add pytorch data convertor

freqtrade/freqai/torch/PyTorchDataConvertor.py

@@ -0,0 +1,56 @@
+from abc import ABC, abstractmethod
+from typing import Optional, Tuple
+
+import pandas as pd
+import torch
+
+
+class PyTorchDataConvertor(ABC):
+
+    @abstractmethod
+    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
+        """
+        :param df: "*_features" dataframe.
+        :param device: cpu/gpu.
+        :returns: tuple of tensors.
+        """
+
+    @abstractmethod
+    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
+        """
+        :param df: "*_labels" dataframe.
+        :param device: cpu/gpu.
+        :returns: tuple of tensors.
+        """
+
+
+class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
+
+    def __init__(
+            self,
+            target_tensor_type: Optional[torch.dtype] = None,
+            squeeze_target_tensor: bool = False
+    ):
+        self._target_tensor_type = target_tensor_type
+        self._squeeze_target_tensor = squeeze_target_tensor
+
+    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
+        x = torch.from_numpy(df.values).float()
+        if device:
+            x = x.to(device)
+
+        return x,
+
+    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> Tuple[torch.Tensor, ...]:
+        y = torch.from_numpy(df.values)
+
+        if self._target_tensor_type:
+            y = y.to(self._target_tensor_type)
+
+        if self._squeeze_target_tensor:
+            y = y.squeeze()
+
+        if device:
+            y = y.to(device)
+
+        return y,