add pytorch regressor example

freqtrade/freqai/base_models/PyTorchModelTrainer.py

@@ -1,6 +1,6 @@
 import logging
 from pathlib import Path
-from typing import Any, Dict, Optional
+from typing import Any, Dict, Optional, Type
 
 import pandas as pd
 import torch
@@ -20,6 +20,7 @@ class PyTorchModelTrainer:
             criterion: nn.Module,
             device: str,
             init_model: Dict,
+            target_tensor_type: torch.dtype,
             model_meta_data: Dict[str, Any] = {},
             **kwargs
     ):
@@ -30,6 +31,8 @@ class PyTorchModelTrainer:
         :param device: The device to use for training (e.g. 'cpu', 'cuda').
         :param init_model: A dictionary containing the initial model/optimizer
             state_dict and model_meta_data saved by self.save() method.
+        :param target_tensor_type: type of target tensor, for classification usually
+            torch.long, for regressor usually torch.float.
         :param model_meta_data: Additional metadata about the model (optional).
         :param max_iters: The number of training iterations to run.
             iteration here refers to the number of times we call
@@ -42,6 +45,7 @@ class PyTorchModelTrainer:
         self.criterion = criterion
         self.model_meta_data = model_meta_data
         self.device = device
+        self.target_tensor_type = target_tensor_type
         self.max_iters: int = kwargs.get("max_iters", 100)
         self.batch_size: int = kwargs.get("batch_size", 64)
         self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
@@ -123,8 +127,8 @@ class PyTorchModelTrainer:
             labels_view = labels_shape[0] if labels_shape[1] == 1 else labels_shape
             dataset = TensorDataset(
                 torch.from_numpy(data_dictionary[f"{split}_features"].values).float(),
-                torch.from_numpy(data_dictionary[f"{split}_labels"].astype(float).values)
-                .long()
+                torch.from_numpy(data_dictionary[f"{split}_labels"].values)
+                .to(self.target_tensor_type)
                 .view(labels_view)
             )