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={})

ベースクラス: 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, rng=None)

Calculating permutation importance of model

パラメータ:

• X (numpy.ndarray) -- inputs

• t (numpy.ndarray) -- target (label)

• n_perm (int) -- number of permutations

• comm (MPI.Comm) -- MPI communicator

戻り値:

• numpy.ndarray -- importance_mean

• numpy.ndarray -- importance_std

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

calculates posterior covariance of model

パラメータ:

• 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)

戻り値:

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.

戻り値の型:

numpy.ndarray

参考

physbo.blm.inf.exact.get_post_fcov

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

calculates posterior mean of model (function)

パラメータ:

• X (numpy.ndarray) -- inputs

• Psi (numpy.ndarray) -- feature maps

• w (numpy.ndarray) -- weight

参考

physbo.blm.inf.exact.get_post_fmean

get_post_params_mean()

calculates posterior mean of weights

戻り値の型:

numpy.ndarray

参考

physbo.blm.inf.exact.get_post_params_mean

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

draws samples of mean value of model

パラメータ:

• 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

戻り値の型:

numpy.ndarray

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

draws samples from model

パラメータ:

• Xtest (numpy.ndarray) -- inputs

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

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

戻り値の型:

numpy.ndarray (N x len(Xtest))

prepare(X, t, Psi=None)

initializes model by using the first training dataset

パラメータ:

• X (numpy.ndarray) -- inputs

• t (numpy.ndarray) -- target (label)

• Psi (numpy.ndarray) -- feature maps

参考

physbo.blm.inf.exact.prepare

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

draws samples of weights

パラメータ:

• 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)

戻り値:

samples of weights

戻り値の型:

numpy.ndarray

参考

physbo.blm.inf.exact.sampling

update_stats(x, t, psi=None)

updates model by using another training data

パラメータ:

• x (numpy.ndarray) -- input

• t (float) -- target (label)

• psi (numpy.ndarray) -- feature map

参考

physbo.blm.inf.exact.update_stats

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

ベースクラス: BasePredictor

Predictor using Baysean linear model

blm

Type:

physbo.blm.core.Model

config

configuration

Type:

physbo.misc.SetConfig

パラメータ:

• config (physbo.misc.SetConfig) -- configuration

• model (physbo.gp.core.Model)

参考

physbo.predictor.BasePredictor

delete_stats()

resets model

draw_post_sample_params(training, alpha=1.0, objective_index=0, rng=None)

draws one sample of the posterior weight parameters

Together with evaluate_post_sample, this splits Thompson sampling into a random "draw" part and a deterministic "evaluate" part. Under MPI, the sample can be drawn on rank 0, broadcast, and evaluated on all ranks so that every rank scores its candidates with the same posterior sample.

パラメータ:

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

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

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

• rng (rng object, optional) -- random number generator (default: global numpy.random state)

戻り値:

sampled weight parameters (nbasis,)

戻り値の型:

numpy.ndarray

evaluate_post_sample(w_hat, test, objective_index=0)

evaluates a posterior weight sample at test points (deterministic)

パラメータ:

• w_hat (numpy.ndarray) -- weight parameters drawn by draw_post_sample_params

• test (physbo.variable) -- inputs

• objective_index (int) -- Index of objective column to use when test.Z is 3D (default: 0)

戻り値:

sampled function values at the test points

戻り値の型:

numpy.ndarray

fit(training, num_basis=None, comm=None, objective_index=0, rng=None)

fit model to training dataset

パラメータ:

• training (physbo.variable) -- dataset for training

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

• comm (MPI.Comm) -- MPI communicator

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

get_basis(X)

calculates feature maps Psi(X)

パラメータ:

X (numpy.ndarray) -- inputs

戻り値:

Psi -- feature maps

戻り値の型:

numpy.ndarray

get_post_fcov(training, test, diag=True, objective_index=0)

calculates posterior variance-covariance matrix of model

パラメータ:

• 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.

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

戻り値:

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.

戻り値の型:

numpy.ndarray

get_post_fmean(training, test, objective_index=0)

calculates posterior mean value of model

パラメータ:

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

• test (physbo.variable) -- inputs

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

戻り値:

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

戻り値の型:

numpy.ndarray

get_post_params(training, test, objective_index=0)

calculates posterior weights

パラメータ:

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

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

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

戻り値の型:

numpy.ndarray

get_post_samples(training, test, N=1, alpha=1.0, objective_index=0, rng=None)

draws samples of mean values of model

パラメータ:

• 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)

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

戻り値の型:

numpy.ndarray

get_predict_samples(training, test, N=1, objective_index=0, rng=None)

draws samples of values of model

パラメータ:

• 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)

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

戻り値の型:

numpy.ndarray (N x len(test))

prepare(training, objective_index=0)

initializes model by using training data set

パラメータ:

• training (physbo.variable) -- dataset for training

• objective_index (int) -- Index of objective column to use when training.t is 2D (default: 0)

update(training, test, objective_index=0)

updates the model.

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

パラメータ:

• 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.

• objective_index (int) -- Index of objective column to use when training.t or test.t is 2D (default: 0)