{"id":26246048,"url":"https://github.com/firefly-cpp/niaarmts","last_synced_at":"2025-08-17T18:11:20.497Z","repository":{"id":271782379,"uuid":"914374598","full_name":"firefly-cpp/NiaARMTS","owner":"firefly-cpp","description":null,"archived":false,"fork":false,"pushed_at":"2025-04-14T21:14:40.000Z","size":621,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-14T22:25:39.301Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/firefly-cpp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-01-09T13:25:55.000Z","updated_at":"2025-04-14T21:14:43.000Z","dependencies_parsed_at":"2025-04-14T22:22:57.209Z","dependency_job_id":null,"html_url":"https://github.com/firefly-cpp/NiaARMTS","commit_stats":null,"previous_names":["firefly-cpp/niaarmts"],"tags_count":4,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/firefly-cpp%2FNiaARMTS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/firefly-cpp%2FNiaARMTS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/firefly-cpp%2FNiaARMTS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/firefly-cpp%2FNiaARMTS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/firefly-cpp","download_url":"https://codeload.github.com/firefly-cpp/NiaARMTS/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250508133,"owners_count":21442165,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-13T13:17:19.415Z","updated_at":"2025-08-17T18:11:20.477Z","avatar_url":"https://github.com/firefly-cpp.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\"\u003e\n  \u003cimg alt=\"logo\" width=\"300\" src=\".github/images/NiaARMTS.png\"\u003e\n\u003c/p\u003e\n\n\u003ch2 align=\"center\"\u003e\n    Nature-Inspired Algorithms for Time Series Numerical Association Rule Mining\n\u003c/h2\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"PyPI version\" src=\"https://img.shields.io/pypi/v/niaarmts.svg\" /\u003e\n    \u003cimg alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/niaarmts.svg\"\u003e\n    \u003ca href=\"https://pepy.tech/project/niaarmts\"\u003e\n        \u003cimg alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/niaarmts.svg\"\u003e\n    \u003c/a\u003e\n    \u003cimg alt=\"Downloads\" src=\"https://static.pepy.tech/badge/niaarmts\"\u003e\n    \u003cimg alt=\"NiaARMTS\" src=\"https://github.com/firefly-cpp/NiaARMTS/actions/workflows/test.yml/badge.svg\" /\u003e\n    \u003cimg alt=\"Documentation status\" src=\"https://readthedocs.org/projects/niaarmts/badge/?version=latest\" /\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cimg alt=\"Repository size\" src=\"https://img.shields.io/github/repo-size/firefly-cpp/NiaARMTS\" /\u003e\n    \u003cimg alt=\"License\" src=\"https://img.shields.io/github/license/firefly-cpp/NiaARMTS.svg\" /\u003e\n    \u003cimg alt=\"GitHub commit activity\" src=\"https://img.shields.io/github/commit-activity/w/firefly-cpp/NiaARMTS.svg\"\u003e\n    \u003ca href=\"http://isitmaintained.com/project/firefly-cpp/NiaARMTS\"\u003e\n        \u003cimg alt=\"Percentage of issues still open\" src=\"http://isitmaintained.com/badge/open/firefly-cpp/NiaARMTS.svg\"\u003e\n    \u003c/a\u003e\n    \u003ca href=\"http://isitmaintained.com/project/firefly-cpp/NiaARMTS\"\u003e\n        \u003cimg alt=\"Average time to resolve an issue\" src=\"http://isitmaintained.com/badge/resolution/firefly-cpp/NiaARMTS.svg\"\u003e\n    \u003c/a\u003e\n    \u003cimg alt=\"GitHub contributors\" src=\"https://img.shields.io/github/contributors/firefly-cpp/NiaARMTS.svg\"/\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003ca href=\"#-features\"\u003e✨ Features\u003c/a\u003e •\n    \u003ca href=\"#-installation\"\u003e📦 Installation\u003c/a\u003e •\n    \u003ca href=\"#-basic-example\"\u003e🚀 Basic example\u003c/a\u003e •\n    \u003ca href=\"#-reference-papers\"\u003e📚 Reference Papers\u003c/a\u003e •\n    \u003ca href=\"#-license\"\u003e🔑 License\u003c/a\u003e •\n    \u003ca href=\"#-cite-us\"\u003e📄 Cite us\u003c/a\u003e\n\u003c/p\u003e\n\nThis framework is designed for **numerical association rule mining in time series data** using **stochastic population-based nature-inspired algorithms**[^1]. It provides tools to extract association rules from time series datasets while incorporating key metrics such as **support**, **confidence**, **inclusion**, and **amplitude**. Although independent from the NiaARM framework, this software can be viewed as an extension, with additional support for time series numerical association rule mining.\n\n[^1]: Fister Jr, I., Yang, X. S., Fister, I., Brest, J., \u0026 Fister, D. (2013). [A brief review of nature-inspired algorithms for optimization](https://arxiv.org/abs/1307.4186). arXiv preprint arXiv:1307.4186.\n\n* **Free software:** MIT license\n* **Python**: 3.11, 3.12\n* **Documentation**: [https://niaarmts.readthedocs.io](https://niaarmts.readthedocs.io)\n\n## ✨ Features\n\nThe current version of the framework supports two types of time series numerical association rule mining:\n\n- **Fixed Interval Time Series Numerical Association Rule Mining**\n- **Segmented Interval Time Series Numerical Association Rule Mining**\n\n## 📦 Installation\n\nTo install `NiaARMTS` with pip, use:\n\n```sh\npip install niaarmts\n```\n\n## 🚀 Basic example\n\n### Fixed Interval Time Series Numerical Association Rule Mining example\n\n```python\nfrom niapy.algorithms.basic import ParticleSwarmAlgorithm\nfrom niapy.task import Task\nfrom niaarmts import Dataset\nfrom niaarmts.NiaARMTS import NiaARMTS\n\n# Load dataset\ndataset = Dataset()\ndataset.load_data_from_csv('intervals.csv', timestamp_col='timestamp')\n\n# Create an instance of NiaARMTS\nniaarmts_problem = NiaARMTS(\n    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically\n    lower=0.0,  # Lower bound of solution space\n    upper=1.0,  # Upper bound of solution space\n    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata\n    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions\n    interval='true',  # Whether we're dealing with interval data\n    alpha=1.0,  # Weight for support in fitness calculation\n    beta=1.0,  # Weight for confidence in fitness calculation\n    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted\n    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted\n)\n\n# Define the optimization task\ntask = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations\n\n# Initialize the Particle Swarm Optimization algorithm\npso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)\n\n# Run the algorithm\nbest_solution = pso.run(task)\n\n# Save discovered rules to CSV\nniaarmts_problem.save_rules_to_csv(\"interval_rules.csv\")\n\n# Print all rules to the terminal\nprint(\"\\n=== All Identified Rules (Interval Data, Sorted by Fitness) ===\")\nfor idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):\n    print(f\"\\nRule #{idx}:\")\n    print(f\"  Antecedent: {rule['antecedent']}\")\n    print(f\"  Consequent: {rule['consequent']}\")\n    print(f\"  Support: {rule['support']:.4f}\")\n    print(f\"  Confidence: {rule['confidence']:.4f}\")\n    print(f\"  Inclusion: {rule['inclusion']:.4f}\")\n    print(f\"  Amplitude: {rule['amplitude']:.4f}\")\n    print(f\"  Fitness: {rule['fitness']:.4f}\")\n    print(f\"  Interval: {rule['start']} (start) to {rule['end']} (end)\")\n```\n\n### Segmented Interval Time Series Numerical Association Rule Mining example\n\n```python\nfrom niapy.algorithms.basic import ParticleSwarmAlgorithm\nfrom niapy.task import Task\nfrom niaarmts import Dataset\nfrom niaarmts.NiaARMTS import NiaARMTS\n\n# Load dataset\ndataset = Dataset()\ndataset.load_data_from_csv('ts.csv', timestamp_col='timestamp')\n\n# Create an instance of NiaARMTS\nniaarmts_problem = NiaARMTS(\n    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically\n    lower=0.0,  # Lower bound of solution space\n    upper=1.0,  # Upper bound of solution space\n    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata\n    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions\n    interval='false',  # Whether we're dealing with interval data\n    alpha=1.0,  # Weight for support in fitness calculation\n    beta=1.0,  # Weight for confidence in fitness calculation\n    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted\n    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted\n)\n\n# Define the optimization task\ntask = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations\n\n# Initialize the Particle Swarm Optimization algorithm\npso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)\n\n# Run the algorithm\nbest_solution = pso.run(task)\n\n# Output the best solution and its fitness value\nprint(f\"Best solution: {best_solution[0]}\")\nprint(f\"Fitness value: {best_solution[1]}\")\n\n# Save all discovered rules to a CSV file\nniaarmts_problem.save_rules_to_csv(\"discovered_rules.csv\")\n\n# Print all rules to the terminal\nprint(\"\\n=== All Identified Rules (Sorted by Fitness) ===\")\nfor idx, rule in enumerate(niaarmts_problem.get_rule_archive(), 1):\n    print(f\"\\nRule #{idx}:\")\n    print(f\"  Antecedent: {rule['antecedent']}\")\n    print(f\"  Consequent: {rule['consequent']}\")\n    print(f\"  Support: {rule['support']:.4f}\")\n    print(f\"  Confidence: {rule['confidence']:.4f}\")\n    print(f\"  Inclusion: {rule['inclusion']:.4f}\")\n    print(f\"  Amplitude: {rule['amplitude']:.4f}\")\n    print(f\"  Fitness: {rule['fitness']:.4f}\")\n    print(f\"  Time window: {rule['start']} to {rule['end']}\")\n```\n\n## 📚 Reference Papers\n\nIdeas are based on the following research papers:\n\n[1] Iztok Fister Jr., Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.\n\n[2] Iztok Fister Jr., Iztok Fister, Sancho Salcedo-Sanz. [Time Series Numerical Association Rule Mining for assisting Smart Agriculture](https://iztok.link/static/publications/298.pdf). In: International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022.\n\n[3] 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](http://www.iztok-jr-fister.eu/static/publications/231.pdf) In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.\n\n[4] I. Fister Jr., V. Podgorelec, I. Fister. [Improved Nature-Inspired Algorithms for Numeric Association Rule Mining](https://iztok-jr-fister.eu/static/publications/324.pdf). In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.\n\n[5] I. Fister Jr., I. Fister [A brief overview of swarm intelligence-based algorithms for numerical association rule mining](https://arxiv.org/abs/2010.15524). arXiv preprint arXiv:2010.15524 (2020).\n\n[6] Fister, I. et al. (2020). [Visualization of Numerical Association Rules by Hill Slopes](http://www.iztok-jr-fister.eu/static/publications/280.pdf).\n    In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020.\n    IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10\n\n[7] I. Fister, S. Deb, I. Fister, [Population-based metaheuristics for Association Rule Text Mining](http://www.iztok-jr-fister.eu/static/publications/260.pdf),\n    In: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics \u0026 Swarm Intelligence,\n    New York, NY, USA, mar. 2020, pp. 19–23. doi: [10.1145/3396474.3396493](https://dl.acm.org/doi/10.1145/3396474.3396493).\n\n[8] I. Fister, I. Fister Jr., D. Novak and D. Verber, [Data squashing as preprocessing in association rule mining](https://iztok-jr-fister.eu/static/publications/300.pdf), 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 1720-1725, doi: [10.1109/SSCI51031.2022.10022240](https://doi.org/10.1109/SSCI51031.2022.10022240).\n\n## See also\n\n[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)\n\n[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)\n\n## 🔑 License\n\nThis package is distributed under the MIT License. This license can be found online at \u003chttp://www.opensource.org/licenses/MIT\u003e.\n\n## Disclaimer\n\nThis 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!\n\n## 📄 Cite us\n\n[1] Fister, I., Jr.; Salcedo-Sanz, S.; Alexandre-Cortizo, E.; Novak, D.; Fister, I.; Podgorelec, V.; Gorenjak, M. [Toward Explainable Time-Series Numerical Association Rule Mining: A Case Study in Smart-Agriculture](https://doi.org/10.3390/math13132122). Mathematics 2025, 13, 2122. [https://doi.org/10.3390/math13132122](https://doi.org/10.3390/math13132122) \n\n[2] Iztok Fister Jr., Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. [Time series numerical association rule mining variants in smart agriculture](https://iztok.link/static/publications/314.pdf). Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffirefly-cpp%2Fniaarmts","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffirefly-cpp%2Fniaarmts","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffirefly-cpp%2Fniaarmts/lists"}