# Tests findHnsw().
# library(BiocNeighbors); library(testthat); source('setup.R'); source("test-find-hnsw.R")
library(RcppHNSW)
REFFUN <- function(X, k, M=16, ef_construction=200, ef_search=10) {
out <- RcppHNSW::hnsw_knn(X, k = k+1, distance = "euclidean", M=M, ef_construction = ef_construction, ef=ef_search)
list(index=out$idx[,-1,drop=FALSE], distance=out$dist[,-1,drop=FALSE])
}
set.seed(7001)
test_that("findHnsw() 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 <- findHnsw(X, k=k)
ref <- REFFUN(X, k=k)
expect_identical(out$index, ref$index)
expect_equal(out$distance, ref$distance, tol=1e-6) # imprecision due to differences between R and C++'s sqrt()?
}
}
})
set.seed(7002)
test_that("findHnsw() works correctly with subsetting", {
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
ref <- findHnsw(X, k=k)
i <- sample(nobs, 20)
sub <- findHnsw(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 <- findHnsw(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 <- findHnsw(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(7003)
test_that("findHnsw() behaves correctly with alternative options", {
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findHnsw(X, k=k)
# Checking what we extract.
out2 <- findHnsw(X, k=k, get.distance=FALSE)
expect_identical(out2$distance, NULL)
expect_identical(out2$index, out$index)
out3 <- findHnsw(X, k=k, get.index=FALSE)
expect_identical(out3$index, NULL)
expect_identical(out3$distance, out$distance)
# Checking precomputation (does not need X).
pre <- buildHnsw(X)
out4 <- findHnsw(k=k, precomputed=pre)
expect_identical(out4, out)
})
set.seed(70031)
test_that("findHnsw() works with Manhattan distances", {
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
# Can't compare directly as L1Space doesn't exist in RcppHNSW.
# We just check that the distance calculation is about-right.
out <- findHnsw(X, k=k, distance="Manhattan")
for (i in seq_len(k)) {
val <- rowSums(abs(X - X[out$index[,i],,drop=FALSE]))
expect_equal(out$distance[,i], val, tol=1e-6)
}
})
set.seed(7003101)
test_that("findHnsw() behaves correctly with Cosine distances", {
# No need for fancy stuff here.
nobs <- 1000
ndim <- 10
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findHnsw(X, k=5, distance="Cosine")
ref <- findHnsw(X / sqrt(rowSums(X^2)), k=5)
expect_identical(out, ref)
})
set.seed(700311)
test_that("findHnsw() behaves correctly when only the last distance is requested", {
nobs <- 500
for (ndim in c(1, 5, 10)) {
for (k in c(1, 5, 20)) {
X <- matrix(runif(nobs * ndim), nrow=nobs)
ref <- findHnsw(X, k=k)
out <- findHnsw(X, k=k, last=1)
expect_identical(out$distance, ref$distance[,k,drop=FALSE])
expect_identical(out$index, ref$index[,k,drop=FALSE])
ref <- findHnsw(X, k=k, distance="Manhattan")
out <- findHnsw(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(70032)
test_that("findHnsw() responds to run-time 'ef.search'", {
nobs <- 1000
ndim <- 10
X <- matrix(runif(nobs * ndim), nrow=nobs)
k <- 7
ref <- findHnsw(X, k=k)
alt <- findHnsw(X, k=k, ef.search=20)
expect_false(identical(alt$index, ref$index))
# As a control:
alt <- findHnsw(X, k=k, ef.search=10)
expect_true(identical(alt$index, ref$index))
})
set.seed(7004)
test_that("findHnsw() behaves correctly with parallelization", {
library(BiocParallel)
nobs <- 1000
ndim <- 10
k <- 5
X <- matrix(runif(nobs * ndim), nrow=nobs)
out <- findHnsw(X, k=k)
# Trying out different types of parallelization.
out1 <- findHnsw(X, k=k, BPPARAM=safeBPParam(2))
expect_identical(out$index, out1$index)
expect_identical(out$distance, out1$distance)
out2 <- findHnsw(X, k=k, BPPARAM=SnowParam(3))
expect_identical(out$index, out2$index)
expect_identical(out$distance, out2$distance)
})
set.seed(7005)
test_that("findHnsw() 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(findHnsw(X, k=0), "positive")
expect_error(findHnsw(X, k=-1), "positive")
# What happens when 'k' > dataset size.
restrict <- 10
expect_warning(out <- findHnsw(X[seq_len(restrict),], k=20), "capped")
expect_warning(ref <- findHnsw(X[seq_len(restrict),], k=restrict-1L), NA)
expect_equal(out, ref)
# What happens when there are no dimensions.
out <- findHnsw(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 with nothing.
expect_identical(findHnsw(X, k=10, get.distance=FALSE, get.index=FALSE), list())
})
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