mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-29 03:37:45 +00:00
Mark Aron Szulyovszky
d047b7417e
* refactor(WalkForward): cleaned up training & evaluation code * refactor: added run_whole_pipeline(), moved all previous models to archive
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
from pytorch_forecasting.data import GroupNormalizer
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from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer
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from lightning.models.custom_model import FullyConnectedModel
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training_options = dict(
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max_epochs=100,
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gpus=0,
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gradient_clip_val=0.1,
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limit_train_batches=30
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)
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model_options_fcn = dict(
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hidden_size=64,
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n_hidden_layers=2,
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)
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model_options_tft = dict(
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hidden_size=64,
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n_hidden_layers=2,
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)
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dataset_options_tft = dict(
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time_idx= 'time_idx',
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target= 'returns',
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# weight="weight",
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group_ids=[ 'ticker' ],
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min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
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max_encoder_length = 36,
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min_prediction_length = 1,
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max_prediction_length = 1,
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static_categoricals=[ ],
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static_reals=[ ],
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time_varying_known_categoricals=[ 'day_month', 'day_week', 'month' ],
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time_varying_known_reals=[ 'vol_10', 'vol_20', 'vol_30', 'vol_60', 'mom_10', 'mom_20', 'mom_30', 'mom_60', 'mom_90'],
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time_varying_unknown_categoricals=[ ],
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time_varying_unknown_reals=[ 'returns' ],
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allow_missing_timesteps=True,
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target_normalizer=GroupNormalizer(
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groups=['ticker'], transformation="softplus"),
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batch_size=128
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)
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dataset_options_fcn = dict(
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time_idx= 'time_idx',
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target= 'returns',
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# weight="weight",
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group_ids=[ 'ticker' ],
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min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
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max_encoder_length = 36,
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min_prediction_length = 1,
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max_prediction_length = 1,
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static_categoricals=[ ],
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static_reals=[ ],
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time_varying_known_categoricals=[ ],
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time_varying_known_reals=[ ],
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time_varying_unknown_categoricals=[ ],
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time_varying_unknown_reals=[ 'returns' ],
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allow_missing_timesteps=True,
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target_normalizer=GroupNormalizer(
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groups=['ticker'], transformation="softplus"),
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batch_size=128
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)
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