Source code for caliber.binary_classification.minimizing.linear_scaling.performance.righteousness_linear_scaling
from typing import Optional
import numpy as np
from caliber.binary_classification.minimizing.linear_scaling.performance.base import (
PerformanceLinearScalingBinaryClassificationModel,
)
[docs]
class RighteousnessLinearScalingBinaryClassificationModel(
PerformanceLinearScalingBinaryClassificationModel
):
def __init__(
self,
threshold: float,
lam: float = 0.01,
minimize_options: Optional[dict] = None,
has_intercept: bool = True,
):
super().__init__(
loss_fn=_righteousness_loss_fn,
threshold=threshold,
minimize_options=minimize_options,
has_intercept=has_intercept,
)
self._lam = lam
def _righteousness_loss_fn(targets: np.ndarray, preds: np.ndarray) -> float:
p11 = np.sum(targets * preds)
p00 = np.sum((1 - targets) * (1 - preds))
p01 = np.sum((1 - targets) * preds)
p10 = np.sum(targets * (1 - preds))
return -(1 - p01 * p10) * p00 * p11