pyfair

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Factor Analysis of Information Risk (FAIR) model written in Python.

This package endeavors to create a simple API for automating the creation of FAIR <https://en.wikipedia.org/wiki/Factor_analysis_of_information_risk>_ Monte Carlo risk simulations.

This is based on the terms found in:

1. Open FAIR™ RISK TAXONOMY (O-RT) <https://publications.opengroup.org/c13k>_; and,
2. Open FAIR™ RISK ANALYSIS (O-RA) <https://publications.opengroup.org/c13g>_

"Open FAIR" is a trademark of the Open Group.

Installation

pyfair is available on PyPI <https://pypi.org/project/pyfair/>_. To use pyfair with your Python installation, you can run:

.. code-block:: python

pip install pyfair

Documentation

Documentation can be found at the Read the Docs site <https://pyfair.readthedocs.io/en/latest/>_.

Code

.. code-block:: python

import pyfair

# Create using LEF (PERT), PL, (PERT), and SL (constant)
model1 = pyfair.FairModel(name="Regular Model 1", n_simulations=10_000)
model1.input_data('Loss Event Frequency', low=20, mode=100, high=900)
model1.input_data('Primary Loss', low=3_000_000, mode=3_500_000, high=5_000_000)
model1.input_data('Secondary Loss', constant=3_500_000)
model1.calculate_all()

# Create another model using LEF (Normal) and LM (PERT)
model2 = pyfair.FairModel(name="Regular Model 2", n_simulations=10_000)
model2.input_data('Loss Event Frequency', mean=.3, stdev=.1)
model2.input_data('Loss Magnitude', low=2_000_000_000, mode=3_000_000_000, high=5_000_000_000)
model2.calculate_all()

# Create metamodel by combining 1 and 2
mm = pyfair.FairMetaModel(name='My Meta Model!', models=[model1, model2])
mm.calculate_all()

# Create report comparing 2 vs metamodel.
fsr = pyfair.FairSimpleReport([model1, mm])
fsr.to_html('output.html')

Report Output

.. image:: static/overview.PNG :alt: Overview

.. image:: /static/tree.PNG :alt: Tree

.. image:: static/violin.PNG :alt: Violin

Serialized Model

.. code-block:: json

{
    "Loss Magnitude": {
        "mean": 100000,
        "stdev": 20000
    },
    "Loss Event Frequency": {
        "low": 20,
        "mode": 90,
        "high": 95,
        "gamma": 4
    },
    "name": "Sample Model",
    "n_simulations": 10000,
    "random_seed": 42,
    "model_uuid": "2e55fba4-c897-11ea-881b-f26e0bbd6dbc",
    "type": "FairModel",
    "creation_date": "2020-07-17 20:37:03.122525"
}