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DropColumns.scala
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DropColumns.scala
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// Copyright (C) Microsoft Corporation. All rights reserved.
// Licensed under the MIT License. See LICENSE in project root for information.
package com.microsoft.azure.synapse.ml.stages
import com.microsoft.azure.synapse.ml.codegen.Wrappable
import com.microsoft.azure.synapse.ml.logging.{FeatureNames, SynapseMLLogging}
import org.apache.spark.ml.Transformer
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.sql.types._
import org.apache.spark.sql.{DataFrame, Dataset}
object DropColumns extends DefaultParamsReadable[DropColumns]
/** <code>DropColumns</code> takes a dataframe and a list of columns to drop as input and returns
* a dataframe comprised of only those columns not listed in the input list.
*
*/
class DropColumns(val uid: String) extends Transformer with Wrappable with DefaultParamsWritable with SynapseMLLogging {
logClass(FeatureNames.Core)
def this() = this(Identifiable.randomUID("DropColumns"))
val cols: StringArrayParam = new StringArrayParam(this, "cols", "Comma separated list of column names")
/** @group getParam */
final def getCols: Array[String] = $(cols)
/** @group setParam */
def setCols(value: Array[String]): this.type = set(cols, value)
def setCol(value: String): this.type = set(cols, Array(value))
/** @param dataset - The input dataset, to be transformed
* @return The DataFrame that results from column selection
*/
override def transform(dataset: Dataset[_]): DataFrame = {
logTransform[DataFrame]({
dataset.toDF().drop(getCols: _*)
}, dataset.columns.length)
}
def transformSchema(schema: StructType): StructType = {
val droppedCols = getCols.toSet
StructType(schema.fields.filter(f => !droppedCols(f.name)))
}
def copy(extra: ParamMap): DropColumns = defaultCopy(extra)
}