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[R] maintenance Apr 2017 (#2237)

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* [R] make sure things work for a single split model; fixes #2191

* [R] add option use_int_id to xgb.model.dt.tree

* [R] add example of exporting tree plot to a file

* [R] set save_period = NULL as default in xgboost() to be the same as in xgb.train; fixes #2182

* [R] it's a good practice after CRAN releases to bump up package version in dev

* [R] allow xgb.DMatrix construction from integer dense matrices

* [R] xgb.DMatrix: silent parameter; improve documentation

* [R] xgb.model.dt.tree code style changes

* [R] update NEWS with parameter changes

* [R] code safety & style; handle non-strict matrix and inherited classes of input and model; fixes #2242

* [R] change to x.y.z.p R-package versioning scheme and set version to 0.6.4.3

* [R] add an R package versioning section to the contributors guide

* [R] R-package/README.md: clean up the redundant old installation instructions, link the contributors guide
This commit is contained in:
Vadim Khotilovich
2017-05-02 00:51:34 -05:00
committed by Tong He
parent d769b6bcb5
commit a375ad2822
29 changed files with 351 additions and 246 deletions
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8
R-package/R/xgb.importance.R
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@@ -58,13 +58,13 @@ xgb.importance <- function(feature_names = NULL, model = NULL,
if (!(is.null(data) && is.null(label) && is.null(target)))
warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
if (class(model) != "xgb.Booster")
stop("Either 'model' has to be an object of class xgb.Booster")
if (!inherits(model, "xgb.Booster"))
stop("model: must be an object of class xgb.Booster")
if (is.null(feature_names) && !is.null(model$feature_names))
feature_names <- model$feature_names
if (!class(feature_names) %in% c("character", "NULL"))
if (!(is.null(feature_names) || is.character(feature_names)))
stop("feature_names: Has to be a character vector")
model_text_dump <- xgb.dump(model = model, with_stats = TRUE)
@@ -76,6 +76,8 @@ xgb.importance <- function(feature_names = NULL, model = NULL,
as.numeric
if(is.null(feature_names))
feature_names <- seq(to = length(weights))
if (length(feature_names) != length(weights))
stop("feature_names has less elements than there are features used in the model")
result <- data.table(Feature = feature_names, Weight = weights)[order(-abs(Weight))]
} else {
# tree model