DAWG-Python 0.1

DAWG-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 an access to DAWGs without requiring compiled extensions. It is also quite fast under PyPy (see benchmarks).

Installation

pip install DAWG-Python

Usage

The aim of DAWG-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 (lenghts of the words), PyPy 1.9, macbook air i5 1.8 Ghz):

dict __getitem__ (hits):        10.978M ops/sec
DAWG __getitem__ (hits):        not supported
BytesDAWG __getitem__ (hits):   0.423M ops/sec
RecordDAWG __getitem__ (hits):  0.348M ops/sec

dict get() (hits):              10.127M ops/sec
DAWG get() (hits):              not supported
BytesDAWG get() (hits):         0.438M ops/sec
RecordDAWG get() (hits):        0.363M ops/sec
dict get() (misses):            14.885M ops/sec
DAWG get() (misses):            not supported
BytesDAWG get() (misses):       1.228M ops/sec
RecordDAWG get() (misses):      1.239M ops/sec

dict __contains__ (hits):           10.341M ops/sec
DAWG __contains__ (hits):           1.086M ops/sec
BytesDAWG __contains__ (hits):      0.904M ops/sec
RecordDAWG __contains__ (hits):     0.886M ops/sec

dict __contains__ (misses):         9.823M ops/sec
DAWG __contains__ (misses):         1.491M ops/sec
BytesDAWG __contains__ (misses):    1.451M ops/sec
RecordDAWG __contains__ (misses):   1.437M ops/sec

dict items():           44.401 ops/sec
DAWG items():           not supported
BytesDAWG items():      3.437 ops/sec
RecordDAWG items():     3.210 ops/sec
dict keys():            426.250 ops/sec
DAWG keys():            not supported
BytesDAWG keys():       6.347 ops/sec
RecordDAWG keys():      6.428 ops/sec


RecordDAWG.keys(prefix="xxx"), avg_len(res)==415:       1.531K ops/sec
RecordDAWG.keys(prefix="xxxxx"), avg_len(res)==17:      39.823K ops/sec
RecordDAWG.keys(prefix="xxxxxxxx"), avg_len(res)==3:    165.236K ops/sec
RecordDAWG.keys(prefix="xxxxx..xx"), avg_len(res)==1.4: 237.831K ops/sec
RecordDAWG.keys(prefix="xxx"), NON_EXISTING:            4183.149K ops/sec

Under CPython expect it to be about 50x slower.

I think these results are quite good for pure-Python package. For example, under PyPy it has faster lookups and uses 2.5x less memory than marisa-trie under Python 3.2 (marisa-trie is much slower/doesn’t work under PyPy).

It is several times slower under PyPy than Cython-based DAWG under CPython though, so DAWG + CPython > DAWG-Python + PyPy.

Memory consumption of DAWG-Python should be the same as of DAWG.

Current limitations

• This package is not capable of creating DAWGs;

• IntDAWG is not implemented;

• all the limitations of DAWG apply.

Contributions are welcome!

Contributing

Development happens at github and bitbucket:

• https://github.com/kmike/DAWG-Python

• https://bitbucket.org/kmike/DAWG-Python

The main issue tracker is at github: https://github.com/kmike/DAWG-Python/issues

Feel free to submit ideas, bugs, pull requests (git or hg) or regular patches.

$ tox

$ tox -c bench.ini -e pypy

License

This package is licensed under MIT License.