"""
.. module:: MetricTHatFPR
:synopsis: ROC Threshold @ False Positive Rate
.. moduleauthor:: Marco Melis <marco.melis@unica.it>
"""
from secml.array import CArray
from secml.ml.peval.metrics import CMetric
from secml.ml.peval.metrics import CRoc
[docs]class CMetricTHatFPR(CMetric):
"""Performance evaluation metric: ROC Threshold @ False Positive Rate.
The metric uses:
- y_true (true ground labels)
- score (estimated target values)
Parameters
----------
fpr : float
Desired False Positive Rate in the interval [0,1]. Default 0.01 (1%)
Attributes
----------
class_type : 'th-at-fpr'
Notes
-----
This implementation is restricted to the binary classification task.
Examples
--------
>>> from secml.ml.peval.metrics import CMetricTHatFPR
>>> from secml.array import CArray
>>> peval = CMetricTHatFPR(fpr=0.5)
>>> peval.performance_score(CArray([0, 1, 0, 0]), score=CArray([0, 0, 0, 0]))
0.0005
"""
__class_type = 'th-at-fpr'
best_value = 1.0
def __init__(self, fpr=0.01):
self.fpr = float(fpr)
def _performance_score(self, y_true, score):
"""Computes the ROC Threshold at given False Positive Rate.
Parameters
----------
y_true : CArray
Flat array with true binary labels in range
{0, 1} or {-1, 1} for each pattern.
score : CArray
Flat array with target scores for each pattern, can either be
probability estimates of the positive class or confidence values.
Returns
-------
metric : float
Returns metric value as float.
Notes
-----
This implementation is restricted to the binary classification task.
"""
fp, tp, th = CRoc().compute(y_true, score)
return CArray(self.fpr).interp(fp, th).item()