One hot encoding of string categorical features

Question

I\'m trying to perform a one hot encoding of a trivial dataset.

data = [[\'a\', \'dog\', \'red\']
        [\'b\', \'cat\', \'green\']]

Wha

Answer 1

If you are on sklearn>0.20.dev0

In [11]: from sklearn.preprocessing import OneHotEncoder
    ...: cat = OneHotEncoder()
    ...: X = np.array([['a', 'b', 'a', 'c'], [0, 1, 0, 1]], dtype=object).T
    ...: cat.fit_transform(X).toarray()
    ...: 
Out[11]: array([[1., 0., 0., 1., 0.],
           [0., 1., 0., 0., 1.],
           [1., 0., 0., 1., 0.],
           [0., 0., 1., 0., 1.]])

If you are on sklearn==0.20.dev0

In [30]: cat = CategoricalEncoder()

In [31]: X = np.array([['a', 'b', 'a', 'c'], [0, 1, 0, 1]], dtype=object).T

In [32]: cat.fit_transform(X).toarray()
Out[32]:
array([[ 1.,  0., 0.,  1.,  0.],
       [ 0.,  1.,  0.,  0.,  1.],
       [ 1.,  0.,  0.,  1.,  0.],
       [ 0.,  0.,  1.,  0.,  1.]])

Another way to do it is to use category_encoders.

Here is an example:

% pip install category_encoders
import category_encoders as ce
le =  ce.OneHotEncoder(return_df=False, impute_missing=False, handle_unknown="ignore")
X = np.array([['a', 'dog', 'red'], ['b', 'cat', 'green']])
le.fit_transform(X)
array([[1, 0, 1, 0, 1, 0],
       [0, 1, 0, 1, 0, 1]])

Answer 2

Very nice question.

However, in some sense, it is a private case of something that comes up (at least for me) rather often - given sklearn stages applicable to subsets of the X matrix, I'd like to apply (possibly several) given the entire matrix. Here, for example, you have a stage which knows to run on a single column, and you'd like to apply it thrice - once per column.

This is a classic case for using the Composite Design Pattern.

Here is a (sketch of a) reusable stage that accepts a dictionary mapping a column index into the transformation to apply to it:

class ColumnApplier(object):
    def __init__(self, column_stages):
        self._column_stages = column_stages

    def fit(self, X, y):
        for i, k in self._column_stages.items():
            k.fit(X[:, i])

        return self

    def transform(self, X):
        X = X.copy()
        for i, k in self._column_stages.items():
            X[:, i] = k.transform(X[:, i])

        return X

Now, to use it in this context, starting with

X = np.array([['a', 'dog', 'red'], ['b', 'cat', 'green']])
y = np.array([1, 2])
X

you would just use it to map each column index to the transformation you want:

multi_encoder = \
    ColumnApplier(dict([(i, preprocessing.LabelEncoder()) for i in range(3)]))
multi_encoder.fit(X, None).transform(X)

Once you develop such a stage (I can't post the one I use), you can use it over and over for various settings.

Answer 3

I've faced this problem many times and I found a solution in this book at his page 100 :

We 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:

and the sample code is here :

from sklearn.preprocessing import LabelBinarizer
encoder = LabelBinarizer()
housing_cat_1hot = encoder.fit_transform(data)
housing_cat_1hot

and as a result : 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.

And you can find more about the LabelBinarizer, here in the sklearn official documentation