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adjust the API signature as well as the docs

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CodingCat 2016-03-11 15:22:44 -05:00
parent 97e4dcde98
commit 400b1faecc
23 changed files with 58 additions and 52 deletions
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12
doc/jvm/xgboost4j-intro.md
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@ -24,7 +24,7 @@ Many of these machine learning libraries(e.g. [XGBoost](https://github.com/dmlc/
requires new computation abstraction and native support(e.g. C++ for GPU computing).
They are also often [much more efficient](http://arxiv.org/abs/1603.02754).
The gap between the implementation fundamentals of the general data processing frameworks and the more specific machine learning libraries/systems prohibits the smooth connection between these two types of systems, thus brings unnecessary inconvenience to the end user. The common workflow to the user is to utilize the systems like Flink/Spark to preprocess/clean data, pass the results to machine learning systems like [XGBoost](https://github.com/dmlc/xgboost)/[MxNet](https://github.com/dmlc/mxnet)) via the file system and then conduct the following machine learning phase. While such process won't hurt performance as much in data processing case(because machine learning takes a lot of time compared to data loading), it create a bit inconvenience for the users.
The gap between the implementation fundamentals of the general data processing frameworks and the more specific machine learning libraries/systems prohibits the smooth connection between these two types of systems, thus brings unnecessary inconvenience to the end user. The common workflow to the user is to utilize the systems like Flink/Spark to preprocess/clean data, pass the results to machine learning systems like [XGBoost](https://github.com/dmlc/xgboost)/[MxNet](https://github.com/dmlc/mxnet)) via the file system and then conduct the following machine learning phase. While such process won't hurt performance as much in data processing case(because machine learning takes a lot of time compared to data loading), it creates a bit inconvenience for the users.
We want best of both worlds, so we can use the data processing frameworks like Flink and Spark toghether with
the best distributed machine learning solutions.
@ -37,7 +37,7 @@ XGBoost and XGBoost4J adopts Unix Philosophy.
XGBoost **does its best in one thing -- tree boosting** and is **being designed to work with other systems**.
We strongly believe that machine learning solution should not be restricted to certain language or certain platform.
Specifically, users will be able to use distributed XGBoost in both Flink and Spark.
Specifically, users will be able to use distributed XGBoost in both Flink and Spark, and possibly more frameworks in Future.
We have made the API in a portable way so it **can be easily ported to other Dataflow frameworks provided by the Cloud**.
XGBoost4J shares its core with other XGBoost libraries, which means data scientists can use R/python
read and visualize the model trained distributedly.
@ -85,10 +85,10 @@ watches += "test" -> testMax
val round = 2
// train a model
val booster = XGBoost.train(params.toMap, trainMax, round, watches.toMap)
val booster = XGBoost.train(trainMax, params.toMap, round, watches.toMap)
```
In Scala:
We then evaluate our model:
```scala
val predicts = booster.predict(testMax)
@ -111,7 +111,7 @@ In Spark, the dataset is represented as the [Resilient Distributed Dataset (RDD)
val trainRDD = MLUtils.loadLibSVMFile(sc, inputTrainPath).repartition(args(1).toInt)
```
We move forward to train the models, in Spark:
We move forward to train the models:
```scala
val xgboostModel = XGBoost.train(trainRDD, paramMap, numRound)
@ -169,6 +169,8 @@ xgboostModel.predict(testData.map{x => x.vector})
It is the first release of XGBoost4J package, we are actively move forward for more charming features in the next release. You can watch our progress in [XGBoost4J Road Map](https://github.com/dmlc/xgboost/issues/935).
While we are trying our best to keep the minimum changes to the APIs, it is still subject to the incompatible changes.
## Further Readings
If you are interested in knowing more about XGBoost, you can find rich resources in

4
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java
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@ -67,7 +67,7 @@ public class BasicWalkThrough {
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
//predict
float[][] predicts = booster.predict(testMat);
@ -111,7 +111,7 @@ public class BasicWalkThrough {
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);
Booster booster3 = XGBoost.train(trainMat2, params, round, watches2, null, null);
float[][] predicts3 = booster3.predict(testMat2);
//check predicts

4
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java
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@ -48,7 +48,7 @@ public class BoostFromPrediction {
watches.put("test", testMat);
//train xgboost for 1 round
Booster booster = XGBoost.train(params, trainMat, 1, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, 1, watches, null, null);
float[][] trainPred = booster.predict(trainMat, true);
float[][] testPred = booster.predict(testMat, true);
@ -57,6 +57,6 @@ public class BoostFromPrediction {
testMat.setBaseMargin(testPred);
System.out.println("result of running from initial prediction");
Booster booster2 = XGBoost.train(params, trainMat, 1, watches, null, null);
Booster booster2 = XGBoost.train(trainMat, params, 1, watches, null, null);
}
}

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java
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@ -49,7 +49,7 @@ public class CrossValidation {
//set additional eval_metrics
String[] metrics = null;
String[] evalHist = XGBoost.crossValidation(params, trainMat, round, nfold, metrics, null,
String[] evalHist = XGBoost.crossValidation(trainMat, params, round, nfold, metrics, null,
null);
}
}

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java
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@ -163,6 +163,6 @@ public class CustomObjective {
//train a booster
System.out.println("begin to train the booster model");
Booster booster = XGBoost.train(params, trainMat, round, watches, obj, eval);
Booster booster = XGBoost.train(trainMat, params, round, watches, obj, eval);
}
}

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java
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@ -56,6 +56,6 @@ public class ExternalMemory {
int round = 2;
//train a boost model
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
}
}

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java
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@ -60,7 +60,7 @@ public class GeneralizedLinearModel {
//train a booster
int round = 4;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
float[][] predicts = booster.predict(testMat);

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java
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@ -51,7 +51,7 @@ public class PredictFirstNtree {
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
//predict use 1 tree
float[][] predicts1 = booster.predict(testMat, false, 1);

2
jvm-packages/xgboost4j-example/src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java
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@ -49,7 +49,7 @@ public class PredictLeafIndices {
//train a booster
int round = 3;
Booster booster = XGBoost.train(params, trainMat, round, watches, null, null);
Booster booster = XGBoost.train(trainMat, params, round, watches, null, null);
//predict using first 2 tree
float[][] leafindex = booster.predictLeaf(testMat, 2);

4
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala
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@ -43,7 +43,7 @@ class BasicWalkThrough {
val round = 2
// train a model
val booster = XGBoost.train(params.toMap, trainMax, round, watches.toMap)
val booster = XGBoost.train(trainMax, params.toMap, round, watches.toMap)
// predict
val predicts = booster.predict(testMax)
// save model to model path
@ -78,7 +78,7 @@ class BasicWalkThrough {
val watches2 = new mutable.HashMap[String, DMatrix]
watches2 += "train" -> trainMax2
watches2 += "test" -> testMax2
val booster3 = XGBoost.train(params.toMap, trainMax2, round, watches2.toMap, null, null)
val booster3 = XGBoost.train(trainMax2, params.toMap, round, watches2.toMap, null, null)
val predicts3 = booster3.predict(testMax2)
println(checkPredicts(predicts, predicts3))
}

4
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala
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@ -39,7 +39,7 @@ class BoostFromPrediction {
val round = 2
// train a model
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
val trainPred = booster.predict(trainMat, true)
val testPred = booster.predict(testMat, true)
@ -48,6 +48,6 @@ class BoostFromPrediction {
testMat.setBaseMargin(testPred)
System.out.println("result of running from initial prediction")
val booster2 = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
val booster2 = XGBoost.train(trainMat, params.toMap, 1, watches.toMap, null, null)
}
}

2
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala
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@ -41,6 +41,6 @@ class CrossValidation {
val metrics: Array[String] = null
val evalHist: Array[String] =
XGBoost.crossValidation(params.toMap, trainMat, round, nfold, metrics, null, null)
XGBoost.crossValidation(trainMat, params.toMap, round, nfold, metrics, null, null)
}
}

4
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala
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@ -150,8 +150,8 @@ class CustomObjective {
val round = 2
// train a model
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
XGBoost.train(params.toMap, trainMat, round, watches.toMap, new LogRegObj, new EvalError)
val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
XGBoost.train(trainMat, params.toMap, round, watches.toMap, new LogRegObj, new EvalError)
}
}

4
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala
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@ -45,7 +45,7 @@ class ExternalMemory {
val round = 2
// train a model
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
val trainPred = booster.predict(trainMat, true)
val testPred = booster.predict(testMat, true)
@ -54,6 +54,6 @@ class ExternalMemory {
testMat.setBaseMargin(testPred)
System.out.println("result of running from initial prediction")
val booster2 = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
val booster2 = XGBoost.train(trainMat, params.toMap, 1, watches.toMap, null, null)
}
}

2
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala
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@ -52,7 +52,7 @@ class GeneralizedLinearModel {
watches += "test" -> testMat
val round = 4
val booster = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
val booster = XGBoost.train(trainMat, params.toMap, 1, watches.toMap, null, null)
val predicts = booster.predict(testMat)
val eval = new CustomEval
println(s"error=${eval.eval(predicts, testMat)}")

2
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala
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@ -38,7 +38,7 @@ class PredictFirstNTree {
val round = 3
// train a model
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
// predict use 1 tree
val predicts1 = booster.predict(testMat, false, 1)

2
jvm-packages/xgboost4j-example/src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala
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@ -39,7 +39,7 @@ class PredictLeafIndices {
watches += "test" -> testMat
val round = 3
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
val booster = XGBoost.train(trainMat, params.toMap, round, watches.toMap)
// predict using first 2 tree
val leafIndex = booster.predictLeaf(testMat, 2)

2
jvm-packages/xgboost4j-flink/src/main/scala/ml/dmlc/xgboost4j/scala/flink/XGBoost.scala
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@ -56,7 +56,7 @@ object XGBoost {
val trainMat = new DMatrix(dataIter, null)
val watches = List("train" -> trainMat).toMap
val round = 2
val booster = XGBoostScala.train(paramMap, trainMat, round, watches, null, null)
val booster = XGBoostScala.train(trainMat, paramMap, round, watches, null, null)
Rabit.shutdown()
collector.collect(new XGBoostModel(booster))
}

7
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoost.scala
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@ -56,9 +56,10 @@ object XGBoost extends Serializable {
trainingSamples =>
rabitEnv.put("DMLC_TASK_ID", TaskContext.getPartitionId().toString)
Rabit.init(rabitEnv.asJava)
val dMatrix = new DMatrix(new JDMatrix(trainingSamples, null))
val booster = SXGBoost.train(xgBoostConfMap, dMatrix, round,
watches = new mutable.HashMap[String, DMatrix]{put("train", dMatrix)}.toMap, obj, eval)
val trainingSet = new DMatrix(new JDMatrix(trainingSamples, null))
val booster = SXGBoost.train(trainingSet, xgBoostConfMap, round,
watches = new mutable.HashMap[String, DMatrix]{put("train", trainingSet)}.toMap,
obj, eval)
Rabit.shutdown()
Iterator(booster)
}.cache()

18
jvm-packages/xgboost4j/src/main/java/ml/dmlc/xgboost4j/java/XGBoost.java
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@ -60,8 +60,8 @@ public class XGBoost {
/**
* Train a booster with given parameters.
*
* @param params Booster params.
* @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.
@ -70,11 +70,13 @@ 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(
DMatrix dtrain,
Map<String, Object> params,
int round,
Map<String, DMatrix> watches,
IObjective obj,
IEvaluation eval) throws XGBoostError {
//collect eval matrixs
String[] evalNames;
@ -139,8 +141,8 @@ public class XGBoost {
/**
* Cross-validation with given parameters.
*
* @param params Booster params.
* @param data Data to be trained.
* @param params Booster params.
* @param round Number of boosting iterations.
* @param nfold Number of folds in CV.
* @param metrics Evaluation metrics to be watched in CV.
@ -150,8 +152,8 @@ public class XGBoost {
* @throws XGBoostError native error
*/
public static String[] crossValidation(
Map<String, Object> params,
DMatrix data,
Map<String, Object> params,
int round,
int nfold,
String[] metrics,

19
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
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@ -28,8 +28,8 @@ object XGBoost {
/**
* Train a booster given parameters.
*
* @param params 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.
@ -39,8 +39,8 @@ object XGBoost {
*/
@throws(classOf[XGBoostError])
def train(
params: Map[String, Any],
dtrain: DMatrix,
params: Map[String, Any],
round: Int,
watches: Map[String, DMatrix] = Map[String, DMatrix](),
obj: ObjectiveTrait = null,
@ -49,10 +49,11 @@ object XGBoost {
val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
val xgboostInJava = JXGBoost.train(
dtrain.jDMatrix,
params.map{
case (key: String, value) => (key, value.toString)
}.toMap[String, AnyRef].asJava,
dtrain.jDMatrix, round, jWatches.asJava,
round, jWatches.asJava,
obj, eval)
new Booster(xgboostInJava)
}
@ -60,8 +61,8 @@ object XGBoost {
/**
* Cross-validation with given parameters.
*
* @param params Booster params.
* @param data Data to be trained.
* @param params Booster params.
* @param round Number of boosting iterations.
* @param nfold Number of folds in CV.
* @param metrics Evaluation metrics to be watched in CV.
@ -71,17 +72,17 @@ object XGBoost {
*/
@throws(classOf[XGBoostError])
def crossValidation(
params: Map[String, Any],
data: DMatrix,
params: Map[String, Any],
round: Int,
nfold: Int = 5,
metrics: Array[String] = null,
obj: ObjectiveTrait = null,
eval: EvalTrait = null): Array[String] = {
JXGBoost.crossValidation(params.map{
case (key: String, value) => (key, value.toString)
}.toMap[String, AnyRef].asJava,
data.jDMatrix, round, nfold, metrics, obj, eval)
JXGBoost.crossValidation(
data.jDMatrix, params.map{ case (key: String, value) => (key, value.toString)}.
toMap[String, AnyRef].asJava,
round, nfold, metrics, obj, eval)
}
/**

4
jvm-packages/xgboost4j/src/test/java/ml/dmlc/xgboost4j/java/BoosterImplTest.java
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@ -94,7 +94,7 @@ public class BoosterImplTest {
int round = 5;
//train a boost model
return XGBoost.train(paramMap, trainMat, round, watches, null, null);
return XGBoost.train(trainMat, paramMap, round, watches, null, null);
}
@Test
@ -177,6 +177,6 @@ public class BoosterImplTest {
//do 5-fold cross validation
int round = 2;
int nfold = 5;
String[] evalHist = XGBoost.crossValidation(param, trainMat, round, nfold, null, null, null);
String[] evalHist = XGBoost.crossValidation(trainMat, param, round, nfold, null, null, null);
}
}

4
jvm-packages/xgboost4j/src/test/scala/ml/dmlc/xgboost4j/scala/ScalaBoosterImplSuite.scala
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@ -74,7 +74,7 @@ class ScalaBoosterImplSuite extends FunSuite {
val watches = List("train" -> trainMat, "test" -> testMat).toMap
val round = 2
XGBoost.train(paramMap, trainMat, round, watches, null, null)
XGBoost.train(trainMat, paramMap, round, watches, null, null)
}
test("basic operation of booster") {
@ -126,6 +126,6 @@ class ScalaBoosterImplSuite extends FunSuite {
"objective" -> "binary:logistic", "gamma" -> "1.0", "eval_metric" -> "error").toMap
val round = 2
val nfold = 5
XGBoost.crossValidation(params, trainMat, round, nfold, null, null, null)
XGBoost.crossValidation(trainMat, params, round, nfold, null, null, null)
}
}