last check

tests/python/test_basic.py

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