apriorib1
apriorib1 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
- Displays stage-wise final itemset as pandas DataFrames.
Installation
Use the package manager pip to install apriorib1.
pip install apriorib1Quick Start
from apriorib1 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.
