33 lines
1020 B
Python
33 lines
1020 B
Python
import numpy as np
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import xgboost as xgb
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dpath = 'demo/data/'
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dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
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dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
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def test_glm():
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param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear',
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'alpha': 0.0001, 'lambda': 1 }
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 4
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bst = xgb.train(param, dtrain, num_round, watchlist)
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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def test_custom_objective():
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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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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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bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
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