PyProbables
pyprobables is a pure-python library for probabilistic data structures. The goal is to provide the developer with a pure-python implementation of common probabilistic data-structures to use in their work.
To achieve better raw performance, it is recommended supplying an alternative hashing algorithm that has been compiled in C. This could include using the md5 and sha512 algorithms provided or installing a third party package and writing your own hashing strategy. Some options include the murmur hash mmh3 or those from the pyhash library. Each data object in pyprobables makes it easy to pass in a custom hashing function.
Read more about how to use Supplying a pre-defined, alternative hashing strategies or Defining hashing function using the provided decorators.
Installation
Pip Installation:
$ pip install pyprobables
To install from source:
To install pyprobables, simply clone the repository on GitHub, then run from the folder:
$ python setup.py install
pyprobables supports python 3.5 - 3.9+
For python 2.7 support, install release 0.3.2
$ pip install pyprobables==0.3.2
API Documentation
The documentation of is hosted on readthedocs.io
You can build the documentation locally by running:
$ pip install sphinx $ cd docs/ $ make html
Automated Tests
To run automated tests, one must simply run the following command from the downloaded folder:
$ python setup.py test
Quickstart
Import pyprobables and setup a Bloom Filter
from probables import (BloomFilter)
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05)
blm.add('google.com')
blm.check('facebook.com') # should return False
blm.check('google.com') # should return True
Import pyprobables and setup a Count-Min Sketch
from probables import (CountMinSketch)
cms = CountMinSketch(width=1000, depth=5)
cms.add('google.com') # should return 1
cms.add('facebook.com', 25) # insert 25 at once; should return 25
Import pyprobables and setup a Cuckoo Filter
from probables import (CuckooFilter)
cko = CuckooFilter(capacity=100, max_swaps=10)
cko.add('google.com')
cko.check('facebook.com') # should return False
cko.check('google.com') # should return True
Supplying a pre-defined, alternative hashing strategies
from probables import (BloomFilter)
from probables.hashes import (default_sha256)
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05,
hash_function=default_sha256)
blm.add('google.com')
blm.check('facebook.com') # should return False
blm.check('google.com') # should return True
Defining hashing function using the provided decorators
import mmh3 # murmur hash 3 implementation (pip install mmh3)
from pyprobables.hashes import (hash_with_depth_bytes)
from pyprobables import (BloomFilter)
@hash_with_depth_bytes
def my_hash(key):
return mmh3.hash_bytes(key)
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05, hash_function=my_hash)
import mmh3 # murmur hash 3 implementation (pip install mmh3)
from pyprobables.hashes import (hash_with_depth_int)
from pyprobables import (BloomFilter)
@hash_with_depth_int
def my_hash(key, encoding='utf-8'):
max64mod = UINT64_T_MAX + 1
val = int(hashlib.sha512(key.encode(encoding)).hexdigest(), 16)
return val % max64mod
blm = BloomFilter(est_elements=1000, false_positive_rate=0.05, hash_function=my_hash)
See the API documentation for other data structures available and the quickstart page for more examples!
Changelog
Please see the changelog for a list of all changes.
27 Oct 29, 2022
1.3k Jan 1, 2023
3 Oct 22, 2021
1 Oct 30, 2021
21 Sep 26, 2022
7 Jul 10, 2022
5 Dec 9, 2022
1 Dec 15, 2021
1 Jan 9, 2022
2 Feb 2, 2022
1 Jan 31, 2022
124 Dec 7, 2022
0 Feb 13, 2022
2 Jan 9, 2022
1 Jan 27, 2022
262 Nov 18, 2022
72 Jan 1, 2023
3 Jan 22, 2022
23 Dec 1, 2022