wideboost 0.1.3

Implements Wide Boosting functions for popular boosting packages

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!