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prepare.py
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def prep_env():
settings = {
"path_to_test_x": "./data/new_tests/test_x",
"path_to_test_y": "./data/new_tests/test_y",
"data_path": "./data",
"filename": "wtbdata_245days.csv",
"location_file": "sdwpf_baidukddcup2022_turb_location.csv",
"start_col": 3,
"checkpoints": "checkpoints",
"pred_file": "predict.py",
"framework": "pytorch",
"device": "cuda:0",
"is_debug": True,
'mdlinear': {
"model_id": "50_alr3_time_split_72step_FE6_abs",
"seed": 2021,
# model settings
"seq_len": 50,
"pred_len": 288,
"label_len": 0,
"target": "Patv",
"horizons": [72, 144, 216, 288],
"step_size": 72,
"enc_in": 7, # number of features
"individual": False,
"moving_avg": 25, # window size of the moving average
"scale": True,
# experiment settings
"train_epochs": 100,
"batch_size": 32,
"weight_decay": 0.0001,
"learning_rate": 0.001,
"lradj": "3", # adjustable learning rate
"patience": 3,
"num_workers": 16,
},
'xtgn': {
"model_name": "xtgn-2",
"seed": 2022,
# data
"feature_dim": 4,
"num_nodes": 134,
"seq_length_x": 288,
"seq_length_y": 288,
"embed_dim": 2,
# experiment settings
"batch_size": 32,
"max_epoch": 100,
"milestone": [5, 20, 40, 70],
"learning_rate": 0.003,
"dropout_rate": 0.2,
"weight_decay": 0.0001,
"print_freq": 5,
"test_time": 5,
"ratio": 9, # time split ratio
# WaveNet model
"blocks": 2,
"wavenet_layers": 7,
"kernel_size": 2,
"residual_channels": 32,
"dilation_channels": 32,
"skip_channels": 128,
"end_channels": 256,
"receptive_field": 13,
}
}
return settings