sklearn.preprocessing.MultiLabelBinarizer

class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)

Transform between iterable of iterables and a multilabel format

Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label.

Parameters:

classes : array-like of shape [n_classes] (optional)

Indicates an ordering for the class labels. All entries should be unique (cannot contain duplicate classes).

sparse_output : boolean (default: False),

Set to true if output binary array is desired in CSR sparse format

Attributes:

classes_ : array of labels

A copy of the classes parameter where provided, or otherwise, the sorted set of classes found when fitting.

See also

sklearn.preprocessing.OneHotEncoder

encode categorical features using a one-hot aka one-of-K scheme.

Examples

>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> mlb = MultiLabelBinarizer()
>>> mlb.fit_transform([(1, 2), (3,)])
array([[1, 1, 0],
       [0, 0, 1]])
>>> mlb.classes_
array([1, 2, 3])

>>> mlb.fit_transform([set(['sci-fi', 'thriller']), set(['comedy'])])
array([[0, 1, 1],
       [1, 0, 0]])
>>> list(mlb.classes_)
['comedy', 'sci-fi', 'thriller']

Methods

fit(y)	Fit the label sets binarizer, storing classes_
fit_transform(y)	Fit the label sets binarizer and transform the given label sets
get_params([deep])	Get parameters for this estimator.
inverse_transform(yt)	Transform the given indicator matrix into label sets
set_params(**params)	Set the parameters of this estimator.
transform(y)	Transform the given label sets

__init__(classes=None, sparse_output=False)

Initialize self. See help(type(self)) for accurate signature.

fit(y)

Fit the label sets binarizer, storing classes_

Parameters:

y : iterable of iterables

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.

Returns:

self : returns this MultiLabelBinarizer instance

fit_transform(y)

Fit the label sets binarizer and transform the given label sets

Parameters:

y : iterable of iterables

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.

Returns:

y_indicator : array or CSR matrix, shape (n_samples, n_classes)

A matrix such that y_indicator[i, j] = 1 iff classes_[j] is in y[i], and 0 otherwise.

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(yt)

Transform the given indicator matrix into label sets

Parameters:

yt : array or sparse matrix of shape (n_samples, n_classes)

A matrix containing only 1s ands 0s.

Returns:

y : list of tuples

The set of labels for each sample such that y[i] consists of classes_[j] for each yt[i, j] == 1.

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 latter 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 the given label sets

Parameters:

y : iterable of iterables

A set of labels (any orderable and hashable object) for each sample. If the classes parameter is set, y will not be iterated.

Returns:

y_indicator : array or CSR matrix, shape (n_samples, n_classes)

A matrix such that y_indicator[i, j] = 1 iff classes_[j] is in y[i], and 0 otherwise.