Performance Robustness Evaluation for Statistical Classifiers

Overview

Mozilla is planning to invest more substantially in privacy-preserving machine learning models for applications such as recommending personalized content and detecting malicious behaviour. As such solutions move towards production, it is essential for us to have confidence in the selection of the model and its parameters for a particular dataset, as well as an accurate view into how it will perform in new instances or as the training data evolves.

While the literature contains a broad array of models, evaluation techniques, and metrics, their choice in practice is often guided by convention or convenience, and their sensitivity to different datasets is not always well-understood. Additionally, to our knowledge there is no existing software tool that provides a comprehensive report on the performance of a given model under consideration.

The eventual goal of this project is to build a standard set of tools that Mozilla can use to evaluate the performance of machine learning models in various contexts on the basis of the following principles:

• Holistic view of model performance
  • Eg. evaluating a model in terms of multiple metrics (ie as a battery of tests), and understanding the tradeoffs between them
  • For classification, separating predicted class membership scores from the decision rule assigning a final class
• Stability of performance metrics as an additional metric to optimize for
  • Eg. the variability of performance metrics across difference splits of the dataset
• Generalizability of the model to unseen data
• Explainability/parsimony when possible
• Reproducibility of results
  • Eg. looking for ways to leverage existing public datasets in the evaluation process when the actual data cannot be made public
• Informative failures
  • We can learn a lot by studying the cases where the model performs poorly, eg. misclassifications or cases near the decision boundary
  • Failures may have implications for generalizability, appropriateness of the choice of model, ethical considerations/bias
  • Eg. do we see systematic failures with a homogeneous distribution within themselves but differing from training data.

At this early stage, the focus is on implementing evaluation methodologies in line with these principles, and testing them out across different models and datasets.

Notes for contributors

• We are currently restricting scope to classification supervised learning models (both binary and multiclass).
• Tests and analyses should use the datasets provided in the repo. Please do not include external datasets in your contributions at this point.
• We are working in Python using the standard data science stack (Numpy/Pandas/Scikit-learn/Jupyter).
• Your code should run in the provided Conda environment. If you feel you need an external dependency, you may include an update to the environment file in your PR.

Contribution guidelines

To keep the project structure and review process manageable at this initial stage, please structure your contributions using the following steps:

• Create a directory with your username in the dev dir
• Structure your code into one or more Python modules in that directory
  • Code should be well-documented. Each function should include a docstring.
• Include a Jupyter notebook that demonstrates a run of your code showing printed output, a graph, etc.
  • Code cells in the notebook should only call functions defined in your modules. Please do not include any actual code logic in the notebook itself.
  • The notebooks should be well-documented. Ideally, each code cell should be preceded by a Markdown cell describing why you have included the code cell. It can also include commments on the output generated, eg. describing features of a graph. These text cells should be more high-level than actual code comments, describing the narrative or thought process behind your contribution.

We request that contributions be structured in this way prior to getting reviewed. If you make subsequent contributions, you can include them in the same directory and reuse module code, but each contribution should include a separate demonstration notebook.

If you wish to build on someone else's contribution, you can import code from their modules into yours. Please do not submit PRs directly modifying code from other contributions at this point, unless to resolve errors or typos.

Information for Outreachy participants

This project is intentionally broadly scoped, and the initial phase will be exploratory.

• The goal is to propose and test out ideas related to the evaluation of classifiers, rather than jumping straight into building features.
• Tasks are open-ended and can be worked on by multiple contributors at the same time, as different people may propose complimentary ideas or approaches.

You can ask for help and discuss your ideas on gitter.

Issues

Tasks are managed using the GitHub issue tracker.

• As issues represent general exploratory tasks at this point, they will generally not be assigned to a single person.
• If you want to work on a task, drop a comment in the issue.
• You are welcome to make a contribution to an issue even if others are already working on it. You may also expand on someone else's work, eg. testing out the methodology with different datasets or models.
• As the project matures, we may start filing targeted issues, eg. to fix specific bugs, which will get assigned to specific person
• You are also welcome to contribute your own issues if there is a direction you would like to explore relating to the project focus.

Contributions

Contributions can be made by submitting a pull request against this repository.

• We ask each Outreachy participant to make a contribution completing #2 (train and test a classification model). This will help you to become familiar with machine learning and the tools if you are not already. Please submit as a PR following the guidelines above.
  • This task must be completed in order to be considered as an intern on this project
• If you would like initial feedback on your contribution before it is ready for submission, you may open a PR with "WIP:" at the start of the title and request review. This tag ('work in progress') indicates that the PR is not ready to be merged. When it is ready for final submission, you can modify the title to remove the "WIP:" tag.

Getting started

1. Install Anaconda or Miniconda.

2. Setup and activate environment:

 $ conda env create -f environment.yml
 $ conda activate presc

3. Run Jupyter. The notebook will open in your browser at localhost:8888 by default.

 $ jupyter notebook

We recommend everyone start by working on #2.

Getting started with GitHub

The git/GitHub open-source workflow can be rather confusing if you haven't used it before. To make a contribution to the project, the general steps you need to take are:

• Install git on your computer
• Fork the repo on Github (ie. make your own personal copy)
• Clone your fork to your local computer
• Commit changes locally on your computer
• Push your changes to your fork on Github
• Submit a pull request (PR) against the main repo from your fork.

Here are some resources to learn more about parts of this workflow you are unfamiliar with:

• GitHub Guides
  • In particular, the git handbook explains some of the basics of the version control system
  • There is a page explaining the forking/pull request workflow you will be using to contribute.
• The Git Book is much more detailed but a good reference
  • The Getting Started section is worth reading
  • There are also some videos on getting set up
• This repo by a previous Outreachy contributor lists many other resources and tutorials.
• This video tutorial series on Youtube may also be helpful

Feel free to reach out to the mentors by email or on Gitter if you have further questions or are having trouble getting set up!

Resources

• This is a great tutorial to learn Pandas.
• Tutorial on Jupyter Notebook.
• The scikit-learn user guide is a good place to start learning the scikit-learn library as well as machine learning methodology and comes with lots of examples.
• This page has a nice overview of classification models.