# Tests the MbkmeansParam class.
# library(bluster); library(testthat); source('test-mbkmeans-param.R')
test_that("MbkmeansParam constructor and utilities work correctly", {
X <- MbkmeansParam(centers=10)
expect_output(show(X), "MbkmeansParam")
expect_output(show(X), "centers: 10")
expect_output(show(X), "batch_size: default")
expect_output(show(X), "BPPARAM: SerialParam")
expect_identical(X[["centers"]], 10L)
X[["centers"]] <- 2L
expect_identical(X[["centers"]], 2L)
X <- MbkmeansParam(centers=log)
expect_true(is.function(X[["centers"]]))
X <- MbkmeansParam(centers=10, batch_size=500)
expect_identical(X[["batch_size"]], 500L)
X[["batch_size"]] <- 100L
expect_identical(X[["batch_size"]], 100L)
})
test_that("clusterRows works correctly", {
m <- matrix(runif(10000), ncol=10)
set.seed(100)
out <- clusterRows(m, MbkmeansParam(5))
expect_true(is.factor(out))
expect_identical(length(out), nrow(m))
expect_identical(nlevels(out), 5L)
# Consistent with the defaults in the original function.
set.seed(100)
ref <- mbkmeans::mbkmeans(t(m), 5)
expect_identical(out, factor(ref$Clusters))
# Trying with fewer cells.
m <- matrix(runif(1000), ncol=10)
set.seed(100)
out <- clusterRows(m, MbkmeansParam(5))
expect_true(is.factor(out))
expect_identical(length(out), nrow(m))
expect_identical(nlevels(out), 5L)
set.seed(100)
ref <- mbkmeans::mbkmeans(t(m), 5)
expect_identical(out, factor(ref$Clusters))
})
test_that("clusterRows responds to the functions and full=TRUE", {
m <- matrix(runif(10000), ncol=10)
set.seed(9999)
out <- clusterRows(m, MbkmeansParam(sqrt))
expect_identical(length(out), nrow(m))
expect_equal(nlevels(out), round(sqrt(nrow(m))))
set.seed(9999)
full <- clusterRows(m, MbkmeansParam(sqrt), full=TRUE)
expect_identical(out, full$cluster)
expect_true(is.list(full$objects))
expect_identical(names(full$objects), "mbkmeans")
})
test_that("clusterRows responds to the options", {
m <- matrix(runif(10000), ncol=10)
set.seed(100000)
suppressWarnings(ref <- mbkmeans::mbkmeans(t(m), 10, batch_size=120, max_iters=5, num_init=10, init_fraction=0.1)$Cluster)
set.seed(100000)
suppressWarnings(out <- clusterRows(m, MbkmeansParam(10, batch_size=120, max_iters=5, num_init=10, init_fraction=0.1)))
expect_identical(factor(ref), out)
})
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