"""Sensitivity at Low Alert Rates objective."""
import logging
import numpy as np
from .binary_classification_objective import BinaryClassificationObjective
logger = logging.getLogger(__name__)
[docs]class SensitivityLowAlert(BinaryClassificationObjective):
"""Create instance of SensitivityLowAlert.
Args:
alert_rate (float): percentage of top scores to classify as high risk.
"""
name = "Sensitivity at Low Alert Rates"
greater_is_better = True
score_needs_proba = False
perfect_score = 1.0
is_bounded_like_percentage = True
expected_range = [0, 1]
def __init__(self, alert_rate=0.01):
if (alert_rate > 1) or (alert_rate < 0):
raise ValueError("Alert rate is outside of valid range [0,1]")
self.alert_rate = alert_rate
[docs] def decision_function(self, ypred_proba, **kwargs):
"""Determine if an observation is high risk given an alert rate.
Args:
ypred_proba (pd.Series): Predicted probabilities.
**kwargs: Additional abritrary parameters.
Returns:
pd.Series: Whether or not an observation is high risk given an alert rate.
"""
ypred_proba = self._standardize_input_type(ypred_proba)
if len(ypred_proba.unique()) == 1:
logger.debug(
f"All predicted probabilities have the same value: {ypred_proba.unique()}"
)
prob_thresh = np.quantile(ypred_proba, 1 - self.alert_rate)
if (prob_thresh == 0) or (prob_thresh == 1):
logger.debug(f"Extreme threshold of {prob_thresh}")
return ypred_proba.astype(float) >= prob_thresh
[docs] def objective_function(self, y_true, y_predicted, **kwargs):
"""Calculate sensitivity across all predictions, using the top alert_rate percent of observations as the predicted positive class.
Args:
y_true (pd.Series): True labels.
y_predicted (pd.Series): Predicted labels based on alert_rate.
**kwargs: Additional abritrary parameters.
Returns:
float: sensitivity using the observations with the top scores as the predicted positive class.
"""
y_true = self._standardize_input_type(y_true)
y_predicted = self._standardize_input_type(y_predicted)
self.validate_inputs(y_true, y_predicted)
tp = y_true & y_predicted
fn = y_true & (~y_predicted)
if (tp.sum() + fn.sum()) > 0:
sensitivity = tp.sum() / (tp.sum() + fn.sum())
else:
sensitivity = np.nan
return sensitivity