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[R-package] Allow for valid data to be a slice of the training data #6844

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[R-package] Allow for valid data to be a slice of the training data #6844

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1c3699f
Only set reference if one does not exist
walkerjameschris Feb 28, 2025
b6ac834
Adding unit test for training on slices of ldb.Dataset
walkerjameschris Feb 28, 2025
25d681b
Correcting whitespace
walkerjameschris Mar 1, 2025
6b1add4
Adding types for constants
walkerjameschris Mar 2, 2025
1153a10
Adding integer qualifier
walkerjameschris Mar 2, 2025
97a6691
Merge branch 'master' into fix/slice-reference
walkerjameschris Mar 2, 2025
d39bc5d
Merge branch 'master' into fix/slice-reference
walkerjameschris Mar 5, 2025
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9 changes: 7 additions & 2 deletions R-package/R/lgb.train.R
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Original file line number Diff line number Diff line change
@@ -184,9 +184,14 @@ lgb.train <- function(params = list(),
next
}

# Update parameters, data
# Update parameters
valid_data$update_params(params)
valid_data$set_reference(data)

# No need to set reference if one exists (e.g., a slice)
if (is.null(valid_data$.__enclos_env__$private$reference)) {
valid_data$set_reference(data)
}
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I don't think this is quite right.

This would result in not attempting the update if the validation Dataset has any reference. That's a problem, because it means that if the training data and validation data have different bin mappers, we might not find that out until much later in the program (and probably with hard-to-understand errors or maybe even more serious problems like segfaults).

(if you're not sure what the terms "reference" or "bin mapper" mean in the context of LightGBM, please tell me and I'll explain)

I think the root cause of this bug is actually right here:

LightGBM/R-package/R/lgb.Dataset.R

Lines 647 to 652 in 6437645

set_reference = function(reference) {
# setting reference to this same Dataset object doesn't require any changes
if (identical(private$reference, reference)) {
return(invisible(self))
}

When private$reference is not NULL, it's an lgb.Dataset (an {R6} class instance).

library(lightgbm)

data("iris")

ds <- lgb.Dataset(
    as.matrix(iris[, seq_len(3L)])
    , label = iris[, 4L]
)

dtrain <- lgb.slice.Dataset(ds, seq_len(100L))
dtrain$.__enclos_env__$private$reference
# <lgb.Dataset>
#   Public:
#     construct: function () 
#     create_valid: function (data, label = NULL, weight = NULL, group = NULL, init_score = NULL,

Using identical() on 2 {R6} class instances is not reliable... it can be affected by implementation details of how {R6} works:

But for that check, it does not matter whether they're the same exact R object (in fact, we can be confident that they aren't)... it's just important that their handles point to the same Dataset on the C++ side.

I don't really want to take on maintaining or implementing an ==.lgb.Dataset() generic here (as suggested in r-lib/R6#211)... for this purpose, I think we should try an internal function that only checks the equality of the characteristics we care about.

Maybe something like this in lgb.Dataset.R (you may have to experiment with this a bit):

.datasets_are_equal <- function(ds1, ds2) {
    if (is.null(ds1) && is.null(ds2)) {
        return(TRUE)
    }
    if (is.null(ds1) || is.null(ds2)) {
        return(FALSE)
    }
    return(
        identical(
            ds1$.__enclos_env__$private$get_handle()
            , ds1$.__enclos_env__$private$get_handle()
        )
    )
}

And then replacing identical() with a call to that function in the spot I linked to above.

Are you interested in attempting that?

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I appreciate you taking the time to break that down! This makes sense so far. I’d like to make an attempt at those changes so I can understand the codebase better. Thanks!


reduced_valid_sets[[key]] <- valid_data

}
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