bart.bart#

Module: bart.bart#

Inheritance diagram for ISLP.bart.bart:

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Classes#

BART#

class ISLP.bart.bart.BART(num_trees=200, num_particles=10, max_stages=5000, split_prob=<function BART.<lambda>>, min_depth=0, std_scale=2, split_prior=None, ndraw=10, burnin=100, sigma_prior=(5, 0.9), num_quantile=50, random_state=None, n_jobs=-1)#

Bases: BaseEnsemble, RegressorMixin

Particle Gibbs BART sampling step

Parameters:
num_particlesint

Number of particles for the conditional SMC sampler. Defaults to 10

max_stagesint

Maximum number of iterations of the conditional SMC sampler. Defaults to 100.

Notes

This sampler is inspired by the [Lakshminarayanan2015] Particle Gibbs sampler, but introduces several changes. The changes will be properly documented soon.

References

[Lakshminarayanan2015]

Lakshminarayanan, B. and Roy, D.M. and Teh, Y. W., (2015), Particle Gibbs for Bayesian Additive Regression Trees. ArviX, link

Methods

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

init_particles(base_particle, sigmasq, resid)

Initialize particles

score(X, y[, sample_weight])

Return the coefficient of determination of the prediction.

set_fit_request(*[, sample_weight])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, sample_weight])

Request metadata passed to the score method.

fit

predict

staged_predict

__init__(num_trees=200, num_particles=10, max_stages=5000, split_prob=<function BART.<lambda>>, min_depth=0, std_scale=2, split_prior=None, ndraw=10, burnin=100, sigma_prior=(5, 0.9), num_quantile=50, random_state=None, n_jobs=-1)#
fit(X, Y, sample_weight=None)#
get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

init_particles(base_particle: ParticleTree, sigmasq: float, resid: ndarray) ndarray#

Initialize particles

predict(X)#
score(X, y, sample_weight=None)#

Return the coefficient of determination of the prediction.

The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)** 2).sum() and \(v\) is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.

Parameters:
Xarray-like of shape (n_samples, n_features)

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

yarray-like of shape (n_samples,) or (n_samples, n_outputs)

True values for X.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

\(R^2\) of self.predict(X) w.r.t. y.

Notes

The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') BART#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') BART#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for sample_weight parameter in score.

Returns:
selfobject

The updated object.

staged_predict(X, start_idx=0)#

SampleSplittingVariable#

class ISLP.bart.bart.SampleSplittingVariable(alpha_prior, random_state)#

Bases: object

Methods

rvs

__init__(alpha_prior, random_state)#

Sample splitting variables proportional to alpha_prior.

This is equivalent as sampling weights from a Dirichlet distribution with alpha_prior parameter and then using those weights to sample from the available spliting variables. This enforce sparsity.

rvs()#