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author | wm624@hotmail.com <wm624@hotmail.com> | 2017-01-26 21:01:59 -0800 |
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committer | Felix Cheung <felixcheung@apache.org> | 2017-01-26 21:01:59 -0800 |
commit | c0ba284300e494354f5bb205a10a12ac7daa2b5e (patch) | |
tree | 43592dfb0b4576ca018414721f809ecfa1972739 /mllib/src | |
parent | 1191fe267d2faad2a99a83f3375ce2d9d382cfa0 (diff) | |
download | spark-c0ba284300e494354f5bb205a10a12ac7daa2b5e.tar.gz spark-c0ba284300e494354f5bb205a10a12ac7daa2b5e.tar.bz2 spark-c0ba284300e494354f5bb205a10a12ac7daa2b5e.zip |
[SPARK-18821][SPARKR] Bisecting k-means wrapper in SparkR
## What changes were proposed in this pull request?
Add R wrapper for bisecting Kmeans.
As JIRA is down, I will update title to link with corresponding JIRA later.
## How was this patch tested?
Add new unit tests.
Author: wm624@hotmail.com <wm624@hotmail.com>
Closes #16566 from wangmiao1981/bk.
Diffstat (limited to 'mllib/src')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/BisectingKMeansWrapper.scala | 143 | ||||
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala | 2 |
2 files changed, 145 insertions, 0 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/BisectingKMeansWrapper.scala b/mllib/src/main/scala/org/apache/spark/ml/r/BisectingKMeansWrapper.scala new file mode 100644 index 0000000000..71712c1c5e --- /dev/null +++ b/mllib/src/main/scala/org/apache/spark/ml/r/BisectingKMeansWrapper.scala @@ -0,0 +1,143 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You 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 org.apache.spark.ml.r + +import org.apache.hadoop.fs.Path +import org.json4s._ +import org.json4s.JsonDSL._ +import org.json4s.jackson.JsonMethods._ + +import org.apache.spark.ml.{Pipeline, PipelineModel} +import org.apache.spark.ml.attribute.AttributeGroup +import org.apache.spark.ml.clustering.{BisectingKMeans, BisectingKMeansModel} +import org.apache.spark.ml.feature.RFormula +import org.apache.spark.ml.util._ +import org.apache.spark.sql.{DataFrame, Dataset} + +private[r] class BisectingKMeansWrapper private ( + val pipeline: PipelineModel, + val features: Array[String], + val size: Array[Long], + val isLoaded: Boolean = false) extends MLWritable { + private val bisectingKmeansModel: BisectingKMeansModel = + pipeline.stages.last.asInstanceOf[BisectingKMeansModel] + + lazy val coefficients: Array[Double] = bisectingKmeansModel.clusterCenters.flatMap(_.toArray) + + lazy val k: Int = bisectingKmeansModel.getK + + // If the model is loaded from a saved model, cluster is NULL. It is checked on R side + lazy val cluster: DataFrame = bisectingKmeansModel.summary.cluster + + def fitted(method: String): DataFrame = { + if (method == "centers") { + bisectingKmeansModel.summary.predictions.drop(bisectingKmeansModel.getFeaturesCol) + } else if (method == "classes") { + bisectingKmeansModel.summary.cluster + } else { + throw new UnsupportedOperationException( + s"Method (centers or classes) required but $method found.") + } + } + + def transform(dataset: Dataset[_]): DataFrame = { + pipeline.transform(dataset).drop(bisectingKmeansModel.getFeaturesCol) + } + + override def write: MLWriter = new BisectingKMeansWrapper.BisectingKMeansWrapperWriter(this) +} + +private[r] object BisectingKMeansWrapper extends MLReadable[BisectingKMeansWrapper] { + + def fit( + data: DataFrame, + formula: String, + k: Int, + maxIter: Int, + seed: String, + minDivisibleClusterSize: Double + ): BisectingKMeansWrapper = { + + val rFormula = new RFormula() + .setFormula(formula) + .setFeaturesCol("features") + RWrapperUtils.checkDataColumns(rFormula, data) + val rFormulaModel = rFormula.fit(data) + + // get feature names from output schema + val schema = rFormulaModel.transform(data).schema + val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol)) + .attributes.get + val features = featureAttrs.map(_.name.get) + + val bisectingKmeans = new BisectingKMeans() + .setK(k) + .setMaxIter(maxIter) + .setMinDivisibleClusterSize(minDivisibleClusterSize) + .setFeaturesCol(rFormula.getFeaturesCol) + + if (seed != null && seed.length > 0) bisectingKmeans.setSeed(seed.toInt) + + val pipeline = new Pipeline() + .setStages(Array(rFormulaModel, bisectingKmeans)) + .fit(data) + + val bisectingKmeansModel: BisectingKMeansModel = + pipeline.stages.last.asInstanceOf[BisectingKMeansModel] + val size: Array[Long] = bisectingKmeansModel.summary.clusterSizes + + new BisectingKMeansWrapper(pipeline, features, size) + } + + override def read: MLReader[BisectingKMeansWrapper] = new BisectingKMeansWrapperReader + + override def load(path: String): BisectingKMeansWrapper = super.load(path) + + class BisectingKMeansWrapperWriter(instance: BisectingKMeansWrapper) extends MLWriter { + + override protected def saveImpl(path: String): Unit = { + val rMetadataPath = new Path(path, "rMetadata").toString + val pipelinePath = new Path(path, "pipeline").toString + + val rMetadata = ("class" -> instance.getClass.getName) ~ + ("features" -> instance.features.toSeq) ~ + ("size" -> instance.size.toSeq) + val rMetadataJson: String = compact(render(rMetadata)) + + sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath) + instance.pipeline.save(pipelinePath) + } + } + + class BisectingKMeansWrapperReader extends MLReader[BisectingKMeansWrapper] { + + override def load(path: String): BisectingKMeansWrapper = { + implicit val format = DefaultFormats + val rMetadataPath = new Path(path, "rMetadata").toString + val pipelinePath = new Path(path, "pipeline").toString + val pipeline = PipelineModel.load(pipelinePath) + + val rMetadataStr = sc.textFile(rMetadataPath, 1).first() + val rMetadata = parse(rMetadataStr) + val features = (rMetadata \ "features").extract[Array[String]] + val size = (rMetadata \ "size").extract[Array[Long]] + new BisectingKMeansWrapper(pipeline, features, size, isLoaded = true) + } + } + +} diff --git a/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala b/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala index b59fe29234..c44179281b 100644 --- a/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala +++ b/mllib/src/main/scala/org/apache/spark/ml/r/RWrappers.scala @@ -64,6 +64,8 @@ private[r] object RWrappers extends MLReader[Object] { GBTRegressorWrapper.load(path) case "org.apache.spark.ml.r.GBTClassifierWrapper" => GBTClassifierWrapper.load(path) + case "org.apache.spark.ml.r.BisectingKMeansWrapper" => + BisectingKMeansWrapper.load(path) case _ => throw new SparkException(s"SparkR read.ml does not support load $className") } |