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make some fix

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This commit is contained in:
yanqingmen 2015-06-10 20:09:49 -07:00
parent f91a098770
commit c110111f52
22 changed files with 234 additions and 162 deletions
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4
java/README.md
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@ -17,11 +17,11 @@ core of this wrapper is two classes:
## build native library
for windows: open the xgboost.sln in windows folder, you will found the xgboostjavawrapper project, you should do the following steps to build wrapper library:
for windows: open the xgboost.sln in "../windows" folder, you will found the xgboostjavawrapper project, you should do the following steps to build wrapper library:
* Select x64/win32 and Release in build
* (if you have setted `JAVA_HOME` properly in windows environment variables, escape this step) right click on xgboostjavawrapper project -> choose "Properties" -> click on "C/C++" in the window -> change the "Additional Include Directories" to fit your jdk install path.
* rebuild all
* move the dll "xgboostjavawrapper.dll" to "xgboost4j/src/main/resources/lib/"(you may need to create this folder if necessary.)
* double click "create_wrap.bat" to set library to proper place
for linux:
* make sure you have installed jdk and `JAVA_HOME` has been setted properly

20
java/create_wrap.bat Normal file
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@ -0,0 +1,20 @@
echo "move native library"
set libsource=..\windows\x64\Release\xgboostjavawrapper.dll
if not exist %libsource% (
goto end
)
set libfolder=xgboost4j\src\main\resources\lib
set libpath=%libfolder%\xgboostjavawrapper.dll
if not exist %libfolder% (mkdir %libfolder%)
if exist %libpath% (del %libpath%)
move %libsource% %libfolder%
echo complete
pause
exit
:end
echo "source library not found, please build it first from ..\windows\xgboost.sln"
pause
exit

2
java/create_wrap.sh
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@ -6,7 +6,7 @@ echo "move native lib"
libPath="xgboost4j/src/main/resources/lib"
if [ ! -d "$libPath" ]; then
mkdir "$libPath"
mkdir -p "$libPath"
fi
rm -f xgboost4j/src/main/resources/lib/libxgboostjavawrapper.so

25
java/doc/xgboost4j.md
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@ -82,9 +82,9 @@ import org.dmlc.xgboost4j.util.Params;
```java
Params params = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
put("eval_metric", "logloss");
}
@ -94,9 +94,9 @@ Params params = new Params() {
```java
Params params = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
put("eval_metric", "logloss");
put("eval_metric", "error");
@ -110,16 +110,19 @@ With parameters and data, you are able to train a booster model.
```java
import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
```
* Training
```java
DMatrix trainMat = new DMatrix("train.svm.txt");
DMatrix validMat = new DMatrix("valid.svm.txt");
DMatrix[] evalMats = new DMatrix[] {trainMat, validMat};
String[] evalNames = new String[] {"train", "valid"};
//specifiy a watchList to see the performance
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
int round = 2;
Booster booster = Trainer.train(params, trainMat, round, evalMats, evalNames, null, null);
Booster booster = Trainer.train(params, trainMat, round, watchs, null, null);
```
* Saving model
@ -139,8 +142,8 @@ booster.dumpModel("modelInfo.txt", "featureMap.txt", false)
```java
Params param = new Params() {
{
put("silent", "1");
put("nthread", "6");
put("silent", 1);
put("nthread", 6);
}
};
Booster booster = new Booster(param, "model.bin");

23
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BasicWalkThrough.java
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@ -24,6 +24,7 @@ import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.demo.util.DataLoader;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* a simple example of java wrapper for xgboost
@ -53,22 +54,23 @@ public class BasicWalkThrough {
//specify parameters
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
}
};
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
//predict
float[][] predicts = booster.predict(testMat);
@ -107,8 +109,11 @@ public class BasicWalkThrough {
DMatrix trainMat2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data, DMatrix.SparseType.CSR);
trainMat2.setLabel(spData.labels);
dmats = new DMatrix[] {trainMat2, testMat};
Booster booster3 = Trainer.train(param, trainMat2, round, dmats, evalNames, null, null);
//specify watchList
WatchList watchs2 = new WatchList();
watchs2.put("train", trainMat2);
watchs2.put("test", testMat);
Booster booster3 = Trainer.train(param, trainMat2, round, watchs2, null, null);
float[][] predicts3 = booster3.predict(testMat2);
//check predicts

18
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/BoostFromPrediction.java
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@ -19,6 +19,7 @@ import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* example for start from a initial base prediction
@ -35,19 +36,20 @@ public class BoostFromPrediction {
//specify parameters
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
}
};
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//train xgboost for 1 round
Booster booster = Trainer.train(param, trainMat, 1, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, 1, watchs, null, null);
float[][] trainPred = booster.predict(trainMat, true);
float[][] testPred = booster.predict(testMat, true);
@ -56,6 +58,6 @@ public class BoostFromPrediction {
testMat.setBaseMargin(testPred);
System.out.println("result of running from initial prediction");
Booster booster2 = Trainer.train(param, trainMat, 1, dmats, evalNames, null, null);
Booster booster2 = Trainer.train(param, trainMat, 1, watchs, null, null);
}
}

10
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CrossValidation.java
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@ -32,12 +32,12 @@ public class CrossValidation {
//set params
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "3");
put("silent", "1");
put("nthread", "6");
put("eta", 1.0);
put("max_depth", 3);
put("silent", 1);
put("nthread", 6);
put("objective", "binary:logistic");
put("gamma", "1.0");
put("gamma", 1.0);
put("eval_metric", "error");
}
};

17
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/CustomObjective.java
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@ -16,7 +16,6 @@
package org.dmlc.xgboost4j.demo;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.IEvaluation;
@ -24,6 +23,7 @@ import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.IObjective;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* an example user define objective and eval
@ -130,18 +130,19 @@ public class CustomObjective {
//set params
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
}
};
//set round
int round = 2;
//set evaluation data
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "eval"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//user define obj and eval
IObjective obj = new LogRegObj();
@ -149,6 +150,6 @@ public class CustomObjective {
//train a booster
System.out.println("begin to train the booster model");
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, obj, eval);
Booster booster = Trainer.train(param, trainMat, round, watchs, obj, eval);
}
}

18
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/ExternalMemory.java
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@ -19,6 +19,7 @@ import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* simple example for using external memory version
@ -35,25 +36,26 @@ public class ExternalMemory {
//specify parameters
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
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");
//param.put("nthread", num_real_cpu);
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//set round
int round = 2;
//train a boost model
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
}
}

14
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/GeneralizedLinearModel.java
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@ -20,6 +20,7 @@ import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.demo.util.CustomEval;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* this is an example of fit generalized linear model in xgboost
@ -39,8 +40,8 @@ public class GeneralizedLinearModel {
//you can also set lambda_bias which is L2 regularizer on the bias term
Params param = new Params() {
{
put("alpha", "0.0001");
put("silent", "1");
put("alpha", 0.0001);
put("silent", 1);
put("objective", "binary:logistic");
put("booster", "gblinear");
}
@ -52,13 +53,14 @@ public class GeneralizedLinearModel {
//param.put("eta", "0.5");
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//train a booster
int round = 4;
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
float[][] predicts = booster.predict(testMat);

16
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictFirstNtree.java
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@ -21,6 +21,7 @@ import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.demo.util.CustomEval;
import org.dmlc.xgboost4j.util.WatchList;
/**
* predict first ntree
@ -35,20 +36,21 @@ public class PredictFirstNtree {
//specify parameters
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
}
};
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//train a booster
int round = 3;
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
//predict use 1 tree
float[][] predicts1 = booster.predict(testMat, false, 1);

16
java/xgboost4j-demo/src/main/java/org/dmlc/xgboost4j/demo/PredictLeafIndices.java
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@ -20,6 +20,7 @@ import org.dmlc.xgboost4j.Booster;
import org.dmlc.xgboost4j.DMatrix;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.Trainer;
import org.dmlc.xgboost4j.util.WatchList;
/**
* predict leaf indices
@ -34,20 +35,21 @@ public class PredictLeafIndices {
//specify parameters
Params param = new Params() {
{
put("eta", "1.0");
put("max_depth", "2");
put("silent", "1");
put("eta", 1.0);
put("max_depth", 2);
put("silent", 1);
put("objective", "binary:logistic");
}
};
//specify evaluate datasets and evaluate names
DMatrix[] dmats = new DMatrix[] {trainMat, testMat};
String[] evalNames = new String[] {"train", "test"};
//specify watchList
WatchList watchs = new WatchList();
watchs.put("train", trainMat);
watchs.put("test", testMat);
//train a booster
int round = 3;
Booster booster = Trainer.train(param, trainMat, round, dmats, evalNames, null, null);
Booster booster = Trainer.train(param, trainMat, round, watchs, null, null);
//predict using first 2 tree
float[][] leafindex = booster.predict(testMat, 2, true);

22
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/Booster.java
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@ -30,7 +30,6 @@ import org.apache.commons.logging.LogFactory;
import org.dmlc.xgboost4j.util.Initializer;
import org.dmlc.xgboost4j.util.Params;
import org.dmlc.xgboost4j.util.TransferUtil;
import org.dmlc.xgboost4j.wrapper.XgboostJNI;
@ -85,7 +84,7 @@ public final class Booster {
private void init(DMatrix[] dMatrixs) {
long[] handles = null;
if(dMatrixs != null) {
handles = TransferUtil.dMatrixs2handles(dMatrixs);
handles = dMatrixs2handles(dMatrixs);
}
handle = XgboostJNI.XGBoosterCreate(handles);
}
@ -105,8 +104,8 @@ public final class Booster {
*/
public void setParams(Params params) {
if(params!=null) {
for(Map.Entry<String, String> entry : params) {
setParam(entry.getKey(), entry.getValue());
for(Map.Entry<String, Object> entry : params) {
setParam(entry.getKey(), entry.getValue().toString());
}
}
}
@ -154,7 +153,7 @@ public final class Booster {
* @return eval information
*/
public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter) {
long[] handles = TransferUtil.dMatrixs2handles(evalMatrixs);
long[] handles = dMatrixs2handles(evalMatrixs);
String evalInfo = XgboostJNI.XGBoosterEvalOneIter(handle, iter, handles, evalNames);
return evalInfo;
}
@ -424,6 +423,19 @@ public final class Booster {
return featureScore;
}
/**
* transfer DMatrix array to handle array (used for native functions)
* @param dmatrixs
* @return handle array for input dmatrixs
*/
private static long[] dMatrixs2handles(DMatrix[] dmatrixs) {
long[] handles = new long[dmatrixs.length];
for(int i=0; i<dmatrixs.length; i++) {
handles[i] = dmatrixs[i].getHandle();
}
return handles;
}
@Override
protected void finalize() {
delete();

21
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/DMatrix.java
View File

@ -19,7 +19,6 @@ import java.io.IOException;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.dmlc.xgboost4j.util.Initializer;
import org.dmlc.xgboost4j.util.TransferUtil;
import org.dmlc.xgboost4j.wrapper.XgboostJNI;
/**
@ -126,7 +125,7 @@ public class DMatrix {
* @param baseMargin
*/
public void setBaseMargin(float[][] baseMargin) {
float[] flattenMargin = TransferUtil.flatten(baseMargin);
float[] flattenMargin = flatten(baseMargin);
setBaseMargin(flattenMargin);
}
@ -203,6 +202,24 @@ public class DMatrix {
return handle;
}
/**
* flatten a mat to array
* @param mat
* @return
*/
private static float[] flatten(float[][] mat) {
int size = 0;
for (float[] array : mat) size += array.length;
float[] result = new float[size];
int pos = 0;
for (float[] ar : mat) {
System.arraycopy(ar, 0, result, pos, ar.length);
pos += ar.length;
}
return result;
}
@Override
protected void finalize() {
delete();

4
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/CVPack.java
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@ -28,7 +28,6 @@ public class CVPack {
DMatrix dtrain;
DMatrix dtest;
DMatrix[] dmats;
long[] dataArray;
String[] names;
Booster booster;
@ -41,7 +40,6 @@ public class CVPack {
public CVPack(DMatrix dtrain, DMatrix dtest, Params params) {
dmats = new DMatrix[] {dtrain, dtest};
booster = new Booster(params, dmats);
dataArray = TransferUtil.dMatrixs2handles(dmats);
names = new String[] {"train", "test"};
this.dtrain = dtrain;
this.dtest = dtest;
@ -70,7 +68,7 @@ public class CVPack {
* @return
*/
public String eval(int iter) {
return booster.evalSet(dataArray, names, iter);
return booster.evalSet(dmats, names, iter);
}
/**

2
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Initializer.java
View File

@ -43,7 +43,7 @@ public class Initializer {
}
/**
* load native library, this method will first try to load library from java.library.path, then try to load from library in jar package.
* load native library, this method will first try to load library from java.library.path, then try to load library in jar package.
* @param libName
* @throws IOException
*/

16
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/NativeUtils.java
View File

@ -1,7 +1,17 @@
/*
* To change this license header, choose License Headers in Project Properties.
* To change this template file, choose Tools | Templates
* and open the template in the editor.
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.util;

10
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Params.java
View File

@ -26,29 +26,29 @@ import java.util.AbstractMap;
* a util class for handle params
* @author hzx
*/
public class Params implements Iterable<Entry<String, String>>{
List<Entry<String, String>> params = new ArrayList<>();
public class Params implements Iterable<Entry<String, Object>>{
List<Entry<String, Object>> params = new ArrayList<>();
/**
* put param key-value pair
* @param key
* @param value
*/
public void put(String key, String value) {
public void put(String key, Object value) {
params.add(new AbstractMap.SimpleEntry<>(key, value));
}
@Override
public String toString(){
String paramsInfo = "";
for(Entry<String, String> param : params) {
for(Entry<String, Object> param : params) {
paramsInfo += param.getKey() + ":" + param.getValue() + "\n";
}
return paramsInfo;
}
@Override
public Iterator<Entry<String, String>> iterator() {
public Iterator<Entry<String, Object>> iterator() {
return params.iterator();
}
}

33
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/Trainer.java
View File

@ -20,6 +20,7 @@ import java.util.Collections;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Map.Entry;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.dmlc.xgboost4j.IEvaluation;
@ -40,14 +41,26 @@ public class Trainer {
* @param params Booster params.
* @param dtrain Data to be trained.
* @param round Number of boosting iterations.
* @param evalMats Data to be evaluated (may include dtrain)
* @param evalNames name of data (used for evaluation info)
* @param watchs a group of items to be evaluated during training, this allows user to watch performance on the validation set.
* @param obj customized objective (set to null if not used)
* @param eval customized evaluation (set to null if not used)
* @return trained booster
*/
public static Booster train(Params params, DMatrix dtrain, int round,
DMatrix[] evalMats, String[] evalNames, IObjective obj, IEvaluation eval) {
public static Booster train(Params params, DMatrix dtrain, int round,
WatchList watchs, IObjective obj, IEvaluation eval) {
//collect eval matrixs
int len = watchs.size();
int i = 0;
String[] evalNames = new String[len];
DMatrix[] evalMats = new DMatrix[len];
for(Entry<String, DMatrix> evalEntry : watchs) {
evalNames[i] = evalEntry.getKey();
evalMats[i] = evalEntry.getValue();
i++;
}
//collect all data matrixs
DMatrix[] allMats;
if(evalMats!=null && evalMats.length>0) {
@ -63,16 +76,6 @@ public class Trainer {
//initialize booster
Booster booster = new Booster(params, allMats);
//used for evaluation
long[] dataArray = null;
String[] names = null;
if(dataArray==null || names==null) {
//prepare data for evaluation
dataArray = TransferUtil.dMatrixs2handles(evalMats);
names = evalNames;
}
//begin to train
for(int iter=0; iter<round; iter++) {
if(obj != null) {
@ -88,7 +91,7 @@ public class Trainer {
evalInfo = booster.evalSet(evalMats, evalNames, iter, eval);
}
else {
evalInfo = booster.evalSet(dataArray, names, iter);
evalInfo = booster.evalSet(evalMats, evalNames, iter);
}
logger.info(evalInfo);
}

55
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/TransferUtil.java
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@ -1,55 +0,0 @@
/*
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.util;
import org.dmlc.xgboost4j.DMatrix;
/**
*
* @author hzx
*/
public class TransferUtil {
/**
* transfer DMatrix array to handle array (used for native functions)
* @param dmatrixs
* @return handle array for input dmatrixs
*/
public static long[] dMatrixs2handles(DMatrix[] dmatrixs) {
long[] handles = new long[dmatrixs.length];
for(int i=0; i<dmatrixs.length; i++) {
handles[i] = dmatrixs[i].getHandle();
}
return handles;
}
/**
* flatten a mat to array
* @param mat
* @return
*/
public static float[] flatten(float[][] mat) {
int size = 0;
for (float[] array : mat) size += array.length;
float[] result = new float[size];
int pos = 0;
for (float[] ar : mat) {
System.arraycopy(ar, 0, result, pos, ar.length);
pos += ar.length;
}
return result;
}
}

49
java/xgboost4j/src/main/java/org/dmlc/xgboost4j/util/WatchList.java Normal file
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@ -0,0 +1,49 @@
/*
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.util;
import java.util.AbstractMap;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;
import java.util.Map.Entry;
import org.dmlc.xgboost4j.DMatrix;
/**
* class to handle evaluation dmatrix
* @author hzx
*/
public class WatchList implements Iterable<Entry<String, DMatrix> >{
List<Entry<String, DMatrix>> watchList = new ArrayList<>();
/**
* put eval dmatrix and it's name
* @param name
* @param dmat
*/
public void put(String name, DMatrix dmat) {
watchList.add(new AbstractMap.SimpleEntry<>(name, dmat));
}
public int size() {
return watchList.size();
}
@Override
public Iterator<Entry<String, DMatrix>> iterator() {
return watchList.iterator();
}
}

1
java/xgboost4j/src/main/resources/lib/README.md
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please put native library in this package.