pyprobables 0.2.0

Probabilistic data structures in python

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 versions 2.7 and 3.3 - 3.6

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.