spark QuantileDiscretizerExample 源码
spark QuantileDiscretizerExample 代码
文件路径：/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.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.
*/
package org.apache.spark.examples.ml
// $example on$
import org.apache.spark.ml.feature.QuantileDiscretizer
// $example off$
import org.apache.spark.sql.SparkSession
object QuantileDiscretizerExample {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("QuantileDiscretizerExample")
.getOrCreate()
// $example on$
val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
val df = spark.createDataFrame(data).toDF("id", "hour")
// $example off$
// Output of QuantileDiscretizer for such small datasets can depend on the number of
// partitions. Here we force a single partition to ensure consistent results.
// Note this is not necessary for normal use cases
.repartition(1)
// $example on$
val discretizer = new QuantileDiscretizer()
.setInputCol("hour")
.setOutputCol("result")
.setNumBuckets(3)
val result = discretizer.fit(df).transform(df)
result.show(false)
// $example off$
spark.stop()
}
}
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