pyndl 0.2.4

Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations.

This python3 package is a collection of useful script in order to run tasks on huge amounts of text file corpora. Especially, it allows to efficiently apply the Rescorla-Wagner learning rule to these corpora.

Warning

This package is still in alpha and there might be some API changes in the near future. If you want to suggest us some contact us under konstantin (dot) sering (ät) uni-tuebingen.de.

Note

This package is not intended to be used under python2.

Installation

Install with:

python setup.py install [--user]

or development install with:

python setup.py develop

in order to build a source package run:

python setup.py sdist

Usage

This package is intended to be used as a python package in (small) python script or via the ipython REPL. For example code and explanations look into doc/source/examples.rst.

Development

Documentation

The documentation and the doc-strings within the source code should follow the numpy doc-string conventions (which are used by pandas as well).

https://github.com/numpy/numpy/blob/master/doc/HOWTO_DOCUMENT.rst.txt

http://pandas.pydata.org/pandas-docs/stable/contributing.html#contributing-to-the-documentation

Install

You can install this package with pip from a folder, git repository, or sdist bundle:

pip install ~/pyndl/|git+ssh://[e-mail]/<user>/pyndl.git|pyndl-<version>.tar.gz

Testing

Check if the package does what it is supposed to do by running the test with py.test:

py.test-3 --cov-report html --cov=pyndl

The more general way of testing - including unit tests, documentation checks and style checking - can be done via tox. Tox does testing in a virtual environment and installs all dependencies.

(pip install tox)
tox

Important terminology

Some terminology used in these modules and scripts which descripe specific behaviour:

cue :

A cue is something that gives a hint on something else. The something else is called outcome. Examples for cues in a text corpus are trigraphs or preceeding words for the word or meaning of the word.

outcome :

A something that happens or is the result of an event. Examples are words, the meaning of the word, or lexomes.

event :

An event connects cues with outcomes. In any event one or more unordered cues are present and one or more outcomes are present.

weights :

The weights represent the learned experience / association between all cues and outcomes of interest. Usually, some meta data is stored alongside the learned weights.

cue file :

A cue file contains a list of all cues that are interesting for a specific question. It is a utf-8 encoded tab delimitered text file with a header in the first line. It has two columns. The first column contains the cue and the second column contains the frequency of the cue. There is one cue per line. The ordering does not matter.

outcome file :

An outcome file contains a list of all outcomes that are interesting for a specific question. It is a utf-8 encoded tab delimitered text file with a header in the first line. It has two columns. The first column contains the outcome and the second column contains the frequency of the outcome. There is one outcome per line. The ordering does not matter.

symbol file :

A symbol file contains a list of all symbols that are allowed to occur in an outcome or a cue. It is a utf-8 encoded tab delimitered text file with a header in the first line. It has two columns. The first column contains the symbol and the second column contains the frequency of the symbol. There is one symbol per line. The ordering does not matter.

event file :

An event file contains a list of all events that should be learned. The learning will start at the first event and continue to the last event in order of the lines. The event file is a utf-8 encoded tab delimitered text file with a header in the first line. It has three columns. The first column contains an underscore delimitered list of all cues. The second column contains an underscore delimitered list of all outcomes. The third column contains the frequency of the event. The ordering of the cues and outcomes does not matter. There is one event per line. The ordering of the lines in the file does matter.

corpus file :

A corpus file is a utf-8 encoded text file that contains huge amounts of text. A ---end.of.document--- or ---END.OF.DOCUMENT--- string marks where an old document finished and a new document starts.

weights file :

A weights file contains the learned weights between cues and outcomes. The netCDF format is used to store these information along side with meta data, which contains the learning parameters, the time needed to calculate the weights, the version of the software used and other information.