def _get_metric_fct(self):
if self.metric == 'Mutual_information':
metric_fct = lambda TP, FP, TN, FN: BinaryClassificationDataset.mutual_information(
TN, FN, TP, FP)
elif self.metric == 'Normalized_mutual_information':
metric_fct = lambda TP, FP, TN, FN: BinaryClassificationDataset.normalized_mutual_information(
TN, FN, TP, FP)
elif self.metric == "Balanced_accuracy":
metric_fct = self.balanced_accuracy
elif '{TP}' in self.metric or '{FP}' in self.metric or '{TN}' in self.metric or '{FN}' in self.metric:
metric_fct = lambda TP, FP, TN, FN: eval(
self.metric.format(
TP=float(TP), FP=float(FP), TN=float(TN), FN=float(FN)))
elif self.inverse_metric:
metric_fct = lambda TP, FP, TN, FN: \
(-1.0)*BinaryClassificationDataset.calculate_confusion_metrics(
{"True_negatives": TN,
"True_positives": TP,
"False_positives": FP,
"False_negatives": FN},
weight=self.weight,)[self.metric]
else:
metric_fct = lambda TP, FP, TN, FN: \
BinaryClassificationDataset.calculate_confusion_metrics(
{"True_negatives": TN,
"True_positives": TP,
"False_positives": FP,
"False_negatives": FN},
weight=self.weight,)[self.metric]
return metric_fct
def _get_metric_fct(self):
if self.metric == 'Mutual_information':
metric_fct = lambda TP, FP, TN, FN: ClassificationCollection.mutual_information(TN, FN, TP, FP)
elif self.metric == 'Normalized_mutual_information':
metric_fct = lambda TP, FP, TN, FN: ClassificationCollection.normalized_mutual_information(TN, FN, TP, FP)
elif self.metric == "Balanced_accuracy":
metric_fct = self.balanced_accuracy
elif '{TP}' in self.metric or '{FP}' in self.metric or '{TN}' in self.metric or '{FN}' in self.metric:
metric_fct = lambda TP, FP, TN, FN: eval(self.metric.format(TP=float(TP),
FP=float(FP),
TN=float(TN),
FN=float(FN)))
elif self.inverse_metric:
metric_fct = lambda TP, FP, TN, FN: \
(-1.0)*ClassificationCollection.calculate_confusion_metrics(
{"True_negatives":TN,
"True_positives":TP,
"False_positives":FP,
"False_negatives":FN},
weight=self.weight,
)[self.metric]
else:
metric_fct = lambda TP, FP, TN, FN: \
ClassificationCollection.calculate_confusion_metrics(
{"True_negatives":TN,
"True_positives":TP,
"False_positives":FP,
"False_negatives":FN},
weight=self.weight,
)[self.metric]
return metric_fct
