semi_supervised.LabelPropagation()

Label Propagation classifier

Read more in the User Guide.

References

Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf

Examples

>>> from sklearn import datasets
>>> from sklearn.semi_supervised import LabelPropagation
>>> label_prop_model = LabelPropagation()
>>> 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)
... 
LabelPropagation(...)

Methods

__init__(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, 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.

Returns:	self :