Project details for peewit

Screenshot peewit 0.10

by lorenz - May 7, 2014, 16:04:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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peewit intends to facilitate programming, running and result examination of machine learning experiments. It abstains from setting ML data types, engines, or interfaces, what are to be provided by the user herself or by other libraries, for instance the python-weka-wrapper,

The user is to decompose the experimental code into named nodes that correpsond to aspects or dimension of an experiment. This breakdown gives the machine a grip on the compounds, such that it can support the user in

  • keeping track of the dimensions in code, stored (intermediate) results and plots
  • simple parallelization
  • storing and reintergration of intermediate results.

It also provides some generall houskeeping services like

  • regime on the file-names and paths, or automatic storing
  • recovering former experiment versions.

Be aware that the prototype is under development and many features are experimental. Even the underlying experimental model may be modified from time to time.

The core modules depend on python-3 and numpy only but for full func­tion­al­i­ty you fur­ther have to pro­vide uni­son, ssh with agent, git, matplotlib, and graphviz as well as lib­svm for the ex­am­ple project. Feel free to contact us for questions.​

Changes to previous version:

v-cube with side-cubes

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Posix
Data Formats: Agnostic
Tags: Workflow
Archive: download here

Other available revisons

Version Changelog Date

v-cube with side-cubes

May 7, 2014, 16:04:18

switched to python-3

February 11, 2013, 21:21:05

core rewrite

July 9, 2012, 23:32:20

semi-uniform v-cubes

November 4, 2011, 19:54:09

archiving services

September 18, 2010, 16:22:01

parallelization services

August 18, 2010, 14:00:21

platform-independent path handling

July 21, 2010, 15:05:31

incremental e-tree definitions

May 12, 2010, 21:44:21

self-referential descent inputs

May 4, 2010, 13:30:36

services for arg-aggregations

April 26, 2010, 11:17:50

Initial Announcement on

April 23, 2010, 23:14:51


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