GPyOpt.models package

Submodules

GPyOpt.models.base module

class GPyOpt.models.base.BOModel

Bases: object

The abstract Model for Bayesian Optimization

MCMC_sampler = False
analytical_gradient_prediction = False
get_fmin()

Get the minimum of the current model.

predict(X)

Get the predicted mean and std at X.

predict_withGradients(X)

Get the gradients of the predicted mean and variance at X.

updateModel(X_all, Y_all, X_new, Y_new)

Augment the dataset of the model

GPyOpt.models.gpmodel module

class GPyOpt.models.gpmodel.GPModel(kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs', max_iters=1000, optimize_restarts=5, sparse=False, num_inducing=10, verbose=True, ARD=False)

Bases: GPyOpt.models.base.BOModel

General class for handling a Gaussian Process in GPyOpt.

Parameters:
  • kernel – GPy kernel to use in the GP model.
  • noise_var – value of the noise variance if known.
  • exact_feval – whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  • optimizer – optimizer of the model. Check GPy for details.
  • max_iters – maximum number of iterations used to optimize the parameters of the model.
  • optimize_restarts – number of restarts in the optimization.
  • sparse – whether to use a sparse GP (default, False). This is useful when many observations are available.
  • num_inducing – number of inducing points if a sparse GP is used.
  • verbose – print out the model messages (default, False).
  • ARD – whether ARD is used in the kernel (default, False).

Note

This model does Maximum likelihood estimation of the hyper-parameters.

analytical_gradient_prediction = True
copy()

Makes a safe copy of the model.

static fromConfig()
get_fmin()

Returns the location where the posterior mean is takes its minimal value.

get_model_parameters()

Returns a 2D numpy array with the parameters of the model

get_model_parameters_names()

Returns a list with the names of the parameters of the model

predict(X)

Predictions with the model. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.

predict_withGradients(X)

Returns the mean, standard deviation, mean gradient and standard deviation gradient at X.

updateModel(X_all, Y_all, X_new, Y_new)

Updates the model with new observations.

class GPyOpt.models.gpmodel.GPModel_MCMC(kernel=None, noise_var=None, exact_feval=False, n_samples=10, n_burnin=100, subsample_interval=10, step_size=0.1, leapfrog_steps=20, verbose=False)

Bases: GPyOpt.models.base.BOModel

General class for handling a Gaussian Process in GPyOpt.

Parameters:
  • kernel – GPy kernel to use in the GP model.
  • noise_var – value of the noise variance if known.
  • exact_feval – whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios (default, False).
  • n_samples – number of MCMC samples.
  • n_burnin – number of samples not used.
  • subsample_interval – sub-sample interval in the MCMC.
  • step_size – step-size in the MCMC.
  • leapfrog_steps –

    ??

  • verbose – print out the model messages (default, False).

Note

This model does MCMC over the hyperparameters.

MCMC_sampler = True
analytical_gradient_prediction = True
copy()

Makes a safe copy of the model.

get_fmin()

Returns the location where the posterior mean is takes its minimal value.

get_model_parameters()

Returns a 2D numpy array with the parameters of the model

get_model_parameters_names()

Returns a list with the names of the parameters of the model

predict(X)

Predictions with the model for all the MCMC samples. Returns posterior means and standard deviations at X. Note that this is different in GPy where the variances are given.

predict_withGradients(X)

Returns the mean, standard deviation, mean gradient and standard deviation gradient at X for all the MCMC samples.

updateModel(X_all, Y_all, X_new, Y_new)

Updates the model with new observations.

GPyOpt.models.input_warped_gpmodel module

class GPyOpt.models.input_warped_gpmodel.InputWarpedGPModel(space, warping_function=None, kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs', max_iters=1000, optimize_restarts=5, verbose=False, ARD=False)

Bases: GPyOpt.models.gpmodel.GPModel

Bayesian Optimization with Input Warped GP using Kumar Warping

The Kumar warping only applies to the numerical variables: continuous and discrete

space : object
Instance of Design_space defined in GPyOpt.core.task.space
warping_function : object, optional
Warping function defined in GPy.util.input_warping_functions.py. Default is Kumar warping
kernel : object, optional
An instance of kernel function defined in GPy.kern. Default is Matern 52
noise_var : float, optional
Value of the noise variance if known
exact_feval : bool, optional
Whether noiseless evaluations are available. IMPORTANT to make the optimization work well in noiseless scenarios, Default is False
optimizer : string, optional
Optimizer of the model. Check GPy for details. Default to bfgs
max_iter : int, optional
Maximum number of iterations used to optimize the parameters of the model. Default is 1000
optimize_restarts : int, optional
Number of restarts in the optimization. Default is 5
verbose : bool, optional
Whether to print out the model messages. Default is False
analytical_gradient_prediction = False

GPyOpt.models.rfmodel module

class GPyOpt.models.rfmodel.RFModel(bootstrap=True, criterion='mse', max_depth=None, max_features='auto', max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=1, oob_score=False, random_state=None, verbose=0, warm_start=False)

Bases: GPyOpt.models.base.BOModel

General class for handling a Ramdom Forest in GPyOpt.

Note

The model has beed wrapper ‘as it is’ from Scikit-learn. Check

http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html for further details.

analytical_gradient_prediction = False
get_fmin()

Get the minimum of the current model.

predict(X)

Predictions with the model. Returns posterior means and standard deviations at X.

updateModel(X_all, Y_all, X_new, Y_new)

Updates the model with new observations.

GPyOpt.models.warpedgpmodel module

class GPyOpt.models.warpedgpmodel.WarpedGPModel(kernel=None, noise_var=None, exact_feval=False, optimizer='bfgs', max_iters=1000, optimize_restarts=5, warping_function=None, warping_terms=3, verbose=False)

Bases: GPyOpt.models.base.BOModel

analytical_gradient_prediction = False
get_fmin()

Get the minimum of the current model.

predict(X)

Get the predicted mean and std at X.

updateModel(X_all, Y_all, X_new, Y_new)

Augment the dataset of the model

Module contents

GPyOpt.models.select_model(name)