Bayesnet

This gem provides an DSL for constructing Bayesian networks and let to execute basic inference queries. It is also capable of parsing .BIF format (The Interchange Format for Bayesian Networks).

Someone has decided to study how some sleep hours followed by a cup of coffee are affecting a morning mood. Here is the graphical model:

Sleep hours could have values :six, :seven, :eight. Mood could be :bad, :moderate, :good and variable Coffee could take values :yes, :no. Here is how accumulated statistics could be compiled in a Bayesian network and then defined in the code:

net = Bayesnet.define do
      node :coffee do
        values yes: 0.7, no: 0.3
      end

      node :sleep_hours do
        values six: 0.1, seven: 0.3, eight: 0.6
      end

      node :mood, parents: [:coffee, :sleep_hours] do
        values [:good, :moderate, :bad] do
          distributions do
            as [0.8, 0.1, 0.1], given: [:yes, :eight]
            as [0.6, 0.2, 0.2], given: [:yes, :seven]
            as [0.4, 0.4, 0.2], given: [:yes, :six]
            as [0.7, 0.2, 0.1], given: [:no, :eight]
            as [0.5, 0.3, 0.2], given: [:no, :seven]
            as [0.3, 0.4, 0.3], given: [:no, :six]
          end
        end
      end
    end

Above, must be read as: someone could be observed having morning coffee in 70% of all cases and when that person has a morning coffee after sleeping 8 hours, chances to find her in

• :good mood - 80%
• :moderate mood - 10%
• :bad mood - 10%

i.e. [0.8, 0.1, 0.1] is a conditional distribution.

Given the model above the following queries could be executed:

net.chances({coffee: :yes}, evidence: {mood: :good, sleep_hours: :six}) # 0.757

net.most_likely_value(:coffee, evidence: {mood: :good, sleep_hours: :six}) # :yes

net.distribution(over: [:coffee], evidence: {mood: :good, sleep_hours: :six}) # [:yes, 0.757], [:no, 0.243]

Inference algorithm finds distribution for variables listed in :over parameter and there are two available methods to achieve that (See Bayesnet::Graph#distribution's ' :algorithm parameter):

1. Using variable elimination algorithm, this is way much faster and used by default
2. Brute force, i.e. building complete join distribution, it works only for small Baysian networks and used mostly for testing

Add this line to your application's Gemfile:

em 'bayesnet'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install bayesnet

After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Bug reports and pull requests are welcome on GitHub at https://github.com/[USERNAME]/bayesnet. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the code of conduct.

The gem is available as open source under the terms of the MIT License.

Everyone interacting in the Bayesnet project's codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.