huaxingao commented on a change in pull request #27593: [SPARK-30818][SPARKR][ML] Add SparkR LinearRegression wrapper URL: https://github.com/apache/spark/pull/27593#discussion_r396689589
########## File path: mllib/src/main/scala/org/apache/spark/ml/r/LinearRegressionWrapper.scala ########## @@ -0,0 +1,155 @@ +/* + * 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.feature.RFormula +import org.apache.spark.ml.r.RWrapperUtils._ +import org.apache.spark.ml.regression.{LinearRegression, LinearRegressionModel} +import org.apache.spark.ml.util._ +import org.apache.spark.sql.{DataFrame, Dataset} + +private[r] class LinearRegressionWrapper private ( + val pipeline: PipelineModel, + val features: Array[String]) extends MLWritable { + import LinearRegressionWrapper._ + + private val linearRegressionModel: LinearRegressionModel = + pipeline.stages(1).asInstanceOf[LinearRegressionModel] + + lazy val rFeatures: Array[String] = if (linearRegressionModel.getFitIntercept) { + Array("(Intercept)") ++ features + } else { + features + } + + lazy val rCoefficients: Array[Double] = if (linearRegressionModel.getFitIntercept) { + Array(linearRegressionModel.intercept) ++ linearRegressionModel.coefficients.toArray + } else { + linearRegressionModel.coefficients.toArray + } + + lazy val numFeatures: Int = linearRegressionModel.numFeatures + + def transform(dataset: Dataset[_]): DataFrame = { + pipeline.transform(dataset) + .drop(linearRegressionModel.getFeaturesCol) + } + + + override def write: MLWriter = new LinearRegressionWrapper.LinearRegressionWrapperWriter(this) +} + +private[r] object LinearRegressionWrapper + extends MLReadable[LinearRegressionWrapper] { + + def fit( // scalastyle:ignore + data: DataFrame, + formula: String, + maxIter: Int, + regParam: Double, + elasticNetParam: Double, + tol: Double, + standardization: Boolean, + solver: String, + weightCol: String, + aggregationDepth: Int, + loss: String, + epsilon: Double, + stringIndexerOrderType: String): LinearRegressionWrapper = { + + val rFormula = new RFormula() + .setFormula(formula) + .setStringIndexerOrderType(stringIndexerOrderType) + checkDataColumns(rFormula, data) + val rFormulaModel = rFormula.fit(data) + + val fitIntercept = rFormula.hasIntercept + + // 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) + + // assemble and fit the pipeline + val lm = new LinearRegression() + .setMaxIter(maxIter) + .setRegParam(regParam) + .setElasticNetParam(elasticNetParam) + .setTol(tol) + .setFitIntercept(fitIntercept) + .setStandardization(standardization) + .setSolver(solver) + .setAggregationDepth(aggregationDepth) + .setLoss(loss) + .setEpsilon(epsilon) + .setFeaturesCol(rFormula.getFeaturesCol) + + if (weightCol != null) { + lm.setWeightCol(weightCol) + } + + Review comment: super nit: remove extra blank line ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
