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First I do a label encoding to all the columns that are strings so they will be numeric. After that, I take just the columns with the labels, convert them to np array, reshape, and convert them to one-hot encoding.

The "y" is of size 900 (of floats), and in the resize I change it to (900,1) so the one hot will work.

I use scikit-learn OneHotEncoding, and when doing fir_transform the result is:

out put

details

Why do I get a tuple as output and not vectors of 1 and 0?


    def OneHot(self,y):
        ohe = OneHotEncoder()
        y = y.reshape(len(y) , 1)
        y_hot = ohe.fit_transform(y)
        print(y_hot)
        return y_hot
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Why do I get a tuple as output and not vectors of 1 and 0?

You get this because by default OneHotEncoder() uses sparse matrix representation. Hence, it transforms the elements of y into elements of type -

<1x3 sparse matrix of type '<class 'numpy.float64'>'
    with 1 stored elements in Compressed Sparse Row format>

If you want the output as vectors, then just put sparse=False in OneHotEncoder()

Following is an example of the same -

from sklearn import datasets
from sklearn.preprocessing import OneHotEncoder

# Iris dataset
X, y = datasets.load_iris(return_X_y=True)
print("Shape of dataset - ",X.shape, y.shape)

# Your code
def OneHot(y):
    ohe = OneHotEncoder(sparse=False)
    y = y.reshape(len(y) , 1) # you can also use y = y.reshape(-1, 1) instead
    y_hot = ohe.fit_transform(y)
    return y_hot
  
y_oh = OneHot(y)

print("Shape of One Hot Encoded y - ",y_oh.shape)
print("Single element in y - ",y_oh[0])

The code generates the following output -

Shape of dataset -  (150, 4) (150,)
Shape of One Hot Encoded y -  (150, 3)
Single element in y -  [1. 0. 0.]
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  • $\begingroup$ Thanks! In all the tut I watched they didn't use it, I finally used .toarray and this got me all the vectors.. but yours is looking better $\endgroup$ Sep 26 at 12:17
  • $\begingroup$ @JamseGoldman Please consider accepting the answer if it answers your question. :) $\endgroup$ Sep 26 at 21:25

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