mars.learn.metrics.precision_recall_fscore_support#
- mars.learn.metrics.precision_recall_fscore_support(y_true, y_pred, *, beta=1.0, labels=None, pos_label=1, average=None, warn_for=('precision', 'recall', 'f-score'), sample_weight=None, zero_division='warn', session=None, run_kwargs=None)[源代码]#
Compute precision, recall, F-measure and support for each class
The precision is the ratio
tp / (tp + fp)
wheretp
is the number of true positives andfp
the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.The recall is the ratio
tp / (tp + fn)
wheretp
is the number of true positives andfn
the number of false negatives. The recall is intuitively the ability of the classifier to find all the positive samples.The F-beta score can be interpreted as a weighted harmonic mean of the precision and recall, where an F-beta score reaches its best value at 1 and worst score at 0.
The F-beta score weights recall more than precision by a factor of
beta
.beta == 1.0
means recall and precision are equally important.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 precision, recall and F-measure ifaverage
is one of'micro'
,'macro'
,'weighted'
or'samples'
.Read more in the User Guide.
- 参数
y_true (1d array-like, or label indicator array / sparse matrix) – Ground truth (correct) target values.
y_pred (1d array-like, or label indicator array / sparse matrix) – Estimated targets as returned by a classifier.
beta (float, 1.0 by default) – The strength of recall versus precision in the F-score.
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, 1 by default) – The class to report if
average='binary'
and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.average (string, [None (default), 'binary', 'micro', 'macro', 'samples', 'weighted']) –
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 (array-like of shape (n_samples,), default=None) – Sample weights.
zero_division ("warn", 0 or 1, default="warn") –
- Sets the value to return when there is a zero division:
recall: when there are no positive labels
precision: when there are no positive predictions
f-score: both
If set to “warn”, this acts as 0, but warnings are also raised.
- 返回
precision (float (if average is not None) or array of float, shape = [n_unique_labels])
recall (float (if average is not None) or array of float, , shape = [n_unique_labels])
fbeta_score (float (if average is not None) or array of float, shape = [n_unique_labels])
support (None (if average is not None) or array of int, shape = [n_unique_labels]) – The number of occurrences of each label in
y_true
.
引用
- 1
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实际案例
>>> import numpy as np >>> from mars.learn.metrics import precision_recall_fscore_support >>> y_true = np.array(['cat', 'dog', 'pig', 'cat', 'dog', 'pig']) >>> y_pred = np.array(['cat', 'pig', 'dog', 'cat', 'cat', 'dog']) >>> precision_recall_fscore_support(y_true, y_pred, average='macro') (0.22..., 0.33..., 0.26..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='micro') (0.33..., 0.33..., 0.33..., None) >>> precision_recall_fscore_support(y_true, y_pred, average='weighted') (0.22..., 0.33..., 0.26..., None)
It is possible to compute per-label precisions, recalls, F1-scores and supports instead of averaging:
>>> precision_recall_fscore_support(y_true, y_pred, average=None, ... labels=['pig', 'dog', 'cat']) (array([0. , 0. , 0.66...]), array([0., 0., 1.]), array([0. , 0. , 0.8]), array([2, 2, 2]))
提示
When
true positive + false positive == 0
, precision is undefined; Whentrue positive + false negative == 0
, recall is undefined. In such cases, by default the metric will be set to 0, as will f-score, andUndefinedMetricWarning
will be raised. This behavior can be modified withzero_division
.