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package com.collective.sparkext.example
import org.apache.log4j.Logger
import org.apache.log4j.varia.NullAppender
import{VectorAssembler, GatherEncoder, S2CellTransformer, Gather}
import{ParamGridBuilder, CrossValidator}
import org.apache.spark.mllib.evaluation.BinaryModelMetrics
import org.apache.spark.mllib.linalg.DenseVector
import org.apache.spark.sql.functions._
import org.apache.spark.sql.{Row, DataFrame}
import org.apache.spark.sql.types._
object SparkMlExtExample extends App with Sites with Geo with Response {
import sqlContext.implicits._
println(s"Run Spark ML Ext Example application")
println(s"Sites data frame size = ${sitesDf.count()}")
println(s"Geo data frame size = ${geoDf.count()}")
println(s"Response data frame size = ${responseDf.count()} ")
// Gather site visitation log
val gatherSites = new Gather()
// Transform lat/lon into S2 Cell Id
val s2Transformer = new S2CellTransformer()
// Gather S2 CellId log
val gatherS2Cells = new Gather()
// Gather raw data into wide rows
val gatheredSites = gatherSites.transform(sitesDf)
val gatheredCells = gatherS2Cells.transform(s2Transformer.transform(geoDf))
// Assemble input dataset
val dataset ="response")
.join(gatheredSites, responseDf(Response.cookie) === gatheredSites(Sites.cookie))
.join(gatheredCells, responseDf(Response.cookie) === gatheredCells(Sites.cookie))
println(s"Input dataset size = ${dataset.count()}")
// Split dataset into test/train sets
val trainPct = 0.1
val Array(trainSet, testSet) = dataset.randomSplit(Array(1 - trainPct, trainPct))
// Setup ML Pipeline stages
// Encode site data
val encodeSites = new GatherEncoder()
// Encode S2 Cell data
val encodeS2Cells = new GatherEncoder()
// Assemble feature vectors together
val assemble = new VectorAssembler()
.setInputCols(Array("sites_f", "s2_cells_f"))
// Extract features label information
val dummyPipeline = new Pipeline()
.setStages(Array(encodeSites, encodeS2Cells, assemble))
val out =
val attrGroup = AttributeGroup.fromStructField(out.schema("features"))
val attributes = attrGroup.attributes.get
println(s"Num features = ${attributes.length}")
attributes.zipWithIndex.foreach { case (attr, idx) =>
println(s" - $idx = $attr")
// Build logistic regression using featurized statistics
val lr = new LogisticRegression()
// Define pipeline with 4 stages
val pipeline = new Pipeline()
.setStages(Array(encodeSites, encodeS2Cells, assemble, lr))
val evaluator = new BinaryClassificationEvaluator()
val crossValidator = new CrossValidator()
val paramGrid = new ParamGridBuilder()
.addGrid(lr.elasticNetParam, Array(0.1, 0.5))
println(s"Train model on train set")
val cvModel =
println(s"Score test set")
val testScores = cvModel.transform(testSet)
val scoreAndLabels = testScores
.select(col("probability"), col(Response.response))
.map { case Row(probability: DenseVector, label: Double) =>
val predictedActionProbability = probability(1)
(predictedActionProbability, label)
println("Evaluate model")
val metrics = new BinaryModelMetrics(scoreAndLabels)
val auc = metrics.areaUnderROC()
println(s"Model AUC: $auc")
private def turnOffLogging(): Unit = {
Logger.getRootLogger.addAppender(new NullAppender())
trait Sites extends InMemorySparkContext {
object Sites {
val cookie = "cookie"
val site = "site"
val impressions = "impressions"
val schema = StructType(Array(
StructField(cookie, StringType),
StructField(site, StringType),
StructField(impressions, IntegerType)
lazy val sitesDf: DataFrame = {
val lines ="/sites.csv")).getLines()
val rows =",")).drop(1) collect {
case Array(cookie, site, impressions) => Row(cookie, site, impressions.toInt)
val rdd = sc.parallelize(rows.toSeq)
sqlContext.createDataFrame(rdd, Sites.schema)
trait Geo extends InMemorySparkContext {
object Geo {
val cookie = "cookie"
val lat = "lat"
val lon = "lon"
val impressions = "impressions"
val schema = StructType(Array(
StructField(cookie, StringType),
StructField(lat, DoubleType),
StructField(lon, DoubleType),
StructField(impressions, IntegerType)
lazy val geoDf: DataFrame = {
val lines ="/geo.csv")).getLines()
val rows =",")).drop(1) collect {
case Array(cookie, lat, lon, impressions) => Row(cookie, lat.toDouble, lon.toDouble, impressions.toInt)
val rdd = sc.parallelize(rows.toSeq)
sqlContext.createDataFrame(rdd, Geo.schema)
trait Response extends InMemorySparkContext {
object Response {
val cookie = "cookie"
val response = "response"
val schema = StructType(Array(
StructField(cookie, StringType),
StructField(response, DoubleType)
lazy val responseDf: DataFrame = {
val lines ="/response.csv")).getLines()
val rows =",")).drop(1) collect {
case Array(cookie, response) => Row(cookie, response.toDouble)
val rdd = sc.parallelize(rows.toSeq)
sqlContext.createDataFrame(rdd, Response.schema)
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