# Tests findVptree().
# library(BiocNeighbors); library(testthat); source("setup.R"); source("test-find-vptree.R")
set.seed(1001)
test_that("findVptree() behaves correctly on simple inputs", {
nobs <- 1000
for (ndim in c(1, 5, 10, 20)) {
for (k in c(1, 5, 20)) {
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findVptree(X, k=k)
ref <- refFindKNN(X, k=k)
expect_identical(out$index, ref$index)
expect_equal(out$distance, ref$distance)
}
}
})
set.seed(1002)
test_that("findVptree() works correctly with subsetting", {
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
ref <- findVptree(X, k=k)
i <- sample(nobs, 20)
sub <- findVptree(X, k=k, subset=i)
expect_identical(sub$index, ref$index[i,,drop=FALSE])
expect_identical(sub$distance, ref$distance[i,,drop=FALSE])
i <- rbinom(nobs, 1, 0.5) == 0L
sub <- findVptree(X, k=k, subset=i)
expect_identical(sub$index, ref$index[i,,drop=FALSE])
expect_identical(sub$distance, ref$distance[i,,drop=FALSE])
rownames(X) <- paste0("CELL", seq_len(nobs))
i <- sample(rownames(X), 100)
sub <- findVptree(X, k=k, subset=i)
m <- match(i, rownames(X))
expect_identical(sub$index, ref$index[m,,drop=FALSE])
expect_identical(sub$distance, ref$distance[m,,drop=FALSE])
})
set.seed(1003)
test_that("findVptree() behaves correctly with alternative options", {
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findVptree(X, k=k)
# Checking what we extract.
out2 <- findVptree(X, k=k, get.distance=FALSE)
expect_identical(out2$distance, NULL)
expect_identical(out2$index, out$index)
out3 <- findVptree(X, k=k, get.index=FALSE)
expect_identical(out3$index, NULL)
expect_identical(out3$distance, out$distance)
# Checking precomputation (does not need X).
pre <- buildVptree(X)
out4 <- findVptree(k=k, precomputed=pre)
expect_identical(out4, out)
})
set.seed(1003001)
test_that("findVptree() behaves correctly with Manhattan distances", {
nobs <- 500 # fewer observations, as refFindKNN is a slow brute-force method.
for (ndim in c(1, 5, 10)) {
for (k in c(1, 5, 20)) {
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findVptree(X, k=k, distance="Manhattan")
ref <- refFindKNN(X, k=k, type="manhattan")
expect_identical(out$index, ref$index)
expect_equal(out$distance, ref$distance)
}
}
})
set.seed(1003002)
test_that("findVptree() behaves correctly when only the last distance is requested", {
nobs <- 500 # fewer observations, as refFindKNN is a slow brute-force method.
for (ndim in c(1, 5, 10)) {
for (k in c(1, 5, 20)) {
X <- matrix(runif(nobs * ndim), nrow=nobs)
ref <- findVptree(X, k=k)
out <- findVptree(X, k=k, last=1)
expect_identical(out$distance, ref$distance[,k,drop=FALSE])
expect_identical(out$index, ref$index[,k,drop=FALSE])
ref <- findVptree(X, k=k, distance="Manhattan")
out <- findVptree(X, k=k, last=1, distance="Manhattan")
expect_identical(out$distance, ref$distance[,k,drop=FALSE])
expect_identical(out$index, ref$index[,k,drop=FALSE])
}
}
})
set.seed(1003003)
test_that("findVptree() behaves correctly with Cosine distances", {
# No need for fancy stuff here.
nobs <- 1000
ndim <- 10
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findVptree(X, k=5, distance="Cosine")
ref <- findVptree(X / sqrt(rowSums(X^2)), k=5)
expect_identical(out, ref)
})
set.seed(100301)
test_that("findVptree() behaves correctly with parallelization", {
library(BiocParallel)
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findVptree(X, k=k)
# Trying out different types of parallelization.
out1 <- findVptree(X, k=k, BPPARAM=safeBPParam(2))
expect_identical(out$index, out1$index)
expect_identical(out$distance, out1$distance)
out2 <- findVptree(X, k=k, BPPARAM=SnowParam(3))
expect_identical(out$index, out2$index)
expect_identical(out$distance, out2$distance)
})
set.seed(10031)
test_that("findVptree() raw output behaves correctly", {
nobs <- 1000
ndim <- 10
k <- 7
X <- matrix(runif(nobs * ndim), nrow=nobs)
pre <- buildVptree(X)
out <- findVptree(k=k, precomputed=pre, raw.index=TRUE)
ref <- findVptree(t(bndata(pre)), k=k)
expect_identical(out, ref)
# Behaves with subsetting.
i <- sample(nobs, 20)
out <- findVptree(k=k, precomputed=pre, raw.index=TRUE, subset=i)
ref <- findVptree(t(bndata(pre)), k=k, subset=i)
expect_identical(out, ref)
i <- rbinom(nobs, 1, 0.5) == 0L
out <- findVptree(k=k, precomputed=pre, raw.index=TRUE, subset=i)
ref <- findVptree(t(bndata(pre)), k=k, subset=i)
expect_identical(out, ref)
# Adding row names.
rownames(X) <- paste0("CELL", seq_len(nobs))
preN <- buildVptree(X)
i <- sample(rownames(X), 30)
out <- findVptree(k=k, precomputed=preN, raw.index=TRUE, subset=i)
ref <- findVptree(t(bndata(preN)), k=k, subset=i)
expect_identical(out, ref)
})
set.seed(1004)
test_that("findVptree() behaves correctly with silly inputs", {
nobs <- 1000
ndim <- 10
X <- matrix(runif(nobs * ndim), nrow=nobs)
# What happens when k is not positive.
expect_error(findVptree(X, k=0), "positive")
expect_error(findVptree(X, k=-1), "positive")
# What happens when 'k' > dataset size.
restrict <- 10
expect_warning(out <- findVptree(X[seq_len(restrict),], k=20), "capped")
expect_warning(ref <- findVptree(X[seq_len(restrict),], k=restrict-1L), NA)
expect_equal(out, ref)
# What happens when there are no dimensions.
out <- findVptree(X[,0], k=20)
expect_identical(nrow(out$index), as.integer(nobs))
expect_identical(ncol(out$index), 20L)
expect_identical(dim(out$index), dim(out$distance))
expect_true(all(out$distance==0))
# What happens when we request raw.index without precomputed.
expect_error(findVptree(X, k=20, raw.index=TRUE), "not valid")
# What happens with nothing.
expect_identical(findVptree(X, k=10, get.distance=FALSE, get.index=FALSE), list())
})
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