mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-11 10:43:56 +00:00
141 lines
4.7 KiB
Python
141 lines
4.7 KiB
Python
import logging
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from pathlib import Path
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from typing import Dict
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from torch.optim import Optimizer
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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from torch.utils.data import TensorDataset
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import pandas as pd
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logger = logging.getLogger(__name__)
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class PyTorchModelTrainer:
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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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batch_size: int,
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max_iters: int,
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eval_iters: int,
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init_model: Dict
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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.device = device
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self.max_iters = max_iters
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self.batch_size = batch_size
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self.eval_iters = eval_iters
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if init_model:
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self.load_from_checkpoint(init_model)
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def fit(self, data_dictionary: Dict[str, pd.DataFrame]):
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data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary)
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epochs = self.calc_n_epochs(
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n_obs=len(data_dictionary['train_features']),
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batch_size=self.batch_size,
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n_iters=self.max_iters
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)
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for epoch in range(epochs):
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# evaluation
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losses = self.estimate_loss(data_loaders_dictionary, data_dictionary)
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logger.info(
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f"epoch ({epoch}/{epochs}):"
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f" train loss {losses['train']:.4f} ; test loss {losses['test']:.4f}"
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)
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# training
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for batch_data in 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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@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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data_dictionary: Dict[str, pd.DataFrame]
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) -> Dict[str, float]:
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self.model.eval()
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epochs = self.calc_n_epochs(
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n_obs=len(data_dictionary['test_features']),
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batch_size=self.batch_size,
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n_iters=self.eval_iters
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)
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loss_dictionary = {}
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for split in ['train', 'test']:
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losses = torch.zeros(epochs)
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for i, batch in enumerate(data_loader_dictionary[split]):
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xb, yb = batch
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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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losses[i] = loss.item()
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loss_dictionary[split] = losses.mean().item()
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self.model.train()
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return loss_dictionary
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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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) -> Dict[str, DataLoader]:
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data_loader_dictionary = {}
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for split in ['train', 'test']:
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labels_shape = data_dictionary[f'{split}_labels'].shape
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labels_view = labels_shape[0] if labels_shape[1] == 1 else labels_shape
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dataset = TensorDataset(
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torch.from_numpy(data_dictionary[f'{split}_features'].values).float(),
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torch.from_numpy(data_dictionary[f'{split}_labels'].astype(float).values)
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.long()
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.view(labels_view)
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)
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# todo currently assuming class labels are strings ['0.0', '1.0' .. n_classes].
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# need to resolve it per ClassifierModel
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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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@staticmethod
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def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int:
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n_batches = n_obs // batch_size
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epochs = n_iters // n_batches
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return epochs
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def save(self, path: Path):
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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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}, path)
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def load_from_file(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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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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return self
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