tests/testthat/test-sampling.R

In PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

# Copyright 2025 Observational Health Data Sciences and Informatics
#
# This file is part of PatientLevelPrediction
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
testType <- sample(c("none", "underSample", "overSample"), 1)
testNumberOutcomestoNonOutcomes <- 2
testSampleSeed <- sample(10000, 1)

sampleSettingFunc <- function(
    type = testType,
    numberOutcomestoNonOutcomes = testNumberOutcomestoNonOutcomes,
    sampleSeed = testSampleSeed) {
  result <- createSampleSettings(
    type = type,
    numberOutcomestoNonOutcomes = numberOutcomestoNonOutcomes,
    sampleSeed = sampleSeed
  )

  return(result)
}



test_that("createSampleSettings works", {
  sampleSettings <- sampleSettingFunc()
  expect_s3_class(sampleSettings, "sampleSettings")

  sampleFun <- "sameData"
  if (testType == "underSample") {
    sampleFun <- "underSampleData"
  }
  if (testType == "overSample") {
    sampleFun <- "overSampleData"
  }

  expect_equal(
    attr(sampleSettings, "fun"),
    sampleFun
  )

  expect_equal(
    sampleSettings$numberOutcomestoNonOutcomes,
    testNumberOutcomestoNonOutcomes
  )

  # the seed is ignored if sameData
  if (testType == "none") {
    testSampleSeed <- 1
  }
  expect_equal(
    sampleSettings$sampleSeed,
    testSampleSeed
  )
})


test_that("createSampleSettings expected errors", {
  expect_error(
    sampleSettingFunc(numberOutcomestoNonOutcomes = "fsfd")
  )
  expect_error(
    sampleSettingFunc(numberOutcomestoNonOutcomes = -1)
  )

  expect_error(
    sampleSettingFunc(sampleSeed = "fsfd")
  )

  expect_error(
    sampleSettingFunc(type = "fsfd")
  )
  expect_error(
    sampleSettingFunc(type = NULL)
  )
})


test_that("sampleData outputs are correct", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  newTrainData <- trainData
  attr(newTrainData, "metaData")$sampleSettings <- NULL # remove for test

  sampleSettings <- sampleSettingFunc(type = "none")

  sampleData <- sampleData(newTrainData, sampleSettings)

  # make sure metaData captures
  expect_equal(
    length(attr(sampleData, "metaData")),
    length(attr(newTrainData, "metaData")) + 1
  )

  expect_equal(
    attr(sampleData, "metaData")$sampleSettings[[1]],
    sampleSettings
  )

  # check the data is the same:
  expect_equal(
    nrow(sampleData$labels),
    nrow(newTrainData$labels)
  )

  expect_equal(
    nrow(sampleData$folds),
    nrow(newTrainData$folds)
  )

  expect_equal(
    sampleData$covariateData$covariates %>% dplyr::tally() %>% dplyr::pull(),
    newTrainData$covariateData$covariates %>% dplyr::tally() %>% dplyr::pull()
  )
})

# specific functions for sampling


test_that("underSampleData works", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  newTrainData <- trainData

  sampleSettings <- list(
    sampleSeed = 1,
    numberOutcomestoNonOutcomes = 1
  )

  underSampleData <- underSampleData(trainData, sampleSettings)

  expect_true(inherits(underSampleData, "plpData")) # add test based on github issue

  # the sampled data should be smaller...
  expect_true(nrow(underSampleData$labels) <= nrow(newTrainData$labels))

  expect_true(nrow(underSampleData$folds) <= nrow(newTrainData$folds))

  expect_true(
    underSampleData$covariateData$covariates %>%
      dplyr::tally() %>%
      dplyr::pull() <= newTrainData$covariateData$covariates %>%
      dplyr::tally() %>%
      dplyr::pull()
  )

  # perhaps add manual data test
})


test_that("overSampleData works", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  newTrainData <- trainData

  sampleSettings <- list(
    sampleSeed = 1,
    numberOutcomestoNonOutcomes = 0.5
  )

  overSampleData <- overSampleData(newTrainData, sampleSettings)

  expect_true(inherits(overSampleData, "plpData")) # add test based on github issue

  # the sampled data should be smaller...
  expect_true(nrow(overSampleData$labels) >= nrow(newTrainData$labels))

  expect_true(nrow(overSampleData$folds) >= nrow(newTrainData$folds))

  expect_true(
    overSampleData$covariateData$covariates %>%
      dplyr::tally() %>%
      dplyr::pull() >= newTrainData$covariateData$covariates %>%
      dplyr::tally() %>%
      dplyr::pull()
  )

  # perhaps add manual data test
})