4e9fad74eb
* Use built-in label when xgb.DMatrix is given to xgb.cv() * Add a test * Fix test * Bump version number
279 lines
12 KiB
R
279 lines
12 KiB
R
require(xgboost)
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context("basic functions")
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data(agaricus.train, package='xgboost')
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data(agaricus.test, package='xgboost')
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train <- agaricus.train
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test <- agaricus.test
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set.seed(1994)
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# disable some tests for Win32
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windows_flag = .Platform$OS.type == "windows" &&
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.Machine$sizeof.pointer != 8
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solaris_flag = (Sys.info()['sysname'] == "SunOS")
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test_that("train and predict binary classification", {
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nrounds = 2
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expect_output(
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bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = nrounds, objective = "binary:logistic")
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, "train-error")
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expect_equal(class(bst), "xgb.Booster")
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expect_equal(bst$niter, nrounds)
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expect_false(is.null(bst$evaluation_log))
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expect_equal(nrow(bst$evaluation_log), nrounds)
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expect_lt(bst$evaluation_log[, min(train_error)], 0.03)
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pred <- predict(bst, test$data)
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expect_length(pred, 1611)
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pred1 <- predict(bst, train$data, ntreelimit = 1)
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expect_length(pred1, 6513)
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err_pred1 <- sum((pred1 > 0.5) != train$label)/length(train$label)
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err_log <- bst$evaluation_log[1, train_error]
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expect_lt(abs(err_pred1 - err_log), 10e-6)
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})
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test_that("train and predict softprob", {
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lb <- as.numeric(iris$Species) - 1
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set.seed(11)
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expect_output(
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
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objective = "multi:softprob", num_class=3)
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, "train-merror")
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expect_false(is.null(bst$evaluation_log))
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expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
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expect_equal(bst$niter * 3, xgb.ntree(bst))
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pred <- predict(bst, as.matrix(iris[, -5]))
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expect_length(pred, nrow(iris) * 3)
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# row sums add up to total probability of 1:
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expect_equal(rowSums(matrix(pred, ncol=3, byrow=TRUE)), rep(1, nrow(iris)), tolerance = 1e-7)
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# manually calculate error at the last iteration:
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mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
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expect_equal(as.numeric(t(mpred)), pred)
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pred_labels <- max.col(mpred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
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# manually calculate error at the 1st iteration:
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mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
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pred_labels <- max.col(mpred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
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})
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test_that("train and predict softmax", {
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lb <- as.numeric(iris$Species) - 1
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set.seed(11)
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expect_output(
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.5, nthread = 2, nrounds = 5,
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objective = "multi:softmax", num_class=3)
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, "train-merror")
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expect_false(is.null(bst$evaluation_log))
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expect_lt(bst$evaluation_log[, min(train_merror)], 0.025)
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expect_equal(bst$niter * 3, xgb.ntree(bst))
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pred <- predict(bst, as.matrix(iris[, -5]))
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expect_length(pred, nrow(iris))
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err <- sum(pred != lb)/length(lb)
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expect_equal(bst$evaluation_log[5, train_merror], err, tolerance = 5e-6)
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})
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test_that("train and predict RF", {
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set.seed(11)
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lb <- train$label
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# single iteration
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bst <- xgboost(data = train$data, label = lb, max_depth = 5,
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nthread = 2, nrounds = 1, objective = "binary:logistic",
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num_parallel_tree = 20, subsample = 0.6, colsample_bytree = 0.1)
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expect_equal(bst$niter, 1)
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expect_equal(xgb.ntree(bst), 20)
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pred <- predict(bst, train$data)
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pred_err <- sum((pred > 0.5) != lb)/length(lb)
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expect_lt(abs(bst$evaluation_log[1, train_error] - pred_err), 10e-6)
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#expect_lt(pred_err, 0.03)
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pred <- predict(bst, train$data, ntreelimit = 20)
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pred_err_20 <- sum((pred > 0.5) != lb)/length(lb)
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expect_equal(pred_err_20, pred_err)
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#pred <- predict(bst, train$data, ntreelimit = 1)
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#pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
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#expect_lt(pred_err, pred_err_1)
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#expect_lt(pred_err, 0.08)
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})
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test_that("train and predict RF with softprob", {
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lb <- as.numeric(iris$Species) - 1
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nrounds <- 15
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set.seed(11)
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bst <- xgboost(data = as.matrix(iris[, -5]), label = lb,
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max_depth = 3, eta = 0.9, nthread = 2, nrounds = nrounds,
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objective = "multi:softprob", num_class=3, verbose = 0,
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num_parallel_tree = 4, subsample = 0.5, colsample_bytree = 0.5)
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expect_equal(bst$niter, 15)
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expect_equal(xgb.ntree(bst), 15*3*4)
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# predict for all iterations:
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pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE)
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expect_equal(dim(pred), c(nrow(iris), 3))
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pred_labels <- max.col(pred) - 1
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err <- sum(pred_labels != lb)/length(lb)
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expect_equal(bst$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
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# predict for 7 iterations and adjust for 4 parallel trees per iteration
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pred <- predict(bst, as.matrix(iris[, -5]), reshape=TRUE, ntreelimit = 7 * 4)
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err <- sum((max.col(pred) - 1) != lb)/length(lb)
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expect_equal(bst$evaluation_log[7, train_merror], err, tolerance = 5e-6)
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})
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test_that("use of multiple eval metrics works", {
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expect_output(
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bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic",
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eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
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, "train-error.*train-auc.*train-logloss")
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expect_false(is.null(bst$evaluation_log))
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expect_equal(dim(bst$evaluation_log), c(2, 4))
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expect_equal(colnames(bst$evaluation_log), c("iter", "train_error", "train_auc", "train_logloss"))
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})
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test_that("training continuation works", {
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dtrain <- xgb.DMatrix(train$data, label = train$label)
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watchlist = list(train=dtrain)
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param <- list(objective = "binary:logistic", max_depth = 2, eta = 1, nthread = 2)
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# for the reference, use 4 iterations at once:
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set.seed(11)
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bst <- xgb.train(param, dtrain, nrounds = 4, watchlist, verbose = 0)
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# first two iterations:
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set.seed(11)
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bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0)
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# continue for two more:
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1)
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if (!windows_flag && !solaris_flag)
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expect_equal(bst$raw, bst2$raw)
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expect_false(is.null(bst2$evaluation_log))
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expect_equal(dim(bst2$evaluation_log), c(4, 2))
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expect_equal(bst2$evaluation_log, bst$evaluation_log)
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# test continuing from raw model data
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = bst1$raw)
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if (!windows_flag && !solaris_flag)
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expect_equal(bst$raw, bst2$raw)
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expect_equal(dim(bst2$evaluation_log), c(2, 2))
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# test continuing from a model in file
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xgb.save(bst1, "xgboost.model")
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bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0, xgb_model = "xgboost.model")
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if (!windows_flag && !solaris_flag)
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expect_equal(bst$raw, bst2$raw)
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expect_equal(dim(bst2$evaluation_log), c(2, 2))
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})
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test_that("xgb.cv works", {
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set.seed(11)
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expect_output(
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cv <- xgb.cv(data = train$data, label = train$label, max_depth = 2, nfold = 5,
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eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
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verbose=TRUE)
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, "train-error:")
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expect_is(cv, 'xgb.cv.synchronous')
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expect_false(is.null(cv$evaluation_log))
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expect_lt(cv$evaluation_log[, min(test_error_mean)], 0.03)
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expect_lt(cv$evaluation_log[, min(test_error_std)], 0.008)
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expect_equal(cv$niter, 2)
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expect_false(is.null(cv$folds) && is.list(cv$folds))
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expect_length(cv$folds, 5)
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expect_false(is.null(cv$params) && is.list(cv$params))
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expect_false(is.null(cv$callbacks))
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expect_false(is.null(cv$call))
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})
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test_that("xgb.cv works with stratified folds", {
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dtrain <- xgb.DMatrix(train$data, label = train$label)
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set.seed(314159)
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cv <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
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eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
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verbose=TRUE, stratified = FALSE)
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set.seed(314159)
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cv2 <- xgb.cv(data = dtrain, max_depth = 2, nfold = 5,
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eta = 1., nthread = 2, nrounds = 2, objective = "binary:logistic",
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verbose=TRUE, stratified = TRUE)
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# Stratified folds should result in a different evaluation logs
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expect_true(all(cv$evaluation_log[, test_error_mean] != cv2$evaluation_log[, test_error_mean]))
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})
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test_that("train and predict with non-strict classes", {
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# standard dense matrix input
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train_dense <- as.matrix(train$data)
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bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
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pr0 <- predict(bst, train_dense)
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# dense matrix-like input of non-matrix class
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class(train_dense) <- 'shmatrix'
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expect_true(is.matrix(train_dense))
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expect_error(
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bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
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, regexp = NA)
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expect_error(pr <- predict(bst, train_dense), regexp = NA)
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expect_equal(pr0, pr)
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# dense matrix-like input of non-matrix class with some inheritance
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class(train_dense) <- c('pphmatrix','shmatrix')
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expect_true(is.matrix(train_dense))
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expect_error(
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bst <- xgboost(data = train_dense, label = train$label, max_depth = 2,
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eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic", verbose = 0)
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, regexp = NA)
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expect_error(pr <- predict(bst, train_dense), regexp = NA)
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expect_equal(pr0, pr)
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# when someone inhertis from xgb.Booster, it should still be possible to use it as xgb.Booster
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class(bst) <- c('super.Booster', 'xgb.Booster')
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expect_error(pr <- predict(bst, train_dense), regexp = NA)
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expect_equal(pr0, pr)
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})
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test_that("max_delta_step works", {
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dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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watchlist <- list(train = dtrain)
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param <- list(objective = "binary:logistic", eval_metric="logloss", max_depth = 2, nthread = 2, eta = 0.5)
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nrounds = 5
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# model with no restriction on max_delta_step
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bst1 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1)
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# model with restricted max_delta_step
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bst2 <- xgb.train(param, dtrain, nrounds, watchlist, verbose = 1, max_delta_step = 1)
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# the no-restriction model is expected to have consistently lower loss during the initial interations
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expect_true(all(bst1$evaluation_log$train_logloss < bst2$evaluation_log$train_logloss))
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expect_lt(mean(bst1$evaluation_log$train_logloss)/mean(bst2$evaluation_log$train_logloss), 0.8)
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})
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test_that("colsample_bytree works", {
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# Randomly generate data matrix by sampling from uniform distribution [-1, 1]
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set.seed(1)
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train_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
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train_y <- as.numeric(rowSums(train_x) > 0)
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test_x <- matrix(runif(1000, min = -1, max = 1), ncol = 100)
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test_y <- as.numeric(rowSums(test_x) > 0)
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colnames(train_x) <- paste0("Feature_", sprintf("%03d", 1:100))
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colnames(test_x) <- paste0("Feature_", sprintf("%03d", 1:100))
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dtrain <- xgb.DMatrix(train_x, label = train_y)
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dtest <- xgb.DMatrix(test_x, label = test_y)
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watchlist <- list(train = dtrain, eval = dtest)
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# Use colsample_bytree = 0.01, so that roughly one out of 100 features is
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# chosen for each tree
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param <- list(max_depth = 2, eta = 0, silent = 1, nthread = 2,
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colsample_bytree = 0.01, objective = "binary:logistic",
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eval_metric = "auc")
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set.seed(2)
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bst <- xgb.train(param, dtrain, nrounds = 100, watchlist, verbose = 0)
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xgb.importance(model = bst)
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# If colsample_bytree works properly, a variety of features should be used
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# in the 100 trees
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expect_gte(nrow(xgb.importance(model = bst)), 30)
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})
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