com.microsoft.ml.spark.featurize.Featurize.scala Maven / Gradle / Ivy
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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.ml.spark.featurize
import com.microsoft.ml.spark.core.contracts.Wrappable
import org.apache.spark.annotation.DeveloperApi
import org.apache.spark.ml.param._
import org.apache.spark.ml.util._
import org.apache.spark.ml.{Estimator, Pipeline, PipelineModel}
import org.apache.spark.sql._
import org.apache.spark.sql.types._
private[spark] object FeaturizeUtilities
{
// 2^18 features by default
val NumFeaturesDefault = 262144
// 2^12 features for tree-based or NN-based learners
val NumFeaturesTreeOrNNBased = 4096
}
object Featurize extends DefaultParamsReadable[Featurize]
/** Featurizes a dataset. Converts the specified columns to feature columns. */
class Featurize(override val uid: String) extends Estimator[PipelineModel]
with Wrappable with DefaultParamsWritable {
def this() = this(Identifiable.randomUID("Featurize"))
/** Feature columns - the columns to be featurized
* @group param
*/
val featureColumns: MapArrayParam = new MapArrayParam(this, "featureColumns", "Feature columns")
/** @group getParam */
final def getFeatureColumns: Map[String, Seq[String]] = $(featureColumns)
/** @group setParam */
def setFeatureColumns(value: Map[String, Seq[String]]): this.type = set(featureColumns, value)
/** One hot encode categorical columns when true; default is true
* @group param
*/
val oneHotEncodeCategoricals: Param[Boolean] = new BooleanParam(this,
"oneHotEncodeCategoricals",
"One-hot encode categoricals")
setDefault(oneHotEncodeCategoricals -> true)
/** @group getParam */
final def getOneHotEncodeCategoricals: Boolean = $(oneHotEncodeCategoricals)
/** @group setParam */
def setOneHotEncodeCategoricals(value: Boolean): this.type = set(oneHotEncodeCategoricals, value)
/** Number of features to hash string columns to
* @group param
*/
val numberOfFeatures: IntParam = new IntParam(this, "numberOfFeatures",
"Number of features to hash string columns to")
setDefault(numberOfFeatures -> FeaturizeUtilities.NumFeaturesDefault)
/** @group getParam */
final def getNumberOfFeatures: Int = $(numberOfFeatures)
/** @group setParam */
def setNumberOfFeatures(value: Int): this.type = set(numberOfFeatures, value)
/** Specifies whether to allow featurization of images */
val allowImages: Param[Boolean] = new BooleanParam(this, "allowImages", "Allow featurization of images")
setDefault(allowImages -> false)
/** @group getParam */
final def getAllowImages: Boolean = $(allowImages)
/** @group setParam */
def setAllowImages(value: Boolean): this.type = set(allowImages, value)
/** Featurizes the dataset.
*
* @param dataset The input dataset to train.
* @return The featurized model.
*/
override def fit(dataset: Dataset[_]): PipelineModel = {
val pipeline = assembleFeaturesEstimators(getFeatureColumns)
pipeline.fit(dataset)
}
private def assembleFeaturesEstimators(featureColumns: Map[String, Seq[String]]): Pipeline = {
val assembleFeaturesEstimators = featureColumns.map(newColToFeatures => {
new AssembleFeatures()
.setColumnsToFeaturize(newColToFeatures._2.toArray)
.setFeaturesCol(newColToFeatures._1)
.setNumberOfFeatures(getNumberOfFeatures)
.setOneHotEncodeCategoricals(getOneHotEncodeCategoricals)
.setAllowImages(getAllowImages)
}).toArray
new Pipeline().setStages(assembleFeaturesEstimators)
}
override def copy(extra: ParamMap): Estimator[PipelineModel] = {
new Featurize()
}
@DeveloperApi
override def transformSchema(schema: StructType): StructType =
assembleFeaturesEstimators(getFeatureColumns).transformSchema(schema)
}