mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
223 lines
8.5 KiB
Python
223 lines
8.5 KiB
Python
import logging
|
|
from pathlib import Path
|
|
from typing import Any, Dict, List, Optional
|
|
|
|
import pandas as pd
|
|
import torch
|
|
from torch import 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
|
|
|
|
from .datasets import WindowDataset
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class PyTorchModelTrainer(PyTorchTrainerInterface):
|
|
def __init__(
|
|
self,
|
|
model: nn.Module,
|
|
optimizer: Optimizer,
|
|
criterion: nn.Module,
|
|
device: str,
|
|
data_convertor: PyTorchDataConvertor,
|
|
model_meta_data: Dict[str, Any] = {},
|
|
window_size: int = 1,
|
|
tb_logger: Any = None,
|
|
**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: converter from pd.DataFrame to torch.tensor.
|
|
:param n_steps: used to calculate n_epochs. The number of training iterations to run.
|
|
iteration here refers to the number of times optimizer.step() is called.
|
|
ignored if n_epochs is set.
|
|
:param n_epochs: The maximum number batches to use for evaluation.
|
|
:param batch_size: The size of the batches to use during training.
|
|
"""
|
|
self.model = model
|
|
self.optimizer = optimizer
|
|
self.criterion = criterion
|
|
self.model_meta_data = model_meta_data
|
|
self.device = device
|
|
self.n_epochs: Optional[int] = kwargs.get("n_epochs", 10)
|
|
self.n_steps: Optional[int] = kwargs.get("n_steps", None)
|
|
if self.n_steps is None and not self.n_epochs:
|
|
raise Exception("Either `n_steps` or `n_epochs` should be set.")
|
|
|
|
self.batch_size: int = kwargs.get("batch_size", 64)
|
|
self.data_convertor = data_convertor
|
|
self.window_size: int = window_size
|
|
self.tb_logger = tb_logger
|
|
self.test_batch_counter = 0
|
|
|
|
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.
|
|
"""
|
|
self.model.train()
|
|
|
|
data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
|
|
n_obs = len(data_dictionary["train_features"])
|
|
n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs)
|
|
batch_counter = 0
|
|
for _ in range(n_epochs):
|
|
for _, batch_data in enumerate(data_loaders_dictionary["train"]):
|
|
xb, yb = batch_data
|
|
xb = xb.to(self.device)
|
|
yb = yb.to(self.device)
|
|
yb_pred = self.model(xb)
|
|
loss = self.criterion(yb_pred, yb)
|
|
|
|
self.optimizer.zero_grad(set_to_none=True)
|
|
loss.backward()
|
|
self.optimizer.step()
|
|
self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
|
|
batch_counter += 1
|
|
|
|
# evaluation
|
|
if "test" in splits:
|
|
self.estimate_loss(data_loaders_dictionary, "test")
|
|
|
|
@torch.no_grad()
|
|
def estimate_loss(
|
|
self,
|
|
data_loader_dictionary: Dict[str, DataLoader],
|
|
split: str,
|
|
) -> None:
|
|
self.model.eval()
|
|
for _, batch_data in enumerate(data_loader_dictionary[split]):
|
|
xb, yb = batch_data
|
|
xb = xb.to(self.device)
|
|
yb = yb.to(self.device)
|
|
|
|
yb_pred = self.model(xb)
|
|
loss = self.criterion(yb_pred, yb)
|
|
self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
|
|
self.test_batch_counter += 1
|
|
|
|
self.model.train()
|
|
|
|
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"], self.device)
|
|
y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
|
|
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
|
|
|
|
def calc_n_epochs(self, n_obs: 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 `n_steps` is easier to optimize and keep stable,
|
|
across different n_obs - the number of data points.
|
|
"""
|
|
if not isinstance(self.n_steps, int):
|
|
raise ValueError("Either `n_steps` or `n_epochs` should be set.")
|
|
n_batches = n_obs // self.batch_size
|
|
n_epochs = max(self.n_steps // n_batches, 1)
|
|
if n_epochs <= 10:
|
|
logger.warning(
|
|
f"Setting low n_epochs: {n_epochs}. "
|
|
f"Please consider increasing `n_steps` hyper-parameter."
|
|
)
|
|
|
|
return n_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,
|
|
"pytrainer": self,
|
|
},
|
|
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
|
|
|
|
|
|
class PyTorchTransformerTrainer(PyTorchModelTrainer):
|
|
"""
|
|
Creating a trainer for the Transformer model.
|
|
"""
|
|
|
|
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"], self.device)
|
|
y = self.data_convertor.convert_y(data_dictionary[f"{split}_labels"], self.device)
|
|
dataset = WindowDataset(x, y, self.window_size)
|
|
data_loader = DataLoader(
|
|
dataset,
|
|
batch_size=self.batch_size,
|
|
shuffle=False,
|
|
drop_last=True,
|
|
num_workers=0,
|
|
)
|
|
data_loader_dictionary[split] = data_loader
|
|
|
|
return data_loader_dictionary
|