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88 lines
3.4 KiB
Python
Executable File
88 lines
3.4 KiB
Python
Executable File
# 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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from torch.utils.data import DataLoader
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import time
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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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"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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],
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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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],
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"process_type": "independent",
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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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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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t = time.time()
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for idx in np.random.randint(0, len(tsds_train), size=2000):
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data = tsds_train[idx]
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print(f"2000 sample takes {time.time() - t}s")
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# FIXME: Please remove pytorch related function. Otherwise the CI tests will fail
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train_loader = DataLoader(tsds_train, batch_size=800, shuffle=True, num_workers=10)
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t = time.time()
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for data in train_loader:
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pass
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print(f"Passing all training batches takes {time.time() - t}s")
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# Here is an example of ffill+bfill for index
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tsds_train.config(fillna_type="ffill+bfill")
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train_loader = DataLoader(tsds_train, batch_size=800, shuffle=True, num_workers=10)
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t = time.time()
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for data in train_loader:
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pass
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print(f"Passing all training batches with fill takes {time.time() - t}s")
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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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