tests/testthat/test-fit.R

In UBC-MDS/KmeansR: Kmeans Algorithm Implemented in R

library(testthat)
library(tidyverse)

context("fit")

#helper data
test_df1 <- data.frame("x1" = seq(1:14),
                   "x2" = seq(1:14))
test_df2 <- "This a R package"

K <- -3
n <- 0.5
iter <- 6.3

test_df3 <- data.frame('X1' = c(0,0,0,0,0,0,0,0,0,0), 'X2' = c(1,1,1,1,1,1,1,1,1,1))

#km1 <- fit(test_df3, 1)
set.seed(1)
km2 <- fit(test_df1, 2)

label2 <- c(1,1,1,1,1,1,1,1,2,2,2,2,2,2)
center2 <- data.frame("x1" = c(4.5,11.5), "x2" = c(4.5,11.5))

#tests for exception handling
test_that("The input of samples must be in a list", {
  expect_error(fit(test_df2, 2))
})
  
test_that("The given number of clusters must be a positive integer >= 1", {
  expect_error(fit(test_df1, K))
})
  
test_that("The number of initialization of the centroids must be an integer >=1", {
  expect_error(fit(test_df1, 2, n_iter = n))
})
  
test_that("The number of iterations must be an integer >=1", {
  expect_error(fit(test_df1, 2, max_iter = iter))
})
  
# edge case
#test_that("Centers should match", {
#  expect_equal(km1$centers, c(0,1))
#})
  
# checking centers
test_that("Centers should match", {
  expect_equal(km2$centers, center2)
})
  
# checking labels
test_that("Labels should match", {
  expect_equal(km2$labels, label2)
})