physbo.blm package

Subpackages

• physbo.blm.basis package
  • Module contents
    • Fourier
• physbo.blm.core package
  • Module contents
    • Model
• physbo.blm.inf package
  • Submodules
    • physbo.blm.inf.exact module
  • Module contents
• physbo.blm.lik package
  • Module contents
    • Cov
    • Gauss
    • Linear
• physbo.blm.prior package
  • Module contents
    • gauss()

Module contents

Bayesian Linear Model

class physbo.blm.Model(lik, prior, options={})

Bases: object

Baysean Linear Model

prior

prior distribution of weights

Type:

physbo.blm.prior.Gauss

lik

kernel

Type:

physbo.blm.lik.Gauss

nbasis

number of features in random feature map

Type:

int

stats

auxially parameters for sampling

Type:

Tuple

method

sampling method

Type:

str

get_permutation_importance(X, t, n_perm: int, comm=None, split_features_parallel=False)

Calculating permutation importance of model

Parameters:

• X (numpy.ndarray) – inputs

• t (numpy.ndarray) – target (label)

• n_perm (int) – number of permutations

• comm (MPI.Comm) – MPI communicator

Returns:

• numpy.ndarray – importance_mean

• numpy.ndarray – importance_std

get_post_fcov(X, Psi=None, diag=True)

calculates posterior covariance of model

Parameters:

• X (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature maps (default: blm.lik.linear.basis.get_basis(X))

• diag (bool) – if True, returns only variances as a diagonal matrix (default: True)

Returns:

Returned shape is (num_points) if diag=true, (num_points, num_points) if diag=false, where num_points is the number of points in X.

Return type:

numpy.ndarray

See also

physbo.blm.inf.exact.get_post_fcov

get_post_fmean(X, Psi=None, w=None)

calculates posterior mean of model (function)

Parameters:

• X (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature maps

• w (numpy.ndarray) – weight

See also

physbo.blm.inf.exact.get_post_fmean

get_post_params_mean()

calculates posterior mean of weights

Return type:

numpy.ndarray

See also

physbo.blm.inf.exact.get_post_params_mean

post_sampling(Xtest, Psi=None, N=1, alpha=1.0)

draws samples of mean value of model

Parameters:

• Xtest (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature maps (default: blm.lik.get_basis(Xtest))

• N (int) – number of samples (default: 1)

• alpha (float) – noise for sampling source

Return type:

numpy.ndarray

predict_sampling(Xtest, Psi=None, N=1)

draws samples from model

Parameters:

• Xtest (numpy.ndarray) – inputs

• Psi (numpy.ndarray) – feature map (default: blm.lik.get_basis(Xtest))

• N (int) – number of samples (default: 1)

Return type:

numpy.ndarray

prepare(X, t, Psi=None)

initializes model by using the first training dataset

Parameters:

• X (numpy.ndarray) – inputs

• t (numpy.ndarray) – target (label)

• Psi (numpy.ndarray) – feature maps

See also

physbo.blm.inf.exact.prepare

sampling(w_mu=None, N=1, alpha=1.0)

draws samples of weights

Parameters:

• blm (physbo.blm.core.Model) – model

• w_mu (numpy.ndarray) – mean of weight

• N (int) – the number of samples (default: 1)

• alpha (float) – noise for sampling source (default: 1.0)

Returns:

samples of weights

Return type:

numpy.ndarray

See also

physbo.blm.inf.exact.sampling

update_stats(x, t, psi=None)

updates model by using another training data

Parameters:

• x (numpy.ndarray) – input

• t (float) – target (label)

• psi (numpy.ndarray) – feature map

See also

physbo.blm.inf.exact.update_stats

class physbo.blm.Predictor(config, model=None)

Bases: BasePredictor

Predictor using Baysean linear model

blm

Type:

physbo.blm.core.Model

config

configuration

Type:

physbo.misc.SetConfig

Parameters:

• config (physbo.misc.SetConfig) – configuration

• model (physbo.gp.core.Model)

See also

physbo.predictor.BasePredictor

delete_stats()

resets model

fit(training, num_basis=None, comm=None)

fit model to training dataset

Parameters:

• training (physbo.variable) – dataset for training

• num_basis (int) – the number of basis (default: self.config.predict.num_basis)

• comm (MPI.Comm) – MPI communicator

get_basis(X)

calculates feature maps Psi(X)

Parameters:

X (numpy.ndarray) – inputs

Returns:

Psi – feature maps

Return type:

numpy.ndarray

get_post_fcov(training, test, diag=True)

calculates posterior variance-covariance matrix of model

Parameters:

• training (physbo.variable) – training dataset. If already trained, the model does not use this.

• test (physbo.variable) – inputs

• diag (bool) – If true, only variances (diagonal elements) are returned.

Returns:

Returned shape is (num_points) if diag=true, (num_points, num_points) if diag=false, where num_points is the number of points in test.

Return type:

numpy.ndarray

get_post_fmean(training, test)

calculates posterior mean value of model

Parameters:

• training (physbo.variable) – training dataset. If already trained, the model does not use this.

• test (physbo.variable) – inputs

Returns:

Returned shape is (num_points), where num_points is the number of points in test.

Return type:

numpy.ndarray

get_post_params(training, test)

calculates posterior weights

Parameters:

• training (physbo.variable) – training dataset. If already trained, the model does not use this.

• test (physbo.variable) – inputs (not used)

Return type:

numpy.ndarray

get_post_samples(training, test, N=1, alpha=1.0)

draws samples of mean values of model

Parameters:

• training (physbo.variable) – training dataset. If already trained, the model does not use this.

• test (physbo.variable) – inputs

• N (int) – number of samples (default: 1)

• alpha (float) – noise for sampling source (default: 1.0)

Return type:

numpy.ndarray

get_predict_samples(training, test, N=1)

draws samples of values of model

Parameters:

• training (physbo.variable) – training dataset. If already trained, the model does not use this.

• test (physbo.variable) – inputs

• N (int) – number of samples (default: 1)

• alpha (float) – noise for sampling source (default: 1.0)

Return type:

numpy.ndarray (N x len(test))

prepare(training)

initializes model by using training data set

Parameters:

training (physbo.variable) – dataset for training

update(training, test)

updates the model.

If not yet initialized (prepared), the model will be prepared by training. Otherwise, the model will be updated by test.

Parameters:

• training (physbo.variable) – training dataset for initialization (preparation). If already prepared, the model ignore this.

• test (physbo.variable) – training data for update. If not prepared, the model ignore this.