quick-sketches 1.0.0

A Library with Fast Sketching Primitives

Quick Sketches

While implementing a paper, we had to create a new library for Count-Min Sketches and Count-Sketches, as current solutions were inadequate. C++ libraries are difficult to use, and Python packages tend to be slow. Thus, we wrote a Python package that contains Python bindings to a C++ library to calculate the predictions of the count-min sketch and count sketches. In short, you can take advantage of C++ code to get predictions for the count-min sketch and count sketch while writing just Python!

• Quick Sketches
  • Installation
  • Usage

Installation

To install the package, one simply only needs to run

pip install quick_sketches

Usage

Consider the following example to get the count-min sketch when there is three of an element, four of another element, and finally five of a last element. There are five levels in the sketch, and each level contains 100 counters.

import quick_sketches as m
import numpy as np

a = np.array([3, 4, 5])
m.cm_sketch_preds(5, a, 100, 1) # usually [3, 4, 5], but sometimes not!

[numpy array of long longs] cm_sketch_preds(int nhashes, [numpy array of long longs] np_input, ll width, int seed)

takes as input

1. nhashes: The number of hash functions used in the sketch
2. np_input: The numpy array containing the frequencies of each key (note that order doesn't matter)
3. width: The width of the sketch, or the number of entries in each row of the sketch
4. seed: A random seed.

Then, it outputs what a count-min sketch would give as predicted frequencies with that particular set of parameters, where the output prediction at index i corresponds to the key in np_input at index i. For example, if np_input was [3, 4, 5], the output might also be [3, 4, 5], but it could not be [4, 3, 5], by the conservative nature of the sketch.

[numpy array of doubles] count_sketch_preds(int nhashes, [numpy array of long longs] np_input, ll width, int seed)

performs the same function for the Count-Sketch, with parameters:

1. nhashes: The number of hash functions used in the sketch
2. np_input: The numpy array containing the frequencies of each key (note that order doesn't matter)
3. width: The width of the sketch, or the number of entries in each row of the sketch
4. seed: A random seed.

Note one key difference, however: the output is in doubles, because the count-sketch takes medians, which sometimes leads to half-integer outputs!