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Minor changes to code style (#2805)

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Some minor changes to code style in file 'boost_from_prediction.py'.
This commit is contained in:
LevineHuang 2017-10-23 23:46:45 +08:00 committed by Yuan (Terry) Tang
parent 452063c32d
commit c71b62d48d
1 changed files with 5 additions and 5 deletions
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demo/guide-python/boost_from_prediction.py
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@ -4,15 +4,15 @@ import xgboost as xgb
dtrain = xgb.DMatrix('../data/agaricus.txt.train') dtrain = xgb.DMatrix('../data/agaricus.txt.train')
dtest = xgb.DMatrix('../data/agaricus.txt.test') dtest = xgb.DMatrix('../data/agaricus.txt.test')
watchlist = [(dtest,'eval'), (dtrain,'train')] watchlist = [(dtest, 'eval'), (dtrain, 'train')]
### ###
# advanced: start from a initial base prediction # advanced: start from a initial base prediction
# #
print ('start running example to start from a initial prediction') print ('start running example to start from a initial prediction')
# specify parameters via map, definition are same as c++ version # specify parameters via map, definition are same as c++ version
param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' } param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
# train xgboost for 1 round # train xgboost for 1 round
bst = xgb.train( param, dtrain, 1, watchlist ) bst = xgb.train(param, dtrain, 1, watchlist)
# Note: we need the margin value instead of transformed prediction in set_base_margin # 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 # do predict with output_margin=True, will always give you margin values before logistic transformation
ptrain = bst.predict(dtrain, output_margin=True) ptrain = bst.predict(dtrain, output_margin=True)
@ -21,4 +21,4 @@ dtrain.set_base_margin(ptrain)
dtest.set_base_margin(ptest) dtest.set_base_margin(ptest)
print ('this is result of running from initial prediction') print ('this is result of running from initial prediction')
bst = xgb.train( param, dtrain, 1, watchlist ) bst = xgb.train(param, dtrain, 1, watchlist)