Github user shivaram commented on a diff in the pull request:
https://github.com/apache/spark/pull/3070#discussion_r19755664
--- Diff:
examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala ---
@@ -0,0 +1,121 @@
+/*
+ * 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.mllib
+
+import java.io.File
+
+import com.google.common.io.Files
+import scopt.OptionParser
+
+import org.apache.spark.{SparkConf, SparkContext}
+import org.apache.spark.mllib.linalg.Vector
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.stat.MultivariateOnlineSummarizer
+import org.apache.spark.mllib.util.MLUtils
+import org.apache.spark.rdd.RDD
+import org.apache.spark.sql.{Row, SQLContext, SchemaRDD}
+
+/**
+ * An example of how to use [[org.apache.spark.sql.SchemaRDD]] as a
Dataset for ML. Run with
+ * {{{
+ * ./bin/run-example org.apache.spark.examples.mllib.DatasetExample
[options]
+ * }}}
+ * If you use it as a template to create your own app, please use
`spark-submit` to submit your app.
+ */
+object DatasetExample {
+
+ case class Params(
+ input: String = "data/mllib/sample_libsvm_data.txt",
+ dataFormat: String = "libsvm") extends AbstractParams[Params]
+
+ def main(args: Array[String]) {
+ val defaultParams = Params()
+
+ val parser = new OptionParser[Params]("DatasetExample") {
+ head("Dataset: an example app using SchemaRDD as a Dataset for ML.")
+ opt[String]("input")
+ .text(s"input path to dataset")
+ .action((x, c) => c.copy(input = x))
+ opt[String]("dataFormat")
+ .text("data format: libsvm (default), dense (deprecated in Spark
v1.1)")
+ .action((x, c) => c.copy(input = x))
+ checkConfig { params =>
+ success
+ }
+ }
+
+ parser.parse(args, defaultParams).map { params =>
+ run(params)
+ }.getOrElse {
+ sys.exit(1)
+ }
+ }
+
+ def run(params: Params) {
+
+ val conf = new SparkConf().setAppName(s"DatasetExample with $params")
+ val sc = new SparkContext(conf)
+ val sqlContext = new SQLContext(sc)
+ import sqlContext._ // for implicit conversions
+
+ // Load input data
+ val origData: RDD[LabeledPoint] = params.dataFormat match {
+ case "dense" => MLUtils.loadLabeledPoints(sc, params.input)
+ case "libsvm" => MLUtils.loadLibSVMFile(sc, params.input)
+ }
+ println(s"Loaded ${origData.count()} instances from file:
${params.input}")
+
+ // Convert input data to SchemaRDD explicitly.
+ val schemaRDD: SchemaRDD = origData
+ println(s"Inferred schema:\n${schemaRDD.schema.prettyJson}")
+ println(s"Converted to SchemaRDD with ${schemaRDD.count()} records")
+
+ // Select columns, using implicit conversion to SchemaRDD.
+ val labelsSchemaRDD: SchemaRDD = origData.select('label)
+ val labels: RDD[Double] = labelsSchemaRDD.map { case Row(v: Double) =>
v }
+ val numLabels = labels.count()
+ val meanLabel = labels.fold(0.0)(_ + _) / numLabels
+ println(s"Selected label column with average value $meanLabel")
+
+ val featuresSchemaRDD: SchemaRDD = origData.select('features)
--- End diff --
Does this also work for any arbitrary column name ? i.e if I am taking in
the features column name as a command line argument, how would it look ?
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