mindspore.train.ROC
- class mindspore.train.ROC(class_num=None, pos_label=None)[source]
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])]
- eval()[source]
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 tpr numpy array under different thresholds will be returned, otherwise in multiclass case, a list of tpr 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)[source]
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.