apriorib

apriorib is a Python library that applies the very famous unsupervised learning algorithm, apriori, for Association Rule Mining(ARM) on a dataset of transaction/purchase logs and shows the accepted association rules.

Currently, this version is limited to a maximum of 4 items in a certain transaction.

New in this version

1. Displays stage-wise final itemset as pandas DataFrames.

Installation

Use the package manager pip to install apriorib.

pip install apriorib

Quick Start

from apriorib.apriorib import Apriori

data = [['MILK', 'BREAD', 'BISCUIT'],
    ['BREAD', 'MILK', 'BISCUIT', 'CORNFLAKES'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['JAM', 'MAGGI', 'BREAD', 'MILK'],
    ['MAGGI', 'TEA', 'BISCUIT'],
    ['BREAD', 'TEA', 'BOURNVITA'],
    ['MAGGI', 'TEA', 'CORNFLAKES'],
    ['MAGGI', 'BREAD', 'TEA', 'BISCUIT'],
    ['JAM', 'MAGGI', 'BREAD', 'TEA'],
    ['BREAD', 'MILK'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'COCK', 'BISCUIT', 'CORNFLAKES'],
    ['COFFEE', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'COCK'],
    ['BREAD', 'SUGER', 'BISCUIT'],
    ['COFFEE', 'SUGER', 'CORNFLAKES'],
    ['BREAD', 'SUGER', 'BOURNVITA'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['BREAD', 'COFFEE', 'SUGER'],
    ['TEA', 'MILK', 'COFFEE', 'CORNFLAKES']]

# Testing the Apriori class
apr = Apriori(records=data,min_sup=2,min_conf=50)
df1,df2,df3,df4 = apr.show_as_df(stage=1),apr.show_as_df(stage=2),apr.show_as_df(stage=3),apr.show_as_df(stage=4)
print("VIEWING THE ITEMSET DATAFRAMES AT THE DIFFERENT STAGES :\nSTAGE 1\n{}\nSTAGE 2\n{}\nSTAGE 3\n{}\nSTAGE 4\n{}".format(df1,df2,df3,df4))
apr.checkAssc()

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

MIT