tests/testthat/test-sNN.R

In dbscan: Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Related Algorithms

library("testthat")
library("dbscan")

context("sNN")

set.seed(665544)
n <- 1000
x <- cbind(
  x = runif(10, 0, 10) + rnorm(n, sd = 0.2),
  y = runif(10, 0, 10) + rnorm(n, sd = 0.2),
  z = runif(10, 0, 10) + rnorm(n, sd = 0.2)
)

## no duplicates first!
x <- x[!duplicated(x),]

rownames(x) <- paste("Object_", 1:nrow(x), sep="")

k <- 5L
nn <- dbscan::sNN(x, k=k, sort = TRUE)

## check dimensions
expect_equal(nn$k, k)
expect_equal(dim(nn$dist), c(nrow(x), k))
expect_equal(dim(nn$id), c(nrow(x), k))

## check visually
#plot(x)
#points(x[nn$id[1,],], col="red", lwd=5)
#points(x[nn$id[2,],], col="green", lwd=5)

## compare with kNN found using distances
nn_d <- dbscan::sNN(dist(x), k, sort = TRUE)

## check visually
#plot(x)
#points(x[nn_d$id[1,],], col="red", lwd=5)
#points(x[nn_d$id[2,],], col="green", lwd=5)

### will aggree minus some tries
expect_equal(nn, nn_d)

## calculate dist internally
nn_d2 <- dbscan::sNN(x, k, search = "dist", sort = TRUE)
expect_equal(nn, nn_d2)

## missing values, but distances are fine
x_na <- x
x_na[c(1,3,5), 1] <- NA
expect_error(dbscan::sNN(x_na, k = 3), regexp = "NA")
res_d1 <- dbscan::sNN(x_na, k = 3, search = "dist")
res_d2 <- dbscan::sNN(dist(x_na), k = 3)
expect_equal(res_d1, res_d2)

## introduce NAs into dist
x_na[c(1,3,5),] <- NA
expect_error(dbscan::sNN(x_na, k = 3), regexp = "NA")
expect_error(dbscan::sNN(x_na, k = 3, search = "dist"), regexp = "NA")
expect_error(dbscan::sNN(dist(x_na), k = 3), regexp = "NA")


## sort and kNN to reduce k
nn10 <- dbscan::sNN(x, k = 10, sort = FALSE)
expect_equal(nn10$sort_shared, FALSE)
expect_error(dbscan::sNN(nn10, k = 11))

nn5 <- dbscan::sNN(nn10, k = 5, sort = TRUE)
nn5_x <- dbscan::sNN(x, k = 5, sort = TRUE)
expect_equal(nn5, nn5_x)

## test with simple data
x <- data.frame(x=1:10)
nn <- dbscan::sNN(x, k = 5)

i <- 1
j_ind <- 1
j <- nn$id[i,j_ind]
intersect(c(i, nn$id[i,]), nn$id[j,])
nn$shared[i,j_ind]

# compute the sNN simularity in R
ss <- matrix(nrow = nrow(x), ncol = nn$k)
for(i in 1:nrow(x))
  for(j_ind in 1:nn$k)
    ss[i, j_ind] <- length(intersect(c(i, nn$id[i,]), nn$id[nn$id[i,j_ind],]))

expect_equivalent(nn$shared, ss)