Github user dbtsai commented on a diff in the pull request:
https://github.com/apache/spark/pull/6576#discussion_r31576746
--- Diff:
examples/src/main/scala/org/apache/spark/examples/ml/LogisticRegressionExample.scala
---
@@ -0,0 +1,160 @@
+/*
+ * 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.classification.{LogisticRegression,
LogisticRegressionModel}
+import org.apache.spark.ml.feature.StringIndexer
+import org.apache.spark.sql.DataFrame
+
+/**
+ * An example runner for logistic regression with elastic-net (mixing
L1/L2) regularization.
+ * Run with
+ * {{{
+ * bin/run-example ml.LogisticRegressionExample [options]
+ * }}}
+ * A synthetic dataset can be found at `data/mllib/sample_libsvm_data.txt`
which can be
+ * trained by
+ * {{{
+ * bin/run-example ml.LogisticRegressionExample --regParam 0.3
--elasticNetParam 0.8 \
+ * data/mllib/sample_libsvm_data.txt
+ * }}}
+ * If you use it as a template to create your own app, please use
`spark-submit` to submit your app.
+ */
+object LogisticRegressionExample {
+
+ case class Params(
+ input: String = null,
+ testInput: String = "",
+ dataFormat: String = "libsvm",
+ regParam: Double = 0.0,
+ elasticNetParam: Double = 0.0,
+ maxIter: Int = 100,
+ fitIntercept: Boolean = true,
+ tol: Double = 1E-6,
+ fracTest: Double = 0.2) extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("LogisticRegressionExample") {
+ head("LogisticRegressionExample: an example Logistic 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"maximum number of iterations, default:
${defaultParams.maxIter}")
+ .action((x, c) => c.copy(maxIter = x))
+ opt[Boolean]("fitIntercept")
+ .text(s"whether to fit an intercept term, default:
${defaultParams.fitIntercept}")
+ .action((x, c) => c.copy(fitIntercept = x))
+ opt[Double]("tol")
+ .text(s"the convergence tolerance of iterations, Smaller value
will lead " +
+ s"to higher accuracy with the cost of more iterations, default:
${defaultParams.tol}")
+ .action((x, c) => c.copy(tol = x))
+ opt[Double]("fracTest")
+ .text(s"fraction of data to hold out for testing. If given option
testInput, " +
+ s"this option is ignored. default: ${defaultParams.fracTest}")
+ .action((x, c) => c.copy(fracTest = x))
+ opt[String]("testInput")
+ .text(s"input path to test dataset. If given, option fracTest is
ignored." +
+ s" default: ${defaultParams.testInput}")
+ .action((x, c) => c.copy(testInput = x))
+ opt[String]("dataFormat")
+ .text("data format: libsvm (default), dense (deprecated in Spark
v1.1)")
+ .action((x, c) => c.copy(dataFormat = x))
+ arg[String]("<input>")
+ .text("input path to labeled examples")
+ .required()
+ .action((x, c) => c.copy(input = x))
+ checkConfig { params =>
+ if (params.fracTest < 0 || params.fracTest >= 1) {
+ failure(s"fracTest ${params.fracTest} value incorrect; should be
in [0,1).")
+ } else {
+ success
+ }
+ }
+ }
+
+ parser.parse(args, defaultParams).map { params =>
+ run(params)
+ }.getOrElse {
+ sys.exit(1)
+ }
+ }
+
+ def run(params: Params) {
+ val conf = new SparkConf().setAppName(s"LogisticRegressionExample with
$params")
+ val sc = new SparkContext(conf)
+
+ println(s"LogisticRegressionExample with parameters:\n$params")
+
+ // Load training and test data and cache it.
+ val (training: DataFrame, test: DataFrame) =
DecisionTreeExample.loadDatasets(sc, params.input,
+ params.dataFormat, params.testInput, "classification",
params.fracTest)
+
+ // Set up Pipeline
+ val stages = new mutable.ArrayBuffer[PipelineStage]()
+
+ val labelIndexer = new StringIndexer()
+ .setInputCol("labelString")
+ .setOutputCol("indexedLabel")
+ stages += labelIndexer
+
+ val lor = new LogisticRegression()
+ .setFeaturesCol("features")
+ .setLabelCol("indexedLabel")
+ .setRegParam(params.regParam)
+ .setElasticNetParam(params.elasticNetParam)
+ .setMaxIter(params.maxIter)
+ .setTol(params.tol)
+ .setMaxIter(params.maxIter)
--- End diff --
Thanks. Fixed.
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