🔍 Detailed insights • 📦 Installation • 🚀 Usage • 📄 Cite us • 📚 References • 📖 See also • 🔑 License • 🫂 Contributors

NiaARM is a framework for Association Rule Mining based on nature-inspired algorithms for optimization. 🌿 The framework is written fully in Python and runs on all platforms. NiaARM allows users to preprocess the data in a transaction database automatically, to search for association rules and provide a pretty output of the rules found. 📊 This framework also supports integral and real-valued types of attributes besides the categorical ones. Mining the association rules is defined as an optimization problem, and solved using the nature-inspired algorithms that come from the related framework called NiaPy. 🔗

• Documentation: https://niaarm.readthedocs.io/en/latest
• Tested OS: Windows, Ubuntu, Fedora, Alpine, Arch, macOS. However, that does not mean it does not work on others

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

• loading datasets in CSV format 📁
• preprocessing of data 🧹
• searching for association rules 🔎
• providing output of mined association rules 📋
• generating statistics about mined association rules 📊
• visualization of association rules 📈
• association rule text mining (experimental) 📄

To install NiaARM with pip, use:

pip install niaarm

To install NiaARM on Alpine Linux, enable Community repository and use:

$ apk add py3-niaarm

To install NiaARM on Arch Linux, use an AUR helper:

$ yay -Syyu python-niaarm

To install NiaARM on Fedora, use:

$ dnf install python3-niaarm

To install NiaARM on NixOS, use:

nix-env -iA nixos.python311Packages.niaarm

In NiaARM, data loading is done via the Dataset class. There are two options for loading data:

import pandas as pd from niaarm import Dataset df = pd.read_csv('datasets/Abalone.csv') # preprocess data... data = Dataset(df) print(data) # printing the dataset will generate a feature report

from niaarm import Dataset data = Dataset('datasets/Abalone.csv') print(data)

Optionally, a preprocessing technique, called data squashing [5], can be applied. This will significantly reduce the number of transactions, while providing similar results to the original dataset.

from niaarm import Dataset, squash dataset = Dataset('datasets/Abalone.csv') squashed = squash(dataset, threshold=0.9, similarity='euclidean') print(squashed)

Association rule mining can be easily performed using the get_rules function:

from niaarm import Dataset, get_rules from niapy.algorithms.basic import DifferentialEvolution data = Dataset("datasets/Abalone.csv") algo = DifferentialEvolution(population_size=50, differential_weight=0.5, crossover_probability=0.9) metrics = ('support', 'confidence') rules, run_time = get_rules(data, algo, metrics, max_iters=30, logging=True) print(rules) # Prints basic stats about the mined rules print(f'Run Time: {run_time}') rules.to_csv('output.csv')

The above example can be also be implemented using a more low level interface, with the NiaARM class directly:

from niaarm import NiaARM, Dataset from niapy.algorithms.basic import DifferentialEvolution from niapy.task import Task, OptimizationType data = Dataset("datasets/Abalone.csv") # Create a problem # dimension represents the dimension of the problem; # features represent the list of features, while transactions depicts the list of transactions # metrics is a sequence of metrics to be taken into account when computing the fitness; # you can also pass in a dict of the shape {'metric_name': <weight of metric in range [0, 1]>}; # when passing a sequence, the weights default to 1. problem = NiaARM(data.dimension, data.features, data.transactions, metrics=('support', 'confidence'), logging=True) # build niapy task task = Task(problem=problem, max_iters=30, optimization_type=OptimizationType.MAXIMIZATION) # use Differential Evolution (DE) algorithm from the NiaPy library # see full list of available algorithms: https://github.com/NiaOrg/NiaPy/blob/master/Algorithms.md algo = DifferentialEvolution(population_size=50, differential_weight=0.5, crossover_probability=0.9) # run algorithm best = algo.run(task=task) # sort rules problem.rules.sort() # export all rules to csv problem.rules.to_csv('output.csv')

The framework implements several popular interest measures, which can be used to compute the fitness function value of rules and for assessing the quality of the mined rules. A full list of the implemented interest measures along with their descriptions and equations can be found here.

The framework currently supports (visualizations):

• hill slopes (presented in [4]),
• scatter plot and
• grouped matrix plot visualization methods.

More visualization methods are planned to be implemented in future releases.

from matplotlib import pyplot as plt from niaarm import Dataset, get_rules from niaarm.visualize import hill_slopes dataset = Dataset('datasets/Abalone.csv') metrics = ('support', 'confidence') rules, _ = get_rules(dataset, 'DifferentialEvolution', metrics, max_evals=1000, seed=1234) some_rule = rules[150] hill_slopes(some_rule, dataset.transactions) plt.show()

from examples.visualization_examples.prepare_datasets import get_weather_data from niaarm import Dataset, get_rules from niaarm.visualize import scatter_plot # Get prepared data arm_df = get_weather_data() # Prepare Dataset dataset = Dataset(path_or_df=arm_df,delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules(dataset, "DifferentialEvolution", metrics, max_evals=500) # Add lift to metrics metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) # Visualize scatter plot fig = scatter_plot(rules=rules, metrics=metrics, interactive=False) fig.show()

from examples.visualization_examples.prepare_datasets import get_football_player_data from niaarm import Dataset, get_rules from niaarm.visualize import grouped_matrix_plot # Get prepared data arm_df = get_football_player_data() # Prepare Dataset dataset = Dataset(path_or_df=arm_df, delimiter=",") # Get rules metrics = ("support", "confidence") rules, run_time = get_rules(dataset, "DifferentialEvolution", metrics, max_evals=500) # Add lift to metrics metrics = list(metrics) metrics.append("lift") metrics = tuple(metrics) # Visualize grouped matrix plot fig = grouped_matrix_plot(rules=rules, metrics=metrics, k=5, interactive=False) fig.show()

An experimental implementation of association rule text mining using nature-inspired algorithms, based on ideas from [5] is also provided. The niaarm.text module contains the Corpus and Document classes for loading and preprocessing corpora, a TextRule class, representing a text rule, and the NiaARTM class, implementing association rule text mining as a continuous optimization problem. The get_text_rules function, equivalent to get_rules, but for text mining, was also added to the niaarm.mine module.

import pandas as pd from niaarm.text import Corpus from niaarm.mine import get_text_rules from niapy.algorithms.basic import ParticleSwarmOptimization df = pd.read_json('datasets/text/artm_test_dataset.json', orient='records') documents = df['text'].tolist() corpus = Corpus.from_list(documents) algorithm = ParticleSwarmOptimization(population_size=200, seed=123) metrics = ('support', 'confidence', 'aws') rules, time = get_text_rules(corpus, max_terms=5, algorithm=algorithm, metrics=metrics, max_evals=10000, logging=True) print(rules) print(f'Run time: {time:.2f}s') rules.to_csv('output.csv')

Note: You may need to download stopwords and the punkt tokenizer from nltk by running import nltk; nltk.download('stopwords'); nltk.download('punkt').

For a full list of examples see the examples folder in the GitHub repository.

We provide a simple command line interface, which allows you to easily mine association rules on any input dataset, output them to a csv file and/or perform a simple statistical analysis on them. For more details see the documentation.

niaarm -h

usage: niaarm [-h] [-v] [-c CONFIG] [-i INPUT_FILE] [-o OUTPUT_FILE] [--squashing-similarity {euclidean,cosine}] [--squashing-threshold SQUASHING_THRESHOLD] [-a ALGORITHM] [-s SEED] [--max-evals MAX_EVALS] [--max-iters MAX_ITERS] [--metrics METRICS [METRICS ...]] [--weights WEIGHTS [WEIGHTS ...]] [--log] [--stats] Perform ARM, output mined rules as csv, get mined rules' statistics options: -h, --help show this help message and exit -v, --version show program's version number and exit -c CONFIG, --config CONFIG Path to a TOML config file -i INPUT_FILE, --input-file INPUT_FILE Input file containing a csv dataset -o OUTPUT_FILE, --output-file OUTPUT_FILE Output file for mined rules --squashing-similarity {euclidean,cosine} Similarity measure to use for squashing --squashing-threshold SQUASHING_THRESHOLD Threshold to use for squashing -a ALGORITHM, --algorithm ALGORITHM Algorithm to use (niapy class name, e.g. DifferentialEvolution) -s SEED, --seed SEED Seed for the algorithm's random number generator --max-evals MAX_EVALS Maximum number of fitness function evaluations --max-iters MAX_ITERS Maximum number of iterations --metrics METRICS [METRICS ...] Metrics to use in the fitness function. --weights WEIGHTS [WEIGHTS ...] Weights in range [0, 1] corresponding to --metrics --log Enable logging of fitness improvements --stats Display stats about mined rules 

Note: The CLI script can also run as a python module (python -m niaarm ...)

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

Ideas are based on the following research papers:

[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] Fister, I. et al. (2020). Visualization of Numerical Association Rules by Hill Slopes. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_10

[5] I. Fister, S. Deb, I. Fister, Population-based metaheuristics for Association Rule Text Mining, In: Proceedings of the 2020 4th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, New York, NY, USA, mar. 2020, pp. 19–23. doi: 10.1145/3396474.3396493.

[6] I. Fister, I. Fister Jr., D. Novak and D. Verber, Data squashing as preprocessing in association rule mining, 2022 IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, Singapore, 2022, pp. 1720-1725, doi: 10.1109/SSCI51031.2022.10022240.

[1] NiaARM.jl: Numerical Association Rule Mining in Julia

[2] arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)

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

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!

Thanks goes to these wonderful people (emoji key):

zStupan 💻 🐛 📖 🖋 🤔 💡

Iztok Fister Jr. 💻 🐛 🧑‍🏫 🚧 🤔

Erkan Karabulut 💻 🐛

Tadej Lahovnik 📖

Ben Beasley 📖

Dusan Fister 🎨

This project follows the all-contributors specification. Contributions of any kind welcome!