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A framework for accuracy profiling of randomized approximate algorithm implementations
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AxProf
examples
tutorial
ICSE-2019-Paper.pdf
LICENSE
README.md

README.md

AxProf

A framework for accuracy profiling of randomized approximate algorithm implementations. See ICSE-2019-Paper.pdf for a full description of AxProf (to appear in ICSE 2019).


Directory structure

  • AxProf contains the source of AxProf.
  • AxProf/checkerGen contains the checker function generator component of AxProf.
  • tutorial contains a tutorial script that uses AxProf.
  • examples contains example scripts for testing some of the benchmarks from the conference paper.

Setup

First, install the required dependencies. Assuming your system is running Ubuntu 18.04, run the following commands:

sudo apt update
sudo apt install python-pip python3-pip cmake build-essential python3-tk
sudo pip install schema psutil numpy scipy pulp scikit-learn matplotlib
sudo pip3 install mmh3 numpy scipy pulp scikit-learn matplotlib minepy

Next, run the following commands from the root directory of this repository:

cd ./AxProf/checkerGen
make

Tutorial

A tutorial for using AxProf is available in tutorial/tutorial.py


Example

An example script for testing ekzhu/datasketch is provided in examples/hllEkzhu.py. To run the script, you must first clone the datasketch repository. Run the following commands from the root directory of this repository:

cd examples
git clone https://github.com/ekzhu/datasketch.git

Now you can run examples/hllEkzhu.py to test the library.

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