# 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("GBM settings work", {
  skip_if_not_installed("xgboost")
  skip_on_cran()
  seed <- sample(10000000, 1)
  # =====================================
  # checking Gradient Boosting Machine
  # =====================================
  gbmSet <- setGradientBoostingMachine(
    ntrees = c(2, 10),
    nthread = 5,
    earlyStopRound = 25,
    maxDepth = 4,
    minChildWeight = 1,
    learnRate = 0.1,
    alpha = 0,
    lambda = 1,
    seed = seed
  )

  expect_s3_class(gbmSet, "modelSettings")
  expect_equal(gbmSet$fitFunction, "fitRclassifier")
  expect_type(gbmSet$param, "list")

  expect_equal(attr(gbmSet$param, "settings")$modelType, "Xgboost")
  expect_equal(attr(gbmSet$param, "settings")$seed, seed)
  expect_equal(attr(gbmSet$param, "settings")$modelName, "Gradient Boosting Machine")

  expect_equal(attr(gbmSet$param, "settings")$threads, 5)
  expect_equal(attr(gbmSet$param, "settings")$varImpRFunction, "varImpXgboost")
  expect_equal(attr(gbmSet$param, "settings")$trainRFunction, "fitXgboost")
  expect_equal(attr(gbmSet$param, "settings")$predictRFunction, "predictXgboost")

  expect_equal(length(gbmSet$param), 2)

  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$ntrees)))), 2)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$earlyStopRound)))), 1)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$maxDepth)))), 1)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$minChildWeight)))), 1)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$learnRate)))), 1)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$lambda)))), 1)
  expect_equal(length(unique(unlist(lapply(gbmSet$param, function(x) x$alpha)))), 1)
})


test_that("GBM settings expected errors", {
  skip_if_not_installed("xgboost")
  skip_on_cran()
  # =====================================
  # checking Gradient Boosting Machine
  # =====================================

  expect_error(setGradientBoostingMachine(ntrees = -1))
  expect_error(setGradientBoostingMachine(minChildWeight = -1))
  expect_error(setGradientBoostingMachine(maxDepth = 0))
  expect_error(setGradientBoostingMachine(learnRate = -2))
  expect_error(setGradientBoostingMachine(seed = "F"))
  expect_error(setGradientBoostingMachine(lambda = -1))
  expect_error(setGradientBoostingMachine(alpha = -1))
  expect_error(setGradientBoostingMachine(scalePosWeight = -1))
})




test_that("GBM working checks", {
  skip_if_not_installed(c("xgboost", "Eunomia"))
  skip_on_cran()
  skip_if_offline()
  modelSettings <- setGradientBoostingMachine(ntrees = 10,
    maxDepth = 3, learnRate = 0.1)

  fitModel <- fitPlp(
    trainData = trainData,
    modelSettings = modelSettings,
    analysisId = "gbmTest",
    analysisPath = tempdir()
  )

  expect_equal(nrow(fitModel$prediction), nrow(trainData$labels) * 2)
  expect_equal(length(unique(fitModel$prediction$evaluationType)), 2)

  # check prediction between 0 and 1
  expect_gte(min(fitModel$prediction$value), 0)
  expect_lte(max(fitModel$prediction$value), 1)

  expect_equal(class(fitModel$model), "xgb.Booster")

  expect_lte(nrow(fitModel$covariateImportance), trainData$covariateData$covariateRef %>% dplyr::tally() %>% dplyr::pull())

  expect_equal(fitModel$modelDesign$outcomeId, outcomeId)
  expect_equal(fitModel$modelDesign$targetId, 1)
  # TODO check other model design values?

  # test that at least some features have importances that are not zero
  expect_equal(sum(abs(fitModel$covariateImportance$covariateValue)) > 0, TRUE)
})


test_that("GBM without outcomes in early stopping set errors", {
  skip_if_not_installed("xgboost")
  skip_on_cran()
  hyperParameters <- list(
    ntrees = 10,
    earlyStopRound = 2,
    maxDepth = 3,
    learnRate = 0.1,
    minChildWeight = 1,
    scalePosWeight = 1,
    lambda = 1,
    alpha = 0
  )
  observations <- 100
  features <- 10
  data <- createData(
    observations = observations, features = features,
    totalFeatures = 10,
    numCovs = FALSE, outcomeRate = 0.05
  )
  dataMatrix <- Matrix::sparseMatrix(
    i = data$covariates %>% dplyr::pull("rowId"),
    j = data$covariates %>% dplyr::pull("columnId"),
    x = data$covariates %>% dplyr::pull("covariateValue"),
    dims = c(observations, features)
  )
  labels <- data.frame(outcomeCount = data$labels)
  settings <- list(seed = 42, threads = 2)
  expect_error(
    fitXgboost(
      dataMatrix = dataMatrix,
      labels = labels,
      hyperParameters = hyperParameters,
      settings = settings
    ),
    regexp = "* or turn off early stopping"
  )
})