tests/testthat/test-trainsetBias.R

In nalzok/tree.interpreter: Random Forest Prediction Decomposition and Feature Importance Measure

library(ranger)
library(randomForest)

test_that('trainsetBias works for ranger & classification tree', {
  set.seed(42L)
  trainID <- sample(150, 120)
  rfobj <- ranger(Species ~ ., iris[trainID, ], keep.inbag = TRUE)
  tidy.RF <- tidyRF(rfobj, iris[trainID, -5], iris[trainID, 5])

  trainset.bias <- trainsetBias(tidy.RF)
  expect_equal(dim(trainset.bias), c(1, 3))
  expect_equal(dimnames(trainset.bias),
               list('Bias', levels(iris$Species)))
})

test_that('trainsetBias works for randomForest & classification tree', {
  set.seed(42L)
  trainID <- sample(150, 120)
  rfobj <- randomForest(Species ~ ., iris[trainID, ], keep.inbag = TRUE)
  tidy.RF <- tidyRF(rfobj, iris[trainID, -5], iris[trainID, 5])

  trainset.bias <- trainsetBias(tidy.RF)
  expect_equal(dim(trainset.bias), c(1, 3))
  expect_equal(dimnames(trainset.bias),
               list('Bias', levels(iris$Species)))
})

test_that('trainsetBias works for ranger & regression tree', {
  set.seed(42L)
  trainID <- sample(32, 25)
  rfobj <- ranger(mpg ~ ., mtcars[trainID, ], keep.inbag = TRUE)
  tidy.RF <- tidyRF(rfobj, mtcars[trainID, -1], mtcars[trainID, 1])

  trainset.bias <- trainsetBias(tidy.RF)
  expect_equal(dim(trainset.bias), c(1, 1))
  expect_equal(dimnames(trainset.bias),
               list('Bias', 'Response'))
})

test_that('trainsetBias works for randomForest & regression tree', {
  set.seed(42L)
  trainID <- sample(32, 25)
  rfobj <- randomForest(mpg ~ ., mtcars[trainID, ], keep.inbag = TRUE)
  tidy.RF <- tidyRF(rfobj, mtcars[trainID, -1], mtcars[trainID, 1])

  trainset.bias <- trainsetBias(tidy.RF)
  expect_equal(dim(trainset.bias), c(1, 1))
  expect_equal(dimnames(trainset.bias),
               list('Bias', 'Response'))
})