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.

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_iter : float maximum number of iterations allowed tol : float Convergence tolerance: threshold to consider the system at steady state n_jobs : int, optional (default = 1) The number of parallel jobs to run. If `-1`, then the number of jobs is set to the number of CPU cores.
Attributes:	X_ : array, shape = [n_samples, n_features] Input array. classes_ : array, shape = [n_classes] The distinct labels used in classifying instances. label_distributions_ : array, shape = [n_samples, n_classes] Categorical distribution for each item. transduction_ : array, shape = [n_samples] Label assigned to each item via the transduction. n_iter_ : int Number of iterations run.

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:

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.

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:

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.

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:

Parameters:	X : array_like, shape = [n_samples, n_features]
Returns:	y : array_like, shape = [n_samples] Predictions for input data

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:

Parameters:	X : array_like, shape = [n_samples, n_features]
Returns:	probabilities : array, shape = [n_samples, n_classes] Normalized probability distributions across class labels

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:

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.

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.