spark VarianceThresholdSelectorExample 源码
spark VarianceThresholdSelectorExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/VarianceThresholdSelectorExample.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.VarianceThresholdSelector
import org.apache.spark.ml.linalg.Vectors
// $example off$
import org.apache.spark.sql.SparkSession
/**
* An example for VarianceThresholdSelector.
* Run with
* {{{
* bin/run-example ml.VarianceThresholdSelectorExample
* }}}
*/
object VarianceThresholdSelectorExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("VarianceThresholdSelectorExample")
.getOrCreate()
import spark.implicits._
// $example on$
val data = Seq(
(1, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0)),
(2, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0)),
(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0)),
(4, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0)),
(5, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0)),
(6, Vectors.dense(8.0, 9.0, 6.0, 0.0, 0.0, 0.0))
)
val df = spark.createDataset(data).toDF("id", "features")
val selector = new VarianceThresholdSelector()
.setVarianceThreshold(8.0)
.setFeaturesCol("features")
.setOutputCol("selectedFeatures")
val result = selector.fit(df).transform(df)
println(s"Output: Features with variance lower than" +
s" ${selector.getVarianceThreshold} are removed.")
result.show()
// $example off$
spark.stop()
}
}
// scalastyle:on println
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