feat: add the argument verbose to control logging;

pypots/base.py

@@ -15,7 +15,7 @@
 from torch.utils.tensorboard import SummaryWriter
 
 from .utils.file import create_dir_if_not_exist
-from .utils.logging import logger
+from .utils.logging import logger, logger_creator
 
 
 class BaseModel(ABC):
@@ -43,6 +43,9 @@ class BaseModel(ABC):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
+
  Attributes
  ----------
  model : object, default = None
@@ -64,6 +67,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  saving_strategies = [None, "best", "better", "all"]
  assert (
@@ -73,6 +77,10 @@ def __init__(
  self.device = None # set up with _setup_device() below
  self.saving_path = None # set up with _setup_path() below
  self.model_saving_strategy = model_saving_strategy
+ self.verbose = verbose
+
+ if not self.verbose:
+ logger_creator.set_level("warning")
 
  self.model = None
  self.summary_writer = None
@@ -442,6 +450,8 @@ class BaseNNModel(BaseModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
 
  Attributes
  ---------
@@ -475,11 +485,13 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  if patience is None:

pypots/classification/base.py

@@ -51,6 +51,8 @@ class BaseClassifier(BaseModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -59,11 +61,13 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  self.n_classes = n_classes
 
@@ -179,6 +183,8 @@ class BaseNNClassifier(BaseNNModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
 
  Notes
  -----
@@ -200,6 +206,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  batch_size,
@@ -209,6 +216,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  self.n_classes = n_classes
 

pypots/classification/brits/model.py

@@ -81,6 +81,8 @@ class BRITS(BaseNNClassifier):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -99,6 +101,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_classes,
@@ -109,6 +112,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  self.n_steps = n_steps

pypots/classification/grud/model.py

@@ -76,6 +76,8 @@ class GRUD(BaseNNClassifier):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -92,6 +94,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_classes,
@@ -102,6 +105,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  self.n_steps = n_steps

pypots/classification/raindrop/model.py

@@ -102,6 +102,8 @@ class Raindrop(BaseNNClassifier):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -126,6 +128,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_classes,
@@ -136,6 +139,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  self.n_features = n_features

pypots/classification/template/model.py

@@ -40,6 +40,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: Optional[str] = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_classes,
@@ -50,6 +51,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  # set up the hyper-parameters
  # TODO: set up your model's hyper-parameters here

pypots/clustering/base.py

@@ -51,6 +51,8 @@ class BaseClusterer(BaseModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -59,11 +61,13 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  self.n_clusters = n_clusters
 
@@ -178,6 +182,8 @@ class BaseNNClusterer(BaseNNModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
 
  Notes
  -----
@@ -199,6 +205,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  batch_size,
@@ -208,6 +215,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  self.n_clusters = n_clusters
 

pypots/clustering/crli/model.py

@@ -106,6 +106,8 @@ class CRLI(BaseNNClusterer):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -129,6 +131,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: Optional[str] = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_clusters,
@@ -139,6 +142,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  assert G_steps > 0 and D_steps > 0, "G_steps and D_steps should both >0"
 

pypots/clustering/template/model.py

@@ -40,6 +40,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: Optional[str] = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_clusters,
@@ -50,6 +51,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  # set up the hyper-parameters
  # TODO: set up your model's hyper-parameters here

pypots/clustering/vader/model.py

@@ -88,6 +88,8 @@ class VaDER(BaseNNClusterer):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -106,6 +108,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  n_clusters,
@@ -116,6 +119,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  assert (

pypots/forecasting/base.py

@@ -48,18 +48,22 @@ class BaseForecaster(BaseModel):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
  self,
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  @abstractmethod
@@ -168,6 +172,10 @@ class BaseNNForecaster(BaseNNModel):
  The "better" strategy will automatically save the model during training whenever the model performs
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
+
+ verbose :
+ Whether to print out the training logs during the training process.
+
  Notes
  -----
  Optimizers are necessary for training deep-learning neural networks, but we don't put a parameter ``optimizer``
@@ -188,6 +196,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: str = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  batch_size,
@@ -197,6 +206,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
 
  @abstractmethod

pypots/forecasting/csdi/model.py

@@ -119,6 +119,8 @@ class CSDI(BaseNNForecaster):
  better than in previous epochs.
  The "all" strategy will save every model after each epoch training.
 
+ verbose :
+ Whether to print out the training logs during the training process.
  """
 
  def __init__(
@@ -147,6 +149,7 @@ def __init__(
  device: Optional[Union[str, torch.device, list]] = None,
  saving_path: Optional[str] = None,
  model_saving_strategy: Optional[str] = "best",
+ verbose: bool = True,
  ):
  super().__init__(
  batch_size,
@@ -156,6 +159,7 @@ def __init__(
  device,
  saving_path,
  model_saving_strategy,
+ verbose,
  )
  assert n_pred_features == n_features, (
  f"currently n_pred_features of CSDI forecasting model should be equal to n_features, "