[GitHub] [spark] viirya commented on a change in pull request #27593: [SPARK-30818][SPARKR][ML] Add SparkR LinearRegression wrapper

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viirya 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_r399629904
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/r/LinearRegressionWrapper.scala
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 @@ -0,0 +1,153 @@
+/*
+ * 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)
+    }
+
+    val pipeline = new Pipeline()
+      .setStages(Array(rFormulaModel, lm))
+      .fit(data)
+
+    new LinearRegressionWrapper(pipeline, features)
+  }
+
+  override def read: MLReader[LinearRegressionWrapper] = new LinearRegressionWrapperReader
+
+  override def load(path: String): LinearRegressionWrapper = super.load(path)
 
 Review comment:
   Is this redundant?

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