A library for computing the personal information factor (PIF)
Project description
piflib - computing personal information factors (PIF)
Installation
This library requires Python3. To install, execute
pip install piflib
Usage
piflib expects the data as a Pandas DataFrame. Luckily, Pandas supports a wide range of input formats.
In this example, we have data in csv format.
import pandas as pd
import piflib
dataframe = pd.read_csv('datafile.csv')
cigs = piflib.compute_cigs(dataframe)
csfs = piflib.compute_csfs(dataframe)
The compute_cigs and compute_csfs functions return a Pandas DataFrame, containing the CIG and CSF values
respectively. The CIG and CSF values appear in the same position as in the input data.
You can run and experiment with the tutorials online here:
What does it do? How does it work?
The documentation can be found here.
Limitations
Piflib currently only supports discrete feature distributions.
Copyright
Copyright 2021 CSIRO's Data61
License
Piflib is released under the Apache-2 license. Unless required by applicable law or agreed to in writing, software distributed under this license is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the license for the specific language governing permissions and limitations.
Citing
Piflib is designed, developed and supported by CSIRO's Data61. If you use any part of this library in your research, please cite it using the following BibTex entry:
@misc{piflib,
author = {CSIRO's Data61},
title = {piflib - computing personal information factors},
year = {2021},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/PIFtools/piflib}},
}
Thank You
We want to thank Jakub Nabaglo and Joyce Yu for their contributions to this codebase.
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