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TST: More thorough checks for Python tests

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terrytangyuan 2015-09-08 10:14:28 -04:00
parent 82a43f448e
commit 8196d5d680
1 changed files with 19 additions and 12 deletions
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31
tests/python/test_models.py
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@ -6,27 +6,34 @@ dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test') dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
def test_glm(): def test_glm():
param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', param = {'silent':1, 'objective':'binary:logistic', 'booster':'gblinear', 'alpha': 0.0001, 'lambda': 1 }
'alpha': 0.0001, 'lambda': 1 } watchlist = [(dtest,'eval'), (dtrain,'train')]
watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 4 num_round = 4
bst = xgb.train(param, dtrain, num_round, watchlist) bst = xgb.train(param, dtrain, num_round, watchlist)
assert isinstance(bst, xgb.core.Booster)
preds = bst.predict(dtest) preds = bst.predict(dtest)
labels = dtest.get_label() labels = dtest.get_label()
err = sum(1 for i in range(len(preds)) if int(preds[i]>0.5)!=labels[i]) / float(len(preds))
assert err < 0.1
def test_custom_objective(): def test_custom_objective():
param = {'max_depth':2, 'eta':1, 'silent':1 } param = {'max_depth':2, 'eta':1, 'silent':1 }
watchlist = [(dtest,'eval'), (dtrain,'train')] watchlist = [(dtest,'eval'), (dtrain,'train')]
num_round = 2 num_round = 2
def logregobj(preds, dtrain): def logregobj(preds, dtrain):
labels = dtrain.get_label() labels = dtrain.get_label()
preds = 1.0 / (1.0 + np.exp(-preds)) preds = 1.0 / (1.0 + np.exp(-preds))
grad = preds - labels grad = preds - labels
hess = preds * (1.0-preds) hess = preds * (1.0-preds)
return grad, hess return grad, hess
def evalerror(preds, dtrain): def evalerror(preds, dtrain):
labels = dtrain.get_label() labels = dtrain.get_label()
return 'error', float(sum(labels != (preds > 0.0))) / len(labels) return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror) bst = xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
assert isinstance(bst, xgb.core.Booster)
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))
assert err < 0.1