Init model for both dataset

tests/model/test_general_nn.py

@@ -0,0 +1,66 @@
+
+import unittest
+
+from qlib.contrib.model.pytorch_general_nn import GeneralPTNN
+from qlib.data.dataset import DatasetH, TSDatasetH
+from qlib.data.dataset.handler import DataHandlerLP
+from qlib.tests import TestAutoData
+
+
+class TestNN(TestAutoData):
+
+    def test_both_dataset(self):
+        data_handler_config = {
+            "start_time": "2008-01-01",
+            "end_time": "2020-08-01",
+            "instruments": "csi300",
+            "data_loader": {
+                "class": "QlibDataLoader",  # Assuming QlibDataLoader is a string reference to the class
+                "kwargs": {
+                    "config": {
+                        "feature": [
+                            ["$high", "$close", "$low"],
+                            ["H", "C", "L"]
+                        ],
+                        "label": [
+                            ["Ref($close, -2)/Ref($close, -1) - 1"],
+                            ["LABEL0"]
+                        ]
+                    },
+                    "freq": "day"
+                }
+            },
+            # TODO: processors
+            "learn_processors": [
+                {
+                "class": "DropnaLabel",
+                },
+                {
+                    "class": "CSZScoreNorm",
+                    "kwargs": {
+                        "fields_group": "label"
+                    }
+                }
+            ]
+        }
+        segments = {
+            "train": ["2008-01-01", "2014-12-31"],
+            "valid": ["2015-01-01", "2016-12-31"],
+            "test": ["2017-01-01", "2020-08-01"]
+        }
+        data_handler = DataHandlerLP(**data_handler_config)
+
+        # time-series dataset
+        tsds = TSDatasetH(handler=data_handler, segments=segments)
+
+        # tabular dataset
+        tbds = DatasetH(handler=data_handler, segments=segments)
+        
+        for ds in (tsds, tbds):
+            ptnn = GeneralPTNN()
+            ptnn.fit(ds)  # It works
+            ptnn.predict(ds)  # It works
+
+
+if __name__ == "__main__":
+    unittest.main()