63 lines
2.3 KiB
Python
Executable File
63 lines
2.3 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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dtrain = xgb.DMatrix('../data/agaricus.txt.train')
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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic'}
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num_round = 2
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print ('running cross validation')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value+std_value
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# std_value is standard deviation of the metric
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xgb.cv(param, dtrain, num_round, nfold=5,
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metrics={'error'}, seed = 0,
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callbacks=[xgb.callback.print_evaluation(show_stdv=True)])
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print ('running cross validation, disable standard deviation display')
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# do cross validation, this will print result out as
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# [iteration] metric_name:mean_value
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res = xgb.cv(param, dtrain, num_boost_round=10, nfold=5,
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metrics={'error'}, seed = 0,
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callbacks=[xgb.callback.print_evaluation(show_stdv=False),
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xgb.callback.early_stop(3)])
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print (res)
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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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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={'auc'}, seed = 0, fpreproc = fpreproc)
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###
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# you can also do cross validation with cutomized loss function
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# See custom_objective.py
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##
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print ('running cross validation, with cutomsized loss function')
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def logregobj(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds))
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grad = preds - labels
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hess = preds * (1.0-preds)
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return grad, hess
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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param = {'max_depth':2, 'eta':1, 'silent':1}
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# train with customized objective
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xgb.cv(param, dtrain, num_round, nfold = 5, seed = 0,
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obj = logregobj, feval=evalerror)
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