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84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
from typing import Any, Dict
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import torch
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from freqtrade.freqai.base_models.BasePyTorchRegressor import BasePyTorchRegressor
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.torch.PyTorchDataConvertor import (
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DefaultPyTorchDataConvertor,
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PyTorchDataConvertor,
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)
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from freqtrade.freqai.torch.PyTorchMLPModel import PyTorchMLPModel
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from freqtrade.freqai.torch.PyTorchModelTrainer import PyTorchModelTrainer
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class PyTorchMLPRegressor(BasePyTorchRegressor):
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"""
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This class implements the fit method of IFreqaiModel.
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in the fit method we initialize the model and trainer objects.
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the only requirement from the model is to be aligned to PyTorchRegressor
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predict method that expects the model to predict tensor of type float.
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the trainer defines the training loop.
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parameters are passed via `model_training_parameters` under the freqai
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section in the config file. e.g:
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{
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...
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"freqai": {
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...
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"model_training_parameters" : {
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"learning_rate": 3e-4,
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"trainer_kwargs": {
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"n_steps": 5000,
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"batch_size": 64,
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"n_epochs": null,
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},
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"model_kwargs": {
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"hidden_dim": 512,
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"dropout_percent": 0.2,
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"n_layer": 1,
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},
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}
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}
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}
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"""
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@property
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def data_convertor(self) -> PyTorchDataConvertor:
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return DefaultPyTorchDataConvertor(target_tensor_type=torch.float)
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def __init__(self, **kwargs) -> None:
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super().__init__(**kwargs)
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config = self.freqai_info.get("model_training_parameters", {})
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self.learning_rate: float = config.get("learning_rate", 3e-4)
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self.model_kwargs: Dict[str, Any] = config.get("model_kwargs", {})
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self.trainer_kwargs: Dict[str, Any] = config.get("trainer_kwargs", {})
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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User sets up the training and test data to fit their desired model here
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:param data_dictionary: the dictionary holding all data for train, test,
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labels, weights
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:param dk: The datakitchen object for the current coin/model
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"""
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n_features = data_dictionary["train_features"].shape[-1]
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model = PyTorchMLPModel(input_dim=n_features, output_dim=1, **self.model_kwargs)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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criterion = torch.nn.MSELoss()
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# check if continual_learning is activated, and retrieve the model to continue training
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trainer = self.get_init_model(dk.pair)
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if trainer is None:
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trainer = PyTorchModelTrainer(
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model=model,
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optimizer=optimizer,
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criterion=criterion,
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device=self.device,
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data_convertor=self.data_convertor,
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tb_logger=self.tb_logger,
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**self.trainer_kwargs,
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)
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trainer.fit(data_dictionary, self.splits)
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return trainer
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