DAWG2-Python

This pure-python package provides read-only access for files created by dawgdic C++ library and DAWG python package.

This package is not capable of creating DAWGs. It works with DAWGs built by dawgdic C++ library or DAWG Python extension module. The main purpose of DAWG-Python is to provide access to DAWGs without requiring compiled extensions. It is also quite fast under PyPy (see benchmarks).

Installation

pip install DAWG2-Python

Usage

The aim of DAWG2-Python is to be API- and binary-compatible with DAWG when it is possible.

First, you have to create a dawg using DAWG module:

import dawg

d = dawg.DAWG(data)
d.save('words.dawg')

And then this dawg can be loaded without requiring C extensions:

import dawg_python

d = dawg_python.DAWG().load('words.dawg')

Please consult DAWG docs for detailed usage. Some features (like constructor parameters or save method) are intentionally unsupported.

Benchmarks

Benchmark results (100k unicode words, integer values (lengths of the words), PyPy 1.9, macbook air i5 1.8 Ghz):

dict __getitem__ (hits):        11.090M ops/sec
DAWG __getitem__ (hits):        not supported
BytesDAWG __getitem__ (hits):   0.493M ops/sec
RecordDAWG __getitem__ (hits):  0.376M ops/sec

dict get() (hits):              10.127M ops/sec
DAWG get() (hits):              not supported
BytesDAWG get() (hits):         0.481M ops/sec
RecordDAWG get() (hits):        0.402M ops/sec
dict get() (misses):            14.885M ops/sec
DAWG get() (misses):            not supported
BytesDAWG get() (misses):       1.259M ops/sec
RecordDAWG get() (misses):      1.337M ops/sec

dict __contains__ (hits):           11.100M ops/sec
DAWG __contains__ (hits):           1.317M ops/sec
BytesDAWG __contains__ (hits):      1.107M ops/sec
RecordDAWG __contains__ (hits):     1.095M ops/sec

dict __contains__ (misses):         10.567M ops/sec
DAWG __contains__ (misses):         1.902M ops/sec
BytesDAWG __contains__ (misses):    1.873M ops/sec
RecordDAWG __contains__ (misses):   1.862M ops/sec

dict items():           44.401 ops/sec
DAWG items():           not supported
BytesDAWG items():      3.226 ops/sec
RecordDAWG items():     2.987 ops/sec
dict keys():            426.250 ops/sec
DAWG keys():            not supported
BytesDAWG keys():       6.050 ops/sec
RecordDAWG keys():      6.363 ops/sec

DAWG.prefixes (hits):    0.756M ops/sec
DAWG.prefixes (mixed):   1.965M ops/sec
DAWG.prefixes (misses):  1.773M ops/sec

RecordDAWG.keys(prefix="xxx"), avg_len(res)==415:       1.429K ops/sec
RecordDAWG.keys(prefix="xxxxx"), avg_len(res)==17:      36.994K ops/sec
RecordDAWG.keys(prefix="xxxxxxxx"), avg_len(res)==3:    121.897K ops/sec
RecordDAWG.keys(prefix="xxxxx..xx"), avg_len(res)==1.4: 265.015K ops/sec
RecordDAWG.keys(prefix="xxx"), NON_EXISTING:            2450.898K ops/sec

Under CPython expect it to be about 50x slower. Memory consumption of DAWG-Python should be the same as of DAWG.

Current limitations

• This package is not capable of creating DAWGs;
• all the limitations of DAWG apply.

Contributions are welcome!

Contributing

• Development happens at GitHub: https://github.com/pymorphy2-fork/DAWG-Python
• Issue tracker: https://github.com/pymorphy2-fork/DAWG-Python/issues

Feel free to submit ideas, bugs or pull requests.

Running tests and benchmarks

Make sure pytest is installed and run

$ pytest .

from the source checkout. Tests should pass under python 3.8, 3.9, 3.10, 3.11 and PyPy3 >= 7.3.

In order to run benchmarks, type

$ pypy3 -m bench.speed

This runs benchmarks under PyPy (they are about 50x slower under CPython).

Authors & Contributors

• Mikhail Korobov <[email protected]>
• @bt2901
• @insolor

The algorithms are from dawgdic C++ library by Susumu Yata & contributors.

License

This package is licensed under MIT License.