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54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import unittest
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import sys
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from qlib.tests import TestAutoData
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from qlib.data.dataset import TSDatasetH
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import numpy as np
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class TestDataset(TestAutoData):
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def testTSDataset(self):
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tsdh = TSDatasetH(
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handler={
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"class": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": "csi300",
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},
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},
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segments={
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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)
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_ = 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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# The dimension of sample is same as tabular data, but it will return timeseries data of the sample
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# We have two method to get the time-series of a sample
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# 1) sample by int index directly
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tsds[len(tsds) - 1]
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# 2) sample by <datetime,instrument> index
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data_from_ds = tsds["2016-12-31", "SZ300315"]
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# Check the data
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# Get data from DataFrame Directly
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data_from_df = tsdh._handler.fetch().loc(axis=0)["2015-01-01":"2016-12-31", "SZ300315"].iloc[-30:].values
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equal = np.isclose(data_from_df, data_from_ds)
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self.assertTrue(equal[~np.isnan(data_from_df)].all())
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if __name__ == "__main__":
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unittest.main(verbosity=10)
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