109473dae2
* Fix #3545: XGDMatrixCreateFromCSCEx silently discards empty trailing rows Description: The bug is triggered when 1. The data matrix has empty rows at the bottom. More precisely, the rows `n-k+1`, `n-k+2`, ..., `n` of the matrix have missing values in all dimensions (`n` number of instances, `k` number of trailing rows) 2. The data matrix is given as Compressed Sparse Column (CSC) format. Diagnosis: When the CSC matrix is converted to Compressed Sparse Row (CSR) format (this is common format used for DMatrix), the trailing empty rows are silently ignored. More specifically, the row pointer (`offset`) of the newly created CSR matrix does not take account of these rows. Fix: Modify the row pointer. * Add regression test
112 lines
3.6 KiB
R
112 lines
3.6 KiB
R
require(xgboost)
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require(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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})
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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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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 <- 'tmp.libsvm'
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writeLines(tmp, tmp_file)
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dtest4 <- xgb.DMatrix(tmp_file, 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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unlink(tmp_file)
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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(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 a warning
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expect_warning( 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(1: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 <- 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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