Metadata-Version: 2.1
Name: wideboost
Version: 0.0.1
Summary: Implements Wide Boosting functions for popular boosting packages
Home-page: UNKNOWN
Author: Michael Horrell
Author-email: mthorrell@github.com
License: UNKNOWN
Description: # wideboost
        Implements wide boosting using popular boosting frameworks as a backend.
        
        ## Getting started
        
        ```
        pip install wideboost
        ```
        
        ## Sample script
        
        ### XGBoost back-end
        
        ```
        import xgboost as xgb
        from wideboost.wrappers import wxgb
        
        dtrain = xgb.DMatrix('../../xgboost/demo/data/agaricus.txt.train')
        dtest = xgb.DMatrix('../../xgboost/demo/data/agaricus.txt.test')
        
        # Two extra parameters, 'btype' and 'extra_dims'
        param = {'btype':'I','extra_dims':2,'max_depth':2, 'eta':0.1, 'objective':'binary:logistic','eval_metric':['error'] }
        num_round = 50
        watchlist = [(dtrain,'train'),(dtest,'test')]
        wxgb_results = dict()
        bst = wxgb.train(param, dtrain, num_round,watchlist,evals_result=xgb_results)
        ```
        
        ## Parameter Explanations
        `'btype'` indicates how to initialize the beta matrix. Settings are `'I'`, `'In'`, `'R'`, `'Rn'`.
        
        `'extra_dims'` integer indicating how many "wide" dimensions are used.  When `'extra_dims'` is set to `0` (and `'btype'` is set to `'I'`) then wide boosting is equivalent to standard gradient boosting.
        
        ## Reference
        
        Coming Soon!
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
