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Unverified Commit 694e41e4 authored by James Lamb's avatar James Lamb Committed by GitHub
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[R-package] standardize naming of internal functions (#6179)

parent deb70773
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Showing with 149 additions and 140 deletions
R-package/R/callback.R
View file @ 694e41e4
......@@ -323,17 +323,17 @@ cb_early_stop <- function(stopping_rounds, first_metric_only, verbose) {
}
# Extract callback names from the list of callbacks
callback.names <- function(cb_list) {
.callback_names <- function(cb_list) {
return(unlist(lapply(cb_list, attr, "name")))
}
add.cb <- function(cb_list, cb) {
.add_cb <- function(cb_list, cb) {
# Combine two elements
cb_list <- c(cb_list, cb)
# Set names of elements
names(cb_list) <- callback.names(cb_list = cb_list)
names(cb_list) <- .callback_names(cb_list = cb_list)
if ("cb_early_stop" %in% names(cb_list)) {
......@@ -349,7 +349,7 @@ add.cb <- function(cb_list, cb) {
}
categorize.callbacks <- function(cb_list) {
.categorize_callbacks <- function(cb_list) {
# Check for pre-iteration or post-iteration
return(
......
R-package/R/lgb.Booster.R
View file @ 694e41e4
......@@ -31,12 +31,12 @@ Booster <- R6::R6Class(
if (!is.null(train_set)) {
if (!lgb.is.Dataset(train_set)) {
if (!.is_Dataset(train_set)) {
stop("lgb.Booster: Can only use lgb.Dataset as training data")
}
train_set_handle <- train_set$.__enclos_env__$private$get_handle()
params <- utils::modifyList(params, train_set$get_params())
params_str <- lgb.params2str(params = params)
params_str <- .params2str(params = params)
# Store booster handle
handle <- .Call(
LGBM_BoosterCreate_R
......@@ -130,7 +130,7 @@ Booster <- R6::R6Class(
# Add validation data
add_valid = function(data, name) {
if (!lgb.is.Dataset(data)) {
if (!.is_Dataset(data)) {
stop("lgb.Booster.add_valid: Can only use lgb.Dataset as validation data")
}
......@@ -167,7 +167,7 @@ Booster <- R6::R6Class(
params <- utils::modifyList(self$params, params)
}
params_str <- lgb.params2str(params = params)
params_str <- .params2str(params = params)
self$restore_handle()
......@@ -193,7 +193,7 @@ Booster <- R6::R6Class(
if (!is.null(train_set)) {
if (!lgb.is.Dataset(train_set)) {
if (!.is_Dataset(train_set)) {
stop("lgb.Booster.update: Only can use lgb.Dataset as training data")
}
......@@ -340,7 +340,7 @@ Booster <- R6::R6Class(
# Evaluate data on metrics
eval = function(data, name, feval = NULL) {
if (!lgb.is.Dataset(data)) {
if (!.is_Dataset(data)) {
stop("lgb.Booster.eval: Can only use lgb.Dataset to eval")
}
......@@ -508,17 +508,17 @@ Booster <- R6::R6Class(
# NOTE: doing this here instead of in Predictor$predict() to keep
# Predictor$predict() as fast as possible
if (length(params) > 0L) {
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "predict_raw_score"
, params = params
, alternative_kwarg_value = rawscore
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "predict_leaf_index"
, params = params
, alternative_kwarg_value = predleaf
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "predict_contrib"
, params = params
, alternative_kwarg_value = predcontrib
......@@ -586,7 +586,7 @@ Booster <- R6::R6Class(
, predcontrib
, start_iteration
, num_iteration
, lgb.params2str(params = params)
, .params2str(params = params)
)
private$fast_predict_config <- list(
......@@ -622,7 +622,7 @@ Booster <- R6::R6Class(
},
check_null_handle = function() {
return(lgb.is.null.handle(private$handle))
return(.is_null_handle(private$handle))
},
restore_handle = function() {
......@@ -959,7 +959,7 @@ predict.lgb.Booster <- function(object,
params = list(),
...) {
if (!lgb.is.Booster(x = object)) {
if (!.is_Booster(x = object)) {
stop("predict.lgb.Booster: object should be an ", sQuote("lgb.Booster"))
}
......@@ -1114,7 +1114,7 @@ lgb.configure_fast_predict <- function(model,
num_iteration = NULL,
type = "response",
params = list()) {
if (!lgb.is.Booster(x = model)) {
if (!.is_Booster(x = model)) {
stop("lgb.configure_fast_predict: model should be an ", sQuote("lgb.Booster"))
}
if (type == "class") {
......@@ -1160,7 +1160,7 @@ lgb.configure_fast_predict <- function(model,
print.lgb.Booster <- function(x, ...) {
# nolint start
handle <- x$.__enclos_env__$private$handle
handle_is_null <- lgb.is.null.handle(handle)
handle_is_null <- .is_null_handle(handle)
if (!handle_is_null) {
ntrees <- x$current_iter()
......@@ -1316,7 +1316,7 @@ lgb.load <- function(filename = NULL, model_str = NULL) {
#' @export
lgb.save <- function(booster, filename, num_iteration = NULL) {
if (!lgb.is.Booster(x = booster)) {
if (!.is_Booster(x = booster)) {
stop("lgb.save: booster should be an ", sQuote("lgb.Booster"))
}
......@@ -1372,7 +1372,7 @@ lgb.save <- function(booster, filename, num_iteration = NULL) {
#' @export
lgb.dump <- function(booster, num_iteration = NULL) {
if (!lgb.is.Booster(x = booster)) {
if (!.is_Booster(x = booster)) {
stop("lgb.dump: booster should be an ", sQuote("lgb.Booster"))
}
......@@ -1430,7 +1430,7 @@ lgb.dump <- function(booster, num_iteration = NULL) {
#' @export
lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_err = FALSE) {
if (!lgb.is.Booster(x = booster)) {
if (!.is_Booster(x = booster)) {
stop("lgb.get.eval.result: Can only use ", sQuote("lgb.Booster"), " to get eval result")
}
......
R-package/R/lgb.DataProcessor.R
View file @ 694e41e4
......@@ -39,7 +39,7 @@ DataProcessor <- R6::R6Class(
)
}
data_num_class <- length(self$factor_levels)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "num_class"
, params = params
, alternative_kwarg_value = data_num_class
......
R-package/R/lgb.Dataset.R
View file @ 694e41e4
......@@ -55,10 +55,10 @@ Dataset <- R6::R6Class(
init_score = NULL) {
# validate inputs early to avoid unnecessary computation
if (!(is.null(reference) || lgb.is.Dataset(reference))) {
if (!(is.null(reference) || .is_Dataset(reference))) {
stop("lgb.Dataset: If provided, reference must be a ", sQuote("lgb.Dataset"))
}
if (!(is.null(predictor) || lgb.is.Predictor(predictor))) {
if (!(is.null(predictor) || .is_Predictor(predictor))) {
stop("lgb.Dataset: If provided, predictor must be a ", sQuote("lgb.Predictor"))
}
......@@ -135,7 +135,7 @@ Dataset <- R6::R6Class(
construct = function() {
# Check for handle null
if (!lgb.is.null.handle(x = private$handle)) {
if (!.is_null_handle(x = private$handle)) {
return(invisible(self))
}
......@@ -191,7 +191,7 @@ Dataset <- R6::R6Class(
}
# Generate parameter str
params_str <- lgb.params2str(params = private$params)
params_str <- .params2str(params = private$params)
# Get handle of reference dataset
ref_handle <- NULL
......@@ -277,7 +277,7 @@ Dataset <- R6::R6Class(
)
}
if (lgb.is.null.handle(x = handle)) {
if (.is_null_handle(x = handle)) {
stop("lgb.Dataset.construct: cannot create Dataset handle")
}
# Setup class and private type
......@@ -345,7 +345,7 @@ Dataset <- R6::R6Class(
dim = function() {
# Check for handle
if (!lgb.is.null.handle(x = private$handle)) {
if (!.is_null_handle(x = private$handle)) {
num_row <- 0L
num_col <- 0L
......@@ -385,7 +385,7 @@ Dataset <- R6::R6Class(
# Get number of bins for feature
get_feature_num_bin = function(feature) {
if (lgb.is.null.handle(x = private$handle)) {
if (.is_null_handle(x = private$handle)) {
stop("Cannot get number of bins in feature before constructing Dataset.")
}
if (is.character(feature)) {
......@@ -409,7 +409,7 @@ Dataset <- R6::R6Class(
get_colnames = function() {
# Check for handle
if (!lgb.is.null.handle(x = private$handle)) {
if (!.is_null_handle(x = private$handle)) {
private$colnames <- .Call(
LGBM_DatasetGetFeatureNames_R
, private$handle
......@@ -449,7 +449,7 @@ Dataset <- R6::R6Class(
# Write column names
private$colnames <- colnames
if (!lgb.is.null.handle(x = private$handle)) {
if (!.is_null_handle(x = private$handle)) {
# Merge names with tab separation
merged_name <- paste0(as.list(private$colnames), collapse = "\t")
......@@ -478,7 +478,7 @@ Dataset <- R6::R6Class(
# Check for info name and handle
if (is.null(private$info[[field_name]])) {
if (lgb.is.null.handle(x = private$handle)) {
if (.is_null_handle(x = private$handle)) {
stop("Cannot perform Dataset$get_field() before constructing Dataset.")
}
......@@ -536,7 +536,7 @@ Dataset <- R6::R6Class(
# Store information privately
private$info[[field_name]] <- data
if (!lgb.is.null.handle(x = private$handle) && !is.null(data)) {
if (!.is_null_handle(x = private$handle) && !is.null(data)) {
if (length(data) > 0L) {
......@@ -583,14 +583,14 @@ Dataset <- R6::R6Class(
return(invisible(self))
}
new_params <- utils::modifyList(private$params, params)
if (lgb.is.null.handle(x = private$handle)) {
if (.is_null_handle(x = private$handle)) {
private$params <- new_params
} else {
tryCatch({
.Call(
LGBM_DatasetUpdateParamChecking_R
, lgb.params2str(params = private$params)
, lgb.params2str(params = new_params)
, .params2str(params = private$params)
, .params2str(params = new_params)
)
private$params <- new_params
}, error = function(e) {
......@@ -663,7 +663,7 @@ Dataset <- R6::R6Class(
please set ", sQuote("free_raw_data = FALSE"), " when you construct lgb.Dataset")
}
if (!lgb.is.Dataset(reference)) {
if (!.is_Dataset(reference)) {
stop("set_reference: Can only use lgb.Dataset as a reference")
}
......@@ -711,7 +711,7 @@ Dataset <- R6::R6Class(
get_handle = function() {
# Get handle and construct if needed
if (lgb.is.null.handle(x = private$handle)) {
if (.is_null_handle(x = private$handle)) {
self$construct()
}
return(private$handle)
......@@ -734,7 +734,7 @@ Dataset <- R6::R6Class(
if (!is.null(predictor)) {
# Predictor is unknown
if (!lgb.is.Predictor(predictor)) {
if (!.is_Predictor(predictor)) {
stop("set_predictor: Can only use lgb.Predictor as predictor")
}
......@@ -888,7 +888,7 @@ lgb.Dataset.create.valid <- function(dataset,
init_score = NULL,
params = list()) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("lgb.Dataset.create.valid: input data should be an lgb.Dataset object")
}
......@@ -922,7 +922,7 @@ lgb.Dataset.create.valid <- function(dataset,
#' @export
lgb.Dataset.construct <- function(dataset) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("lgb.Dataset.construct: input data should be an lgb.Dataset object")
}
......@@ -954,7 +954,7 @@ lgb.Dataset.construct <- function(dataset) {
#' @export
dim.lgb.Dataset <- function(x) {
if (!lgb.is.Dataset(x = x)) {
if (!.is_Dataset(x = x)) {
stop("dim.lgb.Dataset: input data should be an lgb.Dataset object")
}
......@@ -989,7 +989,7 @@ dim.lgb.Dataset <- function(x) {
#' @export
dimnames.lgb.Dataset <- function(x) {
if (!lgb.is.Dataset(x = x)) {
if (!.is_Dataset(x = x)) {
stop("dimnames.lgb.Dataset: input data should be an lgb.Dataset object")
}
......@@ -1062,7 +1062,7 @@ slice <- function(dataset, idxset) {
#' @export
slice.lgb.Dataset <- function(dataset, idxset) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("slice.lgb.Dataset: input dataset should be an lgb.Dataset object")
}
......@@ -1110,7 +1110,7 @@ get_field <- function(dataset, field_name) {
get_field.lgb.Dataset <- function(dataset, field_name) {
# Check if dataset is not a dataset
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("get_field.lgb.Dataset(): input dataset should be an lgb.Dataset object")
}
......@@ -1158,7 +1158,7 @@ set_field <- function(dataset, field_name, data) {
#' @export
set_field.lgb.Dataset <- function(dataset, field_name, data) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("set_field.lgb.Dataset: input dataset should be an lgb.Dataset object")
}
......@@ -1189,7 +1189,7 @@ set_field.lgb.Dataset <- function(dataset, field_name, data) {
#' @export
lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("lgb.Dataset.set.categorical: input dataset should be an lgb.Dataset object")
}
......@@ -1222,7 +1222,7 @@ lgb.Dataset.set.categorical <- function(dataset, categorical_feature) {
#' @export
lgb.Dataset.set.reference <- function(dataset, reference) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("lgb.Dataset.set.reference: input dataset should be an lgb.Dataset object")
}
......@@ -1248,7 +1248,7 @@ lgb.Dataset.set.reference <- function(dataset, reference) {
#' @export
lgb.Dataset.save <- function(dataset, fname) {
if (!lgb.is.Dataset(x = dataset)) {
if (!.is_Dataset(x = dataset)) {
stop("lgb.Dataset.save: input dataset should be an lgb.Dataset object")
}
......
R-package/R/lgb.Predictor.R
View file @ 694e41e4
......@@ -28,7 +28,7 @@ Predictor <- R6::R6Class(
# Initialize will create a starter model
initialize = function(modelfile, params = list(), fast_predict_config = list()) {
private$params <- lgb.params2str(params = params)
private$params <- .params2str(params = params)
handle <- NULL
if (is.character(modelfile)) {
......@@ -46,7 +46,7 @@ Predictor <- R6::R6Class(
handle <- modelfile
private$need_free_handle <- FALSE
} else if (lgb.is.Booster(modelfile)) {
} else if (.is_Booster(modelfile)) {
handle <- modelfile$get_handle()
private$need_free_handle <- FALSE
......@@ -512,7 +512,7 @@ Predictor <- R6::R6Class(
return(FALSE)
}
if (lgb.is.null.handle(private$fast_predict_config$handle)) {
if (.is_null_handle(private$fast_predict_config$handle)) {
warning(paste0("Model had fast CSR predict configuration, but it is inactive."
, " Try re-generating it through 'lgb.configure_fast_predict'."))
return(FALSE)
......@@ -527,8 +527,8 @@ Predictor <- R6::R6Class(
private$fast_predict_config$rawscore == rawscore &&
private$fast_predict_config$predleaf == predleaf &&
private$fast_predict_config$predcontrib == predcontrib &&
lgb.equal.or.both.null(private$fast_predict_config$start_iteration, start_iteration) &&
lgb.equal.or.both.null(private$fast_predict_config$num_iteration, num_iteration)
.equal_or_both_null(private$fast_predict_config$start_iteration, start_iteration) &&
.equal_or_both_null(private$fast_predict_config$num_iteration, num_iteration)
)
}
)
......
R-package/R/lgb.cv.R
View file @ 694e41e4
......@@ -99,7 +99,7 @@ lgb.cv <- function(params = list()
}
# If 'data' is not an lgb.Dataset, try to construct one using 'label'
if (!lgb.is.Dataset(x = data)) {
if (!.is_Dataset(x = data)) {
if (is.null(label)) {
stop("'label' must be provided for lgb.cv if 'data' is not an 'lgb.Dataset'")
}
......@@ -110,27 +110,27 @@ lgb.cv <- function(params = list()
# in `params`.
# this ensures that the model stored with Booster$save() correctly represents
# what was passed in
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "verbosity"
, params = params
, alternative_kwarg_value = verbose
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "num_iterations"
, params = params
, alternative_kwarg_value = nrounds
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "metric"
, params = params
, alternative_kwarg_value = NULL
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "objective"
, params = params
, alternative_kwarg_value = obj
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "early_stopping_round"
, params = params
, alternative_kwarg_value = early_stopping_rounds
......@@ -148,7 +148,7 @@ lgb.cv <- function(params = list()
# (for backwards compatibility). If it is a list of functions, store
# all of them. This makes it possible to pass any mix of strings like "auc"
# and custom functions to eval
params <- lgb.check.eval(params = params, eval = eval)
params <- .check_eval(params = params, eval = eval)
eval_functions <- list(NULL)
if (is.function(eval)) {
eval_functions <- list(eval)
......@@ -166,7 +166,7 @@ lgb.cv <- function(params = list()
# Check for boosting from a trained model
if (is.character(init_model)) {
predictor <- Predictor$new(modelfile = init_model)
} else if (lgb.is.Booster(x = init_model)) {
} else if (.is_Booster(x = init_model)) {
predictor <- init_model$to_predictor()
}
......@@ -193,7 +193,7 @@ lgb.cv <- function(params = list()
} else if (!is.null(data$get_colnames())) {
cnames <- data$get_colnames()
}
params[["interaction_constraints"]] <- lgb.check_interaction_constraints(
params[["interaction_constraints"]] <- .check_interaction_constraints(
interaction_constraints = interaction_constraints
, column_names = cnames
)
......@@ -232,7 +232,7 @@ lgb.cv <- function(params = list()
}
# Create folds
folds <- generate.cv.folds(
folds <- .generate_cv_folds(
nfold = nfold
, nrows = nrow(data)
, stratified = stratified
......@@ -245,12 +245,12 @@ lgb.cv <- function(params = list()
# Add printing log callback
if (params[["verbosity"]] > 0L && eval_freq > 0L) {
callbacks <- add.cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq))
callbacks <- .add_cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq))
}
# Add evaluation log callback
if (record) {
callbacks <- add.cb(cb_list = callbacks, cb = cb_record_evaluation())
callbacks <- .add_cb(cb_list = callbacks, cb = cb_record_evaluation())
}
# Did user pass parameters that indicate they want to use early stopping?
......@@ -282,7 +282,7 @@ lgb.cv <- function(params = list()
# If user supplied early_stopping_rounds, add the early stopping callback
if (using_early_stopping) {
callbacks <- add.cb(
callbacks <- .add_cb(
cb_list = callbacks
, cb = cb_early_stop(
stopping_rounds = early_stopping_rounds
......@@ -292,7 +292,7 @@ lgb.cv <- function(params = list()
)
}
cb <- categorize.callbacks(cb_list = callbacks)
cb <- .categorize_callbacks(cb_list = callbacks)
# Construct booster for each fold. The data.table() code below is used to
# guarantee that indices are sorted while keeping init_score and weight together
......@@ -387,7 +387,7 @@ lgb.cv <- function(params = list()
})
# Prepare collection of evaluation results
merged_msg <- lgb.merge.cv.result(
merged_msg <- .merge_cv_result(
msg = msg
, showsd = showsd
)
......@@ -463,7 +463,7 @@ lgb.cv <- function(params = list()
}
# Generates random (stratified if needed) CV folds
generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
.generate_cv_folds <- function(nfold, nrows, stratified, label, group, params) {
# Check for group existence
if (is.null(group)) {
......@@ -476,7 +476,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
y <- label[rnd_idx]
y <- as.factor(y)
folds <- lgb.stratified.folds(y = y, k = nfold)
folds <- .stratified_folds(y = y, k = nfold)
} else {
......@@ -528,7 +528,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, group, params) {
# It was borrowed from caret::createFolds and simplified
# by always returning an unnamed list of fold indices.
#' @importFrom stats quantile
lgb.stratified.folds <- function(y, k) {
.stratified_folds <- function(y, k) {
# Group the numeric data based on their magnitudes
# and sample within those groups.
......@@ -594,7 +594,7 @@ lgb.stratified.folds <- function(y, k) {
return(out)
}
lgb.merge.cv.result <- function(msg, showsd) {
.merge_cv_result <- function(msg, showsd) {
if (length(msg) == 0L) {
stop("lgb.cv: size of cv result error")
......
R-package/R/lgb.drop_serialized.R
View file @ 694e41e4
......@@ -13,7 +13,7 @@
#' @seealso \link{lgb.restore_handle}, \link{lgb.make_serializable}.
#' @export
lgb.drop_serialized <- function(model) {
if (!lgb.is.Booster(x = model)) {
if (!.is_Booster(x = model)) {
stop("lgb.drop_serialized: model should be an ", sQuote("lgb.Booster"))
}
model$drop_raw()
......
R-package/R/lgb.importance.R
View file @ 694e41e4
......@@ -39,7 +39,7 @@
#' @export
lgb.importance <- function(model, percentage = TRUE) {
if (!lgb.is.Booster(x = model)) {
if (!.is_Booster(x = model)) {
stop("'model' has to be an object of class lgb.Booster")
}
......
R-package/R/lgb.interprete.R
View file @ 694e41e4
......@@ -86,7 +86,7 @@ lgb.interprete <- function(model,
)
for (i in seq_along(idxset)) {
tree_interpretation_dt_list[[i]] <- single.row.interprete(
tree_interpretation_dt_list[[i]] <- .single_row_interprete(
tree_dt = tree_dt
, num_class = num_class
, tree_index_mat = tree_index_mat_list[[i]]
......@@ -151,7 +151,7 @@ single.tree.interprete <- function(tree_dt,
}
#' @importFrom data.table := rbindlist setorder
multiple.tree.interprete <- function(tree_dt,
.multiple_tree_interprete <- function(tree_dt,
tree_index,
leaf_index) {
......@@ -186,7 +186,7 @@ multiple.tree.interprete <- function(tree_dt,
}
#' @importFrom data.table set setnames
single.row.interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) {
.single_row_interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index_mat) {
# Prepare vector list
tree_interpretation <- vector(mode = "list", length = num_class)
......@@ -194,7 +194,7 @@ single.row.interprete <- function(tree_dt, num_class, tree_index_mat, leaf_index
# Loop throughout each class
for (i in seq_len(num_class)) {
next_interp_dt <- multiple.tree.interprete(
next_interp_dt <- .multiple_tree_interprete(
tree_dt = tree_dt
, tree_index = tree_index_mat[, i]
, leaf_index = leaf_index_mat[, i]
......
R-package/R/lgb.make_serializable.R
View file @ 694e41e4
......@@ -13,7 +13,7 @@
#' @seealso \link{lgb.restore_handle}, \link{lgb.drop_serialized}.
#' @export
lgb.make_serializable <- function(model) {
if (!lgb.is.Booster(x = model)) {
if (!.is_Booster(x = model)) {
stop("lgb.make_serializable: model should be an ", sQuote("lgb.Booster"))
}
model$save_raw()
......
R-package/R/lgb.model.dt.tree.R
View file @ 694e41e4
......@@ -62,7 +62,10 @@ lgb.model.dt.tree <- function(model, num_iteration = NULL) {
)
# Parse tree model
tree_list <- lapply(parsed_json_model$tree_info, single.tree.parse)
tree_list <- lapply(
X = parsed_json_model$tree_info
, FUN = .single_tree_parse
)
# Combine into single data.table
tree_dt <- data.table::rbindlist(l = tree_list, use.names = TRUE)
......@@ -84,7 +87,7 @@ lgb.model.dt.tree <- function(model, num_iteration = NULL) {
#' @importFrom data.table := data.table rbindlist
single.tree.parse <- function(lgb_tree) {
.single_tree_parse <- function(lgb_tree) {
# Traverse tree function
pre_order_traversal <- function(env = NULL, tree_node_leaf, current_depth = 0L, parent_index = NA_integer_) {
......
R-package/R/lgb.plot.interpretation.R
View file @ 694e41e4
......@@ -89,7 +89,7 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
if (num_class == 1L) {
# Only one class, plot straight away
multiple.tree.plot.interpretation(
.multiple_tree_plot_interpretation(
tree_interpretation = tree_interpretation_dt
, top_n = top_n
, title = NULL
......@@ -118,7 +118,7 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
, old = names(plot_dt)
, new = c("Feature", "Contribution")
)
multiple.tree.plot.interpretation(
.multiple_tree_plot_interpretation(
tree_interpretation = plot_dt
, top_n = top_n
, title = paste("Class", i - 1L)
......@@ -131,7 +131,7 @@ lgb.plot.interpretation <- function(tree_interpretation_dt,
}
#' @importFrom graphics barplot
multiple.tree.plot.interpretation <- function(tree_interpretation,
.multiple_tree_plot_interpretation <- function(tree_interpretation,
top_n,
title,
cex) {
......
R-package/R/lgb.restore_handle.R
View file @ 694e41e4
......@@ -35,7 +35,7 @@
#' model_new$check_null_handle()
#' @export
lgb.restore_handle <- function(model) {
if (!lgb.is.Booster(x = model)) {
if (!.is_Booster(x = model)) {
stop("lgb.restore_handle: model should be an ", sQuote("lgb.Booster"))
}
model$restore_handle()
......
R-package/R/lgb.train.R
View file @ 694e41e4
......@@ -63,11 +63,11 @@ lgb.train <- function(params = list(),
if (nrounds <= 0L) {
stop("nrounds should be greater than zero")
}
if (!lgb.is.Dataset(x = data)) {
if (!.is_Dataset(x = data)) {
stop("lgb.train: data must be an lgb.Dataset instance")
}
if (length(valids) > 0L) {
if (!identical(class(valids), "list") || !all(vapply(valids, lgb.is.Dataset, logical(1L)))) {
if (!identical(class(valids), "list") || !all(vapply(valids, .is_Dataset, logical(1L)))) {
stop("lgb.train: valids must be a list of lgb.Dataset elements")
}
evnames <- names(valids)
......@@ -80,27 +80,27 @@ lgb.train <- function(params = list(),
# in `params`.
# this ensures that the model stored with Booster$save() correctly represents
# what was passed in
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "verbosity"
, params = params
, alternative_kwarg_value = verbose
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "num_iterations"
, params = params
, alternative_kwarg_value = nrounds
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "metric"
, params = params
, alternative_kwarg_value = NULL
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "objective"
, params = params
, alternative_kwarg_value = obj
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "early_stopping_round"
, params = params
, alternative_kwarg_value = early_stopping_rounds
......@@ -118,7 +118,7 @@ lgb.train <- function(params = list(),
# (for backwards compatibility). If it is a list of functions, store
# all of them. This makes it possible to pass any mix of strings like "auc"
# and custom functions to eval
params <- lgb.check.eval(params = params, eval = eval)
params <- .check_eval(params = params, eval = eval)
eval_functions <- list(NULL)
if (is.function(eval)) {
eval_functions <- list(eval)
......@@ -136,7 +136,7 @@ lgb.train <- function(params = list(),
# Check for boosting from a trained model
if (is.character(init_model)) {
predictor <- Predictor$new(modelfile = init_model)
} else if (lgb.is.Booster(x = init_model)) {
} else if (.is_Booster(x = init_model)) {
predictor <- init_model$to_predictor()
}
......@@ -166,7 +166,7 @@ lgb.train <- function(params = list(),
} else if (!is.null(data$get_colnames())) {
cnames <- data$get_colnames()
}
params[["interaction_constraints"]] <- lgb.check_interaction_constraints(
params[["interaction_constraints"]] <- .check_interaction_constraints(
interaction_constraints = interaction_constraints
, column_names = cnames
)
......@@ -212,12 +212,18 @@ lgb.train <- function(params = list(),
# Add printing log callback
if (params[["verbosity"]] > 0L && eval_freq > 0L) {
callbacks <- add.cb(cb_list = callbacks, cb = cb_print_evaluation(period = eval_freq))
callbacks <- .add_cb(
cb_list = callbacks
, cb = cb_print_evaluation(period = eval_freq)
)
}
# Add evaluation log callback
if (record && length(valids) > 0L) {
callbacks <- add.cb(cb_list = callbacks, cb = cb_record_evaluation())
callbacks <- .add_cb(
cb_list = callbacks
, cb = cb_record_evaluation()
)
}
# Did user pass parameters that indicate they want to use early stopping?
......@@ -249,7 +255,7 @@ lgb.train <- function(params = list(),
# If user supplied early_stopping_rounds, add the early stopping callback
if (using_early_stopping) {
callbacks <- add.cb(
callbacks <- .add_cb(
cb_list = callbacks
, cb = cb_early_stop(
stopping_rounds = early_stopping_rounds
......@@ -259,7 +265,7 @@ lgb.train <- function(params = list(),
)
}
cb <- categorize.callbacks(cb_list = callbacks)
cb <- .categorize_callbacks(cb_list = callbacks)
# Construct booster with datasets
booster <- Booster$new(params = params, train_set = data)
......
R-package/R/lightgbm.R
View file @ 694e41e4
......@@ -184,21 +184,21 @@ lightgbm <- function(data,
}
if (is.null(num_threads)) {
num_threads <- lgb.get.default.num.threads()
num_threads <- .get_default_num_threads()
}
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "num_threads"
, params = params
, alternative_kwarg_value = num_threads
)
params <- lgb.check.wrapper_param(
params <- .check_wrapper_param(
main_param_name = "verbosity"
, params = params
, alternative_kwarg_value = verbose
)
# Process factors as labels and auto-determine objective
if (!lgb.is.Dataset(data)) {
if (!.is_Dataset(data)) {
data_processor <- DataProcessor$new()
temp <- data_processor$process_label(
label = label
......@@ -220,7 +220,7 @@ lightgbm <- function(data,
dtrain <- data
# Check whether data is lgb.Dataset, if not then create lgb.Dataset manually
if (!lgb.is.Dataset(x = dtrain)) {
if (!.is_Dataset(x = dtrain)) {
dtrain <- lgb.Dataset(data = data, label = label, weight = weights, init_score = init_score)
}
......
R-package/R/saveRDS.lgb.Booster.R
View file @ 694e41e4
......@@ -57,7 +57,7 @@ saveRDS.lgb.Booster <- function(object,
warning("'saveRDS.lgb.Booster' is deprecated and will be removed in a future release. Use saveRDS() instead.")
if (!lgb.is.Booster(x = object)) {
if (!.is_Booster(x = object)) {
stop("saveRDS.lgb.Booster: object should be an ", sQuote("lgb.Booster"))
}
......
R-package/R/utils.R
View file @ 694e41e4
lgb.is.Booster <- function(x) {
.is_Booster <- function(x) {
return(all(c("R6", "lgb.Booster") %in% class(x))) # nolint: class_equals
}
lgb.is.Dataset <- function(x) {
.is_Dataset <- function(x) {
return(all(c("R6", "lgb.Dataset") %in% class(x))) # nolint: class_equals
}
lgb.is.Predictor <- function(x) {
.is_Predictor <- function(x) {
return(all(c("R6", "lgb.Predictor") %in% class(x))) # nolint: class_equals
}
lgb.is.null.handle <- function(x) {
.is_null_handle <- function(x) {
if (is.null(x)) {
return(TRUE)
}
......@@ -19,7 +19,7 @@ lgb.is.null.handle <- function(x) {
)
}
lgb.params2str <- function(params) {
.params2str <- function(params) {
if (!identical(class(params), "list")) {
stop("params must be a list")
......@@ -59,7 +59,7 @@ lgb.params2str <- function(params) {
}
lgb.check_interaction_constraints <- function(interaction_constraints, column_names) {
.check_interaction_constraints <- function(interaction_constraints, column_names) {
# Convert interaction constraints to feature numbers
string_constraints <- list()
......@@ -129,7 +129,7 @@ lgb.check_interaction_constraints <- function(interaction_constraints, column_na
# This has to account for the fact that `eval` could be a character vector,
# a function, a list of functions, or a list with a mix of strings and
# functions
lgb.check.eval <- function(params, eval) {
.check_eval <- function(params, eval) {
if (is.null(params$metric)) {
params$metric <- list()
......@@ -194,7 +194,7 @@ lgb.check.eval <- function(params, eval) {
# [return]
# params with num_iterations set to the chosen value, and other aliases
# of num_iterations removed
lgb.check.wrapper_param <- function(main_param_name, params, alternative_kwarg_value) {
.check_wrapper_param <- function(main_param_name, params, alternative_kwarg_value) {
aliases <- .PARAMETER_ALIASES()[[main_param_name]]
aliases_provided <- aliases[aliases %in% names(params)]
......@@ -225,7 +225,7 @@ lgb.check.wrapper_param <- function(main_param_name, params, alternative_kwarg_v
}
#' @importFrom parallel detectCores
lgb.get.default.num.threads <- function() {
.get_default_num_threads <- function() {
if (requireNamespace("RhpcBLASctl", quietly = TRUE)) { # nolint: undesirable_function
return(RhpcBLASctl::get_num_cores())
} else {
......@@ -247,7 +247,7 @@ lgb.get.default.num.threads <- function() {
}
}
lgb.equal.or.both.null <- function(a, b) {
.equal_or_both_null <- function(a, b) {
if (is.null(a)) {
if (!is.null(b)) {
return(FALSE)
......
R-package/tests/testthat/test_Predictor.R
View file @ 694e41e4
......@@ -17,16 +17,16 @@ test_that("Predictor$finalize() should not fail", {
bst$save_model(filename = model_file)
predictor <- Predictor$new(modelfile = model_file)
expect_true(lgb.is.Predictor(predictor))
expect_true(.is_Predictor(predictor))
expect_false(lgb.is.null.handle(predictor$.__enclos_env__$private$handle))
expect_false(.is_null_handle(predictor$.__enclos_env__$private$handle))
predictor$finalize()
expect_true(lgb.is.null.handle(predictor$.__enclos_env__$private$handle))
expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle))
# calling finalize() a second time shouldn't cause any issues
predictor$finalize()
expect_true(lgb.is.null.handle(predictor$.__enclos_env__$private$handle))
expect_true(.is_null_handle(predictor$.__enclos_env__$private$handle))
})
test_that("predictions do not fail for integer input", {
......@@ -79,7 +79,7 @@ test_that("start_iteration works correctly", {
, valids = list("test" = dtest)
, early_stopping_rounds = 2L
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
pred1 <- predict(bst, newdata = test$data, type = "raw")
pred_contrib1 <- predict(bst, test$data, type = "contrib")
pred2 <- rep(0.0, length(pred1))
......
R-package/tests/testthat/test_basic.R
View file @ 694e41e4
......@@ -1094,7 +1094,7 @@ test_that("lgb.train() works as expected with sparse features", {
, nrounds = nrounds
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
expect_equal(bst$current_iter(), nrounds)
parsed_model <- jsonlite::fromJSON(bst$dump_model())
expect_equal(parsed_model$objective, "binary sigmoid:1")
......@@ -1816,7 +1816,7 @@ test_that("lgb.train() supports non-ASCII feature names", {
)
, colnames = feature_names
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
dumped_model <- jsonlite::fromJSON(bst$dump_model())
# UTF-8 strings are not well-supported on Windows
......@@ -2522,7 +2522,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
, params = params
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
dtrain <- .new_dataset()
bst_linear <- lgb.train(
......@@ -2531,7 +2531,7 @@ test_that("lgb.train() fit on linearly-relatead data improves when using linear
, params = utils::modifyList(params, list(linear_tree = TRUE))
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst_linear))
expect_true(.is_Booster(bst_linear))
bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]]
bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]]
......@@ -2599,7 +2599,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
, params = params
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
dtrain <- .new_dataset()
bst_linear <- lgb.train(
......@@ -2608,7 +2608,7 @@ test_that("lgb.train() works with linear learners even if Dataset has missing va
, params = utils::modifyList(params, list(linear_tree = TRUE))
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst_linear))
expect_true(.is_Booster(bst_linear))
bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]]
bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]]
......@@ -2649,7 +2649,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
, params = params
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
dtrain <- .new_dataset()
bst_linear <- lgb.train(
......@@ -2658,7 +2658,7 @@ test_that("lgb.train() works with linear learners, bagging, and a Dataset that h
, params = utils::modifyList(params, list(linear_tree = TRUE))
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst_linear))
expect_true(.is_Booster(bst_linear))
bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]]
bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]]
......@@ -2699,7 +2699,7 @@ test_that("lgb.train() works with linear learners and data where a feature has o
, nrounds = 10L
, params = utils::modifyList(params, list(linear_tree = TRUE))
)
expect_true(lgb.is.Booster(bst_linear))
expect_true(.is_Booster(bst_linear))
})
test_that("lgb.train() works with linear learners when Dataset has categorical features", {
......@@ -2732,7 +2732,7 @@ test_that("lgb.train() works with linear learners when Dataset has categorical f
, params = params
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst))
expect_true(.is_Booster(bst))
dtrain <- .new_dataset()
bst_linear <- lgb.train(
......@@ -2741,7 +2741,7 @@ test_that("lgb.train() works with linear learners when Dataset has categorical f
, params = utils::modifyList(params, list(linear_tree = TRUE))
, valids = list("train" = dtrain)
)
expect_true(lgb.is.Booster(bst_linear))
expect_true(.is_Booster(bst_linear))
bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]]
bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]]
......
R-package/tests/testthat/test_dataset.R
View file @ 694e41e4
......@@ -206,7 +206,7 @@ test_that("lgb.Dataset: Dataset should be able to construct from matrix and retu
, rawData
, nrow(rawData)
, ncol(rawData)
, lightgbm:::lgb.params2str(params = list())
, lightgbm:::.params2str(params = list())
, ref_handle
)
expect_true(methods::is(handle, "externalptr"))
......@@ -322,7 +322,7 @@ test_that("Dataset$update_parameters() does nothing for empty inputs", {
res <- ds$update_params(
params = list()
)
expect_true(lgb.is.Dataset(res))
expect_true(.is_Dataset(res))
new_params <- ds$get_params()
expect_identical(new_params, initial_params)
......@@ -343,7 +343,7 @@ test_that("Dataset$update_params() works correctly for recognized Dataset parame
res <- ds$update_params(
params = new_params
)
expect_true(lgb.is.Dataset(res))
expect_true(.is_Dataset(res))
updated_params <- ds$get_params()
for (param_name in names(new_params)) {
......@@ -356,17 +356,17 @@ test_that("Dataset$finalize() should not fail on an already-finalized Dataset",
data = test_data
, label = test_label
)
expect_true(lgb.is.null.handle(dtest$.__enclos_env__$private$handle))
expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))
dtest$construct()
expect_false(lgb.is.null.handle(dtest$.__enclos_env__$private$handle))
expect_false(.is_null_handle(dtest$.__enclos_env__$private$handle))
dtest$finalize()
expect_true(lgb.is.null.handle(dtest$.__enclos_env__$private$handle))
expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))
# calling finalize() a second time shouldn't cause any issues
dtest$finalize()
expect_true(lgb.is.null.handle(dtest$.__enclos_env__$private$handle))
expect_true(.is_null_handle(dtest$.__enclos_env__$private$handle))
})
test_that("lgb.Dataset: should be able to run lgb.train() immediately after using lgb.Dataset() on a file", {
......@@ -401,7 +401,7 @@ test_that("lgb.Dataset: should be able to run lgb.train() immediately after usin
, data = dtest_read_in
)
expect_true(lgb.is.Booster(x = bst))
expect_true(.is_Booster(x = bst))
})
test_that("lgb.Dataset: should be able to run lgb.cv() immediately after using lgb.Dataset() on a file", {
......
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