Github user ericl commented on a diff in the pull request:
https://github.com/apache/spark/pull/7381#discussion_r34742729
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/feature/RModelFormula.scala ---
@@ -0,0 +1,136 @@
+/*
+ * 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.feature
+
+import scala.util.parsing.combinator.RegexParsers
+
+import org.apache.spark.annotation.Experimental
+import org.apache.spark.ml.Transformer
+import org.apache.spark.ml.param.{Param, ParamMap}
+import org.apache.spark.ml.param.shared.{HasFeaturesCol, HasLabelCol}
+import org.apache.spark.ml.util.Identifiable
+import org.apache.spark.sql.DataFrame
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * :: Experimental ::
+ * Implements the transforms required for fitting a dataset against an R
model formula. Currently
+ * we support a limited subset of the R operators, including '~' and '+'.
Also see the R formula
+ * docs here: http://www.inside-r.org/r-doc/stats/formula
+ */
+@Experimental
+class RModelFormula(override val uid: String)
+ extends Transformer with HasFeaturesCol with HasLabelCol {
+
+ def this() = this(Identifiable.randomUID("rModelFormula"))
+
+ /**
+ * R formula parameter. The formula is provided in string form.
+ * @group setParam
+ */
+ val formula: Param[String] = new Param(this, "formula", "R model
formula")
+
+ private var parsedFormula: Option[RFormula] = None
+
+ /**
+ * Sets the formula to use for this transformer. Must be called before
use.
+ * @group setParam
+ * @param value an R formula in string form (e.g. "y ~ x + z")
+ */
+ def setFormula(value: String): this.type = {
+ parsedFormula = Some(RFormulaParser.parse(value))
+ set(formula, value)
+ this
+ }
+
+ /** @group getParam */
+ def getFormula: String = $(formula)
+
+ /** @group getParam */
+ def setFeaturesCol(col: String): this.type = set(featuresCol, col)
+
+ /** @group getParam */
+ def setLabelCol(col: String): this.type = set(labelCol, col)
+
+ override def transformSchema(schema: StructType): StructType = {
+ require(parsedFormula.isDefined, "Must call setFormula() first.")
+ val withFeatures = featureTransformer.transformSchema(schema)
+ val nullable = schema(parsedFormula.get.response).dataType match {
+ case _: NumericType | BooleanType => false
+ case _ => true
+ }
+ StructType(withFeatures.fields :+ StructField($(labelCol), DoubleType,
nullable))
+ }
+
+ override def transform(dataset: DataFrame): DataFrame = {
+ require(parsedFormula.isDefined, "Must call setFormula() first.")
+ transformLabel(featureTransformer.transform(dataset))
+ }
+
+ override def copy(extra: ParamMap): RModelFormula = defaultCopy(extra)
+
+ override def toString: String = s"RModelFormula(${get(formula)})"
+
+ private def transformLabel(dataset: DataFrame): DataFrame = {
+ val responseName = parsedFormula.get.response
+ dataset.schema(responseName).dataType match {
+ case _: NumericType | BooleanType =>
+ dataset.select(
+ col("*"),
+ dataset(responseName).cast(DoubleType).as($(labelCol)))
--- End diff --
I added a check for this case, but kept the defaults as "feature" and
"label" unless you think we should always randomize.
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