兼容NYCBike

.vscode/launch.json

@@ -28,6 +28,38 @@
             "console": "integratedTerminal",
             "args": "--config ./config/REPST/PEMSD8.yaml"
         },
+        {
+            "name": "STID-BJTaxi-InFlow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/STID/BJTaxi_Inflow.yaml"
+        },
+        {
+            "name": "STID-BJTaxi-OutFlow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/STID/BJTaxi_Outflow.yaml"
+        },
+        {
+            "name": "STID-NYCBike-InFlow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/STID/NYCBike_Inflow.yaml"
+        },
+        {
+            "name": "STID-NYCBike-OutFlow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/STID/NYCBike_Outflow.yaml"
+        },
         {
             "name": "REPST-BJTaxi-InFlow",
             "type": "debugpy",
@@ -36,6 +68,22 @@
             "console": "integratedTerminal",
             "args": "--config ./config/REPST/BJTaxi-Inflow.yaml"
         },
+        {
+            "name": "REPST-NYCBike-outflow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/REPST/NYCBike-outflow.yaml"
+        },
+        {
+            "name": "REPST-NYCBike-inflow",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "run.py",
+            "console": "integratedTerminal",
+            "args": "--config ./config/REPST/NYCBike-inflow.yaml"
+        },
         {
             "name": "REPST-PEMSBAY",
             "type": "debugpy",

config/REPST/BJTaxi-Inflow.yaml

@@ -11,8 +11,8 @@ data:
   column_wise: false
   days_per_week: 7
   default_graph: true
-  horizon: 24
+  horizon: 12
-  lag: 24
+  lag: 12
   normalizer: std
   num_nodes: 1024
   steps_per_day: 48
@@ -24,8 +24,8 @@ data:
   batch_size: 16
 
 model:
-  pred_len: 24
+  pred_len: 12
-  seq_len: 24
+  seq_len: 12
   patch_len: 6 
   stride: 7
   dropout: 0.2

config/REPST/BJTaxi-outflow.yaml

@@ -0,0 +1,60 @@
+basic:
+  dataset: "BJTaxi-OutFlow"
+  mode : "train"
+  device : "cuda:0"
+  model: "REPST"
+  seed: 2023
+
+data:
+  add_day_in_week: false
+  add_time_in_day: false
+  column_wise: false
+  days_per_week: 7
+  default_graph: true
+  horizon: 24
+  lag: 24
+  normalizer: std
+  num_nodes: 1024
+  steps_per_day: 48
+  test_ratio: 0.2
+  tod: false
+  val_ratio: 0.2
+  sample: 1
+  input_dim: 1
+  batch_size: 16
+
+model:
+  pred_len: 24
+  seq_len: 24
+  patch_len: 6 
+  stride: 7
+  dropout: 0.2
+  gpt_layers: 9
+  d_ff: 128
+  gpt_path: ./GPT-2
+  d_model: 64
+  n_heads: 1
+  input_dim: 1
+  word_num: 1000
+
+train:
+  batch_size: 16
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  loss_func: mae
+  lr_decay: true
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  lr_init: 0.003
+  max_grad_norm: 5
+  real_value: true
+  weight_decay: 0
+  debug: false
+  output_dim: 1
+  log_step: 100
+  plot: false
+  mae_thresh: None
+  mape_thresh: 0.001
+

config/REPST/NYCBike-inflow.yaml

@@ -0,0 +1,60 @@
+basic:
+  dataset: "NYCBike-InFlow"
+  mode : "train"
+  device : "cuda:0"
+  model: "REPST"
+  seed: 2023
+
+data:
+  add_day_in_week: false
+  add_time_in_day: false
+  column_wise: false
+  days_per_week: 7
+  default_graph: true
+  horizon: 24
+  lag: 24
+  normalizer: std
+  num_nodes: 128
+  steps_per_day: 48
+  test_ratio: 0.2
+  tod: false
+  val_ratio: 0.2
+  sample: 1
+  input_dim: 1
+  batch_size: 16
+
+model:
+  pred_len: 24
+  seq_len: 24
+  patch_len: 6 
+  stride: 7
+  dropout: 0.2
+  gpt_layers: 9
+  d_ff: 128
+  gpt_path: ./GPT-2
+  d_model: 64
+  n_heads: 1
+  input_dim: 1
+  word_num: 1000
+
+train:
+  batch_size: 16
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  loss_func: mae
+  lr_decay: true
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  lr_init: 0.003
+  max_grad_norm: 5
+  real_value: true
+  weight_decay: 0
+  debug: false
+  output_dim: 1
+  log_step: 100
+  plot: false
+  mae_thresh: None
+  mape_thresh: 0.001
+

config/REPST/NYCBike-outflow.yaml

@@ -0,0 +1,60 @@
+basic:
+  dataset: "NYCBike-OutFlow"
+  mode : "train"
+  device : "cuda:0"
+  model: "REPST"
+  seed: 2023
+
+data:
+  add_day_in_week: false
+  add_time_in_day: false
+  column_wise: false
+  days_per_week: 7
+  default_graph: true
+  horizon: 24
+  lag: 24
+  normalizer: std
+  num_nodes: 128
+  steps_per_day: 48
+  test_ratio: 0.2
+  tod: false
+  val_ratio: 0.2
+  sample: 1
+  input_dim: 1
+  batch_size: 16
+
+model:
+  pred_len: 24
+  seq_len: 24
+  patch_len: 6 
+  stride: 7
+  dropout: 0.2
+  gpt_layers: 9
+  d_ff: 128
+  gpt_path: ./GPT-2
+  d_model: 64
+  n_heads: 1
+  input_dim: 1
+  word_num: 1000
+
+train:
+  batch_size: 16
+  early_stop: true
+  early_stop_patience: 15
+  epochs: 100
+  grad_norm: false
+  loss_func: mae
+  lr_decay: true
+  lr_decay_rate: 0.3
+  lr_decay_step: "5,20,40,70"
+  lr_init: 0.003
+  max_grad_norm: 5
+  real_value: true
+  weight_decay: 0
+  debug: false
+  output_dim: 1
+  log_step: 100
+  plot: false
+  mae_thresh: None
+  mape_thresh: 0.001
+

config/STID/BJTaxi_Inflow.yaml

@@ -0,0 +1,67 @@
+basic:
+  dataset: "BJTaxi-InFlow"
+  mode: "train"
+  device: "cuda:0"
+  model: "STID"
+  seed: 2023
+
+data:
+  num_nodes: 1024
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 48
+  days_per_week: 7
+  input_dim: 1
+  output_dim: 1
+  batch_size: 64
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 24
+  horizon: 24
+  num_nodes: 1024
+  input_len: 24
+  embed_dim: 32
+  output_len: 24
+  num_layer: 3
+  if_node: True
+  node_dim: 32
+  if_T_i_D: True
+  if_D_i_W: True
+  temp_dim_tid: 32
+  temp_dim_diw: 32
+  time_of_day_size: 288
+  day_of_week_size: 7
+  batch_size: 64
+
+
+train:
+  loss_func: mae
+  seed: 1
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.002
+  weight_decay: 0.0001
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "1,50,80"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  debug: true
+  output_dim: 1
+  mae_thresh: null
+  mape_thresh: 0.0
+  log_step: 200
+  plot: False

config/STID/BJTaxi_Outflow.yaml

@@ -0,0 +1,67 @@
+basic:
+  dataset: "BJTaxi-OutFlow"
+  mode: "train"
+  device: "cuda:0"
+  model: "STID"
+  seed: 2023
+
+data:
+  num_nodes: 1024
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 48
+  days_per_week: 7
+  input_dim: 1
+  output_dim: 1
+  batch_size: 64
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 24
+  horizon: 24
+  num_nodes: 1024
+  input_len: 24
+  embed_dim: 32
+  output_len: 24
+  num_layer: 3
+  if_node: True
+  node_dim: 32
+  if_T_i_D: True
+  if_D_i_W: True
+  temp_dim_tid: 32
+  temp_dim_diw: 32
+  time_of_day_size: 288
+  day_of_week_size: 7
+  batch_size: 64
+
+
+train:
+  loss_func: mae
+  seed: 1
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.002
+  weight_decay: 0.0001
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "1,50,80"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  debug: true
+  output_dim: 1
+  mae_thresh: null
+  mape_thresh: 0.001
+  log_step: 200
+  plot: False

config/STID/NYCBike_Inflow.yaml

@@ -0,0 +1,67 @@
+basic:
+  dataset: "NYCBike-InFlow"
+  mode: "train"
+  device: "cuda:0"
+  model: "STID"
+  seed: 2023
+
+data:
+  num_nodes: 128
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 48
+  days_per_week: 7
+  input_dim: 1
+  output_dim: 1
+  batch_size: 64
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 24
+  horizon: 24
+  num_nodes: 128
+  input_len: 24
+  embed_dim: 32
+  output_len: 24
+  num_layer: 3
+  if_node: True
+  node_dim: 32
+  if_T_i_D: True
+  if_D_i_W: True
+  temp_dim_tid: 32
+  temp_dim_diw: 32
+  time_of_day_size: 288
+  day_of_week_size: 7
+  batch_size: 64
+
+
+train:
+  loss_func: mae
+  seed: 1
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.002
+  weight_decay: 0.0001
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "1,50,80"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  debug: true
+  output_dim: 1
+  mae_thresh: null
+  mape_thresh: 0.001
+  log_step: 200
+  plot: False

config/STID/NYCBike_Outflow.yaml

@@ -0,0 +1,67 @@
+basic:
+  dataset: "NYCBike-OutFlow"
+  mode: "train"
+  device: "cuda:0"
+  model: "STID"
+  seed: 2023
+
+data:
+  num_nodes: 128
+  lag: 24
+  horizon: 24
+  val_ratio: 0.2
+  test_ratio: 0.2
+  tod: False
+  normalizer: std
+  column_wise: False
+  default_graph: True
+  add_time_in_day: True
+  add_day_in_week: True
+  steps_per_day: 48
+  days_per_week: 7
+  input_dim: 1
+  output_dim: 1
+  batch_size: 64
+
+model:
+  input_dim: 3
+  output_dim: 1
+  history: 24
+  horizon: 24
+  num_nodes: 128
+  input_len: 24
+  embed_dim: 32
+  output_len: 24
+  num_layer: 3
+  if_node: True
+  node_dim: 32
+  if_T_i_D: True
+  if_D_i_W: True
+  temp_dim_tid: 32
+  temp_dim_diw: 32
+  time_of_day_size: 288
+  day_of_week_size: 7
+  batch_size: 64
+
+
+train:
+  loss_func: mae
+  seed: 1
+  batch_size: 64
+  epochs: 100
+  lr_init: 0.002
+  weight_decay: 0.0001
+  lr_decay: False
+  lr_decay_rate: 0.3
+  lr_decay_step: "1,50,80"
+  early_stop: True
+  early_stop_patience: 15
+  grad_norm: False
+  max_grad_norm: 5
+  real_value: True
+  debug: true
+  output_dim: 1
+  mae_thresh: null
+  mape_thresh: 0.001
+  log_step: 200
+  plot: False

dataloader/data_selector.py

@@ -59,6 +59,18 @@ def load_st_dataset(config):
             data = read_BeijingTaxi()[:, :, 0:1].astype(np.float32)
         case "BJTaxi-OutFlow":
             data = read_BeijingTaxi()[:, :, 1:2].astype(np.float32)
+        case "NYCBike-InFlow":
+            data_path = os.path.join("./data/NYCBike/NYC16x8.h5")
+            with h5py.File(data_path, 'r') as f:
+                data = f['data'][:].astype(np.float32)
+            data = data.transpose(0,2,3,1).reshape(-1, 16*8, 2)
+            data = data[:, :, 0:1]
+        case "NYCBike-OutFlow":
+            data_path = os.path.join("./data/NYCBike/NYC16x8.h5")
+            with h5py.File(data_path, 'r') as f:
+                data = f['data'][:].astype(np.float32)
+            data = data.transpose(0,2,3,1).reshape(-1, 16*8, 2)
+            data = data[:, :, 1:2]
         case _:
             raise ValueError(f"Unsupported dataset: {dataset}")
 

model/REPST/repst.py

@@ -67,7 +67,7 @@ class repst(nn.Module):
         x_enc = rearrange(x, 'b t n c -> b n c t')
         enc_out, n_vars = self.patch_embedding(x_enc)
         self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
-        masks = self.word_choice(self.mapping_layer.weight.data.permute(1,0))
+        masks = self.word_choice(self.mapping_layer.weight.data.permute(1, 0))
         source_embeddings = self.word_embeddings[masks==1]
    
         enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)

utils/Download_data.py

@@ -161,6 +161,13 @@ def check_and_download_data():
             download_github_data(file_path, taxi_bj_floder)
         # 下载后更新缺失列表
         missing_list = detect_data_integrity(data_dir, file_tree)
+    
+        # 检查并下载TaxiBJ数据
+    if "NYCBike" in missing_list:
+        nycbike_bj_floder = os.path.join(data_dir, "NYCBike")
+        download_and_extract("http://code.zhang-heng.com/static/NYCBike.7z", data_dir)
+        # 下载后更新缺失列表
+        missing_list = detect_data_integrity(data_dir, file_tree)
 
     # 检查并下载pems, bay, metr-la, solar-energy数据
     kaggle_map = {

utils/dataset.json

@@ -37,5 +37,6 @@
     ],
     "BeijingAirQuality": ["data.dat", "desc.json"],
     "AirQuality": ["data.dat"],
-    "BeijingTaxi": ["TaxiBJ2013.npy", "TaxiBJ2014.npy", "TaxiBJ2015.npy", "TaxiBJ2016_1.npy", "TaxiBJ2016_2.npy"]
+    "BeijingTaxi": ["TaxiBJ2013.npy", "TaxiBJ2014.npy", "TaxiBJ2015.npy", "TaxiBJ2016_1.npy", "TaxiBJ2016_2.npy"],
+    "NYCBike": ["NYC16x8.h5"]
 }