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CL Machine-Learning Build Status

CL Machine-Learning is high performance and large scale statistical machine learning package written in Common Lisp developed at MSI.

This repository contains is a authorized fork of the original CLML with the following goals in mind:

  • Remove dependent libraries available from the Quicklisp repository
  • Re-factor code to support Quicklisp packaging
  • Organize code into independent systems based on functional category
  • Support for SBCL, CCL, LispWorks and Allegro Common Lisp
  • Improve documentation

Author(s):

Original

  • Salvi Péter
  • Naganuma Shigeta
  • Tada Masashi
  • Abe Yusuke
  • Jianshi Huang
  • Fujii Ryo
  • Abe Seika
  • Kuroda Hisao

Current Branch Maintainer(s)/Authors(s):

  • Mike Maul

Contributors:

  • Graham Dobbins

Installation

Requirements

  • Language: SBCL, CCL, Allegro or Lispworks
  • Platform: Posix compatibile platforms (Windows, Linux, BSD and derivatives)
  • ASDF3 and optionally Quicklisp (This document assumes Quicklisp)

Note: Default heapsize should be around 2560K (On some systems it may need to be greater) for SBCL this can be done by set with the switch

    sbcl --dynamic-space-size 2560

If running sbcl with roswell

    ros dynamic-space-size=2560 run

Currently development is taking place mostly on SBCL. For the near future SBCL is most stable platform.

Installation Notes

Obtaining code

Code can be obtained by one of the following methods:

Or download zip archive at

https://github.com/mmaul/clml/archive/master.zip

Installing

  1. For Quicklisp **

    1. Place code in ~/quicklisp/local-projects
    2. Start LISP and enter (ql:quickload :clml :verbose t)

  2. DONE For ASDF3 only (Non quicklisp users)

    1. Place in a location on your ASDF search path path such as ~/common-lisp
    2. Start LISP and enter (asdf:load-system :clml)

Documentation

User and API Documentation

and also in the project directories docs/clml-manual.html.

  • Tutorials may be found on the clml.tutorials blog at https://mmaul.github.io/clml.tutorials/
  • Usage examples can be found in the docs/sample project directory
  • Some notes and algorithmic details and background information can be

found in the project directory docs/notes files in memo, notes and docs

Sample Data

The sample datasets are located outside the CLML repository. Fortunately CLML is able to download sample datasets from remote sites via HTTP and HTTPS via the clml.utility.data:fetch function. Shown below is an example:

(clml.utility.data:fetch "https://mmaul.github.io/clml.data/sample/airquality.csv")

The clml.utility.data:fetch function downloads the file to a cache location and returns the path to the downloaded file. Therefore anywhere a path to a file is required the output from clml.utility.data:fetch can be provided instead.

The contents of the Sample dataset repository can be found at:

Usage

This library is organized as a hierarchical tree of systems.

  • clml
  • clml.association-rule
    • clml.association-rule
  • clml.classifiers
    • clml.classifiers.linear-regression
    • clml.classifiers.logistic-regression
    • clml.classifiers.nbayes
  • clml-clml.statistics
    • clml-clml.statistics
  • clml.clustering
    • clml.clustering.cluster-validation
    • clml.clustering.hc
    • clml.clustering.k-means2
    • clml.clustering.nmf
    • clml.clustering.optics
    • clml.clustering.optics-speed
    • clml.clustering.spectral-clustering
  • clml.decision-tree
    • clml.decision-tree.decision-tree
    • clml-decision-tree.random-forest
  • clml.graph
    • clml.graph.graph-anomaly-detection
    • clml.graph.graph-centrality
    • clml.graph.graph-utils
    • clml.graph.read-graph
    • clml.graph.shortest-path
  • clml.hjs
    • clml.hjs.k-means
    • clml.hjs.read-data
    • clml.hjs.vars
    • clml.hjs.eigensystems
    • clml.hjs.matrix
    • clml.hjs.meta
    • clml.hjs.missing-value
    • clml.hjs.vector
  • clml.nearest-search
    • clml.nearest-search.k-nn
    • clml.nearest-search.k-nn-new
    • clml.nearest-search.nearest
  • clml.nonparameteric
    • clml.nonparameteric.statistics
    • clml.nonparametric.blocked-hdp-hmm
    • clml.nonparametric.dpm
    • clml.nonparametric.ftm
    • clml.nonparametric.hdp
    • clml.nonparametric.hdp-hmm
    • clml.nonparametric.hdp-hmm
    • clml.nonparametric.hdp-lda
    • clml.nonparametric.ihmm
    • clml.nonparametric.lfm
    • clml.nonparametric.sticky-hdp-hmm
    • clml.numeric.fast-fourier-transform
  • clml.pca
    • clml.pca
  • clml.som
    • clml.som
  • clml.statistics
    • clml.statistics
    • clml.statistics.rand
  • clml.svm
    • clml.svm.mu
    • clml.svm.one-class
    • clml.svm.pwss3
    • clml.svm.smo
    • clml.svm.svr
    • clml.svm.wss3
  • clml.time-series
    • clml.time-series.anomaly-detection
    • clml.time-series.autoregression
    • clml.time-series.burst-detection
    • clml.time-series.changefinder
    • clml.time-series.exponential-smoothing
    • clml.time-series.read-data
    • clml.time-series.state-space
    • clml.time-series.statistics
    • clml.time-series.util
  • clml.utility
    • clml.utility.csv
    • clml.utility.priority-que
  • fork-future
  • future
  • lapack

Each system can be loaded independantly or the the clml system can be loaded which contains dependencies to all child system definitions.

This library requires that default reader float for mat is set to double-float. This should be done before loading the systems.

(setf *read-default-float-format* 'double-float)
  • Example below is using CLML.EXTRAS

Here is a quick demonstration:

CL-USER (ql:quickload :clml)

CL-USER (clml.text.utilities:calculate-levenshtein-similarity "Howdy" "doody")
0.6
CL-USER
CL-USER (setf *syobu* (clml.hjs.read-data:read-data-from-file
           (clml.utility.data:fetch "https://mmaul.github.io/clml.data/sample/syobu.csv")
           :type :csv :csv-type-spec '(string integer integer integer integer)))


#<HJS.LEARN.READ-DATA:UNSPECIALIZED-DATASET >
DIMENSIONS: 種類 | がく長 | がく幅 | 花びら長 | 花びら幅
TYPES:      UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN | UNKNOWN
NUMBER OF DIMENSIONS: 5
DATA POINTS: 150 POINTS

CL-USER (setf *tree* (clml.decision-tree.decision-tree:make-decision-tree *syobu* "種類"))


(((("花びら長" . 30)
   (("花びら幅" . 18) ("花びら幅" . 23) ("花びら幅" . 20) ("花びら幅" . 19) ("花びら幅" . 25)
    ("花びら幅" . 24) ("花びら幅" . 21) ("花びら幅" . 14) ("花びら幅" . 15) ("花びら幅" . 22)
     ("花びら幅" . 16) ("花びら幅" . 17) ("花びら幅" . 13) ("花びら幅" . 11) ("花びら幅" . 12)
  ...
  (("Virginica" . 50) ("Versicolor" . 50) ("Setosa" . 50))
  ((149 148 147 146 145 144 143 142 141 140 139 138 137 136 135 134 133 132 131
  ...
 (((("花びら幅" . 18)
    (("花びら幅" . 23) ("花びら幅" . 20) ("花びら幅" . 19) ("花びら幅" . 25) ("花びら幅" . 24)
     ("花びら幅" . 21) ("花びら幅" . 14) ("花びら幅" . 15) ("花びら幅" . 22) ("花びら幅" . 16)
     ("花びら幅" . 17) ("花びら幅" . 13) ("花びら幅" . 11) ("花びら幅" . 12) ("花びら幅" . 10)
 ...

)))
CL-USER    
CL-USER  (clml.decision-tree.decision-tree:print-decision-tree *tree*)
    [30 <= 花びら長?]((Virginica . 50) (Versicolor . 50) (Setosa . 50))
       Yes->[18 <= 花びら幅?]((Versicolor . 50) (Virginica . 50))
         Yes->[49 <= 花びら長?]((Virginica . 45) (Versicolor . 1))
             Yes->((Virginica . 43))
             No->[60 <= がく長?]((Versicolor . 1) (Virginica . 2))
                Yes->((Virginica . 2))
                No->((Versicolor . 1))
          No->[50 <= 花びら長?]((Virginica . 5) (Versicolor . 49))
             Yes->[16 <= 花びら幅?]((Versicolor . 2) (Virginica . 4))
                Yes->[70 <= がく長?]((Virginica . 1) (Versicolor . 2))
                   Yes->((Virginica . 1))
                   No->((Versicolor . 2))
                No->((Virginica . 3))
             No->[17 <= 花びら幅?]((Versicolor . 47) (Virginica . 1))
                Yes->((Virginica . 1))
                  No->((Versicolor . 47))
       No->((Setosa . 50))

Tests

CLML uses the [[https://github.com/OdonataResearchLLC/lisp-unit][lispunit] testing framwork. Tests are located in the tests directory. The tests provide useful examples of usage of the CLML API.

Compiling and running all unit tests can be ran as shown below.

(ql:quickload :clml.test :verbose t)
(in-package :clml.test)
(run-all-tests)

More information can gained on the useage of lispunit by visiting the project website. However some basic hints. The run- forms return a TEST-RESULTS-DB object. The test results database can be queried for information about the tests previously ran.

(defparameter myrun (run-all-tests))
(print-errors myrun) ; prints details of test errors
(print-failures myrun) ; prints details of test failures

Individual tests can be ran by the run-tests form. Individual test being dests defined with the form define-test.

(run-tests '(matrix-vecs-conversion-test  matrix-transpose-test))

Tests for CLML systes have been grouped in tests/test-groups.lisp for convience.

(run-tests *clustering-tests*)

Building Documentation

CLML uses the a modified version of the CLOD package for it's dcumentation system. Specific details of using clod can be found most easily in the clod api documentation] at quickdocs

(ql:quickload :clml.docs :verbose t)
(in-package :clml.docs)
(generate-api-docs)

The generate-api-docs form enerates Org API documentation in the clml/docs/api directory from loaded packages for CLML for packages matching the following prefix patterns:

+^clml[.]
+^lapack
+^hjs
+blas
+^future
+^fork-future

Documentation is in the form of Org files where one Org file per package is placed in clml/docs/api. A package index file containing Org INCLUDE directives that include Org files generated by the form generate-clml-api-docs are placed in clml/docs/api/index.org.

The CLML users manual includes the generated API documentation file index.org, HTML documentation can then be generated by opening the clml-manual.org file in Emacs and entering the Org mode export mode with C-c C-e and selecting file export with h h

The README.md file is generated by the org-mode export function. Which can be done by opening the README.org file in emacs and entering org-mode and using the export function C-c C-e and selecting the markdown export option as shown below.

M-x org-md-export-as-markdown
C-x-C-w README.md

The CMLM manual and API documentation can be exported to the desired format by opening the docs/clml-manual.org and using the org-mode export C-c C-e cord.

Related Repositories

Contributing

All contributions are welcome. If the contribution is to resolve and problem with CLML, please open an issue in the github repository accompanied by a pull request.

If you would like to contribute new functionality, again open an issue at the clml github repository, describe the proposed functionality and we will go from there. There is a separate repository clml.extras (https://github.com/mmaul/clml.extras) which is used for functionality that is not core to CLML but adds features and capabilities, such as integration layers with other libraries. If this describes your contribution please open an issue on clml.extras github repository.

If you are interesting in helping to maintain CLML, please contact me via email.

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Common Lisp Machine Learning Library

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