test-bins.R
In rbin: Tools for Binning Data

context("test-bins")

test_that("winsorized binning works as expected", {
  k <- rbin_winsorize(mbank, y, age, 10, winsor_rate = 0.05)
  expect_equal(sum(k$bins$bin_count), 4521)
})

test_that("quantile binning works as expected", {
  k <- rbin_quantiles(mbank, y, age, 10)
  expect_equal(sum(k$bins$bin_count), 4521)
})

test_that("manual binning works as expected", {
  k <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))
  expect_equal(sum(k$bins$bin_count), 4521)
})

test_that("equal length binning works as expected", {
  k <- rbin_equal_length(mbank, y, age, 10)
  expect_equal(sum(k$bins$bin_count), 4521)
})

test_that("equal frequency binning works as expected", {
  k <- rbin_equal_freq(mbank, y, age, 10)
  expect_equal(sum(k$bins$bin_count), 4521)
})

test_that("output from rbin_create is as expected as expected", {
  k <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))
  out <- rbin_create(mbank, age, k)
  expect_equal(ncol(out), 27)
})

test_that("output from rbin_factor_combine is as expected", {
  upper <- c("secondary", "tertiary")
  out <- rbin_factor_combine(mbank, education, upper, "upper")
  expect_equal(nlevels(out$education), 3)
})

test_that("output from rbin_factor is as expected", {
  bins <- rbin_factor(mbank, y, education)
  expect_equal(round(sum(bins$bins$iv), 2), 0.05)
})

test_that("output from rbin_factor_create is as expected", {
  upper <- c("secondary", "tertiary")
  out <- rbin_factor_combine(mbank, education, upper, "upper")
  result <- rbin_factor_create(out, education)
  expect_equal(ncol(result), 20)
})

test_that("output from rbin_print is as expected", {

  x <- cat("Binning Summary
---------------------------
Method               Manual 
Response             y 
Predictor            age 
Bins                 10 
Count                4521 
Goods                517 
Bads                 4004 
Entropy              0.5 
Information Value    0.12 


   cut_point bin_count good bad          woe           iv   entropy
1       < 29       410   71 339 -0.483686036 2.547353e-02 0.6649069
2       < 31       313   41 272 -0.154776266 1.760055e-03 0.5601482
3       < 34       567   55 512  0.183985174 3.953685e-03 0.4594187
4       < 36       396   45 351  0.007117468 4.425063e-06 0.5107878
5       < 39       519   47 472  0.259825118 7.008270e-03 0.4383322
6       < 42       431   33 398  0.442938178 1.575567e-02 0.3899626
7       < 46       449   47 402  0.099298221 9.423907e-04 0.4836486
8       < 51       521   40 481  0.439981550 1.881380e-02 0.3907140
9       < 56       445   49 396  0.042587647 1.756117e-04 0.5002548
10     >= 56       470   89 381 -0.592843261 4.564428e-02 0.7001343")

  bins <- rbin_manual(mbank, y, age, c(29, 31, 34, 36, 39, 42, 46, 51, 56))
  expect_output(print(bins), x)

})

test_that("output from rbin_print_custom  is as expected", {

  x <- cat("Binning Summary
---------------------------
Method               Custom 
Response             y 
Predictor            education 
Levels               4 
Count                4521 
Goods                517 
Bads                 4004 
Entropy              0.51 
Information Value    0.05 


      level bin_count good  bad        woe          iv   entropy
1  tertiary      1299  195 1104 -0.3133106 0.031785905 0.6101292
2 secondary      2352  231 2121  0.1702190 0.014113157 0.4633093
3   primary       691   66  625  0.2010906 0.005717878 0.4546110
4   unknown       179   25  154 -0.2289295 0.002265111 0.5833603")

  bins <- rbin_factor(mbank, y, education)
  expect_output(print(bins), x)

})