mars.learn.preprocessing.LabelBinarizer#

class mars.learn.preprocessing.LabelBinarizer(*, neg_label=0, pos_label=1, sparse_output=False)[source]#

Binarize labels in a one-vs-all fashion.

Several regression and binary classification algorithms are available in scikit-learn. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.

At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.

At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.

Read more in the User Guide.

Parameters

neg_label (int, default=0) – Value with which negative labels must be encoded.
pos_label (int, default=1) – Value with which positive labels must be encoded.
sparse_output (bool, default=False) – True if the returned array from transform is desired to be in sparse CSR format.

classes_#

Holds the label for each class.

Type: ndarray of shape (n_classes,)

y_type_#

Represents the type of the target data as evaluated by utils.multiclass.type_of_target. Possible type are ‘continuous’, ‘continuous-multioutput’, ‘binary’, ‘multiclass’, ‘multiclass-multioutput’, ‘multilabel-indicator’, and ‘unknown’.

Type: str

sparse_input_#

True if the input data to transform is given as a sparse matrix, False otherwise.

Type: bool

Examples

>>> from mars.learn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> lb.classes_
array([1, 2, 4, 6])
>>> lb.transform([1, 6])
array([[1, 0, 0, 0],
       [0, 0, 0, 1]])

Binary targets transform to a column vector

>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
array([[1],
       [0],
       [0],
       [1]])

Passing a 2D matrix for multilabel classification

>>> import numpy as np
>>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
LabelBinarizer()
>>> lb.classes_
array([0, 1, 2])
>>> lb.transform([0, 1, 2, 1])
array([[1, 0, 0],
       [0, 1, 0],
       [0, 0, 1],
       [0, 1, 0]])

See also

label_binarize: Function to perform the transform operation of LabelBinarizer with fixed classes.
OneHotEncoder: Encode categorical features using a one-hot aka one-of-K scheme.

__init__(*, neg_label=0, pos_label=1, sparse_output=False)[source]#

Methods

`__init__`(*[, neg_label, pos_label, ...])
`fit`(y[, session, run_kwargs])	Fit label binarizer.
`fit_transform`(y[, session, run_kwargs])	Fit label binarizer and transform multi-class labels to binary labels.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(Y[, threshold])	Transform binary labels back to multi-class labels.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(y[, session, run_kwargs])	Transform multi-class labels to binary labels.