Label propagate cluster solutions to unclustered subjects

Given a solutions_matrix derived from training subjects and a full_data_list containing both training and test subjects, re-run SNF to generate a total affinity matrix of both train and subjects and use the label propagation algorithm to assigned predicted clusters to test subjects.

Usage

lp_solutions_matrix(
  train_solutions_matrix,
  full_data_list,
  distance_metrics_list = NULL,
  weights_matrix = NULL,
  verbose = FALSE
)

Arguments

train_solutions_matrix

A solutions_matrix derived from the training set. The propagation algorithm is slow and should be used for validating a top or top few meaningful chosen clustering solutions. It is advisable to use only a small subset of rows from the original training solutions_matrix for label propagation.

full_data_list

A data_list containing subjects from both the training and testing sets.

distance_metrics_list

Like above - the distance_metrics_list (if any) that was used for the original batch_snf call.

weights_matrix

Like above.

verbose

If TRUE, print progress to console.

Value

labeled_df a dataframe containing a column for subjectkeys, a column for whether the subject was in the train (original) or test (held out) set, and one column per row of the solutions matrix indicating the original and propagated clusters.