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        "author": "Ji\u0159\u00ed Filip",
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        "description": "# pyARC\n [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n\npyARC is an implementation of CBA (Classification Based on Assocation) algorithm introduced in\n\n\n ```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.```\n\nIn addition, pyARC contains the implementation of QCBA (Quantitative CBA) algorithm introduced in \n\n ```KLIEGR, Tomas. Quantitative CBA: Small and Comprehensible Association Rule Classification Models. arXiv preprint arXiv:1711.10166, 2017.```\n\nThe use of [QCBA](https://arxiv.org/abs/1711.10166) algorithm is demonstrated in [this jupyter notebook](https://github.com/jirifilip/pyARC/tree/master/notebooks/extensions/QCBA_demonstration.ipynb).\n\n\nThe [fim](http://www.borgelt.net/pyfim.html) package is used for the rule generation step. \n\n If you find this package useful in your research, please cite ([EasyChair link](https://easychair.org/publications/preprint/5d6G)):\n\n```\n @techreport{filip2018classification,\n  title={Classification based on Associations (CBA)-a performance analysis},\n  author={Filip, Ji{\\v{r}}{\\'\\i} and Kliegr, Tom{\\'a}{\\v{s}}},\n  year={2018},\n  institution={EasyChair}\n}\n```\n\n\n## Instalation\n```\npip install pyarc\n```\n\nFor using pyARC, the [fim](http://www.borgelt.net/pyfim.html) package needs to be installed (refer to http://www.borgelt.net/pyfim.html for installation guide).\n\n\n## Testing\n```\npython -m unittest discover -s pyarc/test  -p '*test_*.py'\n```\n\n\n## Examples\n\nSimplest example\n\n```python\nfrom pyarc import CBA, TransactionDB\nimport pandas as pd\n\ndata_train = pd.read_csv(\"iris.csv\")\ndata_test = pd.read_csv(\"iris.csv\")\n\ntxns_train = TransactionDB.from_DataFrame(data_train)\ntxns_test = TransactionDB.from_DataFrame(data_test)\n\n\ncba = CBA(support=0.20, confidence=0.5, algorithm=\"m1\")\ncba.fit(txns_train)\n\naccuracy = cba.rule_model_accuracy(txns_test) \n```\n\nUsing top_rules function to mine the best rules possible\n\n```python\nfrom pyarc import TransactionDB\nfrom pyarc.algorithms import (\n    top_rules,\n    createCARs,\n    M1Algorithm\n)\nimport pandas as pd\n\n\ndata_train = pd.read_csv(\"iris.csv\")\ndata_test = pd.read_csv(\"iris.csv\")\n\ntxns_train = TransactionDB.from_DataFrame(data_train)\ntxns_test = TransactionDB.from_DataFrame(data_test)\n\n# get the best association rules\nrules = top_rules(txns_train.string_representation)\n\n# convert them to class association rules\ncars = createCARs(rules)\n\nclassifier = M1Algorithm(cars, txns_train).build()\n\naccuracy = classifier.test_transactions(txns_test)\n\n```\n\n\n\n\n",
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