62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
###
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# advanced: customized loss function
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#
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import os
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import numpy as np
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import xgboost as xgb
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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: what we are getting is margin value in prediction you must know what
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# you are doing
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param = {'max_depth': 2, 'eta': 1, 'objective': 'reg:logistic'}
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watchlist = [(dtest, 'eval'), (dtrain, 'train')]
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num_round = 10
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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)) # transform raw leaf weight
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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, which means the prediction is score before logistic transformation.
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def evalerror(preds, dtrain):
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labels = dtrain.get_label()
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preds = 1.0 / (1.0 + np.exp(-preds)) # transform raw leaf weight
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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
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return 'my-error', float(sum(labels != (preds > 0.5))) / len(labels)
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py_evals_result = {}
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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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py_params = param.copy()
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py_params.update({'disable_default_eval_metric': True})
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py_logreg = xgb.train(py_params, dtrain, num_round, watchlist, obj=logregobj,
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feval=evalerror, evals_result=py_evals_result)
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evals_result = {}
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params = param.copy()
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params.update({'eval_metric': 'error'})
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logreg = xgb.train(params, dtrain, num_boost_round=num_round, evals=watchlist,
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evals_result=evals_result)
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for i in range(len(py_evals_result['train']['my-error'])):
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np.testing.assert_almost_equal(py_evals_result['train']['my-error'],
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evals_result['train']['error'])
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