Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/579#discussion_r28605215
--- Diff:
flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/preprocessing/StandardScaler.scala
---
@@ -0,0 +1,177 @@
+/*
+ * 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.flink.ml.preprocessing
+
+import breeze.linalg
+import breeze.linalg.DenseVector
+import breeze.numerics.sqrt
+import breeze.numerics.sqrt._
+import org.apache.flink.api.common.functions._
+import org.apache.flink.api.scala._
+import org.apache.flink.configuration.Configuration
+import org.apache.flink.ml.common.{Parameter, ParameterMap, Transformer}
+import org.apache.flink.ml.math.Breeze._
+import org.apache.flink.ml.math.Vector
+import org.apache.flink.ml.preprocessing.StandardScaler.{Mean, Std}
+
+/** Scales observations, so that all features have a user-specified mean
and standard deviation.
+ * By default for StandardScaler transformer mean=0.0 and std=1.0.
+ *
+ * This transformer takes a a Vector of values and maps it into the
+ * scaled Vector that each feature has a user-specified mean and standard
deviation.
+ *
+ * This transformer can be prepended to all [[Transformer]] and
+ * [[org.apache.flink.ml.common.Learner]] implementations which expect an
input of
+ * [[Vector]].
+ *
+ * @example
+ * {{{
+ * val trainingDS: DataSet[Vector] =
env.fromCollection(data)
+ *
+ * val transformer =
StandardScaler().setMean(10.0).setStd(2.0)
+ *
+ * transformer.transform(trainingDS)
+ * }}}
+ *
+ * =Parameters=
+ *
+ * - [[StandardScaler.Mean]]: The mean value of transformed data set; by
default equal to 0
+ * - [[StandardScaler.Std]]: The standard deviation of the transformed
data set; by default
+ * equal to 1
+ */
+class StandardScaler extends Transformer[Vector, Vector] with Serializable
{
+
+ def setMean(mu: Double): StandardScaler = {
+ parameters.add(Mean, mu)
+ this
+ }
+
+ /**
+ *
+ * @param std the user-specified std value. In case the user gives 0.0
value as input,
+ * the std is set to the default value: 1.0.
+ * @return the StandardScaler object with its std value set to the
user-specified value
+ */
+ def setStd(std: Double): StandardScaler = {
+ if (std == 0.0) {
+ return this
+ }
+ parameters.add(Std, std)
+ this
+ }
+
+ override def transform(input: DataSet[Vector], parameters: ParameterMap):
+ DataSet[Vector] = {
+ val resultingParameters = this.parameters ++ parameters
+ val mean = resultingParameters(Mean)
+ val std = resultingParameters(Std)
+
+ val featureMetrics = extractFeatureMetrics(input)
+
+ input.map(new RichMapFunction[Vector, Vector]() {
+
+ var broadcastMean: linalg.Vector[Double] = null
+ var broadcastStd: linalg.Vector[Double] = null
+
+ override def open(parameters: Configuration): Unit = {
+ val broadcastedMetrics =
getRuntimeContext().getBroadcastVariable[(linalg.Vector[Double],
+ linalg.Vector[Double])]("broadcastedMetrics").get(0)
+ broadcastMean = broadcastedMetrics._1
+ broadcastStd = broadcastedMetrics._2
+ }
+
+ override def map(vector: Vector): Vector = {
+ var myVector = vector.asBreeze
+
+ myVector :-= broadcastMean
+ myVector :/= broadcastStd
+ myVector = (myVector :* std) :+ mean
+ return myVector.fromBreeze
+ }
+ }).withBroadcastSet(featureMetrics, "broadcastedMetrics")
+ }
+
+ /** Calculates in one pass over the data the features' mean and standard
deviation.
+ * For the calculation of the Standard deviation with one pass over the
data,
+ * the Youngs & Cramer algorithm was used:
+ * http://www.cs.yale.edu/publications/techreports/tr222.pdf
+ *
+ *
+ * @param dataSet The data set for which we want to calculate mean and
variance
+ * @return DataSet containing a single tuple of two vectors
(meanVector, stdVector).
+ * The first vector represents the mean vector and the second
is the standard
+ * deviation vector.
+ */
+ private def extractFeatureMetrics(dataSet: DataSet[Vector]):
+ DataSet[(linalg.Vector[Double], linalg.Vector[Double])] = {
+
+ val metrics = dataSet.map(new MapFunction[Vector, (Double,
linalg.Vector[Double], linalg
+ .Vector[Double])] {
+
+ override def map(inputVector: Vector):
+ (Double, linalg.Vector[Double], linalg.Vector[Double]) = {
+ return (1, inputVector.asBreeze,
DenseVector.zeros[Double](inputVector.size))
+ }
+ }).reduce(new ReduceFunction[(Double, linalg.Vector[Double],
linalg.Vector[Double])] {
+
+ override def reduce(metrics1: (Double, linalg.Vector[Double],
linalg.Vector[Double]),
+ metrics2: (Double, linalg.Vector[Double], linalg.Vector[Double])):
+ (Double, linalg.Vector[Double], linalg.Vector[Double]) = {
+
+ /* We use formula 1.5b of the cited technical report for the
combination of partial
+ * sum of squares. According to 1.5b:
+ * val temp1 : m/n(m+n)
+ * val temp2 : n/m
+ */
+ val temp1 = metrics1._1 / (metrics2._1 * (metrics1._1 +
metrics2._1))
+ val temp2 = metrics2._1 / metrics1._1
+ val tempVector = (metrics1._2 * temp2) :- metrics2._2
+
+ val tempS = (metrics1._3 :+ metrics2._3) :+ (tempVector :*
tempVector) * temp1
+ return (metrics1._1 + metrics2._1, metrics1._2 :+ metrics2._2,
tempS)
+ }
+ }).map(new MapFunction[(Double, linalg.Vector[Double],
linalg.Vector[Double]),
--- End diff --
Scala function.
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