niaarm 0.1.1

Nature-inspired algorithms for Association Rule Mining

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NiaARM - A minimalistic framework for numerical association rule mining.

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General outline of the framework

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 numerical 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.

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.

Installation

pip3

Install NiaARM with pip3:

pip3 install niaarm

Usage

Basic example

from niaarm import NiaARM, Dataset
from niapy.algorithms.basic import DifferentialEvolution
from niapy.task import Task, OptimizationType


# load and preprocess the dataset from csv
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
# the following 4 elements represent weights (support, confidence, coverage, shrinkage)
# None defines that criteria are omitted and are, therefore, excluded from the fitness function
problem = NiaARM(data.dimension, data.features, data.transactions, alpha=1.0, beta=1.0)

# 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.sort_rules()

# export all rules to csv
problem.export_rules('output.csv')

For a full list of examples see the examples folder.

Command line interface

niaarm -h
usage: niaarm [-h] -i INPUT_FILE [-o OUTPUT_FILE] -a ALGORITHM [-s SEED]
              [--max-evals MAX_EVALS] [--max-iters MAX_ITERS] [--alpha ALPHA]
              [--beta BETA] [--gamma GAMMA] [--delta DELTA] [--log]
              [--show-stats]

Perform ARM, output mined rules as csv, get mined rules' statistics

options:
  -h, --help            show this help message and exit
  -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
  -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
  --alpha ALPHA         Alpha parameter. Default 0
  --beta BETA           Beta parameter. Default 0
  --gamma GAMMA         Gamma parameter. Default 0
  --delta DELTA         Delta parameter. Default 0
  --log                 Enable logging of fitness improvements
  --show-stats          Display stats about mined rules

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

Reference Papers:

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).

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!