Add simple test

.travis.yml

@@ -21,9 +21,11 @@ script:
 - cd $TRAVIS_BUILD_DIR
 - mkdir build && cd build && cmake .. && make -j
 - cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py
+- cd $TRAVIS_BUILD_DIR/tests/python_package_test && python test_basic.py 
 - cd $TRAVIS_BUILD_DIR
 - rm -rf build && mkdir build && cd build && cmake -DUSE_MPI=ON ..&& make -j
-- cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py
+- cd $TRAVIS_BUILD_DIR/tests/c_api_test && python test.py 
+- cd $TRAVIS_BUILD_DIR/tests/python_package_test && python test_basic.py 
 
 notifications:
   email: false

python-package/lightgbm/basic.py

@@ -290,7 +290,7 @@ class Predictor(object):
                 n_preds *= used_iteration
         preds = np.zeros(n_preds, dtype=np.float32)
         out_num_preds = ctypes.c_int64(0)
-        _safe_call(LIB.LGBM_BoosterPredictForMat(
+        _safe_call(_LIB.LGBM_BoosterPredictForMat(
             self.handle,
             ptr_data, 
             type_ptr_data,
@@ -324,7 +324,7 @@ class Predictor(object):
         ptr_indptr, type_ptr_indptr = c_int_array(csr.indptr)
         ptr_data, type_ptr_data = c_float_array(csr.data)
 
-        _safe_call(LIB.LGBM_BoosterPredictForCSR(
+        _safe_call(_LIB.LGBM_BoosterPredictForCSR(
             self.handle,
             ptr_indptr, 
             type_ptr_indptr,
@@ -447,7 +447,7 @@ class Dataset(object):
                 init_score = new_init_score
             self.set_init_score(init_score)
 
-    def new_valid_dataset(self, data, label=None, weight=None, group_id=None, 
+    def create_valid(self, data, label=None, weight=None, group_id=None, 
         silent=False, params=None):
         """
         Create validation data align with current dataset
@@ -487,7 +487,7 @@ class Dataset(object):
             data = np.array(mat.reshape(mat.size), dtype=np.float32)
 
         ptr_data, type_ptr_data = c_float_array(data)
-        _safe_call(LIB.LGBM_CreateDatasetFromMat(
+        _safe_call(_LIB.LGBM_CreateDatasetFromMat(
             ptr_data, 
             type_ptr_data,
             mat.shape[0],
@@ -825,7 +825,7 @@ class Booster(object):
         if self.handle is not None and self.__is_manage_handle:
             _safe_call(_LIB.LGBM_BoosterFree(self.handle))
 
-    def add_valid_data(self, data, name):
+    def add_valid(self, data, name):
         if data.predictor is not self.init_predictor:
             raise Exception("Add validation data failed, you should use same predictor for these data")
         _safe_call(_LIB.LGBM_BoosterAddValidData(
@@ -835,7 +835,7 @@ class Booster(object):
         self.name_valid_sets.append(name)
         self.__num_dataset += 1
 
-    def ResetParameter(self, params, silent=False):
+    def reset_parameter(self, params, silent=False):
         self.__need_reload_eval_info = True
         if silent:
             params["verbose"] = 0

tests/python_package_test/test_basic.py

+import numpy as np
+from sklearn import datasets, metrics, model_selection
+import importlib.util
+spec  = importlib.util.spec_from_file_location("module.name", "../../python-package/lightgbm/basic.py")
+lgb = importlib.util.module_from_spec(spec)
+spec.loader.exec_module(lgb)
+
+
+X, Y = datasets.make_classification(n_samples=100000, n_features=100)
+x_train, x_test, y_train, y_test = model_selection.train_test_split(X, Y, test_size=0.1)
+
+train_data = lgb.Dataset(x_train, max_bin=255, label=y_train)
+valid_data = train_data.create_valid(x_test, label=y_test)
+
+config={"objective":"binary","metric":"auc", "min_data":1, "num_leaves":15}
+bst = lgb.Booster(params=config, train_set=train_data)
+bst.add_valid(valid_data,"valid_1")
+
+for i in range(100):
+	bst.update()
+	print(bst.eval_train())
+	print(bst.eval_valid())
+bst.save_model("model.txt")
+