From c5163bce4dbc3d5af5368f19d425dc9a5057b787 Mon Sep 17 00:00:00 2001
From: Filip Stefaniuk <filip.stefaniuk@gmail.com>
Date: Sat, 14 Sep 2024 14:11:13 +0200
Subject: [PATCH] clean up training script

---
 scripts/train.py | 70 ------------------------------------------------
 1 file changed, 70 deletions(-)

diff --git a/scripts/train.py b/scripts/train.py
index 89b116f..d09f065 100644
--- a/scripts/train.py
+++ b/scripts/train.py
@@ -81,76 +81,6 @@ def get_args():
     return parser.parse_args()
 
 
-# def get_dataset(config, project):
-#     artifact_name = f"{project}/{config['data']['dataset']}"
-#     artifact = wandb.Api().artifact(artifact_name)
-#     base_path = artifact.download()
-#     logging.info(f"Artifacts downloaded to {base_path}")
-
-#     name = artifact.metadata['name']
-#     part_name = f"in-sample-{config['data']['sliding_window']}"
-#     data = pd.read_csv(os.path.join(
-#         base_path, name + '-' + part_name + '.csv'))
-#     logging.info(f"Using part: {part_name}")
-
-#     # TODO: Fix in dataset
-#     data['weekday'] = data['weekday'].astype('str')
-#     data['hour'] = data['hour'].astype('str')
-
-#     validation_part = config['data']['validation']
-#     logging.info(f"Using {validation_part} of in sample part for validation.")
-#     train_data = data.iloc[:int(len(data) * (1 - validation_part))]
-#     val_data = data.iloc[len(train_data) - config['past_window']:]
-#     logging.info(f"Trainin part size: {len(train_data)}")
-#     logging.info(
-#         f"Validation part size: {len(val_data)} "
-#         + f"({len(data) - len(train_data)} + {config['past_window']})")
-
-#     logging.info("Building time series dataset for training.")
-#     train = TimeSeriesDataSet(
-#         train_data,
-#         time_idx=config['data']['fields']['time_index'],
-#         target=config['data']['fields']['target'],
-#         group_ids=config['data']['fields']['group_ids'],
-#         min_encoder_length=config['past_window'],
-#         max_encoder_length=config['past_window'],
-#         min_prediction_length=config['future_window'],
-#         max_prediction_length=config['future_window'],
-#         static_reals=config['data']['fields']['static_real'],
-#         static_categoricals=config['data']['fields']['static_cat'],
-#         time_varying_known_reals=config['data']['fields'][
-#             'dynamic_known_real'],
-#         time_varying_known_categoricals=config['data']['fields'][
-#             'dynamic_known_cat'],
-#         time_varying_unknown_reals=config['data']['fields'][
-#             'dynamic_unknown_real'],
-#         time_varying_unknown_categoricals=config['data']['fields'][
-#             'dynamic_unknown_cat'],
-#         randomize_length=False
-#     )
-
-#     logging.info("Building time series dataset for validation.")
-#     val = TimeSeriesDataSet.from_dataset(
-#         train, val_data, stop_randomization=True)
-
-#     return train, val
-
-
-# def get_loss(config):
-#     loss_name = config['loss']['name']
-
-#     if loss_name == 'Quantile':
-#         return QuantileLoss(config['loss']['quantiles'])
-
-#     if loss_name == 'GMADL':
-#         return GMADL(
-#             a=config['loss']['a'],
-#             b=config['loss']['b']
-#         )
-
-#     raise ValueError("Unknown loss")
-
-
 def main():
     args = get_args()
     logging.basicConfig(level=args.log_level)