Merge remote-tracking branch 'upstream/master'
This commit is contained in:
Vadim Khotilovich
commit
264c222fe0
7
Makefile
7
Makefile
@ -73,7 +73,7 @@ endif
|
||||
|
||||
|
||||
# specify tensor path
|
||||
.PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck java
|
||||
.PHONY: clean all lint clean_all doxygen rcpplint pypack Rpack Rbuild Rcheck java pylint
|
||||
|
||||
|
||||
all: lib/libxgboost.a $(XGBOOST_DYLIB) xgboost
|
||||
@ -131,8 +131,11 @@ rcpplint:
|
||||
python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} R-package/src
|
||||
|
||||
lint: rcpplint
|
||||
python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin
|
||||
python2 dmlc-core/scripts/lint.py xgboost ${LINT_LANG} include src plugin python-package
|
||||
|
||||
pylint:
|
||||
flake8 --ignore E501 python-package
|
||||
flake8 --ignore E501 tests/python
|
||||
clean:
|
||||
$(RM) -rf build build_plugin lib bin *~ */*~ */*/*~ */*/*/*~ */*.o */*/*.o */*/*/*.o xgboost
|
||||
|
||||
|
||||
4
NEWS.md
4
NEWS.md
@ -31,7 +31,9 @@ This file records the changes in xgboost library in reverse chronological order.
|
||||
* JVM Package
|
||||
- Enable xgboost4j for java and scala
|
||||
- XGBoost distributed now runs on Flink and Spark.
|
||||
|
||||
* Support model attributes listing for meta data.
|
||||
- https://github.com/dmlc/xgboost/pull/1198
|
||||
- https://github.com/dmlc/xgboost/pull/1166
|
||||
|
||||
## v0.47 (2016.01.14)
|
||||
|
||||
|
||||
@ -43,12 +43,7 @@ License
|
||||
-------
|
||||
© Contributors, 2016. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
|
||||
|
||||
|
||||
Reference
|
||||
---------
|
||||
- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). Arxiv.1603.02754
|
||||
- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
|
||||
- XGBoost originates from research project at University of Washington, see also the [Project Page at UW](http://dmlc.cs.washington.edu/xgboost.html).
|
||||
|
||||
Acknowledgements
|
||||
----------------
|
||||
- This work was supported in part by ONR (PECASE) N000141010672, NSF IIS 1258741 and the TerraSwarm Research Center sponsored by MARCO and DARPA.
|
||||
|
||||
@ -66,6 +66,7 @@ However, the parameter settings can be applied to all versions
|
||||
|
||||
- [Starter script for Kaggle Higgs Boson](kaggle-higgs)
|
||||
- [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution)
|
||||
- [Benchmarking the most commonly used open source tools for binary classification](https://github.com/szilard/benchm-ml#boosting-gradient-boosted-treesgradient-boosting-machines)
|
||||
|
||||
|
||||
## Machine Learning Challenge Winning Solutions
|
||||
@ -85,6 +86,10 @@ Please send pull requests if you find ones that are missing here.
|
||||
- Owen Zhang, 1st place of the [Avito Context Ad Clicks competition](https://www.kaggle.com/c/avito-context-ad-clicks). Link to [the Kaggle interview](http://blog.kaggle.com/2015/08/26/avito-winners-interview-1st-place-owen-zhang/).
|
||||
- Keiichi Kuroyanagi, 2nd place of the [Airbnb New User Bookings](https://www.kaggle.com/c/airbnb-recruiting-new-user-bookings). Link to [the Kaggle interview](http://blog.kaggle.com/2016/03/17/airbnb-new-user-bookings-winners-interview-2nd-place-keiichi-kuroyanagi-keiku/).
|
||||
- Marios Michailidis, Mathias Müller and Ning Situ, 1st place [Homesite Quote Conversion](https://www.kaggle.com/c/homesite-quote-conversion). Link to [the Kaggle interview](http://blog.kaggle.com/2016/04/08/homesite-quote-conversion-winners-write-up-1st-place-kazanova-faron-clobber/).
|
||||
|
||||
## Talks
|
||||
- [XGBoost: A Scalable Tree Boosting System](http://datascience.la/xgboost-workshop-and-meetup-talk-with-tianqi-chen/) (video+slides) by Tianqi Chen at the Los Angeles Data Science meetup
|
||||
|
||||
## Tutorials
|
||||
|
||||
- [XGBoost Official RMarkdown Tutorials](https://xgboost.readthedocs.org/en/latest/R-package/index.html#tutorials)
|
||||
|
||||
@ -12,15 +12,18 @@ print ('running cross validation')
|
||||
# [iteration] metric_name:mean_value+std_value
|
||||
# std_value is standard deviation of the metric
|
||||
xgb.cv(param, dtrain, num_round, nfold=5,
|
||||
metrics={'error'}, seed = 0)
|
||||
metrics={'error'}, seed = 0,
|
||||
callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
|
||||
|
||||
print ('running cross validation, disable standard deviation display')
|
||||
# do cross validation, this will print result out as
|
||||
# [iteration] metric_name:mean_value+std_value
|
||||
# std_value is standard deviation of the metric
|
||||
xgb.cv(param, dtrain, num_round, nfold=5,
|
||||
metrics={'error'}, seed = 0, show_stdv = False)
|
||||
|
||||
res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
|
||||
metrics={'error'}, seed = 0,
|
||||
callbacks=[xgb.callback.print_evaluation(show_stdv=False),
|
||||
xgb.callback.early_stop(3)])
|
||||
print (res)
|
||||
print ('running cross validation, with preprocessing function')
|
||||
# define the preprocessing function
|
||||
# used to return the preprocessed training, test data, and parameter
|
||||
@ -58,4 +61,3 @@ param = {'max_depth':2, 'eta':1, 'silent':1}
|
||||
# train with customized objective
|
||||
xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
|
||||
obj = logregobj, feval=evalerror)
|
||||
|
||||
|
||||
@ -1 +1 @@
|
||||
Subproject commit 1db0792e1a55355b1f07699bba18c88ded996953
|
||||
Subproject commit 9fd3b48462a7a651e12a197679f71e043dcb25a2
|
||||
@ -9,7 +9,7 @@ You have find XGBoost R Package!
|
||||
Get Started
|
||||
-----------
|
||||
* Checkout the [Installation Guide](../build.md) contains instructions to install xgboost, and [Tutorials](#tutorials) for examples on how to use xgboost for various tasks.
|
||||
* Please visit [walk through example](demo).
|
||||
* Please visit [walk through example](../../R-package/demo).
|
||||
|
||||
Tutorials
|
||||
---------
|
||||
|
||||
20
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20
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<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="640" height="480" viewBox="-5 -5 12.8 9.6">
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<title>
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Flag of the People's Republic of China
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25
doc/_static/js/auto_module_index.js
vendored
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25
doc/_static/js/auto_module_index.js
vendored
Normal file
@ -0,0 +1,25 @@
|
||||
function auto_index(module) {
|
||||
$(document).ready(function () {
|
||||
// find all classes or functions
|
||||
var div_query = "div[class='section'][id='module-" + module + "']";
|
||||
var class_query = div_query + " dl[class='class'] > dt";
|
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var func_query = div_query + " dl[class='function'] > dt";
|
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var targets = $(class_query + ',' + func_query);
|
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|
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var li_node = $("li a[href='#module-" + module + "']").parent();
|
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var html = "<ul>";
|
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|
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for (var i = 0; i < targets.length; ++i) {
|
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var id = $(targets[i]).attr('id');
|
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// remove 'mxnet.' prefix to make menus shorter
|
||||
var id_simple = id.replace(/^mxnet\./, '');
|
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html += "<li><a class='reference internal' href='#";
|
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html += id;
|
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html += "'>" + id_simple + "</a></li>";
|
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}
|
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|
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html += "</ul>";
|
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li_node.append(html);
|
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});
|
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}
|
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|
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117
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<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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<cc:Work rdf:about="">
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|
||||
<polygon id="polygon216" transform="translate(74.1,7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
|
||||
<polygon id="polygon217" transform="translate(90.567 7)" d="M 1.3084868e-6,-4.0040002 0.89895399,-1.237304 3.8080309,-1.2373029 1.4545381,0.47260808 2.3534912,3.239305 0,1.5293919 l -2.3534933,1.7099115 0.8989552,-2.76669531 -2.353492,-1.70991339 2.9090761,1.3e-6 z" points="0 -4.004 0.89895 -1.2373 3.808 -1.2373 1.4545 0.47261 2.3535 3.2393 0 1.5294 -2.3535 3.2393 -1.4545 0.47261 -3.808 -1.2373 -0.89895 -1.2373"/>
|
||||
</g>
|
||||
</g>
|
||||
</g>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 23 KiB |
5
doc/_static/xgboost-theme/footer.html
vendored
Normal file
5
doc/_static/xgboost-theme/footer.html
vendored
Normal file
@ -0,0 +1,5 @@
|
||||
<div class="container">
|
||||
<div class="footer">
|
||||
<p> © 2015-2016 DMLC. All rights reserved. </p>
|
||||
</div>
|
||||
</div>
|
||||
58
doc/_static/xgboost-theme/index.html
vendored
Normal file
58
doc/_static/xgboost-theme/index.html
vendored
Normal file
@ -0,0 +1,58 @@
|
||||
<div class="splash">
|
||||
<div class="container">
|
||||
<div class="row">
|
||||
<div class="col-lg-12">
|
||||
<h1>Scalable and Flexible Gradient Boosting</h1>
|
||||
<div id="social">
|
||||
<span>
|
||||
<iframe src="https://ghbtns.com/github-btn.html?user=dmlc&repo=xgboost&type=star&count=true&v=2"
|
||||
frameborder="0" scrolling="0" width="120px" height="20px"></iframe>
|
||||
<iframe src="https://ghbtns.com/github-btn.html?user=dmlc&repo=xgboost&type=fork&count=true&v=2"
|
||||
frameborder="0" scrolling="0" width="100px" height="20px"></iframe>
|
||||
</span>
|
||||
</div> <!-- end of social -->
|
||||
<div class="get_start">
|
||||
<a href="get_started/" class="get_start_btn">Get Started</a>
|
||||
</div> <!-- end of get started button -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div class="section-tout">
|
||||
<div class="container">
|
||||
<div class="row">
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-flag"></i> Flexible</h3>
|
||||
<p>Supports regression, classification, ranking and user defined objectives.
|
||||
</p>
|
||||
</div>
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-cube"></i> Portable</h3>
|
||||
<p>Runs on Windows, Lunix and OSX, as well as various cloud Platforms</p>
|
||||
</div>
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-wrench"></i>Multiple Lanuages</h3>
|
||||
<p>Supports multiple languages including C++, Python, R, Java, Scala, Julia.</p>
|
||||
</div>
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-cogs"></i> Battle-tested</h3>
|
||||
<p>Wins many data science and machine learning challenges.
|
||||
Used in production by multiple companies.
|
||||
</p>
|
||||
</div>
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-cloud"></i>Distributed on Cloud</h3>
|
||||
<p>Supports distributed training on multiple machines, including AWS,
|
||||
GCE, Azure, and Yarn clusters. Can be integrated with Flink, Spark and other cloud dataflow systems.</p>
|
||||
</div>
|
||||
<div class="col-lg-4 col-sm-6">
|
||||
<h3><i class="fa fa-rocket"></i> Performance</h3>
|
||||
<p>The well-optimized backend system for the best performance with limited resources.
|
||||
The distributed version solves problems beyond billions of examples with same code.
|
||||
</p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
156
doc/_static/xgboost-theme/layout.html
vendored
Normal file
156
doc/_static/xgboost-theme/layout.html
vendored
Normal file
@ -0,0 +1,156 @@
|
||||
{%- block doctype -%}
|
||||
<!DOCTYPE html>
|
||||
{%- endblock %}
|
||||
{%- set reldelim1 = reldelim1 is not defined and ' »' or reldelim1 %}
|
||||
{%- set reldelim2 = reldelim2 is not defined and ' |' or reldelim2 %}
|
||||
{%- set render_sidebar = (not embedded) and (not theme_nosidebar|tobool) and
|
||||
(sidebars != []) %}
|
||||
{%- set url_root = pathto('', 1) %}
|
||||
{%- if url_root == '#' %}{% set url_root = '' %}{% endif %}
|
||||
{%- if not embedded and docstitle %}
|
||||
{%- set titlesuffix = " — "|safe + docstitle|e %}
|
||||
{%- else %}
|
||||
{%- set titlesuffix = "" %}
|
||||
{%- endif %}
|
||||
|
||||
{%- macro searchform(classes, button) %}
|
||||
<form class="{{classes}}" role="search" action="{{ pathto('search') }}" method="get">
|
||||
<div class="form-group">
|
||||
<input type="text" name="q" class="form-control" {{ 'placeholder="Search"' if not button }} >
|
||||
</div>
|
||||
<input type="hidden" name="check_keywords" value="yes" />
|
||||
<input type="hidden" name="area" value="default" />
|
||||
{% if button %}
|
||||
<input type="submit" class="btn btn-default" value="search">
|
||||
{% endif %}
|
||||
</form>
|
||||
{%- endmacro %}
|
||||
|
||||
{%- macro sidebarglobal() %}
|
||||
<ul class="globaltoc">
|
||||
{{ toctree(maxdepth=2|toint, collapse=False,includehidden=theme_globaltoc_includehidden|tobool) }}
|
||||
</ul>
|
||||
{%- endmacro %}
|
||||
|
||||
{%- macro sidebar() %}
|
||||
{%- if render_sidebar %}
|
||||
<div class="sphinxsidebar" role="navigation" aria-label="main navigation">
|
||||
<div class="sphinxsidebarwrapper">
|
||||
{%- block sidebartoc %}
|
||||
{%- include "localtoc.html" %}
|
||||
{%- endblock %}
|
||||
</div>
|
||||
</div>
|
||||
{%- endif %}
|
||||
{%- endmacro %}
|
||||
|
||||
|
||||
{%- macro script() %}
|
||||
<script type="text/javascript">
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: '{{ url_root }}',
|
||||
VERSION: '{{ release|e }}',
|
||||
COLLAPSE_INDEX: false,
|
||||
FILE_SUFFIX: '{{ '' if no_search_suffix else file_suffix }}',
|
||||
HAS_SOURCE: {{ has_source|lower }}
|
||||
};
|
||||
</script>
|
||||
|
||||
{% for name in ['jquery.js', 'underscore.js', 'doctools.js', 'searchtools.js'] %}
|
||||
<script type="text/javascript" src="{{ pathto('_static/' + name, 1) }}"></script>
|
||||
{% endfor %}
|
||||
|
||||
<script type="text/javascript" src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"></script>
|
||||
|
||||
<!-- {%- for scriptfile in script_files %} -->
|
||||
<!-- <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script> -->
|
||||
<!-- {%- endfor %} -->
|
||||
{%- endmacro %}
|
||||
|
||||
{%- macro css() %}
|
||||
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css" integrity="sha384-1q8mTJOASx8j1Au+a5WDVnPi2lkFfwwEAa8hDDdjZlpLegxhjVME1fgjWPGmkzs7" crossorigin="anonymous">
|
||||
{% if pagename == 'index' %}
|
||||
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/font-awesome/4.5.0/css/font-awesome.min.css">
|
||||
{%- else %}
|
||||
<link rel="stylesheet" href="{{ pathto('_static/' + style, 1) }}" type="text/css" />
|
||||
<link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" />
|
||||
{%- endif %}
|
||||
|
||||
<link rel="stylesheet" href="{{ pathto('_static/xgboost.css', 1) }}">
|
||||
{%- endmacro %}
|
||||
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="{{ encoding }}">
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
{# The above 3 meta tags *must* come first in the head; any other head content
|
||||
must come *after* these tags. #}
|
||||
{{ metatags }}
|
||||
{%- block htmltitle %}
|
||||
{%- if pagename != 'index' %}
|
||||
<title>{{ title|striptags|e }}{{ titlesuffix }}</title>
|
||||
{%- else %}
|
||||
<title>XGBoost Documents</title>
|
||||
{%- endif %}
|
||||
{%- endblock %}
|
||||
{{ css() }}
|
||||
{%- if not embedded %}
|
||||
{{ script() }}
|
||||
{%- if use_opensearch %}
|
||||
<link rel="search" type="application/opensearchdescription+xml"
|
||||
title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
|
||||
href="{{ pathto('_static/opensearch.xml', 1) }}"/>
|
||||
{%- endif %}
|
||||
{%- if favicon %}
|
||||
<link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- block linktags %}
|
||||
{%- if hasdoc('about') %}
|
||||
<link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
|
||||
{%- endif %}
|
||||
{%- if hasdoc('genindex') %}
|
||||
<link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
|
||||
{%- endif %}
|
||||
{%- if hasdoc('search') %}
|
||||
<link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
|
||||
{%- endif %}
|
||||
{%- if hasdoc('copyright') %}
|
||||
<link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
|
||||
{%- endif %}
|
||||
{%- if parents %}
|
||||
<link rel="up" title="{{ parents[-1].title|striptags|e }}" href="{{ parents[-1].link|e }}" />
|
||||
{%- endif %}
|
||||
{%- if next %}
|
||||
<link rel="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}" />
|
||||
{%- endif %}
|
||||
{%- if prev %}
|
||||
<link rel="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}" />
|
||||
{%- endif %}
|
||||
{%- endblock %}
|
||||
{%- block extrahead %} {% endblock %}
|
||||
|
||||
<link rel="icon" type="image/png" href="https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/image/mxnet-icon.png">
|
||||
</head>
|
||||
<body role="document">
|
||||
{%- include "navbar.html" %}
|
||||
|
||||
{% if pagename != 'index' %}
|
||||
<div class="container">
|
||||
<div class="row">
|
||||
{{ sidebar() }}
|
||||
<div class="content">
|
||||
{% block body %} {% endblock %}
|
||||
{%- include "footer.html" %}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
{%- else %}
|
||||
{%- include "index.html" %}
|
||||
{%- include "footer.html" %}
|
||||
{%- endif %} <!-- pagename != index -->
|
||||
|
||||
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js" integrity="sha384-0mSbJDEHialfmuBBQP6A4Qrprq5OVfW37PRR3j5ELqxss1yVqOtnepnHVP9aJ7xS" crossorigin="anonymous"></script>
|
||||
</body>
|
||||
</html>
|
||||
40
doc/_static/xgboost-theme/navbar.html
vendored
Normal file
40
doc/_static/xgboost-theme/navbar.html
vendored
Normal file
@ -0,0 +1,40 @@
|
||||
<div class="navbar navbar-default navbar-fixed-top">
|
||||
<div class="container">
|
||||
<div class="navbar-header">
|
||||
<button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false" aria-controls="navbar">
|
||||
<span class="sr-only">Toggle navigation</span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
<span class="icon-bar"></span>
|
||||
</button>
|
||||
</div>
|
||||
<div id="navbar" class="navbar-collapse collapse">
|
||||
<ul id="navbar" class="navbar navbar-left">
|
||||
<li> <a href="{{url_root}}">XGBoost</a> </li>
|
||||
{% for name in ['Get Started', 'Tutorials', 'How To'] %}
|
||||
<li> <a href="{{url_root}}{{name.lower()|replace(" ", "_")}}/index.html">{{name}}</a> </li>
|
||||
{% endfor %}
|
||||
{% for name in ['Packages'] %}
|
||||
<li class="dropdown">
|
||||
<a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="true">{{name}} <span class="caret"></span></a>
|
||||
<ul class="dropdown-menu">
|
||||
<li><a href="{{url_root}}python/index.html">Python</a></li>
|
||||
<li><a href="{{url_root}}R-package/index.html">R</a></li>
|
||||
<li><a href="{{url_root}}jvm/index.html">JVM</a></li>
|
||||
<li><a href="{{url_root}}julia/index.html">Julia</a></li>
|
||||
<li><a href="{{url_root}}cli/index.html">CLI</a></li>
|
||||
</ul>
|
||||
</li>
|
||||
{% endfor %}
|
||||
<li> <a href="{{url_root}}/parameter.html"> Knobs </a> </li>
|
||||
<li> {{searchform('', False)}} </li>
|
||||
</ul>
|
||||
<!--
|
||||
<ul id="navbar" class="navbar navbar-right">
|
||||
<li> <a href="{{url_root}}index.html"><span class="flag-icon flag-icon-us"></span></a> </li>
|
||||
<li> <a href="{{url_root}}/zh/index.html"><span class="flag-icon flag-icon-cn"></span></a> </li>
|
||||
</ul>
|
||||
navbar -->
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
2
doc/_static/xgboost-theme/theme.conf
vendored
Normal file
2
doc/_static/xgboost-theme/theme.conf
vendored
Normal file
@ -0,0 +1,2 @@
|
||||
[theme]
|
||||
inherit = basic
|
||||
232
doc/_static/xgboost.css
vendored
Normal file
232
doc/_static/xgboost.css
vendored
Normal file
@ -0,0 +1,232 @@
|
||||
/* header section */
|
||||
.splash{
|
||||
padding:5em 0 1em 0;
|
||||
background-color:#0079b2;
|
||||
/* background-image:url(../img/bg.jpg); */
|
||||
background-size:cover;
|
||||
background-attachment:fixed;
|
||||
color:#fff;
|
||||
text-align:center
|
||||
}
|
||||
|
||||
.splash h1{
|
||||
font-size: 40px;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
.splash .social{
|
||||
margin:2em 0
|
||||
}
|
||||
|
||||
.splash .get_start {
|
||||
margin:2em 0
|
||||
}
|
||||
|
||||
.splash .get_start_btn {
|
||||
border: 2px solid #FFFFFF;
|
||||
border-radius: 5px;
|
||||
color: #FFFFFF;
|
||||
display: inline-block;
|
||||
font-size: 26px;
|
||||
padding: 9px 20px;
|
||||
}
|
||||
|
||||
.section-tout{
|
||||
padding:3em 0 3em;
|
||||
border-bottom:1px solid rgba(0,0,0,.05);
|
||||
background-color:#eaf1f1
|
||||
}
|
||||
.section-tout .fa{
|
||||
margin-right:.5em
|
||||
}
|
||||
|
||||
.section-tout h3{
|
||||
font-size:20px;
|
||||
}
|
||||
|
||||
.section-tout p {
|
||||
margin-bottom:2em
|
||||
}
|
||||
|
||||
.section-inst{
|
||||
padding:3em 0 3em;
|
||||
border-bottom:1px solid rgba(0,0,0,.05);
|
||||
|
||||
text-align:center
|
||||
}
|
||||
|
||||
.section-inst p {
|
||||
margin-bottom:2em
|
||||
}
|
||||
.section-inst img {
|
||||
-webkit-filter: grayscale(90%); /* Chrome, Safari, Opera */
|
||||
filter: grayscale(90%);
|
||||
margin-bottom:2em
|
||||
}
|
||||
.section-inst img:hover {
|
||||
-webkit-filter: grayscale(0%); /* Chrome, Safari, Opera */
|
||||
filter: grayscale(0%);
|
||||
}
|
||||
|
||||
.footer{
|
||||
padding-top: 40px;
|
||||
}
|
||||
.footer li{
|
||||
float:right;
|
||||
margin-right:1.5em;
|
||||
margin-bottom:1.5em
|
||||
}
|
||||
.footer p{
|
||||
font-size: 15px;
|
||||
color: #888;
|
||||
clear:right;
|
||||
margin-bottom:0
|
||||
}
|
||||
|
||||
|
||||
/* sidebar */
|
||||
div.sphinxsidebar {
|
||||
margin-top: 20px;
|
||||
margin-left: 0;
|
||||
position: fixed;
|
||||
overflow-y: scroll;
|
||||
width: 250px;
|
||||
top: 52px;
|
||||
bottom: 0;
|
||||
display: none
|
||||
}
|
||||
div.sphinxsidebar ul { padding: 0 }
|
||||
div.sphinxsidebar ul ul { margin-left: 15px }
|
||||
|
||||
@media (min-width:1200px) {
|
||||
.content { float: right; width: 66.66666667%; margin-right: 5% }
|
||||
div.sphinxsidebar {display: block}
|
||||
}
|
||||
|
||||
|
||||
.github-btn { border: 0; overflow: hidden }
|
||||
|
||||
.container {
|
||||
margin-right: auto;
|
||||
margin-left: auto;
|
||||
padding-left: 15px;
|
||||
padding-right: 15px
|
||||
}
|
||||
|
||||
body>.container {
|
||||
padding-top: 80px
|
||||
}
|
||||
|
||||
body {
|
||||
font-size: 16px;
|
||||
}
|
||||
|
||||
pre {
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
/* navbar */
|
||||
.navbar {
|
||||
background-color:#0079b2;
|
||||
border: 0px;
|
||||
height: 65px;
|
||||
}
|
||||
.navbar-right li {
|
||||
display:inline-block;
|
||||
vertical-align:top;
|
||||
padding: 22px 4px;
|
||||
}
|
||||
|
||||
.navbar-left li {
|
||||
display:inline-block;
|
||||
vertical-align:top;
|
||||
padding: 17px 10px;
|
||||
/* margin: 0 5px; */
|
||||
}
|
||||
|
||||
.navbar-left li a {
|
||||
font-size: 22px;
|
||||
color: #fff;
|
||||
}
|
||||
|
||||
.navbar-left > li > a:hover{
|
||||
color:#fff;
|
||||
}
|
||||
.flag-icon {
|
||||
background-size: contain;
|
||||
background-position: 50%;
|
||||
background-repeat: no-repeat;
|
||||
position: relative;
|
||||
display: inline-block;
|
||||
width: 1.33333333em;
|
||||
line-height: 1em;
|
||||
}
|
||||
|
||||
.flag-icon:before {
|
||||
content: "\00a0";
|
||||
}
|
||||
|
||||
|
||||
.flag-icon-cn {
|
||||
background-image: url(./cn.svg);
|
||||
}
|
||||
|
||||
.flag-icon-us {
|
||||
background-image: url(./us.svg);
|
||||
}
|
||||
|
||||
|
||||
/* .flags { */
|
||||
/* padding: 10px; */
|
||||
/* } */
|
||||
|
||||
.navbar-brand >img {
|
||||
width: 110px;
|
||||
}
|
||||
|
||||
.dropdown-menu li {
|
||||
padding: 0px 0px;
|
||||
width: 120px;
|
||||
}
|
||||
.dropdown-menu li a {
|
||||
color: #0079b2;
|
||||
font-size: 20px;
|
||||
}
|
||||
|
||||
.section h1 {
|
||||
padding-top: 90px;
|
||||
margin-top: -60px;
|
||||
padding-bottom: 10px;
|
||||
font-size: 28px;
|
||||
}
|
||||
|
||||
.section h2 {
|
||||
padding-top: 80px;
|
||||
margin-top: -60px;
|
||||
padding-bottom: 10px;
|
||||
font-size: 22px;
|
||||
}
|
||||
|
||||
.section h3 {
|
||||
padding-top: 80px;
|
||||
margin-top: -64px;
|
||||
padding-bottom: 8px;
|
||||
}
|
||||
|
||||
.section h4 {
|
||||
padding-top: 80px;
|
||||
margin-top: -64px;
|
||||
padding-bottom: 8px;
|
||||
}
|
||||
|
||||
dt {
|
||||
margin-top: -76px;
|
||||
padding-top: 76px;
|
||||
}
|
||||
|
||||
dt:target, .highlighted {
|
||||
background-color: #fff;
|
||||
}
|
||||
|
||||
.section code.descname {
|
||||
font-size: 1em;
|
||||
}
|
||||
3
doc/cli/index.md
Normal file
3
doc/cli/index.md
Normal file
@ -0,0 +1,3 @@
|
||||
# XGBoost Command Line version
|
||||
|
||||
See [XGBoost Command Line walkthrough](https://github.com/dmlc/xgboost/blob/master/demo/binary_classification/README.md)
|
||||
@ -117,10 +117,11 @@ todo_include_todos = False
|
||||
|
||||
# -- Options for HTML output ----------------------------------------------
|
||||
|
||||
html_theme_path = ['_static']
|
||||
# The theme to use for HTML and HTML Help pages. See the documentation for
|
||||
# a list of builtin themes.
|
||||
# html_theme = 'alabaster'
|
||||
html_theme = 'sphinx_rtd_theme'
|
||||
html_theme = 'xgboost-theme'
|
||||
|
||||
# Add any paths that contain custom static files (such as style sheets) here,
|
||||
# relative to this directory. They are copied after the builtin static files,
|
||||
|
||||
80
doc/get_started/index.md
Normal file
80
doc/get_started/index.md
Normal file
@ -0,0 +1,80 @@
|
||||
# Get Started with XGBoost
|
||||
|
||||
This is a quick started tutorial showing snippets for you to quickly try out xgboost
|
||||
on the demo dataset on a binary classification task.
|
||||
|
||||
## Links to Helpful Other Resources
|
||||
- See [Installation Guide](../build.md) on how to install xgboost.
|
||||
- See [How to pages](../how_to/index.md) on various tips on using xgboost.
|
||||
- See [Tutorials](../tutorials/index.md) on tutorials on specific tasks.
|
||||
- See [Learning to use XGBoost by Examples](../../demo) for more code examples.
|
||||
|
||||
## Python
|
||||
```python
|
||||
import xgboost as xgb
|
||||
# read in data
|
||||
dtrain = xgb.DMatrix('demo/data/agaricus.txt.train')
|
||||
dtest = xgb.DMatrix('demo/data/agaricus.txt.test')
|
||||
# specify parameters via map
|
||||
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
|
||||
num_round = 2
|
||||
bst = xgb.train(param, dtrain, num_round)
|
||||
# make prediction
|
||||
preds = bst.predict(dtest)
|
||||
```
|
||||
|
||||
## R
|
||||
|
||||
```r
|
||||
# load data
|
||||
data(agaricus.train, package='xgboost')
|
||||
data(agaricus.test, package='xgboost')
|
||||
train <- agaricus.train
|
||||
test <- agaricus.test
|
||||
# fit model
|
||||
bst <- xgboost(data = train$data, label = train$label, max.depth = 2, eta = 1, nround = 2,
|
||||
nthread = 2, objective = "binary:logistic")
|
||||
# predict
|
||||
pred <- predict(bst, test$data)
|
||||
|
||||
```
|
||||
|
||||
## Julia
|
||||
```julia
|
||||
using XGBoost
|
||||
# read data
|
||||
train_X, train_Y = readlibsvm("demo/data/agaricus.txt.train", (6513, 126))
|
||||
test_X, test_Y = readlibsvm("demo/data/agaricus.txt.test", (1611, 126))
|
||||
# fit model
|
||||
num_round = 2
|
||||
bst = xgboost(train_X, num_round, label=train_Y, eta=1, max_depth=2)
|
||||
# predict
|
||||
pred = predict(bst, test_X)
|
||||
```
|
||||
|
||||
## Scala
|
||||
```scala
|
||||
import ml.dmlc.xgboost4j.scala.DMatrix
|
||||
import ml.dmlc.xgboost4j.scala.XGBoost
|
||||
|
||||
object XGBoostScalaExample {
|
||||
def main(args: Array[String]) {
|
||||
// read trainining data, available at xgboost/demo/data
|
||||
val trainData =
|
||||
new DMatrix("/path/to/agaricus.txt.train")
|
||||
// define parameters
|
||||
val paramMap = List(
|
||||
"eta" -> 0.1,
|
||||
"max_depth" -> 2,
|
||||
"objective" -> "binary:logistic").toMap
|
||||
// number of iterations
|
||||
val round = 2
|
||||
// train the model
|
||||
val model = XGBoost.train(trainData, paramMap, round)
|
||||
// run prediction
|
||||
val predTrain = model.predict(trainData)
|
||||
// save model to the file.
|
||||
model.saveModel("/local/path/to/model")
|
||||
}
|
||||
}
|
||||
```
|
||||
16
doc/how_to/index.md
Normal file
16
doc/how_to/index.md
Normal file
@ -0,0 +1,16 @@
|
||||
# XGBoost How To
|
||||
|
||||
This page contains guidelines to use and develop mxnets.
|
||||
|
||||
## Installation
|
||||
- [How to Install XGBoost](../build.md)
|
||||
|
||||
## Use XGBoost in Specific Ways
|
||||
- [Parameter tunning guide](param_tuning.md)
|
||||
- [Use out of core computation for large dataset](external_memory.md)
|
||||
|
||||
## Develop and Hack XGBoost
|
||||
- [Contribute to XGBoost](contribute.md)
|
||||
|
||||
## Frequently Ask Questions
|
||||
- [FAQ](../faq.md)
|
||||
58
doc/index.md
58
doc/index.md
@ -1,59 +1,15 @@
|
||||
XGBoost Documentation
|
||||
=====================
|
||||
This is document of xgboost library.
|
||||
XGBoost is short for eXtreme gradient boosting. This is a library that is designed, and optimized for boosted (tree) algorithms.
|
||||
The goal of this library is to push the extreme of the computation limits of machines to provide a ***scalable***, ***portable*** and ***accurate***
|
||||
for large scale tree boosting.
|
||||
|
||||
This document is hosted at http://xgboost.readthedocs.org/. You can also browse most of the documents in github directly.
|
||||
|
||||
|
||||
Package Documents
|
||||
-----------------
|
||||
This section contains language specific package guide.
|
||||
* [XGBoost Command Line Usage Walkthrough](../demo/binary_classification/README.md)
|
||||
These are used to generate the index used in search.
|
||||
|
||||
* [Python Package Document](python/index.md)
|
||||
* [R Package Document](R-package/index.md)
|
||||
* [Java/Scala Package Document](jvm/index.md)
|
||||
* [XGBoost.jl Julia Package](https://github.com/dmlc/XGBoost.jl)
|
||||
|
||||
User Guides
|
||||
-----------
|
||||
This section contains users guides that are general across languages.
|
||||
* [Installation Guide](build.md)
|
||||
* [Introduction to Boosted Trees](model.md)
|
||||
* [Distributed Training Tutorial](tutorial/aws_yarn.md)
|
||||
* [Frequently Asked Questions](faq.md)
|
||||
* [External Memory Version](external_memory.md)
|
||||
* [Learning to use XGBoost by Example](../demo)
|
||||
* [Parameters](parameter.md)
|
||||
* [Text input format](input_format.md)
|
||||
* [Notes on Parameter Tunning](param_tuning.md)
|
||||
|
||||
|
||||
Tutorials
|
||||
---------
|
||||
This section contains official tutorials of XGBoost package.
|
||||
See [Awesome XGBoost](https://github.com/dmlc/xgboost/tree/master/demo) for links to mores resources.
|
||||
* [Introduction to XGBoost in R](R-package/xgboostPresentation.md) (R package)
|
||||
- This is a general presentation about xgboost in R.
|
||||
* [Discover your data with XGBoost in R](R-package/discoverYourData.md) (R package)
|
||||
- This tutorial explaining feature analysis in xgboost.
|
||||
* [Introduction of XGBoost in Python](python/python_intro.md) (python)
|
||||
- This tutorial introduces the python package of xgboost
|
||||
* [Understanding XGBoost Model on Otto Dataset](../demo/kaggle-otto/understandingXGBoostModel.Rmd) (R package)
|
||||
- This tutorial teaches you how to use xgboost to compete kaggle otto challenge.
|
||||
|
||||
Developer Guide
|
||||
---------------
|
||||
* [Contributor Guide](dev-guide/contribute.md)
|
||||
|
||||
|
||||
Indices and tables
|
||||
------------------
|
||||
|
||||
```eval_rst
|
||||
* :ref:`genindex`
|
||||
* :ref:`modindex`
|
||||
* :ref:`search`
|
||||
```
|
||||
* [Julia Package Document](julia/index.md)
|
||||
* [CLI Package Document](cli/index.md)
|
||||
- [Howto Documents](how_to/index.md)
|
||||
- [Get Started Documents](get_started/index.md)
|
||||
- [Tutorials](tutorials/index.md)
|
||||
|
||||
3
doc/julia/index.md
Normal file
3
doc/julia/index.md
Normal file
@ -0,0 +1,3 @@
|
||||
# XGBoost.jl
|
||||
|
||||
See [XGBoost.jl Project page](https://github.com/dmlc/XGBoost.jl)
|
||||
@ -7,20 +7,24 @@ You have find XGBoost JVM Package!
|
||||
|
||||
Installation
|
||||
------------
|
||||
Currently, XGBoost4J only support installation from source. Building XGBoost4J using Maven requires Maven 3 or newer and Java 7+.
|
||||
Currently, XGBoost4J only support installation from source. Building XGBoost4J using Maven requires Maven 3 or newer and Java 7+.
|
||||
|
||||
Before you install XGBoost4J, you need to define environment variable `JAVA_HOME` as your JDK directory to ensure that your compiler can find `jni.h` correctly, since XGBoost4J relies on JNI to implement the interaction between the JVM and native libraries.
|
||||
|
||||
After your `JAVA_HOME` is defined correctly, it is as simple as run `mvn package` under jvm-packages directory to install XGBoost4J.
|
||||
After your `JAVA_HOME` is defined correctly, it is as simple as run `mvn package` under jvm-packages directory to install XGBoost4J. You can also skip the tests by running `mvn -DskipTests=true package`, if you are sure about the correctness of your local setup.
|
||||
|
||||
NOTE: XGBoost4J requires to run with Spark 1.6 or newer
|
||||
XGBoost4J-Spark which integrates XGBoost with Spark requires to run with Spark 1.6 or newer (the default version is 1.6.1). You can build XGBoost4J-Spark as a component of XGBoost4J by running `mvn package` or specifying the spark version by `mvn -Dspark.version=1.6.0 package`.
|
||||
|
||||
Contents
|
||||
--------
|
||||
* [Java Overview Tutorial](java_intro.md)
|
||||
|
||||
Resources
|
||||
---------
|
||||
* [Code Examples](https://github.com/dmlc/xgboost/tree/master/jvm-packages/xgboost4j-example)
|
||||
* [Java API Docs](http://dmlc.ml/docs/javadocs/index.html)
|
||||
* [Scala API Docs]
|
||||
|
||||
## Scala API Docs
|
||||
* [XGBoost4J](http://dmlc.ml/docs/scaladocs/xgboost4j/index.html)
|
||||
* [XGBoost4J-Spark](http://dmlc.ml/docs/scaladocs/xgboost4j-spark/index.html)
|
||||
* [XGBoost4J-Flink](http://dmlc.ml/docs/scaladocs/xgboost4j-flink/index.html)
|
||||
* [XGBoost4J-Flink](http://dmlc.ml/docs/scaladocs/xgboost4j-flink/index.html)
|
||||
|
||||
@ -13,7 +13,8 @@ In R-package, you can use .(dot) to replace under score in the parameters, for e
|
||||
General Parameters
|
||||
------------------
|
||||
* booster [default=gbtree]
|
||||
- which booster to use, can be gbtree or gblinear. gbtree uses tree based model while gblinear uses linear function.
|
||||
- which booster to use, can be gbtree, gblinear or dart.
|
||||
gbtree and dart use tree based model while gblinear uses linear function.
|
||||
* silent [default=0]
|
||||
- 0 means printing running messages, 1 means silent mode.
|
||||
* nthread [default to maximum number of threads available if not set]
|
||||
@ -72,7 +73,29 @@ Parameters for Tree Booster
|
||||
but consider set to lower number for more accurate enumeration.
|
||||
- range: (0, 1)
|
||||
* scale_pos_weight, [default=0]
|
||||
- Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
|
||||
- Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases) See [Parameters Tuning](how_to/param_tuning.md) for more discussion. Also see Higgs Kaggle competition demo for examples: [R](../demo/kaggle-higgs/higgs-train.R ), [py1](../demo/kaggle-higgs/higgs-numpy.py ), [py2](../demo/kaggle-higgs/higgs-cv.py ), [py3](../demo/guide-python/cross_validation.py)
|
||||
|
||||
Additional parameters for Dart Booster
|
||||
--------------------------------------
|
||||
* sample_type [default="uniform"]
|
||||
- type of sampling algorithm.
|
||||
- "uniform": dropped trees are selected uniformly.
|
||||
- "weighted": dropped trees are selected in proportion to weight.
|
||||
* normalize_type [default="tree]
|
||||
- type of normalization algorithm.
|
||||
- "tree": New trees have the same weight of each of dropped trees.
|
||||
weight of new trees are learning_rate / (k + learnig_rate)
|
||||
dropped trees are scaled by a factor of k / (k + learning_rate)
|
||||
- "forest": New trees have the same weight of sum of dropped trees (forest).
|
||||
weight of new trees are learning_rate / (1 + learning_rate)
|
||||
dropped trees are scaled by a factor of 1 / (1 + learning_rate)
|
||||
* rate_drop [default=0.0]
|
||||
- dropout rate.
|
||||
- range: [0.0, 1.0]
|
||||
* skip_drop [default=0.0]
|
||||
- probability of skip dropout.
|
||||
If a dropout is skipped, new trees are added in the same manner as gbtree.
|
||||
- range: [0.0, 1.0]
|
||||
|
||||
Parameters for Linear Booster
|
||||
-----------------------------
|
||||
|
||||
8
doc/tutorials/index.md
Normal file
8
doc/tutorials/index.md
Normal file
@ -0,0 +1,8 @@
|
||||
# XGBoost Tutorials
|
||||
|
||||
This section contains official tutorials inside XGBoost package.
|
||||
See [Awesome XGBoost](https://github.com/dmlc/xgboost/tree/master/demo) for links to mores resources.
|
||||
|
||||
## Contents
|
||||
- [Introduction to Boosted Trees](../model.md)
|
||||
- [Distributed XGBoost YARN on AWS](aws_yarn.md)
|
||||
@ -61,7 +61,6 @@ object DistTrainWithSpark {
|
||||
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
|
||||
sparkConf.registerKryoClasses(Array(classOf[Booster]))
|
||||
val sc = new SparkContext(sparkConf)
|
||||
val sc = new SparkContext(sparkConf)
|
||||
val inputTrainPath = args(1)
|
||||
val outputModelPath = args(2)
|
||||
// number of iterations
|
||||
@ -73,7 +72,8 @@ object DistTrainWithSpark {
|
||||
"max_depth" -> 2,
|
||||
"objective" -> "binary:logistic").toMap
|
||||
// use 5 distributed workers to train the model
|
||||
val model = XGBoost.train(trainRDD, paramMap, numRound, nWorkers = 5)
|
||||
// useExternalMemory indicates whether
|
||||
val model = XGBoost.train(trainRDD, paramMap, numRound, nWorkers = 5, useExternalMemory = true)
|
||||
// save model to HDFS path
|
||||
model.saveModelToHadoop(outputModelPath)
|
||||
}
|
||||
|
||||
@ -23,6 +23,18 @@
|
||||
<module>xgboost4j-spark</module>
|
||||
<module>xgboost4j-flink</module>
|
||||
</modules>
|
||||
<profiles>
|
||||
<profile>
|
||||
<id>spark-1.x</id>
|
||||
<activation>
|
||||
<activeByDefault>true</activeByDefault>
|
||||
</activation>
|
||||
<properties>
|
||||
<spark.version>1.6.1</spark.version>
|
||||
<scala.binary.version>2.10</scala.binary.version>
|
||||
</properties>
|
||||
</profile>
|
||||
</profiles>
|
||||
<build>
|
||||
<plugins>
|
||||
<plugin>
|
||||
|
||||
@ -32,8 +32,8 @@ public class ExternalMemory {
|
||||
//this is the only difference, add a # followed by a cache prefix name
|
||||
//several cache file with the prefix will be generated
|
||||
//currently only support convert from libsvm file
|
||||
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache");
|
||||
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache");
|
||||
DMatrix trainMat = new DMatrix("../demo/data/agaricus.txt.train#dtrain.cache");
|
||||
DMatrix testMat = new DMatrix("../demo/data/agaricus.txt.test#dtest.cache");
|
||||
|
||||
//specify parameters
|
||||
HashMap<String, Object> params = new HashMap<String, Object>();
|
||||
|
||||
@ -28,7 +28,7 @@ object DistTrainWithSpark {
|
||||
"usage: program num_of_rounds num_workers training_path test_path model_path")
|
||||
sys.exit(1)
|
||||
}
|
||||
val sparkConf = new SparkConf().setMaster("local[*]").setAppName("XGBoost-spark-example")
|
||||
val sparkConf = new SparkConf().setAppName("XGBoost-spark-example")
|
||||
.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
|
||||
sparkConf.registerKryoClasses(Array(classOf[Booster]))
|
||||
val sc = new SparkContext(sparkConf)
|
||||
@ -45,7 +45,8 @@ object DistTrainWithSpark {
|
||||
"eta" -> 0.1f,
|
||||
"max_depth" -> 2,
|
||||
"objective" -> "binary:logistic").toMap
|
||||
val xgboostModel = XGBoost.train(trainRDD, paramMap, numRound, nWorkers = args(1).toInt)
|
||||
val xgboostModel = XGBoost.train(trainRDD, paramMap, numRound, nWorkers = args(1).toInt,
|
||||
useExternalMemory = true)
|
||||
xgboostModel.predict(new DMatrix(testSet))
|
||||
// save model to HDFS path
|
||||
xgboostModel.saveModelAsHadoopFile(outputModelPath)
|
||||
|
||||
@ -28,8 +28,8 @@
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>org.apache.spark</groupId>
|
||||
<artifactId>spark-mllib_2.10</artifactId>
|
||||
<version>1.6.1</version>
|
||||
<artifactId>spark-mllib_${scala.binary.version}</artifactId>
|
||||
<version>${spark.version}</version>
|
||||
</dependency>
|
||||
</dependencies>
|
||||
</project>
|
||||
@ -16,6 +16,8 @@
|
||||
|
||||
package ml.dmlc.xgboost4j.scala.spark
|
||||
|
||||
import java.nio.file.Paths
|
||||
|
||||
import scala.collection.mutable
|
||||
import scala.collection.JavaConverters._
|
||||
|
||||
@ -41,7 +43,8 @@ object XGBoost extends Serializable {
|
||||
trainingData: RDD[LabeledPoint],
|
||||
xgBoostConfMap: Map[String, Any],
|
||||
rabitEnv: mutable.Map[String, String],
|
||||
numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait): RDD[Booster] = {
|
||||
numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait,
|
||||
useExternalMemory: Boolean): RDD[Booster] = {
|
||||
import DataUtils._
|
||||
val partitionedData = {
|
||||
if (numWorkers > trainingData.partitions.length) {
|
||||
@ -54,11 +57,19 @@ object XGBoost extends Serializable {
|
||||
trainingData
|
||||
}
|
||||
}
|
||||
val appName = partitionedData.context.appName
|
||||
partitionedData.mapPartitions {
|
||||
trainingSamples =>
|
||||
rabitEnv.put("DMLC_TASK_ID", TaskContext.getPartitionId().toString)
|
||||
Rabit.init(rabitEnv.asJava)
|
||||
val trainingSet = new DMatrix(new JDMatrix(trainingSamples, null))
|
||||
val cacheFileName: String = {
|
||||
if (useExternalMemory && trainingSamples.hasNext) {
|
||||
s"$appName-dtrain_cache-${TaskContext.getPartitionId()}"
|
||||
} else {
|
||||
null
|
||||
}
|
||||
}
|
||||
val trainingSet = new DMatrix(new JDMatrix(trainingSamples, cacheFileName))
|
||||
val booster = SXGBoost.train(trainingSet, xgBoostConfMap, round,
|
||||
watches = new mutable.HashMap[String, DMatrix]{put("train", trainingSet)}.toMap,
|
||||
obj, eval)
|
||||
@ -76,12 +87,15 @@ object XGBoost extends Serializable {
|
||||
* workers equals to the partition number of trainingData RDD
|
||||
* @param obj the user-defined objective function, null by default
|
||||
* @param eval the user-defined evaluation function, null by default
|
||||
* @param useExternalMemory indicate whether to use external memory cache, by setting this flag as
|
||||
* true, the user may save the RAM cost for running XGBoost within Spark
|
||||
* @throws ml.dmlc.xgboost4j.java.XGBoostError when the model training is failed
|
||||
* @return XGBoostModel when successful training
|
||||
*/
|
||||
@throws(classOf[XGBoostError])
|
||||
def train(trainingData: RDD[LabeledPoint], configMap: Map[String, Any], round: Int,
|
||||
nWorkers: Int, obj: ObjectiveTrait = null, eval: EvalTrait = null): XGBoostModel = {
|
||||
nWorkers: Int, obj: ObjectiveTrait = null, eval: EvalTrait = null,
|
||||
useExternalMemory: Boolean = false): XGBoostModel = {
|
||||
require(nWorkers > 0, "you must specify more than 0 workers")
|
||||
val tracker = new RabitTracker(nWorkers)
|
||||
implicit val sc = trainingData.sparkContext
|
||||
@ -97,7 +111,7 @@ object XGBoost extends Serializable {
|
||||
}
|
||||
require(tracker.start(), "FAULT: Failed to start tracker")
|
||||
val boosters = buildDistributedBoosters(trainingData, overridedConfMap,
|
||||
tracker.getWorkerEnvs.asScala, nWorkers, round, obj, eval)
|
||||
tracker.getWorkerEnvs.asScala, nWorkers, round, obj, eval, useExternalMemory)
|
||||
val sparkJobThread = new Thread() {
|
||||
override def run() {
|
||||
// force the job
|
||||
|
||||
@ -17,7 +17,7 @@
|
||||
package ml.dmlc.xgboost4j.scala.spark
|
||||
|
||||
import org.apache.hadoop.fs.{Path, FileSystem}
|
||||
import org.apache.spark.SparkContext
|
||||
import org.apache.spark.{TaskContext, SparkContext}
|
||||
import org.apache.spark.mllib.linalg.Vector
|
||||
import org.apache.spark.rdd.RDD
|
||||
import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
|
||||
@ -27,13 +27,23 @@ class XGBoostModel(_booster: Booster)(implicit val sc: SparkContext) extends Ser
|
||||
|
||||
/**
|
||||
* Predict result with the given testset (represented as RDD)
|
||||
* @param testSet test set representd as RDD
|
||||
* @param useExternalCache whether to use external cache for the test set
|
||||
*/
|
||||
def predict(testSet: RDD[Vector]): RDD[Array[Array[Float]]] = {
|
||||
def predict(testSet: RDD[Vector], useExternalCache: Boolean = false): RDD[Array[Array[Float]]] = {
|
||||
import DataUtils._
|
||||
val broadcastBooster = testSet.sparkContext.broadcast(_booster)
|
||||
val appName = testSet.context.appName
|
||||
testSet.mapPartitions { testSamples =>
|
||||
if (testSamples.hasNext) {
|
||||
val dMatrix = new DMatrix(new JDMatrix(testSamples, null))
|
||||
val cacheFileName = {
|
||||
if (useExternalCache) {
|
||||
s"$appName-dtest_cache-${TaskContext.getPartitionId()}"
|
||||
} else {
|
||||
null
|
||||
}
|
||||
}
|
||||
val dMatrix = new DMatrix(new JDMatrix(testSamples, cacheFileName))
|
||||
Iterator(broadcastBooster.value.predict(dMatrix))
|
||||
} else {
|
||||
Iterator()
|
||||
|
||||
@ -127,7 +127,7 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
|
||||
List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
|
||||
"objective" -> "binary:logistic").toMap,
|
||||
new scala.collection.mutable.HashMap[String, String],
|
||||
numWorkers = 2, round = 5, null, null)
|
||||
numWorkers = 2, round = 5, null, null, false)
|
||||
val boosterCount = boosterRDD.count()
|
||||
assert(boosterCount === 2)
|
||||
val boosters = boosterRDD.collect()
|
||||
@ -210,4 +210,26 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
|
||||
|
||||
println(xgBoostModel.predict(testRDD))
|
||||
}
|
||||
|
||||
test("training with external memory cache") {
|
||||
sc.stop()
|
||||
sc = null
|
||||
val sparkConf = new SparkConf().setMaster("local[*]").setAppName("XGBoostSuite")
|
||||
val customSparkContext = new SparkContext(sparkConf)
|
||||
val eval = new EvalError()
|
||||
val trainingRDD = buildTrainingRDD(Some(customSparkContext))
|
||||
val testSet = readFile(getClass.getResource("/agaricus.txt.test").getFile).iterator
|
||||
import DataUtils._
|
||||
val testSetDMatrix = new DMatrix(new JDMatrix(testSet, null))
|
||||
val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
|
||||
"objective" -> "binary:logistic").toMap
|
||||
val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers, useExternalMemory = true)
|
||||
assert(eval.eval(xgBoostModel.predict(testSetDMatrix), testSetDMatrix) < 0.1)
|
||||
customSparkContext.stop()
|
||||
// clean
|
||||
val dir = new File(".")
|
||||
for (file <- dir.listFiles() if file.getName.startsWith("XGBoostSuite-dtrain_cache")) {
|
||||
file.delete()
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@ -2,8 +2,8 @@
|
||||
|
||||
ignore=tests
|
||||
|
||||
unexpected-special-method-signature,too-many-nested-blocks
|
||||
disiable=unexpected-special-method-signature,too-many-nested-blocks
|
||||
|
||||
dummy-variables-rgx=(unused|)_.*
|
||||
|
||||
reports=no
|
||||
reports=no
|
||||
|
||||
217
python-package/xgboost/callback.py
Normal file
217
python-package/xgboost/callback.py
Normal file
@ -0,0 +1,217 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable= invalid-name
|
||||
"""Training Library containing training routines."""
|
||||
from __future__ import absolute_import
|
||||
|
||||
from . import rabit
|
||||
from .core import EarlyStopException
|
||||
|
||||
|
||||
def _fmt_metric(value, show_stdv=True):
|
||||
"""format metric string"""
|
||||
if len(value) == 2:
|
||||
return '%s:%g' % (value[0], value[1])
|
||||
elif len(value) == 3:
|
||||
if show_stdv:
|
||||
return '%s:%g+%g' % (value[0], value[1], value[2])
|
||||
else:
|
||||
return '%s:%g' % (value[0], value[1])
|
||||
else:
|
||||
raise ValueError("wrong metric value")
|
||||
|
||||
|
||||
def print_evaluation(period=1, show_stdv=True):
|
||||
"""Create a callback that print evaluation result.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
period : int
|
||||
The period to log the evaluation results
|
||||
|
||||
show_stdv : bool, optional
|
||||
Whether show stdv if provided
|
||||
|
||||
Returns
|
||||
-------
|
||||
callback : function
|
||||
A callback that print evaluation every period iterations.
|
||||
"""
|
||||
def callback(env):
|
||||
"""internal function"""
|
||||
if env.rank != 0 or len(env.evaluation_result_list) == 0:
|
||||
return
|
||||
i = env.iteration
|
||||
if (i % period == 0 or i + 1 == env.begin_iteration):
|
||||
msg = '\t'.join([_fmt_metric(x, show_stdv) for x in env.evaluation_result_list])
|
||||
rabit.tracker_print('[%d]\t%s\n' % (i, msg))
|
||||
return callback
|
||||
|
||||
|
||||
def record_evaluation(eval_result):
|
||||
"""Create a call back that records the evaluation history into eval_result.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
eval_result : dict
|
||||
A dictionary to store the evaluation results.
|
||||
|
||||
Returns
|
||||
-------
|
||||
callback : function
|
||||
The requested callback function.
|
||||
"""
|
||||
if not isinstance(eval_result, dict):
|
||||
raise TypeError('eval_result has to be a dictionary')
|
||||
eval_result.clear()
|
||||
|
||||
def init(env):
|
||||
"""internal function"""
|
||||
for k, _ in env.evaluation_result_list:
|
||||
key, metric = k.split('-')
|
||||
if key not in eval_result:
|
||||
eval_result[key] = {}
|
||||
if metric not in eval_result[key]:
|
||||
eval_result[key][metric] = []
|
||||
|
||||
def callback(env):
|
||||
"""internal function"""
|
||||
if len(eval_result) == 0:
|
||||
init(env)
|
||||
for k, v in env.evaluation_result_list:
|
||||
key, metric = k.split('-')
|
||||
eval_result[key][metric].append(v)
|
||||
return callback
|
||||
|
||||
|
||||
def reset_learning_rate(learning_rates):
|
||||
"""Reset learning rate after iteration 1
|
||||
|
||||
NOTE: the initial learning rate will still take in-effect on first iteration.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
learning_rates: list or function
|
||||
List of learning rate for each boosting round
|
||||
or a customized function that calculates eta in terms of
|
||||
current number of round and the total number of boosting round (e.g. yields
|
||||
learning rate decay)
|
||||
- list l: eta = l[boosting round]
|
||||
- function f: eta = f(boosting round, num_boost_round)
|
||||
|
||||
Returns
|
||||
-------
|
||||
callback : function
|
||||
The requested callback function.
|
||||
"""
|
||||
def callback(env):
|
||||
"""internal function"""
|
||||
bst = env.model
|
||||
i = env.iteration
|
||||
if isinstance(learning_rates, list):
|
||||
if len(learning_rates) != env.end_iteration:
|
||||
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
|
||||
bst.set_param('learning_rate', learning_rates[i])
|
||||
else:
|
||||
bst.set_param('learning_rate', learning_rates(i, env.end_iteration))
|
||||
callback.before_iteration = True
|
||||
return callback
|
||||
|
||||
|
||||
def early_stop(stopping_rounds, maximize=False, verbose=True):
|
||||
"""Create a callback that activates early stoppping.
|
||||
|
||||
Validation error needs to decrease at least
|
||||
every <stopping_rounds> round(s) to continue training.
|
||||
Requires at least one item in evals.
|
||||
If there's more than one, will use the last.
|
||||
Returns the model from the last iteration (not the best one).
|
||||
If early stopping occurs, the model will have three additional fields:
|
||||
bst.best_score, bst.best_iteration and bst.best_ntree_limit.
|
||||
(Use bst.best_ntree_limit to get the correct value if num_parallel_tree
|
||||
and/or num_class appears in the parameters)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
stopp_rounds : int
|
||||
The stopping rounds before the trend occur.
|
||||
|
||||
maximize : bool
|
||||
Whether to maximize evaluation metric.
|
||||
|
||||
verbose : optional, bool
|
||||
Whether to print message about early stopping information.
|
||||
|
||||
Returns
|
||||
-------
|
||||
callback : function
|
||||
The requested callback function.
|
||||
"""
|
||||
state = {}
|
||||
|
||||
def init(env):
|
||||
"""internal function"""
|
||||
bst = env.model
|
||||
|
||||
if len(env.evaluation_result_list) == 0:
|
||||
raise ValueError('For early stopping you need at least one set in evals.')
|
||||
if len(env.evaluation_result_list) > 1 and verbose:
|
||||
msg = ("Multiple eval metrics have been passed: "
|
||||
"'{0}' will be used for early stopping.\n\n")
|
||||
rabit.tracker_print(msg.format(env.evaluation_result_list[-1][0]))
|
||||
maximize_metrics = ('auc', 'map', 'ndcg')
|
||||
maximize_score = maximize
|
||||
metric = env.evaluation_result_list[-1][0]
|
||||
if any(env.evaluation_result_list[-1][0].split('-')[1].startswith(x)
|
||||
for x in maximize_metrics):
|
||||
maximize_score = True
|
||||
|
||||
if verbose and env.rank == 0:
|
||||
msg = "Will train until {} hasn't improved in {} rounds.\n"
|
||||
rabit.tracker_print(msg.format(metric, stopping_rounds))
|
||||
|
||||
state['maximize_score'] = maximize_score
|
||||
state['best_iteration'] = 0
|
||||
if maximize_score:
|
||||
state['best_score'] = float('-inf')
|
||||
else:
|
||||
state['best_score'] = float('inf')
|
||||
|
||||
if bst is not None:
|
||||
if bst.attr('best_score') is not None:
|
||||
state['best_score'] = float(bst.attr('best_score'))
|
||||
state['best_iteration'] = int(bst.attr('best_iteration'))
|
||||
state['best_msg'] = bst.attr('best_msg')
|
||||
else:
|
||||
bst.set_attr(best_iteration=str(state['best_iteration']))
|
||||
bst.set_attr(best_score=str(state['best_score']))
|
||||
else:
|
||||
assert env.cvfolds is not None
|
||||
|
||||
def callback(env):
|
||||
"""internal function"""
|
||||
score = env.evaluation_result_list[-1][1]
|
||||
if len(state) == 0:
|
||||
init(env)
|
||||
best_score = state['best_score']
|
||||
best_iteration = state['best_iteration']
|
||||
maximize_score = state['maximize_score']
|
||||
if (maximize_score and score > best_score) or \
|
||||
(not maximize_score and score < best_score):
|
||||
msg = '[%d]\t%s' % (
|
||||
env.iteration,
|
||||
'\t'.join([_fmt_metric(x) for x in env.evaluation_result_list]))
|
||||
state['best_msg'] = msg
|
||||
state['best_score'] = score
|
||||
state['best_iteration'] = env.iteration
|
||||
# save the property to attributes, so they will occur in checkpoint.
|
||||
if env.model is not None:
|
||||
env.model.set_attr(best_score=str(state['best_score']),
|
||||
best_iteration=str(state['best_iteration']),
|
||||
best_msg=state['best_msg'])
|
||||
elif env.iteration - best_iteration >= stopping_rounds:
|
||||
best_msg = state['best_msg']
|
||||
if verbose and env.rank == 0:
|
||||
msg = "Stopping. Best iteration:\n{}\n\n"
|
||||
rabit.tracker_print(msg.format(best_msg))
|
||||
raise EarlyStopException(best_iteration)
|
||||
return callback
|
||||
@ -1,5 +1,5 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable=unused-import, invalid-name, wrong-import-position
|
||||
# pylint: disable= invalid-name, unused-import
|
||||
"""For compatibility"""
|
||||
|
||||
from __future__ import absolute_import
|
||||
@ -14,12 +14,14 @@ if PY3:
|
||||
STRING_TYPES = str,
|
||||
|
||||
def py_str(x):
|
||||
"""convert c string back to python string"""
|
||||
return x.decode('utf-8')
|
||||
else:
|
||||
# pylint: disable=invalid-name
|
||||
STRING_TYPES = basestring,
|
||||
|
||||
def py_str(x):
|
||||
"""convert c string back to python string"""
|
||||
return x
|
||||
|
||||
try:
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable=too-many-arguments, too-many-branches
|
||||
# pylint: disable=too-many-arguments, too-many-branches, invalid-name
|
||||
# pylint: disable=too-many-branches, too-many-lines, W0141
|
||||
"""Core XGBoost Library."""
|
||||
from __future__ import absolute_import
|
||||
|
||||
@ -22,6 +23,31 @@ class XGBoostError(Exception):
|
||||
pass
|
||||
|
||||
|
||||
class EarlyStopException(Exception):
|
||||
"""Exception to signal early stopping.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
best_iteration : int
|
||||
The best iteration stopped.
|
||||
"""
|
||||
def __init__(self, best_iteration):
|
||||
super(EarlyStopException, self).__init__()
|
||||
self.best_iteration = best_iteration
|
||||
|
||||
|
||||
# Callback environment used by callbacks
|
||||
CallbackEnv = collections.namedtuple(
|
||||
"XGBoostCallbackEnv",
|
||||
["model",
|
||||
"cvfolds",
|
||||
"iteration",
|
||||
"begin_iteration",
|
||||
"end_iteration",
|
||||
"rank",
|
||||
"evaluation_result_list"])
|
||||
|
||||
|
||||
def from_pystr_to_cstr(data):
|
||||
"""Convert a list of Python str to C pointer
|
||||
|
||||
@ -657,7 +683,7 @@ class Booster(object):
|
||||
def __copy__(self):
|
||||
return self.__deepcopy__(None)
|
||||
|
||||
def __deepcopy__(self, memo):
|
||||
def __deepcopy__(self, _):
|
||||
return Booster(model_file=self.save_raw())
|
||||
|
||||
def copy(self):
|
||||
@ -975,7 +1001,6 @@ class Booster(object):
|
||||
_check_call(_LIB.XGBoosterLoadModelFromBuffer(self.handle, ptr, length))
|
||||
|
||||
def dump_model(self, fout, fmap='', with_stats=False):
|
||||
# pylint: disable=consider-using-enumerate
|
||||
"""
|
||||
Dump model into a text file.
|
||||
|
||||
@ -1143,10 +1168,12 @@ class Booster(object):
|
||||
msg = 'feature_names mismatch: {0} {1}'
|
||||
|
||||
if dat_missing:
|
||||
msg += '\nexpected ' + ', '.join(str(s) for s in dat_missing) + ' in input data'
|
||||
msg += ('\nexpected ' + ', '.join(str(s) for s in dat_missing) +
|
||||
' in input data')
|
||||
|
||||
if my_missing:
|
||||
msg += '\ntraining data did not have the following fields: ' + ', '.join(str(s) for s in my_missing)
|
||||
msg += ('\ntraining data did not have the following fields: ' +
|
||||
', '.join(str(s) for s in my_missing))
|
||||
|
||||
raise ValueError(msg.format(self.feature_names,
|
||||
data.feature_names))
|
||||
@ -1161,23 +1188,25 @@ class Booster(object):
|
||||
The name of feature map file.
|
||||
bin: int, default None
|
||||
The maximum number of bins.
|
||||
Number of bins equals number of unique split values n_unique, if bins == None or bins > n_unique.
|
||||
Number of bins equals number of unique split values n_unique,
|
||||
if bins == None or bins > n_unique.
|
||||
as_pandas : bool, default True
|
||||
Return pd.DataFrame when pandas is installed.
|
||||
If False or pandas is not installed, return numpy ndarray.
|
||||
|
||||
Returns
|
||||
-------
|
||||
a histogram of used splitting values for the specified feature either as numpy array or pandas DataFrame.
|
||||
a histogram of used splitting values for the specified feature
|
||||
either as numpy array or pandas DataFrame.
|
||||
"""
|
||||
xgdump = self.get_dump(fmap=fmap)
|
||||
values = []
|
||||
regexp = re.compile("\[{0}<([\d.Ee+-]+)\]".format(feature))
|
||||
regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
|
||||
for i in range(len(xgdump)):
|
||||
m = re.findall(regexp, xgdump[i])
|
||||
values.extend(map(float, m))
|
||||
|
||||
n_unique = np.unique(values).shape[0]
|
||||
n_unique = len(np.unique(values))
|
||||
bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
|
||||
|
||||
nph = np.histogram(values, bins=bins)
|
||||
@ -1187,7 +1216,8 @@ class Booster(object):
|
||||
if as_pandas and PANDAS_INSTALLED:
|
||||
return DataFrame(nph, columns=['SplitValue', 'Count'])
|
||||
elif as_pandas and not PANDAS_INSTALLED:
|
||||
sys.stderr.write("Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
|
||||
sys.stderr.write(
|
||||
"Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
|
||||
return nph
|
||||
else:
|
||||
return nph
|
||||
|
||||
@ -1,3 +1,6 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable= invalid-name
|
||||
|
||||
"""Distributed XGBoost Rabit related API."""
|
||||
from __future__ import absolute_import
|
||||
import sys
|
||||
@ -179,7 +182,7 @@ def allreduce(data, op, prepare_fun=None):
|
||||
else:
|
||||
func_ptr = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
|
||||
|
||||
def pfunc(args):
|
||||
def pfunc(_):
|
||||
"""prepare function."""
|
||||
prepare_fun(data)
|
||||
_LIB.RabitAllreduce(buf.ctypes.data_as(ctypes.c_void_p),
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme
|
||||
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912
|
||||
"""Scikit-Learn Wrapper interface for XGBoost."""
|
||||
from __future__ import absolute_import
|
||||
|
||||
@ -42,6 +42,7 @@ def _objective_decorator(func):
|
||||
``dmatrix.get_label()``
|
||||
"""
|
||||
def inner(preds, dmatrix):
|
||||
"""internal function"""
|
||||
labels = dmatrix.get_label()
|
||||
return func(labels, preds)
|
||||
return inner
|
||||
@ -79,9 +80,9 @@ class XGBModel(XGBModelBase):
|
||||
colsample_bylevel : float
|
||||
Subsample ratio of columns for each split, in each level.
|
||||
reg_alpha : float (xgb's alpha)
|
||||
L2 regularization term on weights
|
||||
reg_lambda : float (xgb's lambda)
|
||||
L1 regularization term on weights
|
||||
reg_lambda : float (xgb's lambda)
|
||||
L2 regularization term on weights
|
||||
scale_pos_weight : float
|
||||
Balancing of positive and negative weights.
|
||||
|
||||
@ -183,7 +184,7 @@ class XGBModel(XGBModelBase):
|
||||
|
||||
def fit(self, X, y, eval_set=None, eval_metric=None,
|
||||
early_stopping_rounds=None, verbose=True):
|
||||
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init, redefined-variable-type
|
||||
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
|
||||
"""
|
||||
Fit the gradient boosting model
|
||||
|
||||
@ -351,7 +352,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
|
||||
|
||||
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
|
||||
early_stopping_rounds=None, verbose=True):
|
||||
# pylint: disable = attribute-defined-outside-init,arguments-differ, redefined-variable-type
|
||||
# pylint: disable = attribute-defined-outside-init,arguments-differ
|
||||
"""
|
||||
Fit gradient boosting classifier
|
||||
|
||||
@ -440,6 +441,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
|
||||
evals_result=evals_result, obj=obj, feval=feval,
|
||||
verbose_eval=verbose)
|
||||
|
||||
self.objective = xgb_options["objective"]
|
||||
if evals_result:
|
||||
for val in evals_result.items():
|
||||
evals_result_key = list(val[1].keys())[0]
|
||||
|
||||
@ -1,20 +1,122 @@
|
||||
# coding: utf-8
|
||||
# pylint: disable=too-many-locals, too-many-arguments, invalid-name
|
||||
# pylint: disable=too-many-branches
|
||||
# pylint: disable=too-many-branches, too-many-statements
|
||||
"""Training Library containing training routines."""
|
||||
from __future__ import absolute_import
|
||||
|
||||
import sys
|
||||
import re
|
||||
|
||||
import numpy as np
|
||||
from .core import Booster, STRING_TYPES, XGBoostError
|
||||
from .core import Booster, STRING_TYPES, XGBoostError, CallbackEnv, EarlyStopException
|
||||
from .compat import (SKLEARN_INSTALLED, XGBStratifiedKFold)
|
||||
from . import rabit
|
||||
from . import callback
|
||||
|
||||
|
||||
def _train_internal(params, dtrain,
|
||||
num_boost_round=10, evals=(),
|
||||
obj=None, feval=None,
|
||||
xgb_model=None, callbacks=None):
|
||||
"""internal training function"""
|
||||
callbacks = [] if callbacks is None else callbacks
|
||||
evals = list(evals)
|
||||
if isinstance(params, dict) \
|
||||
and 'eval_metric' in params \
|
||||
and isinstance(params['eval_metric'], list):
|
||||
params = dict((k, v) for k, v in params.items())
|
||||
eval_metrics = params['eval_metric']
|
||||
params.pop("eval_metric", None)
|
||||
params = list(params.items())
|
||||
for eval_metric in eval_metrics:
|
||||
params += [('eval_metric', eval_metric)]
|
||||
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals])
|
||||
nboost = 0
|
||||
num_parallel_tree = 1
|
||||
|
||||
if xgb_model is not None:
|
||||
if not isinstance(xgb_model, STRING_TYPES):
|
||||
xgb_model = xgb_model.save_raw()
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
|
||||
nboost = len(bst.get_dump())
|
||||
else:
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals])
|
||||
|
||||
_params = dict(params) if isinstance(params, list) else params
|
||||
|
||||
if 'num_parallel_tree' in _params:
|
||||
num_parallel_tree = _params['num_parallel_tree']
|
||||
nboost //= num_parallel_tree
|
||||
if 'num_class' in _params:
|
||||
nboost //= _params['num_class']
|
||||
|
||||
# Distributed code: Load the checkpoint from rabit.
|
||||
version = bst.load_rabit_checkpoint()
|
||||
assert(rabit.get_world_size() != 1 or version == 0)
|
||||
rank = rabit.get_rank()
|
||||
start_iteration = int(version / 2)
|
||||
nboost += start_iteration
|
||||
|
||||
callbacks_before_iter = [
|
||||
cb for cb in callbacks if cb.__dict__.get('before_iteration', False)]
|
||||
callbacks_after_iter = [
|
||||
cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)]
|
||||
|
||||
for i in range(start_iteration, num_boost_round):
|
||||
for cb in callbacks_before_iter:
|
||||
cb(CallbackEnv(model=bst,
|
||||
cvfolds=None,
|
||||
iteration=i,
|
||||
begin_iteration=start_iteration,
|
||||
end_iteration=num_boost_round,
|
||||
rank=rank,
|
||||
evaluation_result_list=None))
|
||||
# Distributed code: need to resume to this point.
|
||||
# Skip the first update if it is a recovery step.
|
||||
if version % 2 == 0:
|
||||
bst.update(dtrain, i, obj)
|
||||
bst.save_rabit_checkpoint()
|
||||
version += 1
|
||||
|
||||
assert(rabit.get_world_size() == 1 or version == rabit.version_number())
|
||||
|
||||
nboost += 1
|
||||
evaluation_result_list = []
|
||||
# check evaluation result.
|
||||
if len(evals) != 0:
|
||||
bst_eval_set = bst.eval_set(evals, i, feval)
|
||||
if isinstance(bst_eval_set, STRING_TYPES):
|
||||
msg = bst_eval_set
|
||||
else:
|
||||
msg = bst_eval_set.decode()
|
||||
res = [x.split(':') for x in msg.split()]
|
||||
evaluation_result_list = [(k, float(v)) for k, v in res[1:]]
|
||||
try:
|
||||
for cb in callbacks_after_iter:
|
||||
cb(CallbackEnv(model=bst,
|
||||
cvfolds=None,
|
||||
iteration=i,
|
||||
begin_iteration=start_iteration,
|
||||
end_iteration=num_boost_round,
|
||||
rank=rank,
|
||||
evaluation_result_list=evaluation_result_list))
|
||||
except EarlyStopException:
|
||||
break
|
||||
# do checkpoint after evaluation, in case evaluation also updates booster.
|
||||
bst.save_rabit_checkpoint()
|
||||
version += 1
|
||||
|
||||
if bst.attr('best_score') is not None:
|
||||
bst.best_score = float(bst.attr('best_score'))
|
||||
bst.best_iteration = int(bst.attr('best_iteration'))
|
||||
else:
|
||||
bst.best_iteration = nboost - 1
|
||||
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
|
||||
return bst
|
||||
|
||||
|
||||
def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
|
||||
maximize=False, early_stopping_rounds=None, evals_result=None,
|
||||
verbose_eval=True, learning_rates=None, xgb_model=None):
|
||||
verbose_eval=True, learning_rates=None, xgb_model=None, callbacks=None):
|
||||
# pylint: disable=too-many-statements,too-many-branches, attribute-defined-outside-init
|
||||
"""Train a booster with given parameters.
|
||||
|
||||
@ -70,176 +172,37 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None,
|
||||
xgb_model : file name of stored xgb model or 'Booster' instance
|
||||
Xgb model to be loaded before training (allows training continuation).
|
||||
|
||||
callbacks : list of callback functions
|
||||
List of callback functions that are applied at end of each iteration.
|
||||
|
||||
Returns
|
||||
-------
|
||||
booster : a trained booster model
|
||||
"""
|
||||
evals = list(evals)
|
||||
if isinstance(params, dict) \
|
||||
and 'eval_metric' in params \
|
||||
and isinstance(params['eval_metric'], list):
|
||||
params = dict((k, v) for k, v in params.items())
|
||||
eval_metrics = params['eval_metric']
|
||||
params.pop("eval_metric", None)
|
||||
params = list(params.items())
|
||||
for eval_metric in eval_metrics:
|
||||
params += [('eval_metric', eval_metric)]
|
||||
callbacks = [] if callbacks is None else callbacks
|
||||
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals])
|
||||
nboost = 0
|
||||
num_parallel_tree = 1
|
||||
|
||||
if isinstance(verbose_eval, bool):
|
||||
verbose_eval_every_line = False
|
||||
# Most of legacy advanced options becomes callbacks
|
||||
if isinstance(verbose_eval, bool) and verbose_eval:
|
||||
callbacks.append(callback.print_evaluation())
|
||||
else:
|
||||
if isinstance(verbose_eval, int):
|
||||
verbose_eval_every_line = verbose_eval
|
||||
verbose_eval = True if verbose_eval_every_line > 0 else False
|
||||
callbacks.append(callback.print_evaluation(verbose_eval))
|
||||
|
||||
if rabit.get_rank() != 0:
|
||||
verbose_eval = False
|
||||
|
||||
if xgb_model is not None:
|
||||
if not isinstance(xgb_model, STRING_TYPES):
|
||||
xgb_model = xgb_model.save_raw()
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals], model_file=xgb_model)
|
||||
nboost = len(bst.get_dump())
|
||||
else:
|
||||
bst = Booster(params, [dtrain] + [d[0] for d in evals])
|
||||
|
||||
_params = dict(params) if isinstance(params, list) else params
|
||||
_eta_param_name = 'eta' if 'eta' in _params else 'learning_rate'
|
||||
if 'num_parallel_tree' in _params:
|
||||
num_parallel_tree = _params['num_parallel_tree']
|
||||
nboost //= num_parallel_tree
|
||||
if 'num_class' in _params:
|
||||
nboost //= _params['num_class']
|
||||
if early_stopping_rounds is not None:
|
||||
callbacks.append(callback.early_stop(early_stopping_rounds,
|
||||
maximize=maximize,
|
||||
verbose=bool(verbose_eval)))
|
||||
if learning_rates is not None:
|
||||
callbacks.append(callback.reset_learning_rate(learning_rates))
|
||||
|
||||
if evals_result is not None:
|
||||
if not isinstance(evals_result, dict):
|
||||
raise TypeError('evals_result has to be a dictionary')
|
||||
else:
|
||||
evals_name = [d[1] for d in evals]
|
||||
evals_result.clear()
|
||||
evals_result.update(dict([(key, {}) for key in evals_name]))
|
||||
callbacks.append(callback.record_evaluation(evals_result))
|
||||
|
||||
# early stopping
|
||||
if early_stopping_rounds is not None:
|
||||
if len(evals) < 1:
|
||||
raise ValueError('For early stopping you need at least one set in evals.')
|
||||
|
||||
if verbose_eval:
|
||||
rabit.tracker_print("Will train until {} error hasn't decreased in {} rounds.\n".format(
|
||||
evals[-1][1], early_stopping_rounds))
|
||||
|
||||
# is params a list of tuples? are we using multiple eval metrics?
|
||||
if isinstance(params, list):
|
||||
if len(params) != len(dict(params).items()):
|
||||
params = dict(params)
|
||||
msg = ("Multiple eval metrics have been passed: "
|
||||
"'{0}' will be used for early stopping.\n\n")
|
||||
rabit.tracker_print(msg.format(params['eval_metric']))
|
||||
else:
|
||||
params = dict(params)
|
||||
|
||||
# either minimize loss or maximize AUC/MAP/NDCG
|
||||
maximize_score = False
|
||||
if 'eval_metric' in params:
|
||||
maximize_metrics = ('auc', 'map', 'ndcg')
|
||||
if any(params['eval_metric'].startswith(x) for x in maximize_metrics):
|
||||
maximize_score = True
|
||||
if feval is not None:
|
||||
maximize_score = maximize
|
||||
|
||||
if maximize_score:
|
||||
bst.set_attr(best_score='0.0')
|
||||
else:
|
||||
bst.set_attr(best_score='inf')
|
||||
bst.set_attr(best_iteration='0')
|
||||
|
||||
if isinstance(learning_rates, list) and len(learning_rates) != num_boost_round:
|
||||
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
|
||||
|
||||
# Distributed code: Load the checkpoint from rabit.
|
||||
version = bst.load_rabit_checkpoint()
|
||||
assert(rabit.get_world_size() != 1 or version == 0)
|
||||
start_iteration = int(version / 2)
|
||||
nboost += start_iteration
|
||||
|
||||
for i in range(start_iteration, num_boost_round):
|
||||
if learning_rates is not None:
|
||||
if isinstance(learning_rates, list):
|
||||
bst.set_param(_eta_param_name, learning_rates[i])
|
||||
else:
|
||||
bst.set_param(_eta_param_name, learning_rates(i, num_boost_round))
|
||||
|
||||
# Distributed code: need to resume to this point.
|
||||
# Skip the first update if it is a recovery step.
|
||||
if version % 2 == 0:
|
||||
bst.update(dtrain, i, obj)
|
||||
bst.save_rabit_checkpoint()
|
||||
version += 1
|
||||
|
||||
assert(rabit.get_world_size() == 1 or version == rabit.version_number())
|
||||
|
||||
nboost += 1
|
||||
# check evaluation result.
|
||||
if len(evals) != 0:
|
||||
bst_eval_set = bst.eval_set(evals, i, feval)
|
||||
|
||||
if isinstance(bst_eval_set, STRING_TYPES):
|
||||
msg = bst_eval_set
|
||||
else:
|
||||
msg = bst_eval_set.decode()
|
||||
|
||||
if verbose_eval:
|
||||
if verbose_eval_every_line:
|
||||
if i % verbose_eval_every_line == 0 or i == num_boost_round - 1:
|
||||
rabit.tracker_print(msg + '\n')
|
||||
else:
|
||||
rabit.tracker_print(msg + '\n')
|
||||
|
||||
if evals_result is not None:
|
||||
res = re.findall("([0-9a-zA-Z@]+[-]*):-?([0-9.]+).", msg)
|
||||
for key in evals_name:
|
||||
evals_idx = evals_name.index(key)
|
||||
res_per_eval = len(res) // len(evals_name)
|
||||
for r in range(res_per_eval):
|
||||
res_item = res[(evals_idx * res_per_eval) + r]
|
||||
res_key = res_item[0]
|
||||
res_val = res_item[1]
|
||||
if res_key in evals_result[key]:
|
||||
evals_result[key][res_key].append(res_val)
|
||||
else:
|
||||
evals_result[key][res_key] = [res_val]
|
||||
|
||||
if early_stopping_rounds:
|
||||
score = float(msg.rsplit(':', 1)[1])
|
||||
best_score = float(bst.attr('best_score'))
|
||||
best_iteration = int(bst.attr('best_iteration'))
|
||||
if (maximize_score and score > best_score) or \
|
||||
(not maximize_score and score < best_score):
|
||||
# save the property to attributes, so they will occur in checkpoint.
|
||||
bst.set_attr(best_score=str(score),
|
||||
best_iteration=str(nboost - 1),
|
||||
best_msg=msg)
|
||||
elif i - best_iteration >= early_stopping_rounds:
|
||||
best_msg = bst.attr('best_msg')
|
||||
if verbose_eval:
|
||||
msg = "Stopping. Best iteration:\n{}\n\n"
|
||||
rabit.tracker_print(msg.format(best_msg))
|
||||
break
|
||||
# do checkpoint after evaluation, in case evaluation also updates booster.
|
||||
bst.save_rabit_checkpoint()
|
||||
version += 1
|
||||
|
||||
if early_stopping_rounds:
|
||||
bst.best_score = float(bst.attr('best_score'))
|
||||
bst.best_iteration = int(bst.attr('best_iteration'))
|
||||
else:
|
||||
bst.best_iteration = nboost - 1
|
||||
bst.best_ntree_limit = (bst.best_iteration + 1) * num_parallel_tree
|
||||
return bst
|
||||
return _train_internal(params, dtrain,
|
||||
num_boost_round=num_boost_round,
|
||||
evals=evals,
|
||||
obj=obj, feval=feval,
|
||||
xgb_model=xgb_model, callbacks=callbacks)
|
||||
|
||||
|
||||
class CVPack(object):
|
||||
@ -294,7 +257,7 @@ def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
|
||||
return ret
|
||||
|
||||
|
||||
def aggcv(rlist, show_stdv=True, verbose_eval=None, as_pandas=True, trial=0):
|
||||
def aggcv(rlist):
|
||||
# pylint: disable=invalid-name
|
||||
"""
|
||||
Aggregate cross-validation results.
|
||||
@ -315,50 +278,21 @@ def aggcv(rlist, show_stdv=True, verbose_eval=None, as_pandas=True, trial=0):
|
||||
if k not in cvmap:
|
||||
cvmap[k] = []
|
||||
cvmap[k].append(float(v))
|
||||
|
||||
msg = idx
|
||||
|
||||
if show_stdv:
|
||||
fmt = '\tcv-{0}:{1}+{2}'
|
||||
else:
|
||||
fmt = '\tcv-{0}:{1}'
|
||||
|
||||
index = []
|
||||
results = []
|
||||
for k, v in sorted(cvmap.items(), key=lambda x: x[0]):
|
||||
for k, v in sorted(cvmap.items(), key=lambda x: (x[0].startswith('test'), x[0])):
|
||||
v = np.array(v)
|
||||
if not isinstance(msg, STRING_TYPES):
|
||||
msg = msg.decode()
|
||||
mean, std = np.mean(v), np.std(v)
|
||||
msg += fmt.format(k, mean, std)
|
||||
|
||||
index.extend([k + '-mean', k + '-std'])
|
||||
results.extend([mean, std])
|
||||
|
||||
if as_pandas:
|
||||
try:
|
||||
import pandas as pd
|
||||
results = pd.Series(results, index=index)
|
||||
except ImportError:
|
||||
if verbose_eval is None:
|
||||
verbose_eval = True
|
||||
else:
|
||||
# if verbose_eval is default (None),
|
||||
# result will be np.ndarray as it can't hold column name
|
||||
if verbose_eval is None:
|
||||
verbose_eval = True
|
||||
|
||||
if (isinstance(verbose_eval, int) and verbose_eval > 0 and trial % verbose_eval == 0) or \
|
||||
(isinstance(verbose_eval, bool) and verbose_eval):
|
||||
sys.stderr.write(msg + '\n')
|
||||
sys.stderr.flush()
|
||||
|
||||
results.extend([(k, mean, std)])
|
||||
return results
|
||||
|
||||
|
||||
def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None,
|
||||
metrics=(), obj=None, feval=None, maximize=False, early_stopping_rounds=None,
|
||||
fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0):
|
||||
fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True, seed=0,
|
||||
callbacks=None):
|
||||
# pylint: disable = invalid-name
|
||||
"""Cross-validation with given paramaters.
|
||||
|
||||
@ -404,6 +338,8 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
|
||||
Results are not affected, and always contains std.
|
||||
seed : int
|
||||
Seed used to generate the folds (passed to numpy.random.seed).
|
||||
callbacks : list of callback functions
|
||||
List of callback functions that are applied at end of each iteration.
|
||||
|
||||
Returns
|
||||
-------
|
||||
@ -431,59 +367,63 @@ def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, folds=None
|
||||
|
||||
params.pop("eval_metric", None)
|
||||
|
||||
if early_stopping_rounds is not None:
|
||||
|
||||
if len(metrics) > 1:
|
||||
msg = ('Check your params. '
|
||||
'Early stopping works with single eval metric only.')
|
||||
raise ValueError(msg)
|
||||
if verbose_eval:
|
||||
msg = "Will train until cv error hasn't decreased in {} rounds.\n"
|
||||
sys.stderr.write(msg.format(early_stopping_rounds))
|
||||
|
||||
maximize_score = False
|
||||
if len(metrics) == 1:
|
||||
maximize_metrics = ('auc', 'map', 'ndcg')
|
||||
if any(metrics[0].startswith(x) for x in maximize_metrics):
|
||||
maximize_score = True
|
||||
if feval is not None:
|
||||
maximize_score = maximize
|
||||
|
||||
if maximize_score:
|
||||
best_score = 0.0
|
||||
else:
|
||||
best_score = float('inf')
|
||||
|
||||
best_score_i = 0
|
||||
results = []
|
||||
results = {}
|
||||
cvfolds = mknfold(dtrain, nfold, params, seed, metrics, fpreproc, stratified, folds)
|
||||
|
||||
# setup callbacks
|
||||
callbacks = [] if callbacks is None else callbacks
|
||||
if early_stopping_rounds is not None:
|
||||
callbacks.append(callback.early_stop(early_stopping_rounds,
|
||||
maximize=maximize,
|
||||
verbose=False))
|
||||
if isinstance(verbose_eval, bool) and verbose_eval:
|
||||
callbacks.append(callback.print_evaluation(show_stdv=show_stdv))
|
||||
else:
|
||||
if isinstance(verbose_eval, int):
|
||||
callbacks.append(callback.print_evaluation(verbose_eval, show_stdv=show_stdv))
|
||||
|
||||
callbacks_before_iter = [
|
||||
cb for cb in callbacks if cb.__dict__.get('before_iteration', False)]
|
||||
callbacks_after_iter = [
|
||||
cb for cb in callbacks if not cb.__dict__.get('before_iteration', False)]
|
||||
|
||||
for i in range(num_boost_round):
|
||||
for cb in callbacks_before_iter:
|
||||
cb(CallbackEnv(model=None,
|
||||
cvfolds=cvfolds,
|
||||
iteration=i,
|
||||
begin_iteration=0,
|
||||
end_iteration=num_boost_round,
|
||||
rank=0,
|
||||
evaluation_result_list=None))
|
||||
for fold in cvfolds:
|
||||
fold.update(i, obj)
|
||||
res = aggcv([f.eval(i, feval) for f in cvfolds],
|
||||
show_stdv=show_stdv, verbose_eval=verbose_eval,
|
||||
as_pandas=as_pandas, trial=i)
|
||||
results.append(res)
|
||||
res = aggcv([f.eval(i, feval) for f in cvfolds])
|
||||
|
||||
if early_stopping_rounds is not None:
|
||||
score = res[0]
|
||||
if (maximize_score and score > best_score) or \
|
||||
(not maximize_score and score < best_score):
|
||||
best_score = score
|
||||
best_score_i = i
|
||||
elif i - best_score_i >= early_stopping_rounds:
|
||||
results = results[:best_score_i + 1]
|
||||
if verbose_eval:
|
||||
msg = "Stopping. Best iteration:\n[{}] cv-mean:{}\tcv-std:{}\n"
|
||||
sys.stderr.write(msg.format(best_score_i, results[-1][0], results[-1][1]))
|
||||
break
|
||||
for key, mean, std in res:
|
||||
if key + '-mean' not in results:
|
||||
results[key + '-mean'] = []
|
||||
if key + '-std' not in results:
|
||||
results[key + '-std'] = []
|
||||
results[key + '-mean'].append(mean)
|
||||
results[key + '-std'].append(std)
|
||||
try:
|
||||
for cb in callbacks_after_iter:
|
||||
cb(CallbackEnv(model=None,
|
||||
cvfolds=cvfolds,
|
||||
iteration=i,
|
||||
begin_iteration=0,
|
||||
end_iteration=num_boost_round,
|
||||
rank=0,
|
||||
evaluation_result_list=res))
|
||||
except EarlyStopException as e:
|
||||
for k in results.keys():
|
||||
results[k] = results[k][:(e.best_iteration + 1)]
|
||||
break
|
||||
if as_pandas:
|
||||
try:
|
||||
import pandas as pd
|
||||
results = pd.DataFrame(results)
|
||||
results = pd.DataFrame.from_dict(results)
|
||||
except ImportError:
|
||||
results = np.array(results)
|
||||
else:
|
||||
results = np.array(results)
|
||||
|
||||
pass
|
||||
return results
|
||||
|
||||
2
rabit
2
rabit
@ -1 +1 @@
|
||||
Subproject commit e19fced5cbd4e41b10099facae7caa5cd3e6ada3
|
||||
Subproject commit 8f61535b83e650331459d7f33a1615fa7d27b7bd
|
||||
@ -271,6 +271,7 @@ void CLIDump2Text(const CLIParam& param) {
|
||||
std::unique_ptr<Learner> learner(Learner::Create({}));
|
||||
std::unique_ptr<dmlc::Stream> fi(
|
||||
dmlc::Stream::Create(param.model_in.c_str(), "r"));
|
||||
learner->Configure(param.cfg);
|
||||
learner->Load(fi.get());
|
||||
// dump data
|
||||
std::vector<std::string> dump = learner->Dump2Text(fmap, param.dump_stats);
|
||||
@ -297,6 +298,7 @@ void CLIPredict(const CLIParam& param) {
|
||||
std::unique_ptr<Learner> learner(Learner::Create({}));
|
||||
std::unique_ptr<dmlc::Stream> fi(
|
||||
dmlc::Stream::Create(param.model_in.c_str(), "r"));
|
||||
learner->Configure(param.cfg);
|
||||
learner->Load(fi.get());
|
||||
|
||||
if (param.silent == 0) {
|
||||
|
||||
@ -223,6 +223,10 @@ DMatrix* DMatrix::Load(const std::string& uri,
|
||||
LOG(CONSOLE) << info.base_margin.size()
|
||||
<< " base_margin are loaded from " << fname << ".base_margin";
|
||||
}
|
||||
if (MetaTryLoadFloatInfo(fname + ".weight", &info.weights) && !silent) {
|
||||
LOG(CONSOLE) << info.weights.size()
|
||||
<< " weights are loaded from " << fname << ".weight";
|
||||
}
|
||||
}
|
||||
return dmat;
|
||||
}
|
||||
|
||||
@ -256,41 +256,44 @@ void SparsePageDMatrix::InitColAccess(const std::vector<bool>& enabled,
|
||||
name_shards.push_back(prefix + ".col.page");
|
||||
format_shards.push_back(SparsePage::Format::DecideFormat(prefix).second);
|
||||
}
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
|
||||
double tstart = dmlc::GetTime();
|
||||
size_t bytes_write = 0;
|
||||
// print every 4 sec.
|
||||
const double kStep = 4.0;
|
||||
size_t tick_expected = kStep;
|
||||
{
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
|
||||
while (make_next_col(page.get())) {
|
||||
for (size_t i = 0; i < page->Size(); ++i) {
|
||||
col_size_[i] += page->offset[i + 1] - page->offset[i];
|
||||
}
|
||||
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
if (tdiff >= tick_expected) {
|
||||
LOG(CONSOLE) << "Writing col.page file to " << cache_info_
|
||||
<< " in " << ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " MB writen";
|
||||
tick_expected += kStep;
|
||||
double tstart = dmlc::GetTime();
|
||||
size_t bytes_write = 0;
|
||||
// print every 4 sec.
|
||||
const double kStep = 4.0;
|
||||
size_t tick_expected = kStep;
|
||||
|
||||
while (make_next_col(page.get())) {
|
||||
for (size_t i = 0; i < page->Size(); ++i) {
|
||||
col_size_[i] += page->offset[i + 1] - page->offset[i];
|
||||
}
|
||||
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
if (tdiff >= tick_expected) {
|
||||
LOG(CONSOLE) << "Writing col.page file to " << cache_info_
|
||||
<< " in " << ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " MB writen";
|
||||
tick_expected += kStep;
|
||||
}
|
||||
}
|
||||
// save meta data
|
||||
std::string col_meta_name = cache_shards[0] + ".col.meta";
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(col_meta_name.c_str(), "w"));
|
||||
fo->Write(buffered_rowset_);
|
||||
fo->Write(col_size_);
|
||||
fo.reset(nullptr);
|
||||
}
|
||||
// save meta data
|
||||
std::string col_meta_name = cache_shards[0] + ".col.meta";
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(col_meta_name.c_str(), "w"));
|
||||
fo->Write(buffered_rowset_);
|
||||
fo->Write(col_size_);
|
||||
fo.reset(nullptr);
|
||||
// initialize column data
|
||||
CHECK(TryInitColData());
|
||||
}
|
||||
|
||||
@ -110,58 +110,60 @@ void SparsePageSource::Create(dmlc::Parser<uint32_t>* src,
|
||||
name_shards.push_back(prefix + ".row.page");
|
||||
format_shards.push_back(SparsePage::Format::DecideFormat(prefix).first);
|
||||
}
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
{
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
|
||||
MetaInfo info;
|
||||
size_t bytes_write = 0;
|
||||
double tstart = dmlc::GetTime();
|
||||
// print every 4 sec.
|
||||
const double kStep = 4.0;
|
||||
size_t tick_expected = kStep;
|
||||
MetaInfo info;
|
||||
size_t bytes_write = 0;
|
||||
double tstart = dmlc::GetTime();
|
||||
// print every 4 sec.
|
||||
const double kStep = 4.0;
|
||||
size_t tick_expected = kStep;
|
||||
|
||||
while (src->Next()) {
|
||||
const dmlc::RowBlock<uint32_t>& batch = src->Value();
|
||||
if (batch.label != nullptr) {
|
||||
info.labels.insert(info.labels.end(), batch.label, batch.label + batch.size);
|
||||
}
|
||||
if (batch.weight != nullptr) {
|
||||
info.weights.insert(info.weights.end(), batch.weight, batch.weight + batch.size);
|
||||
}
|
||||
info.num_row += batch.size;
|
||||
info.num_nonzero += batch.offset[batch.size] - batch.offset[0];
|
||||
for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
|
||||
uint32_t index = batch.index[i];
|
||||
info.num_col = std::max(info.num_col,
|
||||
static_cast<uint64_t>(index + 1));
|
||||
}
|
||||
page->Push(batch);
|
||||
if (page->MemCostBytes() >= kPageSize) {
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
while (src->Next()) {
|
||||
const dmlc::RowBlock<uint32_t>& batch = src->Value();
|
||||
if (batch.label != nullptr) {
|
||||
info.labels.insert(info.labels.end(), batch.label, batch.label + batch.size);
|
||||
}
|
||||
if (batch.weight != nullptr) {
|
||||
info.weights.insert(info.weights.end(), batch.weight, batch.weight + batch.size);
|
||||
}
|
||||
info.num_row += batch.size;
|
||||
info.num_nonzero += batch.offset[batch.size] - batch.offset[0];
|
||||
for (size_t i = batch.offset[0]; i < batch.offset[batch.size]; ++i) {
|
||||
uint32_t index = batch.index[i];
|
||||
info.num_col = std::max(info.num_col,
|
||||
static_cast<uint64_t>(index + 1));
|
||||
}
|
||||
page->Push(batch);
|
||||
if (page->MemCostBytes() >= kPageSize) {
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
if (tdiff >= tick_expected) {
|
||||
LOG(CONSOLE) << "Writing row.page to " << cache_info << " in "
|
||||
<< ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " written";
|
||||
tick_expected += kStep;
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
if (tdiff >= tick_expected) {
|
||||
LOG(CONSOLE) << "Writing row.page to " << cache_info << " in "
|
||||
<< ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " written";
|
||||
tick_expected += kStep;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (page->data.size() != 0) {
|
||||
writer.PushWrite(std::move(page));
|
||||
}
|
||||
if (page->data.size() != 0) {
|
||||
writer.PushWrite(std::move(page));
|
||||
}
|
||||
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(name_info.c_str(), "w"));
|
||||
int tmagic = kMagic;
|
||||
fo->Write(&tmagic, sizeof(tmagic));
|
||||
info.SaveBinary(fo.get());
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(name_info.c_str(), "w"));
|
||||
int tmagic = kMagic;
|
||||
fo->Write(&tmagic, sizeof(tmagic));
|
||||
info.SaveBinary(fo.get());
|
||||
}
|
||||
LOG(CONSOLE) << "SparsePageSource: Finished writing to " << name_info;
|
||||
}
|
||||
|
||||
@ -176,38 +178,39 @@ void SparsePageSource::Create(DMatrix* src,
|
||||
name_shards.push_back(prefix + ".row.page");
|
||||
format_shards.push_back(SparsePage::Format::DecideFormat(prefix).first);
|
||||
}
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
{
|
||||
SparsePage::Writer writer(name_shards, format_shards, 6);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
writer.Alloc(&page); page->Clear();
|
||||
|
||||
MetaInfo info;
|
||||
size_t bytes_write = 0;
|
||||
double tstart = dmlc::GetTime();
|
||||
dmlc::DataIter<RowBatch>* iter = src->RowIterator();
|
||||
MetaInfo info;
|
||||
size_t bytes_write = 0;
|
||||
double tstart = dmlc::GetTime();
|
||||
dmlc::DataIter<RowBatch>* iter = src->RowIterator();
|
||||
|
||||
while (iter->Next()) {
|
||||
page->Push(iter->Value());
|
||||
if (page->MemCostBytes() >= kPageSize) {
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
LOG(CONSOLE) << "Writing to " << cache_info << " in "
|
||||
<< ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " written";
|
||||
while (iter->Next()) {
|
||||
page->Push(iter->Value());
|
||||
if (page->MemCostBytes() >= kPageSize) {
|
||||
bytes_write += page->MemCostBytes();
|
||||
writer.PushWrite(std::move(page));
|
||||
writer.Alloc(&page);
|
||||
page->Clear();
|
||||
double tdiff = dmlc::GetTime() - tstart;
|
||||
LOG(CONSOLE) << "Writing to " << cache_info << " in "
|
||||
<< ((bytes_write >> 20UL) / tdiff) << " MB/s, "
|
||||
<< (bytes_write >> 20UL) << " written";
|
||||
}
|
||||
}
|
||||
if (page->data.size() != 0) {
|
||||
writer.PushWrite(std::move(page));
|
||||
}
|
||||
}
|
||||
|
||||
if (page->data.size() != 0) {
|
||||
writer.PushWrite(std::move(page));
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(name_info.c_str(), "w"));
|
||||
int tmagic = kMagic;
|
||||
fo->Write(&tmagic, sizeof(tmagic));
|
||||
info.SaveBinary(fo.get());
|
||||
}
|
||||
|
||||
std::unique_ptr<dmlc::Stream> fo(
|
||||
dmlc::Stream::Create(name_info.c_str(), "w"));
|
||||
int tmagic = kMagic;
|
||||
fo->Write(&tmagic, sizeof(tmagic));
|
||||
info.SaveBinary(fo.get());
|
||||
LOG(CONSOLE) << "SparsePageSource: Finished writing to " << name_info;
|
||||
}
|
||||
|
||||
|
||||
@ -34,6 +34,7 @@ SparsePage::Writer::Writer(
|
||||
fo->Write(format_shard);
|
||||
std::unique_ptr<SparsePage> page;
|
||||
while (wqueue->Pop(&page)) {
|
||||
if (page.get() == nullptr) break;
|
||||
fmt->Write(*page, fo.get());
|
||||
qrecycle_.Push(std::move(page));
|
||||
}
|
||||
@ -45,7 +46,9 @@ SparsePage::Writer::Writer(
|
||||
|
||||
SparsePage::Writer::~Writer() {
|
||||
for (auto& queue : qworkers_) {
|
||||
queue.SignalForKill();
|
||||
// use nullptr to signal termination.
|
||||
std::unique_ptr<SparsePage> sig(nullptr);
|
||||
queue.Push(std::move(sig));
|
||||
}
|
||||
for (auto& thread : workers_) {
|
||||
thread->join();
|
||||
|
||||
@ -17,6 +17,8 @@
|
||||
#include <limits>
|
||||
#include "../common/common.h"
|
||||
|
||||
#include "../common/random.h"
|
||||
|
||||
namespace xgboost {
|
||||
namespace gbm {
|
||||
|
||||
@ -47,6 +49,42 @@ struct GBTreeTrainParam : public dmlc::Parameter<GBTreeTrainParam> {
|
||||
}
|
||||
};
|
||||
|
||||
/*! \brief training parameters */
|
||||
struct DartTrainParam : public dmlc::Parameter<DartTrainParam> {
|
||||
/*! \brief whether to not print info during training */
|
||||
bool silent;
|
||||
/*! \brief type of sampling algorithm */
|
||||
int sample_type;
|
||||
/*! \brief type of normalization algorithm */
|
||||
int normalize_type;
|
||||
/*! \brief how many trees are dropped */
|
||||
float rate_drop;
|
||||
/*! \brief whether to drop trees */
|
||||
float skip_drop;
|
||||
/*! \brief learning step size for a time */
|
||||
float learning_rate;
|
||||
// declare parameters
|
||||
DMLC_DECLARE_PARAMETER(DartTrainParam) {
|
||||
DMLC_DECLARE_FIELD(silent).set_default(false)
|
||||
.describe("Not print information during trainig.");
|
||||
DMLC_DECLARE_FIELD(sample_type).set_default(0)
|
||||
.add_enum("uniform", 0)
|
||||
.add_enum("weighted", 1)
|
||||
.describe("Different types of sampling algorithm.");
|
||||
DMLC_DECLARE_FIELD(normalize_type).set_default(0)
|
||||
.add_enum("tree", 0)
|
||||
.add_enum("forest", 1)
|
||||
.describe("Different types of normalization algorithm.");
|
||||
DMLC_DECLARE_FIELD(rate_drop).set_range(0.0f, 1.0f).set_default(0.0f)
|
||||
.describe("Parameter of how many trees are dropped.");
|
||||
DMLC_DECLARE_FIELD(skip_drop).set_range(0.0f, 1.0f).set_default(0.0f)
|
||||
.describe("Parameter of whether to drop trees.");
|
||||
DMLC_DECLARE_FIELD(learning_rate).set_lower_bound(0.0f).set_default(0.3f)
|
||||
.describe("Learning rate(step size) of update.");
|
||||
DMLC_DECLARE_ALIAS(learning_rate, eta);
|
||||
}
|
||||
};
|
||||
|
||||
/*! \brief model parameters */
|
||||
struct GBTreeModelParam : public dmlc::Parameter<GBTreeModelParam> {
|
||||
/*! \brief number of trees */
|
||||
@ -313,8 +351,9 @@ class GBTree : public GradientBooster {
|
||||
}
|
||||
}
|
||||
// commit new trees all at once
|
||||
inline void CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
|
||||
int bst_group) {
|
||||
virtual void
|
||||
CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
|
||||
int bst_group) {
|
||||
for (size_t i = 0; i < new_trees.size(); ++i) {
|
||||
trees.push_back(std::move(new_trees[i]));
|
||||
tree_info.push_back(bst_group);
|
||||
@ -475,14 +514,236 @@ class GBTree : public GradientBooster {
|
||||
std::vector<std::unique_ptr<TreeUpdater> > updaters;
|
||||
};
|
||||
|
||||
// dart
|
||||
class Dart : public GBTree {
|
||||
public:
|
||||
Dart() {}
|
||||
|
||||
void Configure(const std::vector<std::pair<std::string, std::string> >& cfg) override {
|
||||
GBTree::Configure(cfg);
|
||||
if (trees.size() == 0) {
|
||||
dparam.InitAllowUnknown(cfg);
|
||||
}
|
||||
}
|
||||
|
||||
void Load(dmlc::Stream* fi) override {
|
||||
GBTree::Load(fi);
|
||||
weight_drop.resize(mparam.num_trees);
|
||||
if (mparam.num_trees != 0) {
|
||||
fi->Read(&weight_drop);
|
||||
}
|
||||
}
|
||||
|
||||
void Save(dmlc::Stream* fo) const override {
|
||||
GBTree::Save(fo);
|
||||
if (weight_drop.size() != 0) {
|
||||
fo->Write(weight_drop);
|
||||
}
|
||||
}
|
||||
|
||||
// predict the leaf scores with dropout if ntree_limit = 0
|
||||
void Predict(DMatrix* p_fmat,
|
||||
int64_t buffer_offset,
|
||||
std::vector<float>* out_preds,
|
||||
unsigned ntree_limit) override {
|
||||
DropTrees(ntree_limit);
|
||||
const MetaInfo& info = p_fmat->info();
|
||||
int nthread;
|
||||
#pragma omp parallel
|
||||
{
|
||||
nthread = omp_get_num_threads();
|
||||
}
|
||||
InitThreadTemp(nthread);
|
||||
std::vector<float> &preds = *out_preds;
|
||||
const size_t stride = p_fmat->info().num_row * mparam.num_output_group;
|
||||
preds.resize(stride * (mparam.size_leaf_vector+1));
|
||||
// start collecting the prediction
|
||||
dmlc::DataIter<RowBatch>* iter = p_fmat->RowIterator();
|
||||
|
||||
iter->BeforeFirst();
|
||||
while (iter->Next()) {
|
||||
const RowBatch &batch = iter->Value();
|
||||
// parallel over local batch
|
||||
const bst_omp_uint nsize = static_cast<bst_omp_uint>(batch.size);
|
||||
#pragma omp parallel for schedule(static)
|
||||
for (bst_omp_uint i = 0; i < nsize; ++i) {
|
||||
const int tid = omp_get_thread_num();
|
||||
RegTree::FVec &feats = thread_temp[tid];
|
||||
int64_t ridx = static_cast<int64_t>(batch.base_rowid + i);
|
||||
CHECK_LT(static_cast<size_t>(ridx), info.num_row);
|
||||
// loop over output groups
|
||||
for (int gid = 0; gid < mparam.num_output_group; ++gid) {
|
||||
this->Pred(batch[i],
|
||||
buffer_offset < 0 ? -1 : buffer_offset + ridx,
|
||||
gid, info.GetRoot(ridx), &feats,
|
||||
&preds[ridx * mparam.num_output_group + gid], stride,
|
||||
ntree_limit);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
void Predict(const SparseBatch::Inst& inst,
|
||||
std::vector<float>* out_preds,
|
||||
unsigned ntree_limit,
|
||||
unsigned root_index) override {
|
||||
DropTrees(1);
|
||||
if (thread_temp.size() == 0) {
|
||||
thread_temp.resize(1, RegTree::FVec());
|
||||
thread_temp[0].Init(mparam.num_feature);
|
||||
}
|
||||
out_preds->resize(mparam.num_output_group * (mparam.size_leaf_vector+1));
|
||||
// loop over output groups
|
||||
for (int gid = 0; gid < mparam.num_output_group; ++gid) {
|
||||
this->Pred(inst, -1, gid, root_index, &thread_temp[0],
|
||||
&(*out_preds)[gid], mparam.num_output_group,
|
||||
ntree_limit);
|
||||
}
|
||||
}
|
||||
|
||||
protected:
|
||||
// commit new trees all at once
|
||||
virtual void
|
||||
CommitModel(std::vector<std::unique_ptr<RegTree> >&& new_trees,
|
||||
int bst_group) {
|
||||
for (size_t i = 0; i < new_trees.size(); ++i) {
|
||||
trees.push_back(std::move(new_trees[i]));
|
||||
tree_info.push_back(bst_group);
|
||||
}
|
||||
mparam.num_trees += static_cast<int>(new_trees.size());
|
||||
size_t num_drop = NormalizeTrees(new_trees.size());
|
||||
if (dparam.silent != 1) {
|
||||
LOG(INFO) << "drop " << num_drop << " trees, "
|
||||
<< "weight = " << weight_drop.back();
|
||||
}
|
||||
}
|
||||
// predict the leaf scores without dropped trees
|
||||
inline void Pred(const RowBatch::Inst &inst,
|
||||
int64_t buffer_index,
|
||||
int bst_group,
|
||||
unsigned root_index,
|
||||
RegTree::FVec *p_feats,
|
||||
float *out_pred,
|
||||
size_t stride,
|
||||
unsigned ntree_limit) {
|
||||
float psum = 0.0f;
|
||||
// sum of leaf vector
|
||||
std::vector<float> vec_psum(mparam.size_leaf_vector, 0.0f);
|
||||
const int64_t bid = this->BufferOffset(buffer_index, bst_group);
|
||||
p_feats->Fill(inst);
|
||||
for (size_t i = 0; i < trees.size(); ++i) {
|
||||
if (tree_info[i] == bst_group) {
|
||||
bool drop = (std::find(idx_drop.begin(), idx_drop.end(), i) != idx_drop.end());
|
||||
if (!drop) {
|
||||
int tid = trees[i]->GetLeafIndex(*p_feats, root_index);
|
||||
psum += weight_drop[i] * (*trees[i])[tid].leaf_value();
|
||||
for (int j = 0; j < mparam.size_leaf_vector; ++j) {
|
||||
vec_psum[j] += weight_drop[i] * trees[i]->leafvec(tid)[j];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
p_feats->Drop(inst);
|
||||
// updated the buffered results
|
||||
if (bid >= 0 && ntree_limit == 0) {
|
||||
pred_counter[bid] = static_cast<unsigned>(trees.size());
|
||||
pred_buffer[bid] = psum;
|
||||
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
|
||||
pred_buffer[bid + i + 1] = vec_psum[i];
|
||||
}
|
||||
}
|
||||
out_pred[0] = psum;
|
||||
for (int i = 0; i < mparam.size_leaf_vector; ++i) {
|
||||
out_pred[stride * (i + 1)] = vec_psum[i];
|
||||
}
|
||||
}
|
||||
|
||||
// select dropped trees
|
||||
inline void DropTrees(unsigned ntree_limit_drop) {
|
||||
std::uniform_real_distribution<> runif(0.0, 1.0);
|
||||
auto& rnd = common::GlobalRandom();
|
||||
// reset
|
||||
idx_drop.clear();
|
||||
// sample dropped trees
|
||||
bool skip = false;
|
||||
if (dparam.skip_drop > 0.0) skip = (runif(rnd) < dparam.skip_drop);
|
||||
if (ntree_limit_drop == 0 && !skip) {
|
||||
if (dparam.sample_type == 1) {
|
||||
float sum_weight = 0.0;
|
||||
for (size_t i = 0; i < weight_drop.size(); ++i) {
|
||||
sum_weight += weight_drop[i];
|
||||
}
|
||||
for (size_t i = 0; i < weight_drop.size(); ++i) {
|
||||
if (runif(rnd) < dparam.rate_drop * weight_drop.size() * weight_drop[i] / sum_weight) {
|
||||
idx_drop.push_back(i);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
for (size_t i = 0; i < weight_drop.size(); ++i) {
|
||||
if (runif(rnd) < dparam.rate_drop) {
|
||||
idx_drop.push_back(i);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
// set normalization factors
|
||||
inline size_t NormalizeTrees(size_t size_new_trees) {
|
||||
float lr = 1.0 * dparam.learning_rate / size_new_trees;
|
||||
size_t num_drop = idx_drop.size();
|
||||
if (num_drop == 0) {
|
||||
for (size_t i = 0; i < size_new_trees; ++i) {
|
||||
weight_drop.push_back(1.0);
|
||||
}
|
||||
} else {
|
||||
if (dparam.normalize_type == 1) {
|
||||
// normalize_type 1
|
||||
float factor = 1.0 / (1.0 + lr);
|
||||
for (size_t i = 0; i < idx_drop.size(); ++i) {
|
||||
weight_drop[i] *= factor;
|
||||
}
|
||||
for (size_t i = 0; i < size_new_trees; ++i) {
|
||||
weight_drop.push_back(lr * factor);
|
||||
}
|
||||
} else {
|
||||
// normalize_type 0
|
||||
float factor = 1.0 * num_drop / (num_drop + lr);
|
||||
for (size_t i = 0; i < idx_drop.size(); ++i) {
|
||||
weight_drop[i] *= factor;
|
||||
}
|
||||
for (size_t i = 0; i < size_new_trees; ++i) {
|
||||
weight_drop.push_back(1.0 * lr / (num_drop + lr));
|
||||
}
|
||||
}
|
||||
}
|
||||
// reset
|
||||
idx_drop.clear();
|
||||
return num_drop;
|
||||
}
|
||||
|
||||
// --- data structure ---
|
||||
// training parameter
|
||||
DartTrainParam dparam;
|
||||
/*! \brief prediction buffer */
|
||||
std::vector<float> weight_drop;
|
||||
// indexes of dropped trees
|
||||
std::vector<size_t> idx_drop;
|
||||
};
|
||||
|
||||
// register the ojective functions
|
||||
DMLC_REGISTER_PARAMETER(GBTreeModelParam);
|
||||
DMLC_REGISTER_PARAMETER(GBTreeTrainParam);
|
||||
DMLC_REGISTER_PARAMETER(DartTrainParam);
|
||||
|
||||
XGBOOST_REGISTER_GBM(GBTree, "gbtree")
|
||||
.describe("Tree booster, gradient boosted trees.")
|
||||
.set_body([]() {
|
||||
return new GBTree();
|
||||
});
|
||||
XGBOOST_REGISTER_GBM(Dart, "dart")
|
||||
.describe("Tree booster, dart.")
|
||||
.set_body([]() {
|
||||
return new Dart();
|
||||
});
|
||||
} // namespace gbm
|
||||
} // namespace xgboost
|
||||
|
||||
@ -35,6 +35,22 @@ class TestBasic(unittest.TestCase):
|
||||
# assert they are the same
|
||||
assert np.sum(np.abs(preds2 - preds)) == 0
|
||||
|
||||
def test_record_results(self):
|
||||
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
|
||||
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
|
||||
param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'binary:logistic'}
|
||||
# specify validations set to watch performance
|
||||
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
|
||||
num_round = 2
|
||||
result = {}
|
||||
res2 = {}
|
||||
xgb.train(param, dtrain, num_round, watchlist,
|
||||
callbacks=[xgb.callback.record_evaluation(result)])
|
||||
xgb.train(param, dtrain, num_round, watchlist,
|
||||
evals_result=res2)
|
||||
assert result['train']['error'][0] < 0.1
|
||||
assert res2 == result
|
||||
|
||||
def test_multiclass(self):
|
||||
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
|
||||
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
|
||||
@ -189,5 +205,5 @@ class TestBasic(unittest.TestCase):
|
||||
|
||||
# return np.ndarray
|
||||
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
|
||||
assert isinstance(cv, np.ndarray)
|
||||
assert cv.shape == (10, 4)
|
||||
assert isinstance(cv, dict)
|
||||
assert len(cv) == (4)
|
||||
|
||||
@ -23,6 +23,51 @@ class TestModels(unittest.TestCase):
|
||||
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
|
||||
assert err < 0.1
|
||||
|
||||
def test_dart(self):
|
||||
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
|
||||
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
|
||||
param = {'max_depth': 5, 'objective': 'binary:logistic', 'booster': 'dart', 'silent': False}
|
||||
# specify validations set to watch performance
|
||||
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
|
||||
num_round = 2
|
||||
bst = xgb.train(param, dtrain, num_round, watchlist)
|
||||
# this is prediction
|
||||
preds = bst.predict(dtest, ntree_limit=num_round)
|
||||
labels = dtest.get_label()
|
||||
err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
|
||||
# error must be smaller than 10%
|
||||
assert err < 0.1
|
||||
|
||||
# save dmatrix into binary buffer
|
||||
dtest.save_binary('dtest.buffer')
|
||||
# save model
|
||||
bst.save_model('xgb.model.dart')
|
||||
# load model and data in
|
||||
bst2 = xgb.Booster(params=param, model_file='xgb.model.dart')
|
||||
dtest2 = xgb.DMatrix('dtest.buffer')
|
||||
preds2 = bst2.predict(dtest2, ntree_limit=num_round)
|
||||
# assert they are the same
|
||||
assert np.sum(np.abs(preds2 - preds)) == 0
|
||||
|
||||
# check whether sample_type and normalize_type work
|
||||
num_round = 50
|
||||
param['silent'] = True
|
||||
param['learning_rate'] = 0.1
|
||||
param['rate_drop'] = 0.1
|
||||
preds_list = []
|
||||
for p in [[p0, p1] for p0 in ['uniform', 'weighted'] for p1 in ['tree', 'forest']]:
|
||||
param['sample_type'] = p[0]
|
||||
param['normalize_type'] = p[1]
|
||||
bst = xgb.train(param, dtrain, num_round, watchlist)
|
||||
preds = bst.predict(dtest, ntree_limit=num_round)
|
||||
err = sum(1 for i in range(len(preds)) if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
|
||||
assert err < 0.1
|
||||
preds_list.append(preds)
|
||||
|
||||
for ii in range(len(preds_list)):
|
||||
for jj in range(ii + 1, len(preds_list)):
|
||||
assert np.sum(np.abs(preds_list[ii] - preds_list[jj])) > 0
|
||||
|
||||
def test_eta_decay(self):
|
||||
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
|
||||
num_round = 4
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
import numpy as np
|
||||
import unittest
|
||||
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
import numpy as np
|
||||
import unittest
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
import unittest
|
||||
|
||||
try:
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
import numpy as np
|
||||
import unittest
|
||||
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
import numpy as np
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
import unittest
|
||||
|
||||
try:
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
import numpy as np
|
||||
import random
|
||||
import xgboost as xgb
|
||||
import xgboost.testing as tm
|
||||
import testing as tm
|
||||
|
||||
rng = np.random.RandomState(1994)
|
||||
|
||||
|
||||
@ -17,6 +17,6 @@ def _skip_if_no_pandas():
|
||||
|
||||
def _skip_if_no_matplotlib():
|
||||
try:
|
||||
import matplotlib.pyplot as plt # noqa
|
||||
import matplotlib.pyplot as _ # noqa
|
||||
except ImportError:
|
||||
raise nose.SkipTest()
|
||||
Loading…
x
Reference in New Issue
Block a user