Getting Started

Install the Pypi package using pip

pip install apriori_python

Then use it like

from apriori_python import apriori
itemSetList = [['eggs', 'bacon', 'soup'],
                ['eggs', 'bacon', 'apple'],
                ['soup', 'bacon', 'banana']]
freqItemSet, rules = apriori(itemSetList, minSup=0.5, minConf=0.5)
print(freqItemSet)
print(rules)  
# [[{'beer'}, {'rice'}, 0.6666666666666666], [{'rice'}, {'beer'}, 1.0]]
# rules[0] --> rules[1], confidence = rules[2]

Clone the repo

Get a copy of this repo using git clone

git clone https://github.com/chonyy/apriori_python.git

Run the program with dataset provided and default values for minSupport = 0.5 and minConfidence = 0.5

python apriori.py -f dataset.csv

Run program with dataset and min support and min confidence

python apriori.py -f tesco2.csv -s 0.5 -c 0.5

Concepts of Apriori

• Support: Fraction of transactions that contain an itemset
• Confidence: Measures how often items in Y appear in transactions that contain X
• Frequent itemset: An itemset whose support is greater than or equal to a minSup threshold