mgaido91 commented on a change in pull request #25160: [SPARK-28399][ML] implement RobustScaler URL: https://github.com/apache/spark/pull/25160#discussion_r305575229
########## File path: mllib/src/main/scala/org/apache/spark/ml/feature/RobustScaler.scala ########## @@ -0,0 +1,292 @@ +/* + * 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.hadoop.fs.Path + +import org.apache.spark.annotation.Since +import org.apache.spark.ml.{Estimator, Model} +import org.apache.spark.ml.linalg._ +import org.apache.spark.ml.param._ +import org.apache.spark.ml.param.shared.{HasInputCol, HasOutputCol} +import org.apache.spark.ml.util._ +import org.apache.spark.mllib.util.MLUtils +import org.apache.spark.sql._ +import org.apache.spark.sql.catalyst.util.QuantileSummaries +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{StructField, StructType} + +/** + * Params for [[RobustScaler]] and [[RobustScalerModel]]. + */ +private[feature] trait RobustScalerParams extends Params with HasInputCol with HasOutputCol { + + /** + * Lower quantile to calculate quantile range, shared by all features + * Default: 0.25 + * @group param + */ + val lower: DoubleParam = new DoubleParam(this, "lower", + "Lower quantile to calculate quantile range", + ParamValidators.inRange(0, 1, false, false)) + + /** @group getParam */ + def getLower: Double = $(lower) + + setDefault(lower -> 0.25) + + /** + * Upper quantile to calculate quantile range, shared by all features + * Default: 0.75 + * @group param + */ + val upper: DoubleParam = new DoubleParam(this, "upper", + "Upper quantile to calculate quantile range", + ParamValidators.inRange(0, 1, false, false)) + + /** @group getParam */ + def getUpper: Double = $(upper) + + setDefault(upper -> 0.75) + + /** + * Whether to center the data with median before scaling. + * It will build a dense output, so take care when applying to sparse input. + * Default: false + * @group param + */ + val withCentering: BooleanParam = new BooleanParam(this, "withCentering", + "Whether to center data with median") + + /** @group getParam */ + def getWithCentering: Boolean = $(withCentering) + + setDefault(withCentering -> false) + + /** + * Whether to scale the data to quantile range. + * Default: true + * @group param + */ + val withScaling: BooleanParam = new BooleanParam(this, "withScaling", + "Whether to scale the data to quantile range") + + /** @group getParam */ + def getWithScaling: Boolean = $(withScaling) + + setDefault(withScaling -> true) + + /** Validates and transforms the input schema. */ + protected def validateAndTransformSchema(schema: StructType): StructType = { + require($(lower) < $(upper), s"The specified lower quantile(${$(lower)}) is " + + s"larger or equal to upper quantile(${$(upper)})") + SchemaUtils.checkColumnType(schema, $(inputCol), new VectorUDT) + require(!schema.fieldNames.contains($(outputCol)), + s"Output column ${$(outputCol)} already exists.") + val outputFields = schema.fields :+ StructField($(outputCol), new VectorUDT, false) + StructType(outputFields) + } +} + + +/** + * Scale features using statistics that are robust to outliers. + * RobustScaler removes the median and scales the data according to the quantile range. + * The quantile range is by default IQR (Interquartile Range, quantile range between the + * 1st quartile = 25th quantile and the 3rd quartile = 75th quantile) but can be configured. + * Centering and scaling happen independently on each feature by computing the relevant + * statistics on the samples in the training set. Median and quantile range are then + * stored to be used on later data using the transform method. + * Standardization of a dataset is a common requirement for many machine learning estimators. + * Typically this is done by removing the mean and scaling to unit variance. However, + * outliers can often influence the sample mean / variance in a negative way. + * In such cases, the median and the quantile range often give better results. + */ +@Since("3.0.0") +class RobustScaler (override val uid: String) + extends Estimator[RobustScalerModel] with RobustScalerParams with DefaultParamsWritable { + + def this() = this(Identifiable.randomUID("robustScal")) + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + /** @group setParam */ + def setLower(value: Double): this.type = set(lower, value) + + /** @group setParam */ + def setUpper(value: Double): this.type = set(upper, value) + + /** @group setParam */ + def setWithCentering(value: Boolean): this.type = set(withCentering, value) + + /** @group setParam */ + def setWithScaling(value: Boolean): this.type = set(withScaling, value) + + override def fit(dataset: Dataset[_]): RobustScalerModel = { + transformSchema(dataset.schema, logging = true) + + val summaries = dataset.select($(inputCol)).rdd.map { + case Row(vec: Vector) => vec + }.mapPartitions { iter => + var agg: Array[QuantileSummaries] = null + while (iter.hasNext) { + val vec = iter.next() + if (agg == null) { + agg = Array.fill(vec.size)( + new QuantileSummaries(QuantileSummaries.defaultCompressThreshold, 0.001)) + } + require(vec.size == agg.length) + var i = 0 + while (i < vec.size) { + agg(i) = agg(i).insert(vec(i)) + i += 1 + } + } + + if (agg == null) { + Iterator.empty + } else { + var i = 0 + while (i < agg.length) { + agg(i) = agg(i).compress() + i += 1 + } + Iterator.single(agg) + } + }.treeReduce { (agg1, agg2) => + require(agg1.length == agg2.length) + var i = 0 + while (i < agg1.length) { + agg1(i) = agg1(i).merge(agg2(i)).compress() + i += 1 + } + agg1 + } + + val (range, median) = summaries.map { s => + (s.query($(upper)).get - s.query($(lower)).get, + s.query(0.5).get) + }.unzip + + copyValues(new RobustScalerModel(uid, Vectors.dense(range).compressed, + Vectors.dense(median).compressed).setParent(this)) + } + + override def transformSchema(schema: StructType): StructType = { + validateAndTransformSchema(schema) + } + + override def copy(extra: ParamMap): RobustScaler = defaultCopy(extra) +} + +@Since("3.0.0") +object RobustScaler extends DefaultParamsReadable[RobustScaler] { + + override def load(path: String): RobustScaler = super.load(path) +} + +/** + * Model fitted by [[RobustScaler]]. + * + * @param range quantile range for each original column during fitting + * @param median median value for each original column during fitting + */ +@Since("3.0.0") +class RobustScalerModel private[ml] ( + override val uid: String, + val range: Vector, + val median: Vector) + extends Model[RobustScalerModel] with RobustScalerParams with MLWritable { + + import RobustScalerModel._ + + /** @group setParam */ + def setInputCol(value: String): this.type = set(inputCol, value) + + /** @group setParam */ + def setOutputCol(value: String): this.type = set(outputCol, value) + + override def transform(dataset: Dataset[_]): DataFrame = { + transformSchema(dataset.schema, logging = true) + + val shift = if ($(withCentering)) median.toArray else Array.emptyDoubleArray + val scale = if ($(withScaling)) { + range.toArray.map { v => if (v == 0) 0.0 else 1.0 / v } + } else Array.emptyDoubleArray + + val func = StandardScalerModel.getTransformFunc(shift, scale, Review comment: nit: ``` val func = StandardScalerModel.getTransformFunc( shift, scale, $(withCentering), $(withScaling)) ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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