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https://github.com/webclinic017/drift.git
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Mark Aron Szulyovszky
d047b7417e
* refactor(WalkForward): cleaned up training & evaluation code * refactor: added run_whole_pipeline(), moved all previous models to archive
19 lines
623 B
Python
19 lines
623 B
Python
from pytorch_forecasting import TemporalFusionTransformer
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from pytorch_forecasting.metrics import QuantileLoss
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def create_TemporalFusionTransformer(training_dataset, model_options):
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# create the model
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tft = TemporalFusionTransformer.from_dataset(
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training_dataset,
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learning_rate=0.03,
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hidden_size=32,
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attention_head_size=1,
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dropout=0.1,
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hidden_continuous_size=16,
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output_size=7,
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loss=QuantileLoss(),
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log_interval=2,
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reduce_on_plateau_patience=4
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)
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print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
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return tft |