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xgboost/R-package/tests/testthat/test_basic.R  view on Meta::CPAN

  # manually calculate error at the 1st iteration:
  mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
  pred_labels <- max.col(mpred) - 1
  err <- sum(pred_labels != lb)/length(lb)
  expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
})

test_that("train and predict softmax", {
  lb <- as.numeric(iris$Species) - 1
  set.seed(11)
  expect_output(
    bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
                   max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
                   objective = "multi:softmax", num_class=3)
  , "train-merror")
  expect_false(is.null(bst$evaluation_log))
  expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
  expect_equal(bst$niter * 3, xgb.ntree(bst))
  
  pred <- predict(bst, as.matrix(iris[, -5]))
  expect_length(pred, nrow(iris))
  err <- sum(pred != lb)/length(lb)
  expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
})

test_that("train and predict RF", {
  set.seed(11)
  lb <- train$label
  # single iteration
  bst <- xgboost(data = train$data, label = lb, max_depth = 5,
                 nthread = 2, nrounds = 1, objective = "binary:logistic",
                 num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1)
  expect_equal(bst$niter, 1)
  expect_equal(xgb.ntree(bst), 20)
  
  pred <- predict(bst, train$data)
  pred_err <- sum((pred > 0.5) != lb)/length(lb)
  expect_lt(abs(bst$evaluation_log[1, train_error] - pred_err), 10e-6)
  #expect_lt(pred_err, 0.03)
  
  pred <- predict(bst, train$data, ntreelimit = 20)
  pred_err_20 <- sum((pred > 0.5) != lb)/length(lb)
  expect_equal(pred_err_20, pred_err)

  #pred <- predict(bst, train$data, ntreelimit = 1)
  #pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
  #expect_lt(pred_err, pred_err_1)
  #expect_lt(pred_err, 0.08)
})

test_that("train and predict RF with softprob", {
  lb <- as.numeric(iris$Species) - 1
  nrounds <- 15
  set.seed(11)
  bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
                 max_depth = 3, eta = 0.9, nthread = 2, nrounds = nrounds,
                 objective = "multi:softprob", num_class=3, verbose = 0,
                 num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5)
  expect_equal(bst$niter, 15)
  expect_equal(xgb.ntree(bst), 15*3*4)
  # predict for all iterations:
  pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE)
  expect_equal(dim(pred), c(nrow(iris), 3))
  pred_labels <- max.col(pred) - 1
  err <- sum(pred_labels != lb)/length(lb)
  expect_equal(bst$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
  # predict for 7 iterations and adjust for 4 parallel trees per iteration
  pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE, ntreelimit = 7 * 4)
  err <- sum((max.col(pred) - 1) != lb)/length(lb)
  expect_equal(bst$evaluation_log[7, train_merror], err, tolerance = 5e-6)
})

test_that("use of multiple eval metrics works", {
  expect_output(
    bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
                  eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
                  eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
  , "train-error.*train-auc.*train-logloss")
  expect_false(is.null(bst$evaluation_log))
  expect_equal(dim(bst$evaluation_log), c(2, 4))
  expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
})


test_that("training continuation works", {
  dtrain <- xgb.DMatrix(train$data, label = train$label)
  watchlist = list(train=dtrain)
  param <- list(objective = "binary:logistic", max_depth = 2, eta = 1, nthread = 2)

  # for the reference, use 4 iterations at once:
  set.seed(11)
  bst <- xgb.train(param, dtrain, nrounds = 4, watchlist, verbose = 0)
  # first two iterations:
  set.seed(11)
  bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
  # continue for two more:
  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1)
  if (!windows_flag)
    expect_equal(bst$raw, bst2$raw)
  expect_false(is.null(bst2$evaluation_log))
  expect_equal(dim(bst2$evaluation_log), c(4, 2))
  expect_equal(bst2$evaluation_log, bst$evaluation_log)
  # test continuing from raw model data
  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1$raw)
  if (!windows_flag)
    expect_equal(bst$raw, bst2$raw)
  expect_equal(dim(bst2$evaluation_log), c(2, 2))
  # test continuing from a model in file
  xgb.save(bst1, "xgboost.model")
  bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = "xgboost.model")
  if (!windows_flag)
    expect_equal(bst$raw, bst2$raw)
  expect_equal(dim(bst2$evaluation_log), c(2, 2))
})


test_that("xgb.cv works", {
  set.seed(11)
  expect_output(
    cv <- xgb.cv(data = train$data, label = train$label, max_depth = 2, nfold = 5,
                 eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
                 verbose=TRUE)
  , "train-error:")
  expect_is(cv, 'xgb.cv.synchronous')
  expect_false(is.null(cv$evaluation_log))
  expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
  expect_lt(cv$evaluation_log[, min(test_error_std)], 0.004)
  expect_equal(cv$niter, 2)
  expect_false(is.null(cv$folds) && is.list(cv$folds))
  expect_length(cv$folds, 5)
  expect_false(is.null(cv$params) && is.list(cv$params))
  expect_false(is.null(cv$callbacks))
  expect_false(is.null(cv$call))
})

test_that("train and predict with non-strict classes", {
  # standard dense matrix input
  train_dense <- as.matrix(train$data)
  bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
                 eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
  pr0 <- predict(bst, train_dense)
  
  # dense matrix-like input of non-matrix class
  class(train_dense) <- 'shmatrix'
  expect_true(is.matrix(train_dense))
  expect_error(
    bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
                   eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
    , regexp = NA)
  expect_error(pr <- predict(bst, train_dense), regexp = NA)
  expect_equal(pr0, pr)
  
  # dense matrix-like input of non-matrix class with some inheritance