python - Scikit Learn OneHotEncoder ajusta y transforma Error: ValueError: X tiene una forma diferente que durante el ajuste

pandas machine-learning

Question

Abajo está mi código.

Sé por qué se produce el error durante la transformación. Esto se debe a que la lista de características no coincide durante el ajuste y la transformación. Como puedo resolver esto? ¿Cómo puedo obtener 0 para todas las funciones de descanso?

Después de esto, quiero usar esto para un ajuste parcial del clasificador SGD.

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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

En lugar de usar pd.get_dummies() , necesita LabelEncoder + OneHotEncoder que puede almacenar los valores originales y luego usarlos en los nuevos datos.

Cambiar su código como a continuación le dará los resultados requeridos.

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.]]

Answer 2

Su codificador está instalado en refreshed_df que contiene 10 columnas y su refreshed_df1 contiene solo 4, literalmente lo que se informa en el error. Debe eliminar las columnas que no aparecen en su refreshed_df1 o simplemente ajustar su codificador a una nueva versión de refreshed_df que solo contenga las 4 columnas que aparecen en refreshed_df1 .