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updating documentation a bit

This commit is contained in:
Alejandro Moreo Fernandez 2024-01-19 18:24:38 +01:00
parent 7137e7ac40
commit ff00de18cb
1 changed files with 4 additions and 2 deletions
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6
quapy/method/aggregative.py
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@ -1141,6 +1141,8 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
return candidates return candidates
def aggregate_with_threshold(self, classif_predictions, tprs, fprs, thresholds): def aggregate_with_threshold(self, classif_predictions, tprs, fprs, thresholds):
# This function performs the adjusted count for given tpr, fpr, and threshold.
# Note that, due to broadcasting, tprs, fprs, and thresholds could be arrays of length > 1
prevs_estims = np.mean(classif_predictions[:, None] >= thresholds, axis=0) prevs_estims = np.mean(classif_predictions[:, None] >= thresholds, axis=0)
prevs_estims = (prevs_estims - fprs) / (tprs - fprs) prevs_estims = (prevs_estims - fprs) / (tprs - fprs)
prevs_estims = F.as_binary_prevalence(prevs_estims, clip_if_necessary=True) prevs_estims = F.as_binary_prevalence(prevs_estims, clip_if_necessary=True)
@ -1164,8 +1166,8 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
return FP / (FP + TN) return FP / (FP + TN)
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection): def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
# the standard behavior is to keep the best threshold only
decision_scores, y = classif_predictions.Xy decision_scores, y = classif_predictions.Xy
# the standard behavior is to keep the best threshold only
self.tpr, self.fpr, self.threshold = self._eval_candidate_thresholds(decision_scores, y)[0] self.tpr, self.fpr, self.threshold = self._eval_candidate_thresholds(decision_scores, y)[0]
return self return self
@ -1270,8 +1272,8 @@ class MS(ThresholdOptimization):
return 1 return 1
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection): def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
# keeps all candidates
decision_scores, y = classif_predictions.Xy decision_scores, y = classif_predictions.Xy
# keeps all candidates
tprs_fprs_thresholds = self._eval_candidate_thresholds(decision_scores, y) tprs_fprs_thresholds = self._eval_candidate_thresholds(decision_scores, y)
self.tprs = tprs_fprs_thresholds[:, 0] self.tprs = tprs_fprs_thresholds[:, 0]
self.fprs = tprs_fprs_thresholds[:, 1] self.fprs = tprs_fprs_thresholds[:, 1]