26 lines
984 B
Python
Executable File
26 lines
984 B
Python
Executable File
#!/usr/bin/python
|
|
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)
|