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二元分类器keras回调的敏感性和特异性?

keras scikit-learn tensorflow numpy python

A T · 技术社区 · 6 年前

我如何获得敏感性和特异性,当 class_mode='binary' 是吗?-我目前的解决方案适用于 class_mode='categorical' :

from keras.callbacks import Callback
import numpy as np
from sklearn.metrics import confusion_matrix


class SensitivitySpecificityCallback(Callback):
    def on_epoch_end(self, epoch, logs=None):
        if epoch:
            x_test, y_test = self.validation_data[0], self.validation_data[1]
            predictions = self.model.predict(x_test)
            output_sensitivity_specificity(epoch, predictions, y_test)


def output_sensitivity_specificity(epoch, predictions, y_test):
    y_test = np.argmax(y_test, axis=-1)
    predictions = np.argmax(predictions, axis=-1)
    c = confusion_matrix(y_test, predictions)
    print('Confusion matrix:\n', c)
    print('[{:03d}] sensitivity'.format(epoch), c[0, 0] / (c[0, 1] + c[0, 0]))
    print('[{:03d}] specificity'.format(epoch), c[1, 1] / (c[1, 1] + c[1, 0]))

82 source lines full code example (与python 2&3兼容)

所有输出都是错误的:

Confusion matrix:
 [[40]]
Traceback (most recent call last):
  File "network.py", line 118, in <module>
    callbacks=[SensitivitySpecificityCallback()], verbose=1)
  File "lib/python2.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "lib/python2.7/site-packages/keras/engine/training.py", line 1426, in fit_generator
    initial_epoch=initial_epoch)
  File "lib/python2.7/site-packages/keras/engine/training_generator.py", line 229, in fit_generator
    callbacks.on_epoch_end(epoch, epoch_logs)
  File "lib/python2.7/site-packages/keras/callbacks.py", line 77, in on_epoch_end
    callback.on_epoch_end(epoch, logs)
  File "network.py", line 56, in on_epoch_end
    output_sensitivity_specificity(epoch, predictions, y_test)
  File "network.py", line 64, in output_sensitivity_specificity
    print('[{:03d}] sensitivity'.format(epoch), c[0, 0] / (c[0, 1] + c[0, 0]))
IndexError: index 1 is out of bounds for axis 1 with size 1

1 回复 | 直到 6 年前

today 6 年前

因为在二进制模式下,您实际上是在预测一个值,该值指示正类的概率(即二进制分类),使用 .argmax() 预测总是返回 0 .因此,您需要修改 output_sensitivity_specificity 本案例的功能:

def output_sensitivity_specificity(epoch, predictions, y_test, mode='binary'):
    if mode == 'binary':
        # determine positive class predictions
        idx = predictions >= 0.5
        predictions = np.zeros(predictions.shape)
        predictions[idx] = 1
        # no need to modify y_test since it consists of zeros and ones already
    else:
        y_test = np.argmax(y_test, axis=-1)
        predictions = np.argmax(predictions, axis=-1)

    c = confusion_matrix(y_test, predictions)
    print('Confusion matrix:\n', c)
    print('[{:03d}] sensitivity'.format(epoch), c[0, 0] / (c[0, 1] + c[0, 0]))
    print('[{:03d}] specificity'.format(epoch), c[1, 1] / (c[1, 1] + c[1, 0]))

就这样过去 mode=class_mode 打电话时 输出灵敏度 在你的回调中,它对二进制和分类模式都有效。