class sklearn.semi_supervised.LabelSpreading(kernel=’rbf’, gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, n_jobs=None)
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LabelSpreading model for semisupervised learning
This model is similar to the basic Label Propagation algorithm, but uses affinity matrix based on the normalized graph Laplacian and soft clamping across the labels.
Read more in the User Guide.
Parameters: 


Attributes: 

See also
LabelPropagation
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
>>> import numpy as np >>> from sklearn import datasets >>> from sklearn.semi_supervised import LabelSpreading >>> label_prop_model = LabelSpreading() >>> 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) ... LabelSpreading(...)
fit (X, y)  Fit a semisupervised 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=None)
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fit(X, y)
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Fit a semisupervised 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: 


Returns: 

get_params(deep=True)
[source]
Get parameters for this estimator.
Parameters: 


Returns: 

predict(X)
[source]
Performs inductive inference across the model.
Parameters: 


Returns: 

predict_proba(X)
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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: 


Returns: 

score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multilabel 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: 


Returns: 

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: 


sklearn.semi_supervised.LabelSpreading
© 2007–2018 The scikitlearn developers
Licensed under the 3clause BSD License.
http://scikitlearn.org/stable/modules/generated/sklearn.semi_supervised.LabelSpreading.html