predict() on a umap object with n_components=1 gets two errors -- Looks like missing drop=F

[JenniferSLyon]

Based on the example in the vignette:

iris.data = iris[, grep("Sepal|Petal", colnames(iris))]
iris.labels = iris[, "Species"]
custom.config = umap.defaults
custom.config$n_components = 1
iris.umap = umap(iris.data, config=custom.config)

set.seed(19)
iris.wnoise = iris.data + matrix(rnorm(150*40, 0, 0.1), ncol=4)
colnames(iris.wnoise) = colnames(iris.data)
iris.wnoise.umap = predict(iris.umap, iris.wnoise)

Error in colMeans(embedding[knn.indexes[i, ], ]) :
'x' must be an array of at least two dimensions

traceback()
6: stop("'x' must be an array of at least two dimensions")
5: colMeans(embedding[knn.indexes[i, ], ])
4: make.initial.spectator.embedding(umap$layout, spectator.knn$indexes)
3: implementations[[method]](object, data)
2: predict.umap(iris.umap, iris.wnoise)
1: predict(iris.umap, iris.wnoise)

Looking at make.initial.spectator.embedding, it looks like a drop=F
is missing (line with ## <-----):

trace(umap:::make.initial.spectator.embedding, edit=T)

function (embedding, knn.indexes)
{
result = matrix(0, nrow = nrow(knn.indexes), ncol = ncol(embedding))
rownames(result) = rownames(knn.indexes)
knn.indexes = knn.indexes[, 2:ncol(knn.indexes), drop = FALSE]
for (i in 1:nrow(result)) {
result[i, ] = colMeans(embedding[knn.indexes[i, ], ,
drop = FALSE]) ## <------- added drop = FALSE
}
result
}

This change leads to a new error:

iris.wnoise.umap = predict(iris.umap, iris.wnoise)
Error in temp.embedding[, temp.index] <- result[, indeces[i]] :
incorrect number of subscripts on matrix

traceback()
4: naive.simplicial.set.embedding(graph, embedding, config,
fix.observations = V)
3: implementations[[method]](object, data)
2: predict.umap(iris.umap, iris.wnoise)
1: predict(iris.umap, iris.wnoise)

And it also looks like a drop=F is missing in naive.simlicial.set.embedding:

naive.simplicial.set.embedding
function (g, embedding, config, fix.observations = NULL)
{
if (config$n_epochs == 0) {
return(embedding)
}
result = t(embedding)
gmax = max(g$coo[, "value"])
g$coo[g$coo[, "value"] < gmax/config$n_epochs, "value"] = 0
g = reduce.coo(g)
eps = cbind(g$coo, eps = make.epochs.per.sample(g$coo[, "value"],
config$n_epochs))
if (is.null(fix.observations)) {
result = naive.optimize.embedding(result, config, eps)
}
else {
eps = eps[eps[, "from"] > fix.observations, ]
indeces = seq(fix.observations + 1, ncol(result))
seeds = column.seeds(result[, indeces, drop = FALSE],
key = config$transform_state)
temp.index = fix.observations + 1
temp.embedding = result[, seq_len(fix.observations +
1), drop = FALSE] ## <----- added drop=FALSE
temp.eps = split.data.frame(eps, eps[, "from"])
for (i in seq_along(indeces)) {
temp.embedding[, temp.index] = result[, indeces[i]]
set.seed(seeds[i])
i.eps = temp.eps[[as.character(indeces[i])]]
if (!is.null(i.eps)) {
i.eps[, "from"] = temp.index
temp.result = naive.optimize.embedding(temp.embedding,
config, i.eps)
}
result[, indeces[i]] = temp.result[, temp.index]
}
}
colnames(result) = g$names
t(result)
}

With these two changes predict() now runs without error and returns values. I am not sure if there are deeper issues with predicting with n_components=1, or if these two changes are sufficient.

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.3 LTS

Matrix products: default
BLAS: /mnt/drive2/r-project/R-3.6.1/lib/libRblas.so
LAPACK: /mnt/drive2/r-project/R-3.6.1/lib/libRlapack.so

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

attached base packages:
[1] stats graphics utils datasets grDevices methods base

other attached packages:
[1] umap_0.2.3 colorspace_1.4-1

loaded via a namespace (and not attached):
[1] compiler_3.6.1 Matrix_1.2-17 tools_3.6.1 reticulate_1.13
[5] Rcpp_1.0.2 RSpectra_0.15-0 grid_3.6.1 jsonlite_1.6
[9] openssl_1.4.1 lattice_0.20-38 askpass_1.1

[tkonopka]

Thanks for pointing that out. Yes, those two drop=FALSE will fix this. Would you like to make a pull request, or should I go ahead and edit?

[JenniferSLyon]

You can just go ahead and edit.