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).
pip install DAWG-Python
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.
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).
Memory consumption of DAWG-Python should be the same as of DAWG.
- This package is not capable of creating DAWGs;
- IntDAWG is not implemented;
- all the limitations of DAWG apply.
Contributions are welcome!
Development happens at github and bitbucket:
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.
Running tests and benchmarks
Make sure tox is installed and run
from the source checkout. Tests should pass under python 2.6, 2.7, 3.2, 3.3 and PyPy >= 1.9.
In order to run benchmarks, type
$ tox -c bench.ini -e pypy
This runs benchmarks under PyPy (they are about 50x slower under CPython).
This package is licensed under MIT License.