`statspai.selection`¶

selection ¶

Variable selection tools.

stepwise: Stepwise regression with AIC/BIC/p-value criteria
lasso_select: LASSO-based variable selection with coordinate descent

SelectionResult `dataclass` ¶

Bases: ResultProtocolMixin

Result container for variable selection procedures.

Attributes:

Name	Type	Description
`selected`	`list[str]`	Variable names retained in the final model.
`dropped`	`list[str]`	Variable names excluded from the final model.
`history`	`DataFrame`	Step-by-step log of the selection procedure.
`final_model`	`dict`	Summary statistics for the final model (R², adj-R², AIC, BIC, n, k).
`method`	`str`	Selection method used (`"forward"`, `"backward"`, `"both"`, `"lasso_cv"`, `"lasso_bic"`, `"lasso_aic"`).
`coefficients`	`dict \| None`	Coefficient estimates from the final model (name -> value).
`lasso_path`	`dict \| None`	For LASSO: `{"lambdas": array, "coef_paths": dict}` storing the regularisation path.

Examples:

>>> import statspai as sp
>>> df = sp.cps_wage()
>>> res = sp.stepwise(df, y="log_wage",
...                   x=["female", "education", "experience"],
...                   method="forward", criterion="aic", verbose=False)
>>> isinstance(res, sp.SelectionResult)
True
>>> sorted(res.final_model.keys())
['adj_r_squared', 'aic', 'bic', 'k', 'n', 'r_squared']

summary ¶

summary() -> str

Return a human-readable summary and print it.

plot ¶

plot(figsize: Tuple[float, float] = (10, 6)) -> Tuple[Any, Any]

Plot selection diagnostics.

For stepwise: criterion value at each step. For LASSO: coefficient path across lambda values.

Returns:

Type	Description
`(fig, ax) : matplotlib Figure and Axes`

lasso_select ¶

lasso_select(data: DataFrame, y: str, x: list[str], method: Literal['cv', 'bic', 'aic'] = 'cv', n_folds: int = 10, n_lambda: int = 100, eps: float = 0.001, max_iter: int = 1000, tol: float = 1e-06, verbose: bool = True, seed: int = 42) -> SelectionResult

LASSO-based variable selection.

Solves min (1/2n)||y - Xβ||² + λ||β||₁ via coordinate descent. Selects λ by K-fold cross-validation, BIC, or AIC.

Parameters:

Name	Type	Description	Default
`data`	`DataFrame`		required
`y`	`str`	Dependent variable column.	required
`x`	`list[str]`	Candidate independent variables.	required
`method`	`('cv', 'bic', 'aic')`	How to choose the regularisation parameter λ.	`"cv"`
`n_folds`	`int`	Number of cross-validation folds (only for `method="cv"`).	`10`
`n_lambda`	`int`	Number of λ values in the grid.	`100`
`eps`	`float`	Ratio of lambda_min / lambda_max.	`0.001`
`max_iter`	`int`	Maximum coordinate descent iterations per λ.	`1000`
`tol`	`float`	Convergence tolerance.	`1e-06`
`verbose`	`bool`	Print progress.	`True`
`seed`	`int`	Random seed for CV fold assignment.	`42`