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Add base margin to sklearn interface. (#5151)

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This commit is contained in:
Jiaming Yuan
2019-12-24 09:43:41 +08:00
committed by GitHub
parent 1d0ca49761
commit 0202e04a8e
5 changed files with 103 additions and 61 deletions
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14
demo/guide-python/boost_from_prediction.py
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@@ -1,5 +1,4 @@
#!/usr/bin/python
import numpy as np
import xgboost as xgb
dtrain = xgb.DMatrix('../data/agaricus.txt.train')
@@ -8,18 +7,19 @@ watchlist = [(dtest, 'eval'), (dtrain, 'train')]
###
# 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
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
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
# 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)
bst = xgb.train(param, dtrain, 1, watchlist)