#115: avoid irlba code path unless necessary

R/init.R

@@ -37,7 +37,17 @@ rspectra_laplacian_eigenmap <- function(A, ndim = 2, verbose = FALSE) {
   # and because A is symmetric, they're equivalent
   M <- A / colSums(A)
   res <- rspectra_eigs_asym(M, ndim)
-  if (is.null(res) || ncol(res$vectors) < ndim) {
+  if (is.null(res) ||
+    !is.list(res) ||
+    !"vectors" %in% names(res) ||
+    is.null(res$vectors) ||
+    tryCatch(
+      is.na(ncol(res$vectors)),
+      error = function(e) {
+        TRUE
+      }
+    ) ||
+    ncol(res$vectors) < ndim) {
     message(
       "Laplacian Eigenmap failed to converge, ",
       "using random initialization instead"
@@ -125,7 +135,17 @@ rspectra_normalized_laplacian_init <- function(A, ndim = 2, verbose = FALSE) {
   L <- form_normalized_laplacian(A)
   res <- rspectra_eigs_sym(L, ndim, verbose = verbose)
 
-  if (is.null(res) || ncol(res$vectors) < ndim) {
+  if (is.null(res) ||
+      !is.list(res) ||
+      !"vectors" %in% names(res) ||
+      is.null(res$vectors) ||
+      tryCatch(
+        is.na(ncol(res$vectors)),
+        error = function(e) {
+          TRUE
+        }
+      ) ||
+      ncol(res$vectors) < ndim) {
     message(
       "Spectral initialization failed to converge, ",
       "using random initialization instead"
@@ -215,16 +235,26 @@ irlba_normalized_laplacian_init <- function(A, ndim = 2, verbose = FALSE) {
   # Laplacian and look for largest eigenvalues
   L <- form_modified_laplacian(A)
   res <- irlba_eigs_sym(L, ndim, smallest = FALSE)
-  # shift back the eigenvalues
-  res$values <- 2.0 - res$values
-  if (is.null(res) || ncol(res$vectors) < ndim) {
+  if (is.null(res) ||
+    !is.list(res) ||
+    !"vectors" %in% names(res) ||
+    is.null(res$vectors) ||
+    tryCatch(
+      is.na(ncol(res$vectors)),
+      error = function(e) {
+        TRUE
+      }
+    ) ||
+    ncol(res$vectors) < ndim) {
     message(
       "Spectral initialization failed to converge, ",
       "using random initialization instead"
     )
     n <- nrow(A)
     return(rand_init(n, ndim))
   }
+  # shift back the eigenvalues
+  res$values <- 2.0 - res$values
   sort_eigenvectors(res, ndim)
 }
 

R/rspectra_init.R

@@ -28,7 +28,17 @@ rspectra_eigs_sym <- function(L, ndim, verbose = FALSE) {
         tsmessage("RSpectra calculation failed, retrying with shifted")
       }
     ))
-  if (is.null(res) || ncol(res$vectors) < ndim) {
+  if (is.null(res) ||
+    !is.list(res) ||
+    !"vectors" %in% names(res) ||
+    is.null(res$vectors) ||
+    tryCatch(
+      is.na(ncol(res$vectors)),
+      error = function(e) {
+        TRUE
+      }
+    ) ||
+    ncol(res$vectors) < ndim) {
     suppressWarnings(res <- tryCatch(
       RSpectra::eigs_sym(
         L,

R/uwot.R

@@ -4000,6 +4000,8 @@ save_uwot <- function(model, file, unload = FALSE, verbose = FALSE) {
 #'   used during loading of the model. This directory cannot be removed until
 #'   this model has been unloaded by using \code{\link{unload_uwot}}.
 #' @examples
+#' library(RSpectra)
+#'
 #' iris_train <- iris[c(1:10, 51:60), ]
 #' iris_test <- iris[100:110, ]
 #'

tests/testthat/test_errors.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("Input validation")
 
 expect_error(umap(X = list(X = "bad", Y = "wrong", nn = "what")), "data format")

tests/testthat/test_neighbors.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("neighbors")
 
 i10nn4dist <- matrix(c(

tests/testthat/test_normlaplacian.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("normalized laplacian")
 
 # this exists as a separate file only because it's easier to comment out as
@@ -29,12 +30,14 @@ test_that("normalized laplacian", {
   res <- normalized_laplacian_init(sparse_m)
   expect_equal(abs(res), abs_expected_norm_lap, tolerance = 0.2)
 
-  # ensure irlba code path gets tested
-  res <- irlba_tsvd_normalized_laplacian_init(sparse_m)
-  expect_equal(abs(res), abs_expected_norm_lap, tolerance = 0.2)
+  # 115: ensure irlba code path gets tested if we can avoid Matrix ABI issue
+  if (Matrix::Matrix.Version()$package >= "1.6.3") {
+    res <- irlba_tsvd_normalized_laplacian_init(sparse_m)
+    expect_equal(abs(res), abs_expected_norm_lap, tolerance = 0.2)
 
-  res <- irlba_normalized_laplacian_init(sparse_m)
-  expect_equal(abs(res), abs_expected_norm_lap, tolerance = 0.2)
+    res <- irlba_normalized_laplacian_init(sparse_m)
+    expect_equal(abs(res), abs_expected_norm_lap, tolerance = 0.2)
+  }
 })
 
 test_that("laplacian eigenmap", {

tests/testthat/test_saveload.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("load/save model")
 
 test_that("can save and load simple model", {

tests/testthat/test_similarity_graph.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("similarity graph")
 
 # #96: more convenient way to just get the high dimensional similarity graph

tests/testthat/test_spectral.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("Spectral")
 
 test_that("1 dimensional output gives a matrix", {

tests/testthat/test_transform.R

@@ -1,4 +1,5 @@
 library(uwot)
+library(RSpectra)
 context("Transform")
 
 diagonal1s <- as(Matrix::drop0(diag(1, nrow(V_asymm), ncol(V_asymm))), "generalMatrix")