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GeometricMean

Geometric mean score.

The geometric mean is a good indicator of a classifier's performance in the presence of class imbalance because it is independent of the distribution of examples between classes. This implementation computes the geometric mean of class-wise sensitivity (recall).

\[ gm = \sqrt[n]{s_1\cdot s_2\cdot s_3\cdot \ldots\cdot s_n} \]

where \(s_i\) is the sensitivity (recall) of class \(i\) and \(n\) is the number of classes.

Parameters

  • cm (river.metrics.confusion.ConfusionMatrix) – 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 = ['cat', 'ant', 'cat', 'cat', 'ant', 'bird', 'bird']
>>> y_pred = ['ant', 'ant', 'cat', 'cat', 'ant', 'cat', 'bird']

>>> metric = metrics.GeometricMean()

>>> for yt, yp in zip(y_true, y_pred):
...     metric = metric.update(yt, yp)

>>> metric
GeometricMean: 0.693361

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.

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)

References

  1. Barandela, R. et al. “Strategies for learning in class imbalance problems”, Pattern Recognition, 36(3), (2003), pp 849-851. ↩