jijzepttools.blackbox_optimization.surrogate_model.fmo_surrogate_model

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

n_epochs#

Number of training epochs.

Type:

int, default=100

optimizer_params#

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

Type:

dict, default={}

batch_size#

Batch size for training.

Type:

int, default=1

latent_dim: int = 5#
n_epochs: int = 100#
optimizer_params: dict#
batch_size: int = 1#
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#
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.

Contents