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add proptype of predleaf in R, fix bug in lambda rank

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tqchen 2015-01-19 09:07:37 -08:00
parent b9650f19c1
commit 632fdbbf5c
4 changed files with 16 additions and 8 deletions
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12
R-package/R/predict.xgb.Booster.R
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@ -11,6 +11,7 @@ setClass("xgb.Booster")
#' value of sum of functions, when outputmargin=TRUE, the prediction is
#' untransformed margin value. In logistic regression, outputmargin=T will
#' output value before logistic transformation.
#' @param predleaf whether predict leaf index instead
#' @param ntreelimit limit number of trees used in prediction, this parameter is
#' only valid for gbtree, but not for gblinear. set it to be value bigger
#' than 0. It will use all trees by default.
@ -25,7 +26,7 @@ setClass("xgb.Booster")
#' @export
#'
setMethod("predict", signature = "xgb.Booster",
definition = function(object, newdata, missing = NULL, outputmargin = FALSE, ntreelimit = NULL) {
definition = function(object, newdata, missing = NULL, outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
if (class(newdata) != "xgb.DMatrix") {
if (is.null(missing)) {
newdata <- xgb.DMatrix(newdata)
@ -40,7 +41,14 @@ setMethod("predict", signature = "xgb.Booster",
stop("predict: ntreelimit must be equal to or greater than 1")
}
}
ret <- .Call("XGBoosterPredict_R", object, newdata, as.integer(outputmargin), as.integer(ntreelimit), PACKAGE = "xgboost")
option = 0
if (outputmargin) {
option <- option + 1
}
if (predleaf) {
option <- option + 2
}
ret <- .Call("XGBoosterPredict_R", object, newdata, as.integer(predleaf), as.integer(ntreelimit), PACKAGE = "xgboost")
return(ret)
})

4
R-package/src/xgboost_R.cpp
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@ -248,12 +248,12 @@ extern "C" {
asInteger(iter),
BeginPtr(vec_dmats), BeginPtr(vec_sptr), len));
}
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP output_margin, SEXP ntree_limit) {
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask, SEXP ntree_limit) {
_WrapperBegin();
bst_ulong olen;
const float *res = XGBoosterPredict(R_ExternalPtrAddr(handle),
R_ExternalPtrAddr(dmat),
asInteger(output_margin),
asInteger(option_mask),
asInteger(ntree_limit),
&olen);
SEXP ret = PROTECT(allocVector(REALSXP, olen));

4
R-package/src/xgboost_R.h
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@ -111,10 +111,10 @@ extern "C" {
* \brief make prediction based on dmat
* \param handle handle
* \param dmat data matrix
* \param output_margin whether only output raw margin value
* \param option_mask output_margin:1 predict_leaf:2
* \param ntree_limit limit number of trees used in prediction
*/
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP output_margin, SEXP ntree_limit);
SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask, SEXP ntree_limit);
/*!
* \brief load model from existing file
* \param handle handle

4
src/learner/objective-inl.hpp
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@ -348,9 +348,9 @@ class LambdaRankObj : public IObjFunction {
float h = loss.SecondOrderGradient(p, 1.0f);
// accumulate gradient and hessian in both pid, and nid
gpair[pos.rindex].grad += g * w;
gpair[pos.rindex].hess += 2.0f * h;
gpair[pos.rindex].hess += 2.0f * w * h;
gpair[neg.rindex].grad -= g * w;
gpair[neg.rindex].hess += 2.0f * h;
gpair[neg.rindex].hess += 2.0f * w * h;
}
}
}