parent
f489d824ca
commit
c7e82b5914
3
.github/workflows/r_tests.yml
vendored
3
.github/workflows/r_tests.yml
vendored
@ -44,8 +44,7 @@ jobs:
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- name: Run lintr
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- name: Run lintr
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run: |
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run: |
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MAKEFLAGS="-j$(nproc)" R CMD INSTALL R-package/
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MAKEFLAGS="-j$(nproc)" R CMD INSTALL R-package/
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# Disable lintr errors for now: https://github.com/dmlc/xgboost/issues/8012
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Rscript tests/ci_build/lint_r.R $(pwd)
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Rscript tests/ci_build/lint_r.R $(pwd) || true
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test-R-on-Windows:
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test-R-on-Windows:
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runs-on: ${{ matrix.config.os }}
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runs-on: ${{ matrix.config.os }}
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@ -4,7 +4,7 @@
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#' @rdname xgb.plot.importance
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#' @rdname xgb.plot.importance
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#' @export
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#' @export
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xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
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xgb.ggplot.importance <- function(importance_matrix = NULL, top_n = NULL, measure = NULL,
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rel_to_first = FALSE, n_clusters = c(1:10), ...) {
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rel_to_first = FALSE, n_clusters = seq_len(10), ...) {
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importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure,
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importance_matrix <- xgb.plot.importance(importance_matrix, top_n = top_n, measure = measure,
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rel_to_first = rel_to_first, plot = FALSE, ...)
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rel_to_first = rel_to_first, plot = FALSE, ...)
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@ -97,9 +97,9 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5,
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, by = .(abs.node.position, Feature)
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, by = .(abs.node.position, Feature)
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][, .(Text = paste0(
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][, .(Text = paste0(
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paste0(
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paste0(
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Feature[1:min(length(Feature), features_keep)],
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Feature[seq_len(min(length(Feature), features_keep))],
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" (",
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" (",
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format(Quality[1:min(length(Quality), features_keep)], digits = 5),
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format(Quality[seq_len(min(length(Quality), features_keep))], digits = 5),
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")"
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")"
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),
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),
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collapse = "\n"
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collapse = "\n"
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@ -273,7 +273,7 @@ xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
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}
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}
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top_n <- top_n[1]
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top_n <- top_n[1]
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if (top_n < 1 | top_n > 100) stop("top_n: must be an integer within [1, 100]")
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if (top_n < 1 | top_n > 100) stop("top_n: must be an integer within [1, 100]")
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features <- imp$Feature[1:min(top_n, NROW(imp))]
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features <- imp$Feature[seq_len(min(top_n, NROW(imp)))]
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}
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}
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if (is.character(features)) {
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if (is.character(features)) {
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features <- match(features, colnames(data))
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features <- match(features, colnames(data))
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@ -389,7 +389,7 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
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xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)
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xgb.iter.update(bst$handle, dtrain, iteration - 1, obj)
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if (length(watchlist) > 0)
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if (length(watchlist) > 0)
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bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)
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bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval) # nolint: object_usage_linter
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xgb.attr(bst$handle, 'niter') <- iteration - 1
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xgb.attr(bst$handle, 'niter') <- iteration - 1
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@ -33,7 +33,7 @@ treeInteractions <- function(input_tree, input_max_depth) {
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}
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}
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# Extract nodes with interactions
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# Extract nodes with interactions
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interaction_trees <- trees[!is.na(Split) & !is.na(parent_1),
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interaction_trees <- trees[!is.na(Split) & !is.na(parent_1), # nolint: object_usage_linter
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c('Feature', paste0('parent_feat_', 1:(input_max_depth - 1))),
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c('Feature', paste0('parent_feat_', 1:(input_max_depth - 1))),
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with = FALSE]
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with = FALSE]
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interaction_trees_split <- split(interaction_trees, seq_len(nrow(interaction_trees)))
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interaction_trees_split <- split(interaction_trees, seq_len(nrow(interaction_trees)))
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@ -10,7 +10,7 @@ xgb.ggplot.importance(
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top_n = NULL,
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top_n = NULL,
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measure = NULL,
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measure = NULL,
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rel_to_first = FALSE,
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rel_to_first = FALSE,
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n_clusters = c(1:10),
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n_clusters = seq_len(10),
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...
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...
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)
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)
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@ -26,7 +26,7 @@ print(paste("weight statistics: wpos=", sumwpos, "wneg=", sumwneg, "ratio=", sum
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xgboost.time <- list()
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xgboost.time <- list()
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threads <- c(1, 2, 4, 8, 16)
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threads <- c(1, 2, 4, 8, 16)
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for (i in 1:length(threads)){
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for (i in seq_along(threads)){
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thread <- threads[i]
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thread <- threads[i]
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xgboost.time[[i]] <- system.time({
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xgboost.time[[i]] <- system.time({
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xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
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xgmat <- xgb.DMatrix(data, label = label, weight = weight, missing = -999.0)
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@ -13,7 +13,7 @@ y <- as.integer(y) - 1 # xgboost take features in [0,numOfClass)
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x <- rbind(train[, -ncol(train)], test)
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x <- rbind(train[, -ncol(train)], test)
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x <- as.matrix(x)
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x <- as.matrix(x)
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x <- matrix(as.numeric(x), nrow(x), ncol(x))
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x <- matrix(as.numeric(x), nrow(x), ncol(x))
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trind <- 1:length(y)
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trind <- seq_along(y)
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teind <- (nrow(train) + 1):nrow(x)
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teind <- (nrow(train) + 1):nrow(x)
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# Set necessary parameter
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# Set necessary parameter
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@ -43,6 +43,6 @@ pred <- t(pred)
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# Output submission
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# Output submission
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pred <- format(pred, digits = 2, scientific = FALSE) # shrink the size of submission
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pred <- format(pred, digits = 2, scientific = FALSE) # shrink the size of submission
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pred <- data.frame(1:nrow(pred), pred)
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pred <- data.frame(seq_len(nrow(pred)), pred)
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names(pred) <- c('id', paste0('Class_', 1:9))
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names(pred) <- c('id', paste0('Class_', 1:9))
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write.csv(pred, file = 'submission.csv', quote = FALSE, row.names = FALSE)
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write.csv(pred, file = 'submission.csv', quote = FALSE, row.names = FALSE)
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@ -30,7 +30,7 @@ require(xgboost)
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require(methods)
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require(methods)
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require(data.table)
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require(data.table)
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require(magrittr)
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require(magrittr)
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train <- fread('data/train.csv', header = T, stringsAsFactors = FALSE)
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train <- fread('data/train.csv', header = TRUE, stringsAsFactors = FALSE)
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test <- fread('data/test.csv', header = TRUE, stringsAsFactors = FALSE)
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test <- fread('data/test.csv', header = TRUE, stringsAsFactors = FALSE)
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```
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```
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> `magrittr` and `data.table` are here to make the code cleaner and much more rapid.
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> `magrittr` and `data.table` are here to make the code cleaner and much more rapid.
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@ -67,6 +67,5 @@ noquote(paste0("Total linting issues found: ", issues_found))
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if (issues_found > 0L) {
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if (issues_found > 0L) {
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print(results)
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print(results)
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quit(save = "no", status = 1L)
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}
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}
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quit(save = "no", status = 1L)
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