8.13.2. sklearn.semi_supervised.LabelSpreading

class sklearn.semi_supervised.LabelSpreading(kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iters=30, tol=0.001)

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.

Parameters :

kernel : {‘knn’, ‘rbf’}

String identifier for kernel function to use. Only ‘rbf’ and ‘knn’ kernels are currently supported.

gamma : float

parameter for rbf kernel

n_neighbors : integer > 0

parameter for knn kernel

alpha : float

clamping factor

max_iters : float

maximum number of iterations allowed

tol : float

Convergence tolerance: threshold to consider the system at steady state

See also

LabelPropagation

Unregularized graph based semi-supervised learning

References

Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf. 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
>>> label_prop_model = LabelSpreading()
>>> iris = datasets.load_iris()
>>> random_unlabeled_points = np.where(np.random.random_integers(0, 1,
...    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 the estimator
predict(X)	Performs inductive inference across the model.
predict_proba(X)	Predict probability for each possible outcome.
score(X, y)	Returns the mean accuracy on the given test data and labels.
set_params(**params)	Set the parameters of the estimator.

__init__(kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iters=30, tol=0.001)

fit(X, y)

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)

Get parameters for the estimator

Parameters :

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

predict(X)

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)

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)

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

Parameters :

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

Training set.

y : array-like, shape = [n_samples]

Labels for X.

Returns :

z : float

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :