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Top 23 Interpretability Open-Source Projects
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awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
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shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Awesome-Federated-Learning
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
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imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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transformers-interpret
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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responsible-ai-toolbox
Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.
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Transformer-MM-Explainability
[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
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decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.
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yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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SaaSHub
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Project mention: Exploring Open-Source Alternatives to Landing AI for Robust MLOps | dev.to | 2023-12-13One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
For the two examples we will be looking at, we will be using pytorch_grad_cam, an incredible open source package that makes working with GradCam very easy. There are excellent other tutorials to check out on the repo as well.
Project mention: GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models | /r/learnmachinelearning | 2023-06-26
Project mention: Alibi: Open-source Python lib for ML model inspection and interpretation | news.ycombinator.com | 2023-06-02
https://arxiv.org/abs/2404.03592 https://github.com/stanfordnlp/pyreft
Project mention: Xplique Is a Neural Networks Explainability Toolbox | news.ycombinator.com | 2024-02-02
Project mention: Why do tree-based models still outperform deep learning on tabular data? (2022) | news.ycombinator.com | 2024-03-05Is it this library https://github.com/google/yggdrasil-decision-forests ?
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Interpretability related posts
- penzai: JAX research toolkit for building, editing, and visualizing neural nets
- Shap v0.45.0
- Exploring GradCam and More with FiftyOne
- [D] Convert a ML model into a rule based system
- Safety in Deep Reinforcement Learning
- Adversarial Reinforcement Learning
- Adversarial Reinforcement Learning
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A note from our sponsor - WorkOS
workos.com | 29 Apr 2024
Index
What are some of the best open-source Interpretability projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | shap | 21,632 |
| 2 | awesome-production-machine-learning | 15,947 |
| 3 | pytorch-grad-cam | 9,456 |
| 4 | interpret | 5,998 |
| 5 | lucid | 4,599 |
| 6 | captum | 4,568 |
| 7 | shapash | 2,642 |
| 8 | alibi | 2,289 |
| 9 | Awesome-Federated-Learning | 1,848 |
| 10 | penzai | 1,334 |
| 11 | DALEX | 1,323 |
| 12 | imodels | 1,290 |
| 13 | transformers-interpret | 1,212 |
| 14 | responsible-ai-toolbox | 1,208 |
| 15 | tf-explain | 1,007 |
| 16 | Transformer-MM-Explainability | 704 |
| 17 | decision-forests | 650 |
| 18 | tcav | 616 |
| 19 | pyreft | 610 |
| 20 | xplique | 569 |
| 21 | FastTreeSHAP | 493 |
| 22 | pyvene | 462 |
| 23 | yggdrasil-decision-forests | 423 |
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