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# Central location for parameter aliases.
# [description] List of respected parameter aliases specific to gpb.Dataset. Wrapped in a function to
# take advantage of lazy evaluation (so it doesn't matter what order
# R sources files during installation).
# [return] A named list, where each key is a parameter relevant to gpb.DataSet and each value is a character
# vector of corresponding aliases.
.DATASET_PARAMETERS <- function() {
return(
list(
"bin_construct_sample_cnt" = c(
"bin_construct_sample_cnt"
, "subsample_for_bin"
)
, "categorical_feature" = c(
"categorical_feature"
, "cat_feature"
, "categorical_column"
, "cat_column"
)
, "data_random_seed" = c(
"data_random_seed"
, "data_seed"
)
, "enable_bundle" = c(
"enable_bundle"
, "is_enable_bundle"
, "bundle"
)
, "feature_pre_filter" = "feature_pre_filter"
, "forcedbins_filename" = "forcedbins_filename"
, "group_column" = c(
"group_column"
, "group"
, "group_id"
, "query_column"
, "query"
, "query_id"
)
, "header" = c(
"header"
, "has_header"
)
, "ignore_column" = c(
"ignore_column"
, "ignore_feature"
, "blacklist"
)
, "is_enable_sparse" = c(
"is_enable_sparse"
, "is_sparse"
, "enable_sparse"
, "sparse"
)
, "label_column" = c(
"label_column"
, "label"
)
, "max_bin" = "max_bin"
, "max_bin_by_feature" = "max_bin_by_feature"
, "min_data_in_bin" = "min_data_in_bin"
, "pre_partition" = c(
"pre_partition"
, "is_pre_partition"
)
, "two_round" = c(
"two_round"
, "two_round_loading"
, "use_two_round_loading"
)
, "use_missing" = "use_missing"
, "weight_column" = c(
"weight_column"
, "weight"
)
, "zero_as_missing" = "zero_as_missing"
)
)
}
# [description] List of respected parameter aliases. Wrapped in a function to take advantage of
# lazy evaluation (so it doesn't matter what order R sources files during installation).
# [return] A named list, where each key is a main GPBoost parameter and each value is a character
# vector of corresponding aliases.
.PARAMETER_ALIASES <- function() {
learning_params <- list(
"boosting" = c(
"boosting"
, "boost"
, "boosting_type"
)
, "early_stopping_round" = c(
"early_stopping_round"
, "early_stopping_rounds"
, "early_stopping"
, "n_iter_no_change"
)
, "num_iterations" = c(
"num_iterations"
, "num_iteration"
, "n_iter"
, "num_tree"
, "num_trees"
, "num_round"
, "num_rounds"
, "num_boost_round"
, "n_estimators"
)
)
return(c(learning_params, .DATASET_PARAMETERS()))
}
# [description]
# Per https://github.com/microsoft/LightGBM/blob/master/docs/Parameters.rst#metric,
# a few different strings can be used to indicate "no metrics".
# [returns]
# A character vector
.NO_METRIC_STRINGS <- function() {
return(
c(
"na"
, "None"
, "null"
, "custom"
)
)
}
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