Github user tillrohrmann commented on a diff in the pull request:
https://github.com/apache/flink/pull/3077#discussion_r96203398
--- Diff:
flink-libraries/flink-ml/src/main/scala/org/apache/flink/ml/outlier/StochasticOutlierSelection.scala
---
@@ -0,0 +1,367 @@
+/*
+ * 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.outlier
+
+/** An implementation of the Stochastic Outlier Selection algorithm by
Jeroen Jansen
+ *
+ * For more information about SOS, see
https://github.com/jeroenjanssens/sos
+ * J.H.M. Janssens, F. Huszar, E.O. Postma, and H.J. van den Herik.
Stochastic
+ * Outlier Selection. Technical Report TiCC TR 2012-001, Tilburg
University,
+ * Tilburg, the Netherlands, 2012.
+ *
+ * @example
+ * {{{
+ * val inputDS = env.fromCollection(List(
+ * LabeledVector(0.0, DenseVector(1.0, 1.0)),
+ * LabeledVector(1.0, DenseVector(2.0, 1.0)),
+ * LabeledVector(2.0, DenseVector(1.0, 2.0)),
+ * LabeledVector(3.0, DenseVector(2.0, 2.0)),
+ * LabeledVector(4.0, DenseVector(5.0, 8.0)) // The outlier!
+ * ))
+ *
+ * val sos = StochasticOutlierSelection()
+ * .setPerplexity(3)
+ *
+ * val outputDS = sos.transform(inputDS)
+ *
+ * val expectedOutputDS = Array(
+ * 0.2790094479202896,
+ * 0.25775014551682535,
+ * 0.22136130977995766,
+ * 0.12707053787018444,
+ * 0.9922779902453757 // The outlier!
+ * )
+ *
+ * assert(outputDS == expectedOutputDS)
+ * }}}
+ *
+ * =Parameters=
+ *
+ * -
[[org.apache.flink.ml.outlier.StochasticOutlierSelection.Perplexity]]:
+ * Perplexity can be interpreted as the k in k-nearest neighbor
algorithms. The difference is that
+ * in SOS being a neighbor is not a binary property, but a probabilistic
one. Should be between
+ * 1 and n-1, where n is the number of observations.
+ * (Default value: '''30''')
+ *
+ * -
[[org.apache.flink.ml.outlier.StochasticOutlierSelection.ErrorTolerance]]:
+ * The accepted error tolerance. When increasing this number, it will
sacrifice accuracy in
+ * return for reduced computational time.
+ * (Default value: '''1e-20''')
+ *
+ * -
[[org.apache.flink.ml.outlier.StochasticOutlierSelection.MaxIterations]]:
+ * The maximum number of iterations to perform. (Default value:
'''5000''')
+ */
+
+import breeze.linalg.functions.euclideanDistance
+import breeze.linalg.{sum, DenseVector => BreezeDenseVector, Vector =>
BreezeVector}
+import org.apache.flink.api.common.operators.Order
+import org.apache.flink.api.common.typeinfo.TypeInformation
+import org.apache.flink.api.scala._
+import org.apache.flink.api.scala.utils._
+import org.apache.flink.ml.common.{LabeledVector, Parameter, ParameterMap,
WithParameters}
+import org.apache.flink.ml.math.Breeze._
+import org.apache.flink.ml.math.{BreezeVectorConverter, Vector}
+import org.apache.flink.ml.pipeline.{TransformDataSetOperation,
Transformer}
+
+import scala.language.implicitConversions
+import scala.reflect.ClassTag
+
+class StochasticOutlierSelection extends
Transformer[StochasticOutlierSelection] {
+
+ import StochasticOutlierSelection._
+
+
+ /** Sets the perplexity of the outlier selection algorithm, can be seen
as the k of kNN
+ * For more information, please read the Stochastic Outlier Selection
algorithm paper
+ *
+ * @param perplexity the perplexity of the affinity fit
+ * @return
+ */
+ def setPerplexity(perplexity: Double): StochasticOutlierSelection = {
+ require(perplexity >= 1, "Perplexity must be at least one.")
+ parameters.add(Perplexity, perplexity)
+ this
+ }
+
+ /** The accepted error tolerance to save computational time when
computing the affinity
+ *
+ * @param errorTolerance the accepted error tolerance with respect to
the affinity
+ * @return
+ */
+ def setErrorTolerance(errorTolerance: Double):
StochasticOutlierSelection = {
+ require(errorTolerance >= 0, "Error tolerance cannot be negative.")
+ parameters.add(ErrorTolerance, errorTolerance)
+ this
+ }
+
+ /** The maximum number of iterations to approximate the affinity
+ *
+ * @param maxIterations the maximum number of iterations
+ * @return
+ */
+ def setMaxIterations(maxIterations: Int): StochasticOutlierSelection = {
+ require(maxIterations > 0, "Maximum iterations must be positive.")
+ parameters.add(MaxIterations, maxIterations)
+ this
+ }
+
+}
+
+object StochasticOutlierSelection extends WithParameters {
+
+ // ========================================= Parameters
==========================================
+ case object Perplexity extends Parameter[Double] {
+ val defaultValue: Option[Double] = Some(30)
+ }
+
+ case object ErrorTolerance extends Parameter[Double] {
+ val defaultValue: Option[Double] = Some(1e-20)
+ }
+
+ case object MaxIterations extends Parameter[Int] {
+ val defaultValue: Option[Int] = Some(5000)
+ }
+
+ // ==================================== Factory methods
==========================================
+
+ def apply(): StochasticOutlierSelection = {
+ new StochasticOutlierSelection()
+ }
+
+ // ===================================== Operations
==============================================
+ case class BreezeLabeledVector(idx: Int, data: BreezeVector[Double])
+
+ implicit val transformLabeledVectors = {
+ new TransformDataSetOperation[StochasticOutlierSelection,
LabeledVector, (Int, Double)] {
+ override def transformDataSet(instance: StochasticOutlierSelection,
+ transformParameters: ParameterMap,
+ input: DataSet[LabeledVector]):
DataSet[(Int, Double)] = {
+
+ val resultingParameters = instance.parameters ++
transformParameters
+
+ val vectorsWithIndex = input.map(labeledVector => {
+ BreezeLabeledVector(labeledVector.label.toInt,
labeledVector.vector.asBreeze)
+ })
+
+ // Don't map back to a labeled-vector since the output of the
algorithm is
+ // a single double instead of vector
+ outlierSelection(vectorsWithIndex, resultingParameters)
+ }
+ }
+ }
+
+ /** [[TransformDataSetOperation]] applies the stochastic outlier
selection algorithm on a
+ * [[Vector]] which will transform the high-dimensionaly input to a
single Double output.
+ *
+ * @tparam T Type of the input and output data which has to be a
subtype of [[Vector]]
+ * @return [[TransformDataSetOperation]] a single double which
represents the oulierness of
+ * the input vectors, where the output is in [0, 1]
+ */
+ implicit def transformVectors[T <: Vector : BreezeVectorConverter :
TypeInformation : ClassTag]
+ = {
+ new TransformDataSetOperation[StochasticOutlierSelection, T, Double] {
+ override def transformDataSet(instance: StochasticOutlierSelection,
+ transformParameters: ParameterMap,
+ input: DataSet[T]): DataSet[Double] = {
+
+ val resultingParameters = instance.parameters ++
transformParameters
+
+ // Map to the right format
+ val vectorsWithIndex = input.zipWithIndex.map(vector => {
+ BreezeLabeledVector(vector._1.toInt, vector._2.asBreeze)
+ })
+
+ outlierSelection(vectorsWithIndex, resultingParameters).map(_._2)
+ }
+ }
+ }
+
+ /** Internal entry point which will execute the different stages of the
algorithm using a single
+ * interface
+ *
+ * @param inputVectors Input vectors on which the stochastic
outlier selection algorithm
+ * will be applied
+ * @param transformParameters The user defined parameters of the
algorithm
+ * @return The outlierness of the vectors compared to each other
+ */
+ private def outlierSelection(inputVectors: DataSet[BreezeLabeledVector],
+ transformParameters: ParameterMap):
DataSet[(Int, Double)] = {
+ val dissimilarityVectors = computeDissimilarityVectors(inputVectors)
+ val affinityVectors = computeAffinity(dissimilarityVectors,
transformParameters)
+ val bindingProbabilityVectors =
computeBindingProbabilities(affinityVectors)
+ val outlierProbability =
computeOutlierProbability(bindingProbabilityVectors)
+
+ outlierProbability
+ }
+
+ /** Compute pair-wise distance from each vector, to all other vectors.
+ *
+ * @param inputVectors The input vectors, will compare the vector to
all other vectors based
+ * on an distance method.
+ * @return Returns new set of [[BreezeLabeledVector]] with
dissimilarity vector
+ */
+ def computeDissimilarityVectors(inputVectors:
DataSet[BreezeLabeledVector]):
+ DataSet[BreezeLabeledVector] =
+ inputVectors.cross(inputVectors) {
+ (a, b) => (a.idx, b.idx, euclideanDistance(a.data, b.data))
+ }.filter(dist => dist._1 != dist._2) // Filter out the diagonal, this
contains no information.
+ .groupBy(0)
+ .sortGroup(1, Order.ASCENDING)
+ .reduceGroup {
+ distancesIterator => {
+ val distances = distancesIterator.toList
+ val distanceVector = distances.map(_._3).toArray
+
+ BreezeLabeledVector(distances.head._1,
BreezeDenseVector(distanceVector))
+ }
+ }
+
+ /** Approximate the affinity by fitting a Gaussian-like function
+ *
+ * @param dissimilarityVectors The dissimilarity vectors which
represents the distance to the
+ * other vectors in the data set.
+ * @param resultingParameters The user defined parameters of the
algorithm
+ * @return Returns new set of [[BreezeLabeledVector]] with
dissimilarity vector
+ */
+ def computeAffinity(dissimilarityVectors: DataSet[BreezeLabeledVector],
+ resultingParameters: ParameterMap):
DataSet[BreezeLabeledVector] = {
+ val logPerplexity = Math.log(resultingParameters(Perplexity))
+ val maxIterations = resultingParameters(MaxIterations)
+ val errorTolerance = resultingParameters(ErrorTolerance)
+
+ dissimilarityVectors.map(vec => {
+ val breezeVec = binarySearch(vec.data, logPerplexity, maxIterations,
errorTolerance)
+ BreezeLabeledVector(vec.idx, breezeVec)
+ })
+ }
+
+ /** Normalizes the input vectors so each row sums up to one.
+ *
+ * @param affinityVectors The affinity vectors which is the
quantification of the relationship
+ * between the original vectors.
+ * @return Returns new set of [[BreezeLabeledVector]] with represents
the binding
+ * probabilities, which is in fact the affinity where each row
sums up to one.
+ */
+ def computeBindingProbabilities(affinityVectors:
DataSet[BreezeLabeledVector]):
+ DataSet[BreezeLabeledVector] =
+ affinityVectors.map(vec => BreezeLabeledVector(vec.idx, vec.data :/
sum(vec.data)))
+
+ /** Compute the final outlier probability by taking the product of the
column.
+ *
+ * @param bindingProbabilityVectors The binding probability vectors
where the binding
+ * probability is based on the
affinity and represents the
+ * probability of a vector binding
with another vector.
+ * @return Returns a single double which represents the final
outlierness of the input vector.
+ */
+ def computeOutlierProbability(bindingProbabilityVectors:
DataSet[BreezeLabeledVector]):
+ DataSet[(Int, Double)] = bindingProbabilityVectors
+ .flatMap(vec => vec.data.toArray.zipWithIndex.map(pair => {
+
+ // The DistanceMatrix removed the diagonal, but we need to compute
the product
+ // of the column, so we need to correct the offset.
+ val columnIndex = if (pair._2 >= vec.idx) {
+ 1
+ } else {
+ 0
+ }
+
+ (columnIndex + pair._2, pair._1)
+ })).groupBy(0).reduceGroup {
+ probabilities => {
+ var rowNumber = -1
+ var outlierProbability = 1.0
+ for (probability <- probabilities) {
+ rowNumber = probability._1
+ outlierProbability = outlierProbability * (1.0 - probability._2)
+ }
+
+ (rowNumber, outlierProbability)
+ }
+ }
+
+ /** Performs a binary search to get affinities in such a way that each
conditional Gaussian has
+ * the same perplexity.
+ *
+ * @param dissimilarityVector The input dissimilarity vector which
represents the current
+ * vector distance to the other vectors in
the data set
+ * @param logPerplexity The log of the perplexity, which represents the
probability of having
+ * affinity with another vector.
+ * @param maxIterations The maximum iterations to limit the
computational time.
+ * @param tolerance The allowed tolerance to sacrifice precision for
decreased computational
+ * time.
+ * @param beta: The current beta
+ * @param betaMin The lower bound of beta
+ * @param betaMax The upper bound of beta
+ * @param iteration The current iteration
+ * @return Returns the affinity vector of the input vector.
+ */
+ def binarySearch(dissimilarityVector: BreezeVector[Double],
+ logPerplexity: Double,
+ maxIterations: Int,
+ tolerance: Double,
+ beta: Double = 1.0,
+ betaMin: Double = Double.NegativeInfinity,
+ betaMax: Double = Double.PositiveInfinity,
+ iteration: Int = 0): BreezeVector[Double] = {
--- End diff --
The non written down coding style was to break a long parameter list by
writing each parameter in its own line with double indentation. Something like
```
def fun(
x: Int,
y: Double)
: String = {
// foobar
}
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
