section_15_Lecture_72.scala

import org.apache.spark.sql.SparkSession
import org.apache.spark.ml.feature.PCA
import org.apache.spark.ml.linalg.Vectors

val spark = SparkSession.builder().appName("PCA_Example").getOrCreate()

val data = Array(
    Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
    Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
    Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
)

val df = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")

val pca = (
    new PCA()
    .setInputCol("features")
    .setOutputCol("pcaFeatures")
    .setK(3)
    .fit(df)
)

val pcaDF = pca.transform(df)

val result = pcaDF.select("pcaFeatures")

result.show()