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)
+                torch.from_numpy(data_dictionary[f"{split}_labels"].values)
-                .long()
+                .to(self.target_tensor_type)
                 .view(labels_view)
             )
 

freqtrade/freqai/prediction_models/PyTorchClassifier.py

@@ -22,16 +22,6 @@ class PyTorchClassifier(BasePyTorchModel):
     User must implement fit method
     """
     def __init__(self, **kwargs):
-        """
-        int: The number of nodes in the hidden layer of the neural network.
-        int: The maximum number of iterations to run during training.
-        int: The batch size to use during training.
-        float: The learning rate to use during training.
-        int: The number of training iterations between each evaluation.
-        dict: A dictionary mapping class names to their corresponding indices.
-        dict: A dictionary mapping indices to their corresponding class names.
-        """
-
         super().__init__(**kwargs)
         self.class_name_to_index = None
         self.index_to_class_name = None

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -11,7 +11,7 @@ from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
 class PyTorchMLPClassifier(PyTorchClassifier):
     """
     This class implements the fit method of IFreqaiModel.
-    int the fit method we initialize the model and trainer objects.
+    in the fit method we initialize the model and trainer objects.
     the only requirement from the model is to be aligned to PyTorchClassifier
     predict method that expects the model to predict tensor of type long.
     the trainer defines the training loop.
@@ -75,6 +75,7 @@ class PyTorchMLPClassifier(PyTorchClassifier):
             model_meta_data={"class_names": class_names},
             device=self.device,
             init_model=init_model,
+            target_tensor_type=torch.long,
             **self.trainer_kwargs,
         )
         trainer.fit(data_dictionary)

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -0,0 +1,78 @@
+from typing import Any, Dict
+
+import torch
+
+from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
+from freqtrade.freqai.prediction_models.PyTorchRegressor import PyTorchRegressor
+
+
+class PyTorchMLPRegressor(PyTorchRegressor):
+    """
+    This class implements the fit method of IFreqaiModel.
+    in the fit method we initialize the model and trainer objects.
+    the only requirement from the model is to be aligned to PyTorchRegressor
+    predict method that expects the model to predict tensor of type float.
+    the trainer defines the training loop.
+
+    parameters are passed via `model_training_parameters` under the freqai
+    section in the config file. e.g:
+    {
+        ...
+        "freqai": {
+            ...
+            "model_training_parameters" : {
+                "learning_rate": 3e-4,
+                "trainer_kwargs": {
+                    "max_iters": 5000,
+                    "batch_size": 64,
+                    "max_n_eval_batches": None,
+                },
+                "model_kwargs": {
+                    "hidden_dim": 512,
+                    "dropout_percent": 0.2,
+                    "n_layer": 1,
+                },
+            }
+        }
+    }
+
+
+    """
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        model_training_params = self.freqai_info.get("model_training_parameters", {})
+        self.learning_rate: float = model_training_params.get("learning_rate", 3e-4)
+        self.model_kwargs: Dict[str, any] = model_training_params.get("model_kwargs", {})
+        self.trainer_kwargs: Dict[str, any] = model_training_params.get("trainer_kwargs", {})
+
+    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
+        """
+        User sets up the training and test data to fit their desired model here
+        :param data_dictionary: the dictionary constructed by DataHandler to hold
+                                all the training and test data/labels.
+        """
+
+        n_features = data_dictionary["train_features"].shape[-1]
+        model = PyTorchMLPModel(
+            input_dim=n_features,
+            output_dim=1,
+            **self.model_kwargs
+        )
+        model.to(self.device)
+        optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
+        criterion = torch.nn.MSELoss()
+        init_model = self.get_init_model(dk.pair)
+        trainer = PyTorchModelTrainer(
+            model=model,
+            optimizer=optimizer,
+            criterion=criterion,
+            device=self.device,
+            init_model=init_model,
+            target_tensor_type=torch.float,
+            **self.trainer_kwargs,
+        )
+        trainer.fit(data_dictionary)
+        return trainer

freqtrade/freqai/prediction_models/PyTorchRegressor.py

@@ -0,0 +1,50 @@
+import logging
+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
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+
+
+logger = logging.getLogger(__name__)
+
+
+class PyTorchRegressor(BasePyTorchModel):
+    """
+    A PyTorch implementation of a regressor.
+    User must implement fit method
+    """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def predict(
+        self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
+    ) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
+        """
+        Filter the prediction features data and predict with it.
+        :param unfiltered_df: Full dataframe for the current backtest period.
+        :return:
+        :pred_df: dataframe containing the predictions
+        :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
+        data (NaNs) or felt uncertain about data (PCA and DI index)
+        """
+
+        dk.find_features(unfiltered_df)
+        filtered_df, _ = dk.filter_features(
+            unfiltered_df, dk.training_features_list, training_filter=False
+        )
+        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)
+
+        y = self.model.model(x)
+        pred_df = DataFrame(y.detach().numpy(), columns=[dk.label_list[0]])
+        return (pred_df, dk.do_predict)