bart.bart#
Module: bart.bart#
Inheritance diagram for ISLP.bart.bart:
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,RegressorMixinParticle 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.
Methods
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
fitmethod.set_params(**params)Set the parameters of this estimator.
set_score_request(*[, sample_weight])Request metadata passed to the
scoremethod.fit
predict
staged_predict
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
- __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
MetadataRequestencapsulating 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), wheren_samples_fittedis 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
scoreon a regressor usesmultioutput='uniform_average'from version 0.23 to keep consistent with default value ofr2_score(). This influences thescoremethod of all the multioutput regressors (except forMultiOutputRegressor).
- set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') BART#
Request metadata passed to the
fitmethod.Note that this method is only relevant if
enable_metadata_routing=True(seesklearn.set_config()). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed tofitif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it tofit.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_weightparameter infit.
- 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
scoremethod.Note that this method is only relevant if
enable_metadata_routing=True(seesklearn.set_config()). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toscoreif provided. The request is ignored if metadata is not provided.False: metadata is not requested and the meta-estimator will not pass it toscore.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_weightparameter inscore.
- Returns:
- selfobject
The updated object.
- staged_predict(X, start_idx=0)#
SampleSplittingVariable#
- class ISLP.bart.bart.SampleSplittingVariable(alpha_prior, random_state)#
Bases:
objectMethods
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()#