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

A minimalistic framework for Numerical Association Rule Mining
https://github.com/firefly-cpp/niaarm
association-rule-mining association-rules data-mining data-science evolutionary-algorithms swarm-intelligence
Last synced: 2 months ago
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A minimalistic framework for Numerical Association Rule Mining
Host: GitHub
URL: https://github.com/firefly-cpp/niaarm
Owner: firefly-cpp
License: mit
Created: 2021-11-23T12:11:15.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2025-03-21T08:51:26.000Z (7 months ago)
Last Synced: 2025-04-11T12:07:31.108Z (6 months ago)
Topics: association-rule-mining, association-rules, data-mining, data-science, evolutionary-algorithms, swarm-intelligence
Language: Python
Homepage:
Size: 44 MB
Stars: 16
Watchers: 5
Forks: 7
Open Issues: 6
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README

          


  





  NiaARM





  A minimalistic framework for Numerical Association Rule Mining





  

    

  

  

  

  

    

  

  

    

  

  

    

  

  

    

  

  

    

  

  

  





  

  

    

  

  

    

  

  

    

  





  

    

  





  🔍 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](https://github.com/NiaOrg/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**

## 🔍 Detailed insights

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) 📄

## 📦 Installation

### pip

To install `NiaARM` with pip, use:

```sh

pip install niaarm

```

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

```sh

$ apk add py3-niaarm

```

To install `NiaARM` on Arch Linux, use an [AUR helper](https://wiki.archlinux.org/title/AUR_helpers):

```sh

$ yay -Syyu python-niaarm

```

To install `NiaARM` on Fedora, use:

```sh

$ dnf install python3-niaarm

```

To install `NiaARM` on NixOS, use:

```sh

nix-env -iA nixos.python311Packages.niaarm

```

## 🚀 Usage

### Loading data

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

#### Option 1: From a pandas DataFrame (recommended)

```python

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

```

#### Option 2: Directly from a CSV file

```python

from niaarm import Dataset

data = Dataset('datasets/Abalone.csv')

print(data)

```

### Preprocessing

#### Data Squashing

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.

```python

from niaarm import Dataset, squash

dataset = Dataset('datasets/Abalone.csv')

squashed = squash(dataset, threshold=0.9, similarity='euclidean')

print(squashed)

```

### Mining association rules

#### The easy way (recommended)

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

```python

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 hard way

The above example can be also be implemented using a more low level interface,

with the `NiaARM` class directly:

```python

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': };

# 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')

```

#### Interest measures

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](interest_measures.md).

### Visualization

The framework currently supports ([visualizations](visualization.md)):

- hill slopes (presented in [4]),

- scatter plot and

- grouped matrix plot visualization methods.

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

#### Hill Slopes

```python

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()

```



    



#### Scatter Plot

```python

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()

```



    



#### Grouped Matrix Plot

```python

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()

```



    



### Text Mining (Experimental)

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.

```python

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](https://github.com/firefly-cpp/NiaARM/tree/main/examples)

in the GitHub repository.

### Command line interface

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](https://niaarm.readthedocs.io/en/latest/cli.html).

```shell

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 ...`)

## 📄 Cite us

Stupan, Ž., & Fister Jr., I. (2022). [NiaARM: A minimalistic framework for Numerical Association Rule Mining](https://www.theoj.org/joss-papers/joss.04448/10.21105.joss.04448.pdf). Journal of Open Source Software, 7(77), 4448.

## 📚 References

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](http://www.iztok-jr-fister.eu/static/publications/231.pdf) 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](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.

[3] 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).

[4] Fister, I. et al. (2020). [Visualization of Numerical Association Rules by Hill Slopes](http://www.iztok-jr-fister.eu/static/publications/280.pdf).

    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](http://www.iztok-jr-fister.eu/static/publications/260.pdf),

    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](https://dl.acm.org/doi/10.1145/3396474.3396493).

[6] 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).

## 📖 See also

[1] [NiaARM.jl: Numerical Association Rule Mining in Julia](https://github.com/firefly-cpp/NiaARM.jl)

[2] [arm-preprocessing: Implementation of several preprocessing techniques for Association Rule Mining (ARM)](https://github.com/firefly-cpp/arm-preprocessing)

## 🔑 License

This package is distributed under the MIT License. This license can be found online at .

## 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!

## 🫂 Contributors

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

  

    

      
_zStupan
💻 🐛 📖 🖋 🤔 💡

      
_{Iztok Fister Jr.}
💻 🐛 🧑‍🏫 🚧 🤔

      
_{Erkan Karabulut}
💻 🐛

      
_{Tadej Lahovnik}
📖

      
_{Ben Beasley}
📖

      
_{Dusan Fister}
🎨

    

  

This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!