Kraken

Kraken is a Contextual Bandits engine designed for:

• Simplicity + Power:

  • powerful features achieved in a simple way

• Ease of deployment:

  • minimal configuration
  • no dependencies other than DB connectors (+streamlit for GUI)
  • minimal components: stateless worker + DB

• Ease of scaling:

  • stateless core - serverless friendly
  • easily sharded db keys - Redis cluster friendly

Features include:

• multiple experiments (also known as rooms)
• multiple algorithms (Epsilon Greedy, UCB1, Thompson Sampling over Beta Distribution)
• context handling
• segmentation for more granular control and analysis
• dynamic item pool
• GUI for Monte Carlo simulator

Features not yet included:

• storing configuration in the DB itself
• arm decay handling
• related experiments

Example GUI instance can be accessed here.

Installation

NOTE: This is experimental Proof-of-Concept software - code quality is 'so-so'. The design, functionality, and interface may change without any notice.

To install Kraken, you can use the pip command as follows:

TODO: pip install git+https://github.com/mobarski/kraken.git

Glossary

arm - An option/variant/item/action in a multi-armed bandit experiment. Each arm represents a different version of a product, feature, or strategy.

room - A term used to denote a unique experiment or test scenario within the Kraken system. Each room can contain multiple arms.

pool - A set of arms available in a specific room. It represents the different variations of an experiment that are currently active.

context - The information about the current situation of a user. This can include user attributes, time of day, location, and more. The context is used to personalize the experience for each user.

segment - A subset of the context that is defined by a specific set of characteristics. Each segment has separate tracking of statistics (clicks/views) allowing for more targeted analysis and personalization.

view - The action of a user seeing or being presented with a variant (arm). This is tracked to understand the exposure of each arm in the experiment.

click - The action of a user interacting with or choosing a variant (arm). This is an indication of preference and is used to adjust the probability distribution of the arms.

Diagrams

CTR calculation (for Epsilon Greedy algorithm)

CTR = arm_clicks / arm_views

sequenceDiagram

note over core,db : db operations group 1 (@room:@segment)
loop arm_ids
core ->> db : GET views:@arm
core ->> db : GET clicks:@arm
loop ctx_items
core ->> db : GET views-ctx:@arm:@ctx
core ->> db : GET clicks-ctx:@arm:@ctx
end
end

note over core : CTR = arm_clicks / arm_views
loop arm_ids
core --> core : calculate CTR
loop ctx_items
core --> core : calculate CTR for CTX
end
end

note over core,db : db operations group 2 (@room:@segment)
loop arm_ids
core ->> db : SET ctr:@arm
loop ctx_items
core ->> db : SET ctr-ctx:@arm:@ctx
end
end

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UCB1 (Upper Confidence Bound 1) calculation

UCB1 = ctr + alpha * sqrt(2*log(all_arms_views)) / arm_views

alpha: exploration weight, default=1.0

sequenceDiagram

note over core,db : db operations group 1 (@room:@segment)
core ->> db : GET views-agg
loop arm_ids
core ->> db : GET views:@arm
core ->> db : GET clicks:@arm
loop ctx_items
core ->> db : GET views-ctx:@arm:@ctx
core ->> db : GET clicks-ctx:@arm:@ctx
end
end

note over core : UCB1 = ctr + alpha * sqrt(2*log(all_arms_views)) / arm_views
loop arm_ids
core --> core : calculate UCB1
loop ctx_items
core --> core : calculate UCB1 for CTX
end
end

note over core,db : db operations group 2 (@room:@segment)
loop arm_ids
core ->> db : SET ucb1:@arm
loop ctx_items
core ->> db : SET ucb1-ctx:@arm:@ctx
end
end

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TSBD (Thompson Sampling over Beta Distribution) calculation

TSBD = betavariate(alpha, beta)

alpha = arm_clicks + 1

beta = arm_views - arm_clicks + 1

sequenceDiagram

note over core,db : db operations group 1 (@room:@segment)
loop arm_ids
core ->> db : GET views:@arm
core ->> db : GET clicks:@arm
loop ctx_items
core ->> db : GET views-ctx:@arm:@ctx
core ->> db : GET clicks-ctx:@arm:@ctx
end
end

note over core : TSBD = betavariate(alpha, beta)<br>alpha = arm_clicks + 1<br>beta = arm_views - arm_clicks + 1
loop arm_ids
core --> core : calculate TSBD
loop ctx_items
core --> core : calculate TSBD for CTX
end
end

note over core,db : db operations group 2 (@room:@segment)
loop arm_ids
core ->> db : SET tsbd:@arm
loop ctx_items
core ->> db : SET tsbd-ctx:@arm:@ctx
end
end

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Notes

Example Use Cases

Simple:

• optimize articles to show in a section
• optimize article's title
• optimize article's cover
• optimize which section to show (arm_id = section_id)

Advanced:

• optimize article's title AND cover (2 related experiments)
• optimize article's title/cover AND which articles to show in a section (3 related experiments)
• optimize article's title AND cover AND which articles to show in a section (3 related experiments)
• optimize articles to show in many sections (many unrelated experiments)

Reporting needs

optimize article's title/photo (small number of arms)

• context-free:

  • CTR of each ARM
  • VIEWS of each ARM
  • CLICKS of each ARM

• contextual

  • CTR of each CTX
  • VIEWS of each CTX
  • CLICKS of each CTX
  • CTR of each ARM for CTX
  • VIEWS of each ARM for CTX
  • CLICKS of each ARM for CTX

• segmented

  • TODO

References

• Lihong Li et al. - A Contextual-Bandit Approach to Personalized News Article Recommendation

• DeepMind x UCL - Reinforcement Learning Lecture Series 2021

  • Lecture 2: Exploration & Control (video)
  • Lecture 2: Exploration & Control (slides)

• Alina Beygelzimer, John Langford - Learning for Contextual Bandits

• James LeDoux - Multi-Armed Bandits in Python: Epsilon Greedy, UCB1, Bayesian UCB, and EXP3

• Elaine Zhang - Comparing Multi-Armed Bandit Algorithms on Marketing Use Cases

• John White - Bandit Algorithms for Website Optimization: Developing, Deploying, and Debugging