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complete BigST with preprocess (#206)
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| import os | ||
| import sys | ||
| import torch | ||
| from easydict import EasyDict | ||
| sys.path.append(os.path.abspath(__file__ + '/../../..')) | ||
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| from basicts.metrics import masked_mae, masked_mape, masked_rmse | ||
| from basicts.data import TimeSeriesForecastingDataset | ||
| from basicts.runners import SimpleTimeSeriesForecastingRunner | ||
| from basicts.scaler import ZScoreScaler | ||
| from basicts.utils import get_regular_settings, load_adj | ||
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| from .arch import BigST | ||
| # from .runner import BigSTPreprocessRunner | ||
| from .loss import bigst_loss | ||
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| import pdb | ||
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| ############################## Hot Parameters ############################## | ||
| # Dataset & Metrics configuration | ||
| DATA_NAME = 'PEMS08' # Dataset name | ||
| regular_settings = get_regular_settings(DATA_NAME) | ||
| INPUT_LEN = 2016 # regular_settings['INPUT_LEN'] # Length of input sequence | ||
| OUTPUT_LEN = 12 # regular_settings['OUTPUT_LEN'] # Length of output sequence | ||
| TRAIN_VAL_TEST_RATIO = regular_settings['TRAIN_VAL_TEST_RATIO'] # Train/Validation/Test split ratios | ||
| NORM_EACH_CHANNEL = regular_settings['NORM_EACH_CHANNEL'] # Whether to normalize each channel of the data | ||
| RESCALE = regular_settings['RESCALE'] # Whether to rescale the data | ||
| NULL_VAL = regular_settings['NULL_VAL'] # Null value in the data | ||
| # Model architecture and parameters | ||
| PREPROCESSED_FILE = "checkpoints\\BigSTPreprocess\\PEMS08_100_2016_12\\db8308a2c87de35e5f3db6177c5714ff\\BigSTPreprocess_best_val_MAE.pt" | ||
| MODEL_ARCH = BigST | ||
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| adj_mx, _ = load_adj("datasets/" + DATA_NAME + | ||
| "/adj_mx.pkl", "doubletransition") | ||
| MODEL_PARAM = { | ||
| "bigst_args":{ | ||
| "num_nodes": 170, | ||
| "seq_num": 12, | ||
| "in_dim": 3, | ||
| "out_dim": OUTPUT_LEN, # 源代码固定成12了 | ||
| "hid_dim": 32, | ||
| "tau" : 0.25, | ||
| "random_feature_dim": 64, | ||
| "node_emb_dim": 32, | ||
| "time_emb_dim": 32, | ||
| "use_residual": True, | ||
| "use_bn": True, | ||
| "use_long": True, | ||
| "use_spatial": True, | ||
| "dropout": 0.3, | ||
| "supports": [torch.tensor(i) for i in adj_mx], | ||
| "time_of_day_size": 288, | ||
| "day_of_week_size": 7 | ||
| }, | ||
| "preprocess_path": PREPROCESSED_FILE, | ||
| "preprocess_args":{ | ||
| "num_nodes": 170, | ||
| "in_dim": 3, | ||
| "dropout": 0.3, | ||
| "input_length": 2016, | ||
| "output_length": 12, | ||
| "nhid": 32, | ||
| "tiny_batch_size": 64, | ||
| } | ||
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| } | ||
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| NUM_EPOCHS = 100 | ||
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| ############################## General Configuration ############################## | ||
| CFG = EasyDict() | ||
| # General settings | ||
| CFG.DESCRIPTION = 'An Example Config' | ||
| CFG.GPU_NUM = 1 # Number of GPUs to use (0 for CPU mode) | ||
| # Runner | ||
| CFG.RUNNER = SimpleTimeSeriesForecastingRunner | ||
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| ############################## Environment Configuration ############################## | ||
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| CFG.ENV = EasyDict() # Environment settings. Default: None | ||
| CFG.ENV.SEED = 0 # Random seed. Default: None | ||
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| ############################## Dataset Configuration ############################## | ||
| CFG.DATASET = EasyDict() | ||
| # Dataset settings | ||
| CFG.DATASET.NAME = DATA_NAME | ||
| CFG.DATASET.TYPE = TimeSeriesForecastingDataset | ||
| CFG.DATASET.PARAM = EasyDict({ | ||
| 'dataset_name': DATA_NAME, | ||
| 'train_val_test_ratio': TRAIN_VAL_TEST_RATIO, | ||
| 'input_len': INPUT_LEN, | ||
| 'output_len': OUTPUT_LEN, | ||
| # 'mode' is automatically set by the runner | ||
| }) | ||
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| ############################## Scaler Configuration ############################## | ||
| CFG.SCALER = EasyDict() | ||
| # Scaler settings | ||
| CFG.SCALER.TYPE = ZScoreScaler # Scaler class | ||
| CFG.SCALER.PARAM = EasyDict({ | ||
| 'dataset_name': DATA_NAME, | ||
| 'train_ratio': TRAIN_VAL_TEST_RATIO[0], | ||
| 'norm_each_channel': NORM_EACH_CHANNEL, | ||
| 'rescale': RESCALE, | ||
| }) | ||
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| ############################## Model Configuration ############################## | ||
| CFG.MODEL = EasyDict() | ||
| # Model settings | ||
| CFG.MODEL.NAME = MODEL_ARCH.__name__ | ||
| CFG.MODEL.ARCH = MODEL_ARCH | ||
| CFG.MODEL.PARAM = MODEL_PARAM | ||
| CFG.MODEL.FORWARD_FEATURES = [0, 1, 2] | ||
| CFG.MODEL.TARGET_FEATURES = [0] | ||
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| ############################## Metrics Configuration ############################## | ||
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| CFG.METRICS = EasyDict() | ||
| # Metrics settings | ||
| CFG.METRICS.FUNCS = EasyDict({ | ||
| 'MAE': masked_mae, | ||
| 'MAPE': masked_mape, | ||
| 'RMSE': masked_rmse, | ||
| }) | ||
| CFG.METRICS.TARGET = 'MAE' | ||
| CFG.METRICS.NULL_VAL = NULL_VAL | ||
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| ############################## Training Configuration ############################## | ||
| CFG.TRAIN = EasyDict() | ||
| CFG.TRAIN.NUM_EPOCHS = NUM_EPOCHS | ||
| CFG.TRAIN.CKPT_SAVE_DIR = os.path.join( | ||
| 'checkpoints', | ||
| MODEL_ARCH.__name__, | ||
| '_'.join([DATA_NAME, str(CFG.TRAIN.NUM_EPOCHS), str(INPUT_LEN), str(OUTPUT_LEN)]) | ||
| ) | ||
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| CFG.TRAIN.LOSS = bigst_loss if MODEL_PARAM['bigst_args']['use_spatial'] else masked_mae | ||
| # Optimizer settings | ||
| CFG.TRAIN.OPTIM = EasyDict() | ||
| CFG.TRAIN.OPTIM.TYPE = "AdamW" | ||
| CFG.TRAIN.OPTIM.PARAM = { | ||
| "lr": 0.002, | ||
| "weight_decay": 0.0001, | ||
| } | ||
| # Learning rate scheduler settings | ||
| CFG.TRAIN.LR_SCHEDULER = EasyDict() | ||
| CFG.TRAIN.LR_SCHEDULER.TYPE = "MultiStepLR" | ||
| CFG.TRAIN.LR_SCHEDULER.PARAM = { | ||
| "milestones": [1, 50], | ||
| "gamma": 0.5 | ||
| } | ||
| # Train data loader settings | ||
| CFG.TRAIN.DATA = EasyDict() | ||
| CFG.TRAIN.DATA.BATCH_SIZE = 64 | ||
| CFG.TRAIN.DATA.SHUFFLE = True | ||
| # Gradient clipping settings | ||
| CFG.TRAIN.CLIP_GRAD_PARAM = { | ||
| "max_norm": 5.0 | ||
| } | ||
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| ############################## Validation Configuration ############################## | ||
| CFG.VAL = EasyDict() | ||
| CFG.VAL.INTERVAL = 1 | ||
| CFG.VAL.DATA = EasyDict() | ||
| CFG.VAL.DATA.BATCH_SIZE = 64 | ||
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| ############################## Test Configuration ############################## | ||
| CFG.TEST = EasyDict() | ||
| CFG.TEST.INTERVAL = 1 | ||
| CFG.TEST.DATA = EasyDict() | ||
| CFG.TEST.DATA.BATCH_SIZE = 64 | ||
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| ############################## Evaluation Configuration ############################## | ||
| CFG.EVAL = EasyDict() | ||
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| # Evaluation parameters | ||
| CFG.EVAL.HORIZONS = [3, 6, 12] # Prediction horizons for evaluation. Default: [] | ||
| CFG.EVAL.USE_GPU = True # Whether to use GPU for evaluation. Default: True | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,3 +1,5 @@ | ||
| from .bigst_arch import BigST | ||
| from .preprocess import BigSTPreprocess | ||
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| __all__ = ["BigST"] | ||
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| __all__ = ["BigST", "BigSTPreprocess"] |
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