30 rows x 21 columns]"],"text/html":["\n","  <div id=\"df-65c9493f-2e04-4934-afd5-886d123cb400\" class=\"colab-df-container\">\n","    <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>sequence_local_test_index</th>\n","      <th>start_line</th>\n","      <th>end_line</th>\n","      <th>event_position</th>\n","      <th>line_index</th>\n","      <th>true_label</th>\n","      <th>predicted_label</th>\n","      <th>original_probability</th>\n","      <th>shap_expected_value</th>\n","      <th>shap_value</th>\n","      <th>...</th>\n","      <th>event_id</th>\n","      <th>raw_label</th>\n","      <th>line_label</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>source</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>event_message</th>\n","      <th>model_text_norm</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>9</td>\n","      <td>4216358</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.183815</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.718215</td>\n","      <td>R72-M1-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>6</td>\n","      <td>4216355</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.127583</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.243549</td>\n","      <td>R74-M1-N8-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>2</td>\n","      <td>4216351</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.107895</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.639081</td>\n","      <td>R74-M1-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>4</td>\n","      <td>4216353</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.092286</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.943960</td>\n","      <td>R74-M1-NC-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>10</td>\n","      <td>4216359</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.087858</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.862518</td>\n","      <td>R72-M1-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>5</td>\n","      <td>4216354</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.087168</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.092971</td>\n","      <td>R74-M1-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>7</td>\n","      <td>4216356</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.082599</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.394346</td>\n","      <td>R74-M1-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>1</td>\n","      <td>4216350</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.081822</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.485817</td>\n","      <td>R74-M0-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>3</td>\n","      <td>4216352</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.078832</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.19.788642</td>\n","      <td>R74-M1-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>41109</td>\n","      <td>4181880</td>\n","      <td>4181889</td>\n","      <td>8</td>\n","      <td>4216357</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.070124</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.20.572641</td>\n","      <td>R72-M1-N8-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>1</td>\n","      <td>4216460</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.151871</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.177059</td>\n","      <td>R60-M0-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>3</td>\n","      <td>4216462</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.145576</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.480785</td>\n","      <td>R76-M1-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>9</td>\n","      <td>4216468</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.144859</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.295164</td>\n","      <td>R62-M0-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>8</td>\n","      <td>4216467</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.131237</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.159153</td>\n","      <td>R62-M0-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>5</td>\n","      <td>4216464</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.108652</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.745040</td>\n","      <td>R60-M0-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>7</td>\n","      <td>4216466</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.107364</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.022015</td>\n","      <td>R62-M0-NC-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>6</td>\n","      <td>4216465</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.084865</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.889699</td>\n","      <td>R62-M0-NC-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>4</td>\n","      <td>4216463</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.080261</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.612978</td>\n","      <td>R60-M0-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>2</td>\n","      <td>4216461</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.040125</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.36.346828</td>\n","      <td>R76-M1-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>41120</td>\n","      <td>4181990</td>\n","      <td>4181999</td>\n","      <td>10</td>\n","      <td>4216469</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.005172</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-17-05.34.37.432634</td>\n","      <td>R62-M0-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>1</td>\n","      <td>4221900</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.144374</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.58.361024</td>\n","      <td>R41-M1-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>8</td>\n","      <td>4221907</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.115552</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.513660</td>\n","      <td>R51-M1-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>10</td>\n","      <td>4221909</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.112225</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.847516</td>\n","      <td>R53-M0-N0-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>9</td>\n","      <td>4221908</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.106512</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.681119</td>\n","      <td>R53-M0-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>4</td>\n","      <td>4221903</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.106374</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.58.844161</td>\n","      <td>R53-M1-N8-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>2</td>\n","      <td>4221901</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.104230</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.58.522898</td>\n","      <td>R41-M1-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>6</td>\n","      <td>4221905</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.102055</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.191542</td>\n","      <td>R56-M0-N4-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>5</td>\n","      <td>4221904</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.097398</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.018157</td>\n","      <td>R56-M1-N4-I:J18-U01</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>3</td>\n","      <td>4221902</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.085242</td>\n","      <td>...</td>\n","      <td>21426</td>\n","      <td>APPSEV</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.58.682832</td>\n","      <td>R53-M1-N8-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>41664</td>\n","      <td>4187430</td>\n","      <td>4187439</td>\n","      <td>7</td>\n","      <td>4221906</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.999992</td>\n","      <td>0.00001</td>\n","      <td>0.026020</td>\n","      <td>...</td>\n","      <td>21424</td>\n","      <td>APPRES</td>\n","      <td>1</td>\n","      <td>2005-11-18-05.34.59.353696</td>\n","      <td>R51-M1-N0-I:J18-U11</td>\n","      <td>RAS</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>ciod: Error reading message prefix on CioStrea...</td>\n","      <td>ras app fatal ciod error reading message prefi...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>30 rows × 21 columns</p>\n","</div>\n","    <div 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'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-65c9493f-2e04-4934-afd5-886d123cb400');\n","        const dataTable =\n","          await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                    [key], {});\n","        if (!dataTable) return;\n","\n","        const docLinkHtml = 'Like what you see? Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe"}},"metadata":{}}],"source":["# ============================================================\n","# Step 35 — Attach metadata to SHAP event explanations\n","# ============================================================\n","\n","# Build a lookup from predictions_df for start/end lines.\n","pred_lookup = predictions_df.set_index(\"sequence_local_test_index\")\n","\n","shap_meta_rows = []\n","\n","for _, row in shap_xai_df.iterrows():\n","\n","    local_idx = int(row[\"sequence_local_test_index\"])\n","    event_position = int(row[\"event_position\"])\n","\n","    pred_row = pred_lookup.loc[local_idx]\n","\n","    start_line = int(pred_row[\"start_line\"])\n","    end_line = int(pred_row[\"end_line\"])\n","    true_label = int(pred_row[\"true_label\"])\n","    predicted_label = int(pred_row[\"predicted_label\"])\n","\n","    line_number = start_line + event_position - 1\n","\n","    meta = metadata_df.iloc[line_number]\n","\n","    shap_meta_rows.append({\n","        \"sequence_local_test_index\": local_idx,\n","        \"start_line\": start_line,\n","        \"end_line\": end_line,\n","        \"event_position\": event_position,\n","        \"line_index\": int(meta[\"line_index\"]),\n","        \"true_label\": true_label,\n","        \"predicted_label\": predicted_label,\n","        \"original_probability\": float(row[\"original_probability\"]),\n","        \"shap_expected_value\": float(row[\"shap_expected_value\"]),\n","        \"shap_value\": float(row[\"shap_value\"]),\n","        \"abs_shap_value\": float(row[\"abs_shap_value\"]),\n","        \"event_id\": int(row[\"event_id\"]),\n","        \"raw_label\": meta[\"raw_label\"],\n","        \"line_label\": int(meta[\"label\"]),\n","        \"full_time\": meta[\"full_time\"],\n","        \"node\": meta[\"node\"],\n","        \"source\": meta[\"source\"],\n","        \"component\": meta[\"component\"],\n","        \"severity\": meta[\"severity\"],\n","        \"event_message\": meta[\"event_message\"],\n","        \"model_text_norm\": meta[\"model_text_norm\"],\n","    })\n","\n","shap_meta_df = pd.DataFrame(shap_meta_rows)\n","\n","shap_meta_df = shap_meta_df.sort_values(\n","    [\"sequence_local_test_index\", \"abs_shap_value\"],\n","    ascending=[True, False]\n",").reset_index(drop=True)\n","\n","save_table(shap_meta_df, SHAP_LOCAL_PATH, \"SHAP event explanations with metadata\")\n","\n","print(\"Saved SHAP metadata explanations to:\", SHAP_LOCAL_PATH)\n","display(shap_meta_df.head(30))\n"]},{"cell_type":"markdown","id":"3260b4ad","metadata":{"id":"3260b4ad"},"source":["## Step 36 — SHAP case study for the strongest anomaly\n","\n","This creates a SHAP table for the same strong case selected in Step 26.\n"]},{"cell_type":"code","execution_count":52,"id":"725505bd","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"725505bd","outputId":"7d96bf89-e4f8-46ac-e603-ab17c48c5b85","executionInfo":{"status":"ok","timestamp":1785510852040,"user_tz":-180,"elapsed":838,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Saved SHAP case study to: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_case_study.csv\n"]},{"output_type":"display_data","data":{"text/plain":["   shap_rank  event_position  shap_value  abs_shap_value  \\\n","0          1               1    0.211296        0.211296   \n","1          2               5    0.200292        0.200292   \n","2          3               3    0.197387        0.197387   \n","3          4               4    0.179346        0.179346   \n","4          5               2    0.162928        0.162928   \n","5          6              10    0.054313        0.054313   \n","6          7               9    0.019604        0.019604   \n","7          8               6   -0.012531        0.012531   \n","8          9               7   -0.006883        0.006883   \n","9         10               8   -0.005775        0.005775   \n","\n","   original_probability  shap_expected_value                   full_time  \\\n","0              0.999987              0.00001  2005-11-18-08.28.06.203590   \n","1              0.999987              0.00001  2005-11-18-08.28.06.330682   \n","2              0.999987              0.00001  2005-11-18-08.28.06.308972   \n","3              0.999987              0.00001  2005-11-18-08.28.06.321151   \n","4              0.999987              0.00001  2005-11-18-08.28.06.283344   \n","5              0.999987              0.00001  2005-11-18-11.13.13.077131   \n","6              0.999987              0.00001  2005-11-18-11.13.12.936469   \n","7              0.999987              0.00001  2005-11-18-11.13.12.191313   \n","8              0.999987              0.00001  2005-11-18-11.13.12.343036   \n","9              0.999987              0.00001  2005-11-18-11.13.12.720046   \n","\n","                  node component severity raw_label  \\\n","0             R06-M1-S   MONITOR   SEVERE         -   \n","1             R02-M1-S   MONITOR   SEVERE         -   \n","2             R01-M1-S   MONITOR   SEVERE         -   \n","3             R05-M1-S   MONITOR   SEVERE         -   \n","4             R07-M1-S   MONITOR   SEVERE         -   \n","5  R04-M0-N4-I:J18-U11       APP    FATAL         -   \n","6  R04-M0-NC-I:J18-U01       APP    FATAL         -   \n","7  R04-M0-N0-I:J18-U11       APP    FATAL         -   \n","8  R04-M0-N0-I:J18-U01       APP    FATAL         -   \n","9  R04-M0-NC-I:J18-U11       APP    FATAL         -   \n","\n","                                       event_message  \n","0  Hardware monitor caught java.net.SocketExcepti...  \n","1  Hardware monitor caught java.net.SocketExcepti...  \n","2  Hardware monitor caught java.net.SocketExcepti...  \n","3  Hardware monitor caught java.net.SocketExcepti...  \n","4  Hardware monitor caught java.net.SocketExcepti...  \n","5  ciod: Error loading -mode VN: invalid or missi...  \n","6  ciod: Error loading -mode VN: invalid or missi...  \n","7  ciod: Error loading -mode VN: invalid or missi...  \n","8  ciod: Error loading -mode VN: invalid or missi...  \n","9  ciod: Error loading -mode VN: invalid or missi...  "],"text/html":["\n","  <div id=\"df-a96542d1-9443-4c2d-a03e-9bd63bcc1cf2\" class=\"colab-df-container\">\n","    <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>shap_rank</th>\n","      <th>event_position</th>\n","      <th>shap_value</th>\n","      <th>abs_shap_value</th>\n","      <th>original_probability</th>\n","      <th>shap_expected_value</th>\n","      <th>full_time</th>\n","      <th>node</th>\n","      <th>component</th>\n","      <th>severity</th>\n","      <th>raw_label</th>\n","      <th>event_message</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>0.211296</td>\n","      <td>0.211296</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-08.28.06.203590</td>\n","      <td>R06-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>5</td>\n","      <td>0.200292</td>\n","      <td>0.200292</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-08.28.06.330682</td>\n","      <td>R02-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>0.197387</td>\n","      <td>0.197387</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-08.28.06.308972</td>\n","      <td>R01-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>0.179346</td>\n","      <td>0.179346</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-08.28.06.321151</td>\n","      <td>R05-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>2</td>\n","      <td>0.162928</td>\n","      <td>0.162928</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-08.28.06.283344</td>\n","      <td>R07-M1-S</td>\n","      <td>MONITOR</td>\n","      <td>SEVERE</td>\n","      <td>-</td>\n","      <td>Hardware monitor caught java.net.SocketExcepti...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>6</td>\n","      <td>10</td>\n","      <td>0.054313</td>\n","      <td>0.054313</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-11.13.13.077131</td>\n","      <td>R04-M0-N4-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>7</td>\n","      <td>9</td>\n","      <td>0.019604</td>\n","      <td>0.019604</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-11.13.12.936469</td>\n","      <td>R04-M0-NC-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>8</td>\n","      <td>6</td>\n","      <td>-0.012531</td>\n","      <td>0.012531</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-11.13.12.191313</td>\n","      <td>R04-M0-N0-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>9</td>\n","      <td>7</td>\n","      <td>-0.006883</td>\n","      <td>0.006883</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-11.13.12.343036</td>\n","      <td>R04-M0-N0-I:J18-U01</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>10</td>\n","      <td>8</td>\n","      <td>-0.005775</td>\n","      <td>0.005775</td>\n","      <td>0.999987</td>\n","      <td>0.00001</td>\n","      <td>2005-11-18-11.13.12.720046</td>\n","      <td>R04-M0-NC-I:J18-U11</td>\n","      <td>APP</td>\n","      <td>FATAL</td>\n","      <td>-</td>\n","      <td>ciod: Error loading -mode VN: invalid or missi...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a96542d1-9443-4c2d-a03e-9bd63bcc1cf2')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      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  \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.9999871253967285,\n        \"max\": 0.9999871253967285,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.9999871253967285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shap_expected_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0079813364427537e-05,\n        \"max\": 1.0079813364427537e-05,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0079813364427537e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"full_time\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"2005-11-18-11.13.12.343036\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"node\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"R04-M0-N0-I:J18-U01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"component\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"APP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"severity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"FATAL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"raw_label\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"-\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"event_message\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"ciod: Error loading -mode VN: invalid or missing program image, No such file or directory\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 36 — SHAP case study for the strongest anomaly\n","# ============================================================\n","\n","shap_case_df = shap_meta_df[\n","    shap_meta_df[\"sequence_local_test_index\"] == case_local_idx\n","].copy()\n","\n","shap_case_df = shap_case_df.sort_values(\n","    \"abs_shap_value\",\n","    ascending=False\n",").reset_index(drop=True)\n","\n","shap_case_df.insert(\n","    0,\n","    \"shap_rank\",\n","    range(1, len(shap_case_df) + 1)\n",")\n","\n","shap_case_table = shap_case_df[[\n","    \"shap_rank\",\n","    \"event_position\",\n","    \"shap_value\",\n","    \"abs_shap_value\",\n","    \"original_probability\",\n","    \"shap_expected_value\",\n","    \"full_time\",\n","    \"node\",\n","    \"component\",\n","    \"severity\",\n","    \"raw_label\",\n","    \"event_message\"\n","]]\n","\n","save_table(shap_case_table, SHAP_CASE_PATH, \"SHAP case study\")\n","\n","print(\"Saved SHAP case study to:\", SHAP_CASE_PATH)\n","display(shap_case_table)\n"]},{"cell_type":"markdown","id":"d5ce415c","metadata":{"id":"d5ce415c"},"source":["## Step 37 — Compare SHAP with occlusion\n","\n","This checks whether SHAP and occlusion identify the same top event.\n"]},{"cell_type":"code","execution_count":53,"id":"710a2d9e","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":983},"id":"710a2d9e","outputId":"8d1fc4f6-a9cf-44a8-fd19-d6f99882d56b","executionInfo":{"status":"ok","timestamp":1785510853972,"user_tz":-180,"elapsed":1930,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["/tmp/ipykernel_634/999927284.py:74: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n","  rank_corr, rank_pvalue = spearmanr(\n","/tmp/ipykernel_634/999927284.py:74: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n","  rank_corr, rank_pvalue = spearmanr(\n","/tmp/ipykernel_634/999927284.py:74: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n","  rank_corr, rank_pvalue = spearmanr(\n","/tmp/ipykernel_634/999927284.py:74: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n","  rank_corr, rank_pvalue = spearmanr(\n","/tmp/ipykernel_634/999927284.py:74: ConstantInputWarning: An input array is constant; the correlation coefficient is not defined.\n","  rank_corr, rank_pvalue = spearmanr(\n"]},{"output_type":"stream","name":"stdout","text":["Compared sequences: 21\n","Top-1 agreement rate: 0.09523809523809523\n","Mean top-3 overlap rate: 0.38095238095238093\n","Median Spearman rank correlation: 0.13939393939393938\n"]},{"output_type":"display_data","data":{"text/plain":["    sequence_local_test_index prediction_type  top_shap_event_position  \\\n","0                       42167              FP                        1   \n","1                       41109              TP                        9   \n","2                       41120              TP                        1   \n","3                       41664              TP                        1   \n","4                       41665              TP                        6   \n","5                       41666              TP                        2   \n","6                       41667              TP                       10   \n","7                       41668              TP                        6   \n","8                       41681              TP                        2   \n","9                       43274              TP                        8   \n","10                      43275              TP                        5   \n","11                      42162              FP                        5   \n","12                      42163              FP                        2   \n","13                      42164              FP                        1   \n","14                      42165              FP                        4   \n","15                      42160              FP                        6   \n","16                      42132              FN                        1   \n","17                      86826              FN                        1   \n","18                      86827              FN                        1   \n","19                      86828              FN                        1   \n","20                      86829              FN                        1   \n","\n","    top_occlusion_event_position  same_top_event  top_k  topk_overlap_count  \\\n","0                              3               0      3                   1   \n","1                              1               0      3                   0   \n","2                              1               1      3                   1   \n","3                              1               1      3                   1   \n","4                              1               0      3                   1   \n","5                              1               0      3                   1   \n","6                              1               0      3                   0   \n","7                             10               0      3                   2   \n","8                              3               0      3                   1   \n","9                              1               0      3                   1   \n","10                             1               0      3                   1   \n","11                             3               0      3                   1   \n","12                             3               0      3                   1   \n","13                             3               0      3                   1   \n","14                             3               0      3                   1   \n","15                             3               0      3                   1   \n","16                            10               0      3                   1   \n","17                             3               0      3                   2   \n","18                             3               0      3                   2   \n","19                             3               0      3                   2   \n","20                             3               0      3                   2   \n","\n","    topk_overlap_rate  spearman_rank_correlation  spearman_pvalue  \n","0            0.333333                  -0.612121         0.059972  \n","1            0.000000                  -0.115152         0.751420  \n","2            0.333333                   0.369697         0.293050  \n","3            0.333333                   0.248485         0.488776  \n","4            0.333333                  -0.018182         0.960240  \n","5            0.333333                  -0.090909         0.802772  \n","6            0.000000                  -0.224242         0.533401  \n","7            0.666667                  -0.030303         0.933773  \n","8            0.333333                   0.151515         0.676065  \n","9            0.333333                   0.163636         0.651477  \n","10           0.333333                   0.127273         0.726057  \n","11           0.333333                   0.236364         0.510885  \n","12           0.333333                   0.333333         0.346594  \n","13           0.333333                   0.284848         0.425038  \n","14           0.333333                   0.090909         0.802772  \n","15           0.333333                   0.200000         0.579584  \n","16           0.333333                        NaN              NaN  \n","17           0.666667                        NaN              NaN  \n","18           0.666667                        NaN              NaN  \n","19           0.666667                        NaN              NaN  \n","20           0.666667                        NaN              NaN  "],"text/html":["\n","  <div id=\"df-f40dd07e-cd5b-4c08-aa4a-3d77e6cb321b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th 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<th>1</th>\n","      <td>41109</td>\n","      <td>TP</td>\n","      <td>9</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>-0.115152</td>\n","      <td>0.751420</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>41120</td>\n","      <td>TP</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.369697</td>\n","      <td>0.293050</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>41664</td>\n","      <td>TP</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.248485</td>\n","      <td>0.488776</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>41665</td>\n","      <td>TP</td>\n","      <td>6</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>-0.018182</td>\n","      <td>0.960240</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>41666</td>\n","      <td>TP</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>-0.090909</td>\n","      <td>0.802772</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>41667</td>\n","      <td>TP</td>\n","      <td>10</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>0.000000</td>\n","      <td>-0.224242</td>\n","      <td>0.533401</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>41668</td>\n","      <td>TP</td>\n","      <td>6</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0.666667</td>\n","      <td>-0.030303</td>\n","      <td>0.933773</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>41681</td>\n","      <td>TP</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.151515</td>\n","      <td>0.676065</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>43274</td>\n","      <td>TP</td>\n","      <td>8</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.163636</td>\n","      <td>0.651477</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>43275</td>\n","      <td>TP</td>\n","      <td>5</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.127273</td>\n","      <td>0.726057</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>42162</td>\n","      <td>FP</td>\n","      <td>5</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.236364</td>\n","      <td>0.510885</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>42163</td>\n","      <td>FP</td>\n","      <td>2</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.333333</td>\n","      <td>0.346594</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>42164</td>\n","      <td>FP</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.284848</td>\n","      <td>0.425038</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>42165</td>\n","      <td>FP</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.090909</td>\n","      <td>0.802772</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>42160</td>\n","      <td>FP</td>\n","      <td>6</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>0.200000</td>\n","      <td>0.579584</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>1</td>\n","      <td>10</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>0.333333</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>86826</td>\n","      <td>FN</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0.666667</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>86827</td>\n","      <td>FN</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0.666667</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>86828</td>\n","      <td>FN</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0.666667</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>86829</td>\n","      <td>FN</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>0</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>0.666667</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f40dd07e-cd5b-4c08-aa4a-3d77e6cb321b')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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{local_idx} has {len(shap_seq)} SHAP rows; \"\n","            f\"expected {WINDOW_SIZE}.\"\n","        )\n","\n","    # Recompute occlusion from the same input sequence rather than relying\n","    # on a previously cached local_xai_df subset.\n","    sequence_event_ids = np.asarray(X_test[local_idx], dtype=np.int64)\n","    occ_seq = explain_sequence_by_occlusion(\n","        model,\n","        sequence_event_ids,\n","        baseline_event_id,\n","    ).copy()\n","\n","    occ_seq[\"abs_logit_drop\"] = occ_seq[\"logit_drop\"].abs()\n","    occ_seq = occ_seq.sort_values(\n","        \"abs_logit_drop\",\n","        ascending=False,\n","    ).reset_index(drop=True)\n","\n","    shap_top_positions = (\n","        shap_seq.head(TOP_K_AGREEMENT)[\"event_position\"]\n","        .astype(int)\n","        .tolist()\n","    )\n","    occ_top_positions = (\n","        occ_seq.head(TOP_K_AGREEMENT)[\"event_position\"]\n","        .astype(int)\n","        .tolist()\n","    )\n","\n","    top1_same = int(shap_top_positions[0] == occ_top_positions[0])\n","    topk_overlap_count = len(\n","        set(shap_top_positions).intersection(occ_top_positions)\n","    )\n","    topk_overlap_rate = topk_overlap_count / TOP_K_AGREEMENT\n","\n","    shap_by_position = (\n","        shap_seq\n","        .set_index(\"event_position\")[\"abs_shap_value\"]\n","        .reindex(range(1, WINDOW_SIZE + 1))\n","        .to_numpy(dtype=float)\n","    )\n","    occ_by_position = (\n","        occ_seq\n","        .set_index(\"event_position\")[\"abs_logit_drop\"]\n","        .reindex(range(1, WINDOW_SIZE + 1))\n","        .to_numpy(dtype=float)\n","    )\n","\n","    rank_corr, rank_pvalue = spearmanr(\n","        shap_by_position,\n","        occ_by_position,\n","    )\n","    if not np.isfinite(rank_corr):\n","        rank_corr = np.nan\n","    if not np.isfinite(rank_pvalue):\n","        rank_pvalue = np.nan\n","\n","    pred_meta = predictions_df.loc[\n","        predictions_df[\"sequence_local_test_index\"] == local_idx\n","    ].iloc[0]\n","\n","    comparison_rows.append({\n","        \"sequence_local_test_index\": local_idx,\n","        \"prediction_type\": pred_meta[\"prediction_type\"],\n","        \"top_shap_event_position\": shap_top_positions[0],\n","        \"top_occlusion_event_position\": occ_top_positions[0],\n","        \"same_top_event\": top1_same,\n","        \"top_k\": TOP_K_AGREEMENT,\n","        \"topk_overlap_count\": topk_overlap_count,\n","        \"topk_overlap_rate\": topk_overlap_rate,\n","        \"spearman_rank_correlation\": rank_corr,\n","        \"spearman_pvalue\": rank_pvalue,\n","    })\n","\n","    for event_position in range(1, WINDOW_SIZE + 1):\n","        agreement_detail_rows.append({\n","            \"sequence_local_test_index\": local_idx,\n","            \"event_position\": event_position,\n","            \"abs_shap_value\": float(\n","                shap_seq.set_index(\"event_position\")\n","                .loc[event_position, \"abs_shap_value\"]\n","            ),\n","            \"abs_occlusion_logit_drop\": float(\n","                occ_seq.set_index(\"event_position\")\n","                .loc[event_position, \"abs_logit_drop\"]\n","            ),\n","        })\n","\n","shap_occ_compare_df = pd.DataFrame(comparison_rows)\n","shap_occ_detail_df = pd.DataFrame(agreement_detail_rows)\n","\n","if len(shap_occ_compare_df) != len(shap_sequence_indices):\n","    raise AssertionError(\n","        \"SHAP–Occlusion comparison did not cover every selected sequence.\"\n","    )\n","\n","save_table(\n","    shap_occ_compare_df,\n","    SHAP_COMPARE_PATH,\n","    \"SHAP and occlusion agreement across identical explained sequences\",\n",")\n","\n","SHAP_COMPARE_DETAIL_PATH = (\n","    SHAP_DIR / \"logminilm_shap_occlusion_event_details.csv\"\n",")\n","save_table(\n","    shap_occ_detail_df,\n","    SHAP_COMPARE_DETAIL_PATH,\n","    \"Event-level SHAP and occlusion scores for identical sequences\",\n",")\n","\n","top1_agreement_rate = float(\n","    shap_occ_compare_df[\"same_top_event\"].mean()\n",")\n","mean_topk_overlap = float(\n","    shap_occ_compare_df[\"topk_overlap_rate\"].mean()\n",")\n","median_spearman = float(\n","    shap_occ_compare_df[\n","        \"spearman_rank_correlation\"\n","    ].median(skipna=True)\n",")\n","\n","print(\"Compared sequences:\", len(shap_occ_compare_df))\n","print(\"Top-1 agreement rate:\", top1_agreement_rate)\n","print(\n","    f\"Mean top-{TOP_K_AGREEMENT} overlap rate:\",\n","    mean_topk_overlap,\n",")\n","print(\"Median Spearman rank correlation:\", median_spearman)\n","display(shap_occ_compare_df)\n"]},{"cell_type":"markdown","id":"MWI43wz2-q-c","metadata":{"id":"MWI43wz2-q-c"},"source":["### Figure 4.15 — Top-k Occlusion Faithfulness\n","\n","**Interpretation:** Increasing probability reduction after masking the most influential events supports the faithfulness of the explanation.\n"]},{"cell_type":"code","execution_count":54,"id":"tLTBC0jr-q-c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"tLTBC0jr-q-c","outputId":"ed1fcd12-b29f-4bcf-9ea4-600d6ed27b85","executionInfo":{"status":"ok","timestamp":1785510855462,"user_tz":-180,"elapsed":1489,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["   k  mean_abs_logit_change  median_abs_logit_change  mean_probability_drop  \\\n","0  1               0.096788                 0.069742               0.000001   \n","1  2               0.270942                 0.133791               0.006063   \n","2  3               0.339358                 0.195854               0.004859   \n","3  5               0.475656                 0.326550               0.001007   \n","\n","   median_probability_drop  sequence_count  \n","0             4.768372e-07             140  \n","1             1.072884e-06             140  \n","2             1.549721e-06             140  \n","3             2.861023e-06             140  "],"text/html":["\n","  <div id=\"df-8ff12f5b-1615-446e-bc46-9e1403f85c3a\" class=\"colab-df-container\">\n","    <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>k</th>\n","      <th>mean_abs_logit_change</th>\n","      <th>median_abs_logit_change</th>\n","      <th>mean_probability_drop</th>\n","      <th>median_probability_drop</th>\n","      <th>sequence_count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>0.096788</td>\n","      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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Important: non-monotonic top-k behavior is reported as an interaction diagnostic, not automatically treated as a coding error.\n"]}],"source":["# ============================================================\n","# Figure 4.15 — Corrected Top-k Occlusion Faithfulness\n","# ============================================================\n","\n","if \"topk_faithfulness_df\" not in globals():\n","    topk_faithfulness_df = pd.read_csv(\n","        TOPK_FAITHFULNESS_PATH\n","    )\n","\n","topk_summary_df = (\n","    topk_faithfulness_df\n","    .groupby(\"k\")\n","    .agg(\n","        mean_abs_logit_change=(\"topk_abs_logit_change\", \"mean\"),\n","        median_abs_logit_change=(\"topk_abs_logit_change\", \"median\"),\n","        mean_probability_drop=(\"topk_probability_drop\", \"mean\"),\n","        median_probability_drop=(\"topk_probability_drop\", \"median\"),\n","        sequence_count=(\"sequence_local_test_index\", \"nunique\"),\n","    )\n","    .reset_index()\n",")\n","\n","display(topk_summary_df)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.plot(\n","    topk_summary_df[\"k\"],\n","    topk_summary_df[\"mean_abs_logit_change\"],\n","    marker=\"o\",\n","    label=\"Mean absolute logit change\",\n",")\n","plt.plot(\n","    topk_summary_df[\"k\"],\n","    topk_summary_df[\"median_abs_logit_change\"],\n","    marker=\"o\",\n","    label=\"Median absolute logit change\",\n",")\n","plt.xlabel(\"Number of top events masked\")\n","plt.ylabel(\"Absolute logit change\")\n","plt.title(\"Top-k Occlusion Faithfulness\")\n","plt.legend()\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_15_topk_occlusion_faithfulness.png\",\n","    \"Figure 4.15\",\n","    \"Top-k Occlusion Faithfulness\",\n",")\n","plt.show()\n","\n","print(\n","    \"Important: non-monotonic top-k behavior is reported as an \"\n","    \"interaction diagnostic, not automatically treated as a coding error.\"\n",")\n"]},{"cell_type":"markdown","id":"zCuTT1lM-q-c","metadata":{"id":"zCuTT1lM-q-c"},"source":["### Figure 4.16 — SHAP and Occlusion Top-Event Agreement\n","\n","**Interpretation:** Agreement between the two methods provides a consistency check for the most influential event identified in each explained sequence.\n"]},{"cell_type":"code","execution_count":55,"id":"BnVmVGUG-q-c","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":675},"id":"BnVmVGUG-q-c","outputId":"cf75b767-8861-4b74-f7f7-79f3b996fde4","executionInfo":{"status":"ok","timestamp":1785510856768,"user_tz":-180,"elapsed":1292,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["             agreement  count\n","0  Different top event     19\n","1       Same top event      2"],"text/html":["\n","  <div id=\"df-3f37087d-f9ba-4634-a416-42acfd0c3c4e\" class=\"colab-df-container\">\n","    <div>\n","<style 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Compared sequences: 21\n","Top-1 agreement rate: 0.09523809523809523\n","Mean top-k overlap rate: 0.38095238095238093\n","Median Spearman rank correlation: 0.13939393939393938\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.16 — Corrected SHAP and Occlusion Agreement\n","# ============================================================\n","\n","if \"shap_occ_compare_df\" not in globals():\n","    shap_occ_compare_df = pd.read_csv(SHAP_COMPARE_PATH)\n","\n","if shap_occ_compare_df.empty:\n","    raise ValueError(\"No SHAP–Occlusion comparison rows are available.\")\n","\n","agreement_counts = (\n","    shap_occ_compare_df[\"same_top_event\"]\n","    .value_counts()\n","    .sort_index()\n",")\n","\n","agreement_df = pd.DataFrame({\n","    \"agreement\": [\n","        \"Different top event\",\n","        \"Same top event\",\n","    ],\n","    \"count\": [\n","        int(agreement_counts.get(0, 0)),\n","        int(agreement_counts.get(1, 0)),\n","    ],\n","})\n","\n","agreement_rate = float(\n","    shap_occ_compare_df[\"same_top_event\"].mean()\n",")\n","mean_topk_overlap = float(\n","    shap_occ_compare_df[\"topk_overlap_rate\"].mean()\n",")\n","median_spearman = float(\n","    shap_occ_compare_df[\n","        \"spearman_rank_correlation\"\n","    ].median(skipna=True)\n",")\n","\n","display(agreement_df)\n","print(\"Compared sequences:\", len(shap_occ_compare_df))\n","print(\"Top-1 agreement rate:\", agreement_rate)\n","print(\"Mean top-k overlap rate:\", mean_topk_overlap)\n","print(\"Median Spearman rank correlation:\", median_spearman)\n","\n","plt.figure(figsize=THESIS_FIGSIZE)\n","plt.bar(\n","    agreement_df[\"agreement\"],\n","    agreement_df[\"count\"],\n",")\n","plt.xlabel(\"Agreement result\")\n","plt.ylabel(\"Number of sequences\")\n","plt.title(\n","    \"SHAP vs Occlusion Top-Event Agreement \"\n","    f\"(n={len(shap_occ_compare_df)}, rate={agreement_rate:.2%})\"\n",")\n","finalize_figure()\n","save_current_figure(\n","    \"figure_4_16_shap_occlusion_agreement.png\",\n","    \"Figure 4.16\",\n","    \"SHAP and Occlusion Top-Event Agreement\",\n",")\n","plt.show()\n"]},{"cell_type":"markdown","id":"e4fef8da","metadata":{"id":"e4fef8da"},"source":["## Step 38 — Generate the SHAP Case-Study Figure\n","\n","### Figure 4.17 — Event-Level SHAP Explanation for the Selected Case Study\n","\n","**Interpretation:** Positive values push the prediction toward the anomalous class, while negative values reduce the anomaly score. This figure uses the same strongest case selected by occlusion.\n"]},{"cell_type":"code","execution_count":56,"id":"f1c890c5","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":507},"id":"f1c890c5","outputId":"813cf24e-eb42-4dd7-d045-6b7e66fbdaf2","executionInfo":{"status":"ok","timestamp":1785510858674,"user_tz":-180,"elapsed":1903,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["# ============================================================\n","# Figure 4.17 — Event-Level SHAP Explanation\n","# ============================================================\n","\n","if not shap_case_table.empty:\n","    plot_df = shap_case_table.sort_values(\"event_position\")\n","\n","    plt.figure(figsize=THESIS_FIGSIZE)\n","    plt.bar(\n","        plot_df[\"event_position\"].astype(str),\n","        plot_df[\"shap_value\"]\n","    )\n","    plt.axhline(0, linewidth=1)\n","    plt.xlabel(\"Event position in the 10-line sequence\")\n","    plt.ylabel(\"SHAP value\")\n","    plt.title(\"Event-Level SHAP Explanation for the Selected Case Study\")\n","    finalize_figure()\n","    save_current_figure(\n","        \"figure_4_17_shap_case_study.png\",\n","        \"Figure 4.17\",\n","        \"Event-Level SHAP Explanation for the Selected Case Study\"\n","    )\n","    plt.show()\n","else:\n","    raise ValueError(\"SHAP case-study table is empty.\")\n"]},{"cell_type":"markdown","id":"1b571dd9","metadata":{"id":"1b571dd9"},"source":["## Step 39 — Thesis-ready SHAP summary\n","\n","This prints the supporting SHAP outputs and the agreement rate between SHAP and occlusion.\n"]},{"cell_type":"code","execution_count":57,"id":"c7169377","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"c7169377","outputId":"2f7bb5c6-41b3-40c5-d57b-faf3fdb783e3","executionInfo":{"status":"ok","timestamp":1785510858688,"user_tz":-180,"elapsed":12,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["SHAP event-level explanation completed.\n","Saved SHAP local explanations: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_event_level_explanations.csv\n","Saved SHAP case study: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_case_study.csv\n","Saved SHAP vs Occlusion comparison: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_occlusion_comparison.csv\n","Compared sequences: 21\n","SHAP–Occlusion top-event agreement rate: 0.09523809523809523\n","Mean top-k overlap rate: 0.38095238095238093\n","Median Spearman rank correlation: 0.13939393939393938\n","\n","Thesis note:\n","\n","SHAP was applied as a supporting local XAI method at the event\n","level. Each position in the 10-line input sequence was treated as\n","an interpretable feature. SHAP and occlusion were evaluated on the\n","same selected sequences. Agreement is reported using Top-1\n","agreement, Top-k overlap, and Spearman rank correlation rather than\n","a single case. The strongest case study is presented only as an\n","illustrative example and is not treated as representative of the\n","entire test set. SHAP and occlusion explain the model's decision;\n","they do not establish the true operational root cause.\n","\n"]}],"source":["# ============================================================\n","# Step 39 — Corrected Thesis-Ready SHAP Summary\n","# ============================================================\n","\n","print(\"SHAP event-level explanation completed.\")\n","print(\"Saved SHAP local explanations:\", SHAP_LOCAL_PATH)\n","print(\"Saved SHAP case study:\", SHAP_CASE_PATH)\n","print(\"Saved SHAP vs Occlusion comparison:\", SHAP_COMPARE_PATH)\n","\n","if (\n","    \"shap_occ_compare_df\" in globals()\n","    and len(shap_occ_compare_df) > 0\n","):\n","    agreement_rate = float(\n","        shap_occ_compare_df[\"same_top_event\"].mean()\n","    )\n","    mean_topk_overlap = float(\n","        shap_occ_compare_df[\"topk_overlap_rate\"].mean()\n","    )\n","    median_spearman = float(\n","        shap_occ_compare_df[\n","            \"spearman_rank_correlation\"\n","        ].median(skipna=True)\n","    )\n","\n","    print(\"Compared sequences:\", len(shap_occ_compare_df))\n","    print(\n","        \"SHAP–Occlusion top-event agreement rate:\",\n","        agreement_rate,\n","    )\n","    print(\"Mean top-k overlap rate:\", mean_topk_overlap)\n","    print(\n","        \"Median Spearman rank correlation:\",\n","        median_spearman,\n","    )\n","\n","print(\"\"\"\n","Thesis note:\n","\n","SHAP was applied as a supporting local XAI method at the event\n","level. Each position in the 10-line input sequence was treated as\n","an interpretable feature. SHAP and occlusion were evaluated on the\n","same selected sequences. Agreement is reported using Top-1\n","agreement, Top-k overlap, and Spearman rank correlation rather than\n","a single case. The strongest case study is presented only as an\n","illustrative example and is not treated as representative of the\n","entire test set. SHAP and occlusion explain the model's decision;\n","they do not establish the true operational root cause.\n","\"\"\")\n"]},{"cell_type":"markdown","metadata":{"id":"m_JB3a8MoGRo"},"source":["# Production Audit Notes — Corrected Release\n","\n","The following controls are applied in this release:\n","\n","- The temporal 80/20 scientific protocol is preserved.\n","- Epoch and threshold are selected jointly by validation F1.\n","- Validation loss is used only to break equal-F1 epoch ties.\n","- Equal-F1 threshold plateaus are reported explicitly; the closest tested threshold to the plateau midpoint is selected.\n","- Random seeds and deterministic settings are explicit, but bitwise-identical GPU reruns are not claimed.\n","- The positive-class multiplier of 2.0 is reported as an experimental hyperparameter rather than as the raw class ratio.\n","- Input caches remain separate from generated production artifacts.\n","- Results are organized into models, figures, tables, XAI, SHAP, logs, and summary folders.\n","- Figures are exported as 300-DPI PNG and vector PDF.\n","- Occlusion ranking and faithfulness use logit changes to reduce sigmoid-saturation effects.\n","- Probability changes remain available as complementary human-readable measures.\n","- Non-monotonic top-k behavior is treated as an event-interaction diagnostic, not silently interpreted as proof of faithfulness.\n","- SHAP and occlusion are compared on exactly the same sequences.\n","- Agreement is measured with Top-1 agreement, Top-k overlap, and Spearman rank correlation.\n","- The strongest case is explicitly labeled illustrative and not representative.\n","- SHAP and occlusion explain model behavior; they do not prove operational root cause.\n","- CSV, JSON, README, requirements, figure manifest, and table manifest outputs are generated.\n"],"id":"m_JB3a8MoGRo"},{"cell_type":"markdown","id":"p1CVDY4g-q-d","metadata":{"id":"p1CVDY4g-q-d"},"source":["# Final Thesis Summary\n","\n","This final section consolidates the active configuration, best detection result, XAI outputs, and generated artifact locations. The values are read directly from notebook variables rather than entered manually.\n"]},{"cell_type":"code","execution_count":58,"id":"-0eScmZG-q-d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"-0eScmZG-q-d","outputId":"ad2cb686-ca43-4dce-8c7a-075123e6c45e","executionInfo":{"status":"ok","timestamp":1785510861161,"user_tz":-180,"elapsed":2472,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["         Section                                    Item  \\\n","0     experiment                                 dataset   \n","1     experiment                                   model   \n","2     experiment                        notebook_version   \n","3     experiment                                  run_id   \n","4     experiment                           window_method   \n","5     experiment                             window_size   \n","6     experiment                               step_size   \n","7     experiment                          temporal_split   \n","8     experiment                  development_validation   \n","9   architecture                          minilm_encoder   \n","10  architecture            semantic_embedding_dimension   \n","11  architecture                    projection_dimension   \n","12  architecture                      transformer_layers   \n","13  architecture                         attention_heads   \n","14  architecture                   feedforward_dimension   \n","15  architecture                                 dropout   \n","16     selection                              best_epoch   \n","17     selection              threshold_selection_source   \n","18     selection                          best_threshold   \n","19     selection                      best_validation_f1   \n","20          data                   development_sequences   \n","21          data          development_training_sequences   \n","22          data                    validation_sequences   \n","23          data                          test_sequences   \n","24          data                         unique_messages   \n","25   performance                                accuracy   \n","26   performance                               precision   \n","27   performance                                  recall   \n","28   performance                                f1_score   \n","29   performance                                 roc_auc   \n","30   performance                pr_auc_average_precision   \n","31   performance                                      tn   \n","32   performance                                      fp   \n","33   performance                                      fn   \n","34   performance                                      tp   \n","35           xai                          primary_method   \n","36           xai                       supporting_method   \n","37           xai                         ranking_measure   \n","38           xai           reported_faithfulness_measure   \n","39           xai                     occlusion_sequences   \n","40           xai                          shap_sequences   \n","41           xai      faithfulness_mean_probability_drop   \n","42           xai    faithfulness_median_probability_drop   \n","43           xai       faithfulness_max_probability_drop   \n","44           xai  mean_logit_drop_for_ranking_diagnostic   \n","45           xai      shap_occlusion_top_event_agreement   \n","46           xai        shap_occlusion_mean_topk_overlap   \n","47           xai          shap_occlusion_median_spearman   \n","48           xai       shap_occlusion_compared_sequences   \n","49     artifacts                        model_checkpoint   \n","50     artifacts                           configuration   \n","51     artifacts                     development_history   \n","52     artifacts              validation_threshold_table   \n","53     artifacts                  final_training_history   \n","54     artifacts                           results_table   \n","55     artifacts                     predictions_for_xai   \n","56     artifacts                       figures_directory   \n","57     artifacts                        tables_directory   \n","58     artifacts                           xai_directory   \n","59     artifacts                          shap_directory   \n","\n","                                                Value  \n","0                                                 BGL  \n","1                                           LogMiniLM  \n","2                                       5.2-corrected  \n","3                            LogMiniLM_V5_2_Corrected  \n","4                       Fixed 10-line non-overlapping  \n","5                                                  10  \n","6                                                  10  \n","7                                               80/20  \n","8                    10% of the development partition  \n","9              sentence-transformers/all-MiniLM-L6-v2  \n","10                                                384  \n","11                                                128  \n","12                                                  1  \n","13                                                  2  \n","14                                                256  \n","15                                                0.1  \n","16                                                  3  \n","17                           Temporal validation only  \n","18                                                0.2  \n","19                                           0.965767  \n","20                                             377079  \n","21                                             339372  \n","22                                              37707  \n","23                                              94270  \n","24                                              47012  \n","25                                           0.998419  \n","26                                           0.991973  \n","27                                           0.981642  \n","28                                            0.98678  \n","29                                           0.999309  \n","30                                           0.992511  \n","31                                              88560  \n","32                                                 45  \n","33                                                104  \n","34                                               5561  \n","35                              Event-level occlusion  \n","36                                               SHAP  \n","37                                         Logit drop  \n","38                                   Probability drop  \n","39                                                140  \n","40                                                 21  \n","41                                           0.000001  \n","42                                                0.0  \n","43                                            0.00001  \n","44                                           0.096788  \n","45                                           0.095238  \n","46                                           0.380952  \n","47                                           0.139394  \n","48                                                 21  \n","49  /content/drive/MyDrive/logminilm_cache/product...  \n","50  /content/drive/MyDrive/logminilm_cache/product...  \n","51  /content/drive/MyDrive/logminilm_cache/product...  \n","52  /content/drive/MyDrive/logminilm_cache/product...  \n","53  /content/drive/MyDrive/logminilm_cache/product...  \n","54  /content/drive/MyDrive/logminilm_cache/product...  \n","55  /content/drive/MyDrive/logminilm_cache/product...  \n","56  /content/drive/MyDrive/logminilm_cache/product...  \n","57  /content/drive/MyDrive/logminilm_cache/product...  \n","58  /content/drive/MyDrive/logminilm_cache/product...  \n","59  /content/drive/MyDrive/logminilm_cache/product...  "],"text/html":["\n","  <div id=\"df-d62b3ea4-c070-4547-91a2-f01fe8ee29cb\" class=\"colab-df-container\">\n","    <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>Section</th>\n","      <th>Item</th>\n","      <th>Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>experiment</td>\n","      <td>dataset</td>\n","      <td>BGL</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>experiment</td>\n","      <td>model</td>\n","      <td>LogMiniLM</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>experiment</td>\n","      <td>notebook_version</td>\n","      <td>5.2-corrected</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>experiment</td>\n","      <td>run_id</td>\n","      <td>LogMiniLM_V5_2_Corrected</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>experiment</td>\n","      <td>window_method</td>\n","      <td>Fixed 10-line non-overlapping</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>experiment</td>\n","      <td>window_size</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>experiment</td>\n","      <td>step_size</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>experiment</td>\n","      <td>temporal_split</td>\n","      <td>80/20</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>experiment</td>\n","      <td>development_validation</td>\n","      <td>10% of the development partition</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>architecture</td>\n","      <td>minilm_encoder</td>\n","      <td>sentence-transformers/all-MiniLM-L6-v2</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>architecture</td>\n","      <td>semantic_embedding_dimension</td>\n","      <td>384</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>architecture</td>\n","      <td>projection_dimension</td>\n","      <td>128</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>architecture</td>\n","      <td>transformer_layers</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>architecture</td>\n","      <td>attention_heads</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>architecture</td>\n","      <td>feedforward_dimension</td>\n","      <td>256</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>architecture</td>\n","      <td>dropout</td>\n","      <td>0.1</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>selection</td>\n","      <td>best_epoch</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>selection</td>\n","      <td>threshold_selection_source</td>\n","      <td>Temporal validation only</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>selection</td>\n","      <td>best_threshold</td>\n","      <td>0.2</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>selection</td>\n","      <td>best_validation_f1</td>\n","      <td>0.965767</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>data</td>\n","      <td>development_sequences</td>\n","      <td>377079</td>\n","    </tr>\n","    <tr>\n","      <th>21</th>\n","      <td>data</td>\n","      <td>development_training_sequences</td>\n","      <td>339372</td>\n","    </tr>\n","    <tr>\n","      <th>22</th>\n","      <td>data</td>\n","      <td>validation_sequences</td>\n","      <td>37707</td>\n","    </tr>\n","    <tr>\n","      <th>23</th>\n","      <td>data</td>\n","      <td>test_sequences</td>\n","      <td>94270</td>\n","    </tr>\n","    <tr>\n","      <th>24</th>\n","      <td>data</td>\n","      <td>unique_messages</td>\n","      <td>47012</td>\n","    </tr>\n","    <tr>\n","      <th>25</th>\n","      <td>performance</td>\n","      <td>accuracy</td>\n","      <td>0.998419</td>\n","    </tr>\n","    <tr>\n","      <th>26</th>\n","      <td>performance</td>\n","      <td>precision</td>\n","      <td>0.991973</td>\n","    </tr>\n","    <tr>\n","      <th>27</th>\n","      <td>performance</td>\n","      <td>recall</td>\n","      <td>0.981642</td>\n","    </tr>\n","    <tr>\n","      <th>28</th>\n","      <td>performance</td>\n","      <td>f1_score</td>\n","      <td>0.98678</td>\n","    </tr>\n","    <tr>\n","      <th>29</th>\n","      <td>performance</td>\n","      <td>roc_auc</td>\n","      <td>0.999309</td>\n","    </tr>\n","    <tr>\n","      <th>30</th>\n","      <td>performance</td>\n","      <td>pr_auc_average_precision</td>\n","      <td>0.992511</td>\n","    </tr>\n","    <tr>\n","      <th>31</th>\n","      <td>performance</td>\n","      <td>tn</td>\n","      <td>88560</td>\n","    </tr>\n","    <tr>\n","      <th>32</th>\n","      <td>performance</td>\n","      <td>fp</td>\n","      <td>45</td>\n","    </tr>\n","    <tr>\n","      <th>33</th>\n","      <td>performance</td>\n","      <td>fn</td>\n","      <td>104</td>\n","    </tr>\n","    <tr>\n","      <th>34</th>\n","      <td>performance</td>\n","      <td>tp</td>\n","      <td>5561</td>\n","    </tr>\n","    <tr>\n","      <th>35</th>\n","      <td>xai</td>\n","      <td>primary_method</td>\n","      <td>Event-level occlusion</td>\n","    </tr>\n","    <tr>\n","      <th>36</th>\n","      <td>xai</td>\n","      <td>supporting_method</td>\n","      <td>SHAP</td>\n","    </tr>\n","    <tr>\n","      <th>37</th>\n","      <td>xai</td>\n","      <td>ranking_measure</td>\n","      <td>Logit drop</td>\n","    </tr>\n","    <tr>\n","      <th>38</th>\n","      <td>xai</td>\n","      <td>reported_faithfulness_measure</td>\n","      <td>Probability drop</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>xai</td>\n","      <td>occlusion_sequences</td>\n","      <td>140</td>\n","    </tr>\n","    <tr>\n","      <th>40</th>\n","      <td>xai</td>\n","      <td>shap_sequences</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>41</th>\n","      <td>xai</td>\n","      <td>faithfulness_mean_probability_drop</td>\n","      <td>0.000001</td>\n","    </tr>\n","    <tr>\n","      <th>42</th>\n","      <td>xai</td>\n","      <td>faithfulness_median_probability_drop</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>xai</td>\n","      <td>faithfulness_max_probability_drop</td>\n","      <td>0.00001</td>\n","    </tr>\n","    <tr>\n","      <th>44</th>\n","      <td>xai</td>\n","      <td>mean_logit_drop_for_ranking_diagnostic</td>\n","      <td>0.096788</td>\n","    </tr>\n","    <tr>\n","      <th>45</th>\n","      <td>xai</td>\n","      <td>shap_occlusion_top_event_agreement</td>\n","      <td>0.095238</td>\n","    </tr>\n","    <tr>\n","      <th>46</th>\n","      <td>xai</td>\n","      <td>shap_occlusion_mean_topk_overlap</td>\n","      <td>0.380952</td>\n","    </tr>\n","    <tr>\n","      <th>47</th>\n","      <td>xai</td>\n","      <td>shap_occlusion_median_spearman</td>\n","      <td>0.139394</td>\n","    </tr>\n","    <tr>\n","      <th>48</th>\n","      <td>xai</td>\n","      <td>shap_occlusion_compared_sequences</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>49</th>\n","      <td>artifacts</td>\n","      <td>model_checkpoint</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>50</th>\n","      <td>artifacts</td>\n","      <td>configuration</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>51</th>\n","      <td>artifacts</td>\n","      <td>development_history</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>52</th>\n","      <td>artifacts</td>\n","      <td>validation_threshold_table</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>53</th>\n","      <td>artifacts</td>\n","      <td>final_training_history</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>54</th>\n","      <td>artifacts</td>\n","      <td>results_table</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>55</th>\n","      <td>artifacts</td>\n","      <td>predictions_for_xai</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>56</th>\n","      <td>artifacts</td>\n","      <td>figures_directory</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>57</th>\n","      <td>artifacts</td>\n","      <td>tables_directory</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>58</th>\n","      <td>artifacts</td>\n","      <td>xai_directory</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>59</th>\n","      <td>artifacts</td>\n","      <td>shap_directory</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d62b3ea4-c070-4547-91a2-f01fe8ee29cb')\"\n","            title=\"Convert this dataframe to an interactive 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drop               0.000001\n","7    Median faithfulness probability drop                    0.0\n","8   Maximum faithfulness probability drop                0.00001\n","9        Mean logit drop used for ranking               0.096788\n","10     SHAP–occlusion top-event agreement               0.095238"],"text/html":["\n","  <div id=\"df-10c9515d-0531-4be8-9fd6-b978dd3bc289\" class=\"colab-df-container\">\n","    <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>Item</th>\n","      <th>Value</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Primary XAI</td>\n","      <td>Event-level occlusion</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Supporting XAI</td>\n","      <td>SHAP</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Ranking measure</td>\n","      <td>Logit drop</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Reported faithfulness measure</td>\n","      <td>Probability drop</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Occlusion sequences</td>\n","      <td>140</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>SHAP sequences</td>\n","      <td>21</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Mean faithfulness probability drop</td>\n","      <td>0.000001</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Median faithfulness probability drop</td>\n","      <td>0.0</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Maximum faithfulness probability drop</td>\n","      <td>0.00001</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Mean logit drop used for ranking</td>\n","      <td>0.096788</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>SHAP–occlusion top-event agreement</td>\n","      <td>0.095238</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10c9515d-0531-4be8-9fd6-b978dd3bc289')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n"," 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0.0967876774924142\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Production package: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected\n"]}],"source":["# ============================================================\n","# Final Thesis Summary and Reproducibility Package\n","# ============================================================\n","\n","agreement_rate = (\n","    float(shap_occ_compare_df[\"same_top_event\"].mean())\n","    if not shap_occ_compare_df.empty\n","    else None\n",")\n","mean_topk_overlap = (\n","    float(shap_occ_compare_df[\"topk_overlap_rate\"].mean())\n","    if (\n","        not shap_occ_compare_df.empty\n","        and \"topk_overlap_rate\" in shap_occ_compare_df.columns\n","    )\n","    else None\n",")\n","median_spearman = (\n","    float(\n","        shap_occ_compare_df[\n","            \"spearman_rank_correlation\"\n","        ].median(skipna=True)\n","    )\n","    if (\n","        not shap_occ_compare_df.empty\n","        and \"spearman_rank_correlation\" in shap_occ_compare_df.columns\n","    )\n","    else None\n",")\n","\n","faithfulness_probability_mean = (\n","    float(faithfulness_df[\"top_event_probability_drop\"].mean())\n","    if not faithfulness_df.empty\n","    else None\n",")\n","faithfulness_probability_median = (\n","    float(faithfulness_df[\"top_event_probability_drop\"].median())\n","    if not faithfulness_df.empty\n","    else None\n",")\n","faithfulness_probability_max = (\n","    float(faithfulness_df[\"top_event_probability_drop\"].max())\n","    if not faithfulness_df.empty\n","    else None\n",")\n","faithfulness_logit_mean = (\n","    float(faithfulness_df[\"top_event_logit_drop\"].mean())\n","    if not faithfulness_df.empty\n","    else None\n",")\n","\n","summary_payload = {\n","    \"experiment\": {\n","        \"dataset\": DATASET_NAME,\n","        \"model\": MODEL_NAME,\n","        \"notebook_version\": NOTEBOOK_VERSION,\n","        \"run_id\": RUN_ID,\n","        \"window_method\": \"Fixed 10-line non-overlapping\",\n","        \"window_size\": int(WINDOW_SIZE),\n","        \"step_size\": int(STEP_SIZE),\n","        \"temporal_split\": \"80/20\",\n","        \"development_validation\": (\n","            f\"{VALIDATION_RATIO_WITHIN_TRAIN:.0%} of the development partition\"\n","        ),\n","    },\n","    \"architecture\": {\n","        \"minilm_encoder\": MINILM_MODEL_NAME,\n","        \"semantic_embedding_dimension\": int(unique_embeddings.shape[1]),\n","        \"projection_dimension\": int(PROJECTION_DIM),\n","        \"transformer_layers\": int(NUM_TRANSFORMER_LAYERS),\n","        \"attention_heads\": int(NUM_ATTENTION_HEADS),\n","        \"feedforward_dimension\": int(FEEDFORWARD_DIM),\n","        \"dropout\": float(DROPOUT),\n","    },\n","    \"selection\": {\n","        \"best_epoch\": int(best_epoch),\n","        \"threshold_selection_source\": \"Temporal validation only\",\n","        \"best_threshold\": float(best_threshold),\n","        \"best_validation_f1\": float(best_validation_f1),\n","    },\n","    \"data\": {\n","        \"development_sequences\": int(len(y_train)),\n","        \"development_training_sequences\": int(len(y_dev_train)),\n","        \"validation_sequences\": int(len(y_val)),\n","        \"test_sequences\": int(len(y_test)),\n","        \"unique_messages\": int(len(unique_texts)),\n","    },\n","    \"performance\": {\n","        \"accuracy\": float(final_metrics[\"accuracy\"]),\n","        \"precision\": float(final_metrics[\"precision\"]),\n","        \"recall\": float(final_metrics[\"recall\"]),\n","        \"f1_score\": float(final_metrics[\"f1\"]),\n","        \"roc_auc\": float(test_roc_auc),\n","        \"pr_auc_average_precision\": float(test_pr_auc),\n","        \"tn\": int(final_metrics[\"tn\"]),\n","        \"fp\": int(final_metrics[\"fp\"]),\n","        \"fn\": int(final_metrics[\"fn\"]),\n","        \"tp\": int(final_metrics[\"tp\"]),\n","    },\n","    \"xai\": {\n","        \"primary_method\": \"Event-level occlusion\",\n","        \"supporting_method\": \"SHAP\",\n","        \"ranking_measure\": \"Logit drop\",\n","        \"reported_faithfulness_measure\": \"Probability drop\",\n","        \"occlusion_sequences\": int(len(faithfulness_df)),\n","        \"shap_sequences\": int(len(shap_sequence_indices)),\n","        \"faithfulness_mean_probability_drop\": faithfulness_probability_mean,\n","        \"faithfulness_median_probability_drop\": faithfulness_probability_median,\n","        \"faithfulness_max_probability_drop\": faithfulness_probability_max,\n","        \"mean_logit_drop_for_ranking_diagnostic\": faithfulness_logit_mean,\n","        \"shap_occlusion_top_event_agreement\": agreement_rate,\n","        \"shap_occlusion_mean_topk_overlap\": mean_topk_overlap,\n","        \"shap_occlusion_median_spearman\": median_spearman,\n","        \"shap_occlusion_compared_sequences\": int(len(shap_occ_compare_df)),\n","    },\n","    \"artifacts\": {\n","        \"model_checkpoint\": str(MODEL_PATH),\n","        \"configuration\": str(CONFIG_JSON_PATH),\n","        \"development_history\": str(DEVELOPMENT_HISTORY_PATH),\n","        \"validation_threshold_table\": str(VALIDATION_RESULTS_PATH),\n","        \"final_training_history\": str(FINAL_TRAINING_HISTORY_PATH),\n","        \"results_table\": str(RESULTS_PATH),\n","        \"predictions_for_xai\": str(PREDICTIONS_PATH),\n","        \"figures_directory\": str(FIGURES_DIR),\n","        \"tables_directory\": str(TABLES_DIR),\n","        \"xai_directory\": str(XAI_DIR),\n","        \"shap_directory\": str(SHAP_DIR),\n","    },\n","}\n","\n","summary_rows = [\n","    [section, item, value]\n","    for section, values in summary_payload.items()\n","    for item, value in values.items()\n","]\n","final_summary_df = pd.DataFrame(\n","    summary_rows,\n","    columns=[\"Section\", \"Item\", \"Value\"]\n",")\n","\n","save_table(\n","    final_summary_df,\n","    FINAL_SUMMARY_CSV_PATH,\n","    \"Final LogMiniLM thesis summary\"\n",")\n","save_json(summary_payload, FINAL_SUMMARY_JSON_PATH)\n","\n","xai_summary_df = pd.DataFrame([\n","    [\"Primary XAI\", \"Event-level occlusion\"],\n","    [\"Supporting XAI\", \"SHAP\"],\n","    [\"Ranking measure\", \"Logit drop\"],\n","    [\"Reported faithfulness measure\", \"Probability drop\"],\n","    [\"Occlusion sequences\", len(faithfulness_df)],\n","    [\"SHAP sequences\", len(shap_sequence_indices)],\n","    [\"Mean faithfulness probability drop\", faithfulness_probability_mean],\n","    [\"Median faithfulness probability drop\", faithfulness_probability_median],\n","    [\"Maximum faithfulness probability drop\", faithfulness_probability_max],\n","    [\"Mean logit drop used for ranking\", faithfulness_logit_mean],\n","    [\"SHAP–occlusion top-event agreement\", agreement_rate],\n","], columns=[\"Item\", \"Value\"])\n","\n","save_table(\n","    xai_summary_df,\n","    XAI_SUMMARY_PATH,\n","    \"Unified XAI summary\"\n",")\n","\n","requirements_text = f\"\"\"# Core dependencies used by LogMiniLM Thesis Notebook v{NOTEBOOK_VERSION}\n","torch\n","numpy\n","pandas\n","matplotlib\n","scikit-learn\n","sentence-transformers\n","shap\n","pyarrow\n","\"\"\"\n","\n","readme_text = f\"\"\"LogMiniLM Defense-Ready Results\n","================================\n","\n","Run ID: {RUN_ID}\n","Notebook version: {NOTEBOOK_VERSION}\n","Dataset: {DATASET_NAME}\n","Model: {MODEL_NAME}\n","\n","Directory structure\n","-------------------\n","models/   Final model checkpoint.\n","figures/  Thesis figures in PNG and PDF formats.\n","tables/   Performance, validation, prediction, and comparison tables.\n","xai/      Event-level occlusion explanations and faithfulness results.\n","shap/     SHAP explanations and SHAP–occlusion comparison.\n","logs/     Development and final-training histories.\n","summary/  Configuration, manifests, integrity checks, and final summaries.\n","\n","Scientific protocol\n","-------------------\n","The notebook uses a chronological 80/20 development-test split. The\n","last 10% of the development partition is used as temporal validation\n","for epoch and threshold selection. The untouched test partition is\n","evaluated once after final retraining from scratch on the complete\n","development partition.\n","\n","Explainability protocol\n","-----------------------\n","Event-level occlusion is the primary explanation method. Events are\n","ranked by logit drop to reduce sigmoid-saturation effects. Faithfulness\n","is reported using probability drop. SHAP is used as a supporting\n","event-level explanation method.\n","\"\"\"\n","\n","save_text(readme_text, README_PATH)\n","save_text(requirements_text, REQUIREMENTS_PATH)\n","\n","display(final_summary_df)\n","display(xai_summary_df)\n","print(\"Production package:\", RUN_DIR)\n"]},{"cell_type":"markdown","id":"rdlZwIKL-q-c","metadata":{"id":"rdlZwIKL-q-c"},"source":["## Step 40 — Export Figure and Table Manifests\n","\n","The manifests are generated after all figures, tables, and summaries so they reflect the complete active run.\n"]},{"cell_type":"code","execution_count":59,"id":"PLuBw6iM-q-d","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"PLuBw6iM-q-d","outputId":"ee715f0f-4a4b-43bb-eddd-8572a01b4313","executionInfo":{"status":"ok","timestamp":1785510862175,"user_tz":-180,"elapsed":1011,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["   figure_number                                              title  \\\n","0     Figure 4.1                  BGL Line-Level Class Distribution   \n","1     Figure 4.2                  Sequence-Level Class Distribution   \n","2     Figure 4.3    Development Training and Validation Loss Curves   \n","3     Figure 4.4                             Validation F1 by Epoch   \n","4     Figure 4.5               Validation Threshold versus F1-Score   \n","5     Figure 4.6  Validation Precision, Recall, and F1 across Th...   \n","6     Figure 4.7                        Validation Confusion Matrix   \n","7     Figure 4.8                              Final Retraining Loss   \n","8     Figure 4.9                        Final Test Confusion Matrix   \n","9    Figure 4.10     Final Test Prediction Probability Distribution   \n","10   Figure 4.11                               Final Test ROC Curve   \n","11   Figure 4.12                  Final Test Precision–Recall Curve   \n","12   Figure 4.13  Global Mean Occlusion Importance by Event Posi...   \n","13   Figure 4.14                        Local Occlusion Explanation   \n","14   Figure 4.15                       Top-k Occlusion Faithfulness   \n","15   Figure 4.16             SHAP and Occlusion Top-Event Agreement   \n","16   Figure 4.17  Event-Level SHAP Explanation for the Selected ...   \n","\n","                                         png_filename  \\\n","0    figure_4_1_bgl_line_level_class_distribution.png   \n","1    figure_4_2_sequence_level_class_distribution.png   \n","2              figure_4_3_development_loss_curves.png   \n","3               figure_4_4_validation_f1_by_epoch.png   \n","4           figure_4_5_validation_threshold_vs_f1.png   \n","5     figure_4_6_validation_threshold_sensitivity.png   \n","6          figure_4_7_validation_confusion_matrix.png   \n","7                figure_4_8_final_retraining_loss.png   \n","8          figure_4_9_final_test_confusion_matrix.png   \n","9       figure_4_10_test_probability_distribution.png   \n","10               figure_4_11_final_test_roc_curve.png   \n","11  figure_4_12_final_test_precision_recall_curve.png   \n","12    figure_4_13_xai_mean_importance_by_position.png   \n","13         figure_4_14_local_occlusion_case_study.png   \n","14        figure_4_15_topk_occlusion_faithfulness.png   \n","15           figure_4_16_shap_occlusion_agreement.png   \n","16                    figure_4_17_shap_case_study.png   \n","\n","                                         pdf_filename  \\\n","0    figure_4_1_bgl_line_level_class_distribution.pdf   \n","1    figure_4_2_sequence_level_class_distribution.pdf   \n","2              figure_4_3_development_loss_curves.pdf   \n","3               figure_4_4_validation_f1_by_epoch.pdf   \n","4           figure_4_5_validation_threshold_vs_f1.pdf   \n","5     figure_4_6_validation_threshold_sensitivity.pdf   \n","6          figure_4_7_validation_confusion_matrix.pdf   \n","7                figure_4_8_final_retraining_loss.pdf   \n","8          figure_4_9_final_test_confusion_matrix.pdf   \n","9       figure_4_10_test_probability_distribution.pdf   \n","10               figure_4_11_final_test_roc_curve.pdf   \n","11  figure_4_12_final_test_precision_recall_curve.pdf   \n","12    figure_4_13_xai_mean_importance_by_position.pdf   \n","13         figure_4_14_local_occlusion_case_study.pdf   \n","14        figure_4_15_topk_occlusion_faithfulness.pdf   \n","15           figure_4_16_shap_occlusion_agreement.pdf   \n","16                    figure_4_17_shap_case_study.pdf   \n","\n","                                             png_path  \\\n","0   /content/drive/MyDrive/logminilm_cache/product...   \n","1   /content/drive/MyDrive/logminilm_cache/product...   \n","2   /content/drive/MyDrive/logminilm_cache/product...   \n","3   /content/drive/MyDrive/logminilm_cache/product...   \n","4   /content/drive/MyDrive/logminilm_cache/product...   \n","5   /content/drive/MyDrive/logminilm_cache/product...   \n","6   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/content/drive/MyDrive/logminilm_cache/product...  \n","5   /content/drive/MyDrive/logminilm_cache/product...  \n","6   /content/drive/MyDrive/logminilm_cache/product...  \n","7   /content/drive/MyDrive/logminilm_cache/product...  \n","8   /content/drive/MyDrive/logminilm_cache/product...  \n","9   /content/drive/MyDrive/logminilm_cache/product...  \n","10  /content/drive/MyDrive/logminilm_cache/product...  \n","11  /content/drive/MyDrive/logminilm_cache/product...  \n","12  /content/drive/MyDrive/logminilm_cache/product...  \n","13  /content/drive/MyDrive/logminilm_cache/product...  \n","14  /content/drive/MyDrive/logminilm_cache/product...  \n","15  /content/drive/MyDrive/logminilm_cache/product...  \n","16  /content/drive/MyDrive/logminilm_cache/product...  "],"text/html":["\n","  <div id=\"df-595f5a52-e9d5-4f67-8360-e39e16e23945\" class=\"colab-df-container\">\n","    <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>figure_number</th>\n","      <th>title</th>\n","      <th>png_filename</th>\n","      <th>pdf_filename</th>\n","      <th>png_path</th>\n","      <th>pdf_path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Figure 4.1</td>\n","      <td>BGL Line-Level Class Distribution</td>\n","      <td>figure_4_1_bgl_line_level_class_distribution.png</td>\n","      <td>figure_4_1_bgl_line_level_class_distribution.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Figure 4.2</td>\n","      <td>Sequence-Level Class Distribution</td>\n","      <td>figure_4_2_sequence_level_class_distribution.png</td>\n","      <td>figure_4_2_sequence_level_class_distribution.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Figure 4.3</td>\n","      <td>Development Training and Validation Loss Curves</td>\n","      <td>figure_4_3_development_loss_curves.png</td>\n","      <td>figure_4_3_development_loss_curves.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Figure 4.4</td>\n","      <td>Validation F1 by Epoch</td>\n","      <td>figure_4_4_validation_f1_by_epoch.png</td>\n","      <td>figure_4_4_validation_f1_by_epoch.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Figure 4.5</td>\n","      <td>Validation Threshold versus F1-Score</td>\n","      <td>figure_4_5_validation_threshold_vs_f1.png</td>\n","      <td>figure_4_5_validation_threshold_vs_f1.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Figure 4.6</td>\n","      <td>Validation Precision, Recall, and F1 across Th...</td>\n","      <td>figure_4_6_validation_threshold_sensitivity.png</td>\n","      <td>figure_4_6_validation_threshold_sensitivity.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Figure 4.7</td>\n","      <td>Validation Confusion Matrix</td>\n","      <td>figure_4_7_validation_confusion_matrix.png</td>\n","      <td>figure_4_7_validation_confusion_matrix.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Figure 4.8</td>\n","      <td>Final Retraining Loss</td>\n","      <td>figure_4_8_final_retraining_loss.png</td>\n","      <td>figure_4_8_final_retraining_loss.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Figure 4.9</td>\n","      <td>Final Test Confusion Matrix</td>\n","      <td>figure_4_9_final_test_confusion_matrix.png</td>\n","      <td>figure_4_9_final_test_confusion_matrix.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Figure 4.10</td>\n","      <td>Final Test Prediction Probability Distribution</td>\n","      <td>figure_4_10_test_probability_distribution.png</td>\n","      <td>figure_4_10_test_probability_distribution.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Figure 4.11</td>\n","      <td>Final Test ROC Curve</td>\n","      <td>figure_4_11_final_test_roc_curve.png</td>\n","      <td>figure_4_11_final_test_roc_curve.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Figure 4.12</td>\n","      <td>Final Test Precision–Recall Curve</td>\n","      <td>figure_4_12_final_test_precision_recall_curve.png</td>\n","      <td>figure_4_12_final_test_precision_recall_curve.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>Figure 4.13</td>\n","      <td>Global Mean Occlusion Importance by Event Posi...</td>\n","      <td>figure_4_13_xai_mean_importance_by_position.png</td>\n","      <td>figure_4_13_xai_mean_importance_by_position.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>Figure 4.14</td>\n","      <td>Local Occlusion Explanation</td>\n","      <td>figure_4_14_local_occlusion_case_study.png</td>\n","      <td>figure_4_14_local_occlusion_case_study.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>Figure 4.15</td>\n","      <td>Top-k Occlusion Faithfulness</td>\n","      <td>figure_4_15_topk_occlusion_faithfulness.png</td>\n","      <td>figure_4_15_topk_occlusion_faithfulness.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>Figure 4.16</td>\n","      <td>SHAP and Occlusion Top-Event Agreement</td>\n","      <td>figure_4_16_shap_occlusion_agreement.png</td>\n","      <td>figure_4_16_shap_occlusion_agreement.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>Figure 4.17</td>\n","      <td>Event-Level SHAP Explanation for the Selected ...</td>\n","      <td>figure_4_17_shap_case_study.png</td>\n","      <td>figure_4_17_shap_case_study.pdf</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-595f5a52-e9d5-4f67-8360-e39e16e23945')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    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logminilm_results.csv   \n","6             logminilm_predictions_for_xai.csv   \n","7              logminilm_top_xai_candidates.csv   \n","8      logminilm_xai_local_event_importance.csv   \n","9                logminilm_xai_faithfulness.csv   \n","10             logminilm_xai_global_summary.csv   \n","11                 logminilm_xai_case_study.csv   \n","12          logminilm_xai_topk_faithfulness.csv   \n","13          logminilm_xai_strong_case_study.csv   \n","14     logminilm_xai_strong_case_study_top5.csv   \n","15  logminilm_shap_event_level_explanations.csv   \n","16                logminilm_shap_case_study.csv   \n","17      logminilm_shap_occlusion_comparison.csv   \n","18   logminilm_shap_occlusion_event_details.csv   \n","19                            final_summary.csv   \n","20                              xai_summary.csv   \n","\n","                                          description   rows  columns  \\\n","0                      Unique normalized BGL messages  47012        1   \n","1                             LogMiniLM model summary     19        2   \n","2                        Development training history      6        8   \n","3                        Validation threshold results     13        9   \n","4                            Final retraining history      3        2   \n","5                                  Final test results      1       25   \n","6   Final test predictions linked to inclusive BGL...  94270       10   \n","7   Reproducible TP, FP, and FN sequences selected...    140       10   \n","8                  Event-level occlusion explanations   1400       24   \n","9                      Occlusion faithfulness results    140       10   \n","10    Global event-level occlusion importance summary   1014        9   \n","11         Strongest event-level occlusion case study     10       25   \n","12  Top-k occlusion faithfulness using logit and p...    560       15   \n","13        Thesis-ready strongest occlusion case study     10       12   \n","14  Top five influential events in strongest occlu...      5       12   \n","15              SHAP event explanations with metadata    210       21   \n","16                                    SHAP case study     10       12   \n","17  SHAP and occlusion agreement across identical ...     21       10   \n","18  Event-level SHAP and occlusion scores for iden...    210        4   \n","19                     Final LogMiniLM thesis summary     60        3   \n","20                                Unified XAI summary     11        2   \n","\n","                                                 path  \n","0   /content/drive/MyDrive/logminilm_cache/bgl_uni...  \n","1   /content/drive/MyDrive/logminilm_cache/logmini...  \n","2   /content/drive/MyDrive/logminilm_cache/product...  \n","3   /content/drive/MyDrive/logminilm_cache/product...  \n","4   /content/drive/MyDrive/logminilm_cache/product...  \n","5   /content/drive/MyDrive/logminilm_cache/product...  \n","6   /content/drive/MyDrive/logminilm_cache/product...  \n","7   /content/drive/MyDrive/logminilm_cache/product...  \n","8   /content/drive/MyDrive/logminilm_cache/product...  \n","9   /content/drive/MyDrive/logminilm_cache/product...  \n","10  /content/drive/MyDrive/logminilm_cache/product...  \n","11  /content/drive/MyDrive/logminilm_cache/product...  \n","12  /content/drive/MyDrive/logminilm_cache/product...  \n","13  /content/drive/MyDrive/logminilm_cache/product...  \n","14  /content/drive/MyDrive/logminilm_cache/product...  \n","15  /content/drive/MyDrive/logminilm_cache/product...  \n","16  /content/drive/MyDrive/logminilm_cache/product...  \n","17  /content/drive/MyDrive/logminilm_cache/product...  \n","18  /content/drive/MyDrive/logminilm_cache/product...  \n","19  /content/drive/MyDrive/logminilm_cache/product...  \n","20  /content/drive/MyDrive/logminilm_cache/product...  "],"text/html":["\n","  <div id=\"df-98d2edc1-b7eb-487e-b8da-0b0d95d062ea\" class=\"colab-df-container\">\n","    <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>filename</th>\n","      <th>description</th>\n","      <th>rows</th>\n","      <th>columns</th>\n","      <th>path</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>bgl_unique_normalized_messages.csv</td>\n","      <td>Unique normalized BGL messages</td>\n","      <td>47012</td>\n","      <td>1</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/bgl_uni...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>logminilm_model_summary.csv</td>\n","      <td>LogMiniLM model summary</td>\n","      <td>19</td>\n","      <td>2</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/logmini...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>logminilm_development_history.csv</td>\n","      <td>Development training history</td>\n","      <td>6</td>\n","      <td>8</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>logminilm_validation_threshold_results.csv</td>\n","      <td>Validation threshold results</td>\n","      <td>13</td>\n","      <td>9</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>logminilm_final_training_history.csv</td>\n","      <td>Final retraining history</td>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>logminilm_results.csv</td>\n","      <td>Final test results</td>\n","      <td>1</td>\n","      <td>25</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>logminilm_predictions_for_xai.csv</td>\n","      <td>Final test predictions linked to inclusive BGL...</td>\n","      <td>94270</td>\n","      <td>10</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>logminilm_top_xai_candidates.csv</td>\n","      <td>Reproducible TP, FP, and FN sequences selected...</td>\n","      <td>140</td>\n","      <td>10</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>logminilm_xai_local_event_importance.csv</td>\n","      <td>Event-level occlusion explanations</td>\n","      <td>1400</td>\n","      <td>24</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>logminilm_xai_faithfulness.csv</td>\n","      <td>Occlusion faithfulness results</td>\n","      <td>140</td>\n","      <td>10</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>logminilm_xai_global_summary.csv</td>\n","      <td>Global event-level occlusion importance summary</td>\n","      <td>1014</td>\n","      <td>9</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>logminilm_xai_case_study.csv</td>\n","      <td>Strongest event-level occlusion case study</td>\n","      <td>10</td>\n","      <td>25</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>12</th>\n","      <td>logminilm_xai_topk_faithfulness.csv</td>\n","      <td>Top-k occlusion faithfulness using logit and p...</td>\n","      <td>560</td>\n","      <td>15</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>13</th>\n","      <td>logminilm_xai_strong_case_study.csv</td>\n","      <td>Thesis-ready strongest occlusion case study</td>\n","      <td>10</td>\n","      <td>12</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>14</th>\n","      <td>logminilm_xai_strong_case_study_top5.csv</td>\n","      <td>Top five influential events in strongest occlu...</td>\n","      <td>5</td>\n","      <td>12</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>15</th>\n","      <td>logminilm_shap_event_level_explanations.csv</td>\n","      <td>SHAP event explanations with metadata</td>\n","      <td>210</td>\n","      <td>21</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>16</th>\n","      <td>logminilm_shap_case_study.csv</td>\n","      <td>SHAP case study</td>\n","      <td>10</td>\n","      <td>12</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>17</th>\n","      <td>logminilm_shap_occlusion_comparison.csv</td>\n","      <td>SHAP and occlusion agreement across identical ...</td>\n","      <td>21</td>\n","      <td>10</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>18</th>\n","      <td>logminilm_shap_occlusion_event_details.csv</td>\n","      <td>Event-level SHAP and occlusion scores for iden...</td>\n","      <td>210</td>\n","      <td>4</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>19</th>\n","      <td>final_summary.csv</td>\n","      <td>Final LogMiniLM thesis summary</td>\n","      <td>60</td>\n","      <td>3</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","    <tr>\n","      <th>20</th>\n","      <td>xai_summary.csv</td>\n","      <td>Unified XAI summary</td>\n","      <td>11</td>\n","      <td>2</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98d2edc1-b7eb-487e-b8da-0b0d95d062ea')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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[\n          \"Unique normalized BGL messages\",\n          \"SHAP and occlusion agreement across identical explained sequences\",\n          \"SHAP event explanations with metadata\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rows\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22469,\n        \"min\": 1,\n        \"max\": 94270,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          47012,\n          19,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"columns\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 1,\n        \"max\": 25,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          4,\n          21,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"path\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          \"/content/drive/MyDrive/logminilm_cache/bgl_unique_normalized_messages.csv\",\n          \"/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_occlusion_comparison.csv\",\n          \"/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/shap/logminilm_shap_event_level_explanations.csv\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 40 — Export Figure and Table Manifests\n","# ============================================================\n","\n","figure_manifest_df = (\n","    pd.DataFrame(figure_manifest)\n","    .drop_duplicates(subset=[\"figure_number\"], keep=\"last\")\n","    .copy()\n",")\n","\n","figure_manifest_df[\"figure_order\"] = (\n","    figure_manifest_df[\"figure_number\"]\n","    .str.extract(r\"Figure 4\\.(\\d+)\")[0]\n","    .astype(int)\n",")\n","\n","figure_manifest_df = (\n","    figure_manifest_df\n","    .sort_values(\"figure_order\")\n","    .drop(columns=[\"figure_order\"])\n","    .reset_index(drop=True)\n",")\n","\n","expected_figure_numbers = [f\"Figure 4.{number}\" for number in range(1, 18)]\n","assert figure_manifest_df[\"figure_number\"].tolist() == expected_figure_numbers\n","\n","for path_column in [\"png_path\", \"pdf_path\"]:\n","    assert figure_manifest_df[path_column].map(\n","        lambda value: Path(value).exists() and Path(value).stat().st_size > 0\n","    ).all()\n","\n","table_manifest_df = (\n","    pd.DataFrame(table_manifest)\n","    .drop_duplicates(subset=[\"path\"], keep=\"last\")\n","    .reset_index(drop=True)\n",")\n","\n","for table_path in table_manifest_df[\"path\"]:\n","    table_path = Path(table_path)\n","    assert table_path.exists() and table_path.stat().st_size > 0\n","\n","save_table(\n","    figure_manifest_df,\n","    FIGURE_MANIFEST_PATH,\n","    \"Manifest of thesis figures exported as PNG and PDF\"\n",")\n","save_table(\n","    table_manifest_df,\n","    TABLE_MANIFEST_PATH,\n","    \"Manifest of generated thesis tables\"\n",")\n","\n","display(figure_manifest_df)\n","display(table_manifest_df)\n"]},{"cell_type":"markdown","metadata":{"id":"t9MYoUxpb4bZ"},"source":["## Step 41 — Verify Final Reproducibility Artifacts\n","\n","This final integrity check confirms that the README, configuration, summaries, and manifests were actually created, are non-empty, and contain values consistent with the active run.\n"],"id":"t9MYoUxpb4bZ"},{"cell_type":"code","execution_count":60,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"id":"z9_FZXP9b4bZ","executionInfo":{"status":"ok","timestamp":1785510862816,"user_tz":-180,"elapsed":640,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"c05d6c20-f8fc-4dd8-b1f5-e3180fd71a92"},"outputs":[{"output_type":"display_data","data":{"text/plain":["                     artifact  \\\n","0                      README   \n","1                Requirements   \n","2          Configuration JSON   \n","3   Development configuration   \n","4          Final summary JSON   \n","5           Final summary CSV   \n","6             XAI summary CSV   \n","7             Figure manifest   \n","8              Table manifest   \n","9            Model checkpoint   \n","10              Final results   \n","11        Predictions for XAI   \n","\n","                                                 path  exists  size_bytes  \\\n","0   /content/drive/MyDrive/logminilm_cache/product...    True        1235   \n","1   /content/drive/MyDrive/logminilm_cache/product...    True         147   \n","2   /content/drive/MyDrive/logminilm_cache/product...    True        1104   \n","3   /content/drive/MyDrive/logminilm_cache/product...    True         752   \n","4   /content/drive/MyDrive/logminilm_cache/product...    True        3557   \n","5   /content/drive/MyDrive/logminilm_cache/product...    True        3445   \n","6   /content/drive/MyDrive/logminilm_cache/product...    True         467   \n","7   /content/drive/MyDrive/logminilm_cache/product...    True        6888   \n","8   /content/drive/MyDrive/logminilm_cache/product...    True        4157   \n","9   /content/drive/MyDrive/logminilm_cache/product...    True    72955767   \n","10  /content/drive/MyDrive/logminilm_cache/product...    True         519   \n","11  /content/drive/MyDrive/logminilm_cache/product...    True     6060770   \n","\n","    non_empty  \n","0        True  \n","1        True  \n","2        True  \n","3        True  \n","4        True  \n","5        True  \n","6        True  \n","7        True  \n","8        True  \n","9        True  \n","10       True  \n","11       True  "],"text/html":["\n","  <div id=\"df-622bc655-daf3-40b2-bcd8-43009df098b8\" class=\"colab-df-container\">\n","    <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>artifact</th>\n","      <th>path</th>\n","      <th>exists</th>\n","      <th>size_bytes</th>\n","      <th>non_empty</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>README</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>1235</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Requirements</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>147</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Configuration JSON</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>1104</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Development configuration</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>752</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Final summary JSON</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>3557</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Final summary CSV</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>3445</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>XAI summary CSV</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>467</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Figure manifest</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>6888</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Table manifest</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>4157</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>Model checkpoint</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>72955767</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>10</th>\n","      <td>Final results</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>519</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>11</th>\n","      <td>Predictions for XAI</td>\n","      <td>/content/drive/MyDrive/logminilm_cache/product...</td>\n","      <td>True</td>\n","      <td>6060770</td>\n","      <td>True</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-622bc655-daf3-40b2-bcd8-43009df098b8')\"\n","            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\"/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/tables/logminilm_results.csv\",\n          \"/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/models/logminilm_model.pt\",\n          \"/content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/README.txt\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exists\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"size_bytes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20973321,\n        \"min\": 147,\n        \"max\": 72955767,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          519\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"non_empty\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["All final reproducibility checks passed.\n"]}],"source":["# ============================================================\n","# Step 41 — Verify Final Reproducibility Artifacts\n","# ============================================================\n","\n","required_artifacts = {\n","    \"README\": README_PATH,\n","    \"Requirements\": REQUIREMENTS_PATH,\n","    \"Configuration JSON\": CONFIG_JSON_PATH,\n","    \"Development configuration\": DEVELOPMENT_CONFIG_PATH,\n","    \"Final summary JSON\": FINAL_SUMMARY_JSON_PATH,\n","    \"Final summary CSV\": FINAL_SUMMARY_CSV_PATH,\n","    \"XAI summary CSV\": XAI_SUMMARY_PATH,\n","    \"Figure manifest\": FIGURE_MANIFEST_PATH,\n","    \"Table manifest\": TABLE_MANIFEST_PATH,\n","    \"Model checkpoint\": MODEL_PATH,\n","    \"Final results\": RESULTS_PATH,\n","    \"Predictions for XAI\": PREDICTIONS_PATH,\n","}\n","\n","artifact_verification_rows = []\n","for artifact_name, artifact_path in required_artifacts.items():\n","    artifact_path = Path(artifact_path)\n","    exists = artifact_path.exists()\n","    size_bytes = artifact_path.stat().st_size if exists else 0\n","    artifact_verification_rows.append({\n","        \"artifact\": artifact_name,\n","        \"path\": str(artifact_path),\n","        \"exists\": bool(exists),\n","        \"size_bytes\": int(size_bytes),\n","        \"non_empty\": bool(size_bytes > 0),\n","    })\n","\n","artifact_verification_df = pd.DataFrame(artifact_verification_rows)\n","assert artifact_verification_df[\"exists\"].all()\n","assert artifact_verification_df[\"non_empty\"].all()\n","\n","with CONFIG_JSON_PATH.open(\"r\", encoding=\"utf-8\") as file:\n","    verified_config = json.load(file)\n","\n","with FINAL_SUMMARY_JSON_PATH.open(\"r\", encoding=\"utf-8\") as file:\n","    verified_summary = json.load(file)\n","\n","assert verified_config[\"notebook_version\"] == NOTEBOOK_VERSION\n","assert verified_config[\"run_id\"] == RUN_ID\n","assert verified_config[\"window_size\"] == WINDOW_SIZE\n","assert verified_config[\"step_size\"] == STEP_SIZE\n","\n","assert verified_summary[\"experiment\"][\"notebook_version\"] == NOTEBOOK_VERSION\n","assert verified_summary[\"experiment\"][\"run_id\"] == RUN_ID\n","assert verified_summary[\"performance\"][\"tp\"] == int(final_metrics[\"tp\"])\n","assert verified_summary[\"performance\"][\"tn\"] == int(final_metrics[\"tn\"])\n","assert verified_summary[\"performance\"][\"fp\"] == int(final_metrics[\"fp\"])\n","assert verified_summary[\"performance\"][\"fn\"] == int(final_metrics[\"fn\"])\n","assert np.isclose(\n","    verified_summary[\"performance\"][\"f1_score\"],\n","    float(final_metrics[\"f1\"]),\n","    atol=1e-12\n",")\n","assert np.isclose(\n","    verified_summary[\"performance\"][\"roc_auc\"],\n","    float(test_roc_auc),\n","    atol=1e-12\n",")\n","assert np.isclose(\n","    verified_summary[\"performance\"][\"pr_auc_average_precision\"],\n","    float(test_pr_auc),\n","    atol=1e-12\n",")\n","\n","verified_results_df = pd.read_csv(RESULTS_PATH)\n","assert len(verified_results_df) == 1\n","for metric_name in [\"accuracy\", \"precision\", \"recall\", \"f1\"]:\n","    assert np.isclose(\n","        float(verified_results_df.loc[0, metric_name]),\n","        float(final_metrics[metric_name]),\n","        atol=1e-12\n","    )\n","\n","verified_predictions_df = pd.read_csv(PREDICTIONS_PATH)\n","assert len(verified_predictions_df) == len(y_test)\n","assert verified_predictions_df[\"sequence_local_test_index\"].is_unique\n","\n","verified_figure_manifest = pd.read_csv(FIGURE_MANIFEST_PATH)\n","assert len(verified_figure_manifest) == 17\n","\n","save_table(\n","    artifact_verification_df,\n","    SUMMARY_DIR / \"artifact_verification.csv\",\n","    \"Post-run verification of required reproducibility artifacts\"\n",")\n","\n","display(artifact_verification_df)\n","print(\"All final reproducibility checks passed.\")\n"],"id":"z9_FZXP9b4bZ"},{"cell_type":"markdown","metadata":{"id":"iV6r23_jtiZk"},"source":["# Step 42 — Unified XAI Benchmark: SHAP versus Occlusion\n","\n","This section evaluates SHAP and event-level Occlusion on the same sequences, model, event positions, and replacement baselines.\n","\n","The benchmark does **not** assume that agreement between the methods implies correctness. Each method is evaluated independently using:\n","\n","- Faithfulness: comprehensiveness, sufficiency, and decision-flip rate.\n","- Stability: repeated SHAP runs and deterministic Occlusion checks.\n","- Baseline robustness: ranking consistency across three replacement baselines.\n","- Sparsity: concentration of attribution on a small number of events.\n","- Computational efficiency: wall-clock time per explained sequence.\n","- Cross-method agreement: reported only as a secondary diagnostic.\n","\n","The primary comparison uses anomaly **logits** rather than sigmoid probabilities to avoid saturation near probabilities of zero or one.\n"],"id":"iV6r23_jtiZk"},{"cell_type":"code","execution_count":62,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XAxOgLLhtiZl","executionInfo":{"status":"ok","timestamp":1785510942393,"user_tz":-180,"elapsed":39,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"b46ab87b-f33f-4198-9577-22e518a2f4a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["XAI benchmark output: /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai_benchmark\n","Benchmark counts: {'TP': 25, 'FP': 10, 'FN': 10, 'TN': 5}\n","Baselines: {'pad_event': 0, 'most_frequent_normal_event': 27136, 'alternative_common_normal_event': 26774}\n","SHAP repeats: 3\n","SHAP nsamples: 300\n"]}],"source":["from time import perf_counter\n","from scipy.stats import spearmanr\n","import warnings\n","\n","XAI_BENCHMARK_DIR = RUN_DIR / \"xai_benchmark\"\n","XAI_BENCHMARK_DIR.mkdir(parents=True, exist_ok=True)\n","\n","# Balanced, reproducible benchmark sample.\n","# Increase these values only when runtime permits.\n","BENCHMARK_COUNTS = {\n","    \"TP\": 25,\n","    \"FP\": 10,\n","    \"FN\": 10,\n","    \"TN\": 5,\n","}\n","\n","BENCHMARK_TOP_K = [1, 2, 3, 5]\n","BENCHMARK_SHAP_REPEATS = 3\n","BENCHMARK_SHAP_NSAMPLES = 300\n","BENCHMARK_RANDOM_SEED = int(SEED if \"SEED\" in globals() else 42)\n","\n","# Three valid event-ID replacement baselines.\n","PAD_BASELINE_EVENT_ID = 0\n","PRIMARY_NORMAL_BASELINE_EVENT_ID = int(baseline_event_id)\n","\n","# Select an alternative common normal event different from the primary baseline.\n","normal_train_sequences = np.asarray(X_train)[np.asarray(y_train).astype(int) == 0]\n","normal_event_values, normal_event_counts = np.unique(\n","    normal_train_sequences.reshape(-1),\n","    return_counts=True,\n",")\n","normal_event_order = np.argsort(-normal_event_counts)\n","alternative_candidates = [\n","    int(normal_event_values[i])\n","    for i in normal_event_order\n","    if int(normal_event_values[i]) not in {\n","        PAD_BASELINE_EVENT_ID,\n","        PRIMARY_NORMAL_BASELINE_EVENT_ID,\n","    }\n","]\n","ALTERNATIVE_NORMAL_BASELINE_EVENT_ID = (\n","    alternative_candidates[0]\n","    if alternative_candidates\n","    else PRIMARY_NORMAL_BASELINE_EVENT_ID\n",")\n","\n","XAI_BASELINES = {\n","    \"pad_event\": PAD_BASELINE_EVENT_ID,\n","    \"most_frequent_normal_event\": PRIMARY_NORMAL_BASELINE_EVENT_ID,\n","    \"alternative_common_normal_event\": ALTERNATIVE_NORMAL_BASELINE_EVENT_ID,\n","}\n","\n","print(\"XAI benchmark output:\", XAI_BENCHMARK_DIR)\n","print(\"Benchmark counts:\", BENCHMARK_COUNTS)\n","print(\"Baselines:\", XAI_BASELINES)\n","print(\"SHAP repeats:\", BENCHMARK_SHAP_REPEATS)\n","print(\"SHAP nsamples:\", BENCHMARK_SHAP_NSAMPLES)\n"],"id":"XAxOgLLhtiZl"},{"cell_type":"code","execution_count":63,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":224},"id":"OsZaNu9PtiZl","executionInfo":{"status":"ok","timestamp":1785510965564,"user_tz":-180,"elapsed":101,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"099fa3b5-761b-4313-fb37-9b59a30d0f50"},"outputs":[{"output_type":"stream","name":"stdout","text":["Selected benchmark cases: 50\n"]},{"output_type":"display_data","data":{"text/plain":["                 count\n","prediction_type       \n","FN                  10\n","FP                  10\n","TN                   5\n","TP                  25"],"text/html":["\n","  <div id=\"df-13c194b0-abf0-41a7-9da6-f9699c6f6e69\" class=\"colab-df-container\">\n","    <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>count</th>\n","    </tr>\n","    <tr>\n","      <th>prediction_type</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>FN</th>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>FP</th>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>TN</th>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>TP</th>\n","      <td>25</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-13c194b0-abf0-41a7-9da6-f9699c6f6e69')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    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       \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8,\n        \"min\": 5,\n        \"max\": 25,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          10,\n          5,\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 43 — Select the Common Benchmark Sample\n","# ============================================================\n","rng = np.random.default_rng(BENCHMARK_RANDOM_SEED)\n","\n","def sample_prediction_type(frame, prediction_type, n):\n","    subset = frame[\n","        frame[\"prediction_type\"] == prediction_type\n","    ].copy()\n","    if subset.empty or n <= 0:\n","        return subset.head(0)\n","\n","    # Preserve difficult and operationally relevant cases:\n","    # TP/FP: highest anomaly probabilities\n","    # FN/TN: lowest anomaly probabilities for FN, highest-confidence normal for TN\n","    if prediction_type in {\"TP\", \"FP\"}:\n","        subset = subset.sort_values(\n","            [\"anomaly_probability\", \"sequence_local_test_index\"],\n","            ascending=[False, True],\n","        )\n","    elif prediction_type == \"FN\":\n","        subset = subset.sort_values(\n","            [\"anomaly_probability\", \"sequence_local_test_index\"],\n","            ascending=[True, True],\n","        )\n","    else:  # TN\n","        subset = subset.sort_values(\n","            [\"anomaly_probability\", \"sequence_local_test_index\"],\n","            ascending=[True, True],\n","        )\n","    return subset.head(min(n, len(subset)))\n","\n","benchmark_parts = [\n","    sample_prediction_type(\n","        predictions_df,\n","        prediction_type,\n","        BENCHMARK_COUNTS[prediction_type],\n","    )\n","    for prediction_type in [\"TP\", \"FP\", \"FN\", \"TN\"]\n","]\n","\n","xai_benchmark_cases_df = (\n","    pd.concat(benchmark_parts, ignore_index=True)\n","    .drop_duplicates(subset=[\"sequence_local_test_index\"])\n","    .sort_values([\"prediction_type\", \"sequence_local_test_index\"])\n","    .reset_index(drop=True)\n",")\n","\n","if xai_benchmark_cases_df.empty:\n","    raise ValueError(\"No cases were selected for the XAI benchmark.\")\n","\n","XAI_BENCHMARK_CASES_PATH = (\n","    XAI_BENCHMARK_DIR / \"xai_benchmark_cases.csv\"\n",")\n","save_table(\n","    xai_benchmark_cases_df,\n","    XAI_BENCHMARK_CASES_PATH,\n","    \"Common cases used for SHAP and Occlusion benchmark\",\n",")\n","\n","print(\"Selected benchmark cases:\", len(xai_benchmark_cases_df))\n","display(\n","    xai_benchmark_cases_df[\n","        [\n","            \"sequence_local_test_index\",\n","            \"prediction_type\",\n","            \"true_label\",\n","            \"predicted_label\",\n","            \"anomaly_probability\",\n","        ]\n","    ].groupby(\"prediction_type\").size().rename(\"count\").to_frame()\n",")\n"],"id":"OsZaNu9PtiZl"},{"cell_type":"code","execution_count":64,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jZdeQ-XytiZl","executionInfo":{"status":"ok","timestamp":1785510965611,"user_tz":-180,"elapsed":21,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"52780556-a1d1-4257-dc83-b19839390711"},"outputs":[{"output_type":"stream","name":"stdout","text":["Common XAI benchmark utilities are ready.\n"]}],"source":["# ============================================================\n","# Step 44 — Common XAI Utilities\n","# ============================================================\n","def predict_sequence_logit_and_probability(sequence_event_ids):\n","    model.eval()\n","    tensor = torch.tensor(\n","        np.asarray(sequence_event_ids, dtype=np.int64),\n","        dtype=torch.long,\n","    ).unsqueeze(0).to(device)\n","    with torch.no_grad():\n","        logit = float(model(tensor).reshape(-1)[0].item())\n","        probability = float(torch.sigmoid(\n","            torch.tensor(logit)\n","        ).item())\n","    return logit, probability\n","\n","\n","def mask_positions(sequence_event_ids, positions, replacement_event_id):\n","    masked = np.asarray(sequence_event_ids, dtype=np.int64).copy()\n","    for position in positions:\n","        masked[int(position) - 1] = int(replacement_event_id)\n","    return masked\n","\n","\n","def normalized_rank_correlation(values_a, values_b):\n","    values_a = np.asarray(values_a, dtype=float)\n","    values_b = np.asarray(values_b, dtype=float)\n","    if (\n","        len(values_a) != len(values_b)\n","        or np.allclose(values_a, values_a[0])\n","        or np.allclose(values_b, values_b[0])\n","    ):\n","        return np.nan\n","    with warnings.catch_warnings():\n","        warnings.simplefilter(\"ignore\")\n","        corr, _ = spearmanr(values_a, values_b)\n","    return float(corr) if np.isfinite(corr) else np.nan\n","\n","\n","def topk_overlap_rate(ranking_a, ranking_b, k=3):\n","    k = min(int(k), len(ranking_a), len(ranking_b))\n","    if k <= 0:\n","        return np.nan\n","    return len(\n","        set(ranking_a[:k]).intersection(ranking_b[:k])\n","    ) / k\n","\n","\n","def attribution_concentration(abs_scores, top_k=3):\n","    scores = np.abs(np.asarray(abs_scores, dtype=float))\n","    total = scores.sum()\n","    if total <= 0:\n","        return 0.0\n","    k = min(int(top_k), len(scores))\n","    return float(np.sort(scores)[::-1][:k].sum() / total)\n","\n","\n","def evaluate_ranking_faithfulness(\n","    sequence_event_ids,\n","    ranking,\n","    replacement_event_id,\n","    top_k_values,\n","):\n","    original_logit, original_probability = (\n","        predict_sequence_logit_and_probability(sequence_event_ids)\n","    )\n","    rows = []\n","    all_positions = list(range(1, len(sequence_event_ids) + 1))\n","\n","    for k in top_k_values:\n","        effective_k = min(int(k), len(ranking))\n","        selected = [int(x) for x in ranking[:effective_k]]\n","\n","        # Comprehensiveness: remove the top-k events.\n","        removed_top = mask_positions(\n","            sequence_event_ids,\n","            selected,\n","            replacement_event_id,\n","        )\n","        removed_logit, removed_probability = (\n","            predict_sequence_logit_and_probability(removed_top)\n","        )\n","\n","        # Sufficiency: keep only the top-k events and replace all others.\n","        non_selected = [\n","            position\n","            for position in all_positions\n","            if position not in selected\n","        ]\n","        kept_top = mask_positions(\n","            sequence_event_ids,\n","            non_selected,\n","            replacement_event_id,\n","        )\n","        kept_logit, kept_probability = (\n","            predict_sequence_logit_and_probability(kept_top)\n","        )\n","\n","        rows.append({\n","            \"k\": effective_k,\n","            \"original_logit\": original_logit,\n","            \"original_probability\": original_probability,\n","            \"removed_topk_logit\": removed_logit,\n","            \"removed_topk_probability\": removed_probability,\n","            \"comprehensiveness_logit\": (\n","                original_logit - removed_logit\n","            ),\n","            \"comprehensiveness_probability\": (\n","                original_probability - removed_probability\n","            ),\n","            # Smaller absolute sufficiency gap is better.\n","            \"kept_topk_logit\": kept_logit,\n","            \"kept_topk_probability\": kept_probability,\n","            \"sufficiency_abs_logit_gap\": abs(\n","                original_logit - kept_logit\n","            ),\n","            \"sufficiency_abs_probability_gap\": abs(\n","                original_probability - kept_probability\n","            ),\n","            \"decision_flip_after_removal\": int(\n","                (removed_probability >= selected_threshold)\n","                != (original_probability >= selected_threshold)\n","            ),\n","        })\n","    return rows\n","\n","\n","print(\"Common XAI benchmark utilities are ready.\")\n"],"id":"jZdeQ-XytiZl"},{"cell_type":"code","execution_count":68,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":262},"id":"EyHTjwdutiZl","executionInfo":{"status":"ok","timestamp":1785511025167,"user_tz":-180,"elapsed":10840,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"f785eb5b-68af-434e-c919-068ed556c906"},"outputs":[{"output_type":"stream","name":"stdout","text":["Occlusion benchmark completed.\n","Attribution rows: 1500\n"]},{"output_type":"display_data","data":{"text/plain":["      method  sequence_local_test_index prediction_type  \\\n","0  Occlusion                      42132              FN   \n","1  Occlusion                      42132              FN   \n","2  Occlusion                      42132              FN   \n","3  Occlusion                      42132              FN   \n","4  Occlusion                      42132              FN   \n","\n","                baseline_name  repeat  runtime_seconds  top3_concentration  k  \\\n","0                   pad_event       1         0.077274            0.731305  1   \n","1                   pad_event       1         0.077274            0.731305  2   \n","2                   pad_event       1         0.077274            0.731305  3   \n","3                   pad_event       1         0.077274            0.731305  5   \n","4  most_frequent_normal_event       1         0.073255            0.482347  1   \n","\n","   original_logit  original_probability  removed_topk_logit  \\\n","0      -11.415568              0.000011          -11.505061   \n","1      -11.415568              0.000011          -11.505015   \n","2      -11.415568              0.000011          -11.505183   \n","3      -11.415568              0.000011          -11.505781   \n","4      -11.415568              0.000011          -11.504519   \n","\n","   removed_topk_probability  comprehensiveness_logit  \\\n","0                   0.00001                 0.089493   \n","1                   0.00001                 0.089447   \n","2                   0.00001                 0.089615   \n","3                   0.00001                 0.090213   \n","4                   0.00001                 0.088951   \n","\n","   comprehensiveness_probability  kept_topk_logit  kept_topk_probability  \\\n","0                   9.435671e-07       -11.370189               0.000012   \n","1                   9.431051e-07       -11.375319               0.000011   \n","2                   9.447967e-07       -11.380144               0.000011   \n","3                   9.508212e-07       -11.390589               0.000011   \n","4                   9.381056e-07       -11.218643               0.000013   \n","\n","   sufficiency_abs_logit_gap  sufficiency_abs_probability_gap  \\\n","0                   0.045380                     5.117099e-07   \n","1                   0.040249                     4.526837e-07   \n","2                   0.035424                     3.974537e-07   \n","3                   0.024980                     2.787992e-07   \n","4                   0.196925                     2.399029e-06   \n","\n","   decision_flip_after_removal  \n","0                            0  \n","1                            0  \n","2                            0  \n","3                            0  \n","4                            0  "],"text/html":["\n","  <div id=\"df-636f846b-6d0d-4fcd-aea6-61c8d53d9e42\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        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 <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Occlusion</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.077274</td>\n","      <td>0.731305</td>\n","      <td>1</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505061</td>\n","      <td>0.00001</td>\n","      <td>0.089493</td>\n","      <td>9.435671e-07</td>\n","      <td>-11.370189</td>\n","      <td>0.000012</td>\n","      <td>0.045380</td>\n","      <td>5.117099e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Occlusion</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.077274</td>\n","      <td>0.731305</td>\n","      <td>2</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505015</td>\n","      <td>0.00001</td>\n","      <td>0.089447</td>\n","      <td>9.431051e-07</td>\n","      <td>-11.375319</td>\n","      <td>0.000011</td>\n","      <td>0.040249</td>\n","      <td>4.526837e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Occlusion</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.077274</td>\n","      <td>0.731305</td>\n","      <td>3</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505183</td>\n","      <td>0.00001</td>\n","      <td>0.089615</td>\n","      <td>9.447967e-07</td>\n","      <td>-11.380144</td>\n","      <td>0.000011</td>\n","      <td>0.035424</td>\n","      <td>3.974537e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Occlusion</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.077274</td>\n","      <td>0.731305</td>\n","      <td>5</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505781</td>\n","      <td>0.00001</td>\n","      <td>0.090213</td>\n","      <td>9.508212e-07</td>\n","      <td>-11.390589</td>\n","      <td>0.000011</td>\n","      <td>0.024980</td>\n","      <td>2.787992e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Occlusion</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>most_frequent_normal_event</td>\n","      <td>1</td>\n","      <td>0.073255</td>\n","      <td>0.482347</td>\n","      <td>1</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.504519</td>\n","      <td>0.00001</td>\n","      <td>0.088951</td>\n","      <td>9.381056e-07</td>\n","      <td>-11.218643</td>\n","      <td>0.000013</td>\n","      <td>0.196925</td>\n","      <td>2.399029e-06</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n"," 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]\n}"}},"metadata":{}}],"source":["selected_threshold = best_threshold\n","# ============================================================\n","# Step 45 — Occlusion Benchmark\n","# ============================================================\n","occlusion_attribution_rows = []\n","occlusion_metric_rows = []\n","\n","for case_number, case in xai_benchmark_cases_df.iterrows():\n","    local_idx = int(case[\"sequence_local_test_index\"])\n","    sequence_event_ids = np.asarray(X_test[local_idx], dtype=np.int64)\n","\n","    for baseline_name, replacement_event_id in XAI_BASELINES.items():\n","        start_time = perf_counter()\n","        original_logit, original_probability = (\n","            predict_sequence_logit_and_probability(sequence_event_ids)\n","        )\n","\n","        scores = []\n","        for position in range(1, WINDOW_SIZE + 1):\n","            masked_sequence = mask_positions(\n","                sequence_event_ids,\n","                [position],\n","                replacement_event_id,\n","            )\n","            masked_logit, masked_probability = (\n","                predict_sequence_logit_and_probability(masked_sequence)\n","            )\n","            signed_logit_drop = original_logit - masked_logit\n","            scores.append({\n","                \"event_position\": position,\n","                \"signed_attribution\": signed_logit_drop,\n","                \"absolute_attribution\": abs(signed_logit_drop),\n","                \"masked_logit\": masked_logit,\n","                \"masked_probability\": masked_probability,\n","            })\n","\n","        elapsed = perf_counter() - start_time\n","        score_df = pd.DataFrame(scores)\n","        ranking = (\n","            score_df.sort_values(\n","                \"absolute_attribution\",\n","                ascending=False,\n","            )[\"event_position\"].astype(int).tolist()\n","        )\n","\n","        for row in scores:\n","            occlusion_attribution_rows.append({\n","                \"method\": \"Occlusion\",\n","                \"sequence_local_test_index\": local_idx,\n","                \"prediction_type\": case[\"prediction_type\"],\n","                \"baseline_name\": baseline_name,\n","                \"repeat\": 1,\n","                **row,\n","            })\n","\n","        faithfulness_rows = evaluate_ranking_faithfulness(\n","            sequence_event_ids,\n","            ranking,\n","            replacement_event_id,\n","            BENCHMARK_TOP_K,\n","        )\n","        for faithfulness_row in faithfulness_rows:\n","            occlusion_metric_rows.append({\n","                \"method\": \"Occlusion\",\n","                \"sequence_local_test_index\": local_idx,\n","                \"prediction_type\": case[\"prediction_type\"],\n","                \"baseline_name\": baseline_name,\n","                \"repeat\": 1,\n","                \"runtime_seconds\": elapsed,\n","                \"top3_concentration\": attribution_concentration(\n","                    score_df[\"absolute_attribution\"].to_numpy(),\n","                    top_k=3,\n","                ),\n","                **faithfulness_row,\n","            })\n","\n","occlusion_attribution_df = pd.DataFrame(\n","    occlusion_attribution_rows\n",")\n","occlusion_metrics_df = pd.DataFrame(occlusion_metric_rows)\n","\n","OCCLUSION_ATTRIBUTIONS_PATH = (\n","    XAI_BENCHMARK_DIR / \"occlusion_attributions.csv\"\n",")\n","OCCLUSION_METRICS_PATH = (\n","    XAI_BENCHMARK_DIR / \"occlusion_metrics.csv\"\n",")\n","save_table(\n","    occlusion_attribution_df,\n","    OCCLUSION_ATTRIBUTIONS_PATH,\n","    \"Event-level Occlusion attributions across benchmark cases and baselines\",\n",")\n","save_table(\n","    occlusion_metrics_df,\n","    OCCLUSION_METRICS_PATH,\n","    \"Occlusion faithfulness, sparsity, and runtime metrics\",\n",")\n","\n","print(\"Occlusion benchmark completed.\")\n","print(\"Attribution rows:\", len(occlusion_attribution_df))\n","display(occlusion_metrics_df.head())"],"id":"EyHTjwdutiZl"},{"cell_type":"code","execution_count":69,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["2374d97285974dd4acd0ec3070a9bfba","9b9ab732a6cc4f8a93ab200b14e8d34e","68d3893769074549a55496b64e292c64","0d7cc0b8d4164bb4ab5501aa2710690b","f1b887b4c43f4bea869d2821c9f6833a","3494a00ce1d045a499203ecfe16decf9","444fbf684c32410f87f67feb0aa3be98","ff3ae4a16ef442b497c88724e81d7e25","7641c6c1a4fb436fbe197f0b90057ab8","3d6004aa4b0b49f2ad5abd1de282a6b3","effd7cded38d451aa6750b45a776d897","ce4f3a21af784bed89d560a1cac81641","e497b1e711e44b7e986a13ecf5d758e1","d8a996fa47b34c64afa70ad070a9c679","1059bb01e987455bb7b5d4ea93d7c5b9","e373ce0e7d55475eba12974c4c1db66c","40db6b888c4b4f909249ce3e5df36819","1932bfbe665041e18d092ae9621012ee","e7f33c330de84fe6823f20a12a391690","69462b5ade44478d8c0beb9bf55adb46","19592fa1c7944656b3401620964c941b","bd2e7a69a7364fada045bd07693de761","7a1e19f9d23c42fcbecb39f9aada6e72","bb966637fe2c44f6aa72d998e7976045","1d5f907f8b0242ef80c25a62b56e6ce0","3f5a198411554c2789f819a386d9d16c","69f6c5764c574c6fb9692678b31d6020","063ddbd5120f43f18683294d03c8555f","35d75985793243429d8354b059c54138","5574efe7ef0e4c8596304d82de35562f","b7bbdf43809041ec97ef84178f37f18e","649b2e20f58b4bb285add1b13c2de396","c98814da5928485fa85f3ae6e096f1e1","f447f58ad4d847c299a552acae3240ce","7dc147d05f944fd4a9ca9b664236db70","b6f67b52495945b3a56e472e60ef0653","0f70e3500b01458ea6f7bce3646a7302","ecd66d6294064202a2b4d01854162074","6908d5ef2f0f479ba6431acd0dde2ffd","d52acaa674ea43a79fac716ba214b719","76c99b96af7d468181c6608c71cf0894","c174005ae4e24b80b7d34fabeeb5377a","b18b480d3686443f96a10b23d6088238","5ed048d1228d492883c4a7906bb2128c","8e21ee1cb4d540258900bad76f105142","a73730a5befd4210a91c5c595682c396","843ccbfd05d34eddbfaf4a7d53e8b4e9","f9fde1d63089480cb9a5b147ded5175f","d7ad9905740340b69c7e89ddca9d78fd","68b01fc51f8240729b04fb2c4a8230ce","899622be077f4ae3be1740b7d587aac3","20d8722170ff4fe09d80d61baa9c194b","0b55226688214f108dfe1a96cf19bc79","5b242af544c14e568cc1ec0e83ffd7c8","d7c01e63ec5f41d386020452edded9f4","3a8727b2cf22486d91ef7b2a62c7fd2a","c76d7782847d44ce8616dad45007ec41","87d15bb3d1274c028e5abd11b3374ef3","e0927716e4af4682b9082ed52273c176","80a1df48805f49a596794855532c6715","a1338d74a65b47adadbf565e02e38ec8","b62767fedcbe4a7ebcc38216fbde9c9f","8e4d736199de47fd828bee0d889e67ee","0ee40e7c08a940f384945165d5fca222","2c8dacea65cc4314ba4cd094fca0aa08","c0adbe0f99124d41a893e673aa62e78e","4fc6577e041847c1992d58d9677a8b06","0763185e195f497fb6370400cc30b6c4","b44f98a2f1e24740a99bd449dfb9b212","543fdfcb6c6041f599d9c1b58f9adc2d","e2cc99ccfe14416ca43dffe368eaf8bf","dd7847f3147149d0afe9060392c268ed","a08176c6d9bf46f8a100a664bd4b54bf","140e50836f6c4ed8a3ac42525bc21252","09cd30854e5c4a298869da6592ac977e","2414453bfa364cde93c94e4b2ec4b1ab","cd473c47b4934ab3a8819bf2d243a6ac","10c22b91e44944a5a6415be3c28b4723","9f32ab2ad9864ee8bd0680ba3aec36e5","eb8c2f4c381547acac86bd83a9ab17f6","9d9e248989fc40bea5b48b016d00e933","f7aa62e1b2b142eea2232050885050cf","065f2dae6c444116a6391cede9194eff","b1df9467296145f0967e9bc78716f478","0d897990cff84a53a10f3ec6f41731f5","067143e9018240d98676f7a1b8546b90","2a33e55805324382a6204e1b019aa762","84df0bb7f27c496ea9d4c814c4081d93","dc9d8932cd0c41dbafc5d9c689f26528","09d631c1165348d1956c8530ef63cf16","4a60930037bd47f4b0fc39910deec953","7dac7c35803b4e24971b574094d7bc65","0accbb33e44a484a93484b3f2dd04df7","c04f1ac779314f33afa66951662cb6f8","0a458ef419e34d72b23543a89e947cc8","0c7d260075b34c44978d08677e493fa1","eb662612666c4e5681b3a1793de1c0f1","633b5c7a7ce54719bd12d5ba340944af","7a810391a08a4c478cd0a9b8249ecacb","ba2f8c2a83f249e9ba84ac373a2eb4af","3eb58657e3874ce0b02f158d130c5ce4","c615477619f14d7bae01340fd8d448ea","8c763fb437a649fa989fbf8b177555c1","3a32269082364d34aac8cb076894ca9d","a1042ed341bc42f78c0572362d286bbd","d2b6f2be0f054520a9277799af8fc0fb","5e2c7eb1277042e888a2a63cd0ccd863","6d7333005bc14564a89ca4f29809ca69","30b5a66bdbf14b44a1e506db0575e822","ce8c7f8bcac74725bda5387d3fe716b5","63b9bc2f009847779df01f3a1b12a278","8dd02f57f4a84b20bacb744c480a224c","c6a6f43c013340e486f8866e09d2a56d","d2c1032d69ad4eb1842179b405bf38c2","3e8f9a43923b4cb182fe4b195c2f3857","6bbf81575d2d476d9f26dbf925fe3038","9ec747acb7af43b48f5ac293b45b337e","ac35a7060dcc4ecc88e3b4fadc35fc59","fc02bfaa57224caf9a47a2eca49319fd","50db1e9611314d33b5bdba02c12a0fa4","63da8155f4f5441081b32bc53267c681","9911b1bc91f749caa23debc7dc0520e4","31655cfc324744199f68da7cd7d04e4d","387155acda284ed8aecb3cbfbda39846","8e67fff998bb441d9615b797764cd1c5","73c35f32a66b4e8784554b09f52f3f13","bd3fc21ed4b04e6b9ec8d2f17f291c91","dbd0b68cd5cb4b0d86ac496d97eeee5b","55b0c69ab15c46128d4368a813662f91","c4ee12945a7d420780e7c4ca1911f2ce","cba2b6aed10c4beab584059a342cd1bf","444c876c48f547bda0473ff8454a558e","10fa4410391d40d8ba55f2fce7de9ba1","bb04edbb683c474a833f1f5a00126f91","a705b1af38b64413ba12183c34fbc169","db7f8bf1b9f2482db662eada743ee456","ee6374b12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\n","2   SHAP                      42132              FN     pad_event       1   \n","3   SHAP                      42132              FN     pad_event       1   \n","4   SHAP                      42132              FN     pad_event       2   \n","\n","   runtime_seconds  top3_concentration  k  original_logit  \\\n","0         0.691691            0.830257  1      -11.415568   \n","1         0.691691            0.830257  2      -11.415568   \n","2         0.691691            0.830257  3      -11.415568   \n","3         0.691691            0.830257  5      -11.415568   \n","4         0.606706            0.827141  1      -11.415568   \n","\n","   original_probability  removed_topk_logit  removed_topk_probability  \\\n","0              0.000011          -11.505061                   0.00001   \n","1              0.000011          -11.505399                   0.00001   \n","2              0.000011          -11.505515                   0.00001   \n","3              0.000011          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sufficiency_abs_probability_gap  decision_flip_after_removal  \n","0                     5.117099e-07                            0  \n","1                     4.590538e-07                            0  \n","2                     4.026715e-07                            0  \n","3                     2.976221e-07                            0  \n","4                     5.117099e-07                            0  "],"text/html":["\n","  <div id=\"df-7bca7f20-086e-4b79-a2ef-8aae9ce80507\" class=\"colab-df-container\">\n","    <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>method</th>\n","      <th>sequence_local_test_index</th>\n","      <th>prediction_type</th>\n","      <th>baseline_name</th>\n","      <th>repeat</th>\n","      <th>runtime_seconds</th>\n","      <th>top3_concentration</th>\n","      <th>k</th>\n","      <th>original_logit</th>\n","      <th>original_probability</th>\n","      <th>removed_topk_logit</th>\n","      <th>removed_topk_probability</th>\n","      <th>comprehensiveness_logit</th>\n","      <th>comprehensiveness_probability</th>\n","      <th>kept_topk_logit</th>\n","      <th>kept_topk_probability</th>\n","      <th>sufficiency_abs_logit_gap</th>\n","      <th>sufficiency_abs_probability_gap</th>\n","      <th>decision_flip_after_removal</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>SHAP</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.691691</td>\n","      <td>0.830257</td>\n","      <td>1</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505061</td>\n","      <td>0.00001</td>\n","      <td>0.089493</td>\n","      <td>9.435671e-07</td>\n","      <td>-11.370189</td>\n","      <td>0.000012</td>\n","      <td>0.045380</td>\n","      <td>5.117099e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>SHAP</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.691691</td>\n","      <td>0.830257</td>\n","      <td>2</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505399</td>\n","      <td>0.00001</td>\n","      <td>0.089830</td>\n","      <td>9.469686e-07</td>\n","      <td>-11.374764</td>\n","      <td>0.000011</td>\n","      <td>0.040804</td>\n","      <td>4.590538e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>SHAP</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.691691</td>\n","      <td>0.830257</td>\n","      <td>3</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505515</td>\n","      <td>0.00001</td>\n","      <td>0.089947</td>\n","      <td>9.481410e-07</td>\n","      <td>-11.379687</td>\n","      <td>0.000011</td>\n","      <td>0.035881</td>\n","      <td>4.026715e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>SHAP</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>1</td>\n","      <td>0.691691</td>\n","      <td>0.830257</td>\n","      <td>5</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505404</td>\n","      <td>0.00001</td>\n","      <td>0.089836</td>\n","      <td>9.470259e-07</td>\n","      <td>-11.388925</td>\n","      <td>0.000011</td>\n","      <td>0.026644</td>\n","      <td>2.976221e-07</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>SHAP</td>\n","      <td>42132</td>\n","      <td>FN</td>\n","      <td>pad_event</td>\n","      <td>2</td>\n","      <td>0.606706</td>\n","      <td>0.827141</td>\n","      <td>1</td>\n","      <td>-11.415568</td>\n","      <td>0.000011</td>\n","      <td>-11.505061</td>\n","      <td>0.00001</td>\n","      <td>0.089493</td>\n","      <td>9.435671e-07</td>\n","      <td>-11.370189</td>\n","      <td>0.000012</td>\n","      <td>0.045380</td>\n","      <td>5.117099e-07</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7bca7f20-086e-4b79-a2ef-8aae9ce80507')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","   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   ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"k\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_logit\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": -11.415568351745605,\n        \"max\": -11.415568351745605,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -11.415568351745605\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.1022418220818508e-05,\n        \"max\": 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\"std\": 0.00785639941845648,\n        \"min\": -11.388924598693848,\n        \"max\": -11.37018871307373,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -11.374764442443848\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"kept_topk_probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.979581042311758e-08,\n        \"min\": 1.132004035753198e-05,\n        \"max\": 1.1534128134371713e-05,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.1481472029117867e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sufficiency_abs_logit_gap\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.00785639941845648,\n        \"min\": 0.026643753051757812,\n        \"max\": 0.045379638671875,\n        \"num_unique_values\": 4,\n        \"samples\": 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============================================================\n","# Step 46 — SHAP Benchmark on Model Logits\n","# ============================================================\n","def make_shap_logit_predict_fn(\n","    original_sequence_event_ids,\n","    replacement_event_id,\n","):\n","    original_sequence_event_ids = np.asarray(\n","        original_sequence_event_ids,\n","        dtype=np.int64,\n","    )\n","\n","    def predict_masks(mask_matrix):\n","        outputs = []\n","        for mask_row in np.asarray(mask_matrix):\n","            keep_mask = (np.asarray(mask_row) >= 0.5)\n","            perturbed = original_sequence_event_ids.copy()\n","            perturbed[~keep_mask] = int(replacement_event_id)\n","            logit, _ = predict_sequence_logit_and_probability(\n","                perturbed\n","            )\n","            outputs.append(logit)\n","        return np.asarray(outputs, dtype=float)\n","    return predict_masks\n","\n","\n","shap_benchmark_attribution_rows = []\n","shap_benchmark_metric_rows = []\n","\n","background_mask = np.zeros((1, WINDOW_SIZE), dtype=float)\n","full_mask = np.ones((1, WINDOW_SIZE), dtype=float)\n","\n","for case_number, case in xai_benchmark_cases_df.iterrows():\n","    local_idx = int(case[\"sequence_local_test_index\"])\n","    sequence_event_ids = np.asarray(X_test[local_idx], dtype=np.int64)\n","\n","    print(\n","        f\"SHAP benchmark case {case_number + 1}/\"\n","        f\"{len(xai_benchmark_cases_df)}: {local_idx}\"\n","    )\n","\n","    for baseline_name, replacement_event_id in XAI_BASELINES.items():\n","        for repeat in range(1, BENCHMARK_SHAP_REPEATS + 1):\n","            # Kernel SHAP uses NumPy's global RNG in several versions.\n","            np.random.seed(\n","                BENCHMARK_RANDOM_SEED\n","                + local_idx\n","                + repeat * 1000\n","                + list(XAI_BASELINES).index(baseline_name) * 10000\n","            )\n","\n","            predict_fn = make_shap_logit_predict_fn(\n","                sequence_event_ids,\n","                replacement_event_id,\n","            )\n","\n","            start_time = perf_counter()\n","            explainer = shap.KernelExplainer(\n","                predict_fn,\n","                background_mask,\n","            )\n","            shap_values = explainer.shap_values(\n","                full_mask,\n","                nsamples=BENCHMARK_SHAP_NSAMPLES,\n","            )\n","            elapsed = perf_counter() - start_time\n","\n","            if isinstance(shap_values, list):\n","                values = np.asarray(\n","                    shap_values[0],\n","                    dtype=float,\n","                ).reshape(-1)\n","            else:\n","                values = np.asarray(\n","                    shap_values,\n","                    dtype=float,\n","                ).reshape(-1)\n","\n","            if len(values) != WINDOW_SIZE:\n","                raise ValueError(\n","                    f\"SHAP returned {len(values)} values; \"\n","                    f\"expected {WINDOW_SIZE}.\"\n","                )\n","\n","            abs_values = np.abs(values)\n","            ranking = (\n","                np.argsort(-abs_values) + 1\n","            ).astype(int).tolist()\n","\n","            for position, value in enumerate(values, start=1):\n","                shap_benchmark_attribution_rows.append({\n","                    \"method\": \"SHAP\",\n","                    \"sequence_local_test_index\": local_idx,\n","                    \"prediction_type\": case[\"prediction_type\"],\n","                    \"baseline_name\": baseline_name,\n","                    \"repeat\": repeat,\n","                    \"event_position\": position,\n","                    \"signed_attribution\": float(value),\n","                    \"absolute_attribution\": float(abs(value)),\n","                })\n","\n","            faithfulness_rows = evaluate_ranking_faithfulness(\n","                sequence_event_ids,\n","                ranking,\n","                replacement_event_id,\n","                BENCHMARK_TOP_K,\n","            )\n","            for faithfulness_row in faithfulness_rows:\n","                shap_benchmark_metric_rows.append({\n","                    \"method\": \"SHAP\",\n","                    \"sequence_local_test_index\": local_idx,\n","                    \"prediction_type\": case[\"prediction_type\"],\n","                    \"baseline_name\": baseline_name,\n","                    \"repeat\": repeat,\n","                    \"runtime_seconds\": elapsed,\n","                    \"top3_concentration\": attribution_concentration(\n","                        abs_values,\n","                        top_k=3,\n","                    ),\n","                    **faithfulness_row,\n","                })\n","\n","shap_benchmark_attribution_df = pd.DataFrame(\n","    shap_benchmark_attribution_rows\n",")\n","shap_benchmark_metrics_df = pd.DataFrame(\n","    shap_benchmark_metric_rows\n",")\n","\n","SHAP_BENCHMARK_ATTRIBUTIONS_PATH = (\n","    XAI_BENCHMARK_DIR / \"shap_attributions.csv\"\n",")\n","SHAP_BENCHMARK_METRICS_PATH = (\n","    XAI_BENCHMARK_DIR / \"shap_metrics.csv\"\n",")\n","save_table(\n","    shap_benchmark_attribution_df,\n","    SHAP_BENCHMARK_ATTRIBUTIONS_PATH,\n","    \"Event-level SHAP logit attributions across cases, baselines, and repeats\",\n",")\n","save_table(\n","    shap_benchmark_metrics_df,\n","    SHAP_BENCHMARK_METRICS_PATH,\n","    \"SHAP faithfulness, sparsity, and runtime metrics\",\n",")\n","\n","print(\"SHAP benchmark completed.\")\n","display(shap_benchmark_metrics_df.head())\n"],"id":"DTa92ISNtiZm"},{"cell_type":"code","execution_count":70,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"ki_3grG1tiZm","executionInfo":{"status":"ok","timestamp":1785511400022,"user_tz":-180,"elapsed":877,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"38c13838-b20a-49e9-ad86-ad56728b78eb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["                          top1_agreement  top3_overlap  spearman\n","method    stability_type                                        \n","Occlusion baseline              0.930000      0.796667  0.720812\n","          repeat                1.000000      1.000000  1.000000\n","SHAP      baseline              0.330000      0.466667  0.234099\n","          repeat                0.343333      0.482222  0.294100"],"text/html":["\n","  <div id=\"df-d5d170da-53e3-490e-9d08-f816c1a6e6bb\" class=\"colab-df-container\">\n","    <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></th>\n","      <th>top1_agreement</th>\n","      <th>top3_overlap</th>\n","      <th>spearman</th>\n","    </tr>\n","    <tr>\n","      <th>method</th>\n","      <th>stability_type</th>\n","      <th></th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">Occlusion</th>\n","      <th>baseline</th>\n","      <td>0.930000</td>\n","      <td>0.796667</td>\n","      <td>0.720812</td>\n","    </tr>\n","    <tr>\n","      <th>repeat</th>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th rowspan=\"2\" valign=\"top\">SHAP</th>\n","      <th>baseline</th>\n","      <td>0.330000</td>\n","      <td>0.466667</td>\n","      <td>0.234099</td>\n","    </tr>\n","    <tr>\n","      <th>repeat</th>\n","      <td>0.343333</td>\n","      <td>0.482222</td>\n","      <td>0.294100</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5d170da-53e3-490e-9d08-f816c1a6e6bb')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 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SHAP has multiple repeats; Occlusion is deterministic\n","# and therefore has one run. Its repeat stability is reported as 1.0 by design.\n","for method in [\"SHAP\", \"Occlusion\"]:\n","    method_df = all_attributions_df[\n","        all_attributions_df[\"method\"] == method\n","    ]\n","    for (local_idx, baseline_name), group in method_df.groupby(\n","        [\"sequence_local_test_index\", \"baseline_name\"]\n","    ):\n","        repeats = sorted(group[\"repeat\"].unique())\n","        if len(repeats) < 2:\n","            stability_rows.append({\n","                \"method\": method,\n","                \"sequence_local_test_index\": int(local_idx),\n","                \"baseline_name\": baseline_name,\n","                \"stability_type\": \"repeat\",\n","                \"top1_agreement\": 1.0,\n","                \"top3_overlap\": 1.0,\n","                \"spearman\": 1.0,\n","                \"note\": \"Deterministic single-run method\",\n","            })\n","            continue\n","\n","        reference_key = (\n","            method,\n","            int(local_idx),\n","            baseline_name,\n","            int(repeats[0]),\n","        )\n","        reference_ranking = rankings[reference_key]\n","        reference_scores = score_vectors[reference_key]\n","\n","        for repeat in repeats[1:]:\n","            key = (\n","                method,\n","                int(local_idx),\n","                baseline_name,\n","                int(repeat),\n","            )\n","            ranking = rankings[key]\n","            scores = score_vectors[key]\n","            stability_rows.append({\n","                \"method\": method,\n","                \"sequence_local_test_index\": int(local_idx),\n","                \"baseline_name\": baseline_name,\n","                \"stability_type\": \"repeat\",\n","                \"top1_agreement\": float(\n","                    reference_ranking[0] == ranking[0]\n","                ),\n","                \"top3_overlap\": topk_overlap_rate(\n","                    reference_ranking,\n","                    ranking,\n","                    k=3,\n","                ),\n","                \"spearman\": normalized_rank_correlation(\n","                    reference_scores,\n","                    scores,\n","                ),\n","                \"note\": \"Compared with repeat 1\",\n","            })\n","\n","# Baseline robustness: compare each alternative baseline with the primary one.\n","primary_baseline_name = \"most_frequent_normal_event\"\n","for method in [\"SHAP\", \"Occlusion\"]:\n","    method_df = all_attributions_df[\n","        all_attributions_df[\"method\"] == method\n","    ]\n","    for local_idx in method_df[\n","        \"sequence_local_test_index\"\n","    ].unique():\n","        reference_repeat = 1\n","        reference_key = (\n","            method,\n","            int(local_idx),\n","            primary_baseline_name,\n","            reference_repeat,\n","        )\n","        if reference_key not in rankings:\n","            continue\n","\n","        for baseline_name in XAI_BASELINES:\n","            if baseline_name == primary_baseline_name:\n","                continue\n","            key = (\n","                method,\n","                int(local_idx),\n","                baseline_name,\n","                reference_repeat,\n","            )\n","            if key not in rankings:\n","                continue\n","\n","            stability_rows.append({\n","                \"method\": method,\n","                \"sequence_local_test_index\": int(local_idx),\n","                \"baseline_name\": baseline_name,\n","                \"stability_type\": \"baseline\",\n","                \"top1_agreement\": float(\n","                    rankings[reference_key][0]\n","                    == rankings[key][0]\n","                ),\n","                \"top3_overlap\": topk_overlap_rate(\n","                    rankings[reference_key],\n","                    rankings[key],\n","                    k=3,\n","                ),\n","                \"spearman\": normalized_rank_correlation(\n","                    score_vectors[reference_key],\n","                    score_vectors[key],\n","                ),\n","                \"note\": (\n","                    f\"Compared with {primary_baseline_name}\"\n","                ),\n","            })\n","\n","xai_stability_df = pd.DataFrame(stability_rows)\n","XAI_STABILITY_PATH = (\n","    XAI_BENCHMARK_DIR / \"xai_stability_and_baseline_robustness.csv\"\n",")\n","save_table(\n","    xai_stability_df,\n","    XAI_STABILITY_PATH,\n","    \"Repeated-run stability and baseline robustness for SHAP and Occlusion\",\n",")\n","\n","display(\n","    xai_stability_df.groupby(\n","        [\"method\", \"stability_type\"]\n","    )[\n","        [\"top1_agreement\", \"top3_overlap\", \"spearman\"]\n","    ].mean(numeric_only=True)\n",")\n"],"id":"ki_3grG1tiZm"},{"cell_type":"code","execution_count":71,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162},"id":"VT2Te9gktiZm","executionInfo":{"status":"ok","timestamp":1785511400188,"user_tz":-180,"elapsed":163,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"3f70d153-b6cf-4e98-f8fb-4e4f25b0ecaf"},"outputs":[{"output_type":"stream","name":"stdout","text":["Cross-method agreement is descriptive only and is not used as the primary winner criterion.\n"]},{"output_type":"display_data","data":{"text/plain":["                    mean\n","top1_agreement  0.306667\n","top3_overlap    0.411111\n","spearman        0.171439"],"text/html":["\n","  <div id=\"df-644b6957-f211-4cc1-a7be-804b1906681e\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","      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Visit the ' +\n","          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","          + ' to learn more about interactive tables.';\n","        element.innerHTML = '';\n","        dataTable['output_type'] = 'display_data';\n","        await google.colab.output.renderOutput(dataTable, element);\n","        const docLink = document.createElement('div');\n","        docLink.innerHTML = docLinkHtml;\n","        element.appendChild(docLink);\n","      }\n","    </script>\n","  </div>\n","\n","\n","    </div>\n","  </div>\n"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"dataframe","summary":"{\n  \"name\": \")\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.12016512500379775,\n        \"min\": 0.17143892137741248,\n        \"max\": 0.41111111111111104,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.30666666666666664,\n          0.41111111111111104,\n          0.17143892137741248\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 48 — Cross-Method Agreement (Secondary Diagnostic)\n","# ============================================================\n","cross_method_rows = []\n","\n","for local_idx in xai_benchmark_cases_df[\n","    \"sequence_local_test_index\"\n","].astype(int):\n","    for baseline_name in XAI_BASELINES:\n","        shap_key = (\n","            \"SHAP\",\n","            local_idx,\n","            baseline_name,\n","            1,\n","        )\n","        occ_key = (\n","            \"Occlusion\",\n","            local_idx,\n","            baseline_name,\n","            1,\n","        )\n","        if shap_key not in rankings or occ_key not in rankings:\n","            continue\n","\n","        cross_method_rows.append({\n","            \"sequence_local_test_index\": local_idx,\n","            \"baseline_name\": baseline_name,\n","            \"top1_agreement\": float(\n","                rankings[shap_key][0] == rankings[occ_key][0]\n","            ),\n","            \"top3_overlap\": topk_overlap_rate(\n","                rankings[shap_key],\n","                rankings[occ_key],\n","                k=3,\n","            ),\n","            \"spearman\": normalized_rank_correlation(\n","                score_vectors[shap_key],\n","                score_vectors[occ_key],\n","            ),\n","        })\n","\n","xai_cross_method_df = pd.DataFrame(cross_method_rows)\n","XAI_CROSS_METHOD_PATH = (\n","    XAI_BENCHMARK_DIR / \"shap_occlusion_cross_method_agreement.csv\"\n",")\n","save_table(\n","    xai_cross_method_df,\n","    XAI_CROSS_METHOD_PATH,\n","    \"SHAP–Occlusion agreement as a secondary diagnostic\",\n",")\n","\n","print(\n","    \"Cross-method agreement is descriptive only and is not used \"\n","    \"as the primary winner criterion.\"\n",")\n","display(\n","    xai_cross_method_df[\n","        [\"top1_agreement\", \"top3_overlap\", \"spearman\"]\n","    ].mean(numeric_only=True).to_frame(\"mean\")\n",")\n"],"id":"VT2Te9gktiZm"},{"cell_type":"code","execution_count":74,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":420},"id":"Z7niBQoFtiZn","executionInfo":{"status":"ok","timestamp":1785512050625,"user_tz":-180,"elapsed":96,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"33fab91a-4f96-4da9-cf48-eb664de45267"},"outputs":[{"output_type":"stream","name":"stdout","text":["================================================================================\n","XAI METHOD SELECTION\n","================================================================================\n","Relative comprehensiveness difference: 1.69%\n","Relative sufficiency difference: 0.76%\n","Meaningful faithfulness difference: False\n","\n","Decision basis:\n","The faithfulness difference was below the predefined 5% practical-significance threshold. The methods were therefore treated as comparable in faithfulness, and the primary method was selected using stability, baseline robustness, and computational efficiency.\n","\n","Primary XAI method: Occlusion\n","Complementary XAI method: SHAP\n"]},{"output_type":"display_data","data":{"text/plain":["      method  mean_comprehensiveness_logit_k3  \\\n","0  Occlusion                         0.458191   \n","1       SHAP                         0.465982   \n","\n","   mean_sufficiency_abs_logit_gap_k3  decision_flip_rate_k3  \\\n","0                           0.422976               0.000000   \n","1                           0.419759               0.013333   \n","\n","   repeat_top3_stability  baseline_top3_robustness  \\\n","0               1.000000                  0.796667   \n","1               0.482222                  0.466667   \n","\n","   baseline_spearman_robustness  mean_top3_concentration  \\\n","0                      0.720812                 0.406489   \n","1                      0.234099                 0.464397   \n","\n","   mean_runtime_seconds_per_sequence  faithfulness_rank  stability_rank  \\\n","0                           0.036889                4.0             3.0   \n","1                           0.766334                2.0             6.0   \n","\n","   efficiency_rank  sparsity_rank  selected_role  \\\n","0              1.0            2.0        Primary   \n","1              2.0            1.0  Complementary   \n","\n","   comprehensiveness_relative_difference  sufficiency_relative_difference  \\\n","0                               0.016862                         0.007634   \n","1                               0.016862                         0.007634   \n","\n","   faithfulness_difference_is_meaningful  \\\n","0                                  False   \n","1                                  False   \n","\n","                                      decision_basis  \n","0  The faithfulness difference was below the pred...  \n","1  The faithfulness difference was below the pred...  "],"text/html":["\n","  <div id=\"df-90c40e0c-3286-4c8d-ae65-10c2e7b2871e\" class=\"colab-df-container\">\n","    <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>method</th>\n","      <th>mean_comprehensiveness_logit_k3</th>\n","      <th>mean_sufficiency_abs_logit_gap_k3</th>\n","      <th>decision_flip_rate_k3</th>\n","      <th>repeat_top3_stability</th>\n","      <th>baseline_top3_robustness</th>\n","      <th>baseline_spearman_robustness</th>\n","      <th>mean_top3_concentration</th>\n","      <th>mean_runtime_seconds_per_sequence</th>\n","      <th>faithfulness_rank</th>\n","      <th>stability_rank</th>\n","      <th>efficiency_rank</th>\n","      <th>sparsity_rank</th>\n","      <th>selected_role</th>\n","      <th>comprehensiveness_relative_difference</th>\n","      <th>sufficiency_relative_difference</th>\n","      <th>faithfulness_difference_is_meaningful</th>\n","      <th>decision_basis</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Occlusion</td>\n","      <td>0.458191</td>\n","      <td>0.422976</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>0.796667</td>\n","      <td>0.720812</td>\n","      <td>0.406489</td>\n","      <td>0.036889</td>\n","      <td>4.0</td>\n","      <td>3.0</td>\n","      <td>1.0</td>\n","      <td>2.0</td>\n","      <td>Primary</td>\n","      <td>0.016862</td>\n","      <td>0.007634</td>\n","      <td>False</td>\n","      <td>The faithfulness difference was below the pred...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>SHAP</td>\n","      <td>0.465982</td>\n","      <td>0.419759</td>\n","      <td>0.013333</td>\n","      <td>0.482222</td>\n","      <td>0.466667</td>\n","      <td>0.234099</td>\n","      <td>0.464397</td>\n","      <td>0.766334</td>\n","      <td>2.0</td>\n","      <td>6.0</td>\n","      <td>2.0</td>\n","      <td>1.0</td>\n","      <td>Complementary</td>\n","      <td>0.016862</td>\n","      <td>0.007634</td>\n","      <td>False</td>\n","      <td>The faithfulness difference was below the pred...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-90c40e0c-3286-4c8d-ae65-10c2e7b2871e')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-90c40e0c-3286-4c8d-ae65-10c2e7b2871e button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\"dtype\": \"number\",\n        \"std\": 0.3441582558964146,\n        \"min\": 0.23409877437931392,\n        \"max\": 0.7208120474706936,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.23409877437931392,\n          0.7208120474706936\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_top3_concentration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04094696233699754,\n        \"min\": 0.4064889991398081,\n        \"max\": 0.46439674861477037,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.46439674861477037,\n          0.4064889991398081\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_runtime_seconds_per_sequence\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5157954507398655,\n        \"min\": 0.03688907439999866,\n        \"max\": 0.7663339962466602,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.7663339962466602,\n          0.03688907439999866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness_rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4142135623730951,\n        \"min\": 2.0,\n        \"max\": 4.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stability_rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1213203435596424,\n        \"min\": 3.0,\n        \"max\": 6.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6.0,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"efficiency_rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sparsity_rank\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7071067811865476,\n        \"min\": 1.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selected_role\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Complementary\",\n          \"Primary\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comprehensiveness_relative_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.01686184958908334,\n        \"max\": 0.01686184958908334,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.01686184958908334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sufficiency_relative_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.007634373370364914,\n        \"max\": 0.007634373370364914,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.007634373370364914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"faithfulness_difference_is_meaningful\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"decision_basis\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"The faithfulness difference was below the predefined 5% practical-significance threshold. The methods were therefore treated as comparable in faithfulness, and the primary method was selected using stability, baseline robustness, and computational efficiency.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{}}],"source":["# ============================================================\n","# Step 49 — Aggregate Comparison and Select the Primary Method\n","#              Using Practical Decision Rules\n","# ============================================================\n","\n","all_metrics_df = pd.concat(\n","    [\n","        occlusion_metrics_df,\n","        shap_benchmark_metrics_df,\n","    ],\n","    ignore_index=True,\n",")\n","\n","# Use the primary baseline for faithfulness comparison.\n","core_metrics_df = all_metrics_df[\n","    all_metrics_df[\"baseline_name\"] == primary_baseline_name\n","].copy()\n","\n","method_summary_rows = []\n","\n","for method, group in core_metrics_df.groupby(\"method\"):\n","    k3 = group[group[\"k\"] == min(3, WINDOW_SIZE)]\n","\n","    repeat_stability = xai_stability_df[\n","        (xai_stability_df[\"method\"] == method)\n","        & (xai_stability_df[\"stability_type\"] == \"repeat\")\n","    ]\n","\n","    baseline_robustness = xai_stability_df[\n","        (xai_stability_df[\"method\"] == method)\n","        & (xai_stability_df[\"stability_type\"] == \"baseline\")\n","    ]\n","\n","    method_summary_rows.append({\n","        \"method\": method,\n","\n","        # Faithfulness: higher comprehensiveness is better.\n","        \"mean_comprehensiveness_logit_k3\": float(\n","            k3[\"comprehensiveness_logit\"].mean()\n","        ),\n","\n","        # Faithfulness: lower sufficiency gap is better.\n","        \"mean_sufficiency_abs_logit_gap_k3\": float(\n","            k3[\"sufficiency_abs_logit_gap\"].mean()\n","        ),\n","\n","        # Higher is better, but this is considered supporting evidence.\n","        \"decision_flip_rate_k3\": float(\n","            k3[\"decision_flip_after_removal\"].mean()\n","        ),\n","\n","        # Stability and robustness: higher is better.\n","        \"repeat_top3_stability\": float(\n","            repeat_stability[\"top3_overlap\"].mean()\n","        ),\n","\n","        \"baseline_top3_robustness\": float(\n","            baseline_robustness[\"top3_overlap\"].mean()\n","        ),\n","\n","        \"baseline_spearman_robustness\": float(\n","            baseline_robustness[\"spearman\"].mean(skipna=True)\n","        ),\n","\n","        # Sparsity/concentration: higher means importance is more focused.\n","        \"mean_top3_concentration\": float(\n","            group[\"top3_concentration\"].mean()\n","        ),\n","\n","        # Efficiency: lower is better.\n","        \"mean_runtime_seconds_per_sequence\": float(\n","            group.groupby(\n","                [\n","                    \"sequence_local_test_index\",\n","                    \"baseline_name\",\n","                    \"repeat\",\n","                ]\n","            )[\"runtime_seconds\"].first().mean()\n","        ),\n","    })\n","\n","xai_method_summary_df = pd.DataFrame(method_summary_rows)\n","\n","if len(xai_method_summary_df) != 2:\n","    raise ValueError(\n","        \"The decision rule expects exactly two XAI methods: \"\n","        \"SHAP and Occlusion.\"\n","    )\n","\n","# ------------------------------------------------------------\n","# Relative differences\n","# ------------------------------------------------------------\n","def relative_difference(value_a, value_b, eps=1e-12):\n","    \"\"\"\n","    Symmetric relative difference.\n","\n","    A small result means that the two methods have practically\n","    similar performance for the evaluated metric.\n","    \"\"\"\n","    denominator = max(\n","        (abs(float(value_a)) + abs(float(value_b))) / 2.0,\n","        eps,\n","    )\n","    return abs(float(value_a) - float(value_b)) / denominator\n","\n","\n","method_a = xai_method_summary_df.iloc[0]\n","method_b = xai_method_summary_df.iloc[1]\n","\n","comprehensiveness_relative_difference = relative_difference(\n","    method_a[\"mean_comprehensiveness_logit_k3\"],\n","    method_b[\"mean_comprehensiveness_logit_k3\"],\n",")\n","\n","sufficiency_relative_difference = relative_difference(\n","    method_a[\"mean_sufficiency_abs_logit_gap_k3\"],\n","    method_b[\"mean_sufficiency_abs_logit_gap_k3\"],\n",")\n","\n","# Faithfulness superiority is considered meaningful only when\n","# at least one central faithfulness metric differs by 5% or more.\n","FAITHFULNESS_PRACTICAL_THRESHOLD = 0.05\n","\n","faithfulness_difference_is_meaningful = (\n","    comprehensiveness_relative_difference\n","    >= FAITHFULNESS_PRACTICAL_THRESHOLD\n","    or sufficiency_relative_difference\n","    >= FAITHFULNESS_PRACTICAL_THRESHOLD\n",")\n","\n","# ------------------------------------------------------------\n","# Rank each raw criterion without Min-Max normalization\n","# ------------------------------------------------------------\n","xai_method_summary_df[\"faithfulness_rank\"] = (\n","    xai_method_summary_df[\n","        \"mean_comprehensiveness_logit_k3\"\n","    ].rank(ascending=False, method=\"min\")\n","    +\n","    xai_method_summary_df[\n","        \"mean_sufficiency_abs_logit_gap_k3\"\n","    ].rank(ascending=True, method=\"min\")\n",")\n","\n","xai_method_summary_df[\"stability_rank\"] = (\n","    xai_method_summary_df[\n","        \"repeat_top3_stability\"\n","    ].rank(ascending=False, method=\"min\")\n","    +\n","    xai_method_summary_df[\n","        \"baseline_top3_robustness\"\n","    ].rank(ascending=False, method=\"min\")\n","    +\n","    xai_method_summary_df[\n","        \"baseline_spearman_robustness\"\n","    ].rank(ascending=False, method=\"min\")\n",")\n","\n","xai_method_summary_df[\"efficiency_rank\"] = (\n","    xai_method_summary_df[\n","        \"mean_runtime_seconds_per_sequence\"\n","    ].rank(ascending=True, method=\"min\")\n",")\n","\n","xai_method_summary_df[\"sparsity_rank\"] = (\n","    xai_method_summary_df[\n","        \"mean_top3_concentration\"\n","    ].rank(ascending=False, method=\"min\")\n",")\n","\n","# ------------------------------------------------------------\n","# Transparent decision logic\n","# ------------------------------------------------------------\n","if faithfulness_difference_is_meaningful:\n","    # If faithfulness differs materially, prioritize faithfulness.\n","    decision_basis = (\n","        \"A practically meaningful faithfulness difference of at \"\n","        \"least 5% was observed. The primary method was selected \"\n","        \"using faithfulness first, followed by stability and runtime.\"\n","    )\n","\n","    ranked_methods_df = xai_method_summary_df.sort_values(\n","        [\n","            \"faithfulness_rank\",\n","            \"stability_rank\",\n","            \"efficiency_rank\",\n","            \"sparsity_rank\",\n","        ],\n","        ascending=True,\n","    ).reset_index(drop=True)\n","\n","else:\n","    # When faithfulness is practically similar, stability,\n","    # baseline robustness, and efficiency determine the winner.\n","    decision_basis = (\n","        \"The faithfulness difference was below the predefined \"\n","        \"5% practical-significance threshold. The methods were \"\n","        \"therefore treated as comparable in faithfulness, and \"\n","        \"the primary method was selected using stability, \"\n","        \"baseline robustness, and computational efficiency.\"\n","    )\n","\n","    ranked_methods_df = xai_method_summary_df.sort_values(\n","        [\n","            \"stability_rank\",\n","            \"efficiency_rank\",\n","            \"faithfulness_rank\",\n","            \"sparsity_rank\",\n","        ],\n","        ascending=True,\n","    ).reset_index(drop=True)\n","\n","xai_method_summary_df = ranked_methods_df.copy()\n","\n","primary_xai_method = xai_method_summary_df.iloc[0][\"method\"]\n","complementary_xai_method = xai_method_summary_df.iloc[1][\"method\"]\n","\n","xai_method_summary_df[\"selected_role\"] = [\n","    \"Primary\",\n","    \"Complementary\",\n","]\n","\n","xai_method_summary_df[\n","    \"comprehensiveness_relative_difference\"\n","] = comprehensiveness_relative_difference\n","\n","xai_method_summary_df[\n","    \"sufficiency_relative_difference\"\n","] = sufficiency_relative_difference\n","\n","xai_method_summary_df[\n","    \"faithfulness_difference_is_meaningful\"\n","] = faithfulness_difference_is_meaningful\n","\n","xai_method_summary_df[\"decision_basis\"] = decision_basis\n","\n","# ------------------------------------------------------------\n","# Save results\n","# ------------------------------------------------------------\n","XAI_METHOD_SUMMARY_PATH = (\n","    XAI_BENCHMARK_DIR / \"xai_method_comparison_summary.csv\"\n",")\n","\n","save_table(\n","    xai_method_summary_df,\n","    XAI_METHOD_SUMMARY_PATH,\n","    \"Unified SHAP versus Occlusion benchmark summary\",\n",")\n","\n","print(\"=\" * 80)\n","print(\"XAI METHOD SELECTION\")\n","print(\"=\" * 80)\n","\n","print(\n","    \"Relative comprehensiveness difference:\",\n","    f\"{comprehensiveness_relative_difference:.2%}\",\n",")\n","\n","print(\n","    \"Relative sufficiency difference:\",\n","    f\"{sufficiency_relative_difference:.2%}\",\n",")\n","\n","print(\n","    \"Meaningful faithfulness difference:\",\n","    faithfulness_difference_is_meaningful,\n",")\n","\n","print(\"\\nDecision basis:\")\n","print(decision_basis)\n","\n","print(\"\\nPrimary XAI method:\", primary_xai_method)\n","print(\"Complementary XAI method:\", complementary_xai_method)\n","\n","display(xai_method_summary_df)"],"id":"Z7niBQoFtiZn"},{"cell_type":"code","execution_count":75,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":937},"id":"h79ih6KZtiZn","executionInfo":{"status":"ok","timestamp":1785512089027,"user_tz":-180,"elapsed":702,"user":{"displayName":"Hesham Bashatah","userId":"16181614520800761792"}},"outputId":"7f1765b9-a320-47f9-9e08-88bffd28df78"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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interpretation:\n","\n","The unified benchmark evaluated SHAP and Occlusion on 50 identical test sequences using three replacement baselines. The differences in the central faithfulness measures were below the predefined 5% practical-significance threshold. The two methods were therefore treated as broadly comparable in faithfulness. Occlusion was selected as the primary explanation method because it demonstrated stronger repeated-run stability, baseline robustness, and computational efficiency. SHAP was retained as a complementary method that provides an alternative attribution perspective.\n","\n","Selection:\n","Primary XAI method: Occlusion\n","Complementary XAI method: SHAP\n","\n","Decision basis:\n","The faithfulness difference was below the predefined 5% practical-significance threshold. The methods were therefore treated as comparable in faithfulness, and the primary method was selected using stability, baseline robustness, and computational efficiency.\n","\n","Saved outputs:\n","- /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai_benchmark/xai_method_comparison_summary.csv\n","- /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai_benchmark/xai_stability_and_baseline_robustness.csv\n","- /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai_benchmark/shap_occlusion_cross_method_agreement.csv\n","- /content/drive/MyDrive/logminilm_cache/production_results/LogMiniLM_V5_2_Corrected/xai_benchmark/xai_method_comparison.png\n"]}],"source":["# ============================================================\n","# Step 50 — Thesis-Ready Tables, Figures, and Interpretation\n","# ============================================================\n","\n","XAI_BENCHMARK_FIGURE_PATH = (\n","    XAI_BENCHMARK_DIR / \"xai_method_comparison.png\"\n",")\n","\n","# ------------------------------------------------------------\n","# Prepare normalized ratios only for visualization.\n","#\n","# Unlike Min-Max, ratio-to-best preserves the magnitude of\n","# differences. A value of 0.98 means that the method achieved\n","# 98% of the best observed result.\n","# ------------------------------------------------------------\n","plot_df = xai_method_summary_df.set_index(\"method\").copy()\n","\n","def ratio_to_best_higher(series, eps=1e-12):\n","    best = max(float(series.max()), eps)\n","    return series.astype(float) / best\n","\n","def ratio_to_best_lower(series, eps=1e-12):\n","    safe_series = series.astype(float).clip(lower=eps)\n","    best = float(safe_series.min())\n","    return best / safe_series\n","\n","plot_metrics = pd.DataFrame(\n","    index=plot_df.index\n",")\n","\n","plot_metrics[\"Comprehensiveness\"] = ratio_to_best_higher(\n","    plot_df[\"mean_comprehensiveness_logit_k3\"]\n",")\n","\n","plot_metrics[\"Sufficiency\"] = ratio_to_best_lower(\n","    plot_df[\"mean_sufficiency_abs_logit_gap_k3\"]\n",")\n","\n","plot_metrics[\"Repeat stability\"] = ratio_to_best_higher(\n","    plot_df[\"repeat_top3_stability\"]\n",")\n","\n","plot_metrics[\"Baseline robustness\"] = ratio_to_best_higher(\n","    plot_df[\"baseline_top3_robustness\"]\n",")\n","\n","plot_metrics[\"Runtime efficiency\"] = ratio_to_best_lower(\n","    plot_df[\"mean_runtime_seconds_per_sequence\"]\n",")\n","\n","ax = plot_metrics.plot(\n","    kind=\"bar\",\n","    figsize=(12, 6),\n",")\n","\n","ax.set_ylim(0, 1.05)\n","ax.set_ylabel(\"Ratio to best observed result\")\n","ax.set_xlabel(\"XAI method\")\n","ax.set_title(\n","    \"SHAP versus Occlusion: Evidence-Based XAI Benchmark\"\n",")\n","ax.legend(loc=\"best\")\n","plt.xticks(rotation=0)\n","plt.tight_layout()\n","\n","plt.savefig(\n","    XAI_BENCHMARK_FIGURE_PATH,\n","    dpi=300,\n","    bbox_inches=\"tight\",\n",")\n","\n","plt.show()\n","\n","winner = xai_method_summary_df.iloc[0]\n","runner_up = xai_method_summary_df.iloc[1]\n","\n","print(\"\\nThesis-ready interpretation:\\n\")\n","\n","if faithfulness_difference_is_meaningful:\n","    interpretation = (\n","        f\"The unified benchmark evaluated SHAP and Occlusion on \"\n","        f\"{len(xai_benchmark_cases_df)} identical test sequences \"\n","        f\"using three replacement baselines. A practically \"\n","        f\"meaningful difference was observed in at least one \"\n","        f\"central faithfulness measure. Accordingly, \"\n","        f\"{winner['method']} was selected as the primary \"\n","        f\"explanation method after considering faithfulness, \"\n","        f\"stability, baseline robustness, and computational \"\n","        f\"efficiency. {runner_up['method']} was retained as a \"\n","        f\"complementary explanation method.\"\n","    )\n","else:\n","    interpretation = (\n","        f\"The unified benchmark evaluated SHAP and Occlusion on \"\n","        f\"{len(xai_benchmark_cases_df)} identical test sequences \"\n","        f\"using three replacement baselines. The differences in \"\n","        f\"the central faithfulness measures were below the \"\n","        f\"predefined 5% practical-significance threshold. The two \"\n","        f\"methods were therefore treated as broadly comparable in \"\n","        f\"faithfulness. {winner['method']} was selected as the \"\n","        f\"primary explanation method because it demonstrated \"\n","        f\"stronger repeated-run stability, baseline robustness, \"\n","        f\"and computational efficiency. \"\n","        f\"{runner_up['method']} was retained as a complementary \"\n","        f\"method that provides an alternative attribution \"\n","        f\"perspective.\"\n","    )\n","\n","print(interpretation)\n","\n","print(\"\\nSelection:\")\n","print(\"Primary XAI method:\", primary_xai_method)\n","print(\"Complementary XAI method:\", complementary_xai_method)\n","\n","print(\"\\nDecision basis:\")\n","print(decision_basis)\n","\n","print(\"\\nSaved outputs:\")\n","print(\"-\", XAI_METHOD_SUMMARY_PATH)\n","print(\"-\", XAI_STABILITY_PATH)\n","print(\"-\", XAI_CROSS_METHOD_PATH)\n","print(\"-\", XAI_BENCHMARK_FIGURE_PATH)"],"id":"h79ih6KZtiZn"},{"cell_type":"markdown","metadata":{"id":"sO177715tiZn"},"source":["## Step 51 — Interpretation Guardrails\n","\n","Use the following wording rules when transferring the benchmark results into the thesis:\n","\n","1. The selected primary method explains the model's decision; it does not prove the operational root cause.\n","2. Comprehensiveness and sufficiency are perturbation-based faithfulness tests, not causal guarantees about the real system.\n","3. SHAP–Occlusion agreement is a secondary diagnostic and must not be presented as ground truth.\n","4. The strongest case study must be labeled as a deliberately selected illustrative case, not a representative average case.\n","5. Report the benchmark sample composition, SHAP sample count, number of repeats, replacement baselines, and score weights.\n","6. Preserve the raw metric table alongside any normalized overall score.\n"],"id":"sO177715tiZn"}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"dataset_path":"/content/drive/MyDrive/bgl_data/bgl.log","epoch_selection_metric":"maximum_validation_f1","experimental_protocol":"two_phase_temporal_validation_then_final_retraining","figure_placement":"immediately_after_generating_step","google_drive_mount_policy":"single_mount_at_notebook_start","imports_policy":"all_required_imports_at_notebook_start","kernelspec":{"display_name":"Python 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