sklearn.semi_supervised.LabelPropagation¶

class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, max_iter=1000, tol=0.001, n_jobs=None)[source]¶

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.

gammafloat

Parameter for rbf kernel

n_neighborsinteger > 0

Parameter for knn kernel

max_iterinteger

Change 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 a joblib.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

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

>>> import numpy as np
>>> 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(self, X, y)	Fit a semi-supervised 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, max_iter=1000, tol=0.001, n_jobs=None)[source]¶

Initialize self. See help(type(self)) for accurate signature.

fit(self, 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

Xarray-like, 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 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

Xarray-like, shape = (n_samples, n_features)

Test samples.

yarray-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weightarray-like, 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