cleaned up logging

config/model_config.yaml

@@ -1,3 +1,4 @@
+device: 1 # 0, cpu
 seq_len: 96 # should not be changed for the current datasets
 input_dim: 2 # or 1 depending on user, but is dynamically set
 noise_dim: 256
@@ -66,7 +67,7 @@ diffusion_ts:
 
 acgan:
   batch_size: 32
-  n_epochs: 200
+  n_epochs: 10
   lr_gen: 3e-4
   lr_discr: 1e-4
-  warm_up_epochs: 50
+  warm_up_epochs: 5

datasets/openpower.py

@@ -12,7 +12,6 @@
 
 warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning)
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 class OpenPowerDataset(Dataset):

datasets/pecanstreet.py

@@ -15,7 +15,6 @@
 
 warnings.filterwarnings("ignore", category=pd.errors.SettingWithCopyWarning)
 ROOT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 class PecanStreetDataManager:

datasets/timeseries_dataset.py

@@ -0,0 +1,42 @@
+import torch
+from torch.utils.data import Dataset
+
+
+class TimeSeriesDataset(Dataset):
+    def __init__(self, dataframe, time_series_column_name, conditioning_vars=None):
+        """
+        Initializes the TimeSeriesDataset.
+
+        Args:
+            dataframe (pd.DataFrame): The input DataFrame containing the time series data and optional conditioning variables.
+            time_series_column_name (str): The name of the column containing the time series data.
+            conditioning_vars (list of str, optional): List of column names to be used as conditioning variables.
+        """
+        self.data = dataframe.reset_index(drop=True)
+        self.conditioning_vars = conditioning_vars or []
+        self.time_series_column_name = time_series_column_name
+
+        if self.time_series_column_name not in self.data.columns:
+            raise ValueError(
+                f"Time series column '{self.time_series_column_name}' not found in DataFrame."
+            )
+
+        for var in self.conditioning_vars:
+            if var not in self.data.columns:
+                raise ValueError(
+                    f"Conditioning variable '{var}' not found in DataFrame."
+                )
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        time_series = self.data.iloc[idx][self.time_series_column]
+        time_series = torch.tensor(time_series, dtype=torch.float32)
+
+        conditioning_vars_dict = {}
+        for var in self.conditioning_vars:
+            value = self.data.iloc[idx][var]
+            conditioning_vars_dict[var] = torch.tensor(value, dtype=torch.long)
+
+        return time_series, conditioning_vars_dict