tests/testthat/test-learningCurves.R

In OHDSI/PatientLevelPrediction: Developing patient level prediction using data in the OMOP Common Data Model

# Copyright 2021 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.
context("LearningCurves")


# learningCurve 
learningCurve <- PatientLevelPrediction::createLearningCurve(
  plpData = plpData,
  outcomeId = outcomeId, parallel = F, cores = -1,
  modelSettings = setLassoLogisticRegression(),
  saveDirectory =  file.path(saveLoc, 'lcc'),
  splitSettings = createDefaultSplitSetting(testFraction = 0.2, nfold=2), 
  trainFractions = c(0.6,0.7),
  trainEvents = NULL,
  preprocessSettings = createPreprocessSettings(
    minFraction = 0.001,
    normalize = T
  )
)

test_that("learningCurve output correct", {

  
  testthat::expect_true(is.data.frame(learningCurve))
  testthat::expect_equal(sum(colnames(learningCurve)%in%c(
    "trainFraction",
    "Train_AUROC",
    "nPredictors",
    "Train_populationSize",
    "Train_outcomeCount") ),5)
  
  testthat::expect_equal(learningCurve$trainFraction,  c(0.6,0.7)*100)
  
})

test_that("plotLearningCurve", {
  
  test <- plotLearningCurve(learningCurve = learningCurve, 
    metric = 'AUROC')
  
  # test the plot works
  testthat::expect_s3_class(test, 'ggplot')
  
  test <- plotLearningCurve(learningCurve = learningCurve,
    metric = "AUPRC")
  testthat::expect_s3_class(test, 'ggplot')
  
  test <- plotLearningCurve(learningCurve = learningCurve,
    metric = "sBrier")
  testthat::expect_s3_class(test, 'ggplot')
  
})

test_that("getTrainFractions works", {
  
  learningCurve <- PatientLevelPrediction::createLearningCurve(
    plpData = tinyPlpData,
    outcomeId = outcomeId, parallel = F, cores = -1,
    modelSettings = setLassoLogisticRegression(seed = 42),
    saveDirectory =  file.path(saveLoc, 'lcc'),
    splitSettings = createDefaultSplitSetting(testFraction = 0.33, nfold = 2,
                                              splitSeed = 42), 
    trainEvents = c(150,200),
    preprocessSettings = createPreprocessSettings(
      minFraction = 0.001,
      normalize = T
    )
  )
  testthat::expect_true(is.data.frame(learningCurve))
  testthat::expect_equal(sum(colnames(learningCurve) %in% c(
    "trainFraction",
    "Train_AUROC",
    "nPredictors",
    "Train_populationSize",
    "Train_outcomeCount") ),5)
  
})