jijzepttools.blackbox_optimization.surrogate_model.bfmo_surrogate_model

jijzepttools.blackbox_optimization.surrogate_model.bfmo_surrogate_model#

Classes#

BFMParams

Parameters for the Bayesian Factorization Machine surrogate model.

BFMSurrogateModel

Bayesian Factorization Machine (BFM) surrogate model.

Module Contents#

class BFMParams#

Bases: jijzepttools.blackbox_optimization.surrogate_model.interface.SurrogateModelParams

Parameters for the Bayesian Factorization Machine surrogate model.

init_stdev, alpha0, beta0, gamma_0, mu_0, reg_0の詳細は以下を参照 参考: tohtsky/myFM

latent_dim#

Dimension of latent factors.

Type:

int, default=5

tune#

Number of burn-in sweeps for Gibbs sampling.

Type:

int, default=100

draws#

Number of samples to average after burn-in.

Type:

int, default=10

init_stdev#

FMのパラメータ初期値の標準偏差

Type:

float

alpha0, beta0

alpha, lambda_w, lambda_vの事前ガンマ分布の形状を決定するパラメータ

Type:

float

gamma_0#

パラメータの正規事前分布の平均mu_w, mu_vが従うハイパー事前分布(正規分布)の精度

Type:

float

mu_0#

パラメータの正規事前分布の平均mu_w, mu_vが従うハイパー事前分布(正規分布)の平均

Type:

float

reg_0#

バイアス項 w0が従う正規事前分布の精度

Type:

float

latent_dim: int = 5#
tune: int = 100#
draws: int = 10#
init_stdev: float = 0.1#
alpha_0: float = 1.0#
beta_0: float = 1.0#
gamma_0: float = 1.0#
mu_0: float = 0.0#
reg_0: float = 1.0#
class BFMSurrogateModel(params: BFMParams | None = None, random_seed: int = 42)#

Bases: jijzepttools.blackbox_optimization.surrogate_model.interface.SurrogateModel

Bayesian Factorization Machine (BFM) surrogate model.

This model wraps the BFM implementation and provides the SurrogateModel interface. It’s effective for problems where interactions between variables are important.

Parameters:

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

params = None#
bfm_regressor#
fit(x: numpy.ndarray, y: numpy.ndarray) → None#

Fit the BFM model to the data using Gibbs sampling.

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 BFM model.

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

Create an OMMX expression from the fitted BFM model.

Parameters:

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

Contents