Add categorical feature support back.

tests/python_package_test/test_engine.py

@@ -153,6 +153,49 @@ class TestEngine(unittest.TestCase):
         for ret in other_ret:
             self.assertAlmostEqual(ret_origin, ret, places=5)
 
+    @unittest.skipIf(not IS_PANDAS_INSTALLED, 'pandas not installed')
+    def test_pandas_categorical(self):
+        X = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'c', 'd'] * 75),  # str
+                          "B": np.random.permutation([1, 2, 3] * 100),  # int
+                          "C": np.random.permutation([0.1, 0.2, -0.1, -0.1, 0.2] * 60),  # float
+                          "D": np.random.permutation([True, False] * 150)})  # bool
+        y = np.random.permutation([0, 1] * 150)
+        X_test = pd.DataFrame({"A": np.random.permutation(['a', 'b', 'e'] * 20),
+                               "B": np.random.permutation([1, 3] * 30),
+                               "C": np.random.permutation([0.1, -0.1, 0.2, 0.2] * 15),
+                               "D": np.random.permutation([True, False] * 30)})
+        for col in ["A", "B", "C", "D"]:
+            X[col] = X[col].astype('category')
+            X_test[col] = X_test[col].astype('category')
+        params = {
+            'objective': 'binary',
+            'metric': 'binary_logloss',
+            'verbose': -1
+        }
+        lgb_train = lgb.Dataset(X, y)
+        gbm0 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False)
+        pred0 = list(gbm0.predict(X_test))
+        lgb_train = lgb.Dataset(X, y)
+        gbm1 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
+                         categorical_feature=[0])
+        pred1 = list(gbm1.predict(X_test))
+        lgb_train = lgb.Dataset(X, y)
+        gbm2 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
+                         categorical_feature=['A'])
+        pred2 = list(gbm2.predict(X_test))
+        lgb_train = lgb.Dataset(X, y)
+        gbm3 = lgb.train(params, lgb_train, num_boost_round=10, verbose_eval=False,
+                         categorical_feature=['A', 'B', 'C', 'D'])
+        pred3 = list(gbm3.predict(X_test))
+        lgb_train = lgb.Dataset(X, y)
+        gbm3.save_model('categorical.model')
+        gbm4 = lgb.Booster(model_file='categorical.model')
+        pred4 = list(gbm4.predict(X_test))
+        self.assertListEqual(pred0, pred1)
+        self.assertListEqual(pred0, pred2)
+        self.assertListEqual(pred0, pred3)
+        self.assertListEqual(pred0, pred4)
+
 
 print("----------------------------------------------------------------------")
 print("running test_engine.py")