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我正在运行一个非常基本的代码来创建编码器类，然后使用相同的类来编码一个新的数据帧。在这段代码中，我不需要使用np.saveand np.load，但是在我的实际实现中，我需要重新加载编码器来转换一个新的数据帧。我试图了解如何在一个数据帧上创建一个编码器类，然后在另一个脚本中加载该编码器并转换一个新的数据帧。

from sklearn.preprocessing import LabelEncoder
import pickle as cPickle
import numpy as np

df_test = pd.DataFrame({'A': [1, 2, 3, 4],
                   'B': ["Yes", "No", "Yes", "Yes"],
                   'C': ["Yes", "No", "No", "Yes"],
                   'D': ["No", "Yes", "No", "Yes"]})

le = LabelEncoder()
df_test['A'] = le.fit_transform(df_test['A'])
le_dict = dict(zip(le.classes_, le.transform(le.classes_)))
class_name = 'classes_' +'A' + '.npy'
np.save(class_name, le_dict, allow_pickle=True)

print(df_test)
print(le.classes_)

le.classes_ = np.load('classes_A.npy', allow_pickle = True)

print(le.classes_)

df_new = pd.DataFrame({'A': [1, 2, 3, 4],
                   'B': ["Yes", "No", "Yes", "Yes"],
                   'C': ["Yes", "No", "No", "Yes"],
                   'D': ["No", "Yes", "No", "Yes"]})

df_new['A'] = le.transform(df_new['A'])

这给了我以下错误：


  File "<ipython-input-42-a1aa630ec7e8>", line 1, in <module>
    df_new['A'] = le.transform(df_new['A'])

  File "/Users/usr/opt/anaconda3/envs/signals_gcp_py36/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 257, in transform
    _, y = _encode(y, uniques=self.classes_, encode=True)

  File "/Users/usr/opt/anaconda3/envs/signals_gcp_py36/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 110, in _encode
    return _encode_numpy(values, uniques, encode)

  File "/Users/usr/opt/anaconda3/envs/signals_gcp_py36/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 49, in _encode_numpy
    % str(diff))

ValueError: y contains previously unseen labels: [1, 2, 3, 4]

当我在从内存加载之前打印 le.classes_ 时，它是这样的： array([1, 2, 3, 4])

但是当我在 np.load() 之后打印它时，它是这样的： {1: 0, 2: 1, 3: 2, 4: 3}

以下是有关 le.classes after 的更多信息np.load()：

In []: le.classes_
Out[]: array({1: 0, 2: 1, 3: 2, 4: 3}, dtype=object)

In []: type(le.classes_)
Out[]: numpy.ndarray

In []: print(le.classes_)
Out[]: {1: 0, 2: 1, 3: 2, 4: 3}

我试图了解这些功能是如何工作的。我运行了相同的代码，但是对于 col.B，我又遇到了另一个错误。

from sklearn.preprocessing import LabelEncoder
import pickle as cPickle
import numpy as np

df_test = pd.DataFrame({'A': [1, 2, 3, 4],
                   'B': ["Yes", "No", "Yes", "Yes"],
                   'C': ["Yes", "No", "No", "Yes"],
                   'D': ["No", "Yes", "No", "Yes"]})

le = LabelEncoder()
df_test['B'] = le.fit_transform(df_test['B'])
le_dict = dict(zip(le.classes_, le.transform(le.classes_)))
class_name = 'classes_' +'B' + '.npy'

np.save(class_name, le_dict, allow_pickle=True)

print(df_test)
print(le.classes_)

le.classes_ = np.load('classes_B.npy', allow_pickle = True)

print(le.classes_)

df_new = pd.DataFrame({'A': [1, 2, 3, 4],
                   'B': ["Yes", "No", "Yes", "Yes"],
                   'C': ["Yes", "No", "No", "Yes"],
                   'D': ["No", "Yes", "No", "Yes"]})

df_new['B'] = le.transform(df_new['B'])

这个错误是TypeError: argument must be a string or number。

这是完整的堆栈：

Traceback (most recent call last):

  File "<ipython-input-71-a0243d411c34>", line 1, in <module>
    df_new['B'] = le.transform(df_new['B'])

  File "/Users/usr/opt/anaconda3/envs/signals_gcp_py36/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 257, in transform
    _, y = _encode(y, uniques=self.classes_, encode=True)

  File "/Users/usr/opt/anaconda3/envs/signals_gcp_py36/lib/python3.6/site-packages/sklearn/preprocessing/label.py", line 107, in _encode
    raise TypeError("argument must be a string or number")

TypeError: argument must be a string or number

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