def test_basic(c, s, a, b):
dtrain = xgb.DMatrix(df, label=labels)
bst = xgb.train(param, dtrain)
ddf = dd.from_pandas(df, npartitions=4)
dlabels = dd.from_pandas(labels, npartitions=4)
dbst = yield dxgb._train(c, param, ddf, dlabels)
dbst = yield dxgb._train(c, param, ddf, dlabels) # we can do this twice
result = bst.predict(dtrain)
dresult = dbst.predict(dtrain)
correct = (result > 0.5) == labels
dcorrect = (dresult > 0.5) == labels
assert dcorrect.sum() >= correct.sum()
predictions = dxgb.predict(c, dbst, ddf)
assert isinstance(predictions, dd.Series)
predictions = yield c.compute(predictions)._result()
assert isinstance(predictions, pd.Series)
assert ((predictions > 0.5) != labels).sum() < 2
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