org.apache.spark.examples.ml.SummarizerExample.scala Maven / Gradle / Ivy
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* 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,
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* See the License for the specific language governing permissions and
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// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.linalg.{Vector, Vectors}
import org.apache.spark.ml.stat.Summarizer
// $example off$
import org.apache.spark.sql.SparkSession
object SummarizerExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("SummarizerExample")
.getOrCreate()
import spark.implicits._
import Summarizer._
// $example on$
val data = Seq(
(Vectors.dense(2.0, 3.0, 5.0), 1.0),
(Vectors.dense(4.0, 6.0, 7.0), 2.0)
)
val df = data.toDF("features", "weight")
val (meanVal, varianceVal) = df.select(metrics("mean", "variance")
.summary($"features", $"weight").as("summary"))
.select("summary.mean", "summary.variance")
.as[(Vector, Vector)].first()
println(s"with weight: mean = ${meanVal}, variance = ${varianceVal}")
val (meanVal2, varianceVal2) = df.select(mean($"features"), variance($"features"))
.as[(Vector, Vector)].first()
println(s"without weight: mean = ${meanVal2}, sum = ${varianceVal2}")
// $example off$
spark.stop()
}
}
// scalastyle:on println
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