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[R-package] allow use of categorical_features in Dataset when raw data does not have column names (fixes #4374) #5184

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merged 5 commits into from
Apr 30, 2022
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[R-package] allow use of categorical_features in Dataset when raw data does not have column names (fixes #4374) #5184

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8591cd9
check for number of columns if data is matrixx for categorical indice…
jmoralez Apr 27, 2022
4ed72e3
check for error when using a greater index than the number of columns
jmoralez Apr 27, 2022
64ab09b
apply suggestion
jmoralez Apr 28, 2022
bc043ac
revert whitespace change
jmoralez Apr 28, 2022
0e6816f
check if is filename instead of matrix
jmoralez Apr 28, 2022
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5 changes: 3 additions & 2 deletions R-package/R/lgb.Dataset.R
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Original file line number Diff line number Diff line change
Expand Up @@ -169,12 +169,13 @@ Dataset <- R6::R6Class(
} else {

# Check if more categorical features were output over the feature space
if (max(private$categorical_feature) > length(private$colnames)) {
data_is_not_filename <- !is.character(private$raw_data)
if (data_is_not_filename && max(private$categorical_feature) > ncol(private$raw_data)) {
stop(
"lgb.self.get.handle: supplied a too large value in categorical_feature: "
, max(private$categorical_feature)
, " but only "
, length(private$colnames)
, ncol(private$raw_data)
, " features"
)
}
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15 changes: 15 additions & 0 deletions R-package/tests/testthat/test_dataset.R
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Expand Up @@ -548,3 +548,18 @@ test_that("lgb.Dataset$get_feature_num_bin() works", {
actual_num_bins <- sapply(1L:5L, ds$get_feature_num_bin)
expect_identical(actual_num_bins, expected_num_bins)
})

test_that("lgb.Dataset can be constructed with categorical features and without colnames", {
# check that dataset can be constructed
raw_mat <- matrix(rep(c(0L, 1L), 50L), ncol = 1L)
ds <- lgb.Dataset(raw_mat, categorical_feature = 1L)$construct()
sparse_mat <- as(raw_mat, "dgCMatrix")
ds2 <- lgb.Dataset(sparse_mat, categorical_feature = 1L)$construct()
# check that the column names are NULL
expect_null(ds$.__enclos_env__$private$colnames)
expect_null(ds2$.__enclos_env__$private$colnames)
# check for error when index is greater than the number of columns
expect_error({
lgb.Dataset(raw_mat, categorical_feature = 2L)$construct()
}, regexp = "supplied a too large value in categorical_feature: 2 but only 1 features")
})