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