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xflr6
Feature set algebra for linguistics
Features
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Features is a simple implementation of feature set algebra in Python.
Linguistic analyses commonly use sets of binary or privative features to
refer to different groups of linguistic objects: for example a group of
phonemes that share some phonological features like [-consonantal, +high]
or a set of morphemes that occur in context of a specific person/number
combination like [-participant, GROUP]. Usually, the features are applied in
a way such that only some of their combinations are valid, while others are
impossible (i.e. refer to no object) |--| for example [+high, +low], or
[-participant, +speaker].
With this package, such feature systems can be defined with a simple contingency
table definition (feature matrix) and stored under a section name in a
simple clear-text configuration file. Each feature system can then be
loaded by its name and provides its own FeatureSet subclass that
implements all comparisons and operations between its feature sets according
to the given definition (compatibility, entailment, intersection, unification,
etc.).
Features creates the complete lattice structure between the possible feature
sets of each feature system and lets you navigate and visualize their
relations using the Graphviz graph layout software_.
Links
- GitHub: https://github.com/xflr6/features
- PyPI: https://pypi.org/project/features/
- Documentation: https://features.readthedocs.io
- Changelog: https://features.readthedocs.io/en/latest/changelog.html
- Issue Tracker: https://github.com/xflr6/features/issues
- Download: https://pypi.org/project/features/#files
Installation
This package runs under Python 3.8+, use pip_ to install:
.. code:: bash
$ pip install features
This will also install the concepts_ package from PyPI providing the Formal Concept Analysis_ (FCA) algorithms on which this package is based.
Quickstart
Load a predefined feature system by name (in this case features for a
six-way person/number distinction, cf. the definitions in the bundled
config.ini in the source repository_).
.. code:: python
>>> import features
>>> fs = features.FeatureSystem('plural')
>>> print(fs.context) # doctest: +ELLIPSIS
<Context object mapping 6 objects to 10 properties [3011c283] at 0x...>
|+1|-1|+2|-2|+3|-3|+sg|+pl|-sg|-pl|
1s|X | | |X | |X |X | | |X |
1p|X | | |X | |X | |X |X | |
2s| |X |X | | |X |X | | |X |
2p| |X |X | | |X | |X |X | |
3s| |X | |X |X | |X | | |X |
3p| |X | |X |X | | |X |X | |
Create feature sets from strings or string sequences. Use feature string parsing, get back string sequences and feature or extent strings in their canonical order (definition order):
.. code:: python
>>> fs('+1 +sg'), fs(['+2', '+2', '+sg']), fs(['+sg', '+3'])
(FeatureSet('+1 +sg'), FeatureSet('+2 +sg'), FeatureSet('+3 +sg'))
>>> fs('SG1').concept.intent
('+1', '-2', '-3', '+sg', '-pl')
>>> fs('1').string, fs('1').string_maximal, fs('1').string_extent
('+1', '+1 -2 -3', '1s 1p')
Use feature algebra: intersection (join) , union/unification (meet),
set inclusion (extension/subsumption). Do feature set comparisons
(logical connectives_).
.. code:: python
>>> fs('+1 +sg') % fs('+2 +sg')
FeatureSet('-3 +sg')
>>> fs('-3') ^ fs('+1') ^ fs('-pl')
FeatureSet('+1 +sg')
>>> fs('+3') > fs('-1') and fs('+pl') < fs('+2 -sg')
True
>>> fs('+1').incompatible_with(fs('+3')) and fs('+sg').complement_of(fs('+pl'))
True
Navigate the created subsumption lattice_ (Hasse graph_) of all valid
feature sets:
.. code:: python
>>> fs('+1').upper_neighbors, fs('+1').lower_neighbors
([FeatureSet('-3'), FeatureSet('-2')], [FeatureSet('+1 +sg'), FeatureSet('+1 +pl')])
>>> fs('+1').upset()
[FeatureSet('+1'), FeatureSet('-3'), FeatureSet('-2'), FeatureSet('')]
>>> for f in fs: # doctest: +ELLIPSIS
... print(f'[{f.string_maximal}] <-> {{{f.string_extent}}}')
[+1 -1 +2 -2 +3 -3 +sg +pl -sg -pl] <-> {}
[+1 -2 -3 +sg -pl] <-> {1s}
...
[-1] <-> {2s 2p 3s 3p}
[] <-> {1s 1p 2s 2p 3s 3p}
See the docs_ on how to define, load, and use your own feature systems.
Further reading
- https://en.wikipedia.org/wiki/Join_and_meet
- https://en.wikipedia.org/wiki/Formal_concept_analysis
- http://www.upriss.org.uk/fca/
See also
- concepts_ |--| Formal Concept Analysis with Python
- fileconfig_ |--| Config file sections as objects
- graphviz_ |--| Simple Python interface for Graphviz
License
Features is distributed under the MIT license_.
.. _pip: https://pip.readthedocs.io
.. _Graphviz graph layout software: http://www.graphviz.org .. _Formal Concept Analysis: https://en.wikipedia.org/wiki/Formal_concept_analysis .. _source repository: https://github.com/xflr6/features/blob/master/features/config.ini .. _logical connectives: https://en.wikipedia.org/wiki/Template:Logical_connectives_table_and_Hasse_diagram .. lattice: https://en.wikipedia.org/wiki/Lattice(order) .. _Hasse graph: https://en.wikipedia.org/wiki/Hasse_diagram .. _the docs: https://features.readthedocs.io/en/stable/manual.html
.. _concepts: https://pypi.org/project/concepts/ .. _fileconfig: https://pypi.org/project/fileconfig/ .. _graphviz: https://pypi.org/project/graphviz/
.. _MIT license: https://opensource.org/licenses/MIT
.. |--| unicode:: U+2013
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