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Deprecate reg:linear' in favor of reg:squarederror'. (#4267)

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* Deprecate `reg:linear' in favor of `reg:squarederror'.
* Replace the use of `reg:linear'.
* Replace the use of `silent`.
This commit is contained in:
Jiaming Yuan
2019-03-17 17:55:04 +08:00
committed by GitHub
parent cf8d5b9b76
commit 29a1356669
34 changed files with 210 additions and 193 deletions
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8
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/LearningTaskParams.scala
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@@ -24,8 +24,8 @@ private[spark] trait LearningTaskParams extends Params {
/**
* Specify the learning task and the corresponding learning objective.
* options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
* multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear
* options: reg:squarederror, reg:logistic, binary:logistic, binary:logitraw, count:poisson,
* multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:squarederror
*/
final val objective = new Param[String](this, "objective", "objective function used for " +
s"training, options: {${LearningTaskParams.supportedObjective.mkString(",")}",
@@ -94,12 +94,12 @@ private[spark] trait LearningTaskParams extends Params {
final def getMaximizeEvaluationMetrics: Boolean = $(maximizeEvaluationMetrics)
setDefault(objective -> "reg:linear", baseScore -> 0.5,
setDefault(objective -> "reg:squarederror", baseScore -> 0.5,
trainTestRatio -> 1.0, numEarlyStoppingRounds -> 0)
}
private[spark] object LearningTaskParams {
val supportedObjective = HashSet("reg:linear", "reg:logistic", "binary:logistic",
val supportedObjective = HashSet("reg:squarederror", "reg:logistic", "binary:logistic",
"binary:logitraw", "count:poisson", "multi:softmax", "multi:softprob", "rank:pairwise",
"rank:ndcg", "rank:map", "reg:gamma", "reg:tweedie")

2
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/PersistenceSuite.scala
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@@ -96,7 +96,7 @@ class PersistenceSuite extends FunSuite with PerTest with BeforeAndAfterAll {
val testDM = new DMatrix(Regression.test.iterator)
val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> "10", "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> "10", "num_workers" -> numWorkers)
val xgbr = new XGBoostRegressor(paramMap)
val xgbrPath = new File(tempDir, "xgbr").getPath
xgbr.write.overwrite().save(xgbrPath)

18
jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressorSuite.scala
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@@ -36,7 +36,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
"eta" -> "1",
"max_depth" -> "6",
"silent" -> "1",
"objective" -> "reg:linear")
"objective" -> "reg:squarederror")
val model1 = ScalaXGBoost.train(trainingDM, paramMap, round)
val prediction1 = model1.predict(testDM)
@@ -69,7 +69,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
"eta" -> "1",
"max_depth" -> "6",
"silent" -> "1",
"objective" -> "reg:linear",
"objective" -> "reg:squarederror",
"num_round" -> round,
"num_workers" -> numWorkers)
@@ -80,7 +80,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
.setEta(1)
.setMaxDepth(6)
.setSilent(1)
.setObjective("reg:linear")
.setObjective("reg:squarederror")
.setNumRound(round)
.setNumWorkers(numWorkers)
.fit(trainingDF)
@@ -108,7 +108,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("use weight") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val getWeightFromId = udf({id: Int => if (id == 0) 1.0f else 0.001f}, DataTypes.FloatType)
val trainingDF = buildDataFrame(Regression.train)
@@ -123,7 +123,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("test predictionLeaf") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val training = buildDataFrame(Regression.train)
val testDF = buildDataFrame(Regression.test)
val groundTruth = testDF.count()
@@ -137,7 +137,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("test predictionLeaf with empty column name") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val training = buildDataFrame(Regression.train)
val testDF = buildDataFrame(Regression.test)
val xgb = new XGBoostRegressor(paramMap)
@@ -149,7 +149,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("test predictionContrib") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val training = buildDataFrame(Regression.train)
val testDF = buildDataFrame(Regression.test)
val groundTruth = testDF.count()
@@ -163,7 +163,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("test predictionContrib with empty column name") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val training = buildDataFrame(Regression.train)
val testDF = buildDataFrame(Regression.test)
val xgb = new XGBoostRegressor(paramMap)
@@ -175,7 +175,7 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
test("test predictionLeaf and predictionContrib") {
val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
"objective" -> "reg:linear", "num_round" -> 5, "num_workers" -> numWorkers)
"objective" -> "reg:squarederror", "num_round" -> 5, "num_workers" -> numWorkers)
val training = buildDataFrame(Regression.train)
val testDF = buildDataFrame(Regression.test)
val groundTruth = testDF.count()