зеркало из https://github.com/microsoft/LightGBM.git
[ci] [R-package] Add paste_linter (#5320)
[ci] [R-package] Add paste_linter (#5303)
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feb4cf4f47
Коммит
4b64b173c1
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@ -50,6 +50,7 @@ LINTERS_TO_USE <- list(
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, "numeric_leading_zero" = lintr::numeric_leading_zero_linter()
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, "outer_negation" = lintr::outer_negation_linter()
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, "package_hooks" = lintr::package_hooks_linter()
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, "paste" = lintr::paste_linter()
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, "regex_subset" = lintr::regex_subset_linter()
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, "semicolon" = lintr::semicolon_linter()
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, "seq" = lintr::seq_linter()
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@ -824,7 +824,7 @@ predict.lgb.Booster <- function(object,
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}
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warning(paste0(
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"predict.lgb.Booster: Found the following passed through '...': "
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, paste(names(additional_params), collapse = ", ")
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, toString(names(additional_params))
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, ". These are ignored. Use argument 'params' instead."
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))
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}
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@ -1131,7 +1131,7 @@ lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_
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"lgb.get.eval.result: data_name "
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, shQuote(data_name)
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, " not found. Only the following datasets exist in record evals: ["
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, paste(data_names, collapse = ", ")
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, toString(data_names)
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, "]"
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))
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}
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@ -1145,7 +1145,7 @@ lgb.get.eval.result <- function(booster, data_name, eval_name, iters = NULL, is_
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, " not found. Only the following eval_names exist for dataset "
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, shQuote(data_name)
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, ": ["
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, paste(eval_names, collapse = ", ")
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, toString(eval_names)
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, "]"
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))
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stop("lgb.get.eval.result: wrong eval name")
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@ -471,7 +471,7 @@ Dataset <- R6::R6Class(
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if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) {
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stop(
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"Dataset$get_field(): field_name must one of the following: "
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, paste0(sQuote(.INFO_KEYS()), collapse = ", ")
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, toString(sQuote(.INFO_KEYS()))
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)
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}
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@ -523,7 +523,7 @@ Dataset <- R6::R6Class(
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if (!is.character(field_name) || length(field_name) != 1L || !field_name %in% .INFO_KEYS()) {
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stop(
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"Dataset$set_field(): field_name must one of the following: "
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, paste0(sQuote(.INFO_KEYS()), collapse = ", ")
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, toString(sQuote(.INFO_KEYS()))
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)
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}
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@ -224,9 +224,7 @@ Predictor <- R6::R6Class(
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, "dsparseVector"
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, "dgRMatrix"
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, "dgCMatrix"
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, paste(class(data)
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, collapse = ", ")))
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, toString(class(data))))
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}
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if (NROW(row.names(data))) {
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@ -20,14 +20,13 @@
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column_classes <- .get_column_classes(df = df)
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unconverted_columns <- column_classes[!(column_classes %in% c("numeric", "integer"))]
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if (length(unconverted_columns) > 0L) {
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col_detail_string <- paste0(
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col_detail_string <- toString(
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paste0(
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names(unconverted_columns)
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, " ("
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, unconverted_columns
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, ")"
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)
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, collapse = ", "
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)
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msg <- paste0(
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function_name
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@ -437,7 +437,7 @@ test_that("predict() keeps row names from data (binary classification)", {
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data(agaricus.train, package = "lightgbm")
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X <- as.matrix(agaricus.train$data)
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y <- agaricus.train$label
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row.names(X) <- paste("rname", seq(1L, nrow(X)), sep = "")
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row.names(X) <- paste0("rname", seq(1L, nrow(X)))
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dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
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bst <- lgb.train(
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data = dtrain
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@ -452,7 +452,7 @@ test_that("predict() keeps row names from data (multi-class classification)", {
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data(iris)
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y <- as.numeric(iris$Species) - 1.0
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X <- as.matrix(iris[, names(iris) != "Species"])
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row.names(X) <- paste("rname", seq(1L, nrow(X)), sep = "")
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row.names(X) <- paste0("rname", seq(1L, nrow(X)))
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dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
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bst <- lgb.train(
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data = dtrain
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@ -70,7 +70,7 @@ unrecognized_args <- setdiff(given_args, recognized_args)
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if (length(unrecognized_args) > 0L) {
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msg <- paste0(
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"Unrecognized arguments: "
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, paste0(unrecognized_args, collapse = ", ")
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, toString(unrecognized_args)
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)
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stop(msg)
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}
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@ -417,7 +417,7 @@ c_api_symbols <- gsub(
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)
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dynlib_statement <- paste0(
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"useDynLib(lib_lightgbm, "
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, paste0(c_api_symbols, collapse = ", ")
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, toString(c_api_symbols)
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, ")"
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)
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namespace_contents[dynlib_line] <- dynlib_statement
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