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CodingCat 2016-03-06 15:16:55 -05:00
commit 16008ebfb8
15 changed files with 471 additions and 404 deletions
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6
jvm-packages/xgboost4j-demo/src/main/java/ml/dmlc/xgboost4j/java/demo/BasicWalkThrough.java
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@ -82,16 +82,16 @@ public class BasicWalkThrough {
booster.saveModel(modelPath);
//dump model
booster.dumpModel("./model/dump.raw.txt", false);
booster.getModelDump("./model/dump.raw.txt", false);
//dump model with feature map
booster.dumpModel("./model/dump.nice.txt", "../../demo/data/featmap.txt", false);
booster.getModelDump("../../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");
Booster booster2 = XGBoost.loadModel("./model/xgb.model");
DMatrix testMat2 = new DMatrix("./model/dtest.buffer");
float[][] predicts2 = booster2.predict(testMat2);

79
jvm-packages/xgboost4j-demo/src/main/java/ml/dmlc/xgboost4j/java/demo/DistTrain.java
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@ -1,79 +0,0 @@
package ml.dmlc.xgboost4j.java.demo;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import ml.dmlc.xgboost4j.java.*;
/**
* Distributed training example, used to quick test distributed training.
*
* @author tqchen
*/
public class DistTrain {
private static final Log logger = LogFactory.getLog(DistTrain.class);
private Map<String, String> envs = null;
private class Worker implements Runnable {
private final int workerId;
Worker(int workerId) {
this.workerId = workerId;
}
public void run() {
try {
Map<String, String> worker_env = new HashMap<String, String>(envs);
worker_env.put("DMLC_TASK_ID", String.valueOf(workerId));
// always initialize rabit module before training.
Rabit.init(worker_env);
// 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("nthread", 2);
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);
// always shutdown rabit module after training.
Rabit.shutdown();
} catch (Exception ex){
logger.error(ex);
}
}
}
void start(int nWorkers) throws IOException, XGBoostError, InterruptedException {
RabitTracker tracker = new RabitTracker(nWorkers);
if (tracker.start()) {
envs = tracker.getWorkerEnvs();
for (int i = 0; i < nWorkers; ++i) {
new Thread(new Worker(i)).start();
}
tracker.waitFor();
}
}
public static void main(String[] args) throws IOException, XGBoostError, InterruptedException {
new DistTrain().start(Integer.parseInt(args[0]));
}
}

4
jvm-packages/xgboost4j-demo/src/main/java/ml/dmlc/xgboost4j/java/demo/PredictLeafIndices.java
View File

@ -52,13 +52,13 @@ public class PredictLeafIndices {
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
//predict using first 2 tree
float[][] leafindex = booster.predict(testMat, 2, true);
float[][] leafindex = booster.predictLeaf(testMat, 2);
for (float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}
//predict all trees
leafindex = booster.predict(testMat, 0, true);
leafindex = booster.predictLeaf(testMat, 0);
for (float[] leafs : leafindex) {
System.out.println(Arrays.toString(leafs));
}

2
jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/flink/Test.scala
View File

@ -37,6 +37,8 @@ object Test {
"objective" -> "binary:logistic").toMap
val round = 2
val model = XGBoost.train(paramMap, data, round)
log.info(model)
}
}

17
jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/flink/XGBoost.scala
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@ -25,6 +25,9 @@ import org.apache.flink.api.scala.DataSet
import org.apache.flink.api.scala._
import org.apache.flink.ml.common.LabeledVector
import org.apache.flink.util.Collector
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path
import org.apache.hadoop.conf.Configuration
object XGBoost {
/**
@ -60,6 +63,20 @@ object XGBoost {
val logger = LogFactory.getLog(this.getClass)
/**
* Load XGBoost model from path, using Hadoop Filesystem API.
*
* @param modelPath The path that is accessible by hadoop filesystem API.
* @return The loaded model
*/
def loadModel(modelPath: String) : XGBoostModel = {
new XGBoostModel(
XGBoostScala.loadModel(
FileSystem
.get(new Configuration)
.open(new Path(modelPath))))
}
/**
* Train a xgboost model with link.
*

39
jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/flink/XGBoostModel.scala
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@ -16,8 +16,45 @@
package ml.dmlc.xgboost4j.flink
import ml.dmlc.xgboost4j.scala.Booster
import ml.dmlc.xgboost4j.LabeledPoint
import ml.dmlc.xgboost4j.scala.{DMatrix, Booster}
import org.apache.flink.api.scala.DataSet
import org.apache.flink.api.scala._
import org.apache.flink.ml.math.Vector
import org.apache.hadoop.fs.FileSystem
import org.apache.hadoop.fs.Path
import org.apache.hadoop.conf.Configuration
class XGBoostModel (booster: Booster) extends Serializable {
/**
* Save the model as a Hadoop filesystem file.
*
* @param modelPath The model path as in Hadoop path.
*/
def saveModel(modelPath: String): Unit = {
booster.saveModel(FileSystem
.get(new Configuration)
.create(new Path(modelPath)))
}
/**
* Predict given vector dataset.
*
* @param data The dataset to be predicted.
* @return The prediction result.
*/
def predict(data: DataSet[Vector]) : DataSet[Array[Float]] = {
val predictMap: Iterator[Vector] => TraversableOnce[Array[Float]] =
(it: Iterator[Vector]) => {
val mapper = (x: Vector) => {
val (index, value) = x.toSeq.unzip
LabeledPoint.fromSparseVector(0.0f,
index.toArray, value.map(z => z.toFloat).toArray)
}
val dataIter = for (x <- it) yield mapper(x)
val dmat = new DMatrix(dataIter, null)
this.booster.predict(dmat)
}
data.mapPartition(predictMap)
}
}

386
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java
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@ -24,20 +24,17 @@ import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
/**
* Booster for xgboost, similar to the python wrapper xgboost.py
* but custom obj function and eval function not supported at present.
*
* @author hzx
* Booster for xgboost, this is a model API that support interactive build of a XGBoost Model
*/
public class Booster implements Serializable {
public class Booster implements Serializable {
private static final Log logger = LogFactory.getLog(Booster.class);
long handle = 0;
// handle to the booster.
private long handle = 0;
//load native library
static {
try {
NativeLibLoader.initXgBoost();
NativeLibLoader.initXGBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);
@ -45,60 +42,70 @@ public class Booster implements Serializable {
}
/**
* init Booster from dMatrixs
* Create a new Booster with empty stage.
*
* @param params parameters
* @param dMatrixs DMatrix array
* @param params Model parameters
* @param cacheMats Cached DMatrix entries,
* the prediction of these DMatrices will become faster than not-cached data.
* @throws XGBoostError native error
*/
Booster(Map<String, Object> params, DMatrix[] dMatrixs) throws XGBoostError {
init(dMatrixs);
Booster(Map<String, Object> params, DMatrix[] cacheMats) throws XGBoostError {
init(cacheMats);
setParam("seed", "0");
setParams(params);
}
/**
* load model from modelPath
*
* @param params parameters
* @param modelPath booster modelPath (model generated by booster.saveModel)
* @throws XGBoostError native error
* Load a new Booster model from modelPath
* @param modelPath The path to the model.
* @return The created Booster.
* @throws XGBoostError
*/
Booster(Map<String, Object> params, String modelPath) throws XGBoostError {
init(null);
static Booster loadModel(String modelPath) throws XGBoostError {
if (modelPath == null) {
throw new NullPointerException("modelPath : null");
}
loadModel(modelPath);
setParam("seed", "0");
setParams(params);
}
private void init(DMatrix[] dMatrixs) throws XGBoostError {
long[] handles = null;
if (dMatrixs != null) {
handles = dmatrixsToHandles(dMatrixs);
}
long[] out = new long[1];
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterCreate(handles, out));
handle = out[0];
Booster ret = new Booster(new HashMap<String, Object>(), new DMatrix[0]);
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterLoadModel(ret.handle, modelPath));
return ret;
}
/**
* set parameter
* Load a new Booster model from a file opened as input stream.
* The assumption is the input stream only contains one XGBoost Model.
* This can be used to load existing booster models saved by other xgboost bindings.
*
* @param in The input stream of the file.
* @return The create boosted
* @throws XGBoostError
* @throws IOException
*/
static Booster loadModel(InputStream in) throws XGBoostError, IOException {
int size;
byte[] buf = new byte[1<<20];
ByteArrayOutputStream os = new ByteArrayOutputStream();
while ((size = in.read(buf)) != -1) {
os.write(buf, 0, size);
}
in.close();
Booster ret = new Booster(new HashMap<String, Object>(), new DMatrix[0]);
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterLoadModelFromBuffer(ret.handle,os.toByteArray()));
return ret;
}
/**
* Set parameter to the Booster.
*
* @param key param name
* @param value param value
* @throws XGBoostError native error
*/
public final void setParam(String key, String value) throws XGBoostError {
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterSetParam(handle, key, value));
public final void setParam(String key, Object value) throws XGBoostError {
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterSetParam(handle, key, value.toString()));
}
/**
* set parameters
* Set parameters to the Booster.
*
* @param params parameters key-value map
* @throws XGBoostError native error
@ -111,9 +118,8 @@ public class Booster implements Serializable {
}
}
/**
* Update (one iteration)
* Update the booster for one iteration.
*
* @param dtrain training data
* @param iter current iteration number
@ -124,14 +130,14 @@ public class Booster implements Serializable {
}
/**
* update with customize obj func
* Update with customize obj func
*
* @param dtrain training data
* @param obj customized objective class
* @throws XGBoostError native error
*/
public void update(DMatrix dtrain, IObjective obj) throws XGBoostError {
float[][] predicts = predict(dtrain, true);
float[][] predicts = this.predict(dtrain, true, 0, false);
List<float[]> gradients = obj.getGradient(predicts, dtrain);
boost(dtrain, gradients.get(0), gradients.get(1));
}
@ -149,8 +155,8 @@ public class Booster implements Serializable {
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));
}
/**
@ -193,18 +199,20 @@ public class Booster implements Serializable {
}
/**
* base function for Predict
* Advanced predict function with all the options.
*
* @param data data
* @param outPutMargin output margin
* @param treeLimit limit number of trees
* @param outputMargin output margin
* @param treeLimit limit number of trees, 0 means all trees.
* @param predLeaf prediction minimum to keep leafs
* @return predict results
*/
private synchronized float[][] pred(DMatrix data, boolean outPutMargin, int treeLimit,
boolean predLeaf) throws XGBoostError {
private synchronized float[][] predict(DMatrix data,
boolean outputMargin,
int treeLimit,
boolean predLeaf) throws XGBoostError {
int optionMask = 0;
if (outPutMargin) {
if (outputMargin) {
optionMask = 1;
}
if (predLeaf) {
@ -225,6 +233,18 @@ public class Booster implements Serializable {
return predicts;
}
/**
* Predict leaf indices given the data
*
* @param data The input data.
* @param treeLimit Number of trees to include, 0 means all trees.
* @return The leaf indices of the instance.
* @throws XGBoostError
*/
public float[][] predictLeaf(DMatrix data, int treeLimit) throws XGBoostError {
return this.predict(data, false, treeLimit, true);
}
/**
* Predict with data
*
@ -233,53 +253,34 @@ public class Booster implements Serializable {
* @throws XGBoostError native error
*/
public float[][] predict(DMatrix data) throws XGBoostError {
return pred(data, false, 0, false);
return this.predict(data, false, 0, false);
}
/**
* Predict with data
*
* @param data dmatrix storing the input
* @param outPutMargin Whether to output the raw untransformed margin value.
* @return predict result
* @throws XGBoostError native error
* @param data data
* @param outputMargin output margin
* @return predict results
*/
public float[][] predict(DMatrix data, boolean outPutMargin) throws XGBoostError {
return pred(data, outPutMargin, 0, false);
public float[][] predict(DMatrix data, boolean outputMargin) throws XGBoostError {
return this.predict(data, outputMargin, 0, false);
}
/**
* Predict with data
* Advanced predict function with all the options.
*
* @param data dmatrix storing the input
* @param outPutMargin Whether to output the raw untransformed margin value.
* @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
* @return predict result
* @throws XGBoostError native error
* @param data data
* @param outputMargin output margin
* @param treeLimit limit number of trees, 0 means all trees.
* @return predict results
*/
public float[][] predict(DMatrix data, boolean outPutMargin, int treeLimit) throws XGBoostError {
return pred(data, outPutMargin, treeLimit, false);
public float[][] predict(DMatrix data, boolean outputMargin, int treeLimit) throws XGBoostError {
return this.predict(data, outputMargin, treeLimit, false);
}
/**
* 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.
* @return predict result
* @throws XGBoostError native error
*/
public float[][] predict(DMatrix data, int treeLimit, boolean predLeaf) throws XGBoostError {
return pred(data, false, treeLimit, predLeaf);
}
/**
* save model to modelPath
* Save model to modelPath
*
* @param modelPath model path
*/
@ -287,8 +288,65 @@ public class Booster implements Serializable {
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterSaveModel(handle, modelPath));
}
private void loadModel(String modelPath) {
XGBoostJNI.XGBoosterLoadModel(handle, modelPath);
/**
* Save the model to file opened as output stream.
* The model format is compatible with other xgboost bindings.
* The output stream can only save one xgboost model.
* This function will close the OutputStream after the save.
*
* @param out The output stream
*/
public void saveModel(OutputStream out) throws XGBoostError, IOException {
out.write(this.toByteArray());
out.close();
}
/**
* Get the dump of the model as a string array
*
* @param withStats Controls whether the split statistics are output.
* @return dumped model information
* @throws XGBoostError native error
*/
public String[] getModelDump(String featureMap, boolean withStats) throws XGBoostError {
int statsFlag = 0;
if (featureMap == null) {
featureMap = "";
}
if (withStats) {
statsFlag = 1;
}
String[][] modelInfos = new String[1][];
JNIErrorHandle.checkCall(
XGBoostJNI.XGBoosterDumpModel(handle, featureMap, statsFlag, modelInfos));
return modelInfos[0];
}
/**
* Get importance of each feature
*
* @return featureMap key: feature index, value: feature importance score, can be nill
* @throws XGBoostError native error
*/
public Map<String, Integer> getFeatureScore(String featureMap) throws XGBoostError {
String[] modelInfos = getModelDump(featureMap, false);
Map<String, Integer> featureScore = new HashMap<String, Integer>();
for (String tree : modelInfos) {
for (String node : tree.split("\n")) {
String[] array = node.split("\\[");
if (array.length == 1) {
continue;
}
String fid = array[1].split("\\]")[0];
fid = fid.split("<")[0];
if (featureScore.containsKey(fid)) {
featureScore.put(fid, 1 + featureScore.get(fid));
} else {
featureScore.put(fid, 1);
}
}
}
return featureScore;
}
/**
@ -309,152 +367,17 @@ public class Booster implements Serializable {
}
/**
* get the dump of the model as a string array
* get the dump of the model as a byte array
*
* @param featureMap featureMap file
* @param withStats Controls whether the split statistics are output.
* @return dumped model information
* @throws XGBoostError native error
*/
private String[] getDumpInfo(String featureMap, boolean withStats) throws XGBoostError {
int statsFlag = 0;
if (withStats) {
statsFlag = 1;
}
String[][] modelInfos = new String[1][];
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterDumpModel(handle, featureMap, statsFlag,
modelInfos));
return modelInfos[0];
}
/**
* Dump model into a text file.
*
* @param modelPath file to save dumped model info
* @param withStats bool
* Controls whether the split statistics are output.
* @throws FileNotFoundException file not found
* @throws UnsupportedEncodingException unsupported feature
* @throws IOException error with model writing
* @throws XGBoostError native error
*/
public void dumpModel(String modelPath, boolean withStats) throws IOException, XGBoostError {
File tf = new File(modelPath);
FileOutputStream out = new FileOutputStream(tf);
BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(out, "UTF-8"));
String[] modelInfos = getDumpInfo(withStats);
for (int i = 0; i < modelInfos.length; i++) {
writer.write("booster [" + i + "]:\n");
writer.write(modelInfos[i]);
}
writer.close();
out.close();
}
/**
* Dump model into a text file.
*
* @param modelPath file to save dumped model info
* @param featureMap featureMap file
* @param withStats bool
* Controls whether the split statistics are output.
* @throws FileNotFoundException exception
* @throws UnsupportedEncodingException exception
* @throws IOException exception
* @throws XGBoostError native error
*/
public void dumpModel(String modelPath, String featureMap, boolean withStats) throws
IOException, XGBoostError {
File tf = new File(modelPath);
FileOutputStream out = new FileOutputStream(tf);
BufferedWriter writer = new BufferedWriter(new OutputStreamWriter(out, "UTF-8"));
String[] modelInfos = getDumpInfo(featureMap, withStats);
for (int i = 0; i < modelInfos.length; i++) {
writer.write("booster [" + i + "]:\n");
writer.write(modelInfos[i]);
}
writer.close();
out.close();
}
/**
* get importance of each feature
*
* @return featureMap key: feature index, value: feature importance score
* @throws XGBoostError native error
*/
public Map<String, Integer> getFeatureScore() throws XGBoostError {
String[] modelInfos = getDumpInfo(false);
Map<String, Integer> featureScore = new HashMap<String, Integer>();
for (String tree : modelInfos) {
for (String node : tree.split("\n")) {
String[] array = node.split("\\[");
if (array.length == 1) {
continue;
}
String fid = array[1].split("\\]")[0];
fid = fid.split("<")[0];
if (featureScore.containsKey(fid)) {
featureScore.put(fid, 1 + featureScore.get(fid));
} else {
featureScore.put(fid, 1);
}
}
}
return featureScore;
}
/**
* get importance of each feature
*
* @param featureMap file to save dumped model info
* @return featureMap key: feature index, value: feature importance score
* @throws XGBoostError native error
*/
public Map<String, Integer> getFeatureScore(String featureMap) throws XGBoostError {
String[] modelInfos = getDumpInfo(featureMap, false);
Map<String, Integer> featureScore = new HashMap<String, Integer>();
for (String tree : modelInfos) {
for (String node : tree.split("\n")) {
String[] array = node.split("\\[");
if (array.length == 1) {
continue;
}
String fid = array[1].split("\\]")[0];
fid = fid.split("<")[0];
if (featureScore.containsKey(fid)) {
featureScore.put(fid, 1 + featureScore.get(fid));
} else {
featureScore.put(fid, 1);
}
}
}
return featureScore;
}
/**
* Save the model as byte array representation.
* Write these bytes to a file will give compatible format with other xgboost bindings.
*
* If java natively support HDFS file API, use toByteArray and write the ByteArray,
*
* @return the saved byte array.
* @throws XGBoostError
*/
public byte[] toByteArray() throws XGBoostError {
byte[][] bytes = new byte[1][];
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterGetModelRaw(this.handle, bytes));
return bytes[0];
}
/**
* Load the booster model from thread-local rabit checkpoint.
* This is only used in distributed training.
@ -476,6 +399,22 @@ public class Booster implements Serializable {
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterSaveRabitCheckpoint(this.handle));
}
/**
* Internal initialization function.
* @param cacheMats The cached DMatrix.
* @throws XGBoostError
*/
private void init(DMatrix[] cacheMats) throws XGBoostError {
long[] handles = null;
if (cacheMats != null) {
handles = dmatrixsToHandles(cacheMats);
}
long[] out = new long[1];
JNIErrorHandle.checkCall(XGBoostJNI.XGBoosterCreate(handles, out));
handle = out[0];
}
/**
* transfer DMatrix array to handle array (used for native functions)
*
@ -499,7 +438,8 @@ public class Booster implements Serializable {
}
}
private void readObject(java.io.ObjectInputStream in) throws IOException, ClassNotFoundException {
private void readObject(java.io.ObjectInputStream in)
throws IOException, ClassNotFoundException {
try {
this.init(null);
byte[] bytes = (byte[])in.readObject();

2
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/DMatrix.java
View File

@ -35,7 +35,7 @@ public class DMatrix {
//load native library
static {
try {
NativeLibLoader.initXgBoost();
NativeLibLoader.initXGBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);

2
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/JNIErrorHandle.java
View File

@ -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);

2
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/NativeLibLoader.java
View File

@ -35,7 +35,7 @@ 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);

2
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Rabit.java
View File

@ -15,7 +15,7 @@ public class Rabit implements Serializable {
//load native library
static {
try {
NativeLibLoader.initXgBoost();
NativeLibLoader.initXGBoost();
} catch (IOException ex) {
logger.error("load native library failed.");
logger.error(ex);

66
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java
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@ -15,6 +15,8 @@
*/
package ml.dmlc.xgboost4j.java;
import java.io.IOException;
import java.io.InputStream;
import java.util.*;
import org.apache.commons.logging.Log;
@ -28,6 +30,33 @@ import org.apache.commons.logging.LogFactory;
public class XGBoost {
private static final Log logger = LogFactory.getLog(XGBoost.class);
/**
* load model from modelPath
*
* @param modelPath booster modelPath (model generated by booster.saveModel)
* @throws XGBoostError native error
*/
public static Booster loadModel(String modelPath)
throws XGBoostError {
return Booster.loadModel(modelPath);
}
/**
* Load a new Booster model from a file opened as input stream.
* The assumption is the input stream only contains one XGBoost Model.
* This can be used to load existing booster models saved by other xgboost bindings.
*
* @param in The input stream of the file,
* will be closed after this function call.
* @return The create boosted
* @throws XGBoostError
* @throws IOException
*/
public static Booster loadModel(InputStream in)
throws XGBoostError, IOException {
return Booster.loadModel(in);
}
/**
* Train a booster with given parameters.
*
@ -41,9 +70,11 @@ public class XGBoost {
* @return trained booster
* @throws XGBoostError native error
*/
public static Booster train(Map<String, Object> params, DMatrix dtrain, int round,
Map<String, DMatrix> watches, IObjective obj,
IEvaluation eval) throws XGBoostError {
public static Booster train(Map<String, Object> params,
DMatrix dtrain, int round,
Map<String, DMatrix> watches,
IObjective obj,
IEvaluation eval) throws XGBoostError {
//collect eval matrixs
String[] evalNames;
@ -106,32 +137,7 @@ public class XGBoost {
}
/**
* init Booster from dMatrixs
*
* @param params parameters
* @param dMatrixs DMatrix array
* @throws XGBoostError native error
*/
public static Booster initBoostingModel(
Map<String, Object> params,
DMatrix[] dMatrixs) throws XGBoostError {
return new Booster(params, dMatrixs);
}
/**
* load model from modelPath
*
* @param params parameters
* @param modelPath booster modelPath (model generated by booster.saveModel)
* @throws XGBoostError native error
*/
public static Booster loadBoostModel(Map<String, Object> params, String modelPath)
throws XGBoostError {
return new Booster(params, modelPath);
}
/**
* Cross-validation with given paramaters.
* Cross-validation with given parameters.
*
* @param params Booster params.
* @param data Data to be trained.
@ -294,7 +300,7 @@ public class XGBoost {
public CVPack(DMatrix dtrain, DMatrix dtest, Map<String, Object> params)
throws XGBoostError {
dmats = new DMatrix[]{dtrain, dtest};
booster = XGBoost.initBoostingModel(params, dmats);
booster = new Booster(params, dmats);
names = new String[]{"train", "test"};
this.dtrain = dtrain;
this.dtest = dtest;

195
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/Booster.scala
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@ -16,86 +16,177 @@
package ml.dmlc.xgboost4j.scala
import ml.dmlc.xgboost4j.java
import java.io.IOException
import ml.dmlc.xgboost4j.java.{Booster => JBooster}
import ml.dmlc.xgboost4j.java.XGBoostError
import scala.collection.JavaConverters._
import scala.collection.mutable
class Booster private[xgboost4j](booster: java.Booster) extends Serializable {
class Booster private[xgboost4j](booster: JBooster) extends Serializable {
def setParam(key: String, value: String): Unit = {
/**
* Set parameter to the Booster.
*
* @param key param name
* @param value param value
*/
@throws(classOf[XGBoostError])
def setParam(key: String, value: AnyRef): Unit = {
booster.setParam(key, value)
}
def update(dtrain: DMatrix, iter: Int): Unit = {
booster.update(dtrain.jDMatrix, iter)
}
def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit = {
booster.update(dtrain.jDMatrix, obj)
}
def dumpModel(modelPath: String, withStats: Boolean): Unit = {
booster.dumpModel(modelPath, withStats)
}
def dumpModel(modelPath: String, featureMap: String, withStats: Boolean): Unit = {
booster.dumpModel(modelPath, featureMap, withStats)
}
/**
* set parameters
*
* @param params parameters key-value map
*/
@throws(classOf[XGBoostError])
def setParams(params: Map[String, AnyRef]): Unit = {
booster.setParams(params.asJava)
}
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int): String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
/**
* Update (one iteration)
*
* @param dtrain training data
* @param iter current iteration number
*/
@throws(classOf[XGBoostError])
def update(dtrain: DMatrix, iter: Int): Unit = {
booster.update(dtrain.jDMatrix, iter)
}
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait):
String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, eval)
}
def dispose: Unit = {
booster.dispose()
}
def predict(data: DMatrix): Array[Array[Float]] = {
booster.predict(data.jDMatrix)
}
def predict(data: DMatrix, outPutMargin: Boolean): Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin)
}
def predict(data: DMatrix, outPutMargin: Boolean, treeLimit: Int):
Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin, treeLimit)
}
def predict(data: DMatrix, treeLimit: Int, predLeaf: Boolean): Array[Array[Float]] = {
booster.predict(data.jDMatrix, treeLimit, predLeaf)
/**
* update with customize obj func
*
* @param dtrain training data
* @param obj customized objective class
*/
@throws(classOf[XGBoostError])
def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit = {
booster.update(dtrain.jDMatrix, obj)
}
/**
* update with give grad and hess
*
* @param dtrain training data
* @param grad first order of gradient
* @param hess seconde order of gradient
*/
@throws(classOf[XGBoostError])
def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float]): Unit = {
booster.boost(dtrain.jDMatrix, grad, hess)
}
def getFeatureScore: mutable.Map[String, Integer] = {
booster.getFeatureScore.asScala
/**
* evaluate with given dmatrixs.
*
* @param evalMatrixs dmatrixs for evaluation
* @param evalNames name for eval dmatrixs, used for check results
* @param iter current eval iteration
* @return eval information
*/
@throws(classOf[XGBoostError])
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int)
: String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
}
def getFeatureScore(featureMap: String): mutable.Map[String, Integer] = {
/**
* evaluate with given customized Evaluation class
*
* @param evalMatrixs evaluation matrix
* @param evalNames evaluation names
* @param eval custom evaluator
* @return eval information
*/
@throws(classOf[XGBoostError])
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait)
: String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, eval)
}
/**
* Predict with data
*
* @param data dmatrix storing the input
* @param outPutMargin Whether to output the raw untransformed margin value.
* @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
* @return predict result
*/
@throws(classOf[XGBoostError])
def predict(data: DMatrix, outPutMargin: Boolean = false, treeLimit: Int = 0)
: Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin, treeLimit)
}
/**
* Predict the leaf indices
*
* @param data dmatrix storing the input
* @param treeLimit Limit number of trees in the prediction; defaults to 0 (use all trees).
* @return predict result
* @throws XGBoostError native error
*/
@throws(classOf[XGBoostError])
def predictLeaf(data: DMatrix, treeLimit: Int = 0)
: Array[Array[Float]] = {
booster.predictLeaf(data.jDMatrix, treeLimit)
}
/**
* save model to modelPath
*
* @param modelPath model path
*/
@throws(classOf[XGBoostError])
def saveModel(modelPath: String): Unit = {
booster.saveModel(modelPath)
}
/**
* save model to Output stream
*
* @param out Output stream
*/
@throws(classOf[XGBoostError])
def saveModel(out: java.io.OutputStream): Unit = {
booster.saveModel(out)
}
/**
* Dump model as Array of string
*
* @param featureMap featureMap file
* @param withStats bool
* Controls whether the split statistics are output.
*/
@throws(classOf[XGBoostError])
def getModelDump(featureMap: String = null, withStats: Boolean = false)
: Array[String] = {
booster.getModelDump(featureMap, withStats)
}
/**
* Get importance of each feature
*
* @return featureMap key: feature index, value: feature importance score
*/
@throws(classOf[XGBoostError])
def getFeatureScore(featureMap: String = null): mutable.Map[String, Integer] = {
booster.getFeatureScore(featureMap).asScala
}
def saveModel(modelPath: String): Unit = {
booster.saveModel(modelPath)
/**
* Dispose the booster when it is no longer needed
*/
def dispose: Unit = {
booster.dispose()
}
override def finalize(): Unit = {
super.finalize()
dispose
}
}

58
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
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@ -16,12 +16,28 @@
package ml.dmlc.xgboost4j.scala
import java.io.InputStream
import ml.dmlc.xgboost4j.java.{XGBoost => JXGBoost, XGBoostError}
import scala.collection.JavaConverters._
import ml.dmlc.xgboost4j.java.{XGBoost => JXGBoost}
/**
* XGBoost Scala Training function.
*/
object XGBoost {
/**
* Train a booster given parameters.
*
* @param params Parameters.
* @param dtrain Data to be trained.
* @param round Number of boosting iterations.
* @param watches a group of items to be evaluated during training, this allows user to watch
* performance on the validation set.
* @param obj customized objective
* @param eval customized evaluation
* @return The trained booster.
*/
@throws(classOf[XGBoostError])
def train(
params: Map[String, AnyRef],
dtrain: DMatrix,
@ -35,6 +51,19 @@ object XGBoost {
new Booster(xgboostInJava)
}
/**
* Cross-validation with given parameters.
*
* @param params Booster params.
* @param data Data to be trained.
* @param round Number of boosting iterations.
* @param nfold Number of folds in CV.
* @param metrics Evaluation metrics to be watched in CV.
* @param obj customized objective
* @param eval customized evaluation
* @return evaluation history
*/
@throws(classOf[XGBoostError])
def crossValidation(
params: Map[String, AnyRef],
data: DMatrix,
@ -46,13 +75,28 @@ object XGBoost {
JXGBoost.crossValidation(params.asJava, data.jDMatrix, round, nfold, metrics, obj, eval)
}
def initBoostModel(params: Map[String, AnyRef], dMatrixs: Array[DMatrix]): Booster = {
val xgboostInJava = JXGBoost.initBoostingModel(params.asJava, dMatrixs.map(_.jDMatrix))
/**
* load model from modelPath
*
* @param modelPath booster modelPath
*/
@throws(classOf[XGBoostError])
def loadModel(modelPath: String): Booster = {
val xgboostInJava = JXGBoost.loadModel(modelPath)
new Booster(xgboostInJava)
}
def loadBoostModel(params: Map[String, AnyRef], modelPath: String): Booster = {
val xgboostInJava = JXGBoost.loadBoostModel(params.asJava, modelPath)
/**
* Load a new Booster model from a file opened as input stream.
* The assumption is the input stream only contains one XGBoost Model.
* This can be used to load existing booster models saved by other XGBoost bindings.
*
* @param in The input stream of the file.
* @return The create booster
*/
@throws(classOf[XGBoostError])
def loadModel(in: InputStream): Booster = {
val xgboostInJava = JXGBoost.loadModel(in)
new Booster(xgboostInJava)
}
}

15
jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java
View File

@ -15,6 +15,10 @@
*/
package ml.dmlc.xgboost4j.java;
import java.io.File;
import java.io.FileInputStream;
import java.io.IOException;
import java.util.Arrays;
import java.util.HashMap;
import java.util.Map;
@ -67,7 +71,7 @@ public class BoosterImplTest {
}
@Test
public void testBoosterBasic() throws XGBoostError {
public void testBoosterBasic() throws XGBoostError, IOException {
DMatrix trainMat = new DMatrix("../../demo/data/agaricus.txt.train");
DMatrix testMat = new DMatrix("../../demo/data/agaricus.txt.test");
@ -94,15 +98,20 @@ public class BoosterImplTest {
Booster booster = XGBoost.train(paramMap, trainMat, round, watches, null, null);
//predict raw output
float[][] predicts = booster.predict(testMat, true);
float[][] predicts = booster.predict(testMat, true, 0);
//eval
IEvaluation eval = new EvalError();
//error must be less than 0.1
TestCase.assertTrue(eval.eval(predicts, testMat) < 0.1f);
//test dump model
// save and load
File temp = File.createTempFile("temp", "model");
temp.deleteOnExit();
booster.saveModel(temp.getAbsolutePath());
Booster bst2 = XGBoost.loadModel(new FileInputStream(temp.getAbsolutePath()));
assert (Arrays.equals(bst2.toByteArray(), booster.toByteArray()));
}
/**