diff --git a/Makefile b/Makefile index e3f3134e4..abe8ccfaa 100644 --- a/Makefile +++ b/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 diff --git a/NEWS.md b/NEWS.md index 81afdbb5a..35cfc74f5 100644 --- a/NEWS.md +++ b/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) diff --git a/README.md b/README.md index edcb68043..8b9c10168 100644 --- a/README.md +++ b/README.md @@ -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. diff --git a/demo/README.md b/demo/README.md index e4117cd8b..fb5505fbc 100644 --- a/demo/README.md +++ b/demo/README.md @@ -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) diff --git a/demo/guide-python/cross_validation.py b/demo/guide-python/cross_validation.py index 6ca13d460..5c8ee0b1b 100755 --- a/demo/guide-python/cross_validation.py +++ b/demo/guide-python/cross_validation.py @@ -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) - diff --git a/dmlc-core b/dmlc-core index 1db0792e1..9fd3b4846 160000 --- a/dmlc-core +++ b/dmlc-core @@ -1 +1 @@ -Subproject commit 1db0792e1a55355b1f07699bba18c88ded996953 +Subproject commit 9fd3b48462a7a651e12a197679f71e043dcb25a2 diff --git a/doc/R-package/index.md b/doc/R-package/index.md index 92df95e9f..6333d43fb 100644 --- a/doc/R-package/index.md +++ b/doc/R-package/index.md @@ -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 --------- diff --git a/doc/_static/cn.svg b/doc/_static/cn.svg new file mode 100644 index 000000000..515176d60 --- /dev/null +++ b/doc/_static/cn.svg @@ -0,0 +1,20 @@ + + + + Flag of the People's Republic of China + + + + + + + + + + + + + + + + diff --git a/doc/_static/js/auto_module_index.js b/doc/_static/js/auto_module_index.js new file mode 100644 index 000000000..b918ecdc1 --- /dev/null +++ b/doc/_static/js/auto_module_index.js @@ -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"; + var func_query = div_query + " dl[class='function'] > dt"; + var targets = $(class_query + ',' + func_query); + + var li_node = $("li a[href='#module-" + module + "']").parent(); + var html = ""; + li_node.append(html); + }); +} + diff --git a/doc/_static/us.svg b/doc/_static/us.svg new file mode 100644 index 000000000..1d621f96d --- /dev/null +++ b/doc/_static/us.svg @@ -0,0 +1,117 @@ + + + + + + image/svg+xml + + + + + The United States of America flag, produced by Daniel McRae + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/_static/xgboost-theme/footer.html b/doc/_static/xgboost-theme/footer.html new file mode 100644 index 000000000..148bcb7df --- /dev/null +++ b/doc/_static/xgboost-theme/footer.html @@ -0,0 +1,5 @@ +
+
+

© 2015-2016 DMLC. All rights reserved.

+
+
diff --git a/doc/_static/xgboost-theme/index.html b/doc/_static/xgboost-theme/index.html new file mode 100644 index 000000000..852e00a1a --- /dev/null +++ b/doc/_static/xgboost-theme/index.html @@ -0,0 +1,58 @@ +
+
+
+
+

Scalable and Flexible Gradient Boosting

+
+ + + + +
+
+ Get Started +
+
+
+
+
+ +
+
+
+
+

Flexible

+

Supports regression, classification, ranking and user defined objectives. +

+
+
+

Portable

+

Runs on Windows, Lunix and OSX, as well as various cloud Platforms

+
+
+

Multiple Lanuages

+

Supports multiple languages including C++, Python, R, Java, Scala, Julia.

+
+
+

Battle-tested

+

Wins many data science and machine learning challenges. + Used in production by multiple companies. +

+
+
+

Distributed on Cloud

+

Supports distributed training on multiple machines, including AWS, + GCE, Azure, and Yarn clusters. Can be integrated with Flink, Spark and other cloud dataflow systems.

+
+
+

Performance

+

The well-optimized backend system for the best performance with limited resources. + The distributed version solves problems beyond billions of examples with same code. +

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+
+
+
+ diff --git a/doc/_static/xgboost-theme/layout.html b/doc/_static/xgboost-theme/layout.html new file mode 100644 index 000000000..2931fe594 --- /dev/null +++ b/doc/_static/xgboost-theme/layout.html @@ -0,0 +1,156 @@ +{%- block doctype -%} + +{%- 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) %} + +{%- endmacro %} + +{%- macro sidebarglobal() %} + +{%- endmacro %} + +{%- macro sidebar() %} + {%- if render_sidebar %} + + {%- endif %} +{%- endmacro %} + + +{%- macro script() %} + + + {% for name in ['jquery.js', 'underscore.js', 'doctools.js', 'searchtools.js'] %} + + {% endfor %} + + + + + + +{%- endmacro %} + +{%- macro css() %} + + {% if pagename == 'index' %} + + {%- else %} + + + {%- endif %} + + +{%- endmacro %} + + + + + + + {# 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|striptags|e }}{{ titlesuffix }} + {%- else %} + XGBoost Documents + {%- endif %} + {%- endblock %} + {{ css() }} + {%- if not embedded %} + {{ script() }} + {%- if use_opensearch %} + + {%- endif %} + {%- if favicon %} + + {%- endif %} + {%- endif %} +{%- block linktags %} + {%- if hasdoc('about') %} + + {%- endif %} + {%- if hasdoc('genindex') %} + + {%- endif %} + {%- if hasdoc('search') %} + + {%- endif %} + {%- if hasdoc('copyright') %} + + {%- endif %} + {%- if parents %} + + {%- endif %} + {%- if next %} + + {%- endif %} + {%- if prev %} + + {%- endif %} +{%- endblock %} +{%- block extrahead %} {% endblock %} + + + + + {%- include "navbar.html" %} + + {% if pagename != 'index' %} +
+
+ {{ sidebar() }} +
+ {% block body %} {% endblock %} + {%- include "footer.html" %} +
+
+
+ {%- else %} + {%- include "index.html" %} + {%- include "footer.html" %} + {%- endif %} + + + + diff --git a/doc/_static/xgboost-theme/navbar.html b/doc/_static/xgboost-theme/navbar.html new file mode 100644 index 000000000..44c66cf9c --- /dev/null +++ b/doc/_static/xgboost-theme/navbar.html @@ -0,0 +1,40 @@ +
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+
+ +
+ +
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diff --git a/doc/_static/xgboost-theme/theme.conf b/doc/_static/xgboost-theme/theme.conf new file mode 100644 index 000000000..89e03bbda --- /dev/null +++ b/doc/_static/xgboost-theme/theme.conf @@ -0,0 +1,2 @@ +[theme] +inherit = basic diff --git a/doc/_static/xgboost.css b/doc/_static/xgboost.css new file mode 100644 index 000000000..f4862a706 --- /dev/null +++ b/doc/_static/xgboost.css @@ -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; +} diff --git a/doc/cli/index.md b/doc/cli/index.md new file mode 100644 index 000000000..23611d699 --- /dev/null +++ b/doc/cli/index.md @@ -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) diff --git a/doc/conf.py b/doc/conf.py index b2f82e726..65ebddccd 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -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, diff --git a/doc/get_started/index.md b/doc/get_started/index.md new file mode 100644 index 000000000..50c960421 --- /dev/null +++ b/doc/get_started/index.md @@ -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") + } +} +``` diff --git a/doc/dev-guide/contribute.md b/doc/how_to/contribute.md similarity index 100% rename from doc/dev-guide/contribute.md rename to doc/how_to/contribute.md diff --git a/doc/external_memory.md b/doc/how_to/external_memory.md similarity index 100% rename from doc/external_memory.md rename to doc/how_to/external_memory.md diff --git a/doc/how_to/index.md b/doc/how_to/index.md new file mode 100644 index 000000000..afc69f777 --- /dev/null +++ b/doc/how_to/index.md @@ -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) diff --git a/doc/param_tuning.md b/doc/how_to/param_tuning.md similarity index 100% rename from doc/param_tuning.md rename to doc/how_to/param_tuning.md diff --git a/doc/index.md b/doc/index.md index 9d95944e6..55d90d95b 100644 --- a/doc/index.md +++ b/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) diff --git a/doc/julia/index.md b/doc/julia/index.md new file mode 100644 index 000000000..470e09644 --- /dev/null +++ b/doc/julia/index.md @@ -0,0 +1,3 @@ +# XGBoost.jl + +See [XGBoost.jl Project page](https://github.com/dmlc/XGBoost.jl) \ No newline at end of file diff --git a/doc/jvm/index.md b/doc/jvm/index.md index e9a16477e..e3ff666c0 100644 --- a/doc/jvm/index.md +++ b/doc/jvm/index.md @@ -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) \ No newline at end of file + * [XGBoost4J-Flink](http://dmlc.ml/docs/scaladocs/xgboost4j-flink/index.html) diff --git a/doc/parameter.md b/doc/parameter.md index 3ca79077b..70575343e 100644 --- a/doc/parameter.md +++ b/doc/parameter.md @@ -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 ----------------------------- diff --git a/doc/tutorial/aws_yarn.md b/doc/tutorials/aws_yarn.md similarity index 100% rename from doc/tutorial/aws_yarn.md rename to doc/tutorials/aws_yarn.md diff --git a/doc/tutorials/index.md b/doc/tutorials/index.md new file mode 100644 index 000000000..a4edf51c1 --- /dev/null +++ b/doc/tutorials/index.md @@ -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) diff --git a/jvm-packages/README.md b/jvm-packages/README.md index e1dfb1576..a9aded6f8 100644 --- a/jvm-packages/README.md +++ b/jvm-packages/README.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) } diff --git a/jvm-packages/pom.xml b/jvm-packages/pom.xml index 5d0cbd00b..c9566016b 100644 --- a/jvm-packages/pom.xml +++ b/jvm-packages/pom.xml @@ -23,6 +23,18 @@ xgboost4j-spark xgboost4j-flink + + + spark-1.x + + true + + + 1.6.1 + 2.10 + + + diff --git a/jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java b/jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java index 349098ae1..a4a1cb703 100644 --- a/jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java +++ b/jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java @@ -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 params = new HashMap(); diff --git a/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/DistTrainWithSpark.scala b/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/DistTrainWithSpark.scala index 978e8f0ee..a5ebfa05a 100644 --- a/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/DistTrainWithSpark.scala +++ b/jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/DistTrainWithSpark.scala @@ -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) diff --git a/jvm-packages/xgboost4j-spark/pom.xml b/jvm-packages/xgboost4j-spark/pom.xml index ac37e78ac..120616c58 100644 --- a/jvm-packages/xgboost4j-spark/pom.xml +++ b/jvm-packages/xgboost4j-spark/pom.xml @@ -28,8 +28,8 @@ org.apache.spark - spark-mllib_2.10 - 1.6.1 + spark-mllib_${scala.binary.version} + ${spark.version} \ No newline at end of file diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala index dc1b5382c..5903bd2c9 100644 --- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala +++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala @@ -16,6 +16,8 @@ package ml.dmlc.xgboost4j.scala.spark +import java.nio.file.Paths + import scala.collection.mutable import scala.collection.JavaConverters._ @@ -41,7 +43,8 @@ object XGBoost extends Serializable { trainingData: RDD[LabeledPoint], xgBoostConfMap: Map[String, Any], rabitEnv: mutable.Map[String, String], - numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait): RDD[Booster] = { + numWorkers: Int, round: Int, obj: ObjectiveTrait, eval: EvalTrait, + useExternalMemory: Boolean): RDD[Booster] = { import DataUtils._ val partitionedData = { if (numWorkers > trainingData.partitions.length) { @@ -54,11 +57,19 @@ object XGBoost extends Serializable { trainingData } } + val appName = partitionedData.context.appName partitionedData.mapPartitions { trainingSamples => rabitEnv.put("DMLC_TASK_ID", TaskContext.getPartitionId().toString) Rabit.init(rabitEnv.asJava) - val trainingSet = new DMatrix(new JDMatrix(trainingSamples, null)) + val cacheFileName: String = { + if (useExternalMemory && trainingSamples.hasNext) { + s"$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 diff --git a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala index 75a91e64c..f81e63048 100644 --- a/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala +++ b/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala @@ -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() diff --git a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala index 711ea35f0..71bb9ecf8 100644 --- a/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala +++ b/jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSuite.scala @@ -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() + } + } } diff --git a/python-package/.pylintrc b/python-package/.pylintrc index 1e63cdabe..e8e957d2b 100644 --- a/python-package/.pylintrc +++ b/python-package/.pylintrc @@ -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 \ No newline at end of file +reports=no diff --git a/python-package/xgboost/callback.py b/python-package/xgboost/callback.py new file mode 100644 index 000000000..3683ea2dd --- /dev/null +++ b/python-package/xgboost/callback.py @@ -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 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 diff --git a/python-package/xgboost/compat.py b/python-package/xgboost/compat.py index 44707c539..8237b1249 100644 --- a/python-package/xgboost/compat.py +++ b/python-package/xgboost/compat.py @@ -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: diff --git a/python-package/xgboost/core.py b/python-package/xgboost/core.py index f22ca7ef1..e31f622cf 100644 --- a/python-package/xgboost/core.py +++ b/python-package/xgboost/core.py @@ -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 diff --git a/python-package/xgboost/rabit.py b/python-package/xgboost/rabit.py index af85b2dd0..89b2a4ec6 100644 --- a/python-package/xgboost/rabit.py +++ b/python-package/xgboost/rabit.py @@ -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), diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py index cafbe073f..2b4c2accb 100644 --- a/python-package/xgboost/sklearn.py +++ b/python-package/xgboost/sklearn.py @@ -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] diff --git a/python-package/xgboost/training.py b/python-package/xgboost/training.py index d21edd30d..3da92ff51 100644 --- a/python-package/xgboost/training.py +++ b/python-package/xgboost/training.py @@ -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 diff --git a/rabit b/rabit index e19fced5c..8f61535b8 160000 --- a/rabit +++ b/rabit @@ -1 +1 @@ -Subproject commit e19fced5cbd4e41b10099facae7caa5cd3e6ada3 +Subproject commit 8f61535b83e650331459d7f33a1615fa7d27b7bd diff --git a/src/cli_main.cc b/src/cli_main.cc index 039ce4070..e79592615 100644 --- a/src/cli_main.cc +++ b/src/cli_main.cc @@ -271,6 +271,7 @@ void CLIDump2Text(const CLIParam& param) { std::unique_ptr learner(Learner::Create({})); std::unique_ptr fi( dmlc::Stream::Create(param.model_in.c_str(), "r")); + learner->Configure(param.cfg); learner->Load(fi.get()); // dump data std::vector dump = learner->Dump2Text(fmap, param.dump_stats); @@ -297,6 +298,7 @@ void CLIPredict(const CLIParam& param) { std::unique_ptr learner(Learner::Create({})); std::unique_ptr fi( dmlc::Stream::Create(param.model_in.c_str(), "r")); + learner->Configure(param.cfg); learner->Load(fi.get()); if (param.silent == 0) { diff --git a/src/data/data.cc b/src/data/data.cc index e8135692a..b1299a53c 100644 --- a/src/data/data.cc +++ b/src/data/data.cc @@ -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; } diff --git a/src/data/sparse_page_dmatrix.cc b/src/data/sparse_page_dmatrix.cc index d25e06492..74a85e9ca 100644 --- a/src/data/sparse_page_dmatrix.cc +++ b/src/data/sparse_page_dmatrix.cc @@ -256,41 +256,44 @@ void SparsePageDMatrix::InitColAccess(const std::vector& 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 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 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 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 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()); } diff --git a/src/data/sparse_page_source.cc b/src/data/sparse_page_source.cc index 3f499739f..ab5d7e650 100644 --- a/src/data/sparse_page_source.cc +++ b/src/data/sparse_page_source.cc @@ -110,58 +110,60 @@ void SparsePageSource::Create(dmlc::Parser* 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 page; - writer.Alloc(&page); page->Clear(); + { + SparsePage::Writer writer(name_shards, format_shards, 6); + std::unique_ptr 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& 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(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& 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(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 fo( - dmlc::Stream::Create(name_info.c_str(), "w")); - int tmagic = kMagic; - fo->Write(&tmagic, sizeof(tmagic)); - info.SaveBinary(fo.get()); + std::unique_ptr 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 page; - writer.Alloc(&page); page->Clear(); + { + SparsePage::Writer writer(name_shards, format_shards, 6); + std::unique_ptr page; + writer.Alloc(&page); page->Clear(); - MetaInfo info; - size_t bytes_write = 0; - double tstart = dmlc::GetTime(); - dmlc::DataIter* iter = src->RowIterator(); + MetaInfo info; + size_t bytes_write = 0; + double tstart = dmlc::GetTime(); + dmlc::DataIter* 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 fo( + dmlc::Stream::Create(name_info.c_str(), "w")); + int tmagic = kMagic; + fo->Write(&tmagic, sizeof(tmagic)); + info.SaveBinary(fo.get()); } - - std::unique_ptr 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; } diff --git a/src/data/sparse_page_writer.cc b/src/data/sparse_page_writer.cc index 33f9172d6..e16d1aee6 100644 --- a/src/data/sparse_page_writer.cc +++ b/src/data/sparse_page_writer.cc @@ -34,6 +34,7 @@ SparsePage::Writer::Writer( fo->Write(format_shard); std::unique_ptr 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 sig(nullptr); + queue.Push(std::move(sig)); } for (auto& thread : workers_) { thread->join(); diff --git a/src/gbm/gbtree.cc b/src/gbm/gbtree.cc index 7e58a060a..a48fb2f94 100644 --- a/src/gbm/gbtree.cc +++ b/src/gbm/gbtree.cc @@ -17,6 +17,8 @@ #include #include "../common/common.h" +#include "../common/random.h" + namespace xgboost { namespace gbm { @@ -47,6 +49,42 @@ struct GBTreeTrainParam : public dmlc::Parameter { } }; +/*! \brief training parameters */ +struct DartTrainParam : public dmlc::Parameter { + /*! \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 { /*! \brief number of trees */ @@ -313,8 +351,9 @@ class GBTree : public GradientBooster { } } // commit new trees all at once - inline void CommitModel(std::vector >&& new_trees, - int bst_group) { + virtual void + CommitModel(std::vector >&& 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 > updaters; }; +// dart +class Dart : public GBTree { + public: + Dart() {} + + void Configure(const std::vector >& 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* 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 &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* 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(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(batch.base_rowid + i); + CHECK_LT(static_cast(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* 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 >&& 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(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 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(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 weight_drop; + // indexes of dropped trees + std::vector 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 diff --git a/tests/python/test_basic.py b/tests/python/test_basic.py index 386462091..710de987d 100644 --- a/tests/python/test_basic.py +++ b/tests/python/test_basic.py @@ -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) diff --git a/tests/python/test_basic_models.py b/tests/python/test_basic_models.py index 9e9c08423..5275e2f04 100644 --- a/tests/python/test_basic_models.py +++ b/tests/python/test_basic_models.py @@ -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 diff --git a/tests/python/test_early_stopping.py b/tests/python/test_early_stopping.py index b015547a1..67e725b74 100644 --- a/tests/python/test_early_stopping.py +++ b/tests/python/test_early_stopping.py @@ -1,5 +1,5 @@ import xgboost as xgb -import xgboost.testing as tm +import testing as tm import numpy as np import unittest diff --git a/tests/python/test_eval_metrics.py b/tests/python/test_eval_metrics.py index 2391bfe28..529ef698c 100644 --- a/tests/python/test_eval_metrics.py +++ b/tests/python/test_eval_metrics.py @@ -1,5 +1,5 @@ import xgboost as xgb -import xgboost.testing as tm +import testing as tm import numpy as np import unittest diff --git a/tests/python/test_plotting.py b/tests/python/test_plotting.py index 7a70bd95e..fde98dcca 100644 --- a/tests/python/test_plotting.py +++ b/tests/python/test_plotting.py @@ -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: diff --git a/tests/python/test_training_continuation.py b/tests/python/test_training_continuation.py index 2cb93f9ac..f7511f685 100644 --- a/tests/python/test_training_continuation.py +++ b/tests/python/test_training_continuation.py @@ -1,5 +1,5 @@ import xgboost as xgb -import xgboost.testing as tm +import testing as tm import numpy as np import unittest diff --git a/tests/python/test_with_pandas.py b/tests/python/test_with_pandas.py index 9536c1e82..0bef20ec2 100644 --- a/tests/python/test_with_pandas.py +++ b/tests/python/test_with_pandas.py @@ -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: diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py index 72ae27948..d079d99fe 100644 --- a/tests/python/test_with_sklearn.py +++ b/tests/python/test_with_sklearn.py @@ -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) diff --git a/python-package/xgboost/testing.py b/tests/python/testing.py similarity index 87% rename from python-package/xgboost/testing.py rename to tests/python/testing.py index 647a89fef..fb368dedd 100644 --- a/python-package/xgboost/testing.py +++ b/tests/python/testing.py @@ -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()