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ROCAUC

Receiving Operating Characteristic Area Under the Curve.

This metric is an approximation of the true ROC AUC. Computing the true ROC AUC would require storing all the predictions and ground truths, which isn't desirable. The approximation error is not significant as long as the predicted probabilities are well calibrated. In any case, this metric can still be used to reliably compare models between each other.

Parameters

  • n_thresholds – defaults to 10

    The number of thresholds used for discretizing the ROC curve. A higher value will lead to more accurate results, but will also cost more time and memory.

  • pos_val – defaults to True

    Value to treat as "positive".

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

Examples

>>> from river import metrics

>>> y_true = [ 0,  0,   1,  1]
>>> y_pred = [.1, .4, .35, .8]

>>> metric = metrics.ROCAUC()

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

>>> metric
ROCAUC: 0.875

The true ROC AUC is in fact 0.75. We can improve the accuracy by increasing the amount of thresholds. This comes at the cost more computation time and more memory usage.

>>> metric = metrics.ROCAUC(n_thresholds=20)

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

>>> metric
ROCAUC: 0.75

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 (bool)
  • y_pred (Union[bool, float, Dict[bool, float]])
  • sample_weight – defaults to 1.0
update

Update the metric.

Parameters

  • y_true (bool)
  • y_pred (Union[bool, float, Dict[bool, float]])
  • 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)