From a88697151aa34293984c5ef816c6848d8928e44f Mon Sep 17 00:00:00 2001
From: lwwang1995 <Lewen.Wang@microsoft.com>
Date: Sun, 6 Dec 2020 17:24:58 +0800
Subject: [PATCH] Test CSRankNorm.

---
 tests/test_dataset.py | 15 ++++++---------
 1 file changed, 6 insertions(+), 9 deletions(-)

diff --git a/tests/test_dataset.py b/tests/test_dataset.py
index 36234c879..01454fff8 100755
--- a/tests/test_dataset.py
+++ b/tests/test_dataset.py
@@ -8,7 +8,7 @@ from qlib.data.dataset import TSDatasetH
 import numpy as np
 from torch.utils.data import DataLoader
 import time
-
+from qlib.data.dataset.handler import DataHandlerLP
 
 class TestDataset(TestAutoData):
     def testTSDataset(self):
@@ -23,17 +23,14 @@ class TestDataset(TestAutoData):
                     "fit_end_time": "2014-12-31",
                     "instruments": "csi300",
                     "infer_processors": [
-                        {"class": "DropCol", "kwargs": {"col_list": ["VWAP0"]}},
                         {"class": "FilterCol", "kwargs": {"col_list": ["RESI5", "WVMA5", "RSQR5"]}},
-                        {"class": "CSZFillna", "kwargs": {"fields_group": "feature"}},
+                        {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature", "clip_outlier":"true"}},
+                        {"class": "Fillna", "kwargs": {"fields_group": "feature"}},
                     ],
                     "learn_processors": [
-                        {"class": "DropCol", "kwargs": {"col_list": ["VWAP0"]}},
-                        {"class": "DropnaProcessor", "kwargs": {"fields_group": "feature"}},
                         "DropnaLabel",
-                        {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
+                        {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}}, # CSRankNorm 
                     ],
-                    "process_type": "independent",
                 },
             },
             segments={
@@ -42,8 +39,8 @@ class TestDataset(TestAutoData):
                 "test": ("2017-01-01", "2020-08-01"),
             },
         )
-        tsds_train = tsdh.prepare("train")  # Test the correctness
-        tsds = tsdh.prepare("valid")  # prepare a dataset with is friendly to converting tabular data to time-series
+        tsds_train = tsdh.prepare("train", data_key=DataHandlerLP.DK_L)  # Test the correctness
+        tsds = tsdh.prepare("valid", data_key=DataHandlerLP.DK_L)
 
         t = time.time()
         for idx in np.random.randint(0, len(tsds_train), size=2000):