32 lines
1.0 KiB
Python
32 lines
1.0 KiB
Python
import numpy as np
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import xgboost as xgb
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dpath = 'demo/data/'
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def test_basic():
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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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param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }
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# specify validations set to watch performance
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watchlist = [(dtest,'eval'), (dtrain,'train')]
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num_round = 2
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bst = xgb.train(param, dtrain, num_round, watchlist)
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# this is prediction
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preds = bst.predict(dtest)
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labels = dtest.get_label()
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err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
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# error must be smaller than 10%
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assert err < 0.1
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# save dmatrix into binary buffer
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dtest.save_binary('dtest.buffer')
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# save model
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bst.save_model('xgb.model')
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# load model and data in
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bst2 = xgb.Booster(model_file='xgb.model')
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dtest2 = xgb.DMatrix('dtest.buffer')
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preds2 = bst2.predict(dtest2)
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# assert they are the same
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assert np.sum(np.abs(preds2-preds)) == 0
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