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 .