File size: 1,707 Bytes
ff4becd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | """Public evaluation entry points for the MacroLens unified API.
Thin wrappers over :func:`whatif_bench.eval.score` and
:func:`whatif_bench.eval.compare_methods`. The eval layer owns all
metric computation, bootstrap-CI logic, and multiple-comparisons
correction; this module exists only so that
``import macrolens as ml; ml.score(...)`` has a stable, lightweight
surface.
"""
from __future__ import annotations
from typing import Any, Literal
from ..eval import compare_methods as _compare_methods
from ..eval import score as _score
from ._types import MetricValue
def score(
task: str,
y_true: Any,
y_pred: Any,
*,
cluster_keys: Any = None,
close_last: Any = None,
resample: Literal["cluster", "iid"] = "cluster",
n_boot: int | Literal["adaptive"] = "adaptive",
alpha: float = 0.05,
seed: int = 42,
return_sensitivity: bool = False,
) -> dict[str, MetricValue]:
"""Score a ``(task, y_true, y_pred)`` triple. Defaults to cluster bootstrap."""
return _score(
task, y_true, y_pred,
cluster_keys=cluster_keys,
close_last=close_last,
resample=resample,
n_boot=n_boot,
alpha=alpha,
seed=seed,
return_sensitivity=return_sensitivity,
)
def compare_methods(
task: str,
records: list,
*,
correction: Literal["holm", "bh"] = "holm",
alpha: float = 0.05,
headline_metric: str | None = None,
):
"""Pairwise compare every method on ``task`` against the best baseline."""
return _compare_methods(
task, records,
correction=correction,
alpha=alpha,
headline_metric=headline_metric,
)
__all__ = ["score", "compare_methods"]
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