Monitoring, drift, and serialization

Prediction monitoring

clf.enable_monitoring(window_size=1000)
clf.predict_proba(X_new)

print(clf.monitor.stats())
print(clf.monitor.report())

The monitor tracks prediction volume, latency, probability summaries, and rolling-window state in a thread-safe object attached to the classifier.

Drift detection

drift_report = clf.detect_drift(X_new, current_labels=y_new)
print(drift_report)

psi_by_feature = clf.get_drift_psi(X_new)
print(psi_by_feature)

The built-in detector combines Population Stability Index, symmetric KL divergence, and optional label drift when current labels are available.

Native missing-value schemes across tabular models

Serialization

from hugiml.serialization import save_model, load_model, generate_sbom

save_model(clf, "model.hugiml")
restored = load_model("model.hugiml")

sbom = generate_sbom(restored, output_path="sbom.json")

The serializer uses a versioned ZIP/JSON/NumPy model format and avoids unrestricted pickle loading for the current schema.

Telemetry

Optional OpenTelemetry and Prometheus hooks are available with the telemetry extra.

pip install "hugiml-core[telemetry]"

from hugiml.telemetry import instrument_classifier

instrumented = instrument_classifier(clf, service_name="credit-risk-hugiml")
instrumented.predict_proba(X_new)