imblearn.metrics.sensitivity_specificity_support¶
-
imblearn.metrics.
sensitivity_specificity_support
(y_true, y_pred, labels=None, pos_label=1, average=None, warn_for=('sensitivity', 'specificity'), sample_weight=None)[source][source]¶ Compute sensitivity, specificity, and support for each class
The sensitivity is the ratio
tp / (tp + fn)
wheretp
is the number of true positives andfn
the number of false negatives. The sensitivity quantifies the ability to avoid false negatives_[1].The specificity is the ratio
tn / (tn + fp)
wheretn
is the number of true negatives andfn
the number of false negatives. The specificity quantifies the ability to avoid false positives_[1].The support is the number of occurrences of each class in
y_true
.If
pos_label is None
and in binary classification, this function returns the average sensitivity and specificity ifaverage
is one of'weighted'
.Parameters: y_true : ndarray, shape (n_samples, )
Ground truth (correct) target values.
y_pred : ndarray, shape (n_samples, )
Estimated targets as returned by a classifier.
labels : list, optional
The set of labels to include when
average != 'binary'
, and their order ifaverage is None
. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels iny_true
andy_pred
are used in sorted order.pos_label : str or int, optional (default=1)
The class to report if
average='binary'
and the data is binary. If the data are multiclass, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.average : str or None, optional (default=None)
If
None
, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:'binary'
:Only report results for the class specified by
pos_label
. This is applicable only if targets (y_{true,pred}
) are binary.'micro'
:Calculate metrics globally by counting the total true positives, false negatives and false positives.
'macro'
:Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
'weighted'
:Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.
'samples'
:Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from
accuracy_score
).
warn_for : tuple or set, for internal use
This determines which warnings will be made in the case that this function is being used to return only one of its metrics.
sample_weight : ndarray, shape (n_samples, )
Sample weights.
Returns: sensitivity : float (if
average
= None) or ndarray, shape (n_unique_labels, )specificity : float (if
average
= None) or ndarray, shape (n_unique_labels, )support : int (if
average
= None) or ndarray, shape (n_unique_labels, )The number of occurrences of each label in
y_true
.References
[R29] Wikipedia entry for the Sensitivity and specificity Examples
>>> import numpy as np >>> from imblearn.metrics import sensitivity_specificity_support >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig']) >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog']) >>> sensitivity_specificity_support(y_true, y_pred, average='macro') (0.33333333333333331, 0.66666666666666663, None) >>> sensitivity_specificity_support(y_true, y_pred, average='micro') (0.33333333333333331, 0.66666666666666663, None) >>> sensitivity_specificity_support(y_true, y_pred, average='weighted') (0.33333333333333331, 0.66666666666666663, None)