From ff00de18cbf0396c8d1ad2f12bb96bb3fe12fb2b Mon Sep 17 00:00:00 2001
From: Alejandro Moreo <alejandro.moreo@isti.cnr.it>
Date: Fri, 19 Jan 2024 18:24:38 +0100
Subject: [PATCH] updating documentation a bit
---
quapy/method/aggregative.py | 6 ++++--
1 file changed, 4 insertions(+), 2 deletions(-)
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index c3e24db..b7b7409 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -1141,6 +1141,8 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
return candidates
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 = (prevs_estims - fprs) / (tprs - fprs)
prevs_estims = F.as_binary_prevalence(prevs_estims, clip_if_necessary=True)
@@ -1164,8 +1166,8 @@ class ThresholdOptimization(BinaryAggregativeQuantifier):
return FP / (FP + TN)
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
+ # the standard behavior is to keep the best threshold only
self.tpr, self.fpr, self.threshold = self._eval_candidate_thresholds(decision_scores, y)[0]
return self
@@ -1270,8 +1272,8 @@ class MS(ThresholdOptimization):
return 1
def aggregation_fit(self, classif_predictions: LabelledCollection, data: LabelledCollection):
- # keeps all candidates
decision_scores, y = classif_predictions.Xy
+ # keeps all candidates
tprs_fprs_thresholds = self._eval_candidate_thresholds(decision_scores, y)
self.tprs = tprs_fprs_thresholds[:, 0]
self.fprs = tprs_fprs_thresholds[:, 1]