ScorerEnsemble: Computes and samples 0 and 1-dimensional laplacian scorers in...

In CamaraLab/RayleighSelection: An R package for feature selection in topological spaces.

Description

Builds an ensemble of LaplacianScorer objects that can be used to score functions and to sample scores by shuffling point labels in tandem.

Fields

new

Constructs a scorer ensemble using lists containing data on the complex and their laplacians.

Use scorer.ensemple <- new(ScorerEnsemble, comb_laplacian, pts_in_vertex, adjacency, one_forms)

Parameters

• comb_laplacian: List containing outputs of combinatorial_laplacian

• pts_in_vertex: List of lists of vectors where the i-th vector of the j-th list contains the index of the points associated to the i-th vertex of the j-th complex

• adjacency: List of adjacency matrices for 1-skeleton as a sparse matrix

• one_forms: boolean indicating if laplacian of one-forms will be computed

score

Pushes functions defined by rows of funcs to the dim-skeleton of all complexes by averaging and computes its laplacian score.

Use scorer$score(funcs, dim)

Parameters

• funcs: functions to be scored as a rows of a dense matrix

• dim: dimension of the laplacian (0 or 1)

Value Scores of functions as a matrix. The value in position (i,j) is the score of the i-th function on the j-th complex.

sample_scores

Takes samples of scores by permuting point labels

Use scorer$sample_scores(funcs, n_perm, dim, n_cores)

Parameters

• funcs: base functions as rows of a dense matrix

• n_perm: number of permutations

• dim: dimension of the laplacian

• n_cores: number of cores to be used, parallelization requires code to be compiled with openmp

Value Dense 3-dimensional array with sampled scores where position (i,j,k) has the score of the j-th permutation of the i-th function in the k-th complex.

sample_with_covariate

Takes samples of scores of function in tandem with scores of covariates by applying the same permutations of labels to both. Use scorer$sample_with_covariate(funcs, cov, n_perm, dim, n_cores) Parameters

• funcs: base functions as rows of a dense matrix

• cov: covariates as rows of a dense matrix

• n_perm: number of permutations

• dim: dimension of the laplacian

• n_cores: number of cores to be used, parallelization requires code to be compiled with openmp

Value A list out with as many elements as there are complexes in the ensemble. Each element of the list is itself a list with two elements:

• func_scores: a dense matrix with sampled scores where the i-th row has samples for the i-th function.

• cov_scores: a dense 3-dimensional array where the position (i, j, k) has the sampled score of the j-th covariate associated to the k-th sample of the i-th function.

Examples

library(RayleighSelection)

# Create a simplicial complex and compute the associated 0- and 1-laplacians
gy.list <- list(
  nerve_complex(list(c(1,4,6,10), c(1,2,7), c(2,3,8), c(3,4,9,10), c(4,5))),
  nerve_complex(list(c(1,6,10), c(1,4,2,7), c(2,3,8), c(3,4,9,10), c(4,5)))
)
lout.list <- lapply(gy.list, combinatorial_laplacian, one_forms = TRUE)

# Create an associated instance of ScorerEnsemble
scorer.ensemble <- new(
  ScorerEnsemble,
  lout.list,
  lapply(gy.list, function(gy) gy$points_in_vertex),
  lapply(gy.list, function(gy) gy$adjacency),
  TRUE
)

# Compute the 0-laplacian scores of a a function
scorer.ensemble$score(t(as.matrix(c(0,1,1,0,0,0,0,0,0,1))), 0)

# Sample scores by shuffling the function
scorer$sample_scores(t(as.matrix(c(0,1,1,0,0,0,0,0,0,1))), 10, 0, 1)

CamaraLab/RayleighSelection documentation built on Aug. 16, 2021, 12:01 p.m.