bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD¶

class bayesvalidrox.surrogate_models.reg_fast_ard.RegressionFastARD(n_iter=300, start=None, tol=0.001, fit_intercept=True, normalize=False, copy_X=True, compute_score=False, verbose=False)¶

Bases: LinearModel, RegressorMixin

Regression with Automatic Relevance Determination (Fast Version uses Sparse Bayesian Learning) https://github.com/AmazaspShumik/sklearn-bayes/blob/master/skbayes/rvm_ard_models/fast_rvm.py

Parameters¶

n_iter: int, optional (DEFAULT = 100): Maximum number of iterations
start: list, optional (DEFAULT = None): Initial selected features.
tol: float, optional (DEFAULT = 1e-3): If absolute change in precision parameter for weights is below threshold algorithm terminates.
fit_interceptboolean, optional (DEFAULT = True): whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (e.g. data is expected to be already centered).
copy_Xboolean, optional (DEFAULT = True): If True, X will be copied; else, it may be overwritten.
compute_scorebool, default=False: If True, compute the log marginal likelihood at each iteration of the optimization.
verboseboolean, optional (DEFAULT = FALSE): Verbose mode when fitting the model

Attributes¶

coef_array, shape = (n_features): Coefficients of the regression model (mean of posterior distribution)
alpha_float: estimated precision of the noise
active_array, dtype = np.bool, shape = (n_features): True for non-zero coefficients, False otherwise
lambda_array, shape = (n_features): estimated precisions of the coefficients
sigma_array, shape = (n_features, n_features): estimated covariance matrix of the weights, computed only for non-zero coefficients
scores_array-like of shape (n_iter_+1,): If computed_score is True, value of the log marginal likelihood (to be maximized) at each iteration of the optimization.

References¶

[1] Fast marginal likelihood maximisation for sparse Bayesian models (Tipping & Faul 2003) (http://www.miketipping.com/papers/met-fastsbl.pdf) [2] Analysis of sparse Bayesian learning (Tipping & Faul 2001)

(http://www.miketipping.com/abstracts.htm#Faul:NIPS01)

__init__(n_iter=300, start=None, tol=0.001, fit_intercept=True, normalize=False, copy_X=True, compute_score=False, verbose=False)¶

Methods

`__init__`([n_iter, start, tol, ...])
`fit`(X, y)	Fits ARD Regression with Sequential Sparse Bayes Algorithm.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`log_marginal_like`(XXa, XYa, Aa, beta)	Computes the log of the marginal likelihood.
`predict`(X[, return_std])	Computes predictive distribution for test set.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, return_std])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

fit(X, y)¶

Fits ARD Regression with Sequential Sparse Bayes Algorithm.

Parameters¶

X: {array-like, sparse matrix} of size (n_samples, n_features): Training data, matrix of explanatory variables
y: array-like of size [n_samples, n_features]: Target values

Returns¶

selfobject: Returns self.

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.

log_marginal_like(XXa, XYa, Aa, beta)¶: Computes the log of the marginal likelihood.

predict(X, return_std=False)¶

Computes predictive distribution for test set. Predictive distribution for each data point is one dimensional Gaussian and therefore is characterised by mean and variance based on Ref.[1] Section 3.3.2.

Parameters¶

X: {array-like, sparse} (n_samples_test, n_features): Test data, matrix of explanatory variables

Returns¶

: list of length two [y_hat, var_hat]

y_hat: numpy array of size (n_samples_test,)

Estimated values of targets on test set (i.e. mean of predictive distribution)

var_hat: numpy array of size (n_samples_test,)
Variance of predictive distribution

References¶

[1] Bishop, C. M. (2006). Pattern recognition and machine learning. springer.

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_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_predict_request(*, return_std: bool | None | str = '$UNCHANGED$') → RegressionFastARD¶

Request metadata passed to the predict 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 predict 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 predict.
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¶

return_stdstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for return_std parameter in predict.

Returns¶

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → RegressionFastARD¶

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.