# SorensenDice¶

Sørensen-Dice coefficient.

Sørensen-Dice coefficient 1 (or Sørensen Index, Dice's coefficient) is a statistic used to gauge the similarity of two samples. Sørensen's original formula was intended to be applied to discrete data. Given two sets, $$X$$ and $$Y$$, it is defined as:

$DSC = \frac{2 |X \cap Y|}{|X| + |Y|}.$

It is equal to twice the number of elements common to both sets divided by the sum of the number of elements in each set.

The coefficient is not very different in form from the Jaccard index. The only difference between the two metrics is that the Jaccard index only counts true positives once in both the numerator and denominator. In fact, both are equivalent in the sense that given a value for the Sorensen-Dice index, once can canculate the respective Jaccard value and vice versa, using the equations

$\begin{equation} J = \frac{S}{2-S}, \\ S = \frac{2J}{1+J}. \end{equation}$

## Parameters¶

• cm (river.metrics.confusion.MultiLabelConfusionMatrix) – 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

• 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: False, 1: True, 2: True},
...     {0: True, 1: True, 2: False},
... ]

>>> y_pred = [
...     {0: True, 1: True, 2: True},
...     {0: True, 1: False, 2: False},
... ]

>>> sorensen_dice = metrics.SorensenDice()
>>> for yt, yp in zip(y_true, y_pred):
...     sorensen_dice = sorensen_dice.update(yt, yp)

>>> sorensen_dice
SorensenDice: 0.736842


## 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 (Dict[Union[str, int], Union[bool, str, int]])
• y_pred (Union[Dict[Union[str, int], Union[bool, str, int]], Dict[Union[str, int], Dict[Union[bool, str, int], float]]])
• sample_weight (numbers.Number) – defaults to 1.0
• correction – defaults to None
update

Update the metric.

Parameters

• y_true (Dict[Union[str, int], Union[bool, str, int]])
• y_pred (Union[Dict[Union[str, int], Union[bool, str, int]], Dict[Union[str, int], Dict[Union[bool, str, int], float]]])
• sample_weight (numbers.Number) – defaults to 1.0
works_with

Indicates whether or not a metric can work with a given model.

Parameters

• model (river.base.estimator.Estimator)