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