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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, Since}
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.attribute.AttributeGroup
import org.apache.spark.ml.param.{IntParam, ParamMap, ParamValidators}
import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol}
import org.apache.spark.ml.util._
import org.apache.spark.mllib.feature
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions.{col, udf}
import org.apache.spark.sql.types.{ArrayType, StructType}
/**
* :: Experimental ::
* Maps a sequence of terms to their term frequencies using the hashing trick.
*/
@Experimental
class HashingTF(override val uid: String)
extends Transformer with HasInputCol with HasOutputCol with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("hashingTF"))
/** @group setParam */
def setInputCol(value: String): this.type = set(inputCol, value)
/** @group setParam */
def setOutputCol(value: String): this.type = set(outputCol, value)
/**
* Number of features. Should be > 0.
* (default = 2^18^)
* @group param
*/
val numFeatures = new IntParam(this, "numFeatures", "number of features (> 0)",
ParamValidators.gt(0))
setDefault(numFeatures -> (1 << 18))
/** @group getParam */
def getNumFeatures: Int = $(numFeatures)
/** @group setParam */
def setNumFeatures(value: Int): this.type = set(numFeatures, value)
override def transform(dataset: DataFrame): DataFrame = {
val outputSchema = transformSchema(dataset.schema)
val hashingTF = new feature.HashingTF($(numFeatures))
val t = udf { terms: Seq[_] => hashingTF.transform(terms) }
val metadata = outputSchema($(outputCol)).metadata
dataset.select(col("*"), t(col($(inputCol))).as($(outputCol), metadata))
}
override def transformSchema(schema: StructType): StructType = {
val inputType = schema($(inputCol)).dataType
require(inputType.isInstanceOf[ArrayType],
s"The input column must be ArrayType, but got $inputType.")
val attrGroup = new AttributeGroup($(outputCol), $(numFeatures))
SchemaUtils.appendColumn(schema, attrGroup.toStructField())
}
override def copy(extra: ParamMap): HashingTF = defaultCopy(extra)
}
@Since("1.6.0")
object HashingTF extends DefaultParamsReadable[HashingTF] {
@Since("1.6.0")
override def load(path: String): HashingTF = super.load(path)
}
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