WeightedFBeta¶

Weighted-average F-Beta score.

This works by computing the F-Beta score per class, and then performs a global weighted average according to the support of each class.

Parameters¶

• beta

Weight of precision in the harmonic mean.

• 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.

• 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.WeightedFBeta(beta=0.8)

>>> for yt, yp in zip(y_true, y_pred):
...     print(metric.update(yt, yp))
WeightedFBeta: 100.00%
WeightedFBeta: 31.06%
WeightedFBeta: 54.04%
WeightedFBeta: 65.53%
WeightedFBeta: 62.63%


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
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)