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* Init model for both dataset * Remove some deprecated code * Add model template; * We must align with previous results * We choose another mode as the initial version * Almost success to run GRU * Successfully run training * Passed general_nn test * gru test * Alignment test passed * comment * fix readme & minor errors * general nn updates & benchmarks * Update examples/benchmarks/GeneralPtNN/workflow_config_gru2mlp.yaml --------- Co-authored-by: Young <afe.young@gmail.com> Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import unittest
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from qlib.tests import TestAutoData
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class TestNN(TestAutoData):
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def test_both_dataset(self):
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try:
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from qlib.contrib.model.pytorch_general_nn import GeneralPTNN
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from qlib.data.dataset import DatasetH, TSDatasetH
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from qlib.data.dataset.handler import DataHandlerLP
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except ImportError:
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print("Import error.")
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return
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"instruments": "csi300",
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"data_loader": {
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"class": "QlibDataLoader", # Assuming QlibDataLoader is a string reference to the class
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"kwargs": {
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"config": {
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"feature": [["$high", "$close", "$low"], ["H", "C", "L"]],
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"label": [["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"]],
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},
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"freq": "day",
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},
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},
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# TODO: processors
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"learn_processors": [
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{
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"class": "DropnaLabel",
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},
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{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
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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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data_handler = DataHandlerLP(**data_handler_config)
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# time-series dataset
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tsds = TSDatasetH(handler=data_handler, segments=segments)
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# tabular dataset
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tbds = DatasetH(handler=data_handler, segments=segments)
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model_l = [
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GeneralPTNN(
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n_epochs=2,
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pt_model_uri="qlib.contrib.model.pytorch_gru_ts.GRUModel",
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pt_model_kwargs={
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"d_feat": 3,
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"hidden_size": 8,
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"num_layers": 1,
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"dropout": 0.0,
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},
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),
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GeneralPTNN(
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n_epochs=2,
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pt_model_uri="qlib.contrib.model.pytorch_nn.Net", # it is a MLP
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pt_model_kwargs={
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"input_dim": 3,
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},
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),
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]
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for ds, model in list(zip((tsds, tbds), model_l)):
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model.fit(ds) # It works
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model.predict(ds) # It works
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if __name__ == "__main__":
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unittest.main()
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