python - Scikit Learn OneHotEncoder 拟合和变换错误 : ValueError: X has different shape than during fitting

Question

下面是我的代码。

我知道为什么在转换过程中会发生错误。这是因为拟合和变换期间特征列表不匹配。我该如何解决这个问题？我如何才能为所有其余功能获得 0？

在此之后，我想用它来部分拟合 SGD 分类器。

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import pandas as pd
from sklearn.preprocessing import OneHotEncoder

input_df = pd.DataFrame(dict(fruit=['Apple', 'Orange', 'Pine'], 
                             color=['Red', 'Orange','Green'],
                             is_sweet = [0,0,1],
                             country=['USA','India','Asia']))
input_df
Out[1]: 
    color country   fruit  is_sweet
0     Red     USA   Apple         0
1  Orange   India  Orange         0
2   Green    Asia    Pine         1



filtered_df = input_df.apply(pd.to_numeric, errors='ignore')
filtered_df.info()
# apply one hot encode
refreshed_df = pd.get_dummies(filtered_df)
refreshed_df
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3 entries, 0 to 2
Data columns (total 4 columns):
color       3 non-null object
country     3 non-null object
fruit       3 non-null object
is_sweet    3 non-null int64
dtypes: int64(1), object(3)
memory usage: 176.0+ bytes


Out[2]: 
   is_sweet  color_Green  color_Orange  color_Red  country_Asia  \
0         0            0             0          1             0   
1         0            0             1          0             0   
2         1            1             0          0             1   

   country_India  country_USA  fruit_Apple  fruit_Orange  fruit_Pine  
0              0            1            1             0           0  
1              1            0            0             1           0  
2              0            0            0             0           1  



enc = OneHotEncoder()
enc.fit(refreshed_df)

Out[3]: 
OneHotEncoder(categorical_features='all', dtype=<class 'numpy.float64'>,
       handle_unknown='error', n_values='auto', sparse=True)



new_df = pd.DataFrame(dict(fruit=['Apple'], 
                             color=['Red'],
                             is_sweet = [0],
                             country=['USA']))
new_df


Out[4]: 
  color country  fruit  is_sweet
0   Red     USA  Apple         0



filtered_df1 = new_df.apply(pd.to_numeric, errors='ignore')
filtered_df1.info()
# apply one hot encode
refreshed_df1 = pd.get_dummies(filtered_df1)
refreshed_df1
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1 entries, 0 to 0
Data columns (total 4 columns):
color       1 non-null object
country     1 non-null object
fruit       1 non-null object
is_sweet    1 non-null int64
dtypes: int64(1), object(3)
memory usage: 112.0+ bytes



Out[5]: 
   is_sweet  color_Red  country_USA  fruit_Apple
0         0          1            1            1

enc.transform(refreshed_df1)
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-6-33a6a884ba3f> in <module>()
----> 1 enc.transform(refreshed_df1)

~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py in transform(self, X)
   2073         """
   2074         return _transform_selected(X, self._transform,
-> 2075                                    self.categorical_features, copy=True)
   2076 
   2077 

~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py in _transform_selected(X, transform, selected, copy)
   1810 
   1811     if isinstance(selected, six.string_types) and selected == "all":
-> 1812         return transform(X)
   1813 
   1814     if len(selected) == 0:

~/anaconda3/lib/python3.6/site-packages/sklearn/preprocessing/data.py in _transform(self, X)
   2030             raise ValueError("X has different shape than during fitting."
   2031                              " Expected %d, got %d."
-> 2032                              % (indices.shape[0] - 1, n_features))
   2033 
   2034         # We use only those categorical features of X that are known using fit.

ValueError: X has different shape than during fitting. Expected 10, got 4.

Answer 1

您需要 LabelEncoder 而不是使用 pd.get_dummies() + OneHotEncoder，它可以存储原始值，然后在新数据上使用它们。

像下面这样更改代码将为您提供所需的结果。

import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
input_df = pd.DataFrame(dict(fruit=['Apple', 'Orange', 'Pine'], 
                             color=['Red', 'Orange','Green'],
                             is_sweet = [0,0,1],
                             country=['USA','India','Asia']))

filtered_df = input_df.apply(pd.to_numeric, errors='ignore')

# This is what you need
le_dict = {}
for col in filtered_df.columns:
    le_dict[col] = LabelEncoder().fit(filtered_df[col])
    filtered_df[col] = le_dict[col].transform(filtered_df[col])

enc = OneHotEncoder()
enc.fit(filtered_df)
refreshed_df = enc.transform(filtered_df).toarray()

new_df = pd.DataFrame(dict(fruit=['Apple'], 
                             color=['Red'],
                             is_sweet = [0],
                             country=['USA']))
for col in new_df.columns:
    new_df[col] = le_dict[col].transform(new_df[col])

new_refreshed_df = enc.transform(new_df).toarray()

print(filtered_df)
      color  country  fruit  is_sweet
0      2        2      0         0
1      1        1      1         0
2      0        0      2         1

print(refreshed_df)
[[ 0.  0.  1.  0.  0.  1.  1.  0.  0.  1.  0.]
 [ 0.  1.  0.  0.  1.  0.  0.  1.  0.  1.  0.]
 [ 1.  0.  0.  1.  0.  0.  0.  0.  1.  0.  1.]]

print(new_df)
      color  country  fruit  is_sweet
0      2        2      0         0

print(new_refreshed_df)
[[ 0.  0.  1.  0.  0.  1.  1.  0.  0.  1.  0.]]