section_12_Lecture_58.scala

import org.apache.spark.ml.classification.LogisticRegression
import org.apache.spark.sql.SparkSession

import org.apache.log4j._
Logger.getLogger("org").setLevel(Level.ERROR)

val spark = SparkSession.builder().getOrCreate()

val data = spark.read.option("header", "true").option("inferSchema", "true").format("csv").load("data/titanic.csv")

val logregdatall = (
    data.select((
        data("Survived").as("label")
    ),
    $"Pclass", $"Name", $"Sex",$"Age", $"SibSp",
    $"Parch", $"Fare", $"Embarked"
))

val logregdata = logregdatall.na.drop()

import org.apache.spark.ml.feature.{VectorAssembler, StringIndexer, VectorIndexer, OneHotEncoder}
import org.apache.spark.ml.linalg.Vectors

val genderIndexer = new StringIndexer().setInputCol("Sex").setOutputCol("SexIndex")
val embarkIndexer = new StringIndexer().setInputCol("Embarked").setOutputCol("EmbarkedIndex")

val genderEncoder = new OneHotEncoder().setInputCol("SexIndex").setOutputCol("SexVec")
val embarkEnconder = new OneHotEncoder().setInputCol("EmbarkedIndex").setOutputCol("EmbarkVec")

val assembler = new VectorAssembler().setInputCols(Array("Pclass", "SexVec", "Age", "SibSp", "Parch", "Fare", "EmbarkVec")).setOutputCol("features")


val Array(training, test) = logregdata.randomSplit(Array(0.7, 0.3), seed=12345)

import org.apache.spark.ml.Pipeline

val lr = new LogisticRegression()

val pipeline = new Pipeline().setStages(Array(genderIndexer,embarkIndexer,genderEncoder,embarkEnconder,assembler, lr))

val model = pipeline.fit(training)

val results = model.transform(test)

import org.apache.spark.mllib.evaluation.MulticlassMetrics

val predictionAndLabels = results.select($"prediction", $"label").as[(Double, Double)].rdd

val metrics = new MulticlassMetrics(predictionAndLabels)

println("Confusion matrix:")
print(metrics.confusionMatrix)