def ks(self, labels, pred_scores):
"""
Compute Kolmogorov-Smirnov
Parameters
----------
labels: value list. The labels of data set.
pred_scores: pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
Returns
----------
max_ks_interval: float max value of each tpr - fpt
fpr:
"""
if self.eval_type == consts.ONE_VS_REST:
try:
rs = classification_metric.KS().compute(labels, pred_scores)
except:
rs = [0, [0], [0], [0], [0]] # in case all labels are 0 or 1
logging.warning("all true labels are 0/1 when running ovr KS")
return rs
else:
return classification_metric.KS().compute(labels, pred_scores)
def ks(self, labels, pred_scores):
"""
Compute Kolmogorov-Smirnov
Parameters
----------
labels: value list. The labels of data set.
pred_scores: pred_scores: value list. The predict results of model. It should be corresponding to labels each data.
Returns
----------
max_ks_interval: float max value of each tpr - fpt
fpr:
"""
return classification_metric.KS().compute(labels, pred_scores)