May 3, 2017

How to read CSV for Spark 2.1.0 mllib for dummies

Intro

It looks like noone is reading CSV for spark 2.1.0 anymore. Only reference I could find was https://elbauldelprogramador.com/en/how-to-convert-column-to-vectorudt-densevector-spark/.
So, here comes my 5 cents on the issue.

Code

    
    val sc: SparkContext = new SparkContext(master, \
       "SuperApp", System.getenv("SPARK_HOME"))
    val session: SparkSession = SparkSession.builder().getOrCreate()

    //Firstly define schema
    val struct = StructType(
      StructField("price", DoubleType, false) ::
        StructField("_id", StringType, false) ::
        StructField("modelYearId", IntegerType, false) ::
        StructField("zip", IntegerType, false) ::
        StructField("modelYear", IntegerType, false) ::
        StructField("modelId", IntegerType, false) ::
        StructField("makeId", IntegerType, false) ::
        StructField("mileage", DoubleType, false) :: Nil)

    val df: DataFrame = session.sqlContext.read.schema(struct)
        .option("header", "true").csv("cars.csv")
    var data: DataFrame = df
    //Transform variable into categorical one
    data = new OneHotEncoder()
      .setInputCol("zip")
      .setOutputCol("zipVec").transform(data)

    //Assemble features that matter
    val assembler = new VectorAssembler().
      setInputCols(Array("modelYearIdVec", "zipVec", 
        "modelYear", "modelIdVec", "makeIdVec", "mileage")).
      setOutputCol("features")
    //to verify our schema is as we want
    data.printSchema()

    data = assembler.transform(data)

 
And then pretty usual mllib tutorial stuff:
   // Split the data into training and 
   //test sets (30% held out for testing).
    val Array(trainingData, testData) = data
        .randomSplit(Array(0.7, 0.3))

    val lr = new LinearRegression()
      .setLabelCol("price")
      .setMaxIter(200)
      .setRegParam(10)
      .setElasticNetParam(0.75)

    // Fit the model
    val lrModel: LinearRegressionModel = lr.fit(trainingData)

    // Print the coefficients and intercept for linear regression
    println(s"Coefficients: ${lrModel.coefficients} 
        Intercept: ${lrModel.intercept}")

    // Summarize the model over the training set 
    //and print out some metrics
    val trainingSummary = lrModel.summary
    println(s"numIterations: ${trainingSummary.totalIterations}")
    println(s"objectiveHistory: 
        [${trainingSummary.objectiveHistory.mkString(",")}]")
    trainingSummary.residuals.show()
    println(s"RMSE: ${trainingSummary.rootMeanSquaredError}")
    println(s"r2: ${trainingSummary.r2}")
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