LPA

class s3l.classification.LPA.LPA(kernel='rbf', gamma=20, n_neighbors=7, max_iter=30, tol=0.001, n_jobs=None)[source]

Bases: s3l.base.TransductiveEstimatorwithGraph

Class for label propagation module.

Parameters:
  • kernel ({'knn', 'rbf', 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.
  • gamma (float (default=20)) – Parameter for rbf kernel
  • n_neighbors (integer > 0 (default=7)) – Parameter for knn kernel
  • max_iter (integer (default=30)) – Change maximum number of iterations allowed
  • tol (float (default=1e-3)) – Convergence tolerance: threshold to consider the system at steady state
  • n_jobs (int 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.
fit(X, y, labeled_idx, W)[source]

Fit a label propagation model

All the input data is provided matrix X (labeled and unlabeled) and corresponding label matrix y with a dedicated marker value for unlabeled samples. Optional matrix W is a graph provided for label propagation.

Parameters:
  • X (array-like, shape = [n_samples, n_features]) – A {n_samples by n_samples} size matrix will be created from this
  • y (array_like, shape = [n_samples]) – n_labeled_samples (unlabeled points are marked as 0)
  • labeled_idx (array_like, shape = [n_samples]) – index of n_labeled_samples in X.
  • W (array_like, shape = [n_samples, n_samples]) – graph of instances
Returns:

self
Return type:

returns an instance of self.
predict(index)[source]

Performs transductive inference across the model.

Parameters:index (array-like) – a row vector with length l, where l is the number of unlabeled instance. Each element is an index of a unlabeled instance.
Returns:y – Predictions for input data
Return type:array_like, shape = [n_samples]
set_params(param)[source]

Parameter setting function.

Parameters:param：dict – Store parameter names and corresponding values {‘name’: value}.