msmbuilder.preprocessing.LabelBinarizer

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

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.

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 : boolean (default: False)

True if the returned array from transform is desired to be in sparse CSR format.

See also

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.

Examples

>>> 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]])
Attributes:
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.

Methods

fit(X[, y]) Fit Preprocessing to X.
fit_transform(sequences[, y]) Fit the model and apply preprocessing
get_params([deep]) Get parameters for this estimator.
inverse_transform(Y[, threshold]) Transform binary labels back to multi-class labels
partial_fit(sequence[, y]) Fit Preprocessing to X.
partial_transform(sequence) Apply preprocessing to single sequence
set_params(**params) Set the parameters of this estimator.
summarize() Return some diagnostic summary statistics about this Markov model
transform(sequences) Apply preprocessing to sequences
__init__(neg_label=0, pos_label=1, sparse_output=False)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__([neg_label, pos_label, sparse_output]) Initialize self.
fit(X[, y]) Fit Preprocessing to X.
fit_transform(sequences[, y]) Fit the model and apply preprocessing
get_params([deep]) Get parameters for this estimator.
inverse_transform(Y[, threshold]) Transform binary labels back to multi-class labels
partial_fit(sequence[, y]) Fit Preprocessing to X.
partial_transform(sequence) Apply preprocessing to single sequence
set_params(**params) Set the parameters of this estimator.
summarize() Return some diagnostic summary statistics about this Markov model
transform(sequences) Apply preprocessing to sequences
fit(X, y=None)

Fit Preprocessing to X.

Parameters:
sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.

y : None

Ignored
Returns:
self
fit_transform(sequences, y=None)

Fit the model and apply preprocessing

Parameters:
sequences: list of array-like, each of shape (n_samples_i, n_features)

Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features.

y : None

Ignored
Returns:
sequence_new : list of array-like, each of shape (n_samples_i, n_components)
get_params(deep=True)

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(Y, threshold=None)

Transform binary labels back to multi-class labels

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

Notes

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.

partial_fit(sequence, y=None)

Fit Preprocessing to X. Parameters ———- sequence : array-like, [sequence_length, n_features]

A multivariate timeseries.
y : None
Ignored

self

partial_transform(sequence)

Apply preprocessing to single sequence

Parameters:
sequence: array like, shape (n_samples, n_features)

A single sequence to transform
Returns:
out : array like, shape (n_samples, n_features)
set_params(**params)

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
summarize()

Return some diagnostic summary statistics about this Markov model

transform(sequences)

Apply preprocessing to sequences

Parameters:
sequences: list of array-like, each of shape (n_samples_i, n_features)

Sequence data to transform, where n_samples_i in the number of samples in sequence i and n_features is the number of features.
Returns:
sequence_new : list of array-like, each of shape (n_samples_i, n_components)