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

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zhengruifeng commented on a change in pull request #25160: [SPARK-28399][ML] implement RobustScaler
URL: https://github.com/apache/spark/pull/25160#discussion_r304205265
 
 

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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
 
 Review comment:
   Yes, after optional remove the median, the features will be divided by the quantile range.
   IQR is a special quantile range, from 25% to 75%. But if the `lower`/`upper` are set to other values, then the range is not IQR.

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