Use-case gallery ================ The use-case gallery is organized by problem theme. This is usually the best way to enter the documentation: choose the topic that looks like your application, then open a card with plots, captured output, and interpretation. Start here ---------- If you are new to the package, start with one of these three examples: .. raw:: html

Fraud and security

Suspicious tabular records, network traffic anomalies, and robust distance ranking.

Finance and risk

Heavy-tailed covariance, portfolio stress, and market-risk style examples.

Real ML datasets

Breast cancer, digits, and wine examples with sklearn baseline comparisons.

Browse by topic --------------- .. raw:: html

Finance
risk

Finance and risk

Portfolio covariance, stress monitoring, and heavy-tailed returns.

Fraud
security

Fraud, security, and networks

Fraud-like screening and network-flow anomaly examples.

Sensors
quality

Sensors and quality control

Sensor anomaly, predictive maintenance, and process monitoring.

Signals
embeddings

Biomedical, image, and embedding data

Feature-vector anomaly detection for signals, images, and embeddings.

Real ML
datasets

Real ML datasets

Reproducible built-in datasets with baseline metrics and plots.

ML
preprocess

Robust ML preprocessing

Use robust distances before downstream classification.

Which topic should I open? -------------------------- .. list-table:: Topic guide :header-rows: 1 * - Problem you have - Open this topic - Good first estimator * - Fraud, suspicious transactions, unusual records - Fraud, security, and networks - ``FastMCD`` or ``AutoRobustAnomalyDetector`` * - Portfolio, returns, covariance, stress periods - Finance and risk - ``RegularizedCauchy`` * - Sensors, process drift, industrial faults - Sensors and quality control - ``FastMCD`` or ``RegularizedCauchy`` * - Signal/image/embedding feature vectors - Biomedical, image, and embedding data - ``AutoRobustScatter`` or ``RegularizedCauchy`` * - Reproducible ML benchmark examples - Real ML datasets - ``RobustOutlierDetector`` with baseline comparison * - Clean training data before a classifier - Robust ML preprocessing - robust-distance filtering Run the gallery --------------- .. code-block:: bash python examples/run_use_case_gallery.py Run every gallery script: .. code-block:: bash python examples/run_use_case_gallery.py --all Regenerate documentation assets ------------------------------- The gallery pages embed captured outputs and plots. Refresh them after changing examples: .. code-block:: bash python docs/generate_gallery_assets.py sphinx-build -b html docs docs/_build/html Topic pages ----------- .. toctree:: :maxdepth: 2 gallery_topics/finance_and_risk gallery_topics/fraud_security_and_networks gallery_topics/sensors_industrial_quality gallery_topics/biomedical_images_embeddings gallery_topics/real_ml_datasets gallery_topics/ml_preprocessing All detailed pages ------------------ .. toctree:: :maxdepth: 1 gallery/finance_risk gallery/portfolio_stress gallery/fraud_screening gallery/network_traffic gallery/sensor_anomaly gallery/maintenance_monitoring gallery/quality_control gallery/biomedical_signal gallery/image_feature_anomaly gallery/text_embedding_outliers gallery/breast_cancer_screening gallery/digits_one_class gallery/wine_class_screening gallery/ml_preprocessing gallery/multimodal_anomaly