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class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)
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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.See also
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sklearn.preprocessing.OneHotEncoder
- encode categorical integer 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)
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fit(y)
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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
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fit_transform(y)
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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.
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get_params(deep=True)
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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.
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inverse_transform(yt)
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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
.
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set_params(**params)
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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 :
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transform(y)
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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.
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preprocessing.MultiLabelBinarizer()
2017-01-15 04:25:10
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