How to use the R package arulesViz from Python using arulespy

This document is also available as an IPython notebook or you can open and run it in Google Colab.

## Windows users: These environment variables may be necessary till rpy2 does this automatically
#from rpy2 import situation
#import os
#
#r_home = situation.r_home_from_registry()
#r_bin = r_home + '\\bin\\x64\\'
#os.environ['R_HOME'] = r_home
#os.environ['PATH'] =  r_bin + ";" + os.environ['PATH']
#os.add_dll_directory(r_bin)

Import Data and Mine Rules

Import the arules module from package arulespy.

from arulespy.arules import Transactions, apriori, parameters
from arulespy.arulesViz import plot, inspectDT

We read Zoo.cvs which contains the description of 101 animals containing nominal variables and one quantitative variable as a pandas dataframe.

import pandas as pd

df = pd.read_csv('Zoo.csv')
df

	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	type
0	True	False	False	True	False	False	True	True	True	True	False	False	4	False	False	True	mammal
1	True	False	False	True	False	False	False	True	True	True	False	False	4	True	False	True	mammal
2	False	False	True	False	False	True	True	True	True	False	False	True	0	True	False	False	fish
3	True	False	False	True	False	False	True	True	True	True	False	False	4	False	False	True	mammal
4	True	False	False	True	False	False	True	True	True	True	False	False	4	True	False	True	mammal
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
96	True	False	False	True	False	False	False	True	True	True	False	False	2	True	False	True	mammal
97	True	False	True	False	True	False	False	False	False	True	True	False	6	False	False	False	insect
98	True	False	False	True	False	False	True	True	True	True	False	False	4	True	False	True	mammal
99	False	False	True	False	False	False	False	False	False	True	False	False	0	False	False	False	mollusc.et.al
100	False	True	True	False	True	False	False	False	True	True	False	False	2	True	False	False	bird

101 rows × 17 columns

Convert the pandas dataframe into a sparse transactions object.

trans = Transactions.from_df(df)

trans.as_df()

	items	transactionID
1	{hair,milk,predator,toothed,backbone,breathes,...	0
2	{hair,milk,toothed,backbone,breathes,legs=[4,8...	1
3	{eggs,aquatic,predator,toothed,backbone,fins,l...	2
4	{hair,milk,predator,toothed,backbone,breathes,...	3
5	{hair,milk,predator,toothed,backbone,breathes,...	4
...	...	...
97	{hair,milk,toothed,backbone,breathes,legs=[2,4...	96
98	{hair,eggs,airborne,breathes,venomous,legs=[4,...	97
99	{hair,milk,predator,toothed,backbone,breathes,...	98
100	{eggs,breathes,legs=[0,2),type=mollusc.et.al}	99
101	{feathers,eggs,airborne,backbone,breathes,legs...	100

101 rows × 2 columns

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

rules.as_df().round(2)

	LHS	RHS	support	confidence	coverage	lift	count
1	{}	{backbone}	0.82	0.82	1.00	1.00	83
2	{type=fish}	{fins}	0.13	1.00	0.13	5.94	13
3	{type=fish}	{legs=[0,2)}	0.13	1.00	0.13	4.39	13
4	{type=fish}	{aquatic}	0.13	1.00	0.13	2.81	13
5	{type=fish}	{eggs}	0.13	1.00	0.13	1.71	13
...	...	...	...	...	...	...	...
5896	{hair,milk,predator,toothed,backbone,breathes,...	{catsize}	0.12	0.86	0.14	1.97	12
5897	{hair,milk,predator,toothed,backbone,breathes,...	{legs=[4,8]}	0.12	0.92	0.13	1.83	12
5898	{milk,predator,toothed,backbone,breathes,legs=...	{hair}	0.12	1.00	0.12	2.35	12
5899	{hair,milk,predator,toothed,backbone,breathes,...	{type=mammal}	0.12	1.00	0.12	2.46	12
5900	{hair,predator,toothed,backbone,breathes,legs=...	{milk}	0.12	1.00	0.12	2.46	12

5900 rows × 7 columns

arulesViz with ggplot

arulesViz provides ggplot as an drawing engine. arulesViz.plot() returns the ggplot object which can be included in the notebook using image_png().

import rpy2.robjects.lib.ggplot2 as gp
from rpy2.ipython.ggplot import image_png

gg = plot(rules, method="scatter")
image_png(gg)

R[write to console]: To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.

R[write to console]: In addition: 
R[write to console]: Warning messages:

R[write to console]: 1: 
R[write to console]: In (function (package, help, pos = 2, lib.loc = NULL, character.only = FALSE,  :
R[write to console]: 
 
R[write to console]:  library ‘/usr/local/lib/R/site-library’ contains no packages

R[write to console]: 2: 
R[write to console]: In (function (package, help, pos = 2, lib.loc = NULL, character.only = FALSE,  :
R[write to console]: 
 
R[write to console]:  library ‘/usr/local/lib/R/site-library’ contains no packages

R[write to console]: 3: Column(s) 13, 17 not logical or factor. Applying default discretization (see '? discretizeDF'). 

gg = plot(rules, method="grouped")
image_png(gg)

rules_20 = rules.sort(by = 'confidence')[0:100]
gg = plot(rules_20, method="graph")
image_png(gg)

Interactive Rules Table with HTML Widget

arulesViz can create interactive HTML widgets which can easily be integrated into a python notebook by saving the widget as a HTML fiel and including it using a IFrame.

from IPython.display import IFrame

import rpy2.robjects.packages as packages
htmlwidgets = packages.importr('htmlwidgets')

m = inspectDT(rules)
htmlwidgets.saveWidget(m, 'datatable.html')

IFrame("datatable.html", "100%", 600)

arulesViz with HTML Widgets

from IPython.display import IFrame
import rpy2.robjects.packages as packages
htmlwidgets = packages.importr('htmlwidgets')

m = plot(rules_20, method="graph", engine="htmlwidget")
htmlwidgets.saveWidget(m, "graph.html", selfcontained = True)
IFrame("graph.html", "100%", 800)

Interactive RuleExplorer

RuleExplorer only has experimental support at this point. The ruleExplorer opens a Shiny up in the web browser. Shutting down the Shiny app from within a Python notebook seems to be difficult. Uncomment the following line to explore the rules.

from arulespy.arulesViz import ruleExplorer

# ruleExplorer(rules)

An example of a running ruleExplorer can be found here.