Issue with OneHotEncoder for categorical features

Question

I want to encode 3 categorical features out of 10 features in my datasets. I use preprocessing from sklearn.preprocessing to do so as the following:

from sklearn import preprocessing cat_features = ['color', 'director_name', 'actor_2_name'] enc = preprocessing.OneHotEncoder(categorical_features=cat_features) enc.fit(dataset.values) 

However, I couldn't proceed as I am getting this error:

    array = np.array(array, dtype=dtype, order=order, copy=copy) ValueError: could not convert string to float: PG 

I am surprised why it is complaining about the string as it is supposed to convert it!! Am I missing something here?

Answer 1

If you read the docs for OneHotEncoder you'll see the input for fit is "Input array of type int". So you need to do two steps for your one hot encoded data

from sklearn import preprocessing cat_features = ['color', 'director_name', 'actor_2_name'] enc = preprocessing.LabelEncoder() enc.fit(cat_features) new_cat_features = enc.transform(cat_features) print new_cat_features # [1 2 0] new_cat_features = new_cat_features.reshape(-1, 1) # Needs to be the correct shape ohe = preprocessing.OneHotEncoder(sparse=False) #Easier to read print ohe.fit_transform(new_cat_features) 

Output:

[[ 0.  1.  0.]  [ 0.  0.  1.]  [ 1.  0.  0.]] 

EDIT

As of 0.20 this became a bit easier, not only because OneHotEncoder now handles strings nicely, but also because we can transform multiple columns easily using ColumnTransformer, see below for an example

from sklearn.compose import ColumnTransformer from sklearn.preprocessing import LabelEncoder, OneHotEncoder import numpy as np  X = np.array([['apple', 'red', 1, 'round', 0],               ['orange', 'orange', 2, 'round', 0.1],               ['bannana', 'yellow', 2, 'long', 0],               ['apple', 'green', 1, 'round', 0.2]]) ct = ColumnTransformer(     [('oh_enc', OneHotEncoder(sparse=False), [0, 1, 3]),],  # the column numbers I want to apply this to     remainder='passthrough'  # This leaves the rest of my columns in place ) print(ct2.fit_transform(X)) # Notice the output is a string 

Output:

[['1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '0.0' '0.0' '1.0' '1' '0']  ['0.0' '0.0' '1.0' '0.0' '1.0' '0.0' '0.0' '0.0' '1.0' '2' '0.1']  ['0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1.0' '0.0' '2' '0']  ['1.0' '0.0' '0.0' '1.0' '0.0' '0.0' '0.0' '0.0' '1.0' '1' '0.2']] 

Answer 2

You can apply both transformations (from text categories to integer categories, then from integer categories to one-hot vectors) in one shot using the LabelBinarizer class:

cat_features = ['color', 'director_name', 'actor_2_name'] encoder = LabelBinarizer() new_cat_features = encoder.fit_transform(cat_features) new_cat_features 

Note that this returns a dense NumPy array by default. You can get a sparse matrix instead by passing sparse_output=True to the LabelBinarizer constructor.

Source Hands-On Machine Learning with Scikit-Learn and TensorFlow