Governance, pruning, and model cards

HUGIML is designed for audit-heavy workflows where model reviewers need to inspect rules, trace feature lineage, remove unsafe patterns, and package validation artifacts.

Model cards

from hugiml.governance import generate_model_card

card = generate_model_card(
    clf,
    model_id="credit-scorer-v1.0.0",
    intended_use="Credit risk assessment for SME lending.",
    training_data_description="German Credit dataset, 1000 samples",
)

print(card.to_markdown())
card.save("model_card.json")
card.save("model_card.md", fmt="markdown")

A starter template is included at docs/model_card_template.md and should be copied into validation packets or repository governance folders as needed.

Pattern pruning

Analysts may need to remove patterns that reference protected attributes, have excessive drift, encode operationally invalid logic, or fail model-risk review. PatternEditor provides a controlled remove/refit/calibrate/finalize workflow with a JSON audit trail.

from hugiml.pruning import PatternEditor

editor = PatternEditor(clf, operator_name="risk-team")
print(editor.list_patterns().head(10))

editor.remove([3, 7], reason="references protected attribute")
editor.remove_by_keyword("postcode", reason="high PSI during monitoring")
editor.remove_low_support(min_support=0.01, reason="low-support noise")

editor.refit(X_train, y_train)
editor.calibrate(X_calibration, y_calibration, method="isotonic")

governed_clf = editor.finalize()
editor.save_audit_report("pattern_pruning_audit.json")

Audit artifacts

from hugiml.governance import GovernanceMetadata, AuditArtifact

metadata = GovernanceMetadata(
    model_id="credit-scorer-v1",
    owner="model-risk-team",
    purpose="Credit application risk ranking",
)

audit = AuditArtifact.from_model(clf, metadata=metadata)
audit.save("training_audit.json")

Calibration

from hugiml.calibration import evaluate_calibration, reliability_diagram_data

result = evaluate_calibration(y_test, proba[:, 1])
print(result.summary())

diagram = reliability_diagram_data(y_test, proba[:, 1], n_bins=10)