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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,
* 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.ml.feature
import org.apache.spark.annotation.{Since, Experimental}
import org.apache.spark.ml.UnaryTransformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types.{ArrayType, DataType, StringType}
/**
* :: Experimental ::
* A feature transformer that converts the input array of strings into an array of n-grams. Null
* values in the input array are ignored.
* It returns an array of n-grams where each n-gram is represented by a space-separated string of
* words.
*
* When the input is empty, an empty array is returned.
* When the input array length is less than n (number of elements per n-gram), no n-grams are
* returned.
*/
@Experimental
class NGram(override val uid: String)
extends UnaryTransformer[Seq[String], Seq[String], NGram] with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("ngram"))
/**
* Minimum n-gram length, >= 1.
* Default: 2, bigram features
* @group param
*/
val n: IntParam = new IntParam(this, "n", "number elements per n-gram (>=1)",
ParamValidators.gtEq(1))
/** @group setParam */
def setN(value: Int): this.type = set(n, value)
/** @group getParam */
def getN: Int = $(n)
setDefault(n -> 2)
override protected def createTransformFunc: Seq[String] => Seq[String] = {
_.iterator.sliding($(n)).withPartial(false).map(_.mkString(" ")).toSeq
}
override protected def validateInputType(inputType: DataType): Unit = {
require(inputType.sameType(ArrayType(StringType)),
s"Input type must be ArrayType(StringType) but got $inputType.")
}
override protected def outputDataType: DataType = new ArrayType(StringType, false)
}
@Since("1.6.0")
object NGram extends DefaultParamsReadable[NGram] {
@Since("1.6.0")
override def load(path: String): NGram = super.load(path)
}
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