Metadata-Version: 2.1
Name: chemrecommender
Version: 0.0.1
Summary: A standalone module to build a recommender pipeline
Home-page: https://github.com/darkreactions/recommendation_engine
Author: DRP Project
Author-email: darkreactionproject@haverford.edu
License: UNKNOWN
Description: # recommendation_engine
        Repo for the recommendation engine that was part of the DRP project
        
        
        ## Recommender Pipeline
        
        Steps to implement a recommender pipeline
        (Specific implementation of this pipeline is available in ./recommender/recommender_pipeline.py)
        
        1. Generate reaction features
            - Get the chemicals in a reaction. For DRP these are referred to as triples
            - Generate descriptors for each of the chemicals in the reaction
            - Generate a sampling grid of reaction parameters
            - Expand grid by associating descriptors with each point on the grid
        
        2. Run trained models with the reaction Sieve
            - Get a trained machine learning model
            - Filter sampling grid by running it through the ML model
            - Make a list of all the potentially successful reactions
              as predicted by the ML model
        
        3. Recommend reactions
            - Calculate the mutual information of the potential reactions
              as compared to the already completed reactions
            - Select the top 'k' reactions with the highest MI
        
        
        ### Progress
        
        - [x] Generate Reaction features
        - [x] Reaction Sieve
        - [x] Reaction Recommender
        - [ ] Test and evaluate against Nature paper
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
