I'm trying to preprossessing data with the OneHotEncoder of scikitlearn. Obviously, I'm doing something wrong. Here is my sample program :
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer
cat = ['ok', 'ko', 'maybe', 'maybe']
label_encoder = LabelEncoder()
label_encoder.fit(cat)
cat = label_encoder.transform(cat)
# returns [2 0 1 1], which seams good.
print(cat)
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0])], remainder='passthrough')
res = ct.fit_transform([cat])
print(res)
Final result : [[1.0 0 1 1]]
Expected result : something like :
[
[ 1 0 0 ]
[ 0 0 1 ]
[ 0 1 0 ]
[ 0 1 0 ]
]
Can someone point out what I'm missing ?
You can consider to using numpy and MultiLabelBinarizer.
import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer
cat = np.array([['ok', 'ko', 'maybe', 'maybe']])
m = MultiLabelBinarizer()
print(m.fit_transform(cat.T))
If you still want to stick with your solution. You just need to update as the following:
# because of it still a row, not a column
# res = ct.fit_transform([cat]) => remove this
# it should works
res = ct.fit_transform(np.array([cat]).T)
Out[2]:
array([[0., 0., 1.],
[1., 0., 0.],
[0., 1., 0.],
[0., 1., 0.]])