Ptnn4both datatypes and alignment tests #1827
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| - Now you can just replace the Pytorch model structure to run a NN model. | ||
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| We provide an example to demonstrate the effectiveness of the current design. | ||
| - `workflow_config_gru.yaml` align with previous results [GRU(Kyunghyun Cho, et al.)](../README.md#Alpha158 dataset) |
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tests/model/test_general_nn.py
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| for ds, model in reversed(list(zip((tsds, tbds), model_l))): | ||
| model.fit(ds) # It works | ||
| model.predict(ds) # It works | ||
| break |
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tests/model/test_general_nn.py
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| ), | ||
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| for ds, model in reversed(list(zip((tsds, tbds), model_l))): |
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| from torch.nn import DataParallel | ||
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| class GeneralPTNN(Model): |
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tests/model/test_general_nn.py
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| if __name__ == "__main__": | ||
| try: |
tests/model/test_general_nn.py
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| import unittest | ||
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| from qlib.contrib.model.pytorch_general_nn import GeneralPTNN |
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| train: [2008-01-01, 2014-12-31] | ||
| valid: [2015-01-01, 2016-12-31] | ||
| test: [2017-01-01, 2020-08-01] | ||
| step_len: 20 |
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Support Pytorch_nn for processing time-series data
Description
We create a new class GeneralPTNN and a test file for it.
Motivation and Context
How Has This Been Tested?
pytest qlib/tests/test_all_pipeline.pyunder upper directory ofqlib.Screenshots of Test Results (if appropriate):
Pipeline test:
Your own tests:
Types of changes