mindspore.train.ROC

class mindspore.train.ROC(class_num=None, pos_label=None)

Calculates the ROC curve. It is suitable for solving binary classification and multi classification problems. In the case of multiclass, the values will be calculated based on a one-vs-the-rest approach.

Parameters

• class_num (int) – The number of classes. It is not necessary to provide this argument under the binary classification scenario. Default: None .

• pos_label (int) – Determine the integer of positive class. For binary problems, it is translated to 1 by default. For multiclass problems, this argument should not be set, as it will iteratively changed in the range [0, num_classes-1]. Default: None .

Supported Platforms:

Ascend GPU CPU

Examples

>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore.train import ROC
>>>
>>> # 1) binary classification example
>>> x = Tensor(np.array([3, 1, 4, 2]))
>>> y = Tensor(np.array([0, 1, 2, 3]))
>>> metric = ROC(pos_label=2)
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[0. 0. 0.33333333 0.6666667 1.]
>>> print(tpr)
[0. 1. 1. 1. 1.]
>>> print(thresholds)
[5 4 3 2 1]
>>>
>>> # 2) multiclass classification example
>>> x = Tensor(np.array([[0.28, 0.55, 0.15, 0.05], [0.10, 0.20, 0.05, 0.05], [0.20, 0.05, 0.15, 0.05],
...                     [0.05, 0.05, 0.05, 0.75]]))
>>> y = Tensor(np.array([0, 1, 2, 3]))
>>> metric = ROC(class_num=4)
>>> metric.clear()
>>> metric.update(x, y)
>>> fpr, tpr, thresholds = metric.eval()
>>> print(fpr)
[array([0., 0., 0.33333333, 0.66666667, 1.]), array([0., 0.33333333, 0.33333333, 1.]),
array([0., 0.33333333, 1.]), array([0., 0., 1.])]
>>> print(tpr)
[array([0., 1., 1., 1., 1.]), array([0., 0., 1., 1.]), array([0., 1., 1.]), array([0., 1., 1.])]
>>> print(thresholds)
[array([1.28, 0.28, 0.2, 0.1, 0.05]), array([1.55, 0.55, 0.2, 0.05]), array([1.15, 0.15, 0.05]),
array([1.75, 0.75, 0.05])]

clear()

Clear the internal evaluation result.

eval()

Computes the ROC curve.

Returns

A tuple, composed of fpr, tpr, and thresholds.

• fpr (np.array) - False positive rate. In binary classification case, a fpr numpy array under different thresholds will be returned, otherwise in multiclass case, a list of fpr numpy arrays will be returned and each element represents one category.

• tpr (np.array) - True positive rates. n binary classification case, a tps numpy array under different thresholds will be returned, otherwise in multiclass case, a list of tps numpy arrays will be returned and each element represents one category.

• thresholds (np.array) - Thresholds used for computing fpr and tpr.

Raises

RuntimeError – If the update method is not called first, an error will be reported.

update(*inputs)

Update state with predictions and targets.

Parameters

inputs – Input y_pred and y. y_pred and y are Tensor, list or numpy.ndarray. In most cases (not strictly), y_pred is 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. y contains values of integers. The shape is \((N, C)\) if one-hot encoding is used. Shape can also be \((N,)\) if category index is used.