fix: mistaken logical code in auto_save_model_if_necessary;

pypots/base.py

@@ -195,25 +195,31 @@ def save_model(
  f'Failed to save the model to "{saving_path}" because of the below error! \n{e}'
  )
 
- def auto_save_model_if_necessary(self, saving_name: str = None):
+ def auto_save_model_if_necessary(
+ self,
+ training_finished: bool = True,
+ saving_name: str = None,
+ ):
  """Automatically save the current model into a file if in need.
 
  Parameters
  ----------
+ training_finished : bool, default = False,
+ Whether the training is already finished when invoke this function.
+ The saving_strategy "better" only works when training_finished is False.
+ The saving_strategy "best" only works when training_finished is True.
+
  saving_name : str, default = None,
  The file name of the saved model.
 
  """
  if self.saving_path is not None and self.auto_save_model:
  name = self.__class__.__name__ if saving_name is None else saving_name
- if self.saving_strategy == "best":
+ if not training_finished and self.saving_strategy == "better":
  self.save_model(self.saving_path, name)
- else: # self.saving_strategy == "better"
+ elif training_finished and self.saving_strategy == "best":
  self.save_model(self.saving_path, name)
 
- logger.info(
- f"Successfully saved the model to {os.path.join(self.saving_path, name)}"
- )
  else:
  return
 

pypots/classification/base.py

@@ -246,7 +246,8 @@ def _train_model(
  self.patience = self.original_patience
  # save the model if necessary
  self.auto_save_model_if_necessary(
- saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}"
+ training_finished=False,
+ saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}",
  )
  else:
  self.patience -= 1

pypots/classification/brits.py

@@ -356,7 +356,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def classify(self, X: Union[dict, str], file_type: str = "h5py"):
  """Classify the input data with the trained model.

pypots/classification/grud.py

@@ -309,7 +309,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def classify(self, X: Union[dict, str], file_type: str = "h5py") -> np.ndarray:
  """Classify the input data with the trained model.

pypots/classification/raindrop.py

@@ -826,7 +826,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def classify(self, X: Union[dict, str], file_type: str = "h5py") -> np.ndarray:
  """Classify the input data with the trained model.

pypots/clustering/crli.py

@@ -533,7 +533,8 @@ def _train_model(
  self.patience = self.original_patience
  # save the model if necessary
  self.auto_save_model_if_necessary(
- saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}"
+ training_finished=False,
+ saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}",
  )
  else:
  self.patience -= 1
@@ -596,7 +597,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def cluster(
  self,

pypots/clustering/vader.py

@@ -616,7 +616,8 @@ def _train_model(
  self.patience = self.original_patience
  # save the model if necessary
  self.auto_save_model_if_necessary(
- saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}"
+ training_finished=False,
+ saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}",
  )
  else:
  self.patience -= 1
@@ -683,7 +684,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def cluster(self, X: Union[dict, str], file_type: str = "h5py") -> np.ndarray:
  """Cluster the input with the trained model.

pypots/imputation/base.py

@@ -290,7 +290,8 @@ def _train_model(
  self.patience = self.original_patience
  # save the model if necessary
  self.auto_save_model_if_necessary(
- saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}"
+ training_finished=False,
+ saving_name=f"{self.__class__.__name__}_epoch{epoch}_loss{mean_loss}",
  )
  else:
  self.patience -= 1

pypots/imputation/brits.py

@@ -687,7 +687,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def impute(
  self,

pypots/imputation/saits.py

@@ -399,7 +399,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def impute(
  self,

pypots/imputation/transformer.py

@@ -483,7 +483,7 @@ def fit(
  self.model.eval() # set the model as eval status to freeze it.
 
  # Step 3: save the model if necessary
- self.auto_save_model_if_necessary()
+ self.auto_save_model_if_necessary(training_finished=True)
 
  def impute(self, X: Union[dict, str], file_type: str = "h5py") -> np.ndarray:
  """Impute missing values in the given data with the trained model.

pypots/utils/files.py

@@ -42,8 +42,6 @@ def create_dir_if_not_exist(path: str, is_dir: bool = True) -> None:
 
  """
  path = extract_parent_dir(path) if not is_dir else path
- if os.path.exists(path):
- logger.info(f'The given directory "{path}" exists.')
- else:
+ if not os.path.exists(path):
  os.makedirs(path, exist_ok=True)
- logger.info(f'Successfully created "{path}".')
+ logger.info(f'Successfully created the given path "{path}".')