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[JVM] Refactor, add filesys API

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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
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131
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/Booster.scala
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@@ -18,20 +18,23 @@ package ml.dmlc.xgboost4j.scala
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
trait Booster extends Serializable {
class Booster private[xgboost4j](booster: JBooster) extends Serializable {
/**
* set parameter
*
* @param key param name
* @param value param value
*/
* Set parameter to the Booster.
*
* @param key param name
* @param value param value
*/
@throws(classOf[XGBoostError])
def setParam(key: String, value: String)
def setParam(key: String, value: AnyRef): Unit = {
booster.setParam(key, value)
}
/**
* set parameters
@@ -39,7 +42,9 @@ trait Booster extends Serializable {
* @param params parameters key-value map
*/
@throws(classOf[XGBoostError])
def setParams(params: Map[String, AnyRef])
def setParams(params: Map[String, AnyRef]): Unit = {
booster.setParams(params.asJava)
}
/**
* Update (one iteration)
@@ -48,7 +53,9 @@ trait Booster extends Serializable {
* @param iter current iteration number
*/
@throws(classOf[XGBoostError])
def update(dtrain: DMatrix, iter: Int)
def update(dtrain: DMatrix, iter: Int): Unit = {
booster.update(dtrain.jDMatrix, iter)
}
/**
* update with customize obj func
@@ -57,7 +64,9 @@ trait Booster extends Serializable {
* @param obj customized objective class
*/
@throws(classOf[XGBoostError])
def update(dtrain: DMatrix, obj: ObjectiveTrait)
def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit = {
booster.update(dtrain.jDMatrix, obj)
}
/**
* update with give grad and hess
@@ -67,7 +76,9 @@ trait Booster extends Serializable {
* @param hess seconde order of gradient
*/
@throws(classOf[XGBoostError])
def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float])
def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float]): Unit = {
booster.boost(dtrain.jDMatrix, grad, hess)
}
/**
* evaluate with given dmatrixs.
@@ -78,7 +89,10 @@ trait Booster extends Serializable {
* @return eval information
*/
@throws(classOf[XGBoostError])
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int): String
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int)
: String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
}
/**
* evaluate with given customized Evaluation class
@@ -89,26 +103,11 @@ trait Booster extends Serializable {
* @return eval information
*/
@throws(classOf[XGBoostError])
def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait): String
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
* @return predict result
*/
@throws(classOf[XGBoostError])
def predict(data: DMatrix): Array[Array[Float]]
/**
* Predict with data
*
* @param data dmatrix storing the input
* @param outPutMargin Whether to output the raw untransformed margin value.
* @return predict result
*/
@throws(classOf[XGBoostError])
def predict(data: DMatrix, outPutMargin: Boolean): Array[Array[Float]]
/**
* Predict with data
@@ -119,22 +118,24 @@ trait Booster extends Serializable {
* @return predict result
*/
@throws(classOf[XGBoostError])
def predict(data: DMatrix, outPutMargin: Boolean, treeLimit: Int): Array[Array[Float]]
def predict(data: DMatrix, outPutMargin: Boolean = false, treeLimit: Int = 0)
: Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin, treeLimit)
}
/**
* Predict with data
* 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).
* @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
*/
@throws(classOf[XGBoostError])
def predict(data: DMatrix, treeLimit: Int, predLeaf: Boolean): Array[Array[Float]]
def predictLeaf(data: DMatrix, treeLimit: Int = 0)
: Array[Array[Float]] = {
booster.predictLeaf(data.jDMatrix, treeLimit)
}
/**
* save model to modelPath
@@ -142,46 +143,50 @@ trait Booster extends Serializable {
* @param modelPath model path
*/
@throws(classOf[XGBoostError])
def saveModel(modelPath: String)
def saveModel(modelPath: String): Unit = {
booster.saveModel(modelPath)
}
/**
* 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(classOf[IOException])
* save model to Output stream
*
* @param out Output stream
*/
@throws(classOf[XGBoostError])
def dumpModel(modelPath: String, withStats: Boolean)
def saveModel(out: java.io.OutputStream): Unit = {
booster.saveModel(out)
}
/**
* Dump model into a text file.
* Dump model as Array of string
*
* @param modelPath file to save dumped model info
* @param featureMap featureMap file
* @param withStats bool
* Controls whether the split statistics are output.
*/
@throws(classOf[IOException])
@throws(classOf[XGBoostError])
def dumpModel(modelPath: String, featureMap: String, withStats: Boolean)
def getModelDump(featureMap: String = null, withStats: Boolean = false)
: Array[String] = {
booster.getModelDump(featureMap, withStats)
}
/**
* get importance of each feature
* Get importance of each feature
*
* @return featureMap key: feature index, value: feature importance score
*/
@throws(classOf[XGBoostError])
def getFeatureScore: mutable.Map[String, Integer]
def getFeatureScore(featureMap: String = null): mutable.Map[String, Integer] = {
booster.getFeatureScore(featureMap).asScala
}
/**
* get importance of each feature
*
* @param featureMap file to save dumped model info
* @return featureMap key: feature index, value: feature importance score
*/
@throws(classOf[XGBoostError])
def getFeatureScore(featureMap: String): mutable.Map[String, Integer]
* Dispose the booster when it is no longer needed
*/
def dispose: Unit = {
booster.dispose()
}
def dispose
override def finalize(): Unit = {
super.finalize()
dispose
}
}

99
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/ScalaBoosterImpl.scala
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@@ -1,99 +0,0 @@
/*
Copyright (c) 2014 by Contributors
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
package ml.dmlc.xgboost4j.scala
import ml.dmlc.xgboost4j.java
import scala.collection.JavaConverters._
import scala.collection.mutable
private[scala] class ScalaBoosterImpl private[xgboost4j](booster: java.Booster) extends Booster {
override def setParam(key: String, value: String): Unit = {
booster.setParam(key, value)
}
override def update(dtrain: DMatrix, iter: Int): Unit = {
booster.update(dtrain.jDMatrix, iter)
}
override def update(dtrain: DMatrix, obj: ObjectiveTrait): Unit = {
booster.update(dtrain.jDMatrix, obj)
}
override def dumpModel(modelPath: String, withStats: Boolean): Unit = {
booster.dumpModel(modelPath, withStats)
}
override def dumpModel(modelPath: String, featureMap: String, withStats: Boolean): Unit = {
booster.dumpModel(modelPath, featureMap, withStats)
}
override def setParams(params: Map[String, AnyRef]): Unit = {
booster.setParams(params.asJava)
}
override def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], iter: Int): String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, iter)
}
override def evalSet(evalMatrixs: Array[DMatrix], evalNames: Array[String], eval: EvalTrait):
String = {
booster.evalSet(evalMatrixs.map(_.jDMatrix), evalNames, eval)
}
override def dispose: Unit = {
booster.dispose()
}
override def predict(data: DMatrix): Array[Array[Float]] = {
booster.predict(data.jDMatrix)
}
override def predict(data: DMatrix, outPutMargin: Boolean): Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin)
}
override def predict(data: DMatrix, outPutMargin: Boolean, treeLimit: Int):
Array[Array[Float]] = {
booster.predict(data.jDMatrix, outPutMargin, treeLimit)
}
override def predict(data: DMatrix, treeLimit: Int, predLeaf: Boolean): Array[Array[Float]] = {
booster.predict(data.jDMatrix, treeLimit, predLeaf)
}
override def boost(dtrain: DMatrix, grad: Array[Float], hess: Array[Float]): Unit = {
booster.boost(dtrain.jDMatrix, grad, hess)
}
override def getFeatureScore: mutable.Map[String, Integer] = {
booster.getFeatureScore.asScala
}
override def getFeatureScore(featureMap: String): mutable.Map[String, Integer] = {
booster.getFeatureScore(featureMap).asScala
}
override def saveModel(modelPath: String): Unit = {
booster.saveModel(modelPath)
}
override def finalize(): Unit = {
super.finalize()
dispose
}
}

63
jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala
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@@ -16,11 +16,28 @@
package ml.dmlc.xgboost4j.scala
import ml.dmlc.xgboost4j.java.{XGBoost => JXGBoost}
import java.io.InputStream
import ml.dmlc.xgboost4j.java.{XGBoost => JXGBoost, XGBoostError}
import scala.collection.JavaConverters._
/**
* 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,
@@ -31,9 +48,22 @@ object XGBoost {
val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
val xgboostInJava = JXGBoost.train(params.asJava, dtrain.jDMatrix, round, jWatches.asJava,
obj, eval)
new ScalaBoosterImpl(xgboostInJava)
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,
@@ -45,13 +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))
new ScalaBoosterImpl(xgboostInJava)
/**
* 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)
new ScalaBoosterImpl(xgboostInJava)
/**
* 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)
}
}