43 lines
1.8 KiB
Python
Executable File
43 lines
1.8 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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###
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# advanced: cutomsized loss function
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#
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print ('start running example to used cutomized objective function')
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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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# note: for customized objective function, we leave objective as default
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# note: what we are getting is margin value in prediction
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# you must know what you are doing
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param = {'max_depth':2, 'eta':1, 'silent':1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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# user define objective function, given prediction, return gradient and second order gradient
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# this is loglikelihood loss
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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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# user defined evaluation function, return a pair metric_name, result
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# NOTE: when you do customized loss function, the default prediction value is margin
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# this may make buildin evalution metric not function properly
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# for example, we are doing logistic loss, the prediction is score before logistic transformation
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# the buildin evaluation error assumes input is after logistic transformation
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# Take this in mind when you use the customization, and maybe you need write customized evaluation function
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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# return a pair metric_name, result
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# since preds are margin(before logistic transformation, cutoff at 0)
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return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
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# training with customized objective, we can also do step by step training
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# simply look at xgboost.py's implementation of train
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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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