pcntoolkit.math_functions.basis_function

Classes

BasisFunction	Helper class that provides a standard way to create an ABC using
BsplineBasisFunction	Helper class that provides a standard way to create an ABC using
CompositeBasisFunction	Helper class that provides a standard way to create an ABC using
FractionalPolynomialBasisFunction	Fractional polynomial basis function for modelling smooth nonlinear
LinearBasisFunction	Helper class that provides a standard way to create an ABC using
PolynomialBasisFunction	Helper class that provides a standard way to create an ABC using

Functions

create_basis_function(→ BasisFunction)

Module Contents

class BasisFunction(basis_column: int = 0, **kwargs)

Bases: abc.ABC

Helper class that provides a standard way to create an ABC using inheritance.

fit(X: numpy.ndarray) → None

classmethod from_args(name: str, args: dict) → BasisFunction

classmethod from_dict(my_dict: dict, version: str | None = None) → BasisFunction

to_dict() → dict

transform(X: numpy.ndarray) → numpy.ndarray

basis_column = 0

basis_name: str

compute_max: bool

compute_min: bool

abstract property dimension

is_fitted: bool

max: float | None

min: float | None

class BsplineBasisFunction(basis_column: int = 0, degree: int = 3, nknots: int = 5, left_expand: float = 0.05, right_expand: float = 0.05, knot_method: str = 'uniform', knots: numpy.ndarray | list = None, **kwargs)

Bases: BasisFunction

Helper class that provides a standard way to create an ABC using inheritance.

to_dict() → dict

basis_name = 'bspline'

degree = 3

property dimension

knot_method = 'uniform'

left_expand = 0.05

nknots = 5

right_expand = 0.05

class CompositeBasisFunction(parts)

Bases: BasisFunction

Helper class that provides a standard way to create an ABC using inheritance.

fit(X)

to_dict()

transform(X)

property dimension

parts

class FractionalPolynomialBasisFunction(basis_column: int = 0, order: int = 3, powers: list | tuple | str | None = 'default', power_set: list | tuple | None = None, eps: float = 1e-08, **kwargs)

Bases: BasisFunction

Fractional polynomial basis function for modelling smooth nonlinear effects.

The input must be strictly positive (do not standardize the covariates). Power convention:

p = 0 -> log(x) p != 0 -> x**p

Repeated powers:

[p, p, p] -> x**p, x**p * log(x), x**p * log(x)**2

Initialise the fractional polynomial basis function.

Parameters:

• basis_column (int, default 0) – Column index to transform.

• order (int, default 3) – Fractional polynomial order. Must be 1, 2, or 3.

• powers (list, tuple, str, or None, default "default")

• power_set (list, tuple, or None, default None) – Allowed fractional polynomial powers.

• eps (float, default 1e-8) – Numerical stability constant.

to_dict() → dict

Serialize the basis function configuration.

AGE_FP_POWER_PRESETS

DEFAULT_POWER_SET

basis_name = 'fractional_polynomial'

property dimension: int

Number of generated basis columns.

eps

order = 3

power_set

class LinearBasisFunction(basis_column: int = 0, **kwargs)

Bases: BasisFunction

Helper class that provides a standard way to create an ABC using inheritance.

basis_name = 'linear'

property dimension

class PolynomialBasisFunction(basis_column: int = 0, degree: int = 3, **kwargs)

Bases: BasisFunction

Helper class that provides a standard way to create an ABC using inheritance.

basis_name = 'poly'

degree = 3

property dimension

create_basis_function(basis_type: str | dict | None, basis_column: int = 0, **kwargs) → BasisFunction