Apyori

Apyori is a simple implementation of Apriori algorithm with Python 2.7 and 3.3 - 3.5, provided as APIs and as commandline interfaces.

.. image:: https://travis-ci.org/ymoch/apyori.svg?branch=master :target: https://travis-ci.org/ymoch/apyori .. image:: https://coveralls.io/repos/github/ymoch/apyori/badge.svg?branch=master :target: https://coveralls.io/github/ymoch/apyori?branch=master

Module Features

• Consisted of only one file and depends on no other libraries, which enable you to use it portably.
• Able to used as APIs.

Application Features

• Supports a JSON output format.
• Supports a TSV output format for 2-items relations.

Installation

Choose one from the following.

• Install with pip :code:

  pip install apyori

  .
• Put apyori.py into your project.
• Run :code:

  python setup.py install

  .

API Usage

Here is a basic example:

.. code-block:: python

from apyori import apriori

transactions = [
    ['beer', 'nuts'],
    ['beer', 'cheese'],
]
results = list(apriori(transactions))

For more details, see apyori.apriori pydoc.

CLI Usage

First, prepare input data as tab-separated transactions.

• Each item is separated with a tab.
• Each transactions is separated with a line feed code.

Second, run the application. Input data is given as a standard input or file paths.

• Run with :code:

  python apyori.py

  command.
• If installed, you can also run with :code:

  apyori-run

  command.

For more details, use '-h' option.

Samples

Basic usage

.. code-block:: shell

apyori-run < data/integration_test_input_1.tsv

Use TSV output

.. code-block:: shell

apyori-run -f tsv < data/integration_test_input_1.tsv

Fields of output mean:

• Base item.
• Appended item.
• Support.
• Confidence.
• Lift.

Specify the minimum support

.. code-block:: shell

apyori-run -s 0.5 < data/integration_test_input_1.tsv

Specify the minimum confidence

.. code-block:: shell

apyori-run -c 0.5 < data/integration_test_input_1.tsv