Struct PyBayesianRidge 

pub struct PyBayesianRidge { /* private fields */ }

Bayesian ridge regression.

Fit a Bayesian ridge model. See the Notes section for details on this implementation and the optimization of the regularization parameters lambda (precision of the weights) and alpha (precision of the noise).

Parameters

max_iter : int, default=300 Maximum number of iterations. Should be greater than or equal to 1.

tol : float, default=1e-3 Stop the algorithm if w has converged.

alpha_init : float, default=1.0 Initial value for alpha (precision of the weights). If not provided, alpha_init is set to 1.0.

lambda_init : float, default=1.0 Initial value for lambda (precision of the noise). If not provided, lambda_init is set to 1.0.

fit_intercept : bool, default=True Whether to calculate the intercept for this model. The intercept is not treated as a probabilistic parameter and thus has no associated variance. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

compute_score : bool, default=False If True, compute the log marginal likelihood at each iteration of the optimization.

copy_X : bool, default=True If True, X will be copied; else, it may be overwritten.

Attributes

coef_ : array-like of shape (n_features,) Coefficients of the regression model (mean of distribution)

intercept_ : float Independent term in decision function. Set to 0.0 if fit_intercept = False.

alpha_ : float Estimated precision of the weights.

lambda_ : float Estimated precision of the noise.

sigma_ : array-like of shape (n_features, n_features) Estimated variance-covariance matrix of the weights

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. The array starts with the value of the log marginal likelihood obtained for the initial values of alpha and lambda and ends with the value obtained for the estimated alpha and lambda.

n_iter_ : int The actual number of iterations to reach the stopping criterion.

n_features_in_ : int Number of features seen during fit.

Examples

from sklears_python import BayesianRidge import numpy as np X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])

y = 1 * x_0 + 2 * x_1 + 3

y = np.dot(X, [1, 2]) + 3 reg = BayesianRidge() reg.fit(X, y) BayesianRidge() reg.predict([[1, 0]]) array([4.]) reg.coef_ array([1., 2.])

Notes

There exist several strategies to perform Bayesian ridge regression. This implementation is based on the algorithm described in Appendix A of (Tipping, 2001) where updates of the regularization parameters are done as suggested in (MacKay, 1992). Note that according to A New View of Automatic Relevance Determination (Wipf and Nagarajan, 2008) these update rules do not guarantee that the marginal likelihood is increasing between two consecutive iterations of the optimization.

References

D. J. C. MacKay, Bayesian Interpolation, Computation and Neural Systems, Vol. 4, No. 3, 1992.

M. E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, Vol. 1, 2001.

Trait Implementations

impl<'py> IntoPyObject<'py> for PyBayesianRidge

type Target = PyBayesianRidge

The Python output type

type Output = Bound<'py, <PyBayesianRidge as IntoPyObject<'py>>::Target>

The smart pointer type to use.

type Error = PyErr

The type returned in the event of a conversion error.

fn into_pyobject( self, py: Python<'py>, ) -> Result<<Self as IntoPyObject<'_>>::Output, <Self as IntoPyObject<'_>>::Error>

Performs the conversion.

impl PyClass for PyBayesianRidge

type Frozen = False

Whether the pyclass is frozen.

impl PyClassImpl for PyBayesianRidge

const IS_BASETYPE: bool = false

#[pyclass(subclass)]

const IS_SUBCLASS: bool = false

#[pyclass(extends=…)]

const IS_MAPPING: bool = false

#[pyclass(mapping)]

const IS_SEQUENCE: bool = false

#[pyclass(sequence)]

const IS_IMMUTABLE_TYPE: bool = false

#[pyclass(immutable_type)]

type BaseType = PyAny

Base class

type ThreadChecker = SendablePyClass<PyBayesianRidge>

This handles following two situations:

type PyClassMutability = <<PyAny as PyClassBaseType>::PyClassMutability as PyClassMutability>::MutableChild

Immutable or mutable

type Dict = PyClassDummySlot

Specify this class has #[pyclass(dict)] or not.

type WeakRef = PyClassDummySlot

Specify this class has #[pyclass(weakref)] or not.

type BaseNativeType = PyAny

The closest native ancestor. This is PyAny by default, and when you declare #[pyclass(extends=PyDict)], it’s PyDict.

fn items_iter() -> PyClassItemsIter

fn doc(py: Python<'_>) -> PyResult<&'static CStr>

Rendered class doc

fn lazy_type_object() -> &'static LazyTypeObject<Self>

fn dict_offset() -> Option<isize>

fn weaklist_offset() -> Option<isize>

impl PyClassNewTextSignature<PyBayesianRidge> for PyClassImplCollector<PyBayesianRidge>

fn new_text_signature(self) -> Option<&'static str>

impl<'a, 'py> PyFunctionArgument<'a, 'py, false> for &'a PyBayesianRidge

type Holder = Option<PyRef<'py, PyBayesianRidge>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

impl<'a, 'py> PyFunctionArgument<'a, 'py, false> for &'a mut PyBayesianRidge

type Holder = Option<PyRefMut<'py, PyBayesianRidge>>

fn extract( obj: &'a Bound<'py, PyAny>, holder: &'a mut Self::Holder, ) -> PyResult<Self>

impl PyMethods<PyBayesianRidge> for PyClassImplCollector<PyBayesianRidge>

fn py_methods(self) -> &'static PyClassItems

impl PyTypeInfo for PyBayesianRidge

const NAME: &'static str = "BayesianRidge"

Class name.

const MODULE: Option<&'static str> = ::core::option::Option::None

Module name, if any.

fn type_object_raw(py: Python<'_>) -> *mut PyTypeObject

Returns the PyTypeObject instance for this type.

fn type_object(py: Python<'_>) -> Bound<'_, PyType>

Returns the safe abstraction over the type object.

fn is_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type or a subclass of this type.

fn is_exact_type_of(object: &Bound<'_, PyAny>) -> bool

Checks if object is an instance of this type.

impl DerefToPyAny for PyBayesianRidge