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’} or callable, default=’rbf’ 

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, default=20 

Parameter for rbf kernel.  
n_neighborsint, default=7 

Parameter for knn kernel which need to be strictly positive.  
max_iterint, default=1000 

Change maximum number of iterations allowed.  
tolfloat, 1e-3 

Convergence tolerance: threshold to consider the system at steady state.  
n_jobsint, 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_ndarray of shape (n_samples, n_features) 

Input array.  
classes_ndarray of shape (n_classes,) 

The distinct labels used in classifying instances.  
label_distributions_ndarray of shape (n_samples, n_classes) 

Categorical distribution for each item.  
transduction_ndarray of 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(X, y) Fit a semi-supervised 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]) Return the mean accuracy on the given test data and labels.  
set_params(**params) Set the parameters of this estimator.    
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 
 
Xarray-like of shape (n_samples, n_features) 

A matrix of shape (n_samples, n_samples) will be created from this.  
yarray-like of shape (n_samples,) 

n_labeled_samples (unlabeled points are marked as -1) All unlabeled samples will be transductively assigned labels.    Returns 
 
selfobject 
   
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
predict(X) [source]
 
Performs inductive inference across the model.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

The data matrix.    Returns 
 
yndarray of 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 
 
Xarray-like of shape (n_samples, n_features) 

The data matrix.    Returns 
 
probabilitiesndarray of shape (n_samples, n_classes) 

Normalized probability distributions across class labels.     
  
score(X, y, sample_weight=None) [source]
 
Return 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 of shape (n_samples, n_features) 

Test samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True labels for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

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 Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.