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An implementation of the FP-growth algorithm in pure Python.
Python
Latest commit 49763de Jan 7, 2016 @enaeseth Bump version to 0.1.3

Readme.md

Python FP-Growth

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.

Installation

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.)

Library Usage

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).

Script Usage

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

References

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.

License

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.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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