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sklearn.preprocessing.LabelEncoder

class sklearn.preprocessing.LabelEncoder

Encode labels with value between 0 and n_classes-1.

Examples

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']

Attributes

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

Methods

fit(y) Fit label encoder
fit_transform(y) Fit label encoder and return encoded labels
get_params([deep]) Get parameters for this estimator.
inverse_transform(y) Transform labels back to original encoding.
set_params(**params) Set the parameters of this estimator.
transform(y) Transform labels to normalized encoding.
__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

fit(y)

Fit label encoder

Parameters:

y : array-like of shape (n_samples,)

Target values.

Returns:

self : returns an instance of self.

fit_transform(y)

Fit label encoder and return encoded labels

Parameters:

y : array-like of shape [n_samples]

Target values.

Returns:

y : array-like of shape [n_samples]

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

inverse_transform(y)

Transform labels back to original encoding.

Parameters:

y : numpy array of shape [n_samples]

Target values.

Returns:

y : numpy array of shape [n_samples]

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :
transform(y)

Transform labels to normalized encoding.

Parameters:

y : array-like of shape [n_samples]

Target values.

Returns:

y : array-like of shape [n_samples]

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