remove temp files
This commit is contained in:
hetong
parent
801a17fa02
commit
de08c5a3da
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\relax
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\providecommand\hyper@newdestlabel[2]{}
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\providecommand\HyperFirstAtBeginDocument{\AtBeginDocument}
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\HyperFirstAtBeginDocument{\ifx\hyper@anchor\@undefined
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\global\let\oldcontentsline\contentsline
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\gdef\contentsline#1#2#3#4{\oldcontentsline{#1}{#2}{#3}}
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\global\let\oldnewlabel\newlabel
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\gdef\newlabel#1#2{\newlabelxx{#1}#2}
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\gdef\newlabelxx#1#2#3#4#5#6{\oldnewlabel{#1}{{#2}{#3}}}
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\AtEndDocument{\ifx\hyper@anchor\@undefined
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\let\contentsline\oldcontentsline
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\let\newlabel\oldnewlabel
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\fi}
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\fi}
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\global\let\hyper@last\relax
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\gdef\HyperFirstAtBeginDocument#1{#1}
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\providecommand\HyField@AuxAddToFields[1]{}
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\providecommand\HyField@AuxAddToCoFields[2]{}
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\citation{friedman2001greedy}
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\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}}
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\@writefile{toc}{\contentsline {section}{\numberline {2}Example with iris}{1}{section.2}}
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\@writefile{toc}{\contentsline {section}{\numberline {3}Advanced Examples}{2}{section.3}}
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\bibstyle{jss}
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\citation{*}
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\bibdata{xgboost}
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\bibcite{friedman2000additive}{{1}{2000}{{Friedman \emph {et~al.}}}{{Friedman, Hastie, Tibshirani \emph {et~al.}}}}
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\bibcite{friedman2001greedy}{{2}{2001}{{Friedman}}{{}}}
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\@writefile{toc}{\contentsline {section}{\numberline {4}The Higgs Boson competition}{3}{section.4}}
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@ -1,24 +0,0 @@
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\begin{thebibliography}{2}
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\newcommand{\enquote}[1]{``#1''}
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\providecommand{\natexlab}[1]{#1}
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\providecommand{\url}[1]{\texttt{#1}}
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\providecommand{\urlprefix}{URL }
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\expandafter\ifx\csname urlstyle\endcsname\relax
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\providecommand{\doi}[1]{doi:\discretionary{}{}{}#1}\else
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\providecommand{\doi}{doi:\discretionary{}{}{}\begingroup
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\urlstyle{rm}\Url}\fi
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\providecommand{\eprint}[2][]{\url{#2}}
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\bibitem[{Friedman \emph{et~al.}(2000)Friedman, Hastie, Tibshirani
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\emph{et~al.}}]{friedman2000additive}
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Friedman J, Hastie T, Tibshirani R, \emph{et~al.} (2000).
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\newblock \enquote{Additive logistic regression: a statistical view of boosting
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(with discussion and a rejoinder by the authors).}
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\newblock \emph{The annals of statistics}, \textbf{28}(2), 337--407.
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\bibitem[{Friedman(2001)}]{friedman2001greedy}
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Friedman JH (2001).
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\newblock \enquote{Greedy function approximation: a gradient boosting machine.}
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\newblock \emph{Annals of Statistics}, pp. 1189--1232.
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\end{thebibliography}
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@ -1,47 +0,0 @@
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This is BibTeX, Version 0.99d (TeX Live 2013/Debian)
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Capacity: max_strings=35307, hash_size=35307, hash_prime=30011
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The top-level auxiliary file: xgboost.aux
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The style file: jss.bst
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Database file #1: xgboost.bib
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Reallocated wiz_functions (elt_size=4) to 6000 items from 3000.
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You've used 2 entries,
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3140 wiz_defined-function locations,
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641 strings with 5430 characters,
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and the built_in function-call counts, 1920 in all, are:
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= -- 162
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> -- 44
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< -- 2
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+ -- 17
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- -- 15
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* -- 149
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:= -- 256
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add.period$ -- 8
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call.type$ -- 2
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change.case$ -- 12
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chr.to.int$ -- 2
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cite$ -- 2
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duplicate$ -- 171
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empty$ -- 175
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format.name$ -- 19
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if$ -- 395
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int.to.chr$ -- 1
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int.to.str$ -- 1
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missing$ -- 24
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newline$ -- 21
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num.names$ -- 8
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pop$ -- 51
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preamble$ -- 1
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purify$ -- 12
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quote$ -- 0
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skip$ -- 53
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stack$ -- 0
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substring$ -- 181
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swap$ -- 65
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text.length$ -- 1
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text.prefix$ -- 0
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top$ -- 0
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type$ -- 18
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warning$ -- 0
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while$ -- 16
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width$ -- 0
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write$ -- 36
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@ -1,4 +0,0 @@
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\BOOKMARK [1][-]{section.1}{Introduction}{}% 1
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\BOOKMARK [1][-]{section.2}{Example with iris}{}% 2
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\BOOKMARK [1][-]{section.3}{Advanced Examples}{}% 3
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\BOOKMARK [1][-]{section.4}{The Higgs Boson competition}{}% 4
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@ -1,319 +0,0 @@
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\documentclass{article}\usepackage[]{graphicx}\usepackage[]{color}
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%% maxwidth is the original width if it is less than linewidth
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%% otherwise use linewidth (to make sure the graphics do not exceed the margin)
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\makeatletter
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\def\maxwidth{ %
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\ifdim\Gin@nat@width>\linewidth
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\linewidth
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\else
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\Gin@nat@width
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\fi
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}
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\makeatother
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\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345}
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\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}%
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\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}%
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\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}%
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\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}%
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\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}%
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\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}%
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\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}%
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\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}%
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\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}%
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\usepackage{framed}
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\makeatletter
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\newenvironment{kframe}{%
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\def\at@end@of@kframe{}%
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\ifinner\ifhmode%
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\def\at@end@of@kframe{\end{minipage}}%
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\begin{minipage}{\columnwidth}%
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\fi\fi%
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\def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep
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\colorbox{shadecolor}{##1}\hskip-\fboxsep
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% There is no \\@totalrightmargin, so:
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\hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}%
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\MakeFramed {\advance\hsize-\width
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\@totalleftmargin\z@ \linewidth\hsize
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\@setminipage}}%
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{\par\unskip\endMakeFramed%
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\at@end@of@kframe}
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\makeatother
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\definecolor{shadecolor}{rgb}{.97, .97, .97}
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\definecolor{messagecolor}{rgb}{0, 0, 0}
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\definecolor{warningcolor}{rgb}{1, 0, 1}
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\definecolor{errorcolor}{rgb}{1, 0, 0}
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\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX
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\usepackage{alltt}
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\RequirePackage{url}
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\usepackage{hyperref}
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\RequirePackage{amsmath}
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\RequirePackage{natbib}
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\RequirePackage[a4paper,lmargin={1.25in},rmargin={1.25in},tmargin={1in},bmargin={1in}]{geometry}
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\makeatletter
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% \VignetteIndexEntry{xgboost: eXtreme Gradient Boosting}
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%\VignetteKeywords{xgboost, gbm, gradient boosting machines}
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%\VignettePackage{xgboost}
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% \VignetteEngine{knitr::knitr}
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\makeatother
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\IfFileExists{upquote.sty}{\usepackage{upquote}}{}
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\begin{document}
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%\SweaveOpts{concordance=TRUE}
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%
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%
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\begin{center}
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\vspace*{6\baselineskip}
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\rule{\textwidth}{1.6pt}\vspace*{-\baselineskip}\vspace*{2pt}
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\rule{\textwidth}{0.4pt}\\[2\baselineskip]
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{\LARGE \textbf{xgboost: eXtreme Gradient Boosting}}\\[1.2\baselineskip]
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\rule{\textwidth}{0.4pt}\vspace*{-\baselineskip}\vspace{3.2pt}
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\rule{\textwidth}{1.6pt}\\[2\baselineskip]
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{\Large Tianqi Chen, Tong He}\\[\baselineskip]
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{\large Package Version: 0.3-0}\\[\baselineskip]
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{\large \today}\par
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\vfill
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\end{center}
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\thispagestyle{empty}
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\clearpage
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\setcounter{page}{1}
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\section{Introduction}
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This is an introductory document of using the \verb@xgboost@ package in R.
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\verb@xgboost@ is short for eXtreme Gradient Boosting package. It is an efficient
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and scalable implementation of gradient boosting framework by \citep{friedman2001greedy}.
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The package includes efficient linear model solver and tree learning algorithm.
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It supports various objective functions, including regression, classification
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and ranking. The package is made to be extendible, so that users are also allowed to define their own objectives easily. It has several features:
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\begin{enumerate}
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\item{Speed: }{\verb@xgboost@ can automatically do parallel computation on
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Windows and Linux, with openmp. It is generally over 10 times faster than
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\verb@gbm@.}
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\item{Input Type: }{\verb@xgboost@ takes several types of input data:}
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\begin{itemize}
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\item{Dense Matrix: }{R's dense matrix, i.e. \verb@matrix@}
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\item{Sparse Matrix: }{R's sparse matrix \verb@Matrix::dgCMatrix@}
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\item{Data File: }{Local data files}
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\item{xgb.DMatrix: }{\verb@xgboost@'s own class. Recommended.}
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\end{itemize}
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\item{Sparsity: }{\verb@xgboost@ accepts sparse input for both tree booster
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and linear booster, and is optimized for sparse input.}
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\item{Customization: }{\verb@xgboost@ supports customized objective function
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and evaluation function}
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\item{Performance: }{\verb@xgboost@ has better performance on several different
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datasets.}
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\end{enumerate}
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\section{Example with iris}
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In this section, we will illustrate some common usage of \verb@xgboost@.
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\begin{knitrout}
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\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
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\begin{alltt}
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\hlkwd{library}\hlstd{(xgboost)}
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\hlkwd{data}\hlstd{(iris)}
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\hlstd{bst} \hlkwb{<-} \hlkwd{xgboost}\hlstd{(}\hlkwd{as.matrix}\hlstd{(iris[,}\hlnum{1}\hlopt{:}\hlnum{4}\hlstd{]),}\hlkwd{as.numeric}\hlstd{(iris[,}\hlnum{5}\hlstd{]}\hlopt{==}\hlstr{'setosa'}\hlstd{),}
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\hlkwc{nrounds} \hlstd{=} \hlnum{5}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## [0] train-rmse:0.351971
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## [1] train-rmse:0.247769
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## [2] train-rmse:0.174418
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## [3] train-rmse:0.122783
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## [4] train-rmse:0.086435
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\end{verbatim}
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\begin{alltt}
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\hlkwd{xgb.save}\hlstd{(bst,} \hlstr{'model.save'}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## [1] TRUE
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\end{verbatim}
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\begin{alltt}
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\hlstd{bst} \hlkwb{=} \hlkwd{xgb.load}\hlstd{(}\hlstr{'model.save'}\hlstd{)}
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\hlstd{pred} \hlkwb{<-} \hlkwd{predict}\hlstd{(bst,} \hlkwd{as.matrix}\hlstd{(iris[,}\hlnum{1}\hlopt{:}\hlnum{4}\hlstd{]))}
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\end{alltt}
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\end{kframe}
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\end{knitrout}
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\verb@xgboost@ is the main function to train a \verb@Booster@, i.e. a model.
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\verb@predict@ does prediction on the model.
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Here we can save the model to a binary local file, and load it when needed.
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We can't inspect the trees inside. However we have another function to save the
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model in plain text.
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\begin{knitrout}
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\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
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\begin{alltt}
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\hlkwd{xgb.dump}\hlstd{(bst,} \hlstr{'model.dump'}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## [1] TRUE
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\end{verbatim}
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\end{kframe}
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\end{knitrout}
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The output looks like
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\begin{verbatim}
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booster[0]:
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0:[f2<2.45] yes=1,no=2,missing=1
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1:leaf=0.147059
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2:[f3<1.65] yes=3,no=4,missing=3
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3:leaf=0.464151
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4:leaf=0.722449
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booster[1]:
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0:[f2<2.45] yes=1,no=2,missing=1
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1:leaf=0.103806
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2:[f2<4.85] yes=3,no=4,missing=3
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3:leaf=0.316341
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4:leaf=0.510365
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\end{verbatim}
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It is important to know \verb@xgboost@'s own data type: \verb@xgb.DMatrix@.
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It speeds up \verb@xgboost@, and is needed for advanced features such as
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training from initial prediction value, weighted training instance.
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We can use \verb@xgb.DMatrix@ to construct an \verb@xgb.DMatrix@ object:
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\begin{knitrout}
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\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
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\begin{alltt}
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\hlstd{iris.mat} \hlkwb{<-} \hlkwd{as.matrix}\hlstd{(iris[,}\hlnum{1}\hlopt{:}\hlnum{4}\hlstd{])}
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\hlstd{iris.label} \hlkwb{<-} \hlkwd{as.numeric}\hlstd{(iris[,}\hlnum{5}\hlstd{]}\hlopt{==}\hlstr{'setosa'}\hlstd{)}
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\hlstd{diris} \hlkwb{<-} \hlkwd{xgb.DMatrix}\hlstd{(iris.mat,} \hlkwc{label} \hlstd{= iris.label)}
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\hlkwd{class}\hlstd{(diris)}
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\end{alltt}
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\begin{verbatim}
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## [1] "xgb.DMatrix"
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\end{verbatim}
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\begin{alltt}
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\hlkwd{getinfo}\hlstd{(diris,}\hlstr{'label'}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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## [36] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [71] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [106] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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## [141] 0 0 0 0 0 0 0 0 0 0
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\end{verbatim}
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\end{kframe}
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\end{knitrout}
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We can also save the matrix to a binary file. Then load it simply with
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\verb@xgb.DMatrix@
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\begin{knitrout}
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\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
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\begin{alltt}
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\hlkwd{xgb.DMatrix.save}\hlstd{(diris,} \hlstr{'iris.xgb.DMatrix'}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## 150x4 matrix with 600 entries is saved to iris.xgb.DMatrix
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## [1] TRUE
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\end{verbatim}
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\begin{alltt}
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\hlstd{diris} \hlkwb{=} \hlkwd{xgb.DMatrix}\hlstd{(}\hlstr{'iris.xgb.DMatrix'}\hlstd{)}
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\end{alltt}
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\begin{verbatim}
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## 150x4 matrix with 600 entries is loaded from iris.xgb.DMatrix
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\end{verbatim}
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\end{kframe}
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\end{knitrout}
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\section{Advanced Examples}
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The function \verb@xgboost@ is a simple function with less parameter, in order
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to be R-friendly. The core training function is wrapped in \verb@xgb.train@. It is more flexible than \verb@xgboost@, but it requires users to read the document a bit more carefully.
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\verb@xgb.train@ only accept a \verb@xgb.DMatrix@ object as its input, while it supports advanced features as custom objective and evaluation functions.
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\begin{knitrout}
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\definecolor{shadecolor}{rgb}{0.969, 0.969, 0.969}\color{fgcolor}\begin{kframe}
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\begin{alltt}
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\hlstd{logregobj} \hlkwb{<-} \hlkwa{function}\hlstd{(}\hlkwc{preds}\hlstd{,} \hlkwc{dtrain}\hlstd{) \{}
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\hlstd{labels} \hlkwb{<-} \hlkwd{getinfo}\hlstd{(dtrain,} \hlstr{"label"}\hlstd{)}
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\hlstd{preds} \hlkwb{<-} \hlnum{1}\hlopt{/}\hlstd{(}\hlnum{1} \hlopt{+} \hlkwd{exp}\hlstd{(}\hlopt{-}\hlstd{preds))}
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\hlstd{grad} \hlkwb{<-} \hlstd{preds} \hlopt{-} \hlstd{labels}
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\hlstd{hess} \hlkwb{<-} \hlstd{preds} \hlopt{*} \hlstd{(}\hlnum{1} \hlopt{-} \hlstd{preds)}
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\hlkwd{return}\hlstd{(}\hlkwd{list}\hlstd{(}\hlkwc{grad} \hlstd{= grad,} \hlkwc{hess} \hlstd{= hess))}
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\hlstd{\}}
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\hlstd{evalerror} \hlkwb{<-} \hlkwa{function}\hlstd{(}\hlkwc{preds}\hlstd{,} \hlkwc{dtrain}\hlstd{) \{}
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\hlstd{labels} \hlkwb{<-} \hlkwd{getinfo}\hlstd{(dtrain,} \hlstr{"label"}\hlstd{)}
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\hlstd{err} \hlkwb{<-} \hlkwd{sqrt}\hlstd{(}\hlkwd{mean}\hlstd{((preds}\hlopt{-}\hlstd{labels)}\hlopt{^}\hlnum{2}\hlstd{))}
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\hlkwd{return}\hlstd{(}\hlkwd{list}\hlstd{(}\hlkwc{metric} \hlstd{=} \hlstr{"MSE"}\hlstd{,} \hlkwc{value} \hlstd{= err))}
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\hlstd{\}}
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\hlstd{dtest} \hlkwb{<-} \hlkwd{slice}\hlstd{(diris,}\hlnum{1}\hlopt{:}\hlnum{100}\hlstd{)}
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\hlstd{watchlist} \hlkwb{<-} \hlkwd{list}\hlstd{(}\hlkwc{eval} \hlstd{= dtest,} \hlkwc{train} \hlstd{= diris)}
|
||||
\hlstd{param} \hlkwb{<-} \hlkwd{list}\hlstd{(}\hlkwc{max_depth} \hlstd{=} \hlnum{2}\hlstd{,} \hlkwc{eta} \hlstd{=} \hlnum{1}\hlstd{,} \hlkwc{silent} \hlstd{=} \hlnum{1}\hlstd{)}
|
||||
|
||||
\hlstd{bst} \hlkwb{<-} \hlkwd{xgb.train}\hlstd{(param, diris,} \hlkwc{nround} \hlstd{=} \hlnum{2}\hlstd{, watchlist, logregobj, evalerror)}
|
||||
\end{alltt}
|
||||
\begin{verbatim}
|
||||
## [1] eval-MSE:1.601 train-MSE:1.76
|
||||
## [2] eval-MSE:2.567 train-MSE:2.745
|
||||
\end{verbatim}
|
||||
\end{kframe}
|
||||
\end{knitrout}
|
||||
|
||||
The gradient and second order gradient is required for the output of customized
|
||||
objective function.
|
||||
|
||||
We also have \verb@slice@ for row extraction. It is useful in
|
||||
cross-validation.
|
||||
|
||||
For a walkthrough demo, please see \verb@R-package/inst/examples/demo.R@ for further
|
||||
details.
|
||||
|
||||
\section{The Higgs Boson competition}
|
||||
|
||||
We have made a demo for \href{http://www.kaggle.com/c/higgs-boson}{the Higgs
|
||||
Boson Machine Learning Challenge}.
|
||||
|
||||
Here are the instructions to make a submission
|
||||
\begin{enumerate}
|
||||
\item Download the \href{http://www.kaggle.com/c/higgs-boson/data}{datasets}
|
||||
and extract them to \verb@data/@.
|
||||
\item Run scripts under \verb@xgboost/demo/kaggle-higgs/@:
|
||||
\href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/higgs-train.R}{higgs-train.R}
|
||||
and \href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/higgs-pred.R}{higgs-pred.R}.
|
||||
The computation will take less than a minute on Intel i7.
|
||||
\item Go to the \href{http://www.kaggle.com/c/higgs-boson/submissions/attach}{submission page}
|
||||
and submit your result.
|
||||
\end{enumerate}
|
||||
|
||||
We provide \href{https://github.com/tqchen/xgboost/blob/master/demo/kaggle-higgs/speedtest.R}{a script}
|
||||
to compare the time cost on the higgs dataset with \verb@gbm@ and \verb@xgboost@.
|
||||
The training set contains 350000 records and 30 features.
|
||||
|
||||
\verb@xgboost@ can automatically do parallel computation. On a machine with Intel
|
||||
i7-4700MQ and 24GB memories, we found that \verb@xgboost@ costs about 35 seconds, which is about 20 times faster
|
||||
than \verb@gbm@. When we limited \verb@xgboost@ to use only one thread, it was
|
||||
still about two times faster than \verb@gbm@.
|
||||
|
||||
Meanwhile, the result from \verb@xgboost@ reaches
|
||||
\href{http://www.kaggle.com/c/higgs-boson/details/evaluation}{3.60@AMS} with a
|
||||
single model. This results stands in the
|
||||
\href{http://www.kaggle.com/c/higgs-boson/leaderboard}{top 30\%} of the
|
||||
competition.
|
||||
|
||||
\bibliographystyle{jss}
|
||||
\nocite{*} % list uncited references
|
||||
\bibliography{xgboost}
|
||||
|
||||
\end{document}
|
||||
|
||||
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Reference in New Issue
Block a user