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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
import scala.annotation.varargs
import org.apache.spark.Logging
import org.apache.spark.annotation.AlphaComponent
import org.apache.spark.ml.param._
import org.apache.spark.ml.param.shared._
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions._
import org.apache.spark.sql.types._
/**
* :: AlphaComponent ::
* Abstract class for transformers that transform one dataset into another.
*/
@AlphaComponent
abstract class Transformer extends PipelineStage with Params {
/**
* Transforms the dataset with optional parameters
* @param dataset input dataset
* @param paramPairs optional list of param pairs, overwrite embedded params
* @return transformed dataset
*/
@varargs
def transform(dataset: DataFrame, paramPairs: ParamPair[_]*): DataFrame = {
val map = new ParamMap()
paramPairs.foreach(map.put(_))
transform(dataset, map)
}
/**
* Transforms the dataset with provided parameter map as additional parameters.
* @param dataset input dataset
* @param paramMap additional parameters, overwrite embedded params
* @return transformed dataset
*/
def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame
}
/**
* Abstract class for transformers that take one input column, apply transformation, and output the
* result as a new column.
*/
private[ml] abstract class UnaryTransformer[IN, OUT, T <: UnaryTransformer[IN, OUT, T]]
extends Transformer with HasInputCol with HasOutputCol with Logging {
/** @group setParam */
def setInputCol(value: String): T = set(inputCol, value).asInstanceOf[T]
/** @group setParam */
def setOutputCol(value: String): T = set(outputCol, value).asInstanceOf[T]
/**
* Creates the transform function using the given param map. The input param map already takes
* account of the embedded param map. So the param values should be determined solely by the input
* param map.
*/
protected def createTransformFunc(paramMap: ParamMap): IN => OUT
/**
* Returns the data type of the output column.
*/
protected def outputDataType: DataType
/**
* Validates the input type. Throw an exception if it is invalid.
*/
protected def validateInputType(inputType: DataType): Unit = {}
override def transformSchema(schema: StructType, paramMap: ParamMap): StructType = {
val map = extractParamMap(paramMap)
val inputType = schema(map(inputCol)).dataType
validateInputType(inputType)
if (schema.fieldNames.contains(map(outputCol))) {
throw new IllegalArgumentException(s"Output column ${map(outputCol)} already exists.")
}
val outputFields = schema.fields :+
StructField(map(outputCol), outputDataType, nullable = false)
StructType(outputFields)
}
override def transform(dataset: DataFrame, paramMap: ParamMap): DataFrame = {
transformSchema(dataset.schema, paramMap, logging = true)
val map = extractParamMap(paramMap)
dataset.withColumn(map(outputCol),
callUDF(this.createTransformFunc(map), outputDataType, dataset(map(inputCol))))
}
}
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