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Top 23 explainable-ml Open-Source Projects
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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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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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shapash
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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awesome-explainable-graph-reasoning
A collection of research papers and software related to explainability in graph machine learning.
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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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imodels
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
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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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CARLA
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms (by carla-recourse)
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explainable-cnn
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
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shapley
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
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Deep_XF
Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.
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TalkToModel
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
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SegGradCAM
SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
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cnn-raccoon
Create interactive dashboards for your Convolutional Neural Networks with a single line of code!
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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
Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.
Project mention: Xplique Is a Neural Networks Explainability Toolbox | news.ycombinator.com | 2024-02-02
Just for some respite from the discussion of our soon-to-be AI overlords (LLMs), I'm one of the contributors to an open-source Python package, Xplainable (https://github.com/xplainable/xplainable). Xplainable is a novel (structured) machine learning algorithm that's inherently explainable, as opposed to being a post-hoc explainer (like SHAP or Lime).
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).
explainable-ml related posts
- Why Vector Compression Matters
- Explainable (Structured) Machine Learning Algorithm
- How are generative AI companies monitoring their systems in production?
- [P] TruLens-Eval is an open source project for eval & tracking LLM experiments.
- Deep_XF: NEW Data - star count:100.0
- Deep_XF: NEW Data - star count:100.0
- GitHub - MAIF/shapash: Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
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A note from our sponsor - SaaSHub
www.saashub.com | 27 Apr 2024
Index
What are some of the best open-source explainable-ml projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | pytorch-grad-cam | 9,410 |
| 2 | interpret | 5,988 |
| 3 | shapash | 2,642 |
| 4 | awesome-explainable-graph-reasoning | 1,934 |
| 5 | trulens | 1,612 |
| 6 | AIX360 | 1,527 |
| 7 | DALEX | 1,318 |
| 8 | imodels | 1,290 |
| 9 | DiCE | 1,267 |
| 10 | responsible-ai-toolbox | 1,208 |
| 11 | OmniXAI | 805 |
| 12 | xplique | 569 |
| 13 | tf-keras-vis | 305 |
| 14 | CARLA | 263 |
| 15 | explainable-cnn | 212 |
| 16 | shapley | 210 |
| 17 | awesome-shapley-value | 127 |
| 18 | Deep_XF | 110 |
| 19 | TalkToModel | 102 |
| 20 | sagemaker-explaining-credit-decisions | 94 |
| 21 | SegGradCAM | 94 |
| 22 | xplainable | 52 |
| 23 | cnn-raccoon | 31 |
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