Release: Version 0.0.1

Release for the Version 1.0.0 of the gam library

README.md

@@ -7,14 +7,15 @@ This implementation is based on "[Global Explanations for Neural Networks](https
 
 ## Installation
 ```sh
-python3 -m pip install git+https://github.com/capitalone/global-attribution-mapping.git
+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
@@ -32,34 +33,12 @@ First generate local attributions using your favorite technique, then:
 # bar chart of feature importance with subpopulation size
 ```
 
-# Acknowledgements
-Thank you to Paul Zeng for his contribution to the implementation and valuable feedback.
-
-# Development
-* `Python 3.7`, `Pytest`, `requirements.txt` for dependencies
-* Branching: master is protected (need one other reviewer to merge)
-* Input/Output: csv (columns: features, rows: local/global attribution)
-* Underlying data structures: `numpy arrays`
-
 ### Tests
 To run tests:
 ```bash
 $ python -m pytest tests/
 ```
 
-### MVP
-* assume local attributions are given
-* K is a specified parameter
-* accompany csv output with a simple visualization showing top 5 clusters and their top 5 features
-* accelerated distance calc using merge sort (from O(n^2) to O(nlogn))
-
-### Post-MVP
-* optimize k based on silouhette score
-* generate local attributions (using appropriate local method)
-
-
-
-
 ## Contributors
 
 We welcome Your interest in Capital One’s Open Source Projects (the
@@ -79,4 +58,4 @@ impact Your rights to use Your own Contributions for any other purpose.
 
 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.
+expected to honor this code.

setup.py

@@ -1,6 +1,6 @@
 # SPDX-Copyright: Copyright (c) Capital One Services, LLC
 # SPDX-License-Identifier: Apache-2.0
-# Copyright 2018 Capital One Services, LLC
+# Copyright 2020 Capital One Services, LLC
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.