arulespy 0.1.2

Python interface to the R package arules

Python interface to the R package arules

arulespy is a Python module available from PyPI. The arules module in arulespy provides an easy to install Python interface to the R package arules for association rule mining built with rpy2.

The R arules package implements a comprehensive infrastructure for representing, manipulating and analyzing transaction data and patterns using frequent itemsets and association rules. The package also provides a wide range of interest measures and mining algorithms including the code of Christian Borgelt’s popular and efficient C implementations of the association mining algorithms Apriori and Eclat, and optimized C/C++ code for mining and manipulating association rules using sparse matrix representation.

The arulesViz module provides plot() for visualizing association rules using the R package arulesViz.

arulespy provides Python classes for

• Transactions: Convert pandas dataframes into transaction data
• Rules: Association rules
• Itemsets: Itemsets
• ItemMatrix: sparse matrix representation of sets of items.

with Phyton-style slicing and len().

Most arules functions are interfaced as methods for the four classes with conversion from the R data structures to Python. Documentation is avaialible in Python via help(). Detailed online documentation for the R package is available here.

Low-level arules functions can also be directly used in the form R.<arules R function>(). The result will be a rpy2 data type. Transactions, itemsets and rules can manually be converted to Python classes using the helper function a2p().

Installation

arulespy is based on the python package rpy2 which requires an R installation. Here are the installation steps:

1. Install the latest version of R from https://www.r-project.org/

2. Install required libraries/set path depending on your OS:

   • libcurl is needed by R package curl.
     • Ubuntu: sudo apt-get install libcurl4-openssl-dev
     • MacOS: brew install curl
     • Windows: no installation necessary
   • Environment variable R_HOME may need to be set for Windows

3. Install arulespy which will automatically install rpy2 and pandas.

   pip install arulespy
   

4. Optional: Set the environment variable R_LIBS_USER to decide where R packages are stored (see libPaths() for details). If not set then R will determine a suitable location.

5. Optional: arulespy will install the needed R packages when it is imported for the first time. This may take a while. R packages can also be preinstalled. Start R and run install.packages(c("arules", "arulesViz"))

The most likely issue is that rpy2 does not find R. This will lead the python kernel to die or exit without explanation when the package arulespy is imported. Check python -m rpy2.situation to see if R and R's libraries are found. Details can be found here.

Example

from arulespy.arules import Transactions, apriori, parameters
import pandas as pd

# define the data as a pandas dataframe
df = pd.DataFrame (
    [
        [True,True, True],
        [True, False,False],
        [True, True, True],
        [True, False, False],
        [True, True, True]
    ],
    columns=list ('ABC')) 

# convert dataframe to transactions
trans = transactions.from_df(df)

# mine association rules
rules = apriori(trans,
                    parameter = parameters({"supp": 0.1, "conf": 0.8}), 
                    control = parameters({"verbose": False}))  

# display the rules as a pandas dataframe
rules.as_df()

	LHS	RHS	support	confidence	coverage	lift	count
1	{}	{A}	0.8	0.8	1	1	8
2	{}	{C}	0.8	0.8	1	1	8
3	{B}	{A}	0.4	0.8	0.5	1	4
4	{B}	{C}	0.5	1	0.5	1.25	5
5	{A,B}	{C}	0.4	1	0.4	1.25	4
6	{B,C}	{A}	0.4	0.8	0.5	1	4

Complete examples:

• Using arules
• Using arulesViz

References

• Michael Hahsler. ARULESPY: Exploring association rules and frequent itemsets in Python. arXiv:2305.15263 [cs.DB], May 2023.
• Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, and Christian Buchta. The arules R-package ecosystem: Analyzing interesting patterns from large transaction datasets. Journal of Machine Learning Research, 12:1977-1981, 2011.
• Michael Hahsler, Bettina Grün and Kurt Hornik. arules - A Computational Environment for Mining Association Rules and Frequent Item Sets. Journal of Statistical Software, 14(15), 2005.
• Hahsler, Michael. A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures.
• Michael Hahsler. An R Companion for Introduction to Data Mining: Chapter 5, 2021, URL: https://mhahsler.github.io/Introduction_to_Data_Mining_R_Examples/book/