mindspore.nn.Recall
- class mindspore.nn.Recall(eval_type='classification')[source]
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) – Metric to calculate the recall over a dataset, for classification or multilabel. Default: ‘classification’.
Examples
>>> 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]
- eval(average=False)[source]
Computes the recall.
- Parameters
average (bool) – Specify whether calculate the average recall. Default value is False.
- Returns
Float, the computed result.
- update(*inputs)[source]
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.