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tinynarm

https://github.com/firefly-cpp/tinynarm

Science Score: 67.0%

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    Low similarity (12.0%) to scientific vocabulary

Keywords from Contributors

evolutionary-algorithms interactive numeric agriculture data-preprocessing-pipelines data-collection esp32 network-simulation toml hacking
Last synced: 4 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: firefly-cpp
  • License: mit
  • Language: Python
  • Default Branch: main
  • Size: 407 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created 12 months ago · Last pushed 12 months ago
Metadata Files
Readme License Citation

README.md

tinyNARM

PyPI Version PyPI - Python Version PyPI - Downloads Downloads

🔍 Detailed insights • 📦 Installation • 🚀 Usage • 🔑 License • 📄 Cite us • 📝 References

tinyNARM is an experimental effort in approaching/tailoring the classical Numerical Association Rule Mining (NARM) to limited hardware devices, e.g., ESP32 microcontrollers so that devices do not need to depend on remote servers for making decisions. Motivation mainly lies in smart agriculture, where Internet connectivity is unavailable in rural areas.

The current repository hosts a tinyNARM algorithm prototype initially developed in Python for fast prototyping.

🔍 Detailed insights

The current version includes (but is not limited to) the following functions:

  • loading datasets in CSV format,
  • discretizing numerical features to discrete classes,
  • association rule mining using the tinynarm approach,
  • easy comparison with the NiaARM approach.

📦 Installation

pip

To install tinyNARM with pip, use:

sh pip install tinynarm

🚀 Usage

Basic run

```python from tinynarm import TinyNarm from tinynarm.utils import Utils

tnarm = TinyNarm("newdataset.csv") tnarm.createrules()

postprocess = Utils(tnarm.rules) postprocess.addfitness() postprocess.sortrules() postprocess.rulestocsv("rules.csv") postprocess.generatestatistics() postprocess.generatestats_report(20) ```

Discretization

```python from tinynarm.discretization import Discretization

dataset = Discretization("datasets/sportydatagen.csv", 5) data = dataset.generatedataset() dataset.datasettocsv(data, "newdataset.csv") ```

🔑 License

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

📄 Cite us

Fister Jr, I., Fister, I., Galvez, A., & Iglesias, A. (2023, August). TinyNARM: Simplifying Numerical Association Rule Mining for Running on Microcontrollers. In International Conference on Soft Computing Models in Industrial and Environmental Applications (pp. 122-131). Cham: Springer Nature Switzerland.

📝 References

[1] I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. Differential evolution for association rule mining using categorical and numerical attributes In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.

[2] I. Fister Jr., V. Podgorelec, I. Fister. Improved Nature-Inspired Algorithms for Numeric Association Rule Mining. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.

[3] I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020).

[4] Stupan, Ž., Fister, I. Jr. (2022). NiaARM: A minimalistic framework for Numerical Association Rule Mining. Journal of Open Source Software, 7(77), 4448.

Owner

  • Name: Iztok Fister Jr.
  • Login: firefly-cpp
  • Kind: user
  • Location: Slovenia

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: tinyNARM
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Iztok Jr.
    family-names: Fister
    orcid: 'https://orcid.org/0000-0002-6418-1272'
identifiers:
  - type: doi
    value: 10.1007/978-3-031-42529-5_12
    description: Conference paper
repository-code: 'https://gitlab.com/firefly-cpp/tinynarm'
keywords:
  - association rule mining
  - numerical association rule mining
  - tinyML
license: MIT

GitHub Events

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  • Push event: 5
  • Pull request event: 5
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Last Year
  • Release event: 1
  • Delete event: 3
  • Push event: 5
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Committers

Last synced: 5 months ago

All Time
  • Total Commits: 48
  • Total Committers: 3
  • Avg Commits per committer: 16.0
  • Development Distribution Score (DDS): 0.229
Past Year
  • Commits: 16
  • Committers: 3
  • Avg Commits per committer: 5.333
  • Development Distribution Score (DDS): 0.563
Top Committers
Name Email Commits
firefly-cpp i****k@i****u 37
Tadej Lahovnik t****k@s****i 8
dependabot[bot] 4****] 3
Committer Domains (Top 20 + Academic)
student.um.si: 1 iztok-jr-fister.eu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 0
  • Total pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 minutes
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 6
Past Year
  • Issues: 0
  • Pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: 10 minutes
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 6
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 28 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: tinynarm

Simplify numerical association rule mining

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 28 Last month
Rankings
Dependent packages count: 6.6%
Downloads: 14.8%
Average: 24.3%
Forks count: 30.5%
Dependent repos count: 30.6%
Stargazers count: 39.1%
Maintainers (1)
firefly-cpp
Last synced: 4 months ago