shap
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shap | captum | |
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38 | 11 | |
21,580 | 4,552 | |
1.8% | 2.2% | |
9.4 | 8.4 | |
6 days ago | 7 days ago | |
Jupyter Notebook | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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shap
- Shap v0.45.0
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[D] Convert a ML model into a rule based system
something like GitHub - shap/shap: A game theoretic approach to explain the output of any machine learning model.?
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[P] tinyshap: A minimal implementation of the SHAP algorithm
A less than 100 lines of code implementation of KernelSHAP because I had a hard time understanding shap's code.
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What’s after model adequacy?
We use tools like SHAP to explain what the model is doing to stakeholders.
- Feature importance with feature engineering?
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Model interpretation with many features
https://github.com/slundberg/shap this or https://github.com/marcotcr/lime would be relevant to you, especially if you want to look at explaining a single prediction.
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SHAP Value Interpretation
See this closed topic for more detail: https://github.com/slundberg/shap/issues/29
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Christoph Molnar on SHAP Library
Dr. Molnar recently had a semi-viral post on LinkedIn and on Twitter, where he essentially highlights the booming popularity [and power] of using SHAP for explainable AI (which I agree with), but that it also comes with problems; i.e., the open source implementation has thousands of pull requests, bugs, and issues and yet there is no permanent or significant funding to go in and fix them.
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Random Forest Estimation Question
Option 4) create SHAP values https://github.com/slundberg/shap to better understand what the RF did.
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Model explainability
txtai pipelines are wrappers around Hugging Face pipelines with logic to easily integrate with txtai's workflow framework. Given that, we can use the SHAP library to explain predictions.
captum
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[D] [R] Research Problem about Weakly Supervised Learning for CT Image Semantic Segmentation
Most likely, NNs in general love shortcut learning (see Geirhos et al. 2020). In general, local explanations such as grad-cam are quite noisy, and sometimes even inconsistent (see Seo et al. 2018 ). Now, in my experience, I've seen that integrated gradients (see Sundararajan et al 2017) does a better job compared to Grad-CAM (also, add a noise tunnel), but this is only based on my limited experience. I would totally recommend using the implementations from the Captum library for loca explanations.
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[D] Off-the-shelf image saliency scoring models?
Take a look at captum
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Can you interrogate a machine learning model to find out why it gave certain predictions?
Sometimes. If explainable predictions are part of your business requirements, it's probably better not to rely entirely on black box models and instead design a system that gives you the information you need as part of it. If you end up using black box models, there are still methods that attempt to help attribute explanations to your prediction. Here's an example of a toolkit for attributing explanations post-hoc to black box model predictions: https://github.com/pytorch/captum
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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What kind of explainability techniques exist for Reinforcement learning?
The straightforward way to interpret RL agent's decision is to use captum library.
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[D] How do you choose which Black-Box Explainability method to use?
My use-case is research-oriented. I work on Explainable AI. Generally, the best package I've come across to compute attributions in Pytorch Captum. If your object detector is in PyTorch, you can perhaps build it in.
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PyTorch vs. TensorFlow in 2022
Do any JAX experts know if there is an equivalent to https://captum.ai/ - a model interpretability library for pytorch?
In particular i want to be able to measure feature importance on both inputs and internal layers on a sample by sample basis. This is the only thing currently holding me back from using JAX right now.
Alternatively a simle to read/understand/port implementation of DeepLIFT would work too.
thanks
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DeepLIFT or other explainable api implementations for JAX (like captum for pytorch)?
I'm interested to use JAX but am having a hard time finding anything similar to captum for the pytorch world.
- how to extract features from a (CNN) convolutional network having raw data with (XAI) explainable techinques?
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Looking for help regarding explainable AI
Do you actually want to implement something? There are decent explainability libraries now, e.g., [AIX360](https://aix360.mybluemix.net/), [InterpretML](https://interpret.ml/), or [captum](https://captum.ai/). Pytorch + maybe pytorch lightning + captum might be the quickest way to actually implement something like an explainable neural net yourself. Do the standard tutorials for each of them and watch a few YT videos (or follow a coursera course or something like that) about how these things work in theory and practice, and you'll get up to speed relatively quickly. You will not be able to do useful work in ML without actually learning the ropes.
What are some alternatives?
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
DALEX - moDel Agnostic Language for Exploration and eXplanation
Transformer-Explainability - [CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.
lucid - A collection of infrastructure and tools for research in neural network interpretability.
lime - Lime: Explaining the predictions of any machine learning classifier
flax - Flax is a neural network library for JAX that is designed for flexibility.
interpret - Fit interpretable models. Explain blackbox machine learning.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
awesome-production-machine-learning - A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
WeightWatcher - The WeightWatcher tool for predicting the accuracy of Deep Neural Networks
anchor - Code for "High-Precision Model-Agnostic Explanations" paper
alibi - Algorithms for explaining machine learning models