tests/testthat/test_advancePipeline.R

In NADIA: NA Data Imputation Algorithms

library(testthat)
library(NADIA)
context("Testing advance imputation pipeline")

test_that("Creting PipeLines", {
  # Amelia
  expect_is(PipeOpAmelia$new(), "PipeOpImpute")

  # mice
  expect_is(PipeOpMice$new(), "PipeOpImpute")

  # mice_A
  expect_is(PipeOpMice_A$new(), "PipeOpImpute")

  # missForest
  expect_is(PipeOpmissForest$new(), "PipeOpImpute")

  # missRanger
  expect_is(PipeOpmissRanger$new(), "PipeOpImpute")

  # softImpute
  expect_is(PipeOpSoftImpute$new(), "PipeOpImpute")

  # VIM_HD
  expect_is(PipeOpVIM_HD$new(), "PipeOpImpute")

  # VIM_IRMI
  expect_is(PipeOpVIM_IRMI$new(), "PipeOpImpute")

  # VIM_KNN
  expect_is(PipeOpVIM_kNN$new(), "PipeOpImpute")

  # VIM_rergrImp
  expect_is(PipeOpVIM_regrImp$new(), "PipeOpImpute")

  # missMDA FMAD...  A
  expect_is(PipeOpMissMDA_PCA_MCA_FMAD_A$new(),"PipeOpImpute")

  # missMDA MFA
  expect_is(PipeOpMissMDA_MFA_A$new(),"PipeOpImpute")

})

raw_data <- data.frame(
  a = (sample(c("red", "yellow", "blue", NA), 1000, replace = TRUE)),
  b = 1:1000,
  c = (sample(c("YES", "NO", NA), 1000, replace = TRUE)),
  d = runif(1000, 1, 10),
  e = (sample(c("YES", "NO"), 1000, replace = TRUE)),
  f = (sample(c("male", "female", "trans", "other", NA), 1000, replace = TRUE)))
raw_data$b[sample(1:1000, 200)] <- NA
raw_data$a <- as.factor(raw_data$a)
raw_data$c <- as.factor(raw_data$c)
raw_data$e <- as.factor(raw_data$e)
raw_data$f <- as.factor(raw_data$f)

task <- TaskClassif$new("test", raw_data, "e")

test_that("Adavance pipe simple imputation ", {

  # Amelia
  expect_equal(PipeOpAmelia$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))


  # mice_A
  expect_equal(as.integer(PipeOpMice_A$new()$train(list(tsk("pima")))[[1]]$missings()), rep(0, 9))

  # mice
  expect_equal(as.integer(PipeOpMice$new()$train(list(tsk("pima")))[[1]]$missings()), rep(0, 9))

  # missForest
  expect_equal(PipeOpmissForest$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # missRanger
  expect_equal(PipeOpmissRanger$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # softImpute
  expect_equal(as.integer(PipeOpSoftImpute$new()$train(list(tsk("pima")))[[1]]$missings()), rep(0, 9))

  # VIM_HD
  expect_equal(PipeOpVIM_HD$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # VIM_kNN
  expect_equal(PipeOpVIM_kNN$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # VIM_IRMI
  expect_equal(PipeOpVIM_IRMI$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # VIM_regrImp
  expect_equal(PipeOpVIM_regrImp$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # missMDA FMAD ...
  expect_equal(PipeOpMissMDA_PCA_MCA_FMAD$new(optimize_ncp = F)$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # missMDA MFA
  expect_equal(PipeOpMissMDA_MFA$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # missMDA FMAD ...  A
  expect_equal(PipeOpMissMDA_PCA_MCA_FMAD_A$new(optimize_ncp = F)$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

  # missMDA MFA A
  expect_equal(PipeOpMissMDA_MFA_A$new()$train(list(task))[[1]]$missings(), c("e" = 0, "d" = 0, "a" = 0, "b" = 0, "c" = 0, "f" = 0))

})