Problems while extractiong association rules with Orange?

Question

I have a dataset with the dimensions (878049, 6).

It looks like this:

I would like to extract association rules that link the category column with the other columns. Thus, from the documentation I tried the following with Orange-Associate:

In:

import Orange
data = Orange.data.Table("data.csv")

In:

data.domain.attributes

Out:

   (DiscreteVariable('Category', values=['ARSON', 'ASSAULT', 'BAD CHECKS', 'BRIBERY', 'BURGLARY', ...]),
 DiscreteVariable('Descript', values=['ABANDONMENT OF CHILD', 'ABORTION', 'ACCESS CARD INFORMATION, PUBLICATION OF', 'ACCESS CARD INFORMATION, THEFT OF', 'ACCIDENTAL BURNS', ...]),
 DiscreteVariable('DayOfWeek', values=['Friday', 'Monday', 'Saturday', 'Sunday', 'Thursday', ...]),
 DiscreteVariable('PdDistrict', values=['BAYVIEW', 'CENTRAL', 'INGLESIDE', 'MISSION', 'NORTHERN', ...]),
 DiscreteVariable('Resolution', values=['ARREST, BOOKED', 'ARREST, CITED', 'CLEARED-CONTACT JUVENILE FOR MORE INFO', 'COMPLAINANT REFUSES TO PROSECUTE', 'DISTRICT ATTORNEY REFUSES TO PROSECUTE', ...]))

In:

from orangecontrib.associate.fpgrowth import *  

X, mapping = OneHot.encode(data, include_class=True)

X

Out:
array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ..., 
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]], dtype=bool)

In:

 sorted(mapping.items())

Out:

[(0, (0, 0)),
 (1, (0, 1)),
 (2, (0, 2)),
 (3, (0, 3)),
 (4, (0, 4)),
 (5, (0, 5)),
 (6, (0, 6)),
 (7, (0, 7)),
....
 (950, (4, 15)),
 (951, (4, 16))]

Then:

In:

itemsets = dict(frequent_itemsets(X, .4))

len(itemsets)

Out:

1 

In:

 class_items = {item

                for item, var, _ in OneHot.decode(mapping, data, mapping)

                if var is data.domain.class_var}
In:
sorted(class_items)

Out:

[]

I believe that the problem is that I did not yield correctly the Orange table. Thus, How should I load the dataset with orange in order to generate association rules?.

update

By @K3---rnc answer I tried this:

itemsets = dict(frequent_itemsets(X, .1))

print (len(itemsets))

print( itemsets)

for itemset, _support in itemsets:

    print(' '.join('{}={}'.format(var.name, val)

                   for _, var, val in OneHot.decode(itemset, data, mapping)))

18
{frozenset({935}): 206403, frozenset({20}): 92304, frozenset({928}): 119908, frozenset({924}): 129211, frozenset({946}): 526790, frozenset({921}): 116707, frozenset({946, 932}): 93924, frozenset({919}): 121584, frozenset({932}): 157182, frozenset({21}): 126182, frozenset({922}): 125038, frozenset({16}): 174900, frozenset({929}): 105296, frozenset({918}): 133734, frozenset({16, 946}): 156586, frozenset({925}): 89431, frozenset({923}): 124965, frozenset({920}): 126810}

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-83-83a24c082126> in <module>()
      2 print (len(itemsets))
      3 print( itemsets)
----> 4 for itemset, _support in itemsets:
      5     print(' '.join('{}={}'.format(var.name, val)
      6                    for _, var, val in OneHot.decode(itemset, data, mapping)))

ValueError: not enough values to unpack (expected 2, got 1)

However, I still with the same issues... I can not extract the association rules.

Answer 1

You are trying to induce classification rules without having any class variable in your data domain. If you print data.domain, you will see you only have regular attributes and metas.

[Category, DayOfWeek, PdDistrict, Resolution] {Descript, Address}

To solve this, you need to set one of your attributes as a class variable.

new_domain = Orange.data.Domain(list(data.domain.attributes[1:]), 
             data.domain.attributes[0], 
             metas=data.domain.metas)

This will set 'Category' attribute as a class variable. Of course you can set your own class variable by the above example. If you now print new_domain, you should see something like this:

[DayOfWeek, PdDistrict, Resolution | Category] {Descript, Address}