Sklearn LabelEncoder inverse transform

[BRN]

Hi everyone!

I need to inverse an label transform with sklearn. I found this example on web:

from sklearn.preprocessing import LabelEncoder

np.random.seed(1)
y = np.random.randint(0, 2, (10, 7))
y = y[np.where(y.sum(axis=1) != 0)[0]]

array([[1, 1, 0, 0, 1, 1, 1],
       [1, 1, 0, 0, 1, 0, 1],
       [1, 0, 0, 1, 0, 0, 0],
       [1, 0, 0, 1, 0, 0, 0],
       [1, 0, 0, 0, 1, 1, 1],
       [1, 1, 0, 0, 0, 1, 1],
       [1, 1, 1, 1, 0, 1, 1],
       [0, 0, 1, 0, 0, 1, 1],
       [1, 0, 1, 0, 0, 1, 1],
       [0, 1, 1, 1, 1, 0, 0]])

le = LabelEncoder()
y_new = le.fit_transform([''.join(str(l)) for l in y])

array([7, 6, 3, 3, 2, 5, 8, 0, 4, 1])

y_old = le.inverse_transform(y_new)

array(['[1 1 0 0 1 1 1]', '[1 1 0 0 1 0 1]', '[1 0 0 1 0 0 0]',
       '[1 0 0 1 0 0 0]', '[1 0 0 0 1 1 1]', '[1 1 0 0 0 1 1]',
       '[1 1 1 1 0 1 1]', '[0 0 1 0 0 1 1]', '[1 0 1 0 0 1 1]',
       '[0 1 1 1 1 0 0]'], dtype='<U15')

With 'inverse_transform' How can I obtain an array with shape (10, 7)?

Thaks!

 

[lofgran]

Hi there!

To obtain an array with shape (10, 7), you can use the 'reshape' function after applying the 'inverse_transform' method. Here's an example:

y_old = le.inverse_transform(y_new).reshape(10,7)

This will reshape the array to have 10 rows and 7 columns. Hope this helps!