jijzepttools.blackbox_optimization.surrogate_model.fmo_surrogate_model

Classes

FMParams	Parameters for Factorization Machine surrogate model.
FMSurrogateModel	Factorization Machine surrogate model implementation.

Module Contents

class FMParams

Bases: jijzepttools.blackbox_optimization.surrogate_model.interface.SurrogateModelParams

Parameters for Factorization Machine surrogate model.

latent_dim

Dimension of latent factors in FM.

Type:

int, default=5

optimizer_params

Parameters for PyTorch optimizer (e.g., {‘lr’: 0.001, ‘weight_decay’: 1e-4}).

Type:

dict, default={}

n_epochs

Number of training epochs.

Type:

int, default=1000

batch_size

Batch size for training.

Type:

int, default=32

early_stopping

Whether to use early stopping.

Type:

bool, default=True

es_min_epochs

Minimum number of epochs.

Type:

int, default=10

es_patience

Patience for early stopping.

Type:

int, default=10

es_threshold_of_loss_rel_imp

Threshold of loss relative improvement for early stopping.

Type:

float, default=5e-4

es_verbose

Whether to print early stopping message.

Type:

bool, default=False

warm_start

Whether to warm start the FM model. If True, the FM model will be warm started from the previous fit. If False, the FM model will be initialized in each fit.

Type:

bool, default=True

latent_dim: int = 5

optimizer_params: dict

n_epochs: int = 1000

batch_size: int = 32

early_stopping: bool = True

es_min_epochs: int = 10

es_patience: int = 10

es_threshold_of_loss_rel_imp: float = 0.0005

es_verbose: bool = False

warm_start: bool = True

class FMSurrogateModel(n_features: int, params: FMParams | None = None)

Bases: jijzepttools.blackbox_optimization.surrogate_model.interface.SurrogateModel

Factorization Machine surrogate model implementation.

This model uses second-order feature interactions to approximate the objective function. The mathematical form is: f(x) = w0 + Σ wi*xi + ΣΣ <vi,vj>*xi*xj

Parameters:

• n_features (int) – Number of input features.

• params (FMParams, optional) – Parameters for the model. If None, default parameters are used.

n_features

fm_trainer

reset_state()

fit(x: numpy.ndarray, y: numpy.ndarray) → None

Fit the Factorization Machine to the data.

Parameters:

• x (np.ndarray, shape (n_samples, n_features)) – Input features.

• y (np.ndarray, shape (n_samples,)) – Target values.

predict(x: numpy.ndarray) → numpy.ndarray

Predict target values using the fitted FM model.

surrogate_to_ommx_objective(decision_variables: list[ommx.v1.DecisionVariable])

Create OMMX expression for the fitted FM model.

Parameters:

decision_variables (list[ommx.v1.DecisionVariable]) – Decision variables from OMMX instance.