niaarmts 0.1.2

Time series numerical association rule mining variants

Nature-Inspired Algorithms for Time Series Numerical Association Rule Mining

✨ Features • 📦 Installation • 🚀 Basic example • 📚 Reference Papers • 🔑 License • 📄 Cite us

This 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.

✨ Features

The current version of the framework supports two types of time series numerical association rule mining:

• Fixed Interval Time Series Numerical Association Rule Mining
• Segmented Interval Time Series Numerical Association Rule Mining

📦 Installation

To install NiaARMTS with pip, use:

pip install niaarmts

🚀 Basic example

Fixed Interval Time Series Numerical Association Rule Mining example

from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS

# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('intervals.csv', timestamp_col='timestamp')

# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically
    lower=0.0,  # Lower bound of solution space
    upper=1.0,  # Upper bound of solution space
    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata
    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions
    interval='true',  # Whether we're dealing with interval data
    alpha=1.0,  # Weight for support in fitness calculation
    beta=1.0,  # Weight for confidence in fitness calculation
    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)

# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations

# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)

# Run the algorithm
best_solution = pso.run(task)

# Output the best solution and its fitness value
print(f"Best solution: {best_solution[0]}")
print(f"Fitness value: {best_solution[1]}")

Segmented Interval Time Series Numerical Association Rule Mining example

from niapy.algorithms.basic import ParticleSwarmAlgorithm
from niapy.task import Task
from niaarmts import Dataset
from niaarmts.NiaARMTS import NiaARMTS

# Load dataset
dataset = Dataset()
dataset.load_data_from_csv('ts.csv', timestamp_col='timestamp')

# Create an instance of NiaARMTS
niaarmts_problem = NiaARMTS(
    dimension=dataset.calculate_problem_dimension(),  # Adjust dimension dynamically
    lower=0.0,  # Lower bound of solution space
    upper=1.0,  # Upper bound of solution space
    features=dataset.get_all_features_with_metadata(),  # Pass feature metadata
    transactions=dataset.get_all_transactions(),  # Dataframe containing all transactions
    interval='false',  # Whether we're dealing with interval data
    alpha=1.0,  # Weight for support in fitness calculation
    beta=1.0,  # Weight for confidence in fitness calculation
    gamma=1.0,  # Weight for inclusion in fitness calculation # if 0.0 then inclusion metric is omitted
    delta=1.0  # Weight for amplitude in fitness calculation # if 0.0 then amplitude metric is omitted
)

# Define the optimization task
task = Task(problem=niaarmts_problem, max_iters=100)  # Run for 100 iterations

# Initialize the Particle Swarm Optimization algorithm
pso = ParticleSwarmAlgorithm(population_size=40, min_velocity=-1.0, max_velocity=1.0, c1=2.0, c2=2.0)

# Run the algorithm
best_solution = pso.run(task)

# Output the best solution and its fitness value
print(f"Best solution: {best_solution[0]}")
print(f"Fitness value: {best_solution[1]}")

📚 Reference Papers

Ideas are based on the following research papers:

[1] Iztok Fister Jr., Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. Time series numerical association rule mining variants in smart agriculture. Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.

[2] Iztok Fister Jr., Iztok Fister, Sancho Salcedo-Sanz. Time Series Numerical Association Rule Mining for assisting Smart Agriculture. In: International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2022.

[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 In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.

[4] 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.

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

[6] 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

[7] 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.

[8] 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.

See also

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

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

🔑 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

[^1] Fister Jr, I., Yang, X. S., Fister, I., Brest, J., & Fister, D. (2013). A brief review of nature-inspired algorithms for optimization. arXiv preprint arXiv:1307.4186.

[^2] Iztok Fister Jr., Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz. Time series numerical association rule mining variants in smart agriculture. Journal of Ambient Intelligence and Humanized Computing (2023): 1-14.