Probabilistic Structures

Probstructs is easy to use Python wrapper for C++ library probstructs . It supports Exponential Histograms, Count Min Sketch (CM-Sketch), and Exponential Count Min Sketch (ECM-Sketch).

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Installation

With pip:

pip install probstructs

From source:

pip install .

Classes

CountMinSketch

Count–min sketch (CM sketch) is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space, at the expense of overcounting some events due to collisions.

C++ documentation: https://probstructs.readthedocs.io/en/latest/classes.html#countminsketch

from probstructs import CountMinSketch

cm_sketch = CountMinSketch(100, 4)
cm_sketch.inc("aaa", 1)
cm_sketch.inc("bbb", 5)
cm_sketch.inc("aaa", 2)

print(cm_sketch.get("aaa"))
# 3
print(cm_sketch.get("bbb"))
# 5
print(cm_sketch.get("ccc"))
# 0


cm_sketch = CountMinSketch(width=100, depth=4)
cm_sketch.inc(key="bbb", delta=5)
print(cm_sketch.get(key="bbb"))
# 5

ExponentialHistorgram

Exponential histogram (EH) is a probabilistic data structure that serves as a frequency counter for specific elements in the last N elements from stream..

C++ documentation: https://probstructs.readthedocs.io/en/latest/classes.html#exponentialhistorgram

from probstructs import ExponentialHistorgram


eh = ExponentialHistorgram(1)
eh.inc(1, 1)
print(eh.get(1, 1))
# 1
eh.inc(1, 1)
print(eh.get(1, 1))
# 2
eh.inc(2, 1)
print(eh.get(1, 2))
# 1

eh = ExponentialHistorgram(window=1)
eh.inc(tick=1, delta=1)
print(eh.get(window=1, tick=1))
# 1
eh.inc(tick=1, delta=1)
print(eh.get(window=1, tick=1))
# 2
eh.inc(tick=2, delta=1)
print(eh.get(window=1, tick=2))
# 1

ExponentialCountMinSketch

Exponential count-min sketch (ECM-Sketch) combines CM-Sketch with EH to count number of different elements in the last N elements in the stream.

C++ documentation: https://probstructs.readthedocs.io/en/latest/classes.html#exponentialcountminsketch

from probstructs import ExponentialCountMinSketch


ecm_sketch = ExponentialCountMinSketch(100, 4, 8)

ts = 0
ecm_sketch.inc("aaa", ts, 1)
ecm_sketch.inc("bbb", ts, 4)
ecm_sketch.inc("ccc", ts, 8)

print(ecm_sketch.get("aaa", 4, ts))
# 1
print(ecm_sketch.get("bbb", 4, ts))
# 4
print(ecm_sketch.get("ccc", 4, ts))
# 8
print(ecm_sketch.get("ddd", 4, ts))
# 0

ecm_sketch = ExponentialCountMinSketch(width=100, depth=4, window=8)

ts = 0
ecm_sketch.inc(key="aaa", tick=ts, delta=1)
ecm_sketch.inc(key="bbb", tick=ts, delta=4)
ecm_sketch.inc(key="ccc", tick=ts, delta=8)

print(ecm_sketch.get(key="aaa", window=4, tick=ts))
# 1
print(ecm_sketch.get(key="bbb", window=4, tick=ts))
# 4
print(ecm_sketch.get(key="ccc", window=4, tick=ts))
# 8
print(ecm_sketch.get(key="ddd", window=4, tick=ts))
# 0

Changelog

0.2.0

  • Introduce named parameters

  • Update documentation to contain examples

0.1.0

  • Initial version

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for martin.majlis from gravatar.com martin.majlis

Credits

Author: Martin Majlis

License

MIT License (MIT)

Tags

probabilistic structures exponential count–min sketch histogram

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for martin.majlis from gravatar.com martin.majlis

Credits

Author: Martin Majlis

License

MIT License (MIT)

Tags

probabilistic structures exponential count–min sketch histogram

Classifiers

Report project as malware

Release files for probstructs 0.2.8

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Source distribution for probstructs 0.2.8
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probstructs-0.2.8-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl 599.6 kB PyPy 3.9 PyPy 3.9 7.3 Linux glibc 2.17+ x86-32, Linux glibc 2.5+ x86-32 Details
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