calibrated-bo 0.2.0

Calibrated Bayesian optimization with composable conformal prediction (Weighted + Localized + Adaptive) on top of bayesian-gp-cvloss.

calibrated-bo

Calibrated Bayesian optimization with composable conformal prediction, built on top of bayesian-gp-cvloss.

Phase 1 (this release) — core single-objective stack:

• CompositeConformalCalibrator with three orthogonal switches:
  • localized=True — pick only x*'s k-NN from the calibration set
  • weighted=True — RBF weights against x* on the surviving subset
  • adaptive=True — ACI controller updates α from rolling coverage
• CalibratedUCB, CalibratedEI — drop-in calibrated acquisitions
• CalibratedBOLoop — single-objective loop (suggest() / observe())
• Platform adapter (create_bo_session(config))

All three calibrator switches are independent: turn them all off and the calibrator is numerically equivalent to standard split CP.

Phase 2 (planned) — multi-objective EHVI, q-batch with fantasies, diagnostics dashboard, native gp-cv platform integration.

Install

pip install -e D:/mypackage/calibrated-bo

Requires bayesian-gp-cvloss>=0.3.1.

Quickstart

import numpy as np
from calibrated_bo import CalibratedBOLoop

# 2D toy: minimise (x-0.3)^2 + (y-0.7)^2
def f(x):
    return np.sum((x - np.array([0.3, 0.7]))**2)

bo = CalibratedBOLoop(
    bounds=[(0.0, 1.0), (0.0, 1.0)],
    objective="minimize",
    calibrator_config={
        "alpha": 0.1,
        "localized": True, "k": 20,
        "weighted": True,
        "adaptive": True, "gamma": 0.05,
    },
    acquisition="cUCB",
    acquisition_kwargs={"beta": 2.0},
    batch_size=1,
    initial_random=5,
    gp_max_evals=30,
)

for step in range(20):
    X_next = bo.suggest()
    y_next = np.array([f(x) for x in X_next])
    bo.observe(X_next, y_next)

print("best so far:", bo.best)
print("diagnostics:", bo.diagnostics())

Calibrator-only usage

If you only want the calibrator (no BO loop), drop it on top of any fitted GPCrossValidatedOptimizer:

from bayesian_gp_cvloss import GPCrossValidatedOptimizer
from calibrated_bo import CompositeConformalCalibrator

opt = GPCrossValidatedOptimizer(X_train, y_train, scoring="cv_rmse")
opt.optimize(max_evals=50)

cal = CompositeConformalCalibrator(
    alpha=0.1, localized=True, weighted=True, adaptive=False
).fit_cv(opt)

mean, lower, upper = cal.predict_interval(X_new)

Design

See the project spec (calibrated-bo design doc) for the math behind Localized + Weighted + Adaptive coordination and the joint half-width formula.