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Merge pull request #138 from pommedeterresautee/master

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new parameters, refactoring...
This commit is contained in:
Tianqi Chen 2015-01-11 14:27:38 -08:00
commit 9a2ad91b48
8 changed files with 88 additions and 46 deletions
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10
R-package/R/xgb.dump.R
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@ -37,11 +37,15 @@
#'
xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with.stats=FALSE) {
if (class(model) != "xgb.Booster") {
stop("xgb.dump: first argument must be type xgb.Booster")
stop("model: argument must be type xgb.Booster")
}
if (!class(fname) %in% c("character", "NULL")) {
stop("xgb.dump: second argument must be type character when provided")
if (!(class(fname) %in% c("character", "NULL") && length(fname) <= 1)) {
stop("fname: argument must be type character (when provided)")
}
if (!(class(fmap) %in% c("character", "NULL") && length(fname) <= 1)) {
stop("fmap: argument must be type character (when provided)")
}
result <- .Call("XGBoosterDumpModel_R", model, fmap, as.integer(with.stats), PACKAGE = "xgboost")
if(is.null(fname)) {

25
R-package/R/xgb.importance.R
View File

@ -9,6 +9,7 @@
#' @importFrom magrittr %>%
#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).
#' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#'
#' @return A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
#'
@ -38,24 +39,34 @@
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.importance(agaricus.test$data@@Dimnames[[2]], 'xgb.model.dump')
#' xgb.importance(agaricus.test$data@@Dimnames[[2]], model = bst)
#'
#' @export
xgb.importance <- function(feature_names = NULL, filename_dump = NULL){
xgb.importance <- function(feature_names = NULL, filename_dump = NULL, model = NULL){
if (!class(feature_names) %in% c("character", "NULL")) {
stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
}
if (class(filename_dump) != "character" || !file.exists(filename_dump)) {
if (!(class(filename_dump) %in% c("character", "NULL") && length(filename_dump) <= 1)) {
stop("filename_dump: Has to be a path to the model dump file.")
}
text <- readLines(filename_dump)
if (!class(model) %in% c("xgb.Booster", "NULL")) {
stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
}
if(is.null(model)){
text <- readLines(filename_dump)
} else {
text <- xgb.dump(model = model, with.stats = T)
}
if(text[2] == "bias:"){
result <- linearDump(feature_names, text)
result <- readLines(filename_dump) %>% linearDump(feature_names, .)
} else {
result <- treeDump(feature_names, text)
result <- treeDump(feature_names, text = text)
}
result
}

28
R-package/R/xgb.model.dt.tree.R
View File

@ -16,6 +16,8 @@
#' @importFrom stringr str_trim
#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
#' @param model dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @param text dump generated by the \code{xgb.dump} function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
#' @param n_first_tree limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.
#'
#' @return A \code{data.table} of the features used in the model with their gain, cover and few other thing.
@ -49,29 +51,37 @@
#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.model.dt.tree(agaricus.train$data@@Dimnames[[2]], 'xgb.model.dump')
#' xgb.model.dt.tree(agaricus.train$data@@Dimnames[[2]], filename_dump = 'xgb.model.dump')
#'
#' @export
xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, text = NULL, n_first_tree = NULL){
xgb.model.dt.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, text = NULL, n_first_tree = NULL){
if (!class(feature_names) %in% c("character", "NULL")) {
stop("feature_names: Has to be a vector of character or NULL if the model dump already contains feature name. Look at this function documentation to see where to get feature names.")
}
if (!class(filename_dump) %in% c("character", "NULL")) {
stop("filename_dump: Has to be a character vector representing the path to the model dump file.")
} else if (class(filename_dump) == "character" && !file.exists(filename_dump)) {
if (!(class(filename_dump) %in% c("character", "NULL") && length(filename_dump) <= 1)) {
stop("filename_dump: Has to be a character vector of size 1 representing the path to the model dump file.")
} else if (!is.null(filename_dump) && !file.exists(filename_dump)) {
stop("filename_dump: path to the model doesn't exist.")
} else if(is.null(filename_dump) & is.null(text)){
stop("filename_dump: no path and no string version of the model dump have been provided.")
} else if(is.null(filename_dump) && is.null(model) && is.null(text)){
stop("filename_dump & model & text: no path to dump model, no model, no text dump, have been provided.")
}
if (!class(text) %in% c("character", "NULL")) {
if (!class(model) %in% c("xgb.Booster", "NULL")) {
stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
}
if (!class(text) %in% c("character", "NULL")) {
stop("text: Has to be a vector of character or NULL if a path to the model dump has already been provided.")
}
if (!class(n_first_tree) %in% c("numeric", "NULL") | length(n_first_tree) > 1) {
stop("n_first_tree: Has to be a numeric vector of size 1.")
}
if(is.null(text)){
if(!is.null(model)){
text = xgb.dump(model = model, with.stats = T)
} else if(!is.null(filename_dump)){
text <- readLines(filename_dump) %>% str_trim(side = "both")
}

33
R-package/R/xgb.plot.tree.R
View File

@ -17,9 +17,12 @@
#' @importFrom stringr str_trim
#' @importFrom DiagrammeR DiagrammeR
#' @param feature_names names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.
#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).
#' @param filename_dump the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}). Possible to provide a model directly (see \code{model} argument).
#' @param model generated by the \code{xgb.train} function. Avoid the creation of a dump file.
#' @param n_first_tree limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.
#' @param style a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.
#' @param CSSstyle a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.
#' @param width the width of the diagram in pixels.
#' @param height the height of the diagram in pixels.
#'
#' @return A \code{DiagrammeR} of the model.
#'
@ -45,33 +48,41 @@
#'
#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
#' eta = 1, nround = 2,objective = "binary:logistic")
#' xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#'
#' #agaricus.test$data@@Dimnames[[2]] represents the column names of the sparse matrix.
#' xgb.plot.tree(agaricus.train$data@@Dimnames[[2]], 'xgb.model.dump')
#' xgb.plot.tree(agaricus.train$data@@Dimnames[[2]], model = bst)
#'
#' @export
xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, n_first_tree = NULL, styles = NULL){
#'
xgb.plot.tree <- function(feature_names = NULL, filename_dump = NULL, model = NULL, n_first_tree = NULL, CSSstyle = NULL, width = NULL, height = NULL){
if (!class(styles) %in% c("character", "NULL") | length(styles) > 1) {
if (!(class(CSSstyle) %in% c("character", "NULL") && length(CSSstyle) <= 1)) {
stop("style: Has to be a character vector of size 1.")
}
if (!class(model) %in% c("xgb.Booster", "NULL")) {
stop("model: Has to be an object of class xgb.Booster model generaged by the xgb.train function.")
}
allTrees <- xgb.model.dt.tree(feature_names, filename_dump, n_first_tree)
if(is.null(model)){
allTrees <- xgb.model.dt.tree(feature_names = feature_names, filename_dump = filename_dump, n_first_tree = n_first_tree)
} else {
allTrees <- xgb.model.dt.tree(feature_names = feature_names, model = model, n_first_tree = n_first_tree)
}
allTrees[Feature!="Leaf" ,yesPath:= paste(ID,"(", Feature, "<br/>Cover: ", Cover, "<br/>Gain: ", Quality, ")-->|< ", Split, "|", Yes, ">", Yes.Feature, "]", sep = "")]
allTrees[Feature!="Leaf" ,noPath:= paste(ID,"(", Feature, ")-->|>= ", Split, "|", No, ">", No.Feature, "]", sep = "")]
if(is.null(styles)){
styles <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
if(is.null(CSSstyle)){
CSSstyle <- "classDef greenNode fill:#A2EB86, stroke:#04C4AB, stroke-width:2px;classDef redNode fill:#FFA070, stroke:#FF5E5E, stroke-width:2px"
}
yes <- allTrees[Feature!="Leaf", c(Yes)] %>% paste(collapse = ",") %>% paste("class ", ., " greenNode", sep = "")
no <- allTrees[Feature!="Leaf", c(No)] %>% paste(collapse = ",") %>% paste("class ", ., " redNode", sep = "")
path <- allTrees[Feature!="Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(styles, yes, no, sep = ";")
DiagrammeR(path)
path <- allTrees[Feature!="Leaf", c(yesPath, noPath)] %>% .[order(.)] %>% paste(sep = "", collapse = ";") %>% paste("graph LR", .,collapse = "", sep = ";") %>% paste(CSSstyle, yes, no, sep = ";")
DiagrammeR(path, width, height)
}

7
R-package/man/xgb.importance.Rd
View File

@ -4,12 +4,14 @@
\alias{xgb.importance}
\title{Show importance of features in a model}
\usage{
xgb.importance(feature_names = NULL, filename_dump = NULL)
xgb.importance(feature_names = NULL, filename_dump = NULL, model = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (\code{with.stats = T} in function \code{xgb.dump}).}
\item{model}{generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
}
\value{
A \code{data.table} of the features used in the model with their average gain (and their weight for boosted tree model) in the model.
@ -43,9 +45,8 @@ test <- agaricus.test
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.importance(agaricus.test$data@Dimnames[[2]], 'xgb.model.dump')
xgb.importance(agaricus.test$data@Dimnames[[2]], model = bst)
}

10
R-package/man/xgb.model.dt.tree.Rd
View File

@ -4,14 +4,18 @@
\alias{xgb.model.dt.tree}
\title{Convert tree model dump to data.table}
\usage{
xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL, text = NULL,
n_first_tree = NULL)
xgb.model.dt.tree(feature_names = NULL, filename_dump = NULL,
model = NULL, text = NULL, n_first_tree = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
\item{model}{dump generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{text}{dump generated by the \code{xgb.dump} function. Avoid the creation of a dump file. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
\item{n_first_tree}{limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.}
}
\value{
@ -49,6 +53,6 @@ bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], 'xgb.model.dump')
xgb.model.dt.tree(agaricus.train$data@Dimnames[[2]], filename_dump = 'xgb.model.dump')
}

13
R-package/man/xgb.plot.tree.Rd
View File

@ -4,17 +4,19 @@
\alias{xgb.plot.tree}
\title{Plot a boosted tree model}
\usage{
xgb.plot.tree(feature_names = NULL, filename_dump = NULL,
n_first_tree = NULL, styles = NULL)
xgb.plot.tree(feature_names = NULL, filename_dump = NULL, model = NULL,
n_first_tree = NULL, CSSstyle = NULL)
}
\arguments{
\item{feature_names}{names of each feature as a character vector. Can be extracted from a sparse matrix (see example). If model dump already contains feature names, this argument should be \code{NULL}.}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}).}
\item{filename_dump}{the path to the text file storing the model. Model dump must include the gain per feature and per tree (parameter \code{with.stats = T} in function \code{xgb.dump}). Possible to provide a model directly (see \code{model} argument).}
\item{model}{generated by the \code{xgb.train} function. Avoid the creation of a dump file.}
\item{n_first_tree}{limit the plot to the n first trees. If \code{NULL}, all trees of the model are plotted. Performance can be low for huge models.}
\item{style}{a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.}
\item{CSSstyle}{a \code{character} vector storing a css style to customize the appearance of nodes. Look at the \href{https://github.com/knsv/mermaid/wiki}{Mermaid wiki} for more information.}
}
\value{
A \code{DiagrammeR} of the model.
@ -44,9 +46,8 @@ train <- agaricus.train
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
eta = 1, nround = 2,objective = "binary:logistic")
xgb.dump(bst, 'xgb.model.dump', with.stats = T)
#agaricus.test$data@Dimnames[[2]] represents the column names of the sparse matrix.
xgb.plot.tree(agaricus.train$data@Dimnames[[2]], 'xgb.model.dump')
xgb.plot.tree(agaricus.train$data@Dimnames[[2]], model = bst)
}

8
R-package/src/xgboost_R.cpp
View File

@ -3,6 +3,7 @@
#include <utility>
#include <cstring>
#include <cstdio>
#include <sstream>
#include "xgboost_R.h"
#include "wrapper/xgboost_wrapper.h"
#include "src/utils/utils.h"
@ -280,11 +281,10 @@ extern "C" {
asInteger(with_stats),
&olen);
SEXP out = PROTECT(allocVector(STRSXP, olen));
char buffer [2000];
for (size_t i = 0; i < olen; ++i) {
memset(buffer, 0, sizeof buffer);
sprintf (buffer, "booster[%u]:\n%s", static_cast<unsigned>(i), res[i]);
SET_STRING_ELT(out, i, mkChar(buffer));
stringstream stream;
stream << "booster["<<i<<"]\n" << res[i];
SET_STRING_ELT(out, i, mkChar(stream.str().c_str()));
}
_WrapperEnd();
UNPROTECT(1);