Welcome to the Iguanas documentation!

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What is Iguanas?

Iguanas is a fast, flexible and modular Python package for:

  • Generating new fraud-capture rules using a labelled dataset.

  • Optimising existing rules using a labelled or unlabelled dataset.

  • Combining rule sets and removing/filtering those which are unnecessary.

  • Generating rule scores based on their performance.

It aims to help streamline the process for developing a deployment-ready rules-based system (RBS) for binary classification use cases.

What are rules and rule-based systems (RBS)?

A rule is a set of conditions which, if met, trigger a certain response.​ An example of a rule is one that captures a particular type of fraudulent behaviour for an e-commerce company:

If the number of transactions made from a given email address in the past 4 hours is greater than 10, reject the transaction.​

An RBS is one that leverages a number of these rules to provide a certain outcome.​

For example, an e-commerce company might employ an RBS to accept, reject and review its transactions.​

The pros and cons of an RBS

As with any approach, there are pros and cons to RBS’s.​

Pros

  • Rules are intuitive, so the outcome given by the RBS is easy to understand.​​

  • RBS’s are flexible, since rules can be quickly added to address new behaviour.​​

  • Rules can be built using domain knowledge, which may capture behaviour an ML model would have missed.​​

Cons

  • Linked to the last pro - domain knowledge is usually required to build rules.​ If you don’t have the domain knowledge, creating rules can be difficult.​

  • Generating these rules can be challenging and time consuming, especially if a data-guided approach is used.​

  • Difficult to tweak existing rules to address new trends.​

The solution – Iguanas!

Iguanas addresses the cons of an RBS for binary classification problems:​

  • Iguanas only requires a historic dataset to generate rules – similar to the requirements of an ML model.​​

  • Iguanas quickly and easily generates high performance rules and utilizes an API that is familiar to most data scientists - Sklearn’s fit/transform methology.​

  • Iguanas’ rule optimization module allows the user to tweak the thresholds of current rules using a labelled dataset.​

  • Iguanas also has a host of other modules which help to streamline the RBS set up process.​

Getting started

The Installation section provides instructions on installing Iguanas on your system. Once installed, take a look at the Examples section for how Iguanas can be used. The API section also provides documentation for the classes and methods in each module.