sklearn.preprocessing
.MultiLabelBinarizer¶
-
class
sklearn.preprocessing.
MultiLabelBinarizer
(classes=None, sparse_output=False)[source]¶ 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
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.
Examples
>>> 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 -
fit
(y)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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)[source]¶ 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.
-