About

arm-preprocessing is a lightweight Python library supporting several key steps involving data preparation, manipulation, and discretization for Association Rule Mining (ARM). The design of this framework has a minimalistic design outlook in mind and is intended to be fully extensible and allow easy integration with other related ARM libraries, e.g., NiaARM.

Key features

• Loading various formats of datasets (CSV, JSON, TXT)
• Converting datasets to different formats
• Loading different types of datasets (numerical dataset, discrete dataset, time-series data, text)
• Dataset identification (which type of dataset)
• Dataset statistics
• Discretisation methods
• Data squashing methods

Usage

Data loading

The following example demonstrates how to load a dataset from a file (csv, json, txt). More examples can be found in the examples/data_loading directory:

• Loading a dataset from a CSV file
• Loading a dataset from a JSON file
• Loading a time-series dataset

from arm_preprocessing.dataset import Dataset

# Initialise dataset with filename (without format) and format (csv, json, txt)
dataset = Dataset('path/to/datasets', format='csv')

# Load dataset
dataset.load_data()
df = dataset.data

Data discretisation

The following example demonstrates how to discretise a dataset using the equal width method. More examples can be found in the examples/discretisation directory:

• Discretising a dataset using the equal width method
• Discretising a dataset using the equal frequency method
• Discretising a dataset using k-means clustering

from arm_preprocessing.dataset import Dataset

# Initialise dataset with filename (without format) and format (csv, json, txt)
dataset = Dataset('datasets/sportydatagen', format='csv')
dataset.load_data()

# Discretise dataset using equal width discretisation
dataset.discretise(method='equal_width', num_bins=5, columns=['calories'])

Data squashing

The following example demonstrates how to squash a dataset using the euclidean similarity. More examples can be found in the examples/squashing directory:

• Squashing a dataset using the euclidean similarity
• Squashing a dataset using the cosine similarity

from arm_preprocessing.dataset import Dataset

# Initialise dataset with filename and format
dataset = Dataset('datasets/breast', format='csv')
dataset.load()

# Squash dataset
dataset.squash(threshold=0.75, similarity='euclidean')

Missing values

The following example demonstrates how to handle missing values in a dataset using imputation. More examples can be found in the examples/missing_values directory:

• Handling missing values in a dataset using row deletion
• Handling missing values in a dataset using column deletion
• Handling missing values in a dataset using imputation

from arm_preprocessing.dataset import Dataset

# Initialise dataset with filename and format
dataset = Dataset('examples/missing_values/data', format='csv')
dataset.load()

# Impute missing data
dataset.missing_values(method='impute')

Related frameworks

[1] NiaARM: A minimalistic framework for Numerical Association Rule Mining

References

[1] 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.

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!