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Merge pull request #961 from tqchen/master

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Fix multi-class loading
This commit is contained in:
Tianqi Chen 2016-03-10 19:39:28 -08:00
commit 04f7fe9c36
3 changed files with 31 additions and 3 deletions
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2
dmlc-core

@ -1 +1 @@
Subproject commit 1db0792e1a55355b1f07699bba18c88ded996953
Subproject commit 969fb6455ae41d5d2f7c4ba8921f4885e9aa63c8

4
src/learner.cc
View File

@ -125,8 +125,8 @@ class LearnerImpl : public Learner {
}
void Configure(const std::vector<std::pair<std::string, std::string> >& args) override {
tparam.InitAllowUnknown(args);
// add to configurations
tparam.InitAllowUnknown(args);
cfg_.clear();
for (const auto& kv : args) {
if (kv.first == "eval_metric") {
@ -187,6 +187,8 @@ class LearnerImpl : public Learner {
// set number of features correctly.
cfg_["num_feature"] = common::ToString(mparam.num_feature);
cfg_["num_class"] = common::ToString(mparam.num_class);
if (gbm_.get() != nullptr) {
gbm_->Configure(cfg_.begin(), cfg_.end());
}

28
tests/python/test_basic.py
View File

@ -34,6 +34,33 @@ class TestBasic(unittest.TestCase):
# assert they are the same
assert np.sum(np.abs(preds2 - preds)) == 0
def test_multiclass(self):
dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train')
dtest = xgb.DMatrix(dpath + 'agaricus.txt.test')
param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'num_class' : 2}
# 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 preds[i] != 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
def test_dmatrix_init(self):
data = np.random.randn(5, 5)
@ -135,4 +162,3 @@ class TestBasic(unittest.TestCase):
cv = xgb.cv(params, dm, num_boost_round=10, nfold=10, as_pandas=False)
assert isinstance(cv, np.ndarray)
assert cv.shape == (10, 4)