CODEWITHSUNDEEP
pythonmachine-learningdata-scienceone-hot-encoding
Navin Bondade
Navin Bondade

Reputation: 53

OneHotEncoder : __init__() got an unexpected keyword argument 'categorical_features'

I'm having this error with onehotencoder where thecategorical_features attribute is missing, I'm using google colab.

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
le = LabelEncoder()
X = star.iloc[:,:6].values
y = star.iloc[:,-1].values
X[:,5] = le.fit_transform(X[:,5])
y[:] = le.fit_transform(y[:])

ohe = OneHotEncoder(categorical_features= [5])
X = ohe.fit_transform(X).toarray()


TypeError                                 Traceback (most recent call last)
<ipython-input-47-93f73a1a04ad> in <module>()
----> 1 ohe = OneHotEncoder(categorical_features= [5])
      2 X = ohe.fit_transform(X).toarray()

TypeError: __init__() got an unexpected keyword argument 'categorical_features'

Upvotes: 2

Views: 7966

Answers (1)

furas
furas

Reputation: 143098

In documentation for OneHotEncoder there is no 'categorical_features'

Older documentation (0.20) for OneHotEncoder shows that 'categorical_features' will be removed in 0.22 (and sklearn latest version has number 0.22.1) and you have to use ColumnTransformer.

But I don't know how to use it. But maybe examples in User Guide can help to use it.

EDIT:

Sklearn 0.20

pip install -U scikit-learn==0.20

Code

import sklearn
print(sklearn.__version__)

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import numpy as np

print('--- data ---')
X = [
    ['Male',   1],
    ['Female', 3],
    ['Female', 2]
]
X = np.array(X)
print(X)

print('--- LabelEncoder ---')
le = LabelEncoder()
X[:,0] = le.fit_transform(X[:,0])
print(X)

print('--- OneHotEncoder ---')
ohe = OneHotEncoder(categorical_features=[0])
X = ohe.fit_transform(X).toarray()
print(X)

Result:

0.20.0
--- data ---
[['Male' '1']
 ['Female' '3']
 ['Female' '2']]
--- LabelEncoder ---
[['1' '1']
 ['0' '3']
 ['0' '2']]
--- OneHotEncoder ---
[[0. 1. 1.]
 [1. 0. 3.]
 [1. 0. 2.]]

Sklearn 0.22

pip install -U scikit-learn==0.22

Code:

import sklearn
print(sklearn.__version__)

from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
import numpy as np

print('--- data ---')
X = [
    ['Male',   1],
    ['Female', 3],
    ['Female', 2]
]
X = np.array(X)
print(X)

print('--- LabelEncoder ---')
le = LabelEncoder()
X[:,0] = le.fit_transform(X[:,0])
print(X)

print('--- OneHotEncoder ---')
ct = ColumnTransformer([('my_ohe', OneHotEncoder(), [0])], remainder='passthrough')
X = ct.fit_transform(X) #.toarray()
print(X)

Result:

0.22.2.post1
--- data ---
[['Male' '1']
 ['Female' '3']
 ['Female' '2']]
--- LabelEncoder ---
[['1' '1']
 ['0' '3']
 ['0' '2']]
--- OneHotEncoder ---
[['0.0' '1.0' '1']
 ['1.0' '0.0' '3']
 ['1.0' '0.0' '2']]

In 0.22 it works even without LabelEncoder but 0.20 needs LabelEncoder 

import sklearn
print(sklearn.__version__)

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
import numpy as np

print('--- data ---')
X = [
    ['Male',   1],
    ['Female', 3],
    ['Female', 2]
]
X = np.array(X)
print(X)

print('--- OneHotEncoder ---')
ct = ColumnTransformer([('my_ohe', OneHotEncoder(), [0])], remainder='passthrough')
X = ct.fit_transform(X) #.toarray()
print(X)

Upvotes: 7

Related Questions