Metadata-Version: 2.1
Name: acd
Version: 0.0.2
Summary: Hierarchical interpretatations and contextual decomposition in pytorch
Home-page: https://github.com/csinva/hierarchical-dnn-interpretations
Author: Chandan Singh
License: UNKNOWN
Description: <h1 align="center"> Hierarchical neural-net interpretations (ACD) 🧠</h1>
        
        <p align="center"> Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code for <i>Hierarchical interpretations for neural network predictions</i> (ICLR 2019 <a href="https://openreview.net/pdf?id=SkEqro0ctQ">pdf</a>). </p>
        
        <p align="center">
          <img src="https://img.shields.io/badge/license-mit-blue.svg">
          <img src="https://img.shields.io/badge/python-3.6--3.8-blue">
          <img src="https://img.shields.io/github/checks-status/csinva/hierarchical-dnn-interpretations/master">
          <img src="https://img.shields.io/pypi/v/acd?color=orange">
          <img src="https://static.pepy.tech/personalized-badge/acd?period=total&units=none&left_color=gray&right_color=orange&left_text=downloads">
        </p>  
        <p align="center">
        	<a href="https://csinva.io/hierarchical-dnn-interpretations/">Documentation</a> •
          <a href="https://github.com/csinva/hierarchical-dnn-interpretations/tree/master/reproduce_figs">Demo notebooks</a>
        </p>  
        <p align="center">
        	<i>Note: this repo is actively maintained. For any questions please file an issue.</i>
        </p>
        
        
        ![](https://csinva.io/hierarchical-dnn-interpretations/intro.svg?sanitize=True)
        
        
        
        # examples/documentation
        
        - **installation**: `pip install acd` (or clone and run `python setup.py install`)
        - **examples**: the [reproduce_figs](https://github.com/csinva/hierarchical-dnn-interpretations/tree/master/reproduce_figs) folder has notebooks with many demos
        - **src**: the [acd](acd) folder contains the source for the method implementation
        - allows for different types of interpretations by changing hyperparameters (explained in examples)
        - test with pytorch >1.0 with/without gpu 
        - all required data/models/code for reproducing are included in the [dsets](dsets) folder
        
        | Inspecting NLP sentiment models    | Detecting adversarial examples      | Analyzing imagenet models           |
        | ---------------------------------- | ----------------------------------- | ----------------------------------- |
        | ![](reproduce_figs/figs/fig_2.png) | ![](reproduce_figs/figs/fig_s3.png) | ![](reproduce_figs/figs/fig_s2.png) |
        
        
        # notes on using ACD on your own data
        - the current CD implementation doesn't always work for all types of networks. If you are getting an error inside of `cd.py`, you may need to write a custom function that iterates through the layers of your network (for examples see `cd.py`). Should work out-of-the-box for many common layers though, including antyhing in alexnet, vgg, or resnet.
        - to use baselines such build-up and occlusion, replace the pred_ims function by a function, which gets predictions from your model given a batch of examples.
        
        
        # related work
        
        - PDR framework (PNAS 2019 [pdf](https://arxiv.org/abs/1901.04592)) - an overarching framewwork for guiding and framing interpretable machine learning
        - CDEP (ICML 2020 [pdf](https://arxiv.org/abs/1909.13584), [github](https://github.com/laura-rieger/deep-explanation-penalization)) - penalizes CD / ACD scores during training to make models generalize better
        - TRIM (ICLR 2020 workshop [pdf](https://arxiv.org/abs/2003.01926), [github](https://github.com/csinva/transformation-importance)) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)
        - DAC (arXiv 2019 [pdf](https://arxiv.org/abs/1905.07631), [github](https://github.com/csinva/disentangled-attribution-curves)) - finds disentangled interpretations for random forests
        - Baseline interpretability methods - the file `scores/score_funcs.py` also contains simple pytorch implementations of [integrated gradients](https://arxiv.org/abs/1703.01365) and the simple interpration technique `gradient * input`
        
        # reference
        
        - feel free to use/share this code openly
        - if you find this code useful for your research, please cite the following:
        
         ```r
        @inproceedings{
            singh2019hierarchical,
            title={Hierarchical interpretations for neural network predictions},
            author={Chandan Singh and W. James Murdoch and Bin Yu},
            booktitle={International Conference on Learning Representations},
            year={2019},
            url={https://openreview.net/forum?id=SkEqro0ctQ},
        }
         ```
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
