Function Differences with tf.keras.metrics.AUC

tf.keras.metrics.AUC

tf.keras.metrics.AUC(
    num_thresholds=200, curve='ROC', summation_method='interpolation', name=None,
    dtype=None, thresholds=None
)

For more information, see tf.keras.metrics.AUC.

mindspore.train.auc

mindspore.train.auc(x, y, reorder=False)

For more information, see mindspore.train.auc.

Usage

TensorFlow: Input y_pred and y_true, and control whether the return value is based on the ROC curve or the Precision-Recall curve via the input curve. In addition, users can set their own parameters such as the number of thresholds num_thresholds and the threshold value thresholds. Support interpolate_pr_auc() method (there is no corresponding function in MindSpore). Please check the API interface for details of implementation and usage. TensorFlow version 1.15 only supports binary classification.

MindSpore: Before calling the mindspore.nn.auc interface, FPR(false positive rate) and TPR(true positive rate) should be derived using mindspore.nn.ROC, and the threshold value is determined by the y_pred element value size during calculation. The computed FPR and TPR are passed into mindspore.nn.auc for AUC calculation. Binary classification and multiclassification are supported.

Code Examplefrom mindspore.train import ROC, auc
import numpy as np

x = ms.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], [0.05, 0.05, 0.05, 0.75]]))
y = ms.Tensor(np.array([0, 1, 2, 3, 2]))
metric = ROC(class_num=4)
metric.update(x, y)
fpr, tpr, thresholds = metric.eval()
print(fpr)
# out: [array([0.        , 0.33333333, 0.33333333, 0.66666667, 1.        ]), array([0.        , 0.33333333, 1.        ]),
# array([0.        , 0.33333333, 1.        ]), array([0.        , 0.33333333, 1.        ])]

print(tpr)
# out: [array([0., 0., 1., 1., 1.]), array([0., 0., 1.]), array([0., 0., 1.]), array([0., 0., 1.])]

# calculate auc for class 0
output = auc(fpr[0], tpr[0])
print(output)
# out: 0.6666666666666667


import tensorflow as tf
tf.enable_eager_execution()

m = tf.keras.metrics.AUC(num_thresholds=3)
m.update_state([0, 0, 1, 1], [0, 0.5, 0.3, 0.9])
print(m.result().numpy())
# out: 0.75