MultiLabelBinarizer¶

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

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

Note

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

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.

classes : array-like of shape [n_classes] (optional)
Indicates an ordering for the class labels
sparse_output : boolean (default: False),
Set to true if output binary array is desired in CSR sparse format
classes_ : array of labels
A copy of the classes parameter where provided, or otherwise, the sorted set of classes found when fitting.
>>> 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']

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 the label sets binarizer, storing classes_

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.

self : returns this MultiLabelBinarizer instance

fit_transform(y)[source]

Note

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

Fit the label sets binarizer and transform the given label sets

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.
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.
inverse_transform(yt)[source]

Note

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

Transform the given indicator matrix into label sets

yt : array or sparse matrix of shape (n_samples, n_classes)
A matrix containing only 1s ands 0s.
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.
transform(y)[source]

Note

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

Transform the given label sets

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.
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.