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Probabilistic data structures

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  • License: MIT License (MIT)
  • Author: Martin Majlis
  • Tags probabilistic, structures, exponential, count–min, sketch, histogram
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Project description

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

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