ClassificationReport

A report for monitoring a classifier.

This class maintains a set of metrics and updates each of them every time update is called. You can print this class at any time during a model's lifetime to get a tabular visualization of various metrics.

You can wrap a metrics.ClassificationReport with metrics.Rolling in order to obtain a classification report over a window of observations. You can also wrap it with metrics.TimeRolling to obtain a report over a period of time.

Parameters

• decimals – defaults to 3

  The number of decimals to display in each cell.

• 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 = ['pear', 'apple', 'banana', 'banana', 'banana']
>>> y_pred = ['apple', 'pear', 'banana', 'banana', 'apple']

>>> report = metrics.ClassificationReport()

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

>>> print(report)
           Precision   Recall   F1      Support
<BLANKLINE>
   apple       0.000    0.000   0.000         1
  banana       1.000    0.667   0.800         3
    pear       0.000    0.000   0.000         1
<BLANKLINE>
   Macro       0.333    0.222   0.267
   Micro       0.400    0.400   0.400
Weighted       0.600    0.400   0.480
<BLANKLINE>
                 40.0% accuracy

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)