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add style check for java and scala code

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CodingCat 2016-03-01 20:19:49 -05:00
parent 3b246c2420
commit 55e36893cd
30 changed files with 1252 additions and 583 deletions
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33
jvm-packages/checkstyle-suppressions.xml Normal file
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@ -0,0 +1,33 @@
<!--
~ Licensed to the Apache Software Foundation (ASF) under one or more
~ contributor license agreements. See the NOTICE file distributed with
~ this work for additional information regarding copyright ownership.
~ The ASF licenses this file to You under the Apache License, Version 2.0
~ (the "License"); you may not use this file except in compliance with
~ the License. You may obtain a copy of the License at
~
~ http://www.apache.org/licenses/LICENSE-2.0
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS,
~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~ See the License for the specific language governing permissions and
~ limitations under the License.
-->
<!DOCTYPE suppressions PUBLIC
"-//Puppy Crawl//DTD Suppressions 1.1//EN"
"http://www.puppycrawl.com/dtds/suppressions_1_1.dtd">
<!--
This file contains suppression rules for Checkstyle checks.
Ideally only files that cannot be modified (e.g. third-party code)
should be added here. All other violations should be fixed.
-->
<suppressions>
<suppress checks=".*"
files="xgboost4j/src/main/java/org/dmlc/xgboost4j/XgboostJNI.java"/>
</suppressions>

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jvm-packages/checkstyle.xml Normal file
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<!--
~ Licensed to the Apache Software Foundation (ASF) under one or more
~ contributor license agreements. See the NOTICE file distributed with
~ this work for additional information regarding copyright ownership.
~ The ASF licenses this file to You under the Apache License, Version 2.0
~ (the "License"); you may not use this file except in compliance with
~ the License. You may obtain a copy of the License at
~
~ http://www.apache.org/licenses/LICENSE-2.0
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS,
~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~ See the License for the specific language governing permissions and
~ limitations under the License.
-->
<!DOCTYPE module PUBLIC
"-//Puppy Crawl//DTD Check Configuration 1.3//EN"
"http://www.puppycrawl.com/dtds/configuration_1_3.dtd">
<!--
Checkstyle configuration based on the Google coding conventions from:
- Google Java Style
https://google-styleguide.googlecode.com/svn-history/r130/trunk/javaguide.html
with Spark-specific changes from:
https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
Checkstyle is very configurable. Be sure to read the documentation at
http://checkstyle.sf.net (or in your downloaded distribution).
Most Checks are configurable, be sure to consult the documentation.
To completely disable a check, just comment it out or delete it from the file.
Authors: Max Vetrenko, Ruslan Diachenko, Roman Ivanov.
-->
<module name = "Checker">
<property name="charset" value="UTF-8"/>
<property name="severity" value="error"/>
<property name="fileExtensions" value="java, properties, xml"/>
<module name="SuppressionFilter">
<property name="file" value="checkstyle-suppressions.xml"/>
</module>
<!-- Checks for whitespace -->
<!-- See http://checkstyle.sf.net/config_whitespace.html -->
<module name="FileTabCharacter">
<property name="eachLine" value="true"/>
</module>
<module name="RegexpSingleline">
<!-- \s matches whitespace character, $ matches end of line. -->
<property name="format" value="\s+$"/>
<property name="message" value="No trailing whitespace allowed."/>
</module>
<module name="TreeWalker">
<module name="OuterTypeFilename"/>
<module name="IllegalTokenText">
<property name="tokens" value="STRING_LITERAL, CHAR_LITERAL"/>
<property name="format" value="\\u00(08|09|0(a|A)|0(c|C)|0(d|D)|22|27|5(C|c))|\\(0(10|11|12|14|15|42|47)|134)"/>
<property name="message" value="Avoid using corresponding octal or Unicode escape."/>
</module>
<module name="AvoidEscapedUnicodeCharacters">
<property name="allowEscapesForControlCharacters" value="true"/>
<property name="allowByTailComment" value="true"/>
<property name="allowNonPrintableEscapes" value="true"/>
</module>
<!-- TODO: 11/09/15 disabled - the lengths are currently > 100 in many places -->
<module name="LineLength">
<property name="max" value="100"/>
<property name="ignorePattern" value="^package.*|^import.*|a href|href|http://|https://|ftp://"/>
</module>
<module name="NoLineWrap"/>
<module name="EmptyBlock">
<property name="option" value="TEXT"/>
<property name="tokens" value="LITERAL_TRY, LITERAL_FINALLY, LITERAL_IF, LITERAL_ELSE, LITERAL_SWITCH"/>
</module>
<module name="NeedBraces">
<property name="allowSingleLineStatement" value="true"/>
</module>
<module name="OneStatementPerLine"/>
<module name="ArrayTypeStyle"/>
<module name="FallThrough"/>
<module name="UpperEll"/>
<module name="ModifierOrder"/>
<module name="SeparatorWrap">
<property name="tokens" value="DOT"/>
<property name="option" value="nl"/>
</module>
<module name="SeparatorWrap">
<property name="tokens" value="COMMA"/>
<property name="option" value="EOL"/>
</module>
<module name="PackageName">
<property name="format" value="^[a-z]+(\.[a-z][a-z0-9]*)*$"/>
<message key="name.invalidPattern"
value="Package name ''{0}'' must match pattern ''{1}''."/>
</module>
<module name="ClassTypeParameterName">
<property name="format" value="([A-Z][a-zA-Z0-9]*$)"/>
<message key="name.invalidPattern"
value="Class type name ''{0}'' must match pattern ''{1}''."/>
</module>
<module name="MethodTypeParameterName">
<property name="format" value="([A-Z][a-zA-Z0-9]*)"/>
<message key="name.invalidPattern"
value="Method type name ''{0}'' must match pattern ''{1}''."/>
</module>
<module name="GenericWhitespace">
<message key="ws.followed"
value="GenericWhitespace ''{0}'' is followed by whitespace."/>
<message key="ws.preceded"
value="GenericWhitespace ''{0}'' is preceded with whitespace."/>
<message key="ws.illegalFollow"
value="GenericWhitespace ''{0}'' should followed by whitespace."/>
<message key="ws.notPreceded"
value="GenericWhitespace ''{0}'' is not preceded with whitespace."/>
</module>
<!-- TODO: 11/09/15 disabled - indentation is currently inconsistent -->
<!--
<module name="Indentation">
<property name="basicOffset" value="4"/>
<property name="braceAdjustment" value="0"/>
<property name="caseIndent" value="4"/>
<property name="throwsIndent" value="4"/>
<property name="lineWrappingIndentation" value="4"/>
<property name="arrayInitIndent" value="4"/>
</module>
-->
<!-- TODO: 11/09/15 disabled - order is currently wrong in many places -->
<!--
<module name="ImportOrder">
<property name="separated" value="true"/>
<property name="ordered" value="true"/>
<property name="groups" value="/^javax?\./,scala,*,org.apache.spark"/>
</module>
-->
<module name="MethodParamPad"/>
<module name="AnnotationLocation">
<property name="tokens" value="CLASS_DEF, INTERFACE_DEF, ENUM_DEF, METHOD_DEF, CTOR_DEF"/>
</module>
<module name="AnnotationLocation">
<property name="tokens" value="VARIABLE_DEF"/>
<property name="allowSamelineMultipleAnnotations" value="true"/>
</module>
<module name="MethodName">
<property name="format" value="^[a-z][a-z0-9][a-zA-Z0-9_]*$"/>
<message key="name.invalidPattern"
value="Method name ''{0}'' must match pattern ''{1}''."/>
</module>
<module name="EmptyCatchBlock">
<property name="exceptionVariableName" value="expected"/>
</module>
<module name="CommentsIndentation"/>
</module>
</module>

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jvm-packages/pom.xml
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@ -23,6 +23,47 @@
</modules>
<build>
<plugins>
<plugin>
<groupId>org.scalastyle</groupId>
<artifactId>scalastyle-maven-plugin</artifactId>
<version>0.8.0</version>
<configuration>
<verbose>false</verbose>
<failOnViolation>true</failOnViolation>
<includeTestSourceDirectory>true</includeTestSourceDirectory>
<sourceDirectory>${basedir}/src/main/scala</sourceDirectory>
<testSourceDirectory>${basedir}/src/test/scala</testSourceDirectory>
<configLocation>scalastyle-config.xml</configLocation>
<outputEncoding>UTF-8</outputEncoding>
</configuration>
<executions>
<execution>
<id>checkstyle</id>
<phase>validate</phase>
<goals>
<goal>check</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-checkstyle-plugin</artifactId>
<version>2.17</version>
<configuration>
<configLocation>checkstyle.xml</configLocation>
<failOnViolation>true</failOnViolation>
</configuration>
<executions>
<execution>
<id>checkstyle</id>
<phase>validate</phase>
<goals>
<goal>check</goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
@ -53,6 +94,7 @@
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-surefire-plugin</artifactId>
<version>2.19.1</version>
<configuration>
<argLine>-Djava.library.path=lib/</argLine>
</configuration>
@ -65,16 +107,6 @@
<artifactId>commons-logging</artifactId>
<version>1.2</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-compiler</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.scalatest</groupId>
<artifactId>scalatest_${scala.binary.version}</artifactId>

291
jvm-packages/scalastyle-config.xml Normal file
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@ -0,0 +1,291 @@
<!--
~ Licensed to the Apache Software Foundation (ASF) under one or more
~ contributor license agreements. See the NOTICE file distributed with
~ this work for additional information regarding copyright ownership.
~ The ASF licenses this file to You under the Apache License, Version 2.0
~ (the "License"); you may not use this file except in compliance with
~ the License. You may obtain a copy of the License at
~
~ http://www.apache.org/licenses/LICENSE-2.0
~
~ Unless required by applicable law or agreed to in writing, software
~ distributed under the License is distributed on an "AS IS" BASIS,
~ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
~ See the License for the specific language governing permissions and
~ limitations under the License.
-->
<!--
If you wish to turn off checking for a section of code, you can put a comment in the source
before and after the section, with the following syntax:
// scalastyle:off
... // stuff that breaks the styles
// scalastyle:on
You can also disable only one rule, by specifying its rule id, as specified in:
http://www.scalastyle.org/rules-0.7.0.html
// scalastyle:off no.finalize
override def finalize(): Unit = ...
// scalastyle:on no.finalize
This file is divided into 3 sections:
(1) rules that we enforce.
(2) rules that we would like to enforce, but haven't cleaned up the codebase to turn on yet
(or we need to make the scalastyle rule more configurable).
(3) rules that we don't want to enforce.
-->
<scalastyle>
<name>Scalastyle standard configuration</name>
<!-- ================================================================================ -->
<!-- rules we enforce -->
<!-- ================================================================================ -->
<check level="error" class="org.scalastyle.file.FileTabChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.file.HeaderMatchesChecker" enabled="true">
<parameters>
<parameter name="header"><![CDATA[/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/]]></parameter>
</parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.SpacesAfterPlusChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.SpacesBeforePlusChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.file.WhitespaceEndOfLineChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.file.FileLineLengthChecker" enabled="true">
<parameters>
<parameter name="maxLineLength"><![CDATA[100]]></parameter>
<parameter name="tabSize"><![CDATA[2]]></parameter>
<parameter name="ignoreImports">true</parameter>
</parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.ClassNamesChecker" enabled="true">
<parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.ObjectNamesChecker" enabled="true">
<parameters><parameter name="regex"><![CDATA[[A-Z][A-Za-z]*]]></parameter></parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.PackageObjectNamesChecker" enabled="true">
<parameters><parameter name="regex"><![CDATA[^[a-z][A-Za-z]*$]]></parameter></parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.ParameterNumberChecker" enabled="true">
<parameters><parameter name="maxParameters"><![CDATA[10]]></parameter></parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.NoFinalizeChecker" enabled="false"></check>
<check level="error" class="org.scalastyle.scalariform.CovariantEqualsChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.StructuralTypeChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.UppercaseLChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.IfBraceChecker" enabled="true">
<parameters>
<parameter name="singleLineAllowed"><![CDATA[true]]></parameter>
<parameter name="doubleLineAllowed"><![CDATA[true]]></parameter>
</parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.PublicMethodsHaveTypeChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.file.NewLineAtEofChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.NonASCIICharacterChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.SpaceAfterCommentStartChecker" enabled="true"></check>
<check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceBeforeTokenChecker" enabled="true">
<parameters>
<parameter name="tokens">ARROW, EQUALS, ELSE, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
</parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.EnsureSingleSpaceAfterTokenChecker" enabled="true">
<parameters>
<parameter name="tokens">ARROW, EQUALS, COMMA, COLON, IF, ELSE, DO, WHILE, FOR, MATCH, TRY, CATCH, FINALLY, LARROW, RARROW</parameter>
</parameters>
</check>
<!-- ??? usually shouldn't be checked into the code base. -->
<check level="error" class="org.scalastyle.scalariform.NotImplementedErrorUsage" enabled="true"></check>
<!-- As of SPARK-7558, all tests in Spark should extend o.a.s.SparkFunSuite instead of FunSuite directly -->
<check customId="funsuite" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
<parameters><parameter name="regex">^FunSuite[A-Za-z]*$</parameter></parameters>
<customMessage>Tests must extend org.apache.spark.SparkFunSuite instead.</customMessage>
</check>
<!-- As of SPARK-7977 all printlns need to be wrapped in '// scalastyle:off/on println' -->
<check customId="println" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
<parameters><parameter name="regex">^println$</parameter></parameters>
<customMessage><![CDATA[Are you sure you want to println? If yes, wrap the code block with
// scalastyle:off println
println(...)
// scalastyle:on println]]></customMessage>
</check>
<check customId="visiblefortesting" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">@VisibleForTesting</parameter></parameters>
<customMessage><![CDATA[
@VisibleForTesting causes classpath issues. Please note this in the java doc instead (SPARK-11615).
]]></customMessage>
</check>
<check customId="runtimeaddshutdownhook" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">Runtime\.getRuntime\.addShutdownHook</parameter></parameters>
<customMessage><![CDATA[
Are you sure that you want to use Runtime.getRuntime.addShutdownHook? In most cases, you should use
ShutdownHookManager.addShutdownHook instead.
If you must use Runtime.getRuntime.addShutdownHook, wrap the code block with
// scalastyle:off runtimeaddshutdownhook
Runtime.getRuntime.addShutdownHook(...)
// scalastyle:on runtimeaddshutdownhook
]]></customMessage>
</check>
<check customId="mutablesynchronizedbuffer" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">mutable\.SynchronizedBuffer</parameter></parameters>
<customMessage><![CDATA[
Are you sure that you want to use mutable.SynchronizedBuffer? In most cases, you should use
java.util.concurrent.ConcurrentLinkedQueue instead.
If you must use mutable.SynchronizedBuffer, wrap the code block with
// scalastyle:off mutablesynchronizedbuffer
mutable.SynchronizedBuffer[...]
// scalastyle:on mutablesynchronizedbuffer
]]></customMessage>
</check>
<check customId="classforname" level="error" class="org.scalastyle.file.RegexChecker" enabled="true">
<parameters><parameter name="regex">Class\.forName</parameter></parameters>
<customMessage><![CDATA[
Are you sure that you want to use Class.forName? In most cases, you should use Utils.classForName instead.
If you must use Class.forName, wrap the code block with
// scalastyle:off classforname
Class.forName(...)
// scalastyle:on classforname
]]></customMessage>
</check>
<!-- As of SPARK-9613 JavaConversions should be replaced with JavaConverters -->
<check customId="javaconversions" level="error" class="org.scalastyle.scalariform.TokenChecker" enabled="true">
<parameters><parameter name="regex">JavaConversions</parameter></parameters>
<customMessage>Instead of importing implicits in scala.collection.JavaConversions._, import
scala.collection.JavaConverters._ and use .asScala / .asJava methods</customMessage>
</check>
<check level="error" class="org.scalastyle.scalariform.ImportOrderChecker" enabled="true">
<parameters>
<parameter name="groups">java,scala,3rdParty,spark</parameter>
<parameter name="group.java">javax?\..*</parameter>
<parameter name="group.scala">scala\..*</parameter>
<parameter name="group.3rdParty">(?!org\.apache\.spark\.).*</parameter>
<parameter name="group.spark">org\.apache\.spark\..*</parameter>
</parameters>
</check>
<check level="error" class="org.scalastyle.scalariform.DisallowSpaceBeforeTokenChecker" enabled="true">
<parameters>
<parameter name="tokens">COMMA</parameter>
</parameters>
</check>
<!-- ================================================================================ -->
<!-- rules we'd like to enforce, but haven't cleaned up the codebase yet -->
<!-- ================================================================================ -->
<!-- We cannot turn the following two on, because it'd fail a lot of string interpolation use cases. -->
<!-- Ideally the following two rules should be configurable to rule out string interpolation. -->
<check level="error" class="org.scalastyle.scalariform.NoWhitespaceBeforeLeftBracketChecker" enabled="false"></check>
<check level="error" class="org.scalastyle.scalariform.NoWhitespaceAfterLeftBracketChecker" enabled="false"></check>
<!-- This breaks symbolic method names so we don't turn it on. -->
<!-- Maybe we should update it to allow basic symbolic names, and then we are good to go. -->
<check level="error" class="org.scalastyle.scalariform.MethodNamesChecker" enabled="false">
<parameters>
<parameter name="regex"><![CDATA[^[a-z][A-Za-z0-9]*$]]></parameter>
</parameters>
</check>
<!-- Should turn this on, but we have a few places that need to be fixed first -->
<check level="error" class="org.scalastyle.scalariform.EqualsHashCodeChecker" enabled="false"></check>
<!-- ================================================================================ -->
<!-- rules we don't want -->
<!-- ================================================================================ -->
<check level="error" class="org.scalastyle.scalariform.IllegalImportsChecker" enabled="false">
<parameters><parameter name="illegalImports"><![CDATA[sun._,java.awt._]]></parameter></parameters>
</check>
<!-- We want the opposite of this: NewLineAtEofChecker -->
<check level="error" class="org.scalastyle.file.NoNewLineAtEofChecker" enabled="false"></check>
<!-- This one complains about all kinds of random things. Disable. -->
<check level="error" class="org.scalastyle.scalariform.SimplifyBooleanExpressionChecker" enabled="false"></check>
<!-- We use return quite a bit for control flows and guards -->
<check level="error" class="org.scalastyle.scalariform.ReturnChecker" enabled="false"></check>
<!-- We use null a lot in low level code and to interface with 3rd party code -->
<check level="error" class="org.scalastyle.scalariform.NullChecker" enabled="false"></check>
<!-- Doesn't seem super big deal here ... -->
<check level="error" class="org.scalastyle.scalariform.NoCloneChecker" enabled="false"></check>
<!-- Doesn't seem super big deal here ... -->
<check level="error" class="org.scalastyle.file.FileLengthChecker" enabled="false">
<parameters><parameter name="maxFileLength">800></parameter></parameters>
</check>
<!-- Doesn't seem super big deal here ... -->
<check level="error" class="org.scalastyle.scalariform.NumberOfTypesChecker" enabled="false">
<parameters><parameter name="maxTypes">30</parameter></parameters>
</check>
<!-- Doesn't seem super big deal here ... -->
<check level="error" class="org.scalastyle.scalariform.CyclomaticComplexityChecker" enabled="false">
<parameters><parameter name="maximum">10</parameter></parameters>
</check>
<!-- Doesn't seem super big deal here ... -->
<check level="error" class="org.scalastyle.scalariform.MethodLengthChecker" enabled="false">
<parameters><parameter name="maxLength">50</parameter></parameters>
</check>
<!-- Not exactly feasible to enforce this right now. -->
<!-- It is also infrequent that somebody introduces a new class with a lot of methods. -->
<check level="error" class="org.scalastyle.scalariform.NumberOfMethodsInTypeChecker" enabled="false">
<parameters><parameter name="maxMethods"><![CDATA[30]]></parameter></parameters>
</check>
<!-- Doesn't seem super big deal here, and we have a lot of magic numbers ... -->
<check level="error" class="org.scalastyle.scalariform.MagicNumberChecker" enabled="false">
<parameters><parameter name="ignore">-1,0,1,2,3</parameter></parameters>
</check>
</scalastyle>

169
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -26,92 +26,93 @@ import java.util.HashMap;
/**
* a simple example of java wrapper for xgboost
*
* @author hzx
*/
public class BasicWalkThrough {
public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
if(fPredicts.length != sPredicts.length) {
return false;
}
for(int i=0; i<fPredicts.length; i++) {
if(!Arrays.equals(fPredicts[i], sPredicts[i])) {
return false;
}
}
return true;
public static boolean checkPredicts(float[][] fPredicts, float[][] sPredicts) {
if (fPredicts.length != sPredicts.length) {
return false;
}
public static void main(String[] args) throws UnsupportedEncodingException, IOException, XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict
float[][] predicts = booster.predict(testMat);
//save model to modelPath
File file = new File("./model");
if(!file.exists()) {
file.mkdirs();
}
String modelPath = "./model/xgb.model";
booster.saveModel(modelPath);
//dump model
booster.dumpModel("./model/dump.raw.txt", false);
//dump model with feature map
booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
//save dmatrix into binary buffer
testMat.saveBinary("./model/dtest.buffer");
//reload model and data
Booster booster2 = XGBoost.loadBoostModel(params, "./model/xgb.model");
DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
float[][] predicts2 = booster2.predict(testMat2);
//check the two predicts
System.out.println(checkPredicts(predicts, predicts2));
System.out.println("start build dmatrix from csr sparse data ...");
//build dmatrix from CSR Sparse Matrix
DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
DMatrix.SparseType.CSR);
trainMat2.setLabel(spData.labels);
//specify watchList
HashMap<String, DMatrix> watches2 = new HashMap<String, DMatrix>();
watches2.put("train", trainMat2);
watches2.put("test", testMat2);
Booster booster3 = XGBoost.train(params, trainMat2, round, watches2, null, null);
float[][] predicts3 = booster3.predict(testMat2);
//check predicts
System.out.println(checkPredicts(predicts, predicts3));
for (int i = 0; i < fPredicts.length; i++) {
if (!Arrays.equals(fPredicts[i], sPredicts[i])) {
return false;
}
}
return true;
}
public static void main(String[] args) throws IOException, XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict
float[][] predicts = booster.predict(testMat);
//save model to modelPath
File file = new File("./model");
if (!file.exists()) {
file.mkdirs();
}
String modelPath = "./model/xgb.model";
booster.saveModel(modelPath);
//dump model
booster.dumpModel("./model/dump.raw.txt", false);
//dump model with feature map
booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
//save dmatrix into binary buffer
testMat.saveBinary("./model/dtest.buffer");
//reload model and data
Booster booster2 = XGBoost.loadBoostModel(params, "./model/xgb.model");
DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
float[][] predicts2 = booster2.predict(testMat2);
//check the two predicts
System.out.println(checkPredicts(predicts, predicts2));
System.out.println("start build dmatrix from csr sparse data ...");
//build dmatrix from CSR Sparse Matrix
DataLoader.CSRSparseData spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train");
DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
DMatrix.SparseType.CSR);
trainMat2.setLabel(spData.labels);
//specify watchList
HashMap<String, DMatrix> watches2 = new HashMap<String, DMatrix>();
watches2.put("train", trainMat2);
watches2.put("test", testMat2);
Booster booster3 = XGBoost.train(params, trainMat2, round, watches2, null, null);
float[][] predicts3 = booster3.predict(testMat2);
//check predicts
System.out.println(checkPredicts(predicts, predicts3));
}
}

67
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -21,38 +21,39 @@ import java.util.HashMap;
/**
* example for start from a initial base prediction
*
* @author hzx
*/
public class BoostFromPrediction {
public static void main(String[] args) throws XGBoostError {
System.out.println("start running example to start from a initial prediction");
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train xgboost for 1 round
Booster booster = XGBoost.train(params, trainMat, 1, watches, null, null);
float[][] trainPred = booster.predict(trainMat, true);
float[][] testPred = booster.predict(testMat, true);
trainMat.setBaseMargin(trainPred);
testMat.setBaseMargin(testPred);
System.out.println("result of running from initial prediction");
Booster booster2 = XGBoost.train(params, trainMat, 1, watches, null, null);
}
public static void main(String[] args) throws XGBoostError {
System.out.println("start running example to start from a initial prediction");
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train xgboost for 1 round
Booster booster = XGBoost.train(params, trainMat, 1, watches, null, null);
float[][] trainPred = booster.predict(trainMat, true);
float[][] testPred = booster.predict(testMat, true);
trainMat.setBaseMargin(trainPred);
testMat.setBaseMargin(testPred);
System.out.println("result of running from initial prediction");
Booster booster2 = XGBoost.train(params, trainMat, 1, watches, null, null);
}
}

4
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software

257
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -29,137 +29,142 @@ import java.util.List;
* this may make buildin evalution metric not function properly
* for example, we are doing logistic loss, the prediction is score before logistic transformation
* he buildin evaluation error assumes input is after logistic transformation
* Take this in mind when you use the customization, and maybe you need write customized evaluation function
* Take this in mind when you use the customization, and maybe you need write customized evaluation
* function
*
* @author hzx
*/
public class CustomObjective {
/**
* loglikelihoode loss obj function
*/
public static class LogRegObj implements IObjective {
private static final Log logger = LogFactory.getLog(LogRegObj.class);
/**
* loglikelihoode loss obj function
* simple sigmoid func
*
* @param input
* @return Note: this func is not concern about numerical stability, only used as example
*/
public static class LogRegObj implements IObjective {
private static final Log logger = LogFactory.getLog(LogRegObj.class);
/**
* simple sigmoid func
* @param input
* @return
* Note: this func is not concern about numerical stability, only used as example
*/
public float sigmoid(float input) {
float val = (float) (1/(1+Math.exp(-input)));
return val;
}
public float[][] transform(float[][] predicts) {
int nrow = predicts.length;
float[][] transPredicts = new float[nrow][1];
for(int i=0; i<nrow; i++) {
transPredicts[i][0] = sigmoid(predicts[i][0]);
}
return transPredicts;
}
@Override
public List<float[]> getGradient(float[][] predicts, org.dmlc.xgboost4j.DMatrix dtrain) {
int nrow = predicts.length;
List<float[]> gradients = new ArrayList<float[]>();
float[] labels;
try {
labels = dtrain.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return null;
}
float[] grad = new float[nrow];
float[] hess = new float[nrow];
float[][] transPredicts = transform(predicts);
for(int i=0; i<nrow; i++) {
float predict = transPredicts[i][0];
grad[i] = predict - labels[i];
hess[i] = predict * (1 - predict);
}
gradients.add(grad);
gradients.add(hess);
return gradients;
}
public float sigmoid(float input) {
float val = (float) (1 / (1 + Math.exp(-input)));
return val;
}
/**
* user defined eval function.
* NOTE: when you do customized loss function, the default prediction value is margin
* this may make buildin evalution metric not function properly
* for example, we are doing logistic loss, the prediction is score before logistic transformation
* the buildin evaluation error assumes input is after logistic transformation
* Take this in mind when you use the customization, and maybe you need write customized evaluation function
*/
public static class EvalError implements IEvaluation {
private static final Log logger = LogFactory.getLog(EvalError.class);
String evalMetric = "custom_error";
public EvalError() {
}
@Override
public String getMetric() {
return evalMetric;
}
@Override
public float eval(float[][] predicts, org.dmlc.xgboost4j.DMatrix dmat) {
float error = 0f;
float[] labels;
try {
labels = dmat.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return -1f;
}
int nrow = predicts.length;
for(int i=0; i<nrow; i++) {
if(labels[i]==0f && predicts[i][0]>0) {
error++;
}
else if(labels[i]==1f && predicts[i][0]<=0) {
error++;
}
}
return error/labels.length;
}
}
public static void main(String[] args) throws XGBoostError {
//load train mat (svmlight format)
org.dmlc.xgboost4j.DMatrix trainMat = new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.train");
//load valid mat (svmlight format)
org.dmlc.xgboost4j.DMatrix testMat = new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.test");
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
public float[][] transform(float[][] predicts) {
int nrow = predicts.length;
float[][] transPredicts = new float[nrow][1];
//set round
int round = 2;
//specify watchList
HashMap<String, org.dmlc.xgboost4j.DMatrix> watches = new HashMap<String, org.dmlc.xgboost4j.DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//user define obj and eval
IObjective obj = new LogRegObj();
IEvaluation eval = new EvalError();
//train a booster
System.out.println("begin to train the booster model");
Booster booster = XGBoost.train(params, trainMat, round, watches, obj, eval);
for (int i = 0; i < nrow; i++) {
transPredicts[i][0] = sigmoid(predicts[i][0]);
}
return transPredicts;
}
@Override
public List<float[]> getGradient(float[][] predicts, org.dmlc.xgboost4j.DMatrix dtrain) {
int nrow = predicts.length;
List<float[]> gradients = new ArrayList<float[]>();
float[] labels;
try {
labels = dtrain.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return null;
}
float[] grad = new float[nrow];
float[] hess = new float[nrow];
float[][] transPredicts = transform(predicts);
for (int i = 0; i < nrow; i++) {
float predict = transPredicts[i][0];
grad[i] = predict - labels[i];
hess[i] = predict * (1 - predict);
}
gradients.add(grad);
gradients.add(hess);
return gradients;
}
}
/**
* user defined eval function.
* NOTE: when you do customized loss function, the default prediction value is margin
* this may make buildin evalution metric not function properly
* for example, we are doing logistic loss, the prediction is score before logistic transformation
* the buildin evaluation error assumes input is after logistic transformation
* Take this in mind when you use the customization, and maybe you need write customized
* evaluation function
*/
public static class EvalError implements IEvaluation {
private static final Log logger = LogFactory.getLog(EvalError.class);
String evalMetric = "custom_error";
public EvalError() {
}
@Override
public String getMetric() {
return evalMetric;
}
@Override
public float eval(float[][] predicts, org.dmlc.xgboost4j.DMatrix dmat) {
float error = 0f;
float[] labels;
try {
labels = dmat.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return -1f;
}
int nrow = predicts.length;
for (int i = 0; i < nrow; i++) {
if (labels[i] == 0f && predicts[i][0] > 0) {
error++;
} else if (labels[i] == 1f && predicts[i][0] <= 0) {
error++;
}
}
return error / labels.length;
}
}
public static void main(String[] args) throws XGBoostError {
//load train mat (svmlight format)
org.dmlc.xgboost4j.DMatrix trainMat =
new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.train");
//load valid mat (svmlight format)
org.dmlc.xgboost4j.DMatrix testMat =
new org.dmlc.xgboost4j.DMatrix("../../demo/data/agaricus.txt.test");
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
//set round
int round = 2;
//specify watchList
HashMap<String, org.dmlc.xgboost4j.DMatrix> watches =
new HashMap<String, org.dmlc.xgboost4j.DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//user define obj and eval
IObjective obj = new LogRegObj();
IEvaluation eval = new EvalError();
//train a booster
System.out.println("begin to train the booster model");
Booster booster = XGBoost.train(params, trainMat, round, watches, obj, eval);
}
}

64
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -21,36 +21,38 @@ import java.util.HashMap;
/**
* simple example for using external memory version
*
* @author hzx
*/
public class ExternalMemory {
public static void main(String[] args) throws XGBoostError {
//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");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//performance notice: set nthread to be the number of your real cpu
//some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case set nthread=4
//param.put("nthread", num_real_cpu);
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
}
public static void main(String[] args) throws XGBoostError {
//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");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//performance notice: set nthread to be the number of your real cpu
//some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case
// set nthread=4
//param.put("nthread", num_real_cpu);
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
}
}

77
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -23,44 +23,45 @@ import java.util.HashMap;
/**
* this is an example of fit generalized linear model in xgboost
* basically, we are using linear model, instead of tree for our boosters
*
* @author hzx
*/
public class GeneralizedLinearModel {
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//specify parameters
//change booster to gblinear, so that we are fitting a linear model
// alpha is the L1 regularizer
//lambda is the L2 regularizer
//you can also set lambda_bias which is L2 regularizer on the bias term
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("alpha", 0.0001);
params.put("silent", 1);
params.put("objective", "binary:logistic");
params.put("booster", "gblinear");
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//normally, you do not need to set eta (step_size)
//XGBoost uses a parallel coordinate descent algorithm (shotgun),
//there could be affection on convergence with parallelization on certain cases
//setting eta to be smaller value, e.g 0.5 can make the optimization more stable
//param.put("eta", "0.5");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train a booster
int round = 4;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
float[][] predicts = booster.predict(testMat);
CustomEval eval = new CustomEval();
System.out.println("error=" + eval.eval(predicts, testMat));
}
//specify parameters
//change booster to gblinear, so that we are fitting a linear model
// alpha is the L1 regularizer
//lambda is the L2 regularizer
//you can also set lambda_bias which is L2 regularizer on the bias term
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("alpha", 0.0001);
params.put("silent", 1);
params.put("objective", "binary:logistic");
params.put("booster", "gblinear");
//normally, you do not need to set eta (step_size)
//XGBoost uses a parallel coordinate descent algorithm (shotgun),
//there could be affection on convergence with parallelization on certain cases
//setting eta to be smaller value, e.g 0.5 can make the optimization more stable
//param.put("eta", "0.5");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train a booster
int round = 4;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
float[][] predicts = booster.predict(testMat);
CustomEval eval = new CustomEval();
System.out.println("error=" + eval.eval(predicts, testMat));
}
}

69
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -22,41 +22,42 @@ import java.util.HashMap;
/**
* predict first ntree
*
* @author hzx
*/
public class PredictFirstNtree {
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
public class PredictFirstNtree {
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict use 1 tree
float[][] predicts1 = booster.predict(testMat, false, 1);
//by default all trees are used to do predict
float[][] predicts2 = booster.predict(testMat);
//use a simple evaluation class to check error result
CustomEval eval = new CustomEval();
System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
}
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict use 1 tree
float[][] predicts1 = booster.predict(testMat, false, 1);
//by default all trees are used to do predict
float[][] predicts2 = booster.predict(testMat);
//use a simple evaluation class to check error result
CustomEval eval = new CustomEval();
System.out.println("error of predicts1: " + eval.eval(predicts1, testMat));
System.out.println("error of predicts2: " + eval.eval(predicts2, testMat));
}
}

69
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java
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@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -22,41 +22,42 @@ import java.util.HashMap;
/**
* predict leaf indices
*
* @author hzx
*/
public class PredictLeafIndices {
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
public static void main(String[] args) throws XGBoostError {
// load file from text file, also binary buffer generated by xgboost4j
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict using first 2 tree
float[][] leafindex = booster.predict(testMat, 2, true);
for(float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}
//predict all trees
leafindex = booster.predict(testMat, 0, true);
for(float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}
//specify parameters
HashMap<String, Object> params = new HashMap<String, Object>();
params.put("eta", 1.0);
params.put("max_depth", 2);
params.put("silent", 1);
params.put("objective", "binary:logistic");
//specify watchList
HashMap<String, DMatrix> watches = new HashMap<String, DMatrix>();
watches.put("train", trainMat);
watches.put("test", testMat);
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict using first 2 tree
float[][] leafindex = booster.predict(testMat, 2, true);
for (float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}
//predict all trees
leafindex = booster.predict(testMat, 0, true);
for (float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}
}
}

60
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/CustomEval.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -23,38 +23,38 @@ import org.dmlc.xgboost4j.XGBoostError;
/**
* a util evaluation class for examples
*
* @author hzx
*/
public class CustomEval implements IEvaluation {
private static final Log logger = LogFactory.getLog(CustomEval.class);
private static final Log logger = LogFactory.getLog(CustomEval.class);
String evalMetric = "custom_error";
@Override
public String getMetric() {
return evalMetric;
String evalMetric = "custom_error";
@Override
public String getMetric() {
return evalMetric;
}
@Override
public float eval(float[][] predicts, DMatrix dmat) {
float error = 0f;
float[] labels;
try {
labels = dmat.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return -1f;
}
int nrow = predicts.length;
for (int i = 0; i < nrow; i++) {
if (labels[i] == 0f && predicts[i][0] > 0.5) {
error++;
} else if (labels[i] == 1f && predicts[i][0] <= 0.5) {
error++;
}
}
@Override
public float eval(float[][] predicts, DMatrix dmat) {
float error = 0f;
float[] labels;
try {
labels = dmat.getLabel();
} catch (XGBoostError ex) {
logger.error(ex);
return -1f;
}
int nrow = predicts.length;
for(int i=0; i<nrow; i++) {
if(labels[i]==0f && predicts[i][0]>0.5) {
error++;
}
else if(labels[i]==1f && predicts[i][0]<=0.5) {
error++;
}
}
return error/labels.length;
}
return error / labels.length;
}
}

187
jvm-packages/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/util/DataLoader.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -23,100 +23,101 @@ import java.util.List;
/**
* util class for loading data
*
* @author hzx
*/
public class DataLoader {
public static class DenseData {
public float[] labels;
public float[] data;
public int nrow;
public int ncol;
public static class DenseData {
public float[] labels;
public float[] data;
public int nrow;
public int ncol;
}
public static class CSRSparseData {
public float[] labels;
public float[] data;
public long[] rowHeaders;
public int[] colIndex;
}
public static DenseData loadCSVFile(String filePath) throws IOException {
DenseData denseData = new DenseData();
File f = new File(filePath);
FileInputStream in = new FileInputStream(f);
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
denseData.nrow = 0;
denseData.ncol = -1;
String line;
List<Float> tlabels = new ArrayList<>();
List<Float> tdata = new ArrayList<>();
while ((line = reader.readLine()) != null) {
String[] items = line.trim().split(",");
if (items.length == 0) {
continue;
}
denseData.nrow++;
if (denseData.ncol == -1) {
denseData.ncol = items.length - 1;
}
tlabels.add(Float.valueOf(items[items.length - 1]));
for (int i = 0; i < items.length - 1; i++) {
tdata.add(Float.valueOf(items[i]));
}
}
public static class CSRSparseData {
public float[] labels;
public float[] data;
public long[] rowHeaders;
public int[] colIndex;
}
public static DenseData loadCSVFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
DenseData denseData = new DenseData();
File f = new File(filePath);
FileInputStream in = new FileInputStream(f);
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
denseData.nrow = 0;
denseData.ncol = -1;
String line;
List<Float> tlabels = new ArrayList<>();
List<Float> tdata = new ArrayList<>();
while((line=reader.readLine()) != null) {
String[] items = line.trim().split(",");
if(items.length==0) {
continue;
}
denseData.nrow++;
if(denseData.ncol == -1) {
denseData.ncol = items.length - 1;
}
tlabels.add(Float.valueOf(items[items.length-1]));
for(int i=0; i<items.length-1; i++) {
tdata.add(Float.valueOf(items[i]));
}
}
reader.close();
in.close();
denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
return denseData;
}
public static CSRSparseData loadSVMFile(String filePath) throws FileNotFoundException, UnsupportedEncodingException, IOException {
CSRSparseData spData = new CSRSparseData();
List<Float> tlabels = new ArrayList<>();
List<Float> tdata = new ArrayList<>();
List<Long> theaders = new ArrayList<>();
List<Integer> tindex = new ArrayList<>();
File f = new File(filePath);
FileInputStream in = new FileInputStream(f);
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
String line;
long rowheader = 0;
theaders.add(rowheader);
while((line=reader.readLine()) != null) {
String[] items = line.trim().split(" ");
if(items.length==0) {
continue;
}
rowheader += items.length - 1;
theaders.add(rowheader);
tlabels.add(Float.valueOf(items[0]));
for(int i=1; i<items.length; i++) {
String[] tup = items[i].split(":");
assert tup.length == 2;
tdata.add(Float.valueOf(tup[1]));
tindex.add(Integer.valueOf(tup[0]));
}
}
spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
return spData;
reader.close();
in.close();
denseData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
denseData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
return denseData;
}
public static CSRSparseData loadSVMFile(String filePath) throws IOException {
CSRSparseData spData = new CSRSparseData();
List<Float> tlabels = new ArrayList<>();
List<Float> tdata = new ArrayList<>();
List<Long> theaders = new ArrayList<>();
List<Integer> tindex = new ArrayList<>();
File f = new File(filePath);
FileInputStream in = new FileInputStream(f);
BufferedReader reader = new BufferedReader(new InputStreamReader(in, "UTF-8"));
String line;
long rowheader = 0;
theaders.add(rowheader);
while ((line = reader.readLine()) != null) {
String[] items = line.trim().split(" ");
if (items.length == 0) {
continue;
}
rowheader += items.length - 1;
theaders.add(rowheader);
tlabels.add(Float.valueOf(items[0]));
for (int i = 1; i < items.length; i++) {
String[] tup = items[i].split(":");
assert tup.length == 2;
tdata.add(Float.valueOf(tup[1]));
tindex.add(Integer.valueOf(tup[0]));
}
}
spData.labels = ArrayUtils.toPrimitive(tlabels.toArray(new Float[tlabels.size()]));
spData.data = ArrayUtils.toPrimitive(tdata.toArray(new Float[tdata.size()]));
spData.colIndex = ArrayUtils.toPrimitive(tindex.toArray(new Integer[tindex.size()]));
spData.rowHeaders = ArrayUtils.toPrimitive(theaders.toArray(new Long[theaders.size()]));
return spData;
}
}

11
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/Booster.java
View File

@ -99,10 +99,10 @@ public interface Booster {
* Predict with data
* @param data dmatrix storing the input
* @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
* @param predLeaf When this option is on, the output will be a matrix of (nsample, ntrees), nsample = data.numRow
with each record indicating the predicted leaf index of each sample in each tree.
Note that the leaf index of a tree is unique per tree, so you may find leaf 1
in both tree 1 and tree 0.
* @param predLeaf When this option is on, the output will be a matrix of (nsample, ntrees),
* nsample = data.numRow with each record indicating the predicted leaf index of
* each sample in each tree. Note that the leaf index of a tree is unique per
* tree, so you may find leaf 1 in both tree 1 and tree 0.
* @return predict result
* @throws XGBoostError native error
*/
@ -131,7 +131,8 @@ public interface Booster {
* @param withStats bool
* Controls whether the split statistics are output.
*/
void dumpModel(String modelPath, String featureMap, boolean withStats) throws IOException, XGBoostError;
void dumpModel(String modelPath, String featureMap, boolean withStats)
throws IOException, XGBoostError;
/**
* get importance of each feature

15
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/DMatrix.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -32,7 +32,7 @@ public class DMatrix {
//load native library
static {
try {
NativeLibLoader.InitXgboost();
NativeLibLoader.initXgBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);
@ -84,8 +84,6 @@ public class DMatrix {
/**
* used for DMatrix slice
*
* @param handle
*/
protected DMatrix(long handle) {
this.handle = handle;
@ -216,8 +214,6 @@ public class DMatrix {
/**
* save DMatrix to filePath
*
* @param filePath file path
*/
public void saveBinary(String filePath) {
XgboostJNI.XGDMatrixSaveBinary(handle, filePath, 1);
@ -225,8 +221,6 @@ public class DMatrix {
/**
* Get the handle
*
* @return native handler id
*/
public long getHandle() {
return handle;
@ -234,9 +228,6 @@ public class DMatrix {
/**
* flatten a mat to array
*
* @param mat
* @return
*/
private static float[] flatten(float[][] mat) {
int size = 0;

4
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/IEvaluation.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software

4
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/IObjective.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software

6
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/JNIErrorHandle.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -30,7 +30,7 @@ class JNIErrorHandle {
//load native library
static {
try {
NativeLibLoader.InitXgboost();
NativeLibLoader.initXgBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);

21
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/JavaBoosterImpl.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -38,7 +38,7 @@ class JavaBoosterImpl implements Booster {
//load native library
static {
try {
NativeLibLoader.InitXgboost();
NativeLibLoader.initXgBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);
@ -80,7 +80,7 @@ class JavaBoosterImpl implements Booster {
private void init(DMatrix[] dMatrixs) throws XGBoostError {
long[] handles = null;
if (dMatrixs != null) {
handles = dMatrixs2handles(dMatrixs);
handles = dmatrixsToHandles(dMatrixs);
}
long[] out = new long[1];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterCreate(handles, out));
@ -151,7 +151,8 @@ class JavaBoosterImpl implements Booster {
throw new AssertionError(String.format("grad/hess length mismatch %s / %s", grad.length,
hess.length));
}
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterBoostOneIter(handle, dtrain.getHandle(), grad, hess));
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterBoostOneIter(handle, dtrain.getHandle(), grad,
hess));
}
/**
@ -164,9 +165,10 @@ class JavaBoosterImpl implements Booster {
* @throws XGBoostError native error
*/
public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter) throws XGBoostError {
long[] handles = dMatrixs2handles(evalMatrixs);
long[] handles = dmatrixsToHandles(evalMatrixs);
String[] evalInfo = new String[1];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames, evalInfo));
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames,
evalInfo));
return evalInfo[0];
}
@ -322,7 +324,8 @@ class JavaBoosterImpl implements Booster {
statsFlag = 1;
}
String[][] modelInfos = new String[1][];
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterDumpModel(handle, featureMap, statsFlag, modelInfos));
JNIErrorHandle.checkCall(XgboostJNI.XGBoosterDumpModel(handle, featureMap, statsFlag,
modelInfos));
return modelInfos[0];
}
@ -444,7 +447,7 @@ class JavaBoosterImpl implements Booster {
* @param dmatrixs
* @return handle array for input dmatrixs
*/
private static long[] dMatrixs2handles(DMatrix[] dmatrixs) {
private static long[] dmatrixsToHandles(DMatrix[] dmatrixs) {
long[] handles = new long[dmatrixs.length];
for (int i = 0; i < dmatrixs.length; i++) {
handles[i] = dmatrixs[i].getHandle();

18
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/NativeLibLoader.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
@ -34,7 +34,7 @@ class NativeLibLoader {
private static final String nativeResourcePath = "/lib/";
private static final String[] libNames = new String[]{"xgboost4j"};
public static synchronized void InitXgboost() throws IOException {
public static synchronized void initXgBoost() throws IOException {
if (!initialized) {
for (String libName : libNames) {
smartLoad(libName);
@ -50,14 +50,17 @@ class NativeLibLoader {
* The temporary file is deleted after exiting.
* Method uses String as filename because the pathname is "abstract", not system-dependent.
* <p/>
* The restrictions of {@link File#createTempFile(java.lang.String, java.lang.String)} apply to {@code path}.
* The restrictions of {@link File#createTempFile(java.lang.String, java.lang.String)} apply to
* {@code path}.
*
* @param path The filename inside JAR as absolute path (beginning with '/'), e.g. /package/File.ext
* @param path The filename inside JAR as absolute path (beginning with '/'),
* e.g. /package/File.ext
* @throws IOException If temporary file creation or read/write operation fails
* @throws IllegalArgumentException If source file (param path) does not exist
* @throws IllegalArgumentException If the path is not absolute or if the filename is shorter than three characters
* @throws IllegalArgumentException If the path is not absolute or if the filename is shorter than
* three characters
*/
private static void loadLibraryFromJar(String path) throws IOException {
private static void loadLibraryFromJar(String path) throws IOException, IllegalArgumentException{
if (!path.startsWith("/")) {
throw new IllegalArgumentException("The path has to be absolute (start with '/').");
@ -126,7 +129,6 @@ class NativeLibLoader {
addNativeDir(nativePath);
try {
System.loadLibrary(libName);
System.out.println("======load " + libName + " successfully");
} catch (UnsatisfiedLinkError e) {
try {
String libraryFromJar = nativeResourcePath + System.mapLibraryName(libName);

4
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/XGBoost.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software

4
jvm-packages/xgboost4j/src/main/java/org/dmlc/xgboost4j/XGBoostError.java
View File

@ -1,10 +1,10 @@
/*
Copyright (c) 2014 by Contributors
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software

24
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/Booster.scala
View File

@ -1,3 +1,19 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import java.io.IOException
@ -111,10 +127,10 @@ trait Booster {
*
* @param data dmatrix storing the input
* @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
* @param predLeaf When this option is on, the output will be a matrix of (nsample, ntrees), nsample = data.numRow
with each record indicating the predicted leaf index of each sample in each tree.
Note that the leaf index of a tree is unique per tree, so you may find leaf 1
in both tree 1 and tree 0.
* @param predLeaf When this option is on, the output will be a matrix of (nsample, ntrees),
* nsample = data.numRow with each record indicating the predicted leaf index of
* each sample in each tree. Note that the leaf index of a tree is unique per
* tree, so you may find leaf 1 in both tree 1 and tree 0.
* @return predict result
* @throws XGBoostError native error
*/

16
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/DMatrix.scala
View File

@ -1,3 +1,19 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import org.dmlc.xgboost4j.{DMatrix => JDMatrix, XGBoostError}

38
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/EvalTrait.scala Normal file
View File

@ -0,0 +1,38 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import org.dmlc.xgboost4j.IEvaluation
trait EvalTrait extends IEvaluation {
/**
* get evaluate metric
*
* @return evalMetric
*/
def getMetric: String
/**
* evaluate with predicts and data
*
* @param predicts predictions as array
* @param dmat data matrix to evaluate
* @return result of the metric
*/
def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float
}

30
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/ObjectiveTrait.scala Normal file
View File

@ -0,0 +1,30 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import org.dmlc.xgboost4j.IObjective
trait ObjectiveTrait extends IObjective {
/**
* user define objective function, return gradient and second order gradient
*
* @param predicts untransformed margin predicts
* @param dtrain training data
* @return List with two float array, correspond to first order grad and second order grad
*/
def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix): java.util.List[Array[Float]]
}

22
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/ScalaBoosterImpl.scala
View File

@ -1,3 +1,19 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import scala.collection.JavaConverters._
@ -35,7 +51,8 @@ private[scala] class ScalaBoosterImpl private[xgboost4j](booster: JBooster) exte
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
}
override def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: IEvaluation): String = {
override def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: IEvaluation):
String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, eval)
}
@ -51,7 +68,8 @@ private[scala] class ScalaBoosterImpl private[xgboost4j](booster: JBooster) exte
booster.predict(data.jDMatrix, outPutMargin)
}
override def predict(data: DMatrix, outPutMargin: Boolean, treeLimit: Int): Array[Array[Float]] = {
override def predict(data: DMatrix, outPutMargin: Boolean, treeLimit: Int):
Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin, treeLimit)
}

49
jvm-packages/xgboost4j/src/main/scala/org/dmlc/xgboost4j/scala/XGBoost.scala
View File

@ -1,30 +1,47 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package org.dmlc.xgboost4j.scala
import _root_.scala.collection.JavaConverters._
import org.dmlc.xgboost4j
import org.dmlc.xgboost4j.{XGBoost => JXGBoost, IEvaluation, IObjective}
import org.dmlc.xgboost4j.{IEvaluation, IObjective, XGBoost => JXGBoost}
object XGBoost {
def train(params: Map[String, AnyRef], dtrain: xgboost4j.DMatrix, round: Int,
watches: Map[String, xgboost4j.DMatrix], obj: IObjective, eval: IEvaluation): Booster = {
val xgboostInJava = JXGBoost.train(params.asJava, dtrain, round, watches.asJava, obj, eval)
def train(params: Map[String, AnyRef], dtrain: DMatrix, round: Int,
watches: Map[String, DMatrix], obj: IObjective, eval: IEvaluation): Booster = {
val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
val xgboostInJava = JXGBoost.train(params.asJava, dtrain.jDMatrix, round, jWatches.asJava,
obj, eval)
new ScalaBoosterImpl(xgboostInJava)
}
def crossValiation(params: Map[String, AnyRef],
data: DMatrix,
round: Int,
nfold: Int,
metrics: Array[String],
obj: IObjective,
eval: IEvaluation): Array[String] = {
JXGBoost.crossValiation(params.asJava, data.jDMatrix, round, nfold, metrics, obj,
eval)
def crossValiation(
params: Map[String, AnyRef],
data: DMatrix,
round: Int,
nfold: Int,
metrics: Array[String],
obj: EvalTrait,
eval: ObjectiveTrait): Array[String] = {
JXGBoost.crossValiation(params.asJava, data.jDMatrix, round, nfold, metrics,
obj.asInstanceOf[IObjective], eval.asInstanceOf[IEvaluation])
}
def initBoostModel(params: Map[String, AnyRef], dMatrixs: Array[DMatrix]): Booster = {
def initBoostModel(params: Map[String, AnyRef], dMatrixs: Array[DMatrix]): Booster = {
val xgboostInJava = JXGBoost.initBoostingModel(params.asJava, dMatrixs.map(_.jDMatrix))
new ScalaBoosterImpl(xgboostInJava)
}

1
tests/travis/run_test.sh
View File

@ -76,7 +76,6 @@ if [ ${TASK} == "java_test" ]; then
make jvm-packages
cd jvm-packages
./create_wrap.sh
cd xgboost4j
mvn clean install -DskipTests=true
mvn test
fi