Merge pull request #961 from tqchen/master

Fix multi-class loading

dmlc-core

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

src/learner.cc

@@ -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());
     }

tests/python/test_basic.py

@@ -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)
-