np_utils.to_categorical反向

Question

import numpy as np
from keras.utils import np_utils
nsample = 100
sample_space = ["HOME","DRAW","AWAY"]
array = np.random.choice(sample_space, nsample, )
uniques, coded_id = np.unique(array, return_inverse=True)
coded_array = np_utils.to_categorical(coded_id)

例

映射字典

 ['AWAY', 'HOME', 'DRAW', 'AWAY', ...]

输入

[[ 0.  1.  0.]
 [ 0.  0.  1.]
 [ 0.  0.  1.]
 ..., 
 [ 0.  0.  1.]
 [ 0.  0.  1.]
 [ 1.  0.  0.]]

编码输出

import numpy as np
from keras.utils import np_utils
nsample = 100
sample_space = ["HOME","DRAW","AWAY"]
array = np.random.choice(sample_space, nsample, )
uniques, coded_id = np.unique(array, return_inverse=True)
coded_array = np_utils.to_categorical(coded_id)

如何反转此功能并获得解码功能？

Answer 1

您可以使用它np.argmax来检索那些ids,然后简单地索引到uniques应该给你原始数组.因此,我们会有一个实现,就像这样 -

uniques[y_code.argmax(1)]

样品运行 -

In [44]: arr
Out[44]: array([5, 7, 3, 2, 4, 3, 7])

In [45]: uniques, ids = np.unique(arr, return_inverse=True)

In [46]: y_code = np_utils.to_categorical(ids, len(uniques))

In [47]: uniques[y_code.argmax(1)]
Out[47]: array([5, 7, 3, 2, 4, 3, 7])