25 lines
1002 B
Python
Executable File
25 lines
1002 B
Python
Executable File
#!/usr/bin/python
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import numpy as np
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import xgboost as xgb
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dtrain = xgb.DMatrix('../data/agaricus.txt.train')
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dtest = xgb.DMatrix('../data/agaricus.txt.test')
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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###
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# advanced: start from a initial base prediction
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#
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print ('start running example to start from a initial prediction')
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# specify parameters via map, definition are same as c++ version
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
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# train xgboost for 1 round
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bst = xgb.train( param, dtrain, 1, watchlist )
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# Note: we need the margin value instead of transformed prediction in set_base_margin
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# do predict with output_margin=True, will always give you margin values before logistic transformation
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ptrain = bst.predict(dtrain, output_margin=True)
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ptest = bst.predict(dtest, output_margin=True)
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dtrain.set_base_margin(ptrain)
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dtest.set_base_margin(ptest)
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print ('this is result of running from initial prediction')
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bst = xgb.train( param, dtrain, 1, watchlist )
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