sklearn.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=None)[source]¶ 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
 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
 gammafloat
parameter for rbf kernel
 n_neighborsinteger > 0
parameter for knn kernel
 alphafloat
Clamping factor. A value in (0, 1) that specifies the relative amount that an instance should adopt the information from its neighbors as opposed to its initial label. alpha=0 means keeping the initial label information; alpha=1 means replacing all initial information.
 max_iterinteger
maximum number of iterations allowed
 tolfloat
Convergence tolerance: threshold to consider the system at steady state
 n_jobsint or None, optional (default=None)
The number of parallel jobs to run.
None
means 1 unless in ajoblib.parallel_backend
context.1
means using all processors. See Glossary for more details.
 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
LabelPropagation
Unregularized graph based semisupervised learning
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
>>> 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(...)
Methods
fit
(self, X, y)Fit a semisupervised label propagation model based
get_params
(self[, deep])Get parameters for this estimator.
predict
(self, X)Performs inductive inference across the model.
predict_proba
(self, X)Predict probability for each possible outcome.
score
(self, X, y[, sample_weight])Returns the mean accuracy on the given test data and labels.
set_params
(self, \*\*params)Set the parameters of this estimator.

__init__
(self, kernel='rbf', gamma=20, n_neighbors=7, alpha=0.2, max_iter=30, tol=0.001, n_jobs=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.

fit
(self, X, y)[source]¶ 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
 Xarraylike, shape = [n_samples, n_features]
A {n_samples by n_samples} size matrix will be created from this
 yarray_like, shape = [n_samples]
n_labeled_samples (unlabeled points are marked as 1) All unlabeled samples will be transductively assigned labels
 Returns
 selfreturns an instance of self.

get_params
(self, deep=True)[source]¶ Get parameters for this estimator.
 Parameters
 deepboolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
 Returns
 paramsmapping of string to any
Parameter names mapped to their values.

predict
(self, X)[source]¶ Performs inductive inference across the model.
 Parameters
 Xarray_like, shape = [n_samples, n_features]
 Returns
 yarray_like, shape = [n_samples]
Predictions for input data

predict_proba
(self, 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
 Xarray_like, shape = [n_samples, n_features]
 Returns
 probabilitiesarray, shape = [n_samples, n_classes]
Normalized probability distributions across class labels

score
(self, X, y, sample_weight=None)[source]¶ 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
 Xarraylike, shape = (n_samples, n_features)
Test samples.
 yarraylike, shape = (n_samples) or (n_samples, n_outputs)
True labels for X.
 sample_weightarraylike, shape = [n_samples], optional
Sample weights.
 Returns
 scorefloat
Mean accuracy of self.predict(X) wrt. y.

set_params
(self, **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