Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[JVM] Refactor, add filesys API

Browse Source
This commit is contained in:
tqchen
2016-03-06 11:33:48 -08:00
parent 457ff82e33
commit 56f7a414d1
17 changed files with 597 additions and 896 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
jvm-packages/xgboost4j-demo/src/main/java/ml/dmlc/xgboost4j/java/demo/BasicWalkThrough.java
View File

@@ -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
View File

@@ -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));
}