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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (FLINK-1807) Stochastic gradient descent optimizer for ML library
Date Tue, 21 Apr 2015 10:01:58 GMT

    [ https://issues.apache.org/jira/browse/FLINK-1807?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14504711#comment-14504711
] 

ASF GitHub Bot commented on FLINK-1807:
---------------------------------------

Github user tillrohrmann commented on a diff in the pull request:

    https://github.com/apache/flink/pull/613#discussion_r28764643
  
    --- Diff: flink-staging/flink-ml/src/main/scala/org/apache/flink/ml/optimization/Solver.scala
---
    @@ -0,0 +1,156 @@
    +/*
    + * 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.optimization
    +
    +import org.apache.flink.api.scala.DataSet
    +import org.apache.flink.ml.common._
    +import org.apache.flink.ml.math.DenseVector
    +import org.apache.flink.api.scala._
    +import org.apache.flink.ml.optimization.IterativeSolver.{Iterations, Stepsize, InitialWeights}
    +import org.apache.flink.ml.optimization.Solver._
    +
    +/** Base trait for optimization algorithms
    + *
    + */
    +abstract class Solver extends Serializable with WithParameters {
    +
    +  /** Provides a solution for the given optimization problem
    +    *
    +    * @param data A Dataset of LabeledVector (input, output) pairs
    +    * @param initialWeights The initial weight that will be optimized
    +    * @return A Vector of weights optimized to the given problem
    +    */
    +  def optimize(data: DataSet[LabeledVector], initialWeights: Option[DataSet[WeightVector]]):
DataSet[WeightVector]
    +
    +  /** Creates a DataSet with one zero vector. The zero vector has dimension d, which
is given
    +    * by the dimensionDS.
    +    *
    +    * @param dimensionDS DataSet with one element d, denoting the dimension of the returned
zero
    +    *                    vector
    +    * @return DataSet of a zero vector of dimension d
    +    */
    +  def createInitialWeightVector(dimensionDS: DataSet[Int]):  DataSet[WeightVector] =
{
    +    dimensionDS.map {
    +      dimension =>
    +        val values = Array.fill(dimension)(0.0)
    +        new WeightVector(DenseVector(values), 0.0)
    +    }
    +  }
    +
    +  //Setters for parameters
    +  def setLossFunction(lossFunction: LossFunction): Solver = {
    +    parameters.add(LossFunction, lossFunction)
    +    this
    +  }
    +
    +  def setRegularizationType(regularization: RegularizationType): Solver = {
    +    parameters.add(RegularizationType, regularization)
    +    this
    +  }
    +
    +  def setRegularizationParameter(regularizationParameter: Double): Solver = {
    +    parameters.add(RegularizationParameter, regularizationParameter)
    +    this
    +  }
    +
    +  def setDimensions(dimensions: Int): Solver = {
    +    parameters.add(Dimensions, dimensions)
    +    this
    +  }
    +}
    --- End diff --
    
    line break


> Stochastic gradient descent optimizer for ML library
> ----------------------------------------------------
>
>                 Key: FLINK-1807
>                 URL: https://issues.apache.org/jira/browse/FLINK-1807
>             Project: Flink
>          Issue Type: Improvement
>          Components: Machine Learning Library
>            Reporter: Till Rohrmann
>            Assignee: Theodore Vasiloudis
>              Labels: ML
>
> Stochastic gradient descent (SGD) is a widely used optimization technique in different
ML algorithms. Thus, it would be helpful to provide a generalized SGD implementation which
can be instantiated with the respective gradient computation. Such a building block would
make the development of future algorithms easier.



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