30 lines
1.0 KiB
Python
30 lines
1.0 KiB
Python
#!/usr/bin/python
|
|
import xgboost as xgb
|
|
|
|
# Always call this before using distributed module
|
|
xgb.rabit.init()
|
|
|
|
# Load file, file will be automatically sharded in distributed mode.
|
|
dtrain = xgb.DMatrix('../../demo/data/agaricus.txt.train')
|
|
dtest = xgb.DMatrix('../../demo/data/agaricus.txt.test')
|
|
|
|
# Specify parameters via map, definition are same as c++ version
|
|
param = {'max_depth': 2, 'eta': 1, 'objective': 'binary:logistic'}
|
|
|
|
# Specify validations set to watch performance
|
|
watchlist = [(dtest, 'eval'), (dtrain, 'train')]
|
|
num_round = 20
|
|
|
|
# Run training, all the features in training API is available.
|
|
# Currently, this script only support calling train once for fault recovery purpose.
|
|
bst = xgb.train(param, dtrain, num_round, watchlist, early_stopping_rounds=2)
|
|
|
|
# Save the model, only ask process 0 to save the model.
|
|
if xgb.rabit.get_rank() == 0:
|
|
bst.save_model("test.model")
|
|
xgb.rabit.tracker_print("Finished training\n")
|
|
|
|
# Notify the tracker all training has been successful
|
|
# This is only needed in distributed training.
|
|
xgb.rabit.finalize()
|