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/*
* 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.classification.{RandomForestClassificationModel, RandomForestClassifier}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.util._
import org.apache.spark.sql.{DataFrame, Dataset}
private[r] class RandomForestClassifierWrapper private (
val pipeline: PipelineModel,
val formula: String,
val features: Array[String]) extends MLWritable {
import RandomForestClassifierWrapper._
private val rfcModel: RandomForestClassificationModel =
pipeline.stages(1).asInstanceOf[RandomForestClassificationModel]
lazy val numFeatures: Int = rfcModel.numFeatures
lazy val featureImportances: Vector = rfcModel.featureImportances
lazy val numTrees: Int = rfcModel.getNumTrees
lazy val treeWeights: Array[Double] = rfcModel.treeWeights
def summary: String = rfcModel.toDebugString
def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset)
.drop(PREDICTED_LABEL_INDEX_COL)
.drop(rfcModel.getFeaturesCol)
.drop(rfcModel.getLabelCol)
}
override def write: MLWriter = new
RandomForestClassifierWrapper.RandomForestClassifierWrapperWriter(this)
}
private[r] object RandomForestClassifierWrapper extends MLReadable[RandomForestClassifierWrapper] {
val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
val PREDICTED_LABEL_COL = "prediction"
def fit( // scalastyle:ignore
data: DataFrame,
formula: String,
maxDepth: Int,
maxBins: Int,
numTrees: Int,
impurity: String,
minInstancesPerNode: Int,
minInfoGain: Double,
checkpointInterval: Int,
featureSubsetStrategy: String,
seed: String,
subsamplingRate: Double,
maxMemoryInMB: Int,
cacheNodeIds: Boolean): RandomForestClassifierWrapper = {
val rFormula = new RFormula()
.setFormula(formula)
.setForceIndexLabel(true)
checkDataColumns(rFormula, data)
val rFormulaModel = rFormula.fit(data)
// get labels and feature names from output schema
val (features, labels) = getFeaturesAndLabels(rFormulaModel, data)
// assemble and fit the pipeline
val rfc = new RandomForestClassifier()
.setMaxDepth(maxDepth)
.setMaxBins(maxBins)
.setNumTrees(numTrees)
.setImpurity(impurity)
.setMinInstancesPerNode(minInstancesPerNode)
.setMinInfoGain(minInfoGain)
.setCheckpointInterval(checkpointInterval)
.setFeatureSubsetStrategy(featureSubsetStrategy)
.setSubsamplingRate(subsamplingRate)
.setMaxMemoryInMB(maxMemoryInMB)
.setCacheNodeIds(cacheNodeIds)
.setFeaturesCol(rFormula.getFeaturesCol)
.setLabelCol(rFormula.getLabelCol)
.setPredictionCol(PREDICTED_LABEL_INDEX_COL)
if (seed != null && seed.length > 0) rfc.setSeed(seed.toLong)
val idxToStr = new IndexToString()
.setInputCol(PREDICTED_LABEL_INDEX_COL)
.setOutputCol(PREDICTED_LABEL_COL)
.setLabels(labels)
val pipeline = new Pipeline()
.setStages(Array(rFormulaModel, rfc, idxToStr))
.fit(data)
new RandomForestClassifierWrapper(pipeline, formula, features)
}
override def read: MLReader[RandomForestClassifierWrapper] =
new RandomForestClassifierWrapperReader
override def load(path: String): RandomForestClassifierWrapper = super.load(path)
class RandomForestClassifierWrapperWriter(instance: RandomForestClassifierWrapper)
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) ~
("formula" -> instance.formula) ~
("features" -> instance.features.toSeq)
val rMetadataJson: String = compact(render(rMetadata))
sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
instance.pipeline.save(pipelinePath)
}
}
class RandomForestClassifierWrapperReader extends MLReader[RandomForestClassifierWrapper] {
override def load(path: String): RandomForestClassifierWrapper = {
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 formula = (rMetadata \ "formula").extract[String]
val features = (rMetadata \ "features").extract[Array[String]]
new RandomForestClassifierWrapper(pipeline, formula, features)
}
}
}
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