limen.calibration

Canonical docs

In the research projects

The Python project calibrates an ordinary fitted classifier and chooses a threshold before Sensor construction. The Compiled project declares the same four calibration/threshold choices in its Manifest. Both calibration scenarios disclose reuse of validation observations.

What this package owns

Owns probability calibration, threshold optimization, and the runtime pipeline that applies those steps to model outputs. Does not own target construction, scaler fitting, model training, or downstream trade decisioning.

Key entry points

Entry point	Use case	Notes
`CalibratorProtocol`, `ThresholdOptimizerProtocol`	Callable contracts for custom calibration or threshold functions	Used by manifest calibration wiring and pipeline helpers.
`fit_calibrator`	Fit the configured calibrator and threshold on validation data	Returns the fitted predictor, selected threshold, and validation score.
`apply_calibrated_predict`	Apply calibration and thresholding to test predictions	Returns `_preds`, `_probs`, `optimal_threshold`, and `val_score`.
`sklearn_probability_calibrator`	Scikit-learn probability calibration	Supports isotonic and sigmoid calibration.
`grid_threshold_optimizer`	Threshold optimizer over calibrated or raw probabilities	Produces the decision cutoff used for binary predictions.

Adjacent modules

limen.experiment.manifest_core wires calibration into manifest execution.
limen.sfd.reference_architecture consumes calibrated probabilities during evaluation.
limen.targets creates the supervised labels that calibration evaluates against.

Quick orientation

Module or family	Responsibility
`pipeline.py`	protocol types, fit step, and calibrated prediction helper
`probability.py`	scikit-learn probability calibrator wrapper
`threshold.py`	grid threshold optimizer

text · README.md

1calibration/2├── README.md3├── __init__.py4├── _objective_threshold.py5├── pipeline.py6├── probability.py7└── threshold.py

Merged source PR · 87894eb8Package layout · Limen 6.0.1 · current package validation · package source

Things to know

Python callers use the fitting and prediction helpers directly; YAML declares the same choices for each native round.
Probability calibration and threshold optimization can be enabled independently.
Validation data is the fitting surface for calibrators and threshold choice.
predict() and evaluate() have different result-schema guarantees; see the canonical Calibration page before changing output fields.