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SampleAverage

Sample-average wrapper.

The provided metric is evaluate on each sample. The arithmetic average over all the samples is returned. This is equivalent to using average='samples' in scikit-learn.

Parameters

  • metric

    A classification or a regression metric.

Attributes

  • bigger_is_better

    Indicate if a high value is better than a low one or not.

  • metric

    Gives access to the wrapped metric.

  • requires_labels

  • works_with_weights

    Indicate whether the model takes into consideration the effect of sample weights

Examples

from river import metrics

y_true = [
    {0: False, 1: True, 2: True},
    {0: True, 1: True, 2: False}
]
y_pred = [
    {0: True, 1: True, 2: True},
    {0: True, 1: False, 2: False}
]

sample_jaccard = metrics.multioutput.SampleAverage(metrics.Jaccard())

for yt, yp in zip(y_true, y_pred):
    sample_jaccard.update(yt, yp)

sample_jaccard

SampleAverage(Jaccard): 58.33%

Methods

get

Return the current value of the metric.

is_better_than

Indicate if the current metric is better than another one.

Parameters

  • other

revert

Revert the metric.

Parameters

  • y_true
  • y_pred
  • w — defaults to 1.0

update

Update the metric.

Parameters

  • y_true
  • y_pred
  • w — defaults to 1.0

works_with

Indicates whether or not a metric can work with a given model.

Parameters