add pytorch data convertor

freqtrade/freqai/base_models/BasePyTorchClassifier.py

@@ -69,12 +69,11 @@ class BasePyTorchClassifier(BasePyTorchModel):
         )
         filtered_df = dk.normalize_data_from_metadata(filtered_df)
         dk.data_dictionary["prediction_features"] = filtered_df
-
         self.data_cleaning_predict(dk)
-        x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
-            .float()\
-            .to(self.device)
-
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"],
+            device=self.device
+        )
         logits = self.model.model(x)
         probs = F.softmax(logits, dim=-1)
         predicted_classes = torch.argmax(probs, dim=-1)

freqtrade/freqai/base_models/BasePyTorchModel.py

@@ -1,4 +1,5 @@
 import logging
+from abc import ABC, abstractmethod
 from time import time
 from typing import Any
 
@@ -7,15 +8,17 @@ from pandas import DataFrame
 
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
+from freqtrade.freqai.torch import PyTorchDataConvertor
 
 
 logger = logging.getLogger(__name__)
 
 
-class BasePyTorchModel(IFreqaiModel):
+class BasePyTorchModel(IFreqaiModel, ABC):
     """
     Base class for PyTorch type models.
-    User *must* inherit from this class and set fit() and predict().
+    User *must* inherit from this class and set fit() and predict() and
+    data_convertor property.
     """
 
     def __init__(self, **kwargs):
@@ -69,3 +72,8 @@ class BasePyTorchModel(IFreqaiModel):
                     f"({end_time - start_time:.2f} secs) --------------------")
 
         return model
+
+    @property
+    @abstractmethod
+    def data_convertor(self) -> PyTorchDataConvertor:
+        raise NotImplementedError("Abstract property")

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -3,7 +3,6 @@ from typing import Tuple
 
 import numpy as np
 import numpy.typing as npt
-import torch
 from pandas import DataFrame
 
 from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
@@ -41,9 +40,12 @@ class BasePyTorchRegressor(BasePyTorchModel):
         dk.data_dictionary["prediction_features"] = filtered_df
 
         self.data_cleaning_predict(dk)
-        x = torch.from_numpy(dk.data_dictionary["prediction_features"].values)\
-            .float()\
-            .to(self.device)
+        x = self.data_convertor.convert_x(
+            dk.data_dictionary["prediction_features"],
+            device=self.device
+        )
+        logger.info(self.model.model)
+        logger.info(self.model.model)
 
         y = self.model.model(x)
         pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])