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[jvm-packages] Scala/Java interface for Fast Histogram Algorithm (#1966)

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* add back train method but mark as deprecated

* fix scalastyle error

* first commit in scala binding for fast histo

* java test

* add missed scala tests

* spark training

* add back train method but mark as deprecated

* fix scalastyle error

* local change

* first commit in scala binding for fast histo

* local change

* fix df frame test
This commit is contained in:
Nan Zhu
2017-03-04 15:37:24 -08:00
committed by GitHub
parent ac30a0aff5
commit ab13fd72bd
10 changed files with 400 additions and 37 deletions
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20
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/Booster.java
View File

@@ -180,6 +180,26 @@ public class Booster implements Serializable, KryoSerializable {
return evalInfo[0];
}
/**
* evaluate with given dmatrixs.
*
* @param evalMatrixs dmatrixs for evaluation
* @param evalNames name for eval dmatrixs, used for check results
* @param iter current eval iteration
* @param metricsOut output array containing the evaluation metrics for each evalMatrix
* @return eval information
* @throws XGBoostError native error
*/
public String evalSet(DMatrix[] evalMatrixs, String[] evalNames, int iter, float[] metricsOut)
throws XGBoostError {
String stringFormat = evalSet(evalMatrixs, evalNames, iter);
String[] metricPairs = stringFormat.split("\t");
for (int i = 1; i < metricPairs.length; i++) {
metricsOut[i - 1] = Float.valueOf(metricPairs[i].split(":")[1]);
}
return stringFormat;
}
/**
* evaluate with given customized Evaluation class
*

50
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java
View File

@@ -57,26 +57,24 @@ public class XGBoost {
return Booster.loadModel(in);
}
/**
* Train a booster with given parameters.
*
* @param dtrain Data to be trained.
* @param params Booster params.
* @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 (set to null if not used)
* @param eval customized evaluation (set to null if not used)
* @return trained booster
* @throws XGBoostError native error
*/
public static Booster train(
DMatrix dtrain,
Map<String, Object> params,
int round,
Map<String, DMatrix> watches,
IObjective obj,
IEvaluation eval) throws XGBoostError {
DMatrix dtrain,
Map<String, Object> params,
int round,
Map<String, DMatrix> watches,
IObjective obj,
IEvaluation eval) throws XGBoostError {
return train(dtrain, params, round, watches, null, obj, eval);
}
public static Booster train(
DMatrix dtrain,
Map<String, Object> params,
int round,
Map<String, DMatrix> watches,
float[][] metrics,
IObjective obj,
IEvaluation eval) throws XGBoostError {
//collect eval matrixs
String[] evalNames;
@@ -94,7 +92,7 @@ public class XGBoost {
//collect all data matrixs
DMatrix[] allMats;
if (evalMats != null && evalMats.length > 0) {
if (evalMats.length > 0) {
allMats = new DMatrix[evalMats.length + 1];
allMats[0] = dtrain;
System.arraycopy(evalMats, 0, allMats, 1, evalMats.length);
@@ -121,12 +119,20 @@ public class XGBoost {
}
//evaluation
if (evalMats != null && evalMats.length > 0) {
if (evalMats.length > 0) {
String evalInfo;
if (eval != null) {
evalInfo = booster.evalSet(evalMats, evalNames, eval);
} else {
evalInfo = booster.evalSet(evalMats, evalNames, iter);
if (metrics == null) {
evalInfo = booster.evalSet(evalMats, evalNames, iter);
} else {
float[] m = new float[evalMats.length];
evalInfo = booster.evalSet(evalMats, evalNames, iter, m);
for (int i = 0; i < m.length; i++) {
metrics[i][iter] = m[i];
}
}
}
if (Rabit.getRank() == 0) {
Rabit.trackerPrint(evalInfo + '\n');

46
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
View File

@@ -25,6 +25,41 @@ import scala.collection.JavaConverters._
* XGBoost Scala Training function.
*/
object XGBoost {
/**
* Train a booster given parameters.
*
* @param dtrain Data to be trained.
* @param params Parameters.
* @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 metrics array containing the evaluation metrics for each matrix in watches for each
* iteration
* @param obj customized objective
* @param eval customized evaluation
* @return The trained booster.
*/
@throws(classOf[XGBoostError])
def train(
dtrain: DMatrix,
params: Map[String, Any],
round: Int,
watches: Map[String, DMatrix],
metrics: Array[Array[Float]],
obj: ObjectiveTrait,
eval: EvalTrait): Booster = {
val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
val xgboostInJava = JXGBoost.train(
dtrain.jDMatrix,
// we have to filter null value for customized obj and eval
params.filter(_._2 != null).map{
case (key: String, value) => (key, value.toString)
}.toMap[String, AnyRef].asJava,
round, jWatches.asJava, metrics, obj, eval)
new Booster(xgboostInJava)
}
/**
* Train a booster given parameters.
*
@@ -45,16 +80,7 @@ object XGBoost {
watches: Map[String, DMatrix] = Map[String, DMatrix](),
obj: ObjectiveTrait = null,
eval: EvalTrait = null): Booster = {
val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
val xgboostInJava = JXGBoost.train(
dtrain.jDMatrix,
// we have to filter null value for customized obj and eval
params.filter(_._2 != null).map{
case (key: String, value) => (key, value.toString)
}.toMap[String, AnyRef].asJava,
round, jWatches.asJava,
obj, eval)
new Booster(xgboostInJava)
train(dtrain, params, round, watches, null, obj, eval)
}
/**