Execution modes

HUGIML 1.1.9 adds an execution_mode parameter for choosing how much fitted training-review state is retained after fit.

Mode	Retained state	Use when
"audit"	Full traceability state, including training matrices and drift baselines.	You need model cards, governance review, drift diagnostics, pattern-support tables, or detailed explanation workflows.
"production"	Prediction-critical state, fitted metadata, selected feature names, and lightweight shape summaries.	You need a smaller deployed model object for prediction and save/load workflows.

audit is the default and preserves the v1.1.8 behavior for governance-heavy workflows.

Basic usage

from hugiml import HUGIMLClassifier

audit_model = HUGIMLClassifier(execution_mode="audit")
audit_model.fit(X_train, y_train)
print(audit_model.get_pattern_info())

production_model = HUGIMLClassifier(execution_mode="production")
production_model.fit(X_train, y_train)
proba = production_model.predict_proba(X_new)

Production-mode guidance

Production mode keeps prediction, predict_proba, score, save_model, load_model, model_summary, feature_importances, and get_hug_features available. Methods that need retained training-review artifacts, such as pattern support details and drift baselines, raise a clear message asking you to refit or load an audit-mode model when complete traceability is required.

Recommended workflow

Use audit mode during development, validation, governance review, and documentation. Save the audit-mode artifact for traceability. Use production mode for deployed scoring services when the model has already passed review and the retained training matrices are not needed in the live prediction object.