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. 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([{'sci-fi', 'thriller'}, {'comedy'}]) array([[0, 1, 1], [1, 0, 0]]) >>> list(mlb.classes_) ['comedy', 'sci-fi', 'thriller']
A common mistake is to pass in a list, which leads to the following issue:
>>> mlb = MultiLabelBinarizer() >>> mlb.fit(['sci-fi', 'thriller', 'comedy']) MultiLabelBinarizer() >>> mlb.classes_ array(['-', 'c', 'd', 'e', 'f', 'h', 'i', 'l', 'm', 'o', 'r', 's', 't', 'y'], dtype=object)
To correct this, the list of labels should be passed in as:
>>> mlb = MultiLabelBinarizer() >>> mlb.fit([['sci-fi', 'thriller', 'comedy']]) MultiLabelBinarizer() >>> mlb.classes_ array(['comedy', 'sci-fi', 'thriller'], dtype=object)
Methods
fit
(y)Fit the label sets binarizer, storing classes_
Fit the label sets binarizer and transform the given label sets
get_params
([deep])Get parameters for this estimator.
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)[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
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
iffclasses_[j]
is iny[i]
, and 0 otherwise.
-
get_params
(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
deep : bool, default=True
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 ofclasses_[j]
for eachyt[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 latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters
**params : dict
Estimator parameters.
- Returns
self : object
Estimator instance.
-
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
iffclasses_[j]
is iny[i]
, and 0 otherwise.