from typing import Any, Dict import torch from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.torch import PyTorchDataConvertor from freqtrade.freqai.torch.PyTorchDataConvertor import DefaultPyTorchDataConvertor from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer class PyTorchMLPRegressor(BasePyTorchRegressor): """ 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": null, }, "model_kwargs": { "hidden_dim": 512, "dropout_percent": 0.2, "n_layer": 1, }, } } } """ @property def data_convertor(self) -> PyTorchDataConvertor: return DefaultPyTorchDataConvertor(target_tensor_type=torch.float) def __init__(self, **kwargs) -> None: super().__init__(**kwargs) config = self.freqai_info.get("model_training_parameters", {}) self.learning_rate: float = config.get("learning_rate", 3e-4) self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {}) self.trainer_kwargs: Dict[str, Any] = config.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, data_convertor=self.data_convertor, **self.trainer_kwargs, ) trainer.fit(data_dictionary, self.splits) return trainer