sandy4321/pyfreqpm

Frequent pattern mining framework (Apriori, Eclat, FP-Growth)

About

Frequent patterns are patterns which appear frequently within a dataset. A frequent itemset is one which is made up of one of these patterns, which is why frequent pattern mining is often alternately referred to as frequent itemset mining.

Frequent pattern mining is most easily explained by introducing market basket analysis (or affinity analysis), a typical usage for which it is well-known. Market basket analysis attempts to identify associations, or patterns, between the various items that have been chosen by a particular shopper and placed in their market basket, be it real or virtual, and assigns support and confidence measures for comparison. The value of this lies in cross-marketing and customer behavior analysis.

The generalization of market basket analysis is frequent pattern mining, and is actually quite similar to classification except that any attribute, or combination of attributes (and not just the class), can be predicted in association. As association does not require the pre-labeling of classes, it is a form of unsupervised learning. source

Framework

Algorithms

This frequent pattern mining framework implementing the following algorithms

1. generic Apriori
2. Apriori-TID
3. Eclat
4. FP-Growth

Usage

1. Get a list if all transactions (previous orders) and a list of items being sold that are encountered in the dataset

from pyfreqpm import FreqPM
dataset = [['i1', 'i2'],
           ['i1', 'i3'],
           ['i1', 'i2']]
items = ['i1', 'i2', 'i3']

2. Create an analyzer and provide algorithm to use and min_support, min_confidence, min_lift parameters.

Possible values for analyzer are apriori-gen, apriori-tid, eclat, fp-growth

test = FreqPM.model(analyzer='apriori-gen')
test.fit(dataset, items, min_support=0.1, min_confidence=0.5, min_lift=1.0)

3. After creating a model a prediction may be made for a new basket based on mined association rules

test.predict(['i1'])
{'confidence': 0.6666666666666666, 'lift': 1.0, 'to': frozenset({'i2'})}

This prediction recommends item i2 for someone having i1 already in the shopping cart.