2c1a439869
* Remove GPU memory usage demo. * Add tests for demos. * Remove `silent`. * Remove shebang as it's not portable.
51 lines
1.9 KiB
Python
51 lines
1.9 KiB
Python
import os
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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: customized loss function
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#
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print('start running example to used customized objective function')
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CURRENT_DIR = os.path.dirname(__file__)
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dtrain = xgb.DMatrix(os.path.join(CURRENT_DIR, '../data/agaricus.txt.train'))
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dtest = xgb.DMatrix(os.path.join(CURRENT_DIR, '../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}
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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
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# order gradient this is log likelihood 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
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# margin. this may make builtin evaluation metric not function properly for
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# example, we are doing logistic loss, the prediction is score before logistic
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# transformation the builtin evaluation error assumes input is after logistic
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# transformation Take this in mind when you use the customization, and maybe
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# 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. The metric name must not contain a
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# colon (:) or a space since preds are margin(before logistic
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# transformation, cutoff at 0)
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return 'my-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, obj=logregobj,
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feval=evalerror)
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