Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6576#discussion_r31558267
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/LinearRegressionExample.scala
---
@@ -0,0 +1,149 @@
+/*
+ * 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.examples.ml
+
+import scala.collection.mutable
+import scala.language.reflectiveCalls
+
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.examples.mllib.AbstractParams
+import org.apache.spark.ml.{Pipeline, PipelineStage}
+import org.apache.spark.ml.regression.{LinearRegression,
LinearRegressionModel}
+import org.apache.spark.sql.DataFrame
+
+/**
+ * An example runner for linear regression with elastic-net (mixing L1/L2)
regularization.
+ * Run with
+ * {{{
+ * bin/run-example ml.LinearRegressionExample [options]
+ * }}}
+ * A synthetic dataset can be found at
`data/mllib/sample_linear_regression_data.txt` which can be
+ * trained by
+ * {{{
+ * bin/run-example ml.LinearRegressionExample --regParam 0.15
--elasticNetParam 1.0 \
+ * data/mllib/sample_linear_regression_data.txt
+ * }}}
+ * If you use it as a template to create your own app, please use
`spark-submit` to submit your app.
+ */
+object LinearRegressionExample {
+
+ case class Params(
+ input: String = null,
+ testInput: String = "",
+ dataFormat: String = "libsvm",
+ regParam: Double = 0.0,
+ elasticNetParam: Double = 0.0,
+ maxIter: Int = 100,
+ tol: Double = 1E-6,
+ fracTest: Double = 0.2) extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("LinearRegressionExample") {
+ head("LinearRegressionExample: an example Linear Regression with
Elastic-Net app.")
+ opt[Double]("regParam")
+ .text(s"regularization parameter, default:
${defaultParams.regParam}")
+ .action((x, c) => c.copy(regParam = x))
+ opt[Double]("elasticNetParam")
+ .text(s"ElasticNet mixing parameter. For alpha = 0, the penalty is
an L2 penalty. " +
+ s"For alpha = 1, it is an L1 penalty. For 0 < alpha < 1, the
penalty is a combination of " +
+ s"L1 and L2, default: ${defaultParams.elasticNetParam}")
+ .action((x, c) => c.copy(elasticNetParam = x))
+ opt[Int]("maxIter")
+ .text(s"maximal number of iterations, default:
${defaultParams.maxIter}")
--- End diff --
I'd say "maximum" since "maximal" often implies there are multiple maxima.
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