multisearch

Parameter optimization similar to GridSearch.

License	GNU General Public License 3
Categories	Search Business Logic Libraries
GroupId	com.github.fracpete
ArtifactId	multisearch-weka-package
Last Version	2021.2.17
Release Date	Feb 17, 2021
Type	jar
Description	multisearch Parameter optimization similar to GridSearch.
Project URL	https://github.com/fracpete/multisearch-weka-package
Project Organization	University of Waikato, Hamilton, NZ
Source Code Management	https://github.com/fracpete/multisearch-weka-package

Download multisearch-weka-package

Filename	Size
multisearch-weka-package-2021.2.17.pom
multisearch-weka-package-2021.2.17.jar	83 KB
multisearch-weka-package-2021.2.17-tests.jar	12 KB
multisearch-weka-package-2021.2.17-test-sources.jar	8 KB
multisearch-weka-package-2021.2.17-sources.jar	71 KB
multisearch-weka-package-2021.2.17-javadoc.jar	257 KB

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/com.github.fracpete/multisearch-weka-package/ -->
<dependency>
    <groupId>com.github.fracpete</groupId>
    <artifactId>multisearch-weka-package</artifactId>
    <version>2021.2.17</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/com.github.fracpete/multisearch-weka-package/
implementation 'com.github.fracpete:multisearch-weka-package:2021.2.17'

Gradle Kotlin

// https://jarcasting.com/artifacts/com.github.fracpete/multisearch-weka-package/
implementation ("com.github.fracpete:multisearch-weka-package:2021.2.17")

Apache Buildr

'com.github.fracpete:multisearch-weka-package:jar:2021.2.17'

Apache Ivy

<dependency org="com.github.fracpete" name="multisearch-weka-package" rev="2021.2.17">
  <artifact name="multisearch-weka-package" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='com.github.fracpete', module='multisearch-weka-package', version='2021.2.17')
)

Scala SBT

libraryDependencies += "com.github.fracpete" % "multisearch-weka-package" % "2021.2.17"

Leiningen

[com.github.fracpete/multisearch-weka-package "2021.2.17"]

Dependencies

compile (1)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	jar	[3.7.13,)

test (2)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	test-jar	[3.7.13,)
junit : junit	jar	4.13.1

Project Modules

There are no modules declared in this project.

multisearch-weka-package

Weka package for parameter optimization, similar to GridSearch. Can be used for optimizing an arbitrary number of parameters, in contrast to GridSearch which always requires you to optimize two parameters. However, it does not offer automatic search space extensions like GridSearch.

Parameters

• weka.core.setupgenerator.MathParameter

  uses mathematical expression to compute the (numeric) parameters

• weka.core.setupgenerator.ListParameter

  list of values; default is blank-separated, but a custom delimiter can be supplied

• weka.core.setupgenerator.MLPLayersParameter

  generates hidden layer definitions for the MultiLayerPerceptron classifier (contributed by Jan van Rijn)

• weka.core.setupgenerator.ParameterGroup

  allows grouping of dependent parameters, e.g., setting on group sets the kernel of SMO to RFBKernel and explores the gamma option, another group sets the kernel to PolyKernel and explores the exponent option.

Note: array elements, e.g., the filters inside a weka.filters.MultiFilter can be accessed using [n] with n being the 0-based index. E.g., if the third filter inside a MultiFilter is a PLSFilter, then its numComponents property can be accessed with filters[2].numComponents.

Supported parameter types

• char, string
• float, double
• int, long
• boolean
• weka.core.SelectedTag
• Java classname (and possible options for classes implementing weka.core.OptionHandler)

Search space exploration

The search space of setups can be explored with different strategies, derived from weka.classifiers.meta.multisearch.AbstractSearch. The following strategies are available:

• weka.classifiers.meta.multisearch.DefaultSearch

  Exhaustive search of parameter space

• weka.classifiers.meta.multisearch.RandomSearch

  Random search of parameter space (contributed by Jan van Rijn)

Example

With the following classifier setup:

weka.classifiers.meta.FilteredClassifier
|
+- weka.filters.supervised.attribute.PLSFilter
|
+- weka.classifiers.functions.LinearRegression

You can explore the filter's PLS components and classifier's ridge parameters by referencing these properties as follows (the MultiSearch's classifier is used as the base for the property paths):

• components: filter.numComponents
• ridge: classifier.ridge

For more examples, please see the following repository:

https://github.com/fracpete/multisearch-weka-package-examples

Releases

Click on one of the following links to download the corresponding Weka package:

• 2021.2.17
• 2020.2.17
• 2019.10.4
• 2019.9.30
• old releases: 2018.9.2, 2018.8.16, 2018.8.11, 2017.10.1, 2017.3.28, 2016.6.6, 2016.5.31, 2016.5.25, 2016.5.15, 2016.4.30, 2016.4.25, 2016.4.14, 2016.1.30, 2016.1.15, 2016.1.14, 2016.1.13, 2015.10.15, 2015.9.2, 2014.12.10

How to use packages

For more information on how to install the package, see:

https://waikato.github.io/weka-wiki/packages/manager/

Maven

Add the following dependency in your pom.xml to include the package:

    <dependency>
      <groupId>com.github.fracpete</groupId>
      <artifactId>multisearch-weka-package</artifactId>
      <version>2021.2.17</version>
      <type>jar</type>
      <exclusions>
        <exclusion>
          <groupId>nz.ac.waikato.cms.weka</groupId>
          <artifactId>weka-dev</artifactId>
        </exclusion>
      </exclusions>
    </dependency>

Versions

Version
2021.2.17 Feb 17, 2021
2020.2.17 Feb 17, 2020
2019.10.4 Oct 3, 2019
2019.9.30 Sep 30, 2019
2018.9.2 Sep 2, 2018
2018.8.16 Aug 16, 2018
2018.8.11 Aug 11, 2018
2017.10.1 Oct 1, 2017
2017.3.28 Mar 28, 2017
2016.6.7 Jun 7, 2016
2016.6.6 Jun 6, 2016
2016.5.31 May 31, 2016
2016.5.25 May 25, 2016
2016.5.15 May 15, 2016
2016.4.30 Apr 30, 2016
2016.4.25 Apr 25, 2016
2016.4.14 Apr 14, 2016
2016.1.30 Jan 30, 2016
2016.1.15 Jan 14, 2016
2016.1.14 Jan 14, 2016
2016.1.13 Jan 13, 2016
2015.10.15 Oct 15, 2015
2015.9.2 Sep 2, 2015
2014.12.10 Dec 10, 2014