[GitHub] [spark] srowen commented on a change in pull request #25160: [SPARK-28399][ML] implement RobustScaler

[GitBox <gi...@apache.org>]

srowen commented on a change in pull request #25160: [SPARK-28399][ML] implement RobustScaler
URL: https://github.com/apache/spark/pull/25160#discussion_r303925618
 
 

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 File path: mllib/src/main/scala/org/apache/spark/ml/feature/RobustScaler.scala
 ##########
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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.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 interquartile range.
+   * Default: true
+   * @group param
+   */
+  val withScaling: BooleanParam = new BooleanParam(this, "withScaling",
+    "Whether to scale the data to interquartile 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($(outputCol), new VectorUDT, false)
+    StructType(outputFields)
+  }
+}
+
+
+/**
+ * Scale features using statistics that are robust to outliers.
+ * This Scaler removes the median and scales the data according to the quantile range
+ * (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile
+ * (25th quantile) and the 3rd quartile (75th quantile).
+ * Centering and scaling happen independently on each feature by computing the relevant
+ * statistics on the samples in the training set. Median and interquartile range are then
+ * stored to be used on later data using the transform method.
+ * Standardization of a dataset is a common requirement for many machine learning estimators.
+ * Typically this is done by removing the mean and scaling to unit variance. However,
+ * outliers can often influence the sample mean / variance in a negative way.
+ * In such cases, the median and the interquartile range often give better results.
+ */
+@Since("3.0.0")
+class RobustScaler (override val uid: String)
+  extends Estimator[RobustScalerModel] with RobustScalerParams with DefaultParamsWritable {
+
+  def this() = this(Identifiable.randomUID("robustScal"))
+
+  /** @group setParam */
+  def setInputCol(value: String): this.type = set(inputCol, value)
+
+  /** @group setParam */
+  def setOutputCol(value: String): this.type = set(outputCol, value)
+
+  /** @group setParam */
+  def setLower(value: Double): this.type = set(lower, value)
+
+  /** @group setParam */
+  def setUpper(value: Double): this.type = set(upper, value)
+
+  /** @group setParam */
+  def setWithCentering(value: Boolean): this.type = set(withCentering, value)
+
+  /** @group setParam */
+  def setWithScaling(value: Boolean): this.type = set(withScaling, value)
+
+  override def fit(dataset: Dataset[_]): RobustScalerModel = {
+    transformSchema(dataset.schema, logging = true)
+
+    val summaries = dataset.select($(inputCol)).rdd.map {
+      case Row(vec: Vector) => vec
+    }.mapPartitions { iter =>
+      var localAgg: Array[QuantileSummaries] = null
+      while (iter.hasNext) {
+        val vec = iter.next()
+        if (localAgg == null) {
+          localAgg = Array.fill(vec.size)(
+            new QuantileSummaries(QuantileSummaries.defaultCompressThreshold, 0.001))
+        }
+        require(vec.size == localAgg.length)
+        var i = 0
+        while (i < vec.size) {
+          localAgg(i) = localAgg(i).insert(vec(i))
+          i += 1
+        }
+      }
+
+      if (localAgg != null) {
 
 Review comment:
   This might be clearer as:
   ```
   if (localAgg == null) {
     Iterator.empty
   } else {
     ...
   }
   ```

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