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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.regression
import org.apache.spark.annotation.Experimental
import org.apache.spark.ml.PredictorParams
import org.apache.spark.ml.param.{Param, ParamMap, BooleanParam}
import org.apache.spark.ml.util.{SchemaUtils, Identifiable}
import org.apache.spark.mllib.regression.{IsotonicRegression => MLlibIsotonicRegression}
import org.apache.spark.mllib.regression.{IsotonicRegressionModel => MLlibIsotonicRegressionModel}
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.types.{DoubleType, DataType}
import org.apache.spark.sql.{Row, DataFrame}
import org.apache.spark.storage.StorageLevel
/**
* Params for isotonic regression.
*/
private[regression] trait IsotonicRegressionParams extends PredictorParams {
/**
* Param for weight column name.
* TODO: Move weightCol to sharedParams.
*
* @group param
*/
final val weightCol: Param[String] =
new Param[String](this, "weightCol", "weight column name")
/** @group getParam */
final def getWeightCol: String = $(weightCol)
/**
* Param for isotonic parameter.
* Isotonic (increasing) or antitonic (decreasing) sequence.
* @group param
*/
final val isotonic: BooleanParam =
new BooleanParam(this, "isotonic", "isotonic (increasing) or antitonic (decreasing) sequence")
/** @group getParam */
final def getIsotonicParam: Boolean = $(isotonic)
}
/**
* :: Experimental ::
* Isotonic regression.
*
* Currently implemented using parallelized pool adjacent violators algorithm.
* Only univariate (single feature) algorithm supported.
*
* Uses [[org.apache.spark.mllib.regression.IsotonicRegression]].
*/
@Experimental
class IsotonicRegression(override val uid: String)
extends Regressor[Double, IsotonicRegression, IsotonicRegressionModel]
with IsotonicRegressionParams {
def this() = this(Identifiable.randomUID("isoReg"))
/**
* Set the isotonic parameter.
* Default is true.
* @group setParam
*/
def setIsotonicParam(value: Boolean): this.type = set(isotonic, value)
setDefault(isotonic -> true)
/**
* Set weight column param.
* Default is weight.
* @group setParam
*/
def setWeightParam(value: String): this.type = set(weightCol, value)
setDefault(weightCol -> "weight")
override private[ml] def featuresDataType: DataType = DoubleType
override def copy(extra: ParamMap): IsotonicRegression = defaultCopy(extra)
private[this] def extractWeightedLabeledPoints(
dataset: DataFrame): RDD[(Double, Double, Double)] = {
dataset.select($(labelCol), $(featuresCol), $(weightCol))
.map { case Row(label: Double, features: Double, weights: Double) =>
(label, features, weights)
}
}
override protected def train(dataset: DataFrame): IsotonicRegressionModel = {
SchemaUtils.checkColumnType(dataset.schema, $(weightCol), DoubleType)
// Extract columns from data. If dataset is persisted, do not persist oldDataset.
val instances = extractWeightedLabeledPoints(dataset)
val handlePersistence = dataset.rdd.getStorageLevel == StorageLevel.NONE
if (handlePersistence) instances.persist(StorageLevel.MEMORY_AND_DISK)
val isotonicRegression = new MLlibIsotonicRegression().setIsotonic($(isotonic))
val parentModel = isotonicRegression.run(instances)
new IsotonicRegressionModel(uid, parentModel)
}
}
/**
* :: Experimental ::
* Model fitted by IsotonicRegression.
* Predicts using a piecewise linear function.
*
* For detailed rules see [[org.apache.spark.mllib.regression.IsotonicRegressionModel.predict()]].
*
* @param parentModel A [[org.apache.spark.mllib.regression.IsotonicRegressionModel]]
* model trained by [[org.apache.spark.mllib.regression.IsotonicRegression]].
*/
class IsotonicRegressionModel private[ml] (
override val uid: String,
private[ml] val parentModel: MLlibIsotonicRegressionModel)
extends RegressionModel[Double, IsotonicRegressionModel]
with IsotonicRegressionParams {
override def featuresDataType: DataType = DoubleType
override protected def predict(features: Double): Double = {
parentModel.predict(features)
}
override def copy(extra: ParamMap): IsotonicRegressionModel = {
copyValues(new IsotonicRegressionModel(uid, parentModel), extra)
}
}
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