sklearn.semi_supervised.LabelPropagation

class sklearn.semi_supervised.LabelPropagation(kernel=’rbf’, gamma=20, n_neighbors=7, alpha=None, max_iter=1000, tol=0.001, n_jobs=1)

Label Propagation classifier

Read more in the User Guide.

Parameters:

kernel : {‘knn’, ‘rbf’, callable}

String identifier for kernel function to use or the kernel function itself. Only ‘rbf’ and ‘knn’ strings are valid inputs. The function passed should take two inputs, each of shape [n_samples, n_features], and return a [n_samples, n_samples] shaped weight matrix.

gamma : float

Parameter for rbf kernel

n_neighbors : integer > 0

Parameter for knn kernel

alpha : float

Clamping factor.

Deprecated since version 0.19: This parameter will be removed in 0.21. ‘alpha’ is fixed to zero in ‘LabelPropagation’.

max_iter : integer

Change 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.

See also

LabelSpreading

Alternate label propagation strategy more robust to noise

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()
>>> rng = np.random.RandomState(42)
>>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3
>>> labels = np.copy(iris.target)
>>> labels[random_unlabeled_points] = -1
>>> label_prop_model.fit(iris.data, labels)
... 
LabelPropagation(...)

Methods

fit(X, y)
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=None, max_iter=1000, tol=0.001, n_jobs=1)

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.

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, sample_weight=None)

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)

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 :