LabelEncoder

class ibex.sklearn.preprocessing.LabelEncoder

Bases: sklearn.preprocessing.label.LabelEncoder, ibex._base.FrameMixin

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Encode labels with value between 0 and n_classes-1.

Read more in the User Guide.

classes_ : array of shape (n_class,)
Holds the label for each class.

LabelEncoder can be used to normalize labels.

>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6]) 
array([0, 0, 1, 2]...)
>>> le.inverse_transform([0, 0, 1, 2])
array([1, 1, 2, 6])

It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

>>> le = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"]) 
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']
sklearn.preprocessing.OneHotEncoder : encode categorical integer features
using a one-hot aka one-of-K scheme.
fit(y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit label encoder

y : array-like of shape (n_samples,)
Target values.

self : returns an instance of self.

fit_transform(y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Fit label encoder and return encoded labels

y : array-like of shape [n_samples]
Target values.

y : array-like of shape [n_samples]

inverse_transform(y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transform labels back to original encoding.

y : numpy array of shape [n_samples]
Target values.

y : numpy array of shape [n_samples]

transform(y)[source]

Note

The documentation following is of the class wrapped by this class. There are some changes, in particular:

Transform labels to normalized encoding.

y : array-like of shape [n_samples]
Target values.

y : array-like of shape [n_samples]