maxframe.learn.preprocessing.LabelEncoder#

class maxframe.learn.preprocessing.LabelEncoder[source]#

Encode target labels with value between 0 and n_classes-1.

This transformer should be used to encode target values, i.e. y, and not the input X.

Read more in the User Guide.

classes_#

Holds the label for each class.

Type:

ndarray of shape (n_classes,)

See also

OrdinalEncoder

Encode categorical features using an ordinal encoding scheme.

OneHotEncoder

Encode categorical features as a one-hot numeric array.

Examples

LabelEncoder can be used to normalize labels.

>>> from maxframe.learn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> le.fit([1, 2, 2, 6]).execute()
LabelEncoder()
>>> le.classes_.to_numpy()
array([1, 2, 6])
>>> le.transform([1, 1, 2, 6]).to_numpy()
array([0, 0, 1, 2]...)
>>> le.inverse_transform([0, 0, 1, 2]).to_numpy()
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"]).execute()
LabelEncoder()
>>> list(le.classes_.to_numpy())
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"]).to_numpy()
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]).to_numpy())
['tokyo', 'tokyo', 'paris']
__init__()#

Methods

__init__()

execute([session, run_kwargs, extra_tileables])

fetch([session, run_kwargs])

fit(y[, execute, session, run_kwargs])

Fit label encoder.

fit_transform(y[, execute, session, run_kwargs])

Fit label encoder and return encoded labels.

inverse_transform(y[, execute, session, ...])

Transform labels back to original encoding.

transform(y[, execute, session, run_kwargs])

Transform labels to normalized encoding.