spark BucketizerExample 源码
spark BucketizerExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/BucketizerExample.scala
/*
* 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,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
// scalastyle:off println
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.feature.Bucketizer
// $example off$
import org.apache.spark.sql.SparkSession
/**
* An example for Bucketizer.
* Run with
* {{{
* bin/run-example ml.BucketizerExample
* }}}
*/
object BucketizerExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("BucketizerExample")
.getOrCreate()
// $example on$
val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity)
val data = Array(-999.9, -0.5, -0.3, 0.0, 0.2, 999.9)
val dataFrame = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")
val bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits)
// Transform original data into its bucket index.
val bucketedData = bucketizer.transform(dataFrame)
println(s"Bucketizer output with ${bucketizer.getSplits.length-1} buckets")
bucketedData.show()
// $example off$
// $example on$
val splitsArray = Array(
Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity),
Array(Double.NegativeInfinity, -0.3, 0.0, 0.3, Double.PositiveInfinity))
val data2 = Array(
(-999.9, -999.9),
(-0.5, -0.2),
(-0.3, -0.1),
(0.0, 0.0),
(0.2, 0.4),
(999.9, 999.9))
val dataFrame2 = spark.createDataFrame(data2).toDF("features1", "features2")
val bucketizer2 = new Bucketizer()
.setInputCols(Array("features1", "features2"))
.setOutputCols(Array("bucketedFeatures1", "bucketedFeatures2"))
.setSplitsArray(splitsArray)
// Transform original data into its bucket index.
val bucketedData2 = bucketizer2.transform(dataFrame2)
println(s"Bucketizer output with [" +
s"${bucketizer2.getSplitsArray(0).length-1}, " +
s"${bucketizer2.getSplitsArray(1).length-1}] buckets for each input column")
bucketedData2.show()
// $example off$
spark.stop()
}
}
// scalastyle:on println
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