add scala examples
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CodingCat
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package ml.dmlc.xgboost4j.scala.example
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import java.io.File
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import java.util
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import scala.collection.mutable
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import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
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import ml.dmlc.xgboost4j.java.example.util.DataLoader
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
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class BasicWalkThrough {
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def main(args: Array[String]): Unit = {
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import BasicWalkThrough._
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val trainMax = new DMatrix("../../demo/data/agaricus.txt.train")
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val testMax = new DMatrix("../../demo/data/agaricus.txt.test")
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val params = new mutable.HashMap[String, Any]()
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params += "eta" -> 1.0
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params += "max_depth" -> 2
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params += "silent" -> 1
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params += "objective" -> "binary:logistic"
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val watches = new mutable.HashMap[String, DMatrix]
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watches += "train" -> trainMax
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watches += "test" -> testMax
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val round = 2
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// train a model
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val booster = XGBoost.train(params.toMap, trainMax, round, watches.toMap)
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// predict
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val predicts = booster.predict(testMax)
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// save model to model path
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val file = new File("./model")
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if (!file.exists()) {
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file.mkdirs()
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}
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booster.saveModel(file.getAbsolutePath + "/xgb.model")
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// dump model
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booster.getModelDump(file.getAbsolutePath + "/dump.raw.txt", false)
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// dump model with feature map
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booster.getModelDump(file.getAbsolutePath + "/featmap.txt", false)
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// save dmatrix into binary buffer
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testMax.saveBinary(file.getAbsolutePath + "/dtest.buffer")
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// reload model and data
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val booster2 = XGBoost.loadModel(file.getAbsolutePath + "/xgb.model")
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val testMax2 = new DMatrix(file.getAbsolutePath + "/dtest.buffer")
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val predicts2 = booster2.predict(testMax2)
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// check predicts
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println(checkPredicts(predicts, predicts2))
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// build dmatrix from CSR Sparse Matrix
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println("start build dmatrix from csr sparse data ...")
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val spData = DataLoader.loadSVMFile("../../demo/data/agaricus.txt.train")
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val trainMax2 = new DMatrix(spData.rowHeaders, spData.colIndex, spData.data,
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JDMatrix.SparseType.CSR)
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trainMax2.setLabel(spData.labels)
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// specify watchList
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val watches2 = new mutable.HashMap[String, DMatrix]
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watches2 += "train" -> trainMax2
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watches2 += "test" -> testMax2
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val booster3 = XGBoost.train(params.toMap, trainMax2, round, watches2.toMap, null, null)
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val predicts3 = booster3.predict(testMax2)
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println(checkPredicts(predicts, predicts3))
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}
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}
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object BasicWalkThrough {
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def checkPredicts(fPredicts: Array[Array[Float]], sPredicts: Array[Array[Float]]): Boolean = {
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require(fPredicts.length == sPredicts.length, "the comparing predicts must be with the same " +
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"length")
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for (i <- fPredicts.indices) {
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if (!java.util.Arrays.equals(fPredicts(i), sPredicts(i))) {
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return false
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}
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}
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true
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}
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}
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package ml.dmlc.xgboost4j.scala.example
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import scala.collection.mutable
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
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class BoostFromPrediction {
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def main(args: Array[String]): Unit = {
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println("start running example to start from a initial prediction")
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val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
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val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
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val params = new mutable.HashMap[String, Any]()
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params += "eta" -> 1.0
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params += "max_depth" -> 2
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params += "silent" -> 1
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params += "objective" -> "binary:logistic"
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val watches = new mutable.HashMap[String, DMatrix]
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watches += "train" -> trainMat
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watches += "test" -> testMat
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val round = 2
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// train a model
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val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
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val trainPred = booster.predict(trainMat, true)
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val testPred = booster.predict(testMat, true)
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trainMat.setBaseMargin(trainPred)
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testMat.setBaseMargin(testPred)
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System.out.println("result of running from initial prediction")
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val booster2 = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
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}
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}
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package ml.dmlc.xgboost4j.scala.example
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import scala.collection.mutable
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
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class CrossValidation {
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def main(args: Array[String]): Unit = {
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val trainMat: DMatrix = new DMatrix("../../demo/data/agaricus.txt.train")
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// set params
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val params = new mutable.HashMap[String, Any]
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params.put("eta", 1.0)
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params.put("max_depth", 3)
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params.put("silent", 1)
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params.put("nthread", 6)
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params.put("objective", "binary:logistic")
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params.put("gamma", 1.0)
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params.put("eval_metric", "error")
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// do 5-fold cross validation
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val round: Int = 2
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val nfold: Int = 5
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// set additional eval_metrics
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val metrics: Array[String] = null
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val evalHist: Array[String] =
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XGBoost.crossValidation(params.toMap, trainMat, round, nfold, metrics, null, null)
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}
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}
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package ml.dmlc.xgboost4j.scala.example
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import scala.collection.mutable
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import scala.collection.mutable.ListBuffer
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import ml.dmlc.xgboost4j.java.XGBoostError
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix, EvalTrait, ObjectiveTrait}
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import org.apache.commons.logging.{LogFactory, Log}
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/**
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* an example user define objective and eval
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* NOTE: when you do customized loss function, the default prediction value is margin
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* this may make buildin evalution metric not function properly
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* for example, we are doing logistic loss, the prediction is score before logistic transformation
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* he buildin evaluation error assumes input is after logistic transformation
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* Take this in mind when you use the customization, and maybe you need write customized evaluation
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* function
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*
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*/
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class CustomObjective {
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/**
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* loglikelihoode loss obj function
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*/
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class LogRegObj extends ObjectiveTrait {
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private val logger: Log = LogFactory.getLog(classOf[LogRegObj])
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/**
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* user define objective function, return gradient and second order gradient
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*
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* @param predicts untransformed margin predicts
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* @param dtrain training data
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* @return List with two float array, correspond to first order grad and second order grad
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*/
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override def getGradient(predicts: Array[Array[Float]], dtrain: DMatrix)
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: List[Array[Float]] = {
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val nrow = predicts.length
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val gradients = new ListBuffer[Array[Float]]
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var labels: Array[Float] = null
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try {
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labels = dtrain.getLabel
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} catch {
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case e: XGBoostError =>
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logger.error(e)
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null
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case _ =>
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null
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}
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val grad = new Array[Float](nrow)
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val hess = new Array[Float](nrow)
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val transPredicts = transform(predicts)
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for (i <- 0 until nrow) {
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val predict = transPredicts(i)(0)
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grad(i) = predict - labels(i)
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hess(i) = predict * (1 - predict)
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}
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gradients += grad
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gradients += hess
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gradients.toList
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}
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/**
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* simple sigmoid func
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*
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* @param input
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* @return Note: this func is not concern about numerical stability, only used as example
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*/
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def sigmoid(input: Float): Float = {
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(1 / (1 + Math.exp(-input))).toFloat
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}
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def transform(predicts: Array[Array[Float]]): Array[Array[Float]] = {
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val nrow = predicts.length
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val transPredicts = Array.fill[Float](nrow, 1)(0)
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for (i <- 0 until nrow) {
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transPredicts(i)(0) = sigmoid(predicts(i)(0))
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}
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transPredicts
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}
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}
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class EvalError extends EvalTrait {
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val logger = LogFactory.getLog(classOf[EvalError])
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private[xgboost4j] var evalMetric: String = "custom_error"
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/**
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* get evaluate metric
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*
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* @return evalMetric
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*/
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override def getMetric: String = evalMetric
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/**
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* evaluate with predicts and data
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*
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* @param predicts predictions as array
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* @param dmat data matrix to evaluate
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* @return result of the metric
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*/
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override def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float = {
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var error: Float = 0f
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var labels: Array[Float] = null
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try {
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labels = dmat.getLabel
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} catch {
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case ex: XGBoostError =>
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logger.error(ex)
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return -1f
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}
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val nrow: Int = predicts.length
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for (i <- 0 until nrow) {
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if (labels(i) == 0.0 && predicts(i)(0) > 0) {
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error += 1
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} else if (labels(i) == 1.0 && predicts(i)(0) <= 0) {
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error += 1
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}
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}
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error / labels.length
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}
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}
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def main(args: Array[String]): Unit = {
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val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
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val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
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val params = new mutable.HashMap[String, Any]()
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params += "eta" -> 1.0
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params += "max_depth" -> 2
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params += "silent" -> 1
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val watches = new mutable.HashMap[String, DMatrix]
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watches += "train" -> trainMat
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watches += "test" -> testMat
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val round = 2
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// train a model
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val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
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XGBoost.train(params.toMap, trainMat, round, watches.toMap, new LogRegObj, new EvalError)
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}
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}
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package ml.dmlc.xgboost4j.scala.example
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import scala.collection.mutable
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
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class ExternalMemory {
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def main(args: Array[String]): Unit = {
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// this is the only difference, add a # followed by a cache prefix name
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// several cache file with the prefix will be generated
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// currently only support convert from libsvm file
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val trainMat = new DMatrix("../../demo/data/agaricus.txt.train#dtrain.cache")
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val testMat = new DMatrix("../../demo/data/agaricus.txt.test#dtest.cache")
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val params = new mutable.HashMap[String, Any]()
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params += "eta" -> 1.0
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params += "max_depth" -> 2
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params += "silent" -> 1
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params += "objective" -> "binary:logistic"
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// performance notice: set nthread to be the number of your real cpu
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// some cpu offer two threads per core, for example, a 4 core cpu with 8 threads, in such case
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// set nthread=4
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// param.put("nthread", num_real_cpu);
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val watches = new mutable.HashMap[String, DMatrix]
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watches += "train" -> trainMat
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watches += "test" -> testMat
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val round = 2
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// train a model
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val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
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val trainPred = booster.predict(trainMat, true)
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val testPred = booster.predict(testMat, true)
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trainMat.setBaseMargin(trainPred)
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testMat.setBaseMargin(testPred)
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System.out.println("result of running from initial prediction")
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val booster2 = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
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}
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}
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/*
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Copyright (c) 2014 by Contributors
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
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||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
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limitations under the License.
|
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*/
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package ml.dmlc.xgboost4j.scala.example
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import scala.collection.mutable
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import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
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import ml.dmlc.xgboost4j.scala.example.util.CustomEval
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/**
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* this is an example of fit generalized linear model in xgboost
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* basically, we are using linear model, instead of tree for our boosters
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*/
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class GeneralizedLinearModel {
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def main(args: Array[String]): Unit = {
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val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
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val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
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// specify parameters
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// change booster to gblinear, so that we are fitting a linear model
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// alpha is the L1 regularizer
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// lambda is the L2 regularizer
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// you can also set lambda_bias which is L2 regularizer on the bias term
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val params = new mutable.HashMap[String, Any]()
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params += "alpha" -> 0.0001
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params += "boosterh" -> "gblinear"
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params += "silent" -> 1
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params += "objective" -> "binary:logistic"
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// normally, you do not need to set eta (step_size)
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// XGBoost uses a parallel coordinate descent algorithm (shotgun),
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// there could be affection on convergence with parallelization on certain cases
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// setting eta to be smaller value, e.g 0.5 can make the optimization more stable
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// param.put("eta", "0.5");
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val watches = new mutable.HashMap[String, DMatrix]
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watches += "train" -> trainMat
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watches += "test" -> testMat
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val round = 4
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val booster = XGBoost.train(params.toMap, trainMat, 1, watches.toMap, null, null)
|
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val predicts = booster.predict(testMat)
|
||||
val eval = new CustomEval
|
||||
println(s"error=${eval.eval(predicts, testMat)}")
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,53 @@
|
||||
/*
|
||||
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.example
|
||||
|
||||
import scala.collection.mutable
|
||||
|
||||
import ml.dmlc.xgboost4j.scala.example.util.CustomEval
|
||||
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
|
||||
|
||||
class PredictFirstNTree {
|
||||
|
||||
def main(args: Array[String]): Unit = {
|
||||
val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
|
||||
val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
|
||||
|
||||
val params = new mutable.HashMap[String, Any]()
|
||||
params += "eta" -> 1.0
|
||||
params += "max_depth" -> 2
|
||||
params += "silent" -> 1
|
||||
params += "objective" -> "binary:logistic"
|
||||
|
||||
val watches = new mutable.HashMap[String, DMatrix]
|
||||
watches += "train" -> trainMat
|
||||
watches += "test" -> testMat
|
||||
|
||||
val round = 3
|
||||
// train a model
|
||||
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
|
||||
|
||||
// predict use 1 tree
|
||||
val predicts1 = booster.predict(testMat, false, 1)
|
||||
// by default all trees are used to do predict
|
||||
val predicts2 = booster.predict(testMat)
|
||||
|
||||
val eval = new CustomEval
|
||||
println("error of predicts1: " + eval.eval(predicts1, testMat))
|
||||
println("error of predicts2: " + eval.eval(predicts2, testMat))
|
||||
}
|
||||
|
||||
}
|
||||
@ -0,0 +1,56 @@
|
||||
/*
|
||||
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.example
|
||||
|
||||
import java.util
|
||||
|
||||
import scala.collection.mutable
|
||||
|
||||
import ml.dmlc.xgboost4j.scala.{XGBoost, DMatrix}
|
||||
|
||||
class PredictLeafIndices {
|
||||
|
||||
def main(args: Array[String]): Unit = {
|
||||
val trainMat = new DMatrix("../../demo/data/agaricus.txt.train")
|
||||
val testMat = new DMatrix("../../demo/data/agaricus.txt.test")
|
||||
|
||||
val params = new mutable.HashMap[String, Any]()
|
||||
params += "eta" -> 1.0
|
||||
params += "max_depth" -> 2
|
||||
params += "silent" -> 1
|
||||
params += "objective" -> "binary:logistic"
|
||||
|
||||
val watches = new mutable.HashMap[String, DMatrix]
|
||||
watches += "train" -> trainMat
|
||||
watches += "test" -> testMat
|
||||
|
||||
val round = 3
|
||||
val booster = XGBoost.train(params.toMap, trainMat, round, watches.toMap)
|
||||
|
||||
// predict using first 2 tree
|
||||
val leafIndex = booster.predictLeaf(testMat, 2)
|
||||
for (leafs <- leafIndex) {
|
||||
println(java.util.Arrays.toString(leafs))
|
||||
}
|
||||
|
||||
// predict all trees
|
||||
val leafIndex2 = booster.predictLeaf(testMat, 0)
|
||||
for (leafs <- leafIndex) {
|
||||
println(java.util.Arrays.toString(leafs))
|
||||
}
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,60 @@
|
||||
/*
|
||||
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.example.util
|
||||
|
||||
import ml.dmlc.xgboost4j.java.XGBoostError
|
||||
import ml.dmlc.xgboost4j.scala.{DMatrix, EvalTrait}
|
||||
import org.apache.commons.logging.{Log, LogFactory}
|
||||
|
||||
class CustomEval extends EvalTrait {
|
||||
private val logger: Log = LogFactory.getLog(classOf[CustomEval])
|
||||
/**
|
||||
* get evaluate metric
|
||||
*
|
||||
* @return evalMetric
|
||||
*/
|
||||
override def getMetric: String = {
|
||||
"custom_error"
|
||||
}
|
||||
|
||||
/**
|
||||
* evaluate with predicts and data
|
||||
*
|
||||
* @param predicts predictions as array
|
||||
* @param dmat data matrix to evaluate
|
||||
* @return result of the metric
|
||||
*/
|
||||
override def eval(predicts: Array[Array[Float]], dmat: DMatrix): Float = {
|
||||
var error: Float = 0f
|
||||
var labels: Array[Float] = null
|
||||
try {
|
||||
labels = dmat.getLabel
|
||||
} catch {
|
||||
case ex: XGBoostError =>
|
||||
logger.error(ex)
|
||||
return -1f
|
||||
}
|
||||
val nrow: Int = predicts.length
|
||||
for (i <- 0 until nrow) {
|
||||
if (labels(i) == 0.0 && predicts(i)(0) > 0.5) {
|
||||
error += 1
|
||||
} else if (labels(i) == 1.0 && predicts(i)(0) <= 0.5) {
|
||||
error += 1
|
||||
}
|
||||
}
|
||||
error / labels.length
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user