2c1a439869
* Remove GPU memory usage demo. * Add tests for demos. * Remove `silent`. * Remove shebang as it's not portable.
38 lines
1.4 KiB
Python
Executable File
38 lines
1.4 KiB
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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### load data in do training
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train = np.loadtxt('./data/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
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label = train[:,32]
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data = train[:,1:31]
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weight = train[:,31]
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dtrain = xgb.DMatrix( data, label=label, missing = -999.0, weight=weight )
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param = {'max_depth':6, 'eta':0.1, 'objective':'binary:logitraw', 'nthread':4}
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num_round = 120
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print ('running cross validation, with preprocessing function')
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# define the preprocessing function
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# used to return the preprocessed training, test data, and parameter
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# we can use this to do weight rescale, etc.
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# as a example, we try to set scale_pos_weight
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def fpreproc(dtrain, dtest, param):
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label = dtrain.get_label()
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ratio = float(np.sum(label == 0)) / np.sum(label==1)
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param['scale_pos_weight'] = ratio
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wtrain = dtrain.get_weight()
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wtest = dtest.get_weight()
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sum_weight = sum(wtrain) + sum(wtest)
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wtrain *= sum_weight / sum(wtrain)
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wtest *= sum_weight / sum(wtest)
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dtrain.set_weight(wtrain)
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dtest.set_weight(wtest)
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return (dtrain, dtest, param)
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# do cross validation, for each fold
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# the dtrain, dtest, param will be passed into fpreproc
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# then the return value of fpreproc will be used to generate
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# results of that fold
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'ams@0.15', 'auc'}, seed = 0, fpreproc = fpreproc)
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