sparsely.tune

module documentation

(source)

This module implements a function to tune the sparsity parameter of a linear model using cross-validation.

Function	`tune_estimator`	Tune the sparsity parameter (i.e. number of non-zero coefficients) of a linear model.
Type Alias	`Estimator`	Undocumented

def tune_estimator(X: np.ndarray, y: np.ndarray, estimator: Estimator, k_min: int = 1, k_max: int = None, step_size: int = 1, max_iters_no_improvement: Optional[int] = None, cv: int = 3, return_search_log: bool = False, show_progress_bar: bool = False) -> Union[Estimator, tuple[Estimator, pd.DataFrame]]: (source) ¶

Tune the sparsity parameter (i.e. number of non-zero coefficients) of a linear model.

The sparsity parameter is tuned by performing a grid search over the range [k_min, k_max] with step size step_size. If the test score does not improve for max_iters_no_improvement iterations, then the search is terminated early.

Parameters
X:`np.ndarray`	The training data. The array should be of shape (n_samples, n_features)
y:`np.ndarray`	The training labels. The array should be of shape (n_samples,).
estimator:`Estimator`	The estimator to tune. This must be a `SparseLinearRegressor` instance (for regression problems) or a `SparseLinearClassifier` instance (for classification problems).
k_min:`int`	The minimum value for the sparsity parameter (i.e. number of non-zero coefficients).
k_max:`int`	The maximum sparsity for the sparsity parameter (i.e. number of non-zero coefficients). If `None`, then this is set to `n_features`.
step_size:`int`	The step size for the search. The sparsity parameter is incremented by this value at each iteration. Must be less than or equal to `k_max - k_min`.
max_iters_no_improvement:`Optional[int]`	The maximum number of iterations without improvement in the CV test score before the search is terminated. If `None`, then no early stopping is performed.
cv:`int`	The number of cross-validation folds.
return_search_log:`bool`	Whether to return the search log.
show_progress_bar:`bool`	Whether to show a progress bar.
Returns
`Union[Estimator, tuple[Estimator, pd.DataFrame]]`	The tuned estimator. If `return_search_log` is `True`, then a tuple of the tuned estimator and the search log.

Estimator = (source) ¶

Undocumented

Value

Union[SparseLinearRegressor, SparseLinearClassifier]