# LabelBinarizer¶

class ibex.sklearn.preprocessing.LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)

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

Note

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

Binarize labels in a one-vs-all fashion

Several regression and binary classification algorithms are available in the scikit. 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.

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 : boolean (default: False)
True if the returned array from transform is desired to be in sparse CSR format.
classes_ : array of shape [n_class]
Holds the label for each class.
y_type_ : str,
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’.
sparse_input_ : boolean,
True if the input data to transform is given as a sparse matrix, False otherwise.
>>> from sklearn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer(neg_label=0, pos_label=1, sparse_output=False)
>>> 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(neg_label=0, pos_label=1, sparse_output=False)
>>> 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]])

label_binarize : function to perform the transform operation of
LabelBinarizer with fixed classes.
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 label binarizer

y : array of shape [n_samples,] or [n_samples, n_classes]
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification.

self : returns an instance of self.

fit_transform(y)[source]

Note

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

Fit label binarizer and transform multi-class labels to binary

labels.

The output of transform is sometimes referred to as the 1-of-K coding scheme.

y : array or sparse matrix of shape [n_samples,] or [n_samples, n_classes]
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.
Y : array or CSR matrix of shape [n_samples, n_classes]
Shape will be [n_samples, 1] for binary problems.
inverse_transform(Y, threshold=None)[source]

Note

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

Transform binary labels back to multi-class labels

Y : numpy array or sparse matrix with shape [n_samples, n_classes]
Target values. All sparse matrices are converted to CSR before inverse transformation.
threshold : float or None

Threshold used in the binary and multi-label cases.

Use 0 when Y contains the output of decision_function (classifier). Use 0.5 when Y contains the output of predict_proba.

If None, the threshold is assumed to be half way between neg_label and pos_label.

y : numpy array or CSR matrix of shape [n_samples] Target values.

In the case when the binary labels are fractional (probabilistic), inverse_transform chooses the class with the greatest value. Typically, this allows to use the output of a linear model’s decision_function method directly as the input of inverse_transform.

transform(y)[source]

Note

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

Transform multi-class labels to binary labels

The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

y : array or sparse matrix of shape [n_samples,] or [n_samples, n_classes]
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.
Y : numpy array or CSR matrix of shape [n_samples, n_classes]
Shape will be [n_samples, 1] for binary problems.