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WeightedF1

Weighted-average F1 score.

This works by computing the F1 score per class, and then performs a global weighted average by using the support of each class.

Parameters

  • cm – defaults to None

    This parameter allows sharing the same confusion matrix between multiple metrics. Sharing a confusion matrix reduces the amount of storage and computation time.

Attributes

  • bigger_is_better

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

  • requires_labels

    Indicates if labels are required, rather than probabilities.

  • sample_correction

  • works_with_weights

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

Examples

>>> from river import metrics

>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1]

>>> metric = metrics.WeightedF1()

>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp))
WeightedF1: 1.
WeightedF1: 0.333333
WeightedF1: 0.555556
WeightedF1: 0.666667
WeightedF1: 0.613333

Methods

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

get

Return the current value of the metric.

is_better_than
revert

Revert the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight – defaults to 1.0
  • correction – defaults to None
update

Update the metric.

Parameters

  • y_true
  • y_pred
  • sample_weight – defaults to 1.0
works_with

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

Parameters

  • model (river.base.estimator.Estimator)