# PairConfusionMatrix¶

Pair Confusion Matrix.

The pair confusion matrix $$C$$ is a 2 by 2 similarity matrix between two clusterings by considering all pairs of samples and counting pairs that are assigned into the same or into different clusters under the true and predicted clusterings.

The pair confusion matrix has the following entries:

• $$C[0][0]$$ (True Negatives): number of pairs of points that are in different clusters in both true and predicted labels

• $$C[0][1]$$ (False Positives): number of pairs of points that are in the same cluster in predicted labels but not in predicted labels;

• $$C[1][0]$$ (False Negatives): number of pairs of points that are in the same cluster in true labels but not in predicted labels;

• $$C[1][1]$$ (True Positives): number of pairs of points that are in the same cluster in both true and predicted labels.

We can also show that the four counts have the following property

$TP + FP + FN + TV = \frac{n(n-1)}{2}$

## 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]

>>> pair_confusion_matrix = metrics.PairConfusionMatrix()

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

>>> pair_confusion_matrix
PairConfusionMatrix: {0: defaultdict(<class 'int'>, {0: 12.0, 1: 2.0}), 1: defaultdict(<class 'int'>, {0: 4.0, 1: 2.0})}


## 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)