/* * 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.NGram // $example off$ import org.apache.spark.sql.SparkSession object NGramExample { def main(args: Array[String]): Unit = { val spark = SparkSession .builder .appName("NGramExample") .getOrCreate() // $example on$ val wordDataFrame = spark.createDataFrame(Seq( (0, Array("Hi", "I", "heard", "about", "Spark")), (1, Array("I", "wish", "Java", "could", "use", "case", "classes")), (2, Array("Logistic", "regression", "models", "are", "neat")) )).toDF("id", "words") val ngram = new NGram().setN(2).setInputCol("words").setOutputCol("ngrams") val ngramDataFrame = ngram.transform(wordDataFrame) ngramDataFrame.select("ngrams").show(false) // $example off$ spark.stop() } } // scalastyle:on println