reformat documentation

freqtrade/freqai/base_models/PyTorchModelTrainer.py

@@ -26,29 +26,6 @@ class PyTorchModelTrainer:
             model_meta_data: Dict[str, Any] = {},
     ):
         """
-        A class for training PyTorch models.
-        Implements the training loop logic, load/save methods.
-
-        fit method - training loop logic:
-         - Calculates the predicted output for the batch using the PyTorch model.
-         - Calculates the loss between the predicted and actual output using a loss function.
-         - Computes the gradients of the loss with respect to the model's parameters using
-           backpropagation.
-         - Updates the model's parameters using an optimizer.
-
-        save method:
-         called by DataDrawer
-         - Saving any nn.Module state_dict
-         - Saving model_meta_data, this dict should contain any additional data that the
-           user needs to store. e.g class_names for classification models.
-
-        load method:
-         currently DataDrawer is responsible for the actual loading.
-         when using continual_learning the DataDrawer will load the dict
-         (saved by self.save(path)). and this class will populate the necessary
-         state_dict of the self.model & self.optimizer and self.model_meta_data.
-
-
         :param model: The PyTorch model to be trained.
         :param optimizer: The optimizer to use for training.
         :param criterion: The loss function to use for training.
@@ -76,7 +53,11 @@ class PyTorchModelTrainer:
 
     def fit(self, data_dictionary: Dict[str, pd.DataFrame]):
         """
-        General training loop.
+         - Calculates the predicted output for the batch using the PyTorch model.
+         - Calculates the loss between the predicted and actual output using a loss function.
+         - Computes the gradients of the loss with respect to the model's parameters using
+           backpropagation.
+         - Updates the model's parameters using an optimizer.
         """
         data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary)
         epochs = self.calc_n_epochs(
@@ -172,6 +153,12 @@ class PyTorchModelTrainer:
         return epochs
 
     def save(self, path: Path):
+        """
+        - Saving any nn.Module state_dict
+        - Saving model_meta_data, this dict should contain any additional data that the
+          user needs to store. e.g class_names for classification models.
+        """
+
         torch.save({
             'model_state_dict': self.model.state_dict(),
             'optimizer_state_dict': self.optimizer.state_dict(),
@@ -183,7 +170,14 @@ class PyTorchModelTrainer:
         return self.load_from_checkpoint(checkpoint)
 
     def load_from_checkpoint(self, checkpoint: Dict):
-        self.model.load_state_dict(checkpoint['model_state_dict'])
-        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+        """
+        when using continual_learning, DataDrawer will load the dictionary
+        (containing state dicts and model_meta_data) by calling torch.load(path).
+        you can access this dict from any class that inherits IFreqaiModel by calling
+        get_init_model method.
+        """
+
+        self.model.load_state_dict(checkpoint["model_state_dict"])
+        self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
         self.model_meta_data = checkpoint["model_meta_data"]
         return self