Metadata-Version: 2.1
Name: gam
Version: 1.1.0
Summary: Global Explanations for Deep Neural Networks
Home-page: https://github.com/capitalone/global-attribution-mapping
Maintainer: Brian Barr
Maintainer-email: brian.barr@capitalone.com
License: Apache License 2.0
Description: # GAM (Global Attribution Mapping)
        > Global Explanations for Deep Neural Networks
        
        GAM explains the landscape of neural network predictions across subpopulations. 
        
        This implementation is based on "[Global Explanations for Neural Networks](https://arxiv.org/abs/1902.02384): Mapping the Landscape of Predictions" (AAAI/ACM AIES 2019).
        
        ## Installation
        ```sh
        python3 -m pip install gam
        ```  
        ## Get Started
        First generate local attributions using your favorite technique, then:
        
        ```Python
        >>> from gam.gam import GAM
        >>> # for a quick example use `attributions_path="tests/test_attributes.csv"`
        >>> # Input/Output: csv (columns: features, rows: local/global attribution)
        >>> gam = GAM(attributions_path="<path_to_your_attributes>.csv", distance="spearman", k=2)
        >>> gam.generate()
        >>> gam.explanations
        [[('height', .6), ('weight', .3), ('hair color', .1)], 
         [('weight', .9), ('weight', .05), ('hair color', .05)]]
         
        >>> gam.subpopulation_sizes
        [90, 10]
        
        >>> gam.subpopulations
        # global explanation assignment
        [0, 1, 0, 0,...]
        
        >>> gam.plot()
        # bar chart of feature importance with subpopulation size
        ```
        
        ### Tests
        To run tests:
        ```bash
        $ python -m pytest tests/
        ```
        
        ## Contributors
        
        We welcome Your interest in Capital One’s Open Source Projects (the
        “Project”). Any Contributor to the Project must accept and sign an
        Agreement indicating agreement to the license terms below. Except for
        the license granted in this Agreement to Capital One and to recipients
        of software distributed by Capital One, You reserve all right, title,
        and interest in and to Your Contributions; this Agreement does not
        impact Your rights to use Your own Contributions for any other purpose.
        
        [Sign the Individual Agreement](https://docs.google.com/forms/d/19LpBBjykHPox18vrZvBbZUcK6gQTj7qv1O5hCduAZFU/viewform)
        
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        ## Code of Conduct
        
        This project adheres to the [Open Code of Conduct](https://developer.capitalone.com/resources/code-of-conduct)
        By participating, you are
        expected to honor this code.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
