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Readiness adjustment evaluation

This section documents evaluation utilities related to the readiness adjustment layer (RAL) provided by eb-evaluation.

These utilities assess the impact of readiness adjustments on forecast outputs and help quantify how RAL policies affect service risk and decision outcomes.

eb_evaluation.adjustment.ral

Readiness Adjustment Layer (RAL): deterministic fit + apply in eb-evaluation.

This module implements a transparent post-processing step that converts a baseline forecast into an operationally conservative readiness forecast via a learned uplift.

Responsibilities
  • Fit a simple uplift policy via grid search that minimizes CWSL.
  • Apply learned uplift factors to new data (global or segmented).
  • Provide before/after diagnostics for auditability.

ReadinessAdjustmentLayer

Readiness Adjustment Layer (RAL) for operational forecast uplift.

transform(df, *, forecast_col, output_col='readiness_forecast', segment_cols=None)

Apply learned uplift factors to produce readiness forecasts.

Test expectation: - If called before explicit fit(), this should still work for global uplift by implicitly fitting on the provided dataframe (requires an actual column), but only when costs (cu/co) are set.