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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.Experimental
import org.apache.spark.ml._
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.ml.util.{Identifiable, SchemaUtils}
import org.apache.spark.mllib.feature
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.sql._
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types.StructType
/**
* Params for [[IDF]] and [[IDFModel]].
*/
private[feature] trait IDFBase extends Params with HasInputCol with HasOutputCol {
/**
* The minimum of documents in which a term should appear.
* @group param
*/
final val minDocFreq = new IntParam(
this, "minDocFreq", "minimum of documents in which a term should appear for filtering")
setDefault(minDocFreq -> 0)
/** @group getParam */
def getMinDocFreq: Int = $(minDocFreq)
/**
* Validate and transform the input schema.
*/
protected def validateAndTransformSchema(schema: StructType): StructType = {
SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT)
SchemaUtils.appendColumn(schema, $(outputCol), new VectorUDT)
}
}
/**
* :: Experimental ::
* Compute the Inverse Document Frequency (IDF) given a collection of documents.
*/
@Experimental
final class IDF(override val uid: String) extends Estimator[IDFModel] with IDFBase {
def this() = this(Identifiable.randomUID("idf"))
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/** @group setParam */
def setMinDocFreq(value: Int): this.type = set(minDocFreq, value)
override def fit(dataset: DataFrame): IDFModel = {
transformSchema(dataset.schema, logging = true)
val input = dataset.select($(inputCol)).map { case Row(v: Vector) => v }
val idf = new feature.IDF($(minDocFreq)).fit(input)
copyValues(new IDFModel(uid, idf).setParent(this))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): IDF = defaultCopy(extra)
}
/**
* :: Experimental ::
* Model fitted by [[IDF]].
*/
@Experimental
class IDFModel private[ml] (
override val uid: String,
idfModel: feature.IDFModel)
extends Model[IDFModel] with IDFBase {
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
override def transform(dataset: DataFrame): DataFrame = {
transformSchema(dataset.schema, logging = true)
val idf = udf { vec: Vector => idfModel.transform(vec) }
dataset.withColumn($(outputCol), idf(col($(inputCol))))
}
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}
override def copy(extra: ParamMap): IDFModel = {
val copied = new IDFModel(uid, idfModel)
copyValues(copied, extra).setParent(parent)
}
}
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