context("Clusterability Test and Print")
library(clusterability)

test_that("clusterabilitytest examples", {
  # Testing clusterability of normals1, a dataset constructed to be nonclusterable.
  data(normals1)
  normals1 <- normals1[, -3]
  expect_gte(clusterabilitytest(normals1, "dip")$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "dip", distance_standardize = "NONE", reduction = "distance")$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "silverman", s_setseed = 123)$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "silverman", distance_standardize = "NONE", reduction = "distance", s_setseed = 123)$pvalue, 0.05)

  expect_gte(clusterabilitytest(normals1, "dip", reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "dip", reduction = "spca", spca_method = "VP")$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_gte(clusterabilitytest(normals1, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP")$pvalue, 0.05)

  # Testing clusterability of normals2
  data(normals2)
  normals2 <- normals2[, -3]
  expect_lte(clusterabilitytest(normals2, "dip")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "dip", reduction = "distance", distance_standardize = "NONE")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "silverman", s_setseed = 123)$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123)$pvalue, 0.05)

  expect_lte(clusterabilitytest(normals2, "dip", reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "dip", reduction = "spca", spca_method = "VP")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals2, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP")$pvalue, 0.05)

  # Testing clusterability of normals3
  normals3 <- normals3[, -3]
  expect_no_error(clusterabilitytest(normals3, "dip"))
  expect_no_error(clusterabilitytest(normals3, "dip", reduction = "distance", distance_standardize = "NONE"))
  expect_no_error(clusterabilitytest(normals3, "silverman", s_setseed = 123))
  expect_no_error(clusterabilitytest(normals3, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123))

  expect_no_error(clusterabilitytest(normals3, "dip", reduction = "spca", spca_method = "EN"))
  expect_no_error(clusterabilitytest(normals3, "dip", reduction = "spca", spca_method = "VP"))
  expect_no_error(clusterabilitytest(normals3, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN"))
  expect_no_error(clusterabilitytest(normals3, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP"))

  # Testing clusterability of normals4
  data(normals4)
  normals4 <- normals4[, -4]
  expect_no_error(clusterabilitytest(normals4, "dip"))
  expect_no_error(clusterabilitytest(normals4, "dip", reduction = "distance", distance_standardize = "NONE"))
  expect_no_error(clusterabilitytest(normals4, "silverman", s_setseed = 123))
  expect_no_error(clusterabilitytest(normals4, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123))

  expect_no_error(clusterabilitytest(normals4, "dip", reduction = "spca", spca_method = "EN"))
  expect_no_error(clusterabilitytest(normals4, "dip", reduction = "spca", spca_method = "VP"))
  expect_no_error(clusterabilitytest(normals4, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN"))
  expect_no_error(clusterabilitytest(normals4, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP"))

  # Testing clusterability of normals5
  data(normals5)
  normals5 <- normals5[, -4]
  expect_lte(clusterabilitytest(normals5, "dip")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "dip", reduction = "distance", distance_standardize = "NONE")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "silverman", s_setseed = 123, s_digits = 20)$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123)$pvalue, 0.05)

  expect_lte(clusterabilitytest(normals5, "dip", reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "dip", reduction = "spca", spca_method = "VP")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(normals5, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP")$pvalue, 0.05)

  # Testing clusterability of iris
  data(iris)
  newiris <- iris[, c(1:4)]
  expect_lte(clusterabilitytest(newiris, "dip")$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "dip", reduction = "distance", distance_standardize = "NONE")$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "silverman", s_setseed = 123)$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123)$pvalue, 0.05)

  expect_lte(clusterabilitytest(newiris, "dip", reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "dip", reduction = "spca", spca_method = "VP")$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_lte(clusterabilitytest(newiris, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP")$pvalue, 0.05)


  # Testing clusterability of cars
  data(cars)

  expect_gte(clusterabilitytest(cars, "dip")$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "dip", reduction = "distance", distance_standardize = "NONE")$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "silverman", s_setseed = 123)$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "silverman", reduction = "distance", distance_standardize = "NONE", s_setseed = 123)$pvalue, 0.05)

  expect_gte(clusterabilitytest(cars, "dip", reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "dip", reduction = "spca", spca_method = "VP")$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "silverman", s_setseed = 123, reduction = "spca", spca_method = "EN")$pvalue, 0.05)
  expect_gte(clusterabilitytest(cars, "silverman", s_setseed = 123, reduction = "spca", spca_method = "VP")$pvalue, 0.05)
})


test_that("mixed data", {
  mixed_data <- data.frame(rnorm(50), as.factor(rbinom(50, 1, 0.5)))
  clusterability_result <- clusterabilitytest(mixed_data, test = "dip", reduction = "distance", distance_metric = "gower", distance_standardize = "none")

  expect_output(print.clusterability(clusterability_result, "gower"))

  expect_error(clusterabilitytest(mixed_data, test = "silverman"), "non-numeric", info = "Expect error for mixed data without using gower metric + distances")
})

test_that("print - errors", {
  iris1 <- data(iris)
  iris1 <- iris[, 1:4]

  cresult <- clusterabilitytest(iris1, test = "Dip", reduction = "PCA")

  expect_error(print.clusterability(iris1), "clusterability", info = "Expect error for incorrect class types")
})

test_that("print - conditionals", {
  iris1 <- data(iris)
  iris1 <- iris[, 1:4]

  miss <- matrix(c(1, 5, 8, NA, 2, 3, 8, 7, 9, 4, 2, 4, 6, 9, 11, 4, 13, 5, 6, 1, 0), ncol = 3)

  cresult <- clusterabilitytest(iris1, test = "Dip", reduction = "PCA", d_simulatepvalue = TRUE, d_reps = 500)
  missresult <- clusterabilitytest(miss, test = "Silverman", reduction = "distance", distance_metric = "euclidean", distance_standardize = "STD", s_m = 1422, s_digits = 7, s_setseed = 1234, s_outseed = TRUE, completecase = TRUE)

  basicdip <- clusterabilitytest(iris1, test = "dip")
  basicsilv <- clusterabilitytest(iris1, test = "silverman")

  # Missing or No Missing
  expect_output(print.clusterability(missresult), "missing at least one")
  expect_output(print.clusterability(cresult), "is complete")

  # Data reduction technique
  expect_output(print.clusterability(cresult), "PCA")
  expect_output(print.clusterability(missresult), "Distance")
  expect_output(print.clusterability(missresult), "euclidean")
  expect_output(print.clusterability(missresult), "STD")

  # Descriptions of data reduction
  expect_output(print.clusterability(missresult), "has mean 0")


  # Test name
  expect_output(print.clusterability(cresult), "Dip Test")
  expect_output(print.clusterability(missresult), "Silverman")

  # Null and Alternative
  expect_output(print.clusterability(cresult), "Null Hypothesis")
  expect_output(print.clusterability(cresult), "Alternative Hypothesis")
  expect_output(print.clusterability(cresult), "p-value")
  expect_output(print.clusterability(cresult), "Dip statistic")

  expect_output(print.clusterability(missresult), "Null Hypothesis")
  expect_output(print.clusterability(missresult), "Alternative Hypothesis")
  expect_output(print.clusterability(missresult), "p-value")
  expect_output(print.clusterability(missresult), "Critical bandwidth")

  # Test options
  expect_output(print.clusterability(cresult), "Test Options")
  expect_output(print.clusterability(missresult), "Test Options")

  # Dip
  expect_output(print.clusterability(cresult), "Monte Carlo")
  expect_output(print.clusterability(cresult), "500")

  expect_output(print.clusterability(basicdip), "Default")

  # Silverman
  expect_output(print.clusterability(missresult), "Seed set")
  expect_output(print.clusterability(missresult), "1234")

  expect_output(print.clusterability(missresult), "1422 bootstrap")

  expect_output(print.clusterability(missresult), "Hall and York")

  expect_output(print.clusterability(missresult), "7 digits")
})