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ExactMatch

Exact match score.

This is the most strict multi-label metric, defined as the number of samples that have all their labels correctly classified, divided by the total number of samples.

Attributes

  • bigger_is_better

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

  • 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},
    {0: True, 1: True, 2: False},
]

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

metric = metrics.multioutput.ExactMatch()
for yt, yp in zip(y_true, y_pred):
    metric = metric.update(yt, yp)

metric

ExactMatch: 33.33%

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 — 'dict[str | int, base.typing.ClfTarget]'
  • y_pred — 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]'
  • sample_weight — defaults to 1.0

update

Update the metric.

Parameters

  • y_true — 'dict[str | int, base.typing.ClfTarget]'
  • y_pred — 'dict[str | int, base.typing.ClfTarget] | dict[str | int, dict[base.typing.ClfTarget, float]]'
  • sample_weight — defaults to 1.0

works_with

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

Parameters

  • model