This module provides a pure Python implementation of the FP-growth algorithm for finding frequent itemsets. FP-growth exploits an (often-valid) assumption that many transactions will have items in common to build a prefix tree. If the assumption holds true, this tree produces a compact representation of the actual transactions and is used to generate itemsets much faster than Apriori can.
After downloading and extracting the package, install the module by running
python setup.py install from within the extracted package directory. (If you
encounter errors, you may need to run setup with elevated permissions:
sudo python setup.py install.)
Usage of the module is very simple. Assuming you have some iterable of transactions (which are themselves iterables of items) called transactions and
an integer minimum support value minsup, you can find the frequent itemsets
in your transactions with the following code:
from fp_growth import find_frequent_itemsets
for itemset in find_frequent_itemsets(transactions, minsup):
print itemset
Note that find_frequent_itemsets returns a generator of itemsets, not a
greedily-populated list. Each item must be hashable (i.e., it must be valid as
a member of a dictionary or a set).
Once installed, the module can also be used as a stand-alone script. It will
read a list of transactions formatted as a CSV file. (An example of such a file
in included in the examples directory.)
python -m fp_growth -s {minimum support} {path to CSV file}
For example, to find the itemsets with support ≥ 4 in the included example file:
python -m fp_growth -s 4 examples/tsk.csv
Options:
-s X or --minimum-support X sets minimum support to X (Default is 2)
-n or --numeric converts the values in datasets to numerals (Default is false)
-d or --no-dopples Changes the support of the patterns so that it is the actual number of its appearances alone disregarding appearance in supersets (default: false)
The following references were used as source descriptions of the algorithm:
- Tan, Pang-Ning, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining. 1st ed. Boston: Pearson / Addison Wesley, 2006. (pp. 363-370)
- Han, Jiawei, Jian Pei, and Yiwen Yin. "Mining Frequent Patterns without Candidate Generation." Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 2000.
The example data included in tsk.csv comes from the section in Introduction
to Data Mining.
The python-fp-growth package is made available under the terms of the
MIT License.
Copyright © 2009 Eric Naeseth
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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