`sklearn.semi_supervised`.LabelSpreading¶

class sklearn.semi_supervised.LabelSpreading(kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, n_jobs=1)[source]¶

LabelSpreading model for semi-supervised learning

This model is similar to the basic Label Propgation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.

See also

LabelPropagation: Unregularized graph based semi-supervised learning

References

Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schoelkopf. Learning with local and global consistency (2004) http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.115.3219

Examples

>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelSpreading
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.randint(0, 2,
...    size=len(iris.target)))
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... 
LabelSpreading(...)

Methods

`fit`(X, y)	Fit a semi-supervised label propagation model based
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Performs inductive inference across the model.
`predict_proba`(X)	Predict probability for each possible outcome.
`score`(X, y[, sample_weight])	Returns the mean accuracy on the given test data and labels.
`set_params`(\\params)	Set the parameters of this estimator.

__init__(kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, n_jobs=1)[source]¶

fit(X, y)[source]¶

Fit a semi-supervised label propagation model based

All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples.

Parameters:

X : array-like, shape = [n_samples, n_features]

A {n_samples by n_samples} size matrix will be created from this

y : array_like, shape = [n_samples]

n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels

Returns:

self : returns an instance of self.

get_params(deep=True)[source]¶

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.

predict(X)[source]¶

Performs inductive inference across the model.

Parameters:

X : array_like, shape = [n_samples, n_features]

Returns:

y : array_like, shape = [n_samples]

Predictions for input data

predict_proba(X)[source]¶

Predict probability for each possible outcome.

Compute the probability estimates for each single sample in X and each possible outcome seen during training (categorical distribution).

Parameters:

X : array_like, shape = [n_samples, n_features]

Returns:

probabilities : array, shape = [n_samples, n_classes]

Normalized probability distributions across class labels

score(X, y, sample_weight=None)[source]¶

Returns the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

Mean accuracy of self.predict(X) wrt. y.

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.