Metadata-Version: 2.1
Name: nam-pt
Version: 0.3
Summary: Neural Additive Models (PyTorch): Intepretable ML with Neural Nets
Home-page: https://github.com/kherud/neural-additive-models-pt
Author: Konstantin Herud
License: Apache 2.0
Description: # [Neural Additive Models (PyTorch)](https://github.com/google-research/google-research/tree/master/neural_additive_models)
        
        This is a PyTorch re-implementation for neural additive models, check out:
        
        - [Neural Additive Models: Interpretable Machine Learning with Neural Nets](https://arxiv.org/abs/2004.13912).
        - [TensorFlow OG Implementation](https://github.com/google-research/google-research/tree/master/neural_additive_models)
        
        <img src="https://i.imgur.com/Hvb7sb2.jpg" width="50%" alt="Neural Additive Model" >
        
        ## Install Package
        
        
        
        ## Dependencies
        
        - torch==1.7.0
        - fsspec==0.8.4
        - pandas==1.1.4
        - tqdm==4.54.0
        - sklearn==0.0
        - absl-py==0.11.0
        - gcsfs==0.7.1
        
        ## Usage
        
        ```
        conda env create -f environment.yml
        conda activate nam-pt
        python run.py
        ```
        
        Citing
        ------
        If you use this code in your research, please cite the following paper:
        
        > Agarwal, R., Frosst, N., Zhang, X., Caruana, R., & Hinton, G. E. (2020).
        > Neural additive models: Interpretable machine learning with neural nets.
        > arXiv preprint arXiv:2004.13912
        
        
              @article{agarwal2020neural,
                title={Neural additive models: Interpretable machine learning with neural nets},
                author={Agarwal, Rishabh and Frosst, Nicholas and Zhang, Xuezhou and
                Caruana, Rich and Hinton, Geoffrey E},
                journal={arXiv preprint arXiv:2004.13912},
                year={2020}
              }
        
        ---
        
        *Disclaimer about COMPAS dataset: It is important to note that
        developing a machine learning model to predict pre-trial detention has a
        number of important ethical considerations. You can learn more about these
        issues in the Partnership on AI
        [Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System](https://www.partnershiponai.org/report-on-machine-learning-in-risk-assessment-tools-in-the-u-s-criminal-justice-system/).
        The Partnership on AI is a multi-stakeholder organization -- of which Google
        is a member -- that creates guidelines around AI.*
        
        *We’re using the COMPAS dataset only as an example of how to identify and
        remediate fairness concerns in data. This dataset is canonical in the
        algorithmic fairness literature.*
        
        *Disclaimer: This is not an official Google product.*
        
Keywords: nam,interpretability,machine,learning,research
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
