MLJ.jl Projects – Summer of Code

MLJ is a machine learning framework for Julia aiming to provide a convenient way to use and combine a multitude of tools and models available in the Julia ML/Stats ecosystem.

List of projects

MLJ is released under the MIT license and sponsored by the Alan Turing Institute.

  1. MLJ.jl Projects – Summer of Code
    1. List of projects
    2. Categorical variable encoding
      1. Description
    3. Prerequisites
    4. Your contribution
    5. References
    6. Machine Learning in Predictive Survival Analysis
      1. Description
    7. Prerequisites
    8. Your contribution
    9. References
    10. Deeper Bayesian Integration
      1. Description
    11. Your contributions
    12. References
    13. Difficulty: Medium to Hard
    14. Tracking and sharing MLJ workflows using MLflow
      1. Description
    15. Prerequisites
    16. Your contribution
    17. References
    18. Speed demons only need apply
      1. Description
    19. Prerequisites
    20. Your contribution
    21. References

Categorical variable encoding

Extend the categorical variable encoding of MLJ.

Difficulty. Moderate. Duration. 350 hours

Description

MLJ provides basic one-hot encoding of categorical variables but no sophisticated encoding techniques. One-hot encoding is rather limited, in particular when a categorical has a very large number of classes. Many other techniques exists, and this project aims to make some of these available to the MLJ user.

Mentors. Anthony Blaom (best contact: direct message on Julia slack)

Prerequisites

Your contribution

In this project you will survey popular existing methods for one-hot encoding categorical variables. In collaboration with the mentor, you will make a plan for integrating some of these techniques into MLJ. You will begin work on the plan, initially focusing on simple methods, providing MLJ interfaces to existing julia packages, or new implementations where needed. If the project advances well, you will implement more advanced techniques, such as entity embedding via MLJFlux.jl (MLJ's neural network interface).

References

Machine Learning in Predictive Survival Analysis

Implement survival analysis models for use in the MLJ machine learning platform.

Difficulty. Moderate - hard. Duration. 350 hours

Description

Survival/time-to-event analysis is an important field of Statistics concerned with understanding the distribution of events over time. Survival analysis presents a unique challenge as we are also interested in events that do not take place, which we refer to as 'censoring'. Survival analysis methods are important in many real-world settings, such as health care (disease prognosis), finance and economics (risk of default), commercial ventures (customer churn), engineering (component lifetime), and many more. This project aims to implement models for performing survivor analysis with the MLJ machine learning framework.

mlr3proba is currently the most complete survival analysis interface, let's get SurvivalAnalysisA.jl to the same standard - but learning from mistakes along the way.

Mentors. Sebastian Vollmer, Anthony Blaom,

Prerequisites

preferred.

Your contribution

You will work towards creating a survival analysis package with a range of metrics, capable of making distribution predictions for classical and ML models. You will bake in competing risks in early, as well as prediction transformations, and include both left and interval censoring. You will code up basic models (Cox PH and AFT), as well as one ML model as a proof of concept (probably decision tree is simplest or Coxnet).

Specifically, you will:

learning models in MLJ.

models not currently implemented in Julia.

References

Deeper Bayesian Integration

Bayesian methods and probabilistic supervised learning provide uncertainty quantification. This project aims increasing integration to combine Bayesian and non-Bayesian methods using Turing.

Difficulty. Difficult. Duration. 350 hours.

Description

As an initial step reproduce SOSSMLJ in Turing. The bulk of the project is to implement methods that combine multiple predictive distributions.

Your contributions

References

Bayesian Stacking SKpro

Difficulty: Medium to Hard

Mentors: Hong Ge Sebastian Vollmer

Tracking and sharing MLJ workflows using MLflow

Help data scientists using MLJ track and share their machine learning experiments using MLflow. The emphasis iin this phase of the project is:

Difficulty. Moderate. Duration. 350 hours.

Description

MLflow is an open source platform for the machine learning life cycle. It allows the data scientist to upload experiment metadata and outputs to the platform for reproducing and sharing purposes. MLJ already allows users to report basic model performance evaluation to an MLflow service and this project seeks to greatly enhance this integration.

Prerequisites

Your contribution

References

Mentors. Anthony Blaom

Speed demons only need apply

Diagnose and exploit opportunities for speeding up common MLJ workflows.

Difficulty. Moderate. Duration. 350 hours.

Description

In addition to investigating a number of known performance bottlenecks, you will have some free reign in this to identify opportunities to speed up common MLJ workflows, as well as making better use of memory resources.

Prerequisites

Your contribution

In this project you will:

References

Mentors. Anthony Blaom, Okon Samuel.