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[jvm-packages] Spark pipeline persistence (#1906)
[jvm-packages] Spark pipeline persistence
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86 changes: 86 additions & 0 deletions
86
...park/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/DefaultXGBoostParamsWriter.scala
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| /* | ||
| 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. | ||
| */ | ||
|
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| package ml.dmlc.xgboost4j.scala.spark.params | ||
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| import org.apache.hadoop.fs.Path | ||
| import org.apache.spark.SparkContext | ||
| import org.apache.spark.ml.param.{ParamPair, Params} | ||
| import org.json4s.JsonDSL._ | ||
| import org.json4s.jackson.JsonMethods._ | ||
| import org.json4s.{JObject, _} | ||
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| // This originates from apache-spark DefaultPramsWriter copy paste | ||
| private[spark] object DefaultXGBoostParamsWriter { | ||
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| /** | ||
| * Saves metadata + Params to: path + "/metadata" | ||
| * - class | ||
| * - timestamp | ||
| * - sparkVersion | ||
| * - uid | ||
| * - paramMap | ||
| * - (optionally, extra metadata) | ||
| * | ||
| * @param extraMetadata Extra metadata to be saved at same level as uid, paramMap, etc. | ||
| * @param paramMap If given, this is saved in the "paramMap" field. | ||
| * Otherwise, all [[org.apache.spark.ml.param.Param]]s are encoded using | ||
| * [[org.apache.spark.ml.param.Param.jsonEncode()]]. | ||
| */ | ||
| def saveMetadata( | ||
| instance: Params, | ||
| path: String, | ||
| sc: SparkContext, | ||
| extraMetadata: Option[JObject] = None, | ||
| paramMap: Option[JValue] = None): Unit = { | ||
| val metadataPath = new Path(path, "metadata").toString | ||
| val metadataJson = getMetadataToSave(instance, sc, extraMetadata, paramMap) | ||
| sc.parallelize(Seq(metadataJson), 1).saveAsTextFile(metadataPath) | ||
| } | ||
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| /** | ||
| * Helper for [[saveMetadata()]] which extracts the JSON to save. | ||
| * This is useful for ensemble models which need to save metadata for many sub-models. | ||
| * | ||
| * @see [[saveMetadata()]] for details on what this includes. | ||
| */ | ||
| def getMetadataToSave( | ||
| instance: Params, | ||
| sc: SparkContext, | ||
| extraMetadata: Option[JObject] = None, | ||
| paramMap: Option[JValue] = None): String = { | ||
| val uid = instance.uid | ||
| val cls = instance.getClass.getName | ||
| val params = instance.extractParamMap().toSeq.asInstanceOf[Seq[ParamPair[Any]]] | ||
| val jsonParams = paramMap.getOrElse(render(params.map { | ||
| case ParamPair(p, v) => | ||
| p.name -> parse(p.jsonEncode(v)) | ||
| }.toList)) | ||
| val basicMetadata = ("class" -> cls) ~ | ||
| ("timestamp" -> System.currentTimeMillis()) ~ | ||
| ("sparkVersion" -> sc.version) ~ | ||
| ("uid" -> uid) ~ | ||
| ("paramMap" -> jsonParams) | ||
| val metadata = extraMetadata match { | ||
| case Some(jObject) => | ||
| basicMetadata ~ jObject | ||
| case None => | ||
| basicMetadata | ||
| } | ||
| val metadataJson: String = compact(render(metadata)) | ||
| metadataJson | ||
| } | ||
| } |
33 changes: 33 additions & 0 deletions
33
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/Utils.scala
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,33 @@ | ||
| /* | ||
| 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. | ||
| */ | ||
|
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| package ml.dmlc.xgboost4j.scala.spark.params | ||
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| // based on org.apache.spark.util copy /paste | ||
| private[spark] object Utils { | ||
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| def getSparkClassLoader: ClassLoader = getClass.getClassLoader | ||
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| def getContextOrSparkClassLoader: ClassLoader = | ||
| Option(Thread.currentThread().getContextClassLoader).getOrElse(getSparkClassLoader) | ||
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| // scalastyle:off classforname | ||
| /** Preferred alternative to Class.forName(className) */ | ||
| def classForName(className: String): Class[_] = { | ||
| Class.forName(className, true, getContextOrSparkClassLoader) | ||
| // scalastyle:on classforname | ||
| } | ||
| } |
101 changes: 101 additions & 0 deletions
101
...-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostSparkPipelinePersistence.scala
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,101 @@ | ||
| /* | ||
| 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. | ||
| */ | ||
|
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| package ml.dmlc.xgboost4j.scala.spark | ||
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| import org.apache.spark.SparkConf | ||
| import org.apache.spark.ml.feature._ | ||
| import org.apache.spark.ml.{Pipeline, PipelineModel} | ||
| import org.apache.spark.sql.SparkSession | ||
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| import scala.concurrent.duration._ | ||
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| case class Foobar(TARGET: Int, bar: Double, baz: Double) | ||
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| class XGBoostSparkPipelinePersistence extends SharedSparkContext with Utils { | ||
| test("test sparks pipeline persistence of dataframe-based model") { | ||
| // maybe move to shared context, but requires session to import implicits. | ||
| // what about introducing https://github.com/holdenk/spark-testing-base ? | ||
| val conf: SparkConf = new SparkConf() | ||
| .setAppName("foo") | ||
| .setMaster("local[*]") | ||
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| val spark: SparkSession = SparkSession | ||
| .builder() | ||
| .config(conf) | ||
| .getOrCreate() | ||
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| import spark.implicits._ | ||
| // maybe move to shared context, but requires session to import implicits | ||
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| val df = Seq(Foobar(0, 0.5, 1), Foobar(1, 0.01, 0.8), | ||
| Foobar(0, 0.8, 0.5), Foobar(1, 8.4, 0.04)) | ||
| .toDS | ||
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| val vectorAssembler = new VectorAssembler() | ||
| .setInputCols(df.columns | ||
| .filter(!_.contains("TARGET"))) | ||
| .setOutputCol("features") | ||
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| val xgbEstimator = new XGBoostEstimator(Map("num_rounds" -> 10, | ||
| "tracker_conf" -> TrackerConf(1 minute, "scala") | ||
| )) | ||
| .setFeaturesCol("features") | ||
| .setLabelCol("TARGET") | ||
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| // separate | ||
| val predModel = xgbEstimator.fit(vectorAssembler.transform(df)) | ||
| predModel.write.overwrite.save("test2xgbPipe") | ||
| val same2Model = XGBoostModel.load("test2xgbPipe") | ||
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| assert(java.util.Arrays.equals(predModel.booster.toByteArray, same2Model.booster.toByteArray)) | ||
| val predParamMap = predModel.extractParamMap() | ||
| val same2ParamMap = same2Model.extractParamMap() | ||
| assert(predParamMap.get(predModel.useExternalMemory) | ||
| === same2ParamMap.get(same2Model.useExternalMemory)) | ||
| assert(predParamMap.get(predModel.featuresCol) === same2ParamMap.get(same2Model.featuresCol)) | ||
| assert(predParamMap.get(predModel.predictionCol) | ||
| === same2ParamMap.get(same2Model.predictionCol)) | ||
| assert(predParamMap.get(predModel.labelCol) === same2ParamMap.get(same2Model.labelCol)) | ||
| assert(predParamMap.get(predModel.labelCol) === same2ParamMap.get(same2Model.labelCol)) | ||
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| // chained | ||
| val predictionModel = new Pipeline().setStages(Array(vectorAssembler, xgbEstimator)).fit(df) | ||
| predictionModel.write.overwrite.save("testxgbPipe") | ||
| val sameModel = PipelineModel.load("testxgbPipe") | ||
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| val predictionModelXGB = predictionModel.stages.collect { case xgb: XGBoostModel => xgb } head | ||
| val sameModelXGB = sameModel.stages.collect { case xgb: XGBoostModel => xgb } head | ||
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| assert(java.util.Arrays.equals( | ||
| predictionModelXGB.booster.toByteArray, | ||
| sameModelXGB.booster.toByteArray | ||
| )) | ||
| val predictionModelXGBParamMap = predictionModel.extractParamMap() | ||
| val sameModelXGBParamMap = sameModel.extractParamMap() | ||
| assert(predictionModelXGBParamMap.get(predictionModelXGB.useExternalMemory) | ||
| === sameModelXGBParamMap.get(sameModelXGB.useExternalMemory)) | ||
| assert(predictionModelXGBParamMap.get(predictionModelXGB.featuresCol) | ||
| === sameModelXGBParamMap.get(sameModelXGB.featuresCol)) | ||
| assert(predictionModelXGBParamMap.get(predictionModelXGB.predictionCol) | ||
| === sameModelXGBParamMap.get(sameModelXGB.predictionCol)) | ||
| assert(predictionModelXGBParamMap.get(predictionModelXGB.labelCol) | ||
| === sameModelXGBParamMap.get(sameModelXGB.labelCol)) | ||
| assert(predictionModelXGBParamMap.get(predictionModelXGB.labelCol) | ||
| === sameModelXGBParamMap.get(sameModelXGB.labelCol)) | ||
| } | ||
| } | ||
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