add documentation

freqtrade/freqai/base_models/BasePyTorchModel.py

@@ -76,4 +76,8 @@ class BasePyTorchModel(IFreqaiModel, ABC):
     @property
     @abstractmethod
     def data_convertor(self) -> PyTorchDataConvertor:
+        """
+        a class responsible for converting `*_features` & `*_labels` pandas dataframes
+        to pytorch tensors.
+        """
         raise NotImplementedError("Abstract property")

freqtrade/freqai/torch/PyTorchDataConvertor.py

@@ -6,6 +6,10 @@ import torch
 
 
 class PyTorchDataConvertor(ABC):
+    """
+    This class is responsible for converting `*_features` & `*_labels` pandas dataframes
+    to pytorch tensors.
+    """
 
     @abstractmethod
     def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
@@ -25,6 +29,9 @@ class PyTorchDataConvertor(ABC):
 
 
 class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
+    """
+    A default conversion that keeps features dataframe shapes.
+    """
 
     def __init__(
             self,

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -36,6 +36,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         :param init_model: A dictionary containing the initial model/optimizer
             state_dict and model_meta_data saved by self.save() method.
         :param model_meta_data: Additional metadata about the model (optional).
+        :param data_convertor: convertor from pd.DataFrame to torch.tensor.
         :param max_iters: The number of training iterations to run.
             iteration here refers to the number of times we call
             self.optimizer.step(). used to calculate n_epochs.