# 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.
library("testthat")
context("Recalibration")
prediction <- data.frame(
rowId = 1:100,
value = c(runif(20)/30,runif(80)/300),
outcomeCount = c(runif(20)>0.5, runif(80)>0.9)*1,
gender = sample(c(8507, 1111), 100, replace = T),
ageYear = sample(1:100,100, replace = T ),
survivalTime = rep(365,100),
evaluationType = rep('Test', 100)
)
metaData <- list(
modelType = "binary",
targetId = 1,
outcomeId = outcomeId,
timepoint = 365
)
attr(prediction, "metaData") <- metaData
test_that("recalibrationInTheLarge", {
test <- recalibratePlp(prediction, analysisId = 'Analysis_1',
method = 'recalibrationInTheLarge')
testthat::expect_true(sum(test$evaluationType == 'recalibrationInTheLarge') == 100)
})
#'weakRecalibration'
test_that("weakRecalibration", {
test <- recalibratePlp(prediction, analysisId = 'Analysis_1',
method = 'weakRecalibration')
testthat::expect_true(sum(test$evaluationType == 'weakRecalibration') == 100)
})
test_that("recalibratePlpRefit", {
newPop <- plpResult$prediction %>% dplyr::select(-"value") %>% dplyr::filter(.data$evaluationType %in% c('Test','Train'))
attr(newPop, 'metaData') <- list(
targetId = 1,
outcomeId = outcomeId,
restrictPlpDataSettings = PatientLevelPrediction::createRestrictPlpDataSettings(),
populationSettings = PatientLevelPrediction::createStudyPopulationSettings()
)
testRecal <- recalibratePlpRefit(
plpModel = plpResult$model,
newPopulation = newPop,
newData = plpData
)
if(!is.null(testRecal)){
testthat::expect_true(
sum(testRecal$evaluationType == 'recalibrationRefit')>0
)
testthat::expect_s3_class(testRecal, 'data.frame')
}
# add more test...
})
test_that("survival", {
# survival
metaData <- list(
modelType = "survival",
targetId = 1,
outcomeId = outcomeId,
timepoint = 365
)
attr(prediction, "metaData") <- metaData
test <- recalibratePlp(prediction, analysisId = 'Analysis_1',
method = 'weakRecalibration')
testthat::expect_true(sum(test$evaluationType == 'weakRecalibration') == 100)
})
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