33 lines
1.1 KiB
Python
33 lines
1.1 KiB
Python
#!/usr/bin/python
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import numpy as np
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import scipy.sparse
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import pickle
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import xgboost as xgb
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# always call this before using distributed module
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xgb.rabit.init()
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# Load file, file will be automatically sharded in distributed mode.
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dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
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dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
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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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# specify validations set to watch performance
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 20
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# Run training, all the features in training API is available.
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# Currently, this script only support calling train once for fault recovery purpose.
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bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)
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# save the model, only ask process 0 to save the model.
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if xgb.rabit.get_rank() == 0:
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bst.save_model("test.model")
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xgb.rabit.tracker_print("Finished training\n")
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# Notify the tracker all training has been successful
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# This is only needed in distributed training.
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xgb.rabit.finalize()
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