boost_from_prediction.py

#!/usr/bin/python
import numpy as np
import xgboost as xgb

dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test')
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
###
# advanced: start from a initial base prediction
#
print ('start running example to start from a initial prediction')
# specify parameters via map, definition are same as c++ version
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
# train xgboost for 1 round
bst = xgb.train(param, dtrain, 1, watchlist)
# Note: we need the margin value instead of transformed prediction in set_base_margin
# do predict with output_margin=True, will always give you margin values before logistic transformation
ptrain = bst.predict(dtrain, output_margin=True)
ptest = bst.predict(dtest, output_margin=True)
dtrain.set_base_margin(ptrain)
dtest.set_base_margin(ptest)


print('this is result of running from initial prediction')
bst = xgb.train(param, dtrain, 1, watchlist)