aub_htp.machine_learning.regressor.AlphaStableLinearRegressor#

class aub_htp.machine_learning.regressor.AlphaStableLinearRegressor(alpha: float = 1.0, *, max_iter: int = 5000, tol: float = 1e-06, optimizer: str = 'Powell')#

Bases: RegressorMixin, BaseEstimator

Scikit-learn compatible Alpha-stable linear regression.

Given $\textbf{x}$ and $\textbf{y}$ as training data, where $\textbf{x}$ is a matrix of shape (n_samples, n_features) and $\textbf{y}$ is a matrix of shape (n_samples, n_targets), the objective is to find the weights $\textbf{w}$ and bias $b$ that minimizes the loss function:

\[\mathrm{arg\,min}_{\mathbf{w}, b} P_\alpha(y - (\mathbf{x}\mathbf{w}^T + b))^\alpha\]

__init__(alpha: float = 1.0, *, max_iter: int = 5000, tol: float = 1e-06, optimizer: str = 'Powell')#

Methods

`__init__`([alpha, max_iter, tol, optimizer])
`fit`(X, y)
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)
`score`(X, y[, sample_weight])	Return coefficient of determination on test data.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.

fit(X, y)#

predict(X)#

score(X, y, sample_weight=None)#

Return coefficient of determination on test data.

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_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → AlphaStableLinearRegressor#

Configure whether metadata should be requested to be passed to the score method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the 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.

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.