add pytorch regressor example

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