# 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.
test_that("prediction inputs", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  # =====================================
  # check prediction
  # =====================================
  expect_error(predictPlp(
    model = NULL, population = population,
    plpData = plpData
  ))
  expect_error(predictPlp(
    model = list(), population = NULL,
    plpData = plpData
  ))
  expect_error(predictPlp(
    model = list(), population = population,
    plpData = NULL
  ))
})


test_that("prediction works", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  # =====================================
  # check prediction
  # =====================================
  pred <- predictPlp(
    plpModel = plpResult$model,
    population = population,
    plpData = plpData
  )
  pred <- pred[order(pred$rowId), ]
  tempPrediction <- plpResult$prediction[order(plpResult$prediction$rowId), ]
  expect_equal(
    nrow(pred),
    nrow(population)
  )

  rowId <- tempPrediction$rowId[tempPrediction$evaluationType == "Test"][1]

  expect_equal(
    pred$value[pred$rowId == rowId],
    tempPrediction$value[
      tempPrediction$evaluationType == "Test" &
        tempPrediction$rowId == rowId
    ]
  )

  # check metaData
  expect_equal(length(names(attr(pred, "metaData"))), 6) # 8 if survivial

  # add single person pred and compare with manual cal

  # add prediction of other models
})

# predict.*


test_that("applyTidyCovariateData", {
  skip_if_not_installed("Eunomia")
  skip_if_offline()
  covariateIds <- plpData$covariateData$covariateRef %>%
    dplyr::select("covariateId") %>%
    dplyr::pull()
  remove <- sample(covariateIds, 10)
  deletedRedundantCovariateIds <- remove[1:5]
  deletedInfrequentCovariateIds <- remove[6:10]

  prepocessSettings <- list(
    normFactors = data.frame(
      covariateId = covariateIds,
      maxValue = rep(0.1, length(covariateIds))
    ),
    deletedRedundantCovariateIds = deletedRedundantCovariateIds,
    deletedInfrequentCovariateIds = deletedInfrequentCovariateIds
  )

  # get covariateSize before
  covariateCount <- plpData$covariateData$covariates %>%
    dplyr::tally() %>%
    dplyr::pull()

  newCovariateData <- applyTidyCovariateData(
    covariateData = plpData$covariateData,
    preprocessSettings = prepocessSettings
  )

  # some covariates removed
  expect_true(newCovariateData$covariates %>% dplyr::tally() %>% dplyr::pull() < covariateCount)

  newCovs <- newCovariateData$covariateRef %>%
    dplyr::select("covariateId") %>%
    dplyr::pull()

  expect_equal(sum(covariateIds[!covariateIds %in% newCovs] %in% remove), 10)
})