maciejwie/capsa-core

A data- and model-agnostic neural network wrapper for risk-aware decision making.

A Library for Risk-Aware and Trustworthy Machine Learning

[🚀 Getting Started] - [📄 Docs] - [🌐 Website] - [🧠 We're Hiring!] - [💡 Tutorials!]

👋 Welcome

We know deploying machine learning models can be tough. Today's models are notoriously bad at understanding their own risks -- they are biased on underrepresented data, brittle on challenging out-of-distribution scenarios, and can fail without warning when insufficiently trained.

Ensuring awareness of not one, but all of these risks, requires a tedious process involving changes to your model, its architecture, loss function, optimization procedure, and more.

Luckily, capsa has got you covered! Capsa automatically wraps your model (i.e., like a capsule!) and makes all of the internal changes so it can be end-to-end risk-aware. Capsa abstracts away all of those changes so you don't have to modify any of your existing training or deployment pipelines in order to build state-of-the-art trustworthy machine learning solutions.

🚀 Quickstart

💾 Installation

capsa is available to be downloaded with Pip:

pip install capsa

⭐ Wrap your model!

Eager to make your models risk-aware? Let's go through a quick example of wrapping your model (e.g., using an MVEWrapper) to estimate risk from noise in your labels (i.e., aleatoric uncertainty).

import capsa
import tensorflow as tf

# Build your model
model = tf.keras.Sequential(...)

# Wrap the model with capsa to make it risk-aware.
#   Capsa takes care of all the architecture, loss,
#   and deployment changes so you don't have to!
model = capsa.MVEWrapper(model)

# Compile and train the wrapped model the
#   same as you would have done with the
#   original model. No changes!
model.compile(...)
model.fit(train_x, train_y, epochs=5)

# The model now outputs `RiskTensor` objects, which
#   behave just like a normal `Tensor`, except they also
#   contain multiple different quantitative risk measures.
pred_y = model(test_x)

# Returns the aleatoric uncertainty of this prediction
risk = pred_y.aleatoric

🧠 Tutorials

Hungry for more?

Checkout our tutorials on some more advanced functions with capsa including other forms of risk, composing wrappers together, high-dimensional datasets, and more! All tutorials can be opened directly in Google Collab so you can play around without needing access to GPUs.

💪 Contribution

Capsa is being actively maintained and advanced. It has been built with research, extensibility, and community development as a priority. We greatly appreciate contributions to the capsa repository and codebase, including issues, enhancements, and pull requests.

For more details please see here.

💡 Support

Capsa currently supports Keras Sequential models, although we are looking at possible solutions for supporting wider range of models. We can't wait to share you some new details soon!