"""Binary classification pipeline mix-in class."""
[docs]class BinaryClassificationPipelineMixin:
"""Binary classification pipeline mix-in class."""
_threshold = None
@property
def threshold(self):
"""Threshold used to make a prediction. Defaults to None."""
return self._threshold
@threshold.setter
def threshold(self, value):
self._threshold = value
def _predict_with_objective(self, X, ypred_proba, objective):
ypred_proba = ypred_proba.iloc[:, 1]
if objective is None:
return ypred_proba > self.threshold
return objective.decision_function(ypred_proba, threshold=self.threshold, X=X)
def _compute_predictions(self, X, y, objectives, time_series=False):
"""Compute predictions/probabilities based on objectives."""
y_predicted = None
y_predicted_proba = None
if any(o.score_needs_proba for o in objectives) or self.threshold is not None:
y_predicted_proba = (
self.predict_proba(X, y) if time_series else self.predict_proba(X)
)
if any(not o.score_needs_proba for o in objectives) and self.threshold is None:
y_predicted = (
self._predict(X, y, pad=True) if time_series else self._predict(X)
)
return y_predicted, y_predicted_proba
def _select_y_pred_for_score(self, X, y, y_pred, y_pred_proba, objective):
y_pred_to_use = y_pred
if self.threshold is not None and not objective.score_needs_proba:
y_pred_to_use = self._predict_with_objective(X, y_pred_proba, objective)
return y_pred_to_use
[docs] def optimize_threshold(self, X, y, y_pred_proba, objective):
"""Optimize the pipeline threshold given the objective to use. Only used for binary problems with objectives whose thresholds can be tuned.
Args:
X (pd.DataFrame): Input features.
y (pd.Series): Input target values.
y_pred_proba (pd.Series): The predicted probabilities of the target outputted by the pipeline.
objective (ObjectiveBase): The objective to threshold with. Must have a tunable threshold.
Raises:
ValueError: If objective is not optimizable.
"""
if self.can_tune_threshold_with_objective(objective):
if self._encoder is not None:
y = self._encode_targets(y)
self.threshold = objective.optimize_threshold(y_pred_proba, y, X)
else:
raise ValueError(
"Problem type must be binary and objective must be optimizable."
)