兼容BeijingAirQuality。重构data，需要更新pip requirement
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@ -52,6 +52,14 @@
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"console": "integratedTerminal",
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"args": "--config ./config/REPST/SolarEnergy.yaml"
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},
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{
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"name": "BeijingAirQuality",
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"type": "debugpy",
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"request": "launch",
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"program": "run.py",
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"console": "integratedTerminal",
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"args": "--config ./config/REPST/BeijingAirQuality.yaml"
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},
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{
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"name": "AEPSA-PEMSBAY",
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"type": "debugpy",
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@ -1,5 +1,11 @@
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{
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"python-envs.defaultEnvManager": "ms-python.python:system",
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"python-envs.defaultPackageManager": "ms-python.python:pip",
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"python-envs.pythonProjects": []
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"python-envs.defaultEnvManager": "ms-python.python:conda",
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"python-envs.defaultPackageManager": "ms-python.python:conda",
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"python-envs.pythonProjects": [
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{
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"path": "data/SolarEnergy",
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"envManager": "ms-python.python:system",
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"packageManager": "ms-python.python:pip"
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}
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]
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}
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@ -0,0 +1,61 @@
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basic:
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dataset: "BeijingAirQuality"
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mode : "train"
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device : "cuda:1"
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model: "REPST"
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seed: 2023
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data:
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add_day_in_week: false
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add_time_in_day: false
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column_wise: false
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days_per_week: 7
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default_graph: true
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horizon: 12
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lag: 12
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normalizer: std
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num_nodes: 7
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steps_per_day: 288
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test_ratio: 0.2
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tod: false
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val_ratio: 0.2
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sample: 1
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input_dim: 3
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batch_size: 16
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model:
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pred_len: 12
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seq_len: 12
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patch_len: 6
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stride: 7
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dropout: 0.2
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gpt_layers: 9
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d_ff: 128
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gpt_path: ./GPT-2
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d_model: 64
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n_heads: 1
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input_dim: 3
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output_dim: 3
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word_num: 1000
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train:
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batch_size: 16
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early_stop: true
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early_stop_patience: 15
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epochs: 100
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grad_norm: false
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loss_func: mae
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lr_decay: true
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lr_decay_rate: 0.3
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lr_decay_step: "5,20,40,70"
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lr_init: 0.003
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max_grad_norm: 5
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real_value: true
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weight_decay: 0
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debug: false
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output_dim: 3
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log_step: 1000
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plot: false
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mae_thresh: None
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mape_thresh: 0.001
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@ -7,6 +7,11 @@ def load_st_dataset(config):
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# sample = config["data"]["sample"]
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# output B, N, D
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match dataset:
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case "BeijingAirQuality":
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data_path = os.path.join("./data/BeijingAirQuality/data.dat")
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data = np.memmap(data_path, dtype=np.float32, mode='r')
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L, N, C = 36000, 7, 3
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data = data.reshape(L, N, C)
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case "PEMS-BAY":
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data_path = os.path.join("./data/PEMS-BAY/pems-bay.h5")
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with h5py.File(data_path, 'r') as f:
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@ -15,13 +15,13 @@ class ReplicationPad1d(nn.Module):
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return output
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class TokenEmbedding(nn.Module):
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def __init__(self, c_in, d_model, patch_num, input_dim):
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def __init__(self, c_in, d_model, patch_num, input_dim, output_dim):
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super(TokenEmbedding, self).__init__()
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padding = 1
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self.tokenConv = nn.Conv1d(in_channels=c_in, out_channels=d_model,
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kernel_size=3, padding=padding, padding_mode='circular', bias=False)
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self.confusion_layer = nn.Linear(patch_num * input_dim, 1)
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self.confusion_layer = nn.Linear(patch_num * input_dim, output_dim)
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for m in self.modules():
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if isinstance(m, nn.Conv1d):
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@ -37,17 +37,16 @@ class TokenEmbedding(nn.Module):
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class PatchEmbedding(nn.Module):
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def __init__(self, d_model, patch_len, stride, dropout, patch_num, input_dim):
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def __init__(self, d_model, patch_len, stride, dropout, patch_num, input_dim, output_dim):
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super(PatchEmbedding, self).__init__()
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# Patching
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self.patch_len = patch_len
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self.stride = stride
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self.padding_patch_layer = ReplicationPad1d((0, stride))
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self.value_embedding = TokenEmbedding(patch_len, d_model, patch_num, input_dim)
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self.value_embedding = TokenEmbedding(patch_len, d_model, patch_num, input_dim, output_dim)
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self.dropout = nn.Dropout(dropout)
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def forward(self, x):
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n_vars = x.shape[2]
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x = self.padding_patch_layer(x)
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x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
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@ -19,6 +19,7 @@ class repst(nn.Module):
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self.gpt_layers = configs['gpt_layers']
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self.d_ff = configs['d_ff']
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self.gpt_path = configs['gpt_path']
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self.output_dim = configs.get('output_dim', 1)
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self.word_choice = GumbelSoftmax(configs['word_num'])
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@ -31,7 +32,7 @@ class repst(nn.Module):
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self.head_nf = self.d_ff * self.patch_nums
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# 词嵌入
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self.patch_embedding = PatchEmbedding(self.d_model, self.patch_len, self.stride, self.dropout, self.patch_nums, self.input_dim)
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self.patch_embedding = PatchEmbedding(self.d_model, self.patch_len, self.stride, self.dropout, self.patch_nums, self.input_dim, self.output_dim)
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# GPT2初始化
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self.gpts = GPT2Model.from_pretrained(self.gpt_path, output_attentions=True, output_hidden_states=True)
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@ -41,7 +42,7 @@ class repst(nn.Module):
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self.word_embeddings = self.gpts.get_input_embeddings().weight.to(self.device)
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self.vocab_size = self.word_embeddings.shape[0]
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self.mapping_layer = nn.Linear(self.vocab_size, 1)
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self.reprogramming_layer = ReprogrammingLayer(self.d_model, self.n_heads, self.d_keys, self.d_llm)
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self.reprogramming_layer = ReprogrammingLayer(self.d_model * self.output_dim, self.n_heads, self.d_keys, self.d_llm)
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self.out_mlp = nn.Sequential(
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nn.Linear(self.d_llm, 128),
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@ -62,7 +63,7 @@ class repst(nn.Module):
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torch.nn.init.zeros_(module.bias)
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def forward(self, x):
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x = x[..., :1]
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x = x[..., :self.output_dim]
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x_enc = rearrange(x, 'b t n c -> b n c t')
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enc_out, n_vars = self.patch_embedding(x_enc)
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self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
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@ -79,25 +80,3 @@ class repst(nn.Module):
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return outputs
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if __name__ == '__main__':
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configs = {
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'device': 'cuda:0',
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'pred_len': 24,
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'seq_len': 24,
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'patch_len': 6,
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'stride': 7,
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'dropout': 0.2,
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'gpt_layers': 9,
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'd_ff': 128,
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'gpt_path': './GPT-2',
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'd_model': 64,
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'n_heads': 1,
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'input_dim': 1
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}
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model = repst(configs)
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x = torch.randn(16, 24, 325, 1)
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y = model(x)
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print(y.shape)
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@ -12,3 +12,4 @@ notebook
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torchcde
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einops
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transformers
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py7zr
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2
run.py
2
run.py
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@ -15,7 +15,7 @@ def main():
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args = init.init_device(args)
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init.init_seed(args["basic"]["seed"])
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# Load model
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model = init.init_model(args)
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# Load dataset
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@ -1,228 +1,114 @@
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import os
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import requests
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import zipfile
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import shutil
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import kagglehub # 假设 kagglehub 是一个可用的库
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import os, json, shutil, requests
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from urllib.parse import urlsplit
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from tqdm import tqdm
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import kagglehub
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import py7zr
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# 定义文件完整性信息的字典
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# ---------- 1. 加载结构 JSON ----------
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def load_structure_json(path="utils/dataset.json"):
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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def check_and_download_data():
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"""
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检查 data 文件夹的完整性，并根据缺失文件类型下载相应数据。
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"""
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current_working_dir = os.getcwd() # 获取当前工作目录
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data_dir = os.path.join(
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current_working_dir, "data"
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) # 假设 data 文件夹在当前工作目录下
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expected_structure = {
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"PEMS03": [
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"PEMS03.csv",
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"PEMS03.npz",
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"PEMS03.txt",
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"PEMS03_dtw_distance.npy",
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"PEMS03_spatial_distance.npy",
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],
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"PEMS04": [
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"PEMS04.csv",
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"PEMS04.npz",
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"PEMS04_dtw_distance.npy",
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"PEMS04_spatial_distance.npy",
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],
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"PEMS07": [
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"PEMS07.csv",
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"PEMS07.npz",
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"PEMS07_dtw_distance.npy",
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"PEMS07_spatial_distance.npy",
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],
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"PEMS08": [
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"PEMS08.csv",
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"PEMS08.npz",
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"PEMS08_dtw_distance.npy",
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"PEMS08_spatial_distance.npy",
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],
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"PEMS-BAY": [
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"adj_mx_bay.pkl",
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"pems-bay-meta.h5",
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"pems-bay.h5"
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],
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"METR-LA": [
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"METR-LA.h5"
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],
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"SolarEnergy": [
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]
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}
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current_dir = os.getcwd() # 获取当前工作目录
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missing_adj = False
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missing_main_files = False
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# 检查 data 文件夹是否存在
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if not os.path.exists(data_dir) or not os.path.isdir(data_dir):
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# print(f"目录 {data_dir} 不存在。")
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print("正在下载所有必要的数据文件...")
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# ---------- 2. 检测完整性 ----------
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def detect_data_integrity(data_dir, expected, check_adj=False):
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missing_adj, missing_main = False, False
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if not os.path.isdir(data_dir): return True, True
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for folder, files in expected.items():
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folder_path = os.path.join(data_dir, folder)
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if not os.path.isdir(folder_path):
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if check_adj:
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missing_adj = True
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missing_main_files = True
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else:
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# 遍历预期的文件结构
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for subfolder, expected_files in expected_structure.items():
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subfolder_path = os.path.join(data_dir, subfolder)
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# 检查子文件夹是否存在
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if not os.path.exists(subfolder_path) or not os.path.isdir(subfolder_path):
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# print(f"子文件夹 {subfolder} 不存在。")
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missing_main_files = True
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continue
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# 获取子文件夹中的实际文件列表
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actual_files = os.listdir(subfolder_path)
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# 检查是否缺少文件
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for expected_file in expected_files:
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if expected_file not in actual_files:
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# print(f"子文件夹 {subfolder} 中缺少文件 {expected_file}。")
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if (
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"_dtw_distance.npy" in expected_file
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or "_spatial_distance.npy" in expected_file
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):
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missing_main = True
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continue
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existing = set(os.listdir(folder_path))
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for f in files:
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if f not in existing:
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if f.endswith(("_dtw_distance.npy", "_spatial_distance.npy")):
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missing_adj = True
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else:
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missing_main_files = True
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elif not check_adj:
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missing_main = True
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return missing_adj, missing_main
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# 根据缺失文件类型调用下载逻辑
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# ---------- 3. 下载 7z 并解压 ----------
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def download_and_extract(url, dst_dir, max_retries=3):
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os.makedirs(dst_dir, exist_ok=True)
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filename = os.path.basename(urlsplit(url).path) or "download.7z"
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file_path = os.path.join(dst_dir, filename)
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for attempt in range(1, max_retries+1):
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try:
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# 下载
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with requests.get(url, stream=True, timeout=30) as r:
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r.raise_for_status()
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total = int(r.headers.get("content-length",0))
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with open(file_path,"wb") as f, tqdm(total=total, unit="B", unit_scale=True, desc=filename) as bar:
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for chunk in r.iter_content(8192):
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f.write(chunk)
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bar.update(len(chunk))
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# 解压 7z
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with py7zr.SevenZipFile(file_path, mode='r') as archive:
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archive.extractall(path=dst_dir)
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os.remove(file_path)
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return
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except Exception as e:
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if attempt==max_retries: raise RuntimeError("下载或解压失败")
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print("错误，重试中...", e)
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# ---------- 4. 下载 Kaggle 数据 ----------
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def download_kaggle_data(base_dir, dataset):
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try:
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path = kagglehub.dataset_download(dataset)
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shutil.copytree(path, os.path.join(base_dir,"data"), dirs_exist_ok=True)
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except Exception as e:
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print("Kaggle 下载失败:", dataset, e)
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# ---------- 5. 整理目录 ----------
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def rearrange_dir():
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data_dir = os.path.join(os.getcwd(), "data")
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nested = os.path.join(data_dir,"data")
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if os.path.isdir(nested):
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for item in os.listdir(nested):
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src,dst = os.path.join(nested,item), os.path.join(data_dir,item)
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if os.path.isdir(src):
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shutil.copytree(src, dst, dirs_exist_ok=True) # 更新已存在的目录
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else:
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shutil.copy2(src, dst)
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shutil.rmtree(nested)
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for kw,tgt in [("bay","PEMS-BAY"),("metr","METR-LA")]:
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dst = os.path.join(data_dir,tgt); os.makedirs(dst,exist_ok=True)
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for f in os.listdir(data_dir):
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if kw in f.lower() and f.endswith((".h5",".pkl")):
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shutil.move(os.path.join(data_dir,f), os.path.join(dst,f))
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solar = os.path.join(data_dir,"solar-energy")
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if os.path.isdir(solar):
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dst = os.path.join(data_dir,"SolarEnergy"); os.makedirs(dst,exist_ok=True)
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csv = os.path.join(solar,"solar_AL.csv")
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if os.path.isfile(csv): shutil.move(csv, os.path.join(dst,"SolarEnergy.csv"))
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shutil.rmtree(solar)
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# ---------- 6. 主流程 ----------
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def check_and_download_data():
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cwd = os.getcwd()
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data_dir = os.path.join(cwd,"data")
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expected = load_structure_json()
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missing_adj,_ = detect_data_integrity(data_dir, expected, check_adj=True)
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if missing_adj:
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download_adj_data(current_dir)
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if missing_main_files:
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download_kaggle_data(current_dir, 'elmahy/pems-dataset')
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download_kaggle_data(current_dir, 'scchuy/pemsbay')
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download_kaggle_data(current_dir, "annnnguyen/metr-la-dataset")
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download_kaggle_data(current_dir, "wangshaoqi/solar-energy")
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download_and_extract("http://code.zhang-heng.com/static/adj.7z", data_dir)
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baq_folder = os.path.join(data_dir,"BeijingAirQuality")
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if not os.path.isdir(baq_folder):
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download_and_extract("http://code.zhang-heng.com/static/BeijingAirQuality.7z", data_dir)
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_,missing_main = detect_data_integrity(data_dir, expected, check_adj=False)
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if missing_main:
|
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for ds in ["elmahy/pems-dataset","scchuy/pemsbay","annnnguyen/metr-la-dataset","wangshaoqi/solar-energy"]:
|
||||
download_kaggle_data(cwd, ds)
|
||||
|
||||
rearrange_dir()
|
||||
|
||||
return True
|
||||
|
||||
|
||||
def download_adj_data(current_dir, max_retries=3):
|
||||
"""
|
||||
下载并解压 adj.zip 文件，并显示下载进度条。
|
||||
如果下载失败，最多重试 max_retries 次。
|
||||
"""
|
||||
url = "http://code.zhang-heng.com/static/adj.zip"
|
||||
retries = 0
|
||||
|
||||
while retries <= max_retries:
|
||||
try:
|
||||
print(f"正在从 {url} 下载邻接矩阵文件...")
|
||||
response = requests.get(url, stream=True)
|
||||
|
||||
if response.status_code == 200:
|
||||
total_size = int(response.headers.get("content-length", 0))
|
||||
block_size = 1024 # 1KB
|
||||
t = tqdm(total=total_size, unit="B", unit_scale=True, desc="下载进度")
|
||||
|
||||
zip_file_path = os.path.join(current_dir, "adj.zip")
|
||||
with open(zip_file_path, "wb") as f:
|
||||
for data in response.iter_content(block_size):
|
||||
f.write(data)
|
||||
t.update(len(data))
|
||||
t.close()
|
||||
|
||||
# print("下载完成，文件已保存到:", zip_file_path)
|
||||
|
||||
if os.path.exists(zip_file_path):
|
||||
with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
|
||||
zip_ref.extractall(current_dir)
|
||||
# print("数据集已解压到:", current_dir)
|
||||
os.remove(zip_file_path) # 删除zip文件
|
||||
else:
|
||||
print("未找到下载的zip文件，跳过解压。")
|
||||
break # 下载成功，退出循环
|
||||
else:
|
||||
print(f"下载失败，状态码: {response.status_code}。请检查链接是否有效。")
|
||||
except Exception as e:
|
||||
print(f"下载或解压数据集时出错: {e}")
|
||||
print("如果链接无效，请检查URL的合法性或稍后重试。")
|
||||
|
||||
retries += 1
|
||||
if retries > max_retries:
|
||||
raise Exception(
|
||||
f"下载失败，已达到最大重试次数（{max_retries}次）。请检查链接或网络连接。"
|
||||
)
|
||||
|
||||
|
||||
def download_kaggle_data(current_dir, kaggle_path):
|
||||
"""
|
||||
下载 KaggleHub 数据集，并将数据直接移动到当前工作目录的 data 文件夹。
|
||||
如果目标文件夹已存在，会覆盖冲突的文件。
|
||||
"""
|
||||
try:
|
||||
print(f"正在下载 {kaggle_path} 数据集...")
|
||||
path = kagglehub.dataset_download(kaggle_path)
|
||||
# print("Path to KaggleHub dataset files:", path)
|
||||
|
||||
if os.path.exists(path):
|
||||
destination_path = os.path.join(current_dir, "data")
|
||||
# 使用 shutil.copytree 将文件夹内容直接放在 data 文件夹下，覆盖冲突的文件
|
||||
shutil.copytree(path, destination_path, dirs_exist_ok=True)
|
||||
except Exception as e:
|
||||
print(f"下载或处理 KaggleHub 数据集时出错: {e}")
|
||||
|
||||
|
||||
|
||||
def rearrange_dir():
|
||||
"""
|
||||
将 data/data 中的文件合并到上级目录，并删除 data/data 目录。
|
||||
"""
|
||||
data_dir = os.path.join(os.getcwd(), "data")
|
||||
nested_data_dir = os.path.join(data_dir, "data")
|
||||
|
||||
if os.path.exists(nested_data_dir) and os.path.isdir(nested_data_dir):
|
||||
for item in os.listdir(nested_data_dir):
|
||||
source_path = os.path.join(nested_data_dir, item)
|
||||
destination_path = os.path.join(data_dir, item)
|
||||
|
||||
if os.path.isdir(source_path):
|
||||
shutil.copytree(source_path, destination_path, dirs_exist_ok=True)
|
||||
else:
|
||||
shutil.copy2(source_path, destination_path)
|
||||
|
||||
shutil.rmtree(nested_data_dir)
|
||||
|
||||
# 将带有 "bay" 的文件移动到 PEMS-BAY 文件夹
|
||||
pems_bay_dir = os.path.join(data_dir, "PEMS-BAY")
|
||||
os.makedirs(pems_bay_dir, exist_ok=True)
|
||||
|
||||
for item in os.listdir(data_dir):
|
||||
if "bay" in item.lower() and (item.endswith(".pkl") or item.endswith(".h5")):
|
||||
source_path = os.path.join(data_dir, item)
|
||||
destination_path = os.path.join(pems_bay_dir, item)
|
||||
shutil.move(source_path, destination_path)
|
||||
|
||||
# metr-la
|
||||
metrla_dir = os.path.join(data_dir, "METR-LA")
|
||||
os.makedirs(metrla_dir, exist_ok=True)
|
||||
for item in os.listdir(data_dir):
|
||||
if "metr" in item.lower() and (item.endswith(".pkl") or item.endswith(".h5")):
|
||||
source_path = os.path.join(data_dir, item)
|
||||
destination_path = os.path.join(metrla_dir, item)
|
||||
shutil.move(source_path, destination_path)
|
||||
|
||||
# solar-energy
|
||||
solar_src = os.path.join(data_dir, "solar-energy")
|
||||
solar_sub = os.path.join(solar_src, "solar_AL.txt")
|
||||
solar_csv = os.path.join(solar_src, "solar_AL.csv")
|
||||
solar_dst_dir = os.path.join(data_dir,"SolarEnergy")
|
||||
solar_dst_csv = os.path.join(solar_dst_dir, "SolarEnergy.csv")
|
||||
if os.path.isdir(solar_sub): shutil.rmtree(solar_sub)
|
||||
if os.path.isdir(solar_src): os.rename(solar_src, solar_dst_dir)
|
||||
if os.path.isfile(solar_csv.replace(solar_src, solar_dst_dir)):
|
||||
os.rename(solar_csv.replace(solar_src, solar_dst_dir), solar_dst_csv)
|
||||
|
||||
|
||||
# 主程序
|
||||
if __name__ == "__main__":
|
||||
if __name__=="__main__":
|
||||
check_and_download_data()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,39 @@
|
|||
{
|
||||
"PEMS03": [
|
||||
"PEMS03.csv",
|
||||
"PEMS03.npz",
|
||||
"PEMS03.txt",
|
||||
"PEMS03_dtw_distance.npy",
|
||||
"PEMS03_spatial_distance.npy"
|
||||
],
|
||||
"PEMS04": [
|
||||
"PEMS04.csv",
|
||||
"PEMS04.npz",
|
||||
"PEMS04_dtw_distance.npy",
|
||||
"PEMS04_spatial_distance.npy"
|
||||
],
|
||||
"PEMS07": [
|
||||
"PEMS07.csv",
|
||||
"PEMS07.npz",
|
||||
"PEMS07_dtw_distance.npy",
|
||||
"PEMS07_spatial_distance.npy"
|
||||
],
|
||||
"PEMS08": [
|
||||
"PEMS08.csv",
|
||||
"PEMS08.npz",
|
||||
"PEMS08_dtw_distance.npy",
|
||||
"PEMS08_spatial_distance.npy"
|
||||
],
|
||||
"PEMS-BAY": [
|
||||
"adj_mx_bay.pkl",
|
||||
"pems-bay-meta.h5",
|
||||
"pems-bay.h5"
|
||||
],
|
||||
"METR-LA": [
|
||||
"METR-LA.h5"
|
||||
],
|
||||
"SolarEnergy": [
|
||||
"SolarEnergy.csv"
|
||||
],
|
||||
"BeijingAirQuality": ["data.dat", "desc.json"]
|
||||
}
|
||||
Loading…
Reference in New Issue