from typing import Any, Dict import torch from freqtrade.freqai.base_models.BasePyTorchClassifier import BasePyTorchClassifier from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer class PyTorchMLPClassifier(BasePyTorchClassifier): """ 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 PyTorchClassifier predict method that expects the model to predict a tensor of type long. 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, }, } } } """ 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. :raises ValueError: If self.class_names is not defined in the parent class. """ class_names = self.get_class_names() self.convert_label_column_to_int(data_dictionary, dk, class_names) n_features = data_dictionary["train_features"].shape[-1] model = PyTorchMLPModel( input_dim=n_features, output_dim=len(class_names), **self.model_kwargs ) model.to(self.device) optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) criterion = torch.nn.CrossEntropyLoss() init_model = self.get_init_model(dk.pair) trainer = PyTorchModelTrainer( model=model, optimizer=optimizer, criterion=criterion, model_meta_data={"class_names": class_names}, device=self.device, init_model=init_model, target_tensor_type=torch.long, squeeze_target_tensor=True, **self.trainer_kwargs, ) trainer.fit(data_dictionary, self.splits) return trainer