1f9a57d17b
Previously, we use `libsvm` as default when format is not specified. However, the dmlc data parser is not particularly robust against errors, and the most common type of error is undefined format. Along with which, we will recommend users to use other data loader instead. We will continue the maintenance of the parsers as it's currently used for many internal tests including federated learning.
218 lines
7.0 KiB
R
218 lines
7.0 KiB
R
library(Matrix)
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context("testing xgb.DMatrix functionality")
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data(agaricus.test, package = "xgboost")
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test_data <- agaricus.test$data[1:100, ]
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test_label <- agaricus.test$label[1:100]
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test_that("xgb.DMatrix: basic construction", {
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# from sparse matrix
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dtest1 <- xgb.DMatrix(test_data, label = test_label)
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# from dense matrix
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dtest2 <- xgb.DMatrix(as.matrix(test_data), label = test_label)
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expect_equal(getinfo(dtest1, "label"), getinfo(dtest2, "label"))
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expect_equal(dim(dtest1), dim(dtest2))
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# from dense integer matrix
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int_data <- as.matrix(test_data)
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storage.mode(int_data) <- "integer"
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dtest3 <- xgb.DMatrix(int_data, label = test_label)
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expect_equal(dim(dtest1), dim(dtest3))
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n_samples <- 100
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X <- cbind(
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x1 = sample(x = 4, size = n_samples, replace = TRUE),
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x2 = sample(x = 4, size = n_samples, replace = TRUE),
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x3 = sample(x = 4, size = n_samples, replace = TRUE)
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)
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X <- matrix(X, nrow = n_samples)
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y <- rbinom(n = n_samples, size = 1, prob = 1 / 2)
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fd <- xgb.DMatrix(X, label = y, missing = 1)
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dgc <- as(X, "dgCMatrix")
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fdgc <- xgb.DMatrix(dgc, label = y, missing = 1.0)
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dgr <- as(X, "dgRMatrix")
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fdgr <- xgb.DMatrix(dgr, label = y, missing = 1)
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params <- list(tree_method = "hist")
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bst_fd <- xgb.train(
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params, nrounds = 8, fd, watchlist = list(train = fd)
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)
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bst_dgr <- xgb.train(
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params, nrounds = 8, fdgr, watchlist = list(train = fdgr)
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)
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bst_dgc <- xgb.train(
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params, nrounds = 8, fdgc, watchlist = list(train = fdgc)
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)
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raw_fd <- xgb.save.raw(bst_fd, raw_format = "ubj")
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raw_dgr <- xgb.save.raw(bst_dgr, raw_format = "ubj")
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raw_dgc <- xgb.save.raw(bst_dgc, raw_format = "ubj")
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expect_equal(raw_fd, raw_dgr)
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expect_equal(raw_fd, raw_dgc)
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})
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test_that("xgb.DMatrix: saving, loading", {
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# save to a local file
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dtest1 <- xgb.DMatrix(test_data, label = test_label)
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tmp_file <- tempfile('xgb.DMatrix_')
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on.exit(unlink(tmp_file))
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expect_true(xgb.DMatrix.save(dtest1, tmp_file))
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# read from a local file
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expect_output(dtest3 <- xgb.DMatrix(tmp_file), "entries loaded from")
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expect_output(dtest3 <- xgb.DMatrix(tmp_file, silent = TRUE), NA)
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unlink(tmp_file)
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expect_equal(getinfo(dtest1, 'label'), getinfo(dtest3, 'label'))
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# from a libsvm text file
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tmp <- c("0 1:1 2:1", "1 3:1", "0 1:1")
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tmp_file <- tempfile(fileext = ".libsvm")
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writeLines(tmp, tmp_file)
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dtest4 <- xgb.DMatrix(paste(tmp_file, "?format=libsvm", sep = ""), silent = TRUE)
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expect_equal(dim(dtest4), c(3, 4))
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expect_equal(getinfo(dtest4, 'label'), c(0, 1, 0))
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# check that feature info is saved
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data(agaricus.train, package = 'xgboost')
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dtrain <- xgb.DMatrix(data = agaricus.train$data, label = agaricus.train$label)
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cnames <- colnames(dtrain)
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expect_equal(length(cnames), 126)
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tmp_file <- tempfile('xgb.DMatrix_')
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xgb.DMatrix.save(dtrain, tmp_file)
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dtrain <- xgb.DMatrix(tmp_file)
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expect_equal(colnames(dtrain), cnames)
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ft <- rep(c("c", "q"), each = length(cnames) / 2)
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setinfo(dtrain, "feature_type", ft)
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expect_equal(ft, getinfo(dtrain, "feature_type"))
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})
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test_that("xgb.DMatrix: getinfo & setinfo", {
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dtest <- xgb.DMatrix(test_data)
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expect_true(setinfo(dtest, 'label', test_label))
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labels <- getinfo(dtest, 'label')
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expect_equal(test_label, getinfo(dtest, 'label'))
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expect_true(setinfo(dtest, 'label_lower_bound', test_label))
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expect_equal(test_label, getinfo(dtest, 'label_lower_bound'))
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expect_true(setinfo(dtest, 'label_upper_bound', test_label))
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expect_equal(test_label, getinfo(dtest, 'label_upper_bound'))
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expect_true(length(getinfo(dtest, 'weight')) == 0)
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expect_true(length(getinfo(dtest, 'base_margin')) == 0)
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expect_true(setinfo(dtest, 'weight', test_label))
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expect_true(setinfo(dtest, 'base_margin', test_label))
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expect_true(setinfo(dtest, 'group', c(50, 50)))
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expect_error(setinfo(dtest, 'group', test_label))
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# providing character values will give an error
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expect_error(setinfo(dtest, 'weight', rep('a', nrow(test_data))))
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# any other label should error
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expect_error(setinfo(dtest, 'asdf', test_label))
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})
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test_that("xgb.DMatrix: slice, dim", {
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dtest <- xgb.DMatrix(test_data, label = test_label)
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expect_equal(dim(dtest), dim(test_data))
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dsub1 <- slice(dtest, 1:42)
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expect_equal(nrow(dsub1), 42)
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expect_equal(ncol(dsub1), ncol(test_data))
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dsub2 <- dtest[1:42, ]
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expect_equal(dim(dtest), dim(test_data))
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expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))
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})
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test_that("xgb.DMatrix: slice, trailing empty rows", {
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data(agaricus.train, package = 'xgboost')
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train_data <- agaricus.train$data
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train_label <- agaricus.train$label
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dtrain <- xgb.DMatrix(data = train_data, label = train_label)
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slice(dtrain, 6513L)
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train_data[6513, ] <- 0
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dtrain <- xgb.DMatrix(data = train_data, label = train_label)
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slice(dtrain, 6513L)
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expect_equal(nrow(dtrain), 6513)
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})
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test_that("xgb.DMatrix: colnames", {
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dtest <- xgb.DMatrix(test_data, label = test_label)
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expect_equal(colnames(dtest), colnames(test_data))
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expect_error(colnames(dtest) <- 'asdf')
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new_names <- make.names(seq_len(ncol(test_data)))
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expect_silent(colnames(dtest) <- new_names)
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expect_equal(colnames(dtest), new_names)
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expect_silent(colnames(dtest) <- NULL)
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expect_null(colnames(dtest))
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})
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test_that("xgb.DMatrix: nrow is correct for a very sparse matrix", {
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set.seed(123)
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nr <- 1000
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x <- Matrix::rsparsematrix(nr, 100, density = 0.0005)
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# we want it very sparse, so that last rows are empty
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expect_lt(max(x@i), nr)
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dtest <- xgb.DMatrix(x)
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expect_equal(dim(dtest), dim(x))
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})
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test_that("xgb.DMatrix: print", {
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data(agaricus.train, package = 'xgboost')
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# core DMatrix with just data and labels
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dtrain <- xgb.DMatrix(
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data = agaricus.train$data
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, label = agaricus.train$label
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)
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txt <- capture.output({
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print(dtrain)
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})
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expect_equal(txt, "xgb.DMatrix dim: 6513 x 126 info: label colnames: yes")
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# verbose=TRUE prints feature names
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txt <- capture.output({
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print(dtrain, verbose = TRUE)
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})
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expect_equal(txt[[1L]], "xgb.DMatrix dim: 6513 x 126 info: label colnames:")
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expect_equal(txt[[2L]], sprintf("'%s'", paste(colnames(dtrain), collapse = "','")))
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# DMatrix with weights and base_margin
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dtrain <- xgb.DMatrix(
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data = agaricus.train$data
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, label = agaricus.train$label
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, weight = seq_along(agaricus.train$label)
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, base_margin = agaricus.train$label
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)
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txt <- capture.output({
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print(dtrain)
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})
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expect_equal(txt, "xgb.DMatrix dim: 6513 x 126 info: label weight base_margin colnames: yes")
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# DMatrix with just features
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dtrain <- xgb.DMatrix(
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data = agaricus.train$data
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)
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txt <- capture.output({
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print(dtrain)
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})
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expect_equal(txt, "xgb.DMatrix dim: 6513 x 126 info: NA colnames: yes")
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# DMatrix with no column names
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data_no_colnames <- agaricus.train$data
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colnames(data_no_colnames) <- NULL
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dtrain <- xgb.DMatrix(
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data = data_no_colnames
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)
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txt <- capture.output({
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print(dtrain)
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})
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expect_equal(txt, "xgb.DMatrix dim: 6513 x 126 info: NA colnames: no")
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})
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