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mindspore.nn.Recall

class mindspore.nn.Recall(eval_type='classification')

Calculates recall for classification and multilabel data.

The recall class creates two local variables, $\text{true\_positive}$ and $\text{false\_negative}$, that are used to compute the recall. This value is ultimately returned as the recall, an idempotent operation that simply divides $\text{true\_positive}$ by the sum of $\text{true\_positive}$ and $\text{false\_negative}$.

$$\text{recall}=\frac{\text{true\_positive}}{\text{true\_positive}+\text{false\_negative}}$$

Note

In the multi-label cases, the elements of $y$ and $y_{pred}$ must be 0 or 1.

Parameters

eval_type (str) – The metric to calculate the recall over a dataset, for classification or multilabel. Default: ‘classification’.

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))
>>> y = Tensor(np.array([1, 0, 1]))
>>> metric = nn.Recall('classification')
>>> metric.clear()
>>> metric.update(x, y)
>>> recall = metric.eval()
>>> print(recall)
[1. 0.5]

clear()

Clears the internal evaluation result.

eval(average=False)

Computes the recall.

Parameters

average (bool) – Specify whether calculate the average recall. Default value is False.

Returns

Float, the computed result.

update(*inputs)

Updates the internal evaluation result with y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are a Tensor, a list or an array. For ‘classification’ evaluation type, y_pred is in most cases (not strictly) a list of floating numbers in range $[0,1]$ and the shape is $(N,C)$, where $N$ is the number of cases and $C$ is the number of categories. Shape of y can be $(N,C)$ with values 0 and 1 if one-hot encoding is used or the shape is $(N,)$ with integer values if index of category is used. For ‘multilabel’ evaluation type, y_pred and y can only be one-hot encoding with values 0 or 1. Indices with 1 indicate positive category. The shape of y_pred and y are both $(N,C)$.

Raises

ValueError – If the number of input is not 2.