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Comparison of Execution Times

Robrecht Cannoodt

2024-10-22

Source: vignettes/articles/timings.Rmd
timings.Rmd

proxyC is optimized for computing proximity between rows or columns of large sparse matrices.

In this vignette, we compare the execution types of proxyC with other R packages for a variable sparsity level.

Euclidean 

require(Matrix, quietly = TRUE)
require(microbenchmark, quietly = TRUE)
require(ggplot2, quietly = TRUE)

n_rows <- 1000L
n_cols <- 1000L

# helper function to compute timings
compute_timings_for_euclidean <- function(
  n_rows = 1000L,
  n_cols = 1000L,
  density = 0.1,
  times = 10L
) {
  sparse_mat <- rsparsematrix(n_rows, n_cols, density)
  dense_mat <- as.matrix(sparse_mat)

  microbenchmark(
    # Disabled due to long execution times
    # "proxy::dist" = proxy::dist(dense_mat, method = "euclidean", diag = TRUE),
    "proxyC::dist" = proxyC::dist(sparse_mat, method = "euclidean", diag = TRUE),
    "Rfast::Dist" = Rfast::Dist(dense_mat, method = "euclidean"),
    "stats::dist" = dist(dense_mat, method = "euclidean"),
    "parallelDist::parDist" = parallelDist::parDist(dense_mat, method = "euclidean"),
    # Force conversion to matrix because distances::distances uses lazy evaluation
    "distances::distances" = as.matrix(distances::distances(dense_mat)),
    times = times
  )
}

1000 rows, 1000 columns, 95% sparsity 

bm <- compute_timings_for_euclidean(n_rows = n_rows, n_cols = n_cols, density = 0.05)

autoplot(bm)

1000 rows, 1000 columns, 50% sparsity 

bm <- compute_timings_for_euclidean(n_rows = n_rows, n_cols = n_cols, density = 0.5)

autoplot(bm)

1000 rows, 1000 columns, 5% sparsity 

bm <- compute_timings_for_euclidean(n_rows = n_rows, n_cols = n_cols, density = 0.95)

autoplot(bm)

Pearson correlation 

n_rows <- 1000L
n_cols <- 1000L

# helper function to compute timings
compute_timings_for_correlation <- function(
  n_rows = 1000L,
  n_cols = 1000L,
  density = 0.1,
  times = 10L
) {
  sparse_mat <- rsparsematrix(n_rows, n_cols, density)
  dense_mat <- as.matrix(sparse_mat)

  microbenchmark(
    "coop::pcor" = coop::pcor(dense_mat),
    "stats::cor" = cor(dense_mat, method = "pearson"),
    "Rfast::cora" = Rfast::cora(dense_mat, large = TRUE),
    # Disabled due to long execution times
    # "proxy::simil" = proxy::simil(dense_mat, margin = 2, method = "correlation", diag = TRUE),
    "proxyC::simil" = proxyC::simil(sparse_mat, margin = 2, method = "correlation", diag = TRUE),
    times = times
  )
}

1000 rows, 1000 columns, 95% sparsity 

bm <- compute_timings_for_correlation(n_rows = n_rows, n_cols = n_cols, density = 0.05)

autoplot(bm)

1000 rows, 1000 columns, 50% sparsity 

bm <- compute_timings_for_correlation(n_rows = n_rows, n_cols = n_cols, density = 0.5)

autoplot(bm)

1000 rows, 1000 columns, 5% sparsity 

bm <- compute_timings_for_correlation(n_rows = n_rows, n_cols = n_cols, density = 0.95)

autoplot(bm)