function.torch_objective

function.torch_objective

Classes

Name	Description
TorchObjective	A callable objective function for SpotOptim that trains and evaluates a PyTorch model.

TorchObjective

function.torch_objective.TorchObjective(experiment, seed=None, use_scaler=False)

A callable objective function for SpotOptim that trains and evaluates a PyTorch model.

Attributes

Name	Description
bounds	Returns the bounds of the hyperparameters.
objective_names	Returns the names of the objectives.
var_name	Returns the names of the hyperparameters.
var_trans	Returns the transformations of the hyperparameters.
var_type	Returns the types of the hyperparameters.

Methods

Name	Description
train_model	Trains the model and returns a dictionary of metrics.

train_model

function.torch_objective.TorchObjective.train_model(
    model,
    train_loader,
    val_loader,
    params,
)

Trains the model and returns a dictionary of metrics.

Executes the training loop for the specified number of epochs. Handles optimizer creation, loss calculation, backward pass, and validation evaluation.

Parameters

Name	Type	Description	Default
model	nn.Module	The PyTorch model to train.	required
train_loader	DataLoader	DataLoader for training data.	required
val_loader	Optional[DataLoader]	DataLoader for validation data (can be None).	required
params	Dict[str, Any]	Hyperparameters dictionary containing ‘epochs’, ‘lr’, ‘optimizer’ name, etc.	required

Returns

Name	Type	Description
	Dict[str, float]	Dict[str, float]: Dictionary containing computed metrics, e.g., {‘val_loss’: …, ‘train_loss’: …, ‘mse’: …, ‘epochs’: …}.

Examples

>>> import torch
>>> import torch.nn as nn
>>> from torch.utils.data import DataLoader, TensorDataset
>>> from spotoptim.function.torch_objective import TorchObjective
>>> from unittest.mock import MagicMock
>>>
>>> # 1. Create dataset and loader
>>> X = torch.randn(10, 2)
>>> y = torch.randn(10, 1)
>>> loader = DataLoader(TensorDataset(X, y), batch_size=2)
>>>
>>> # 2. Create model
>>> model = nn.Linear(2, 1)
>>>
>>> # 3. Mock Objective context
>>> exp = MagicMock()
>>> exp.loss_function = nn.MSELoss()
>>> exp.epochs = 1
>>> exp.torch_device = "cpu"
>>> objective = TorchObjective(exp)
>>>
>>> # 4. Train
>>> params = {'lr': 1e-2, 'optimizer': 'Adam'}
>>> metrics = objective.train_model(model, loader, None, params)
>>> print(f"Train Loss: {metrics['train_loss']:.4f}")
>>> print(f"Epochs: {metrics['epochs']}")