"""
.. module:: CMetricAUC
:synopsis: Performance Metric: Area Under (ROC) Curve
.. moduleauthor:: Marco Melis <marco.melis@unica.it>
"""
import sklearn.metrics as skm
from secml.array import CArray
from secml.ml.peval.metrics import CMetric
from secml.ml.peval.metrics import CRoc
[docs]class CMetricAUC(CMetric):
"""Performance evaluation metric: Area Under (ROC) Curve.
AUC is computed using the trapezoidal rule.
The metric uses:
- y_true (true ground labels)
- score (estimated target values)
Attributes
----------
class_type : 'auc'
Notes
-----
This implementation is restricted to the binary classification task.
Examples
--------
>>> from secml.ml.peval.metrics import CMetricAUC
>>> from secml.array import CArray
>>> peval = CMetricAUC()
>>> print(peval.performance_score(CArray([0, 1, 0, 0]), score=CArray([0, 0, 0, 0])))
0.5
"""
__class_type = 'auc'
best_value = 1.0
def _performance_score(self, y_true, score):
"""Computes the Area Under the ROC Curve (AUC).
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.
"""
fpr, tpr = CRoc().compute(y_true, score)[0:2]
return float(skm.auc(fpr.tondarray(), tpr.tondarray()))