mindspore.nn.ConfusionMatrixMetric

class mindspore.nn.ConfusionMatrixMetric(skip_channel=True, metric_name='sensitivity', calculation_method=False, decrease='mean')[source]

The performance matrix of measurement classification model is the model whose output is binary or multi class. The correlation measure of confusion matrix was calculated from the full-scale tensor, and the average values of batch, class channel and iteration were collected. This function supports the calculation of all measures described below: the metric name in parameter metric_name.

If you want to use confusion matrix to calculate, such as ‘PPV’, ‘TPR’, ‘TNR’, use this class. If you only want to calculate confusion matrix, please use ‘mindspore.metrics.ConfusionMatrix’.

Parameters

  • skip_channel (bool) – Whether to skip the measurement calculation on the first channel of the predicted output. Default: True.

  • metric_name (str) – The names of indicators are in the following range. Of course, you can also set the industry common aliases for these indicators. Choose from: [“sensitivity”, “specificity”, “precision”, “negative predictive value”, “miss rate”, “fall out”, “false discovery rate”, “false omission rate”, “prevalence threshold”, “threat score”, “accuracy”, “balanced accuracy”, “f1 score”, “matthews correlation coefficient”, “fowlkes mallows index”, “informedness”, “markedness”].

  • calculation_method (bool) – If true, the measurement for each sample is calculated first. If it is false, the confusion matrix of all samples is accumulated first. As for classification task, ‘calculation_method’ should be False. Default: False.

  • decrease (str) – Define the mode to reduce the calculation result of one batch of data. Decrease is used only if calculation_method is True. Default: “mean”. Choose from: [“none”, “mean”, “sum”, “mean_batch”, “sum_batch”, “mean_channel”, “sum_channel”].

Examples

>>> metric = ConfusionMatrixMetric(skip_channel=True, metric_name="tpr",
...                                calculation_method=False, decrease="mean")
>>> metric.clear()
>>> x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
>>> y = Tensor(np.array([[[0], [1]], [[0], [1]]]))
>>> metric.update(x, y)
>>> x = Tensor(np.array([[[0], [1]], [[1], [0]]]))
>>> y = Tensor(np.array([[[0], [1]], [[1], [0]]]))
>>> avg_output = metric.eval()
>>> print(avg_output)
[0.5]
clear()[source]

Clears the internal evaluation result.

eval()[source]

Computes confusion matrix metric.

Returns

ndarray, the computed result.

update(*inputs)[source]

Update state with predictions and targets.

inputs:

Input y_pred and y. y_pred and y are a Tensor, a list or an array.

  • y_pred (ndarray) - Input data to compute. It must be one-hot format and first dim is batch. The shape of y_pred is \((N, C, ...)\) or \((N, ...)\). As for classification tasks, y_pred should has the shape [BN] where N is larger than 1. As for segmentation tasks, the shape should be [BNHW] or [BNHWD].

  • y (ndarray) - Compute the true value of the measure. It must be one-hot format and first dim is batch. The shape of y is \((N, C, ...)\).

Raises

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