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).
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.
First, install the required dependencies. Assuming your system is running Ubuntu 20.04 and Python 3.8, run the following commands:
sudo apt update
sudo apt install build-essential default-jdk python3-pip
sudo python3 -m pip install numpy==1.24.3 scipy==1.10.1 matplotlib==3.7.1 minepy==1.2.6
Note that newer versions of Python 3 (e.g., 3.11) may not support AxProf. Use pyenv
if necessary to run AxProf in a Python 3.8 environment.
Next, run the following commands from the root directory of this repository:
cd AxProf/checkerGen
make
A tutorial for using AxProf is available in tutorial/tutorial.py
.
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.