import logging import math from pathlib import Path from typing import Any, Dict, List, Optional import pandas as pd import torch import torch.nn as nn from torch.optim import Optimizer from torch.utils.data import DataLoader, TensorDataset from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface logger = logging.getLogger(__name__) class PyTorchModelTrainer(PyTorchTrainerInterface): def __init__( self, model: nn.Module, optimizer: Optimizer, criterion: nn.Module, device: str, init_model: Dict, data_convertor: PyTorchDataConvertor, model_meta_data: Dict[str, Any] = {}, **kwargs ): """ :param model: The PyTorch model to be trained. :param optimizer: The optimizer to use for training. :param criterion: The loss function to use for training. :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 model_meta_data: Additional metadata about the model (optional). :param data_convertor: convertor from pd.DataFrame to torch.tensor. :param max_iters: The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs. :param batch_size: The size of the batches to use during training. :param max_n_eval_batches: The maximum number batches to use for evaluation. """ self.model = model self.optimizer = optimizer self.criterion = criterion self.model_meta_data = model_meta_data self.device = device 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) self.data_convertor = data_convertor if init_model: self.load_from_checkpoint(init_model) def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]): """ :param data_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. :param splits: splits to use in training, splits must contain "train", optional "test" could be added by setting freqai.data_split_parameters.test_size > 0 in the config file. - Calculates the predicted output for the batch using the PyTorch model. - Calculates the loss between the predicted and actual output using a loss function. - Computes the gradients of the loss with respect to the model's parameters using backpropagation. - Updates the model's parameters using an optimizer. """ data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits) epochs = self.calc_n_epochs( n_obs=len(data_dictionary["train_features"]), batch_size=self.batch_size, n_iters=self.max_iters ) for epoch in range(1, epochs + 1): # training losses = [] for i, batch_data in enumerate(data_loaders_dictionary["train"]): for tensor in batch_data: tensor.to(self.device) xb = batch_data[:-1] yb = batch_data[-1] yb_pred = self.model(xb) loss = self.criterion(yb_pred, yb) self.optimizer.zero_grad(set_to_none=True) loss.backward() self.optimizer.step() losses.append(loss.item()) train_loss = sum(losses) / len(losses) log_message = f"epoch {epoch}/{epochs}: train loss {train_loss:.4f}" # evaluation if "test" in splits: test_loss = self.estimate_loss( data_loaders_dictionary, self.max_n_eval_batches, "test" ) log_message += f" ; test loss {test_loss:.4f}" logger.info(log_message) @torch.no_grad() def estimate_loss( self, data_loader_dictionary: Dict[str, DataLoader], max_n_eval_batches: Optional[int], split: str, ) -> float: self.model.eval() n_batches = 0 losses = [] for i, batch_data in enumerate(data_loader_dictionary[split]): if max_n_eval_batches and i > max_n_eval_batches: n_batches += 1 break for tensor in batch_data: tensor.to(self.device) xb = batch_data[:-1] yb = batch_data[-1] yb_pred = self.model(xb) loss = self.criterion(yb_pred, yb) losses.append(loss.item()) self.model.train() return sum(losses) / len(losses) def create_data_loaders_dictionary( self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str] ) -> Dict[str, DataLoader]: """ Converts the input data to PyTorch tensors using a data loader. """ data_loader_dictionary = {} for split in splits: x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"]) y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"]) dataset = TensorDataset(*x, *y) data_loader = DataLoader( dataset, batch_size=self.batch_size, shuffle=True, drop_last=True, num_workers=0, ) data_loader_dictionary[split] = data_loader return data_loader_dictionary @staticmethod def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int: """ Calculates the number of epochs required to reach the maximum number of iterations specified in the model training parameters. the motivation here is that `max_iters` is easier to optimize and keep stable, across different n_obs - the number of data points. """ n_batches = math.ceil(n_obs // batch_size) epochs = math.ceil(n_iters // n_batches) return epochs def save(self, path: Path): """ - Saving any nn.Module state_dict - Saving model_meta_data, this dict should contain any additional data that the user needs to store. e.g class_names for classification models. """ torch.save({ "model_state_dict": self.model.state_dict(), "optimizer_state_dict": self.optimizer.state_dict(), "model_meta_data": self.model_meta_data, }, path) def load(self, path: Path): checkpoint = torch.load(path) return self.load_from_checkpoint(checkpoint) def load_from_checkpoint(self, checkpoint: Dict): """ when using continual_learning, DataDrawer will load the dictionary (containing state dicts and model_meta_data) by calling torch.load(path). you can access this dict from any class that inherits IFreqaiModel by calling get_init_model method. """ self.model.load_state_dict(checkpoint["model_state_dict"]) self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) self.model_meta_data = checkpoint["model_meta_data"] return self