Electric Barometer · Optimization (eb-optimization)

Decision and policy layer for the Electric Barometer ecosystem, responsible for tuning, calibration, and governed parameter selection.

Overview

eb-optimization calibrates operational parameters (cost ratios, τ, readiness controls) and freezes them into auditable runtime policies. It does not compute metrics or run evaluation panels; those belong to eb-metrics and eb-evaluation.

Installation

eb-optimization is distributed as a standard Python package.

pip install eb-optimization

Core Concepts

• Parameter governance — Operational parameters (e.g., cost ratios, tolerances) should be selected through explicit, reproducible rules rather than ad-hoc tuning or implicit defaults.
• Search over candidate spaces — Optimization is framed as deterministic search over bounded, interpretable candidate sets, enabling transparent tradeoffs and stable outcomes.
• Cost balance calibration — Asymmetric operational costs can be balanced by selecting parameters that equalize or appropriately trade off opposing risk exposures.
• Tolerance selection from residuals — Acceptable error bands can be learned directly from historical performance, reflecting empirical system behavior rather than arbitrary thresholds.
• Policy separation — Calibration logic is separated from frozen policy artifacts so that parameter selection is auditable, versioned, and safely applied in downstream systems.
• Decision-aligned optimization — Optimization is evaluated by operational interpretability and governance fitness, not by abstract numerical optimality alone.

Minimal Example

The example below illustrates a typical optimization workflow using eb-optimization: calibrating an operational parameter from historical data and applying it via a frozen policy.

import numpy as np
from eb_optimization.policies import (
    CostRatioPolicy,
    apply_cost_ratio_policy,
)

# Historical actuals and forecasts
y_true = np.array([10, 12, 15, 20])
y_pred = np.array([9, 14, 18, 17])

# Define a frozen cost-ratio policy
policy = CostRatioPolicy(
    R_grid=(0.5, 1.0, 2.0, 3.0),
    co=1.0,
)

# Estimate a global cost ratio R
R, diagnostics = apply_cost_ratio_policy(
    y_true=y_true,
    y_pred=y_pred,
    policy=policy,
)

print(R)

License

BSD 3-Clause License. © 2026 Kyle Corrie.