peewit intends to facilitate programming, running and result examination of machine learning experiments. It does not provide any sort of ML code but it give some assistance in avoiding a mess.
The concept of the framework is based on two observations: 1) machine learning experiment often have a regular tree structure and 2) experimentors happen to puzzle about which numbers belong to what parameters the next day. The typical regularity is utilized to fan out experiments in an abstract way that is a bit odd but readable for humans. Since this breakdown is readable for machines too, simple services can be precoded that help to keep track of the experimental dimensions. Further, it puts some regime on output files, helps to recover former versions, and handles simple parallelization.
Be aware that the implementation still has many deficiencies: it is weakly tested, many doc-string are missing, erroneous calls are rarely caught by helpful error messages. There is no up-todate documentation outside the code. At least, the examples are decorated with many explicatory comments. We do not recommend peewit at the current state unless you are curious about it. Also, by release of version 0.7 we start to overwork the value-model and thereby will rewrite the core modules.
The core modules depends on numpy only but for full functionality you further have to provide unison, ssh with agent, git, and matplotlib, as well as libsvm for the example project. Feel free to contact us for questions.
- Changes to previous version:
Other available revisons
Version Changelog Date 0.10
v-cube with side-cubes
May 7, 2014, 16:04:18 0.9
switched to python-3
February 11, 2013, 21:21:05 0.8
July 9, 2012, 23:32:20 0.7
November 4, 2011, 19:54:09 0.6
September 18, 2010, 16:22:01 0.5
August 18, 2010, 14:00:21 0.4
platform-independent path handling
July 21, 2010, 15:05:31 0.3
incremental e-tree definitions
May 12, 2010, 21:44:21 0.2
self-referential descent inputs
May 4, 2010, 13:30:36 0.1
services for arg-aggregations
April 26, 2010, 11:17:50 0.0
Initial Announcement on mloss.org.
April 23, 2010, 23:14:51
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