add NumModelPerIteration and NumberOfTotalModel in C_API (#1613)

* added NumberOfTotalModel and NumModelPerIteration to C_API and python-package

* fixed tests

* added tests for current_iteration, num_trees, num_model_per_iteration methods

* break huge line in test

* hotfix

include/LightGBM/c_api.h

@@ -465,6 +465,20 @@ LIGHTGBM_C_EXPORT int LGBM_BoosterRollbackOneIter(BoosterHandle handle);
 */
 LIGHTGBM_C_EXPORT int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration);
 
+/*!
+* \brief Get number of tree per iteration
+* \param out_tree_per_iteration number of tree per iteration
+* \return 0 when succeed, -1 when failure happens
+*/
+LIGHTGBM_C_EXPORT int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration);
+
+/*!
+* \brief Get number of weak sub-models
+* \param out_models number of weak sub-models
+* \return 0 when succeed, -1 when failure happens
+*/
+LIGHTGBM_C_EXPORT int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models);
+
 /*!
 * \brief Get number of eval
 * \param out_len total number of eval results

python-package/lightgbm/basic.py

@@ -1737,6 +1737,34 @@ class Booster(object):
             ctypes.byref(out_cur_iter)))
         return out_cur_iter.value
 
+    def num_model_per_iteration(self):
+        """Get number of models per iteration.
+
+        Returns
+        -------
+        model_per_iter : int
+            The number of models per iteration.
+        """
+        model_per_iter = ctypes.c_int(0)
+        _safe_call(_LIB.LGBM_BoosterNumModelPerIteration(
+            self.handle,
+            ctypes.byref(model_per_iter)))
+        return model_per_iter.value
+
+    def num_trees(self):
+        """Get number of weak sub-models.
+
+        Returns
+        -------
+        num_trees : int
+            The number of weak sub-models.
+        """
+        num_trees = ctypes.c_int(0)
+        _safe_call(_LIB.LGBM_BoosterNumberOfTotalModel(
+            self.handle,
+            ctypes.byref(num_trees)))
+        return num_trees.value
+
     def eval(self, data, name, feval=None):
         """Evaluate for data.
 

src/c_api.cpp

@@ -1017,6 +1017,20 @@ int LGBM_BoosterGetCurrentIteration(BoosterHandle handle, int* out_iteration) {
   API_END();
 }
 
+int LGBM_BoosterNumModelPerIteration(BoosterHandle handle, int* out_tree_per_iteration) {
+  API_BEGIN();
+  Booster* ref_booster = reinterpret_cast<Booster*>(handle);
+  *out_tree_per_iteration = ref_booster->GetBoosting()->NumModelPerIteration();
+  API_END();
+}
+
+int LGBM_BoosterNumberOfTotalModel(BoosterHandle handle, int* out_models) {
+  API_BEGIN();
+  Booster* ref_booster = reinterpret_cast<Booster*>(handle);
+  *out_models = ref_booster->GetBoosting()->NumberOfTotalModel();
+  API_END();
+}
+
 int LGBM_BoosterGetEvalCounts(BoosterHandle handle, int* out_len) {
   API_BEGIN();
   Booster* ref_booster = reinterpret_cast<Booster*>(handle);

tests/c_api_test/test_.py

@@ -253,5 +253,12 @@ def test_booster():
         c_str(''),
         ctypes.byref(num_preb),
         preb.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
-    LIB.LGBM_BoosterPredictForFile(booster2, c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test')), 0, 0, 50, c_str(''), c_str('preb.txt'))
+    LIB.LGBM_BoosterPredictForFile(
+        booster2,
+        c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../../examples/binary_classification/binary.test')),
+        0,
+        0,
+        50,
+        c_str(''),
+        c_str('preb.txt'))
     LIB.LGBM_BoosterFree(booster2)

tests/python_package_test/test_basic.py

@@ -33,6 +33,11 @@ class TestBasic(unittest.TestCase):
             bst.update()
             if i % 10 == 0:
                 print(bst.eval_train(), bst.eval_valid())
+
+        self.assertEqual(bst.current_iteration(), 30)
+        self.assertEqual(bst.num_trees(), 30)
+        self.assertEqual(bst.num_model_per_iteration(), 1)
+
         bst.save_model("model.txt")
         pred_from_matr = bst.predict(X_test)
         with tempfile.NamedTemporaryFile() as f:

tests/python_package_test/test_engine.py

@@ -583,14 +583,14 @@ class TestEngine(unittest.TestCase):
         stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0)
         stacked_features = np.concatenate((stacked_features, np.ones(9, dtype=np.float32).reshape((1, 9))), axis=0)
         # test sliced 2d matrix
-        sliced_features = stacked_features[2:102, 2: 7]
-        assert np.all(sliced_features == features)
+        sliced_features = stacked_features[2:102, 2:7]
+        self.assertTrue(np.all(sliced_features == features))
         sliced_pred = train_and_get_predictions(sliced_features, sliced_labels)
         np.testing.assert_almost_equal(origin_pred, sliced_pred)
         # test sliced CSR
         stacked_csr = csr_matrix(stacked_features)
-        sliced_csr = stacked_csr[2:102, 2: 7]
-        assert np.all(sliced_csr == features)
+        sliced_csr = stacked_csr[2:102, 2:7]
+        self.assertTrue(np.all(sliced_csr == features))
         sliced_pred = train_and_get_predictions(sliced_csr, sliced_labels)
         np.testing.assert_almost_equal(origin_pred, sliced_pred)
 
@@ -632,7 +632,7 @@ class TestEngine(unittest.TestCase):
             'monotone_constraints': '1,-1'
         }
         constrained_model = lgb.train(params, trainset)
-        assert is_correctly_constrained(constrained_model)
+        self.assertTrue(is_correctly_constrained(constrained_model))
 
     def test_refit(self):
         X, y = load_breast_cancer(True)