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feat(WalkForward): added regression/classification switch, archived old experiments, wrapped the process into run_whole_pipeline() (#10)

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* refactor(WalkForward): cleaned up training & evaluation code

* refactor: added run_whole_pipeline(), moved all previous models to archive
This commit is contained in:
Mark Aron Szulyovszky
2021-12-14 18:16:17 +01:00
committed by GitHub
parent 7aedb91069
commit d047b7417e
89 changed files with 10763 additions and 6477 deletions
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archive/lightning/options.py
+63
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from pytorch_forecasting.data import GroupNormalizer
from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer
from lightning.models.custom_model import FullyConnectedModel
training_options = dict(
max_epochs=100,
gpus=0,
gradient_clip_val=0.1,
limit_train_batches=30
)
model_options_fcn = dict(
hidden_size=64,
n_hidden_layers=2,
)
model_options_tft = dict(
hidden_size=64,
n_hidden_layers=2,
)
dataset_options_tft = dict(
time_idx= 'time_idx',
target= 'returns',
# weight="weight",
group_ids=[ 'ticker' ],
min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
max_encoder_length = 36,
min_prediction_length = 1,
max_prediction_length = 1,
static_categoricals=[ ],
static_reals=[ ],
time_varying_known_categoricals=[ 'day_month', 'day_week', 'month' ],
time_varying_known_reals=[ 'vol_10', 'vol_20', 'vol_30', 'vol_60', 'mom_10', 'mom_20', 'mom_30', 'mom_60', 'mom_90'],
time_varying_unknown_categoricals=[ ],
time_varying_unknown_reals=[ 'returns' ],
allow_missing_timesteps=True,
target_normalizer=GroupNormalizer(
groups=['ticker'], transformation="softplus"),
batch_size=128
)
dataset_options_fcn = dict(
time_idx= 'time_idx',
target= 'returns',
# weight="weight",
group_ids=[ 'ticker' ],
min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
max_encoder_length = 36,
min_prediction_length = 1,
max_prediction_length = 1,
static_categoricals=[ ],
static_reals=[ ],
time_varying_known_categoricals=[ ],
time_varying_known_reals=[ ],
time_varying_unknown_categoricals=[ ],
time_varying_unknown_reals=[ 'returns' ],
allow_missing_timesteps=True,
target_normalizer=GroupNormalizer(
groups=['ticker'], transformation="softplus"),
batch_size=128
)