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mindspore.nn.ConfusionMatrix

class mindspore.nn.ConfusionMatrix(num_classes, normalize=NO_NORM, threshold=0.5)

Computes the confusion matrix. The performance matrix of measurement classification model is the model whose output is binary or multi class. The confusion matrix is calculated. An array of shape [BC4] is returned. The third dimension represents each channel of each sample in the input batch.Where B is the batch size and C is the number of classes to be calculated.

If you only want to find confusion matrix, use this class. If you want to find ‘PPV’, ‘TPR’, ‘TNR’, etc., use class ‘mindspore.metrics.ConfusionMatrixMetric’.

Parameters

• num_classes (int) – Number of classes in the dataset.

• normalize (str) –

  The parameter of calculating ConfusionMatrix supports four Normalization modes, Choose from:

  • ’no_norm’ (None) - No Normalization is used. Default: None.

  • ’target’ (str) - Normalization based on target value.

  • ’prediction’ (str) - Normalization based on predicted value.

  • ’all’ (str) - Normalization over the whole matrix.

• threshold (float) – A threshold, which is used to compare with the input tensor. Default: 0.5.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import nn, Tensor
>>>
>>> x = Tensor(np.array([1, 0, 1, 0]))
>>> y = Tensor(np.array([1, 0, 0, 1]))
>>> metric = nn.ConfusionMatrix(num_classes=2, normalize='no_norm', threshold=0.5)
>>> metric.clear()
>>> metric.update(x, y)
>>> output = metric.eval()
>>> print(output)
[[1. 1.]
 [1. 1.]]

clear()

Clears the internal evaluation result.

eval()

Computes confusion matrix.

Returns

numpy.ndarray, the computed result.

update(*inputs)

Update state with y_pred and y.

Parameters

inputs – Input y_pred and y. y_pred and y are a Tensor, a list or an array. y_pred is the predicted value, y is the true value. The shape of y_pred is $(N,C,...)$ or $(N,...)$. The shape of y is $(N,...)$.

Raises

ValueError – If the number of the inputs is not 2.