concrete linear reg

skpro/regression/linear/__init__.py

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+"""Linear regression models."""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+
+from skpro.regression.linear._sklearn import (
+    ARDRegression,
+    BayesianRidge,
+)
+
+__all__ = [
+    "ARDRegression",
+    "BayesianRidge",
+]

skpro/regression/linear/_sklearn.py

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+"""Adapters to sklearnn linear regressors with probabilistic components."""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+# based on sktime pipelines
+
+__author__ = ["fkiraly"]
+
+from skpro.regression.adapters.sklearn import SklearnProbaReg
+from skpro.regression.base._delegate import _DelegatedProbaRegressor
+
+
+class _DelegateWithFittedParamForwarding(_DelegatedProbaRegressor):
+    """Common base class for delegates with attribute forwarding.
+
+    Assumes that delegate has an attribute `estimator_`,
+    from which fitted attributes are forwarded to self.
+    """
+    # attribute for _DelegatedProbaRegressor, which then delegates
+    #     all non-overridden methods are same as of getattr(self, _delegate_name)
+    #     see further details in _DelegatedRegressor docstring
+    _delegate_name = "_estimator"
+    # _estimator, not estimator_, because we do not want to expose it as
+    # fitted params - fitted params are instead forwarded
+
+    def _fit(self, X, y):
+        """Fit regressor to training data.
+
+        Writes to self:
+            Sets fitted model attributes ending in "_".
+
+        Parameters
+        ----------
+        X : pandas DataFrame
+            feature instances to fit regressor to
+        y : pandas DataFrame, must be same length as X
+            labels to fit regressor to
+
+        Returns
+        -------
+        self : reference to self
+        """
+        estimator = self._get_delegate()
+        estimator.fit(X=X, y=y)
+
+        for attr in self.FITTED_PARAMS_TO_FORWARD:
+            setattr(self, attr, getattr(estimator.estimator_, attr))
+
+        return self
+
+
+class ARDRegression(_DelegateWithFittedParamForwarding):
+    """ARD regression, direct adapter to sklearn ARDRegression.
+
+    Fit the weights of a regression model, using an ARD prior. The weights of
+    the regression model are assumed to be in Gaussian distributions.
+    Also estimate the parameters lambda (precisions of the distributions of the
+    weights) and alpha (precision of the distribution of the noise).
+    The estimation is done by an iterative procedures (Evidence Maximization)
+
+    Read more in the :ref:`User Guide <bayesian_regression>`.
+
+    Parameters
+    ----------
+    max_iter : int, default=None
+        Maximum number of iterations. If `None`, it corresponds to `max_iter=300`.
+
+    tol : float, default=1e-3
+        Stop the algorithm if w has converged.
+
+    alpha_1 : float, default=1e-6
+        Hyper-parameter : shape parameter for the Gamma distribution prior
+        over the alpha parameter.
+
+    alpha_2 : float, default=1e-6
+        Hyper-parameter : inverse scale parameter (rate parameter) for the
+        Gamma distribution prior over the alpha parameter.
+
+    lambda_1 : float, default=1e-6
+        Hyper-parameter : shape parameter for the Gamma distribution prior
+        over the lambda parameter.
+
+    lambda_2 : float, default=1e-6
+        Hyper-parameter : inverse scale parameter (rate parameter) for the
+        Gamma distribution prior over the lambda parameter.
+
+    compute_score : bool, default=False
+        If True, compute the objective function at each step of the model.
+
+    threshold_lambda : float, default=10 000
+        Threshold for removing (pruning) weights with high precision from
+        the computation.
+
+    fit_intercept : bool, default=True
+        Whether to calculate the intercept for this model. If set
+        to false, no intercept will be used in calculations
+        (i.e. data is expected to be centered).
+
+    copy_X : bool, default=True
+        If True, X will be copied; else, it may be overwritten.
+
+    verbose : bool, default=False
+        Verbose mode when fitting the model.
+
+    Attributes
+    ----------
+    coef_ : array-like of shape (n_features,)
+        Coefficients of the regression model (mean of distribution)
+
+    alpha_ : float
+       estimated precision of the noise.
+
+    lambda_ : array-like of shape (n_features,)
+       estimated precisions of the weights.
+
+    sigma_ : array-like of shape (n_features, n_features)
+        estimated variance-covariance matrix of the weights
+
+    scores_ : float
+        if computed, value of the objective function (to be maximized)
+
+    n_iter_ : int
+        The actual number of iterations to reach the stopping criterion.
+
+    intercept_ : float
+        Independent term in decision function. Set to 0.0 if
+        ``fit_intercept = False``.
+
+    X_offset_ : float
+        If `fit_intercept=True`, offset subtracted for centering data to a
+        zero mean. Set to np.zeros(n_features) otherwise.
+    """
+
+    def __init__(
+        self,
+        max_iter=None,
+        tol=1e-3,
+        alpha_1=1e-6,
+        alpha_2=1e-6,
+        lambda_1=1e-6,
+        lambda_2=1e-6,
+        compute_score=False,
+        threshold_lambda=10000.0,
+        fit_intercept=True,
+        copy_X=True,
+        verbose=False,
+    ):
+        from sklearn.linear_model import ARDRegression
+
+        skl_estimator=ARDRegression(
+            max_iter=max_iter,
+            tol=tol,
+            alpha_1=alpha_1,
+            alpha_2=alpha_2,
+            lambda_1=lambda_1,
+            lambda_2=lambda_2,
+            compute_score=compute_score,
+            threshold_lambda=threshold_lambda,
+            fit_intercept=fit_intercept,
+            copy_X=copy_X,
+            verbose=verbose,
+        )
+
+        skpro_est = SklearnProbaReg(skl_estimator)
+        self._estimator = skpro_est.clone()
+
+        super().__init__()
+
+    FITTED_PARAMS_TO_FORWARD = [
+        "coef_",
+        "alpha_",
+        "lambda_",
+        "sigma_",
+        "scores_",
+        "n_iter_",
+        "intercept_",
+        "X_offset_",
+    ]
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        param1 = {}
+        param2 = {
+            "max_iter": 300,
+            "tol": 2e-3,
+            "alpha_1": 2e-6,
+            "alpha_2": 2e-6,
+            "lambda_1": 2e-6,
+            "lambda_2": 2e-6,
+            "compute_score": True,
+            "threshold_lambda": 15000.0,
+            "fit_intercept": False,
+        }
+        return [param1, param2]
+
+
+class BayesianRidge(_DelegateWithFittedParamForwarding):
+    """Bayesian ridge regression, direct adapter to sklearn BayesianRidge.
+
+    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).
+
+    Read more in the :ref:`User Guide <bayesian_regression>`.
+
+    Parameters
+    ----------
+    max_iter : int, default=None
+        Maximum number of iterations over the complete dataset before
+        stopping independently of any early stopping criterion. If `None`, it
+        corresponds to `max_iter=300`.
+
+    tol : float, default=1e-3
+        Stop the algorithm if w has converged.
+
+    alpha_1 : float, default=1e-6
+        Hyper-parameter : shape parameter for the Gamma distribution prior
+        over the alpha parameter.
+
+    alpha_2 : float, default=1e-6
+        Hyper-parameter : inverse scale parameter (rate parameter) for the
+        Gamma distribution prior over the alpha parameter.
+
+    lambda_1 : float, default=1e-6
+        Hyper-parameter : shape parameter for the Gamma distribution prior
+        over the lambda parameter.
+
+    lambda_2 : float, default=1e-6
+        Hyper-parameter : inverse scale parameter (rate parameter) for the
+        Gamma distribution prior over the lambda parameter.
+
+    alpha_init : float, default=None
+        Initial value for alpha (precision of the noise).
+        If not set, alpha_init is 1/Var(y).
+
+    lambda_init : float, default=None
+        Initial value for lambda (precision of the weights).
+        If not set, lambda_init is 1.
+
+    compute_score : bool, default=False
+        If True, compute the log marginal likelihood at each iteration of the
+        optimization.
+
+    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).
+
+    copy_X : bool, default=True
+        If True, X will be copied; else, it may be overwritten.
+
+    verbose : bool, default=False
+        Verbose mode when fitting the model.
+
+    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 noise.
+
+    lambda_ : float
+       Estimated precision of the weights.
+
+    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.
+
+    X_offset_ : ndarray of shape (n_features,)
+        If `fit_intercept=True`, offset subtracted for centering data to a
+        zero mean. Set to np.zeros(n_features) otherwise.
+    """
+
+    def __init__(
+        self,
+        max_iter=None,
+        tol=1e-3,
+        alpha_1=1e-6,
+        alpha_2=1e-6,
+        lambda_1=1e-6,
+        lambda_2=1e-6,
+        alpha_init=None,
+        lambda_init=None,
+        compute_score=False,
+        fit_intercept=True,
+        copy_X=True,
+        verbose=False,
+    ):
+
+        from sklearn.linear_model import BayesianRidge
+
+        skl_estimator = BayesianRidge(
+            max_iter=max_iter,
+            tol=tol,
+            alpha_1=alpha_1,
+            alpha_2=alpha_2,
+            lambda_1=lambda_1,
+            lambda_2=lambda_2,
+            alpha_init=alpha_init,
+            lambda_init=lambda_init,
+            compute_score=compute_score,
+            fit_intercept=fit_intercept,
+            copy_X=copy_X,
+            verbose=verbose,
+        )
+
+        skpro_est = SklearnProbaReg(skl_estimator)
+        self._estimator = skpro_est.clone()
+
+        super().__init__()
+
+    FITTED_PARAMS_TO_FORWARD = [
+        "coef_",
+        "alpha_",
+        "lambda_",
+        "sigma_",
+        "scores_",
+        "n_iter_",
+        "intercept_",
+        "X_offset_",
+    ]
+
+    @classmethod
+    def get_test_params(cls, parameter_set="default"):
+        """Return testing parameter settings for the estimator.
+
+        Parameters
+        ----------
+        parameter_set : str, default="default"
+            Name of the set of test parameters to return, for use in tests. If no
+            special parameters are defined for a value, will return `"default"` set.
+
+        Returns
+        -------
+        params : dict or list of dict, default = {}
+            Parameters to create testing instances of the class
+            Each dict are parameters to construct an "interesting" test instance, i.e.,
+            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            `create_test_instance` uses the first (or only) dictionary in `params`
+        """
+        param1 = {}
+        param2 = {
+            "max_iter": 300,
+            "tol": 2e-3,
+            "alpha_1": 2e-6,
+            "alpha_2": 2e-6,
+            "lambda_1": 2e-6,
+            "lambda_2": 2e-6,
+            "compute_score": True,
+            "threshold_lambda": 15000.0,
+            "fit_intercept": False,
+        }
+        return [param1, param2]