pyarc 1.0.23

An implementation of CBA algorithm

pyARC

pyARC is an implementation of CBA (Classification Based on Assocation) algorithm introduced in

Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp 80-86.

In addition, pyARC contains the implementation of QCBA (Quantitative CBA) algorithm introduced in

KLIEGR, Tomas. Quantitative CBA: Small and Comprehensible Association Rule Classification Models. arXiv preprint arXiv:1711.10166, 2017.

The use of QCBA algorithm is demonstrated in this jupyter notebook.

The fim package is used for the rule generation step.

If you find this package useful in your research, please cite (EasyChair link):

 @techreport{filip2018classification,
  title={Classification based on Associations (CBA)-a performance analysis},
  author={Filip, Ji{\v{r}}{\'\i} and Kliegr, Tom{\'a}{\v{s}}},
  year={2018},
  institution={EasyChair}
}

Installation

pip install pyarc

For using pyARC, the fim package needs to be installed (refer to http://www.borgelt.net/pyfim.html for installation guide).

Testing

python -m unittest discover -s pyarc/test  -p '*test_*.py'

Examples

Simplest example

from pyarc import CBA, TransactionDB
import pandas as pd

data_train = pd.read_csv("iris.csv")
data_test = pd.read_csv("iris.csv")

txns_train = TransactionDB.from_DataFrame(data_train)
txns_test = TransactionDB.from_DataFrame(data_test)


cba = CBA(support=0.20, confidence=0.5, algorithm="m1")
cba.fit(txns_train)

accuracy = cba.rule_model_accuracy(txns_test) 

Using top_rules function to mine the best rules possible

from pyarc import TransactionDB
from pyarc.algorithms import (
    top_rules,
    createCARs,
    M1Algorithm,
    M2Algorithm
)
import pandas as pd


data_train = pd.read_csv("iris.csv")
data_test = pd.read_csv("iris.csv")

txns_train = TransactionDB.from_DataFrame(data_train)
txns_test = TransactionDB.from_DataFrame(data_test)

# get the best association rules
rules = top_rules(txns_train.string_representation)

# convert them to class association rules
cars = createCARs(rules)

classifier = M1Algorithm(cars, txns_train).build()
# classifier = M2Algorithm(cars, txns_train).build()

accuracy = classifier.test_transactions(txns_test)