derivkit.local_polynomial_derivative.local_poly_config module

Configuration for the local polynomial regression baseline.

This config controls how LocalPolynomialDerivative chooses sample locations, fits the local polynomial, and decides whether the fit is trustworthy enough to mark ok=True in diagnostics.

class derivkit.local_polynomial_derivative.local_poly_config.LocalPolyConfig(rel_steps=(0.01, 0.02, 0.04, 0.08), tol_rel: float = 0.01, tol_abs: float = 1e-10, min_samples: int = 9, max_trim: int = 10, max_degree: int = 7, center: bool = True)

Bases: object

Configuration for the local polynomial regression baseline.

This config controls how LocalPolynomialDerivative chooses sample locations, fits the local polynomial, and decides whether the fit is trustworthy enough to mark ok=True in diagnostics.

Initialize configuration.

Parameters:

  • rel_steps –

    Symmetric relative offsets around x0 used to build the default sample grid.

    • For x0 != 0 the grid is x = x0 * (1 ± rel_steps[i]).

    • For x0 == 0 the grid is x = ± rel_steps[i].

    Values are deduplicated and sorted; must be a non-empty 1D sequence.

  • tol_rel – Relative residual tolerance used when deciding whether a sample row is consistent with the current polynomial fit. A row is flagged as “bad” if any component satisfies |y_fit - y| / max(|y|, tol_abs) > tol_rel. Lower values make trimming more aggressive.

  • tol_abs – Absolute floor in the residual normalization. Prevents division by very small |y| when computing relative errors. Used as denom = max(|y|, tol_abs).

  • min_samples – Minimum number of sample points that must remain after trimming for a fit to be considered. Also used (together with max_degree) to ensure the system is not underdetermined. If trimming would reduce the usable samples below this threshold, trimming stops.

  • max_trim – Maximum number of trimming iterations. Each iteration may remove at most one point from each edge of the grid. Acts as a safety bound to avoid pathological loops on extremely noisy or adversarial data.

  • max_degree – Maximum polynomial degree allowed for the local fit. The actual degree used in differentiate() is min(max_degree, chosen_degree) where chosen_degree is usually max(order + 2, 3) or an explicit degree= passed by the caller.

  • center – If True, the polynomial is expressed in powers of (x - x0); derivatives at x0 are then read off as k! * a_k. If False, the polynomial is in powers of x directly. Centering generally improves numerical stability and is recommended.