Probabilistic data structures
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).
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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BLAKE2b-256 | 3b310e39945cc68699288d2d4a06c931ee9f70d2bac35a7c286afa9f3b6a6556 |
Hashes for probstructs-0.2.8-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7a797eb2bbfe9f06a10100c7c2566894d3b06536025bf740fc462bf3e4b3053c |
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MD5 | 3cb63733746d3515a90542e4c22d3b57 |
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BLAKE2b-256 | c3089ef5ed984fbf990d06df91249d8dc5461e463db10f381668b233b1089ec3 |
Hashes for probstructs-0.2.8-cp37-cp37m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ab5e3252dc400dc45d480fdd8f8b23dcf66905bed4eceb2af69f0bf3df18f527 |
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MD5 | 10e6fdb68bc0dd371d74c382a9644bb4 |
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BLAKE2b-256 | 3caaa8eb34fb1bf9471a4699f04a36cc77ebfbe5d8e0f29161b8e02ca6491a1d |
Hashes for probstructs-0.2.8-cp37-cp37m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 7aa45a5bab43656f3044c0f5931d2f51b92332de807fe062034b7a7768a51086 |
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MD5 | 31d4bc071326f1d3a837e0eb47fe0a47 |
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BLAKE2b-256 | c6778ae6528ba08efc1a1d8bd27ceef91d6f4b85c6765608e2296c9e57d924a1 |
Hashes for probstructs-0.2.8-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 06a81959287d7ac989b64943b60341493ba5281f84df52eeb0016ca1b4a3bd48 |
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MD5 | 4867aa499c03b7baa9f75820f114fc4e |
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BLAKE2b-256 | 0b0da0bfd1fbcef8ac5531cb721e941f5a3a2d283d5ca7506fa4a5db863c1943 |
Hashes for probstructs-0.2.8-cp37-cp37m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | aecd2adced2472a5dbff5ebeaafded97160a6d7fbf0b70c9743467282467cb28 |
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MD5 | 3e0c70c5832cfa19f8d4c97d8717fa76 |
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BLAKE2b-256 | b77bb200e6e7d1a672843e77fb87d1d5763eb9fe88adb66e19f591b2817259ac |
Hashes for probstructs-0.2.8-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9009a9068b825741554a8828ce0e4f27149319bb33862700183e5ab5c1df901b |
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MD5 | 5ecc11b183ced22d8bfe2874f4663171 |
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BLAKE2b-256 | ea52ef4e149ff2927147619e77ead1de63df26b161fee25c6434895484d92fd6 |
Hashes for probstructs-0.2.8-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | c17d81ea8298ccf742e7672e33906148a0fed66608acd0e37c227f0bd1aab60c |
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MD5 | bdbd17225a7d88fdf599d2595f3427de |
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BLAKE2b-256 | efa5fbc49bb2d62628754727bf90896e5f74cb9f30ad3a05de1f50cd1ed306ea |
Hashes for probstructs-0.2.8-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ca5df533ccf508aedbadb674f034e4529c6f3fa2ede174c903c7efaf1357b329 |
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MD5 | 955e0d4fd0747835c958e783a6e5c9a3 |
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BLAKE2b-256 | 0a6d102ea6dfe5a3d3c2c040d35df04499c93124f354d4d4a91d6ae7bcea7706 |
Hashes for probstructs-0.2.8-cp36-cp36m-win_amd64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ce1eb99cc49e94635187af6026222f4b9fb6a2f7160d91dac90eb033ff82a6f9 |
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MD5 | 52e12c801abd3f2faab4340e42fcc6b0 |
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BLAKE2b-256 | f128f2b19a2139133eded802c2bcab62b2ad543eb66b4b4f6c53cb21b180689c |
Hashes for probstructs-0.2.8-cp36-cp36m-win32.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0b568765cb7c547520aafac1b0b09a499a9c2dae0ce5f1bdcb4d2d3423d305b7 |
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MD5 | 876da5d500cf62becb3c5b8cb3c99cba |
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BLAKE2b-256 | a15208a1994a3771a21d03cf60e24114f05a656aa500993a3e751707f15855ab |
Hashes for probstructs-0.2.8-cp36-cp36m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 341d66adc6dee94ccf0ba6e6c07da92194cf1ac2b146073ceffc76db742abf43 |
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MD5 | 3c90cf3336ee0d72e5dc8267531335c4 |
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BLAKE2b-256 | ee0e2dc0f4a81416f5bada0709ac08c46c266201924a3db30f24c67bbd05dbd0 |
Hashes for probstructs-0.2.8-cp36-cp36m-musllinux_1_1_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 8cf64b838e6ed32f104debe5361b7eb3114504cd63fc1d7eb2fc1d9249853303 |
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MD5 | 93f951cb6352cf01feb2e8ac1078782c |
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BLAKE2b-256 | 600a5b17ace8ca36dc095f87196438a69067b6398cfbe0713ef5482e096ad856 |
Hashes for probstructs-0.2.8-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | fa14f1b9d2a05117084ac11f60e0603ec2dd217ff0ab45afd50c61cc90bf2cb3 |
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MD5 | 7ed3191e290695bd25ec4da4f7e08594 |
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BLAKE2b-256 | f0cf4ccac04a1e41189b68efb7072d751eabc4e89d6f68f5dd273a9a75f48b57 |
Hashes for probstructs-0.2.8-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 20e0ed3c49abb69b2162c990464f900a5126bc478c068a00766e187a714b1b08 |
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MD5 | fe0f0f63f843f4e2f14ef4b2c169d1c6 |
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BLAKE2b-256 | 1a962ce405766cedb0ef2d74bd490eca6b8270a9e61866a39f29939c6875b5a6 |
Hashes for probstructs-0.2.8-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b940e7a6d3e261b52eb16591ecf99cef257a9a7bcd7f4c8c1f6cfec5bec2e7c3 |
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MD5 | 59fb4c7be7cd78d9116e5d400e1c97dd |
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BLAKE2b-256 | ba14353c4f7b2d384b26229956cf180f2a86e259184f4b7d7d1f75ea878d2b5f |