mirror of
https://github.com/freqtrade/freqtrade.git
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223 lines
8.5 KiB
Python
223 lines
8.5 KiB
Python
import logging
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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import pandas as pd
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import torch
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from torch import nn
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from torch.optim import Optimizer
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from torch.utils.data import DataLoader, TensorDataset
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from freqtrade.freqai.torch.PyTorchDataConvertor import PyTorchDataConvertor
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from freqtrade.freqai.torch.PyTorchTrainerInterface import PyTorchTrainerInterface
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from .datasets import WindowDataset
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logger = logging.getLogger(__name__)
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class PyTorchModelTrainer(PyTorchTrainerInterface):
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def __init__(
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self,
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model: nn.Module,
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optimizer: Optimizer,
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criterion: nn.Module,
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device: str,
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data_convertor: PyTorchDataConvertor,
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model_meta_data: Dict[str, Any] = {},
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window_size: int = 1,
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tb_logger: Any = None,
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**kwargs
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):
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"""
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:param model: The PyTorch model to be trained.
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:param optimizer: The optimizer to use for training.
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:param criterion: The loss function to use for training.
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:param device: The device to use for training (e.g. 'cpu', 'cuda').
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:param init_model: A dictionary containing the initial model/optimizer
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state_dict and model_meta_data saved by self.save() method.
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:param model_meta_data: Additional metadata about the model (optional).
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:param data_convertor: converter from pd.DataFrame to torch.tensor.
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:param n_steps: used to calculate n_epochs. The number of training iterations to run.
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iteration here refers to the number of times optimizer.step() is called.
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ignored if n_epochs is set.
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:param n_epochs: The maximum number batches to use for evaluation.
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:param batch_size: The size of the batches to use during training.
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"""
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self.model = model
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self.optimizer = optimizer
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self.criterion = criterion
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self.model_meta_data = model_meta_data
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self.device = device
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self.n_epochs: Optional[int] = kwargs.get("n_epochs", 10)
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self.n_steps: Optional[int] = kwargs.get("n_steps", None)
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if self.n_steps is None and not self.n_epochs:
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raise Exception("Either `n_steps` or `n_epochs` should be set.")
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self.batch_size: int = kwargs.get("batch_size", 64)
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self.data_convertor = data_convertor
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self.window_size: int = window_size
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self.tb_logger = tb_logger
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self.test_batch_counter = 0
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def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
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"""
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:param data_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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:param splits: splits to use in training, splits must contain "train",
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optional "test" could be added by setting freqai.data_split_parameters.test_size > 0
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in the config file.
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- Calculates the predicted output for the batch using the PyTorch model.
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- Calculates the loss between the predicted and actual output using a loss function.
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- Computes the gradients of the loss with respect to the model's parameters using
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backpropagation.
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- Updates the model's parameters using an optimizer.
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"""
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self.model.train()
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data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
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n_obs = len(data_dictionary["train_features"])
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n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs)
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batch_counter = 0
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for _ in range(n_epochs):
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for _, batch_data in enumerate(data_loaders_dictionary["train"]):
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xb, yb = batch_data
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xb = xb.to(self.device)
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yb = yb.to(self.device)
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yb_pred = self.model(xb)
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loss = self.criterion(yb_pred, yb)
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self.optimizer.zero_grad(set_to_none=True)
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loss.backward()
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self.optimizer.step()
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self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
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batch_counter += 1
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# evaluation
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if "test" in splits:
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self.estimate_loss(data_loaders_dictionary, "test")
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@torch.no_grad()
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def estimate_loss(
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self,
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data_loader_dictionary: Dict[str, DataLoader],
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split: str,
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) -> None:
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self.model.eval()
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for _, batch_data in enumerate(data_loader_dictionary[split]):
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xb, yb = batch_data
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xb = xb.to(self.device)
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yb = yb.to(self.device)
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yb_pred = self.model(xb)
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loss = self.criterion(yb_pred, yb)
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self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
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self.test_batch_counter += 1
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self.model.train()
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def create_data_loaders_dictionary(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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splits: List[str]
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) -> Dict[str, DataLoader]:
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"""
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Converts the input data to PyTorch tensors using a data loader.
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"""
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data_loader_dictionary = {}
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for split in splits:
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x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
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y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
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dataset = TensorDataset(x, y)
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data_loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=True,
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drop_last=True,
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num_workers=0,
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)
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data_loader_dictionary[split] = data_loader
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return data_loader_dictionary
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def calc_n_epochs(self, n_obs: int) -> int:
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"""
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Calculates the number of epochs required to reach the maximum number
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of iterations specified in the model training parameters.
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the motivation here is that `n_steps` is easier to optimize and keep stable,
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across different n_obs - the number of data points.
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"""
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assert isinstance(self.n_steps, int), "Either `n_steps` or `n_epochs` should be set."
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n_batches = n_obs // self.batch_size
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n_epochs = max(self.n_steps // n_batches, 1)
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if n_epochs <= 10:
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logger.warning(
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f"Setting low n_epochs: {n_epochs}. "
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f"Please consider increasing `n_steps` hyper-parameter."
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)
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return n_epochs
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def save(self, path: Path):
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"""
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- Saving any nn.Module state_dict
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- Saving model_meta_data, this dict should contain any additional data that the
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user needs to store. e.g. class_names for classification models.
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"""
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torch.save({
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"model_state_dict": self.model.state_dict(),
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"optimizer_state_dict": self.optimizer.state_dict(),
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"model_meta_data": self.model_meta_data,
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"pytrainer": self
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}, path)
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def load(self, path: Path):
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checkpoint = torch.load(path)
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return self.load_from_checkpoint(checkpoint)
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def load_from_checkpoint(self, checkpoint: Dict):
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"""
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when using continual_learning, DataDrawer will load the dictionary
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(containing state dicts and model_meta_data) by calling torch.load(path).
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you can access this dict from any class that inherits IFreqaiModel by calling
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get_init_model method.
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"""
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self.model.load_state_dict(checkpoint["model_state_dict"])
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self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
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self.model_meta_data = checkpoint["model_meta_data"]
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return self
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class PyTorchTransformerTrainer(PyTorchModelTrainer):
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"""
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Creating a trainer for the Transformer model.
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"""
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def create_data_loaders_dictionary(
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self,
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data_dictionary: Dict[str, pd.DataFrame],
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splits: List[str]
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) -> Dict[str, DataLoader]:
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"""
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Converts the input data to PyTorch tensors using a data loader.
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"""
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data_loader_dictionary = {}
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for split in splits:
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x = self.data_convertor.convert_x(data_dictionary[f"{split}_features"], self.device)
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y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
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dataset = WindowDataset(x, y, self.window_size)
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data_loader = DataLoader(
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dataset,
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batch_size=self.batch_size,
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shuffle=False,
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drop_last=True,
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num_workers=0,
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)
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data_loader_dictionary[split] = data_loader
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return data_loader_dictionary
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