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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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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)
}
}