LinearTerm

class pygam.terms.LinearTerm(feature, lam=0.6, penalties='auto', verbose=False)

Bases: Term

Creates an instance of a LinearTerm.

Parameters:

featureint

Index of the feature to use for the feature function.

lamfloat or iterable of floats

Strength of smoothing penalty. Must be a positive float. Larger values enforce stronger smoothing.

If single value is passed, it will be repeated for every penalty.

If iterable is passed, the length of lam must be equal to the length of penalties

penalties{‘auto’, ‘derivative’, ‘l2’, None} or callable or iterable

Type of smoothing penalty to apply to the term.

If an iterable is used, multiple penalties are applied to the term. The length of the iterable must match the length of lam.

If ‘auto’, then 2nd derivative smoothing for ‘numerical’ dtypes, and L2/ridge smoothing for ‘categorical’ dtypes.

Custom penalties can be passed as a callable.

Attributes:

n_coefsint

Number of coefficients contributed by the term to the model.

istensorbool

whether the term is a tensor product of sub-terms

isinterceptbool

whether the term is an intercept

hasconstraintbool

bool, whether the term has any constraints.

infodict

Get information about this term.

Methods

build_columns(X[, verbose])	Construct the model matrix columns for the term.
build_constraints(coef, constraint_lam, ...)	Builds the GAM block-diagonal constraint matrix in quadratic form out of constraint matrices specified for each feature.
build_from_info(info)	Build a Term instance from a dict.
build_penalties([verbose])	Builds the GAM block-diagonal penalty matrix in quadratic form out of penalty matrices specified for each feature.
compile(X[, verbose])	Method to validate and prepare data-dependent parameters.
get_params([deep])	Returns a dict of all of the object's user-facing parameters.
set_params([deep, force])	Sets an object's parameters.

build_columns(X, verbose=False)

Construct the model matrix columns for the term.

Parameters:

Xarray-like

Input dataset with n rows

verbosebool

whether to show warnings

Returns:

scipy sparse array with n rows

build_constraints(coef, constraint_lam, constraint_l2)

Builds the GAM block-diagonal constraint matrix in quadratic form out of constraint matrices specified for each feature.

behaves like a penalty, but with a very large lambda value, ie 1e6.

Parameters:

coefsarray-like containing the coefficients of a term

constraint_lamfloat,

penalty to impose on the constraint.

typically this is a very large number.

constraint_l2float,

loading to improve the numerical conditioning of the constraint matrix.

typically this is a very small number.

Returns:

Csparse CSC matrix containing the model constraints in quadratic form

classmethod build_from_info(info)

Build a Term instance from a dict.

Parameters:

clsclass

infodict

contains all information needed to build the term

build_penalties(verbose=False)

Builds the GAM block-diagonal penalty matrix in quadratic form out of penalty matrices specified for each feature.

each feature penalty matrix is multiplied by a lambda for that feature.

so for m features: P = block_diag[lam0 * P0, lam1 * P1, lam2 * P2, … , lamm * Pm]

Parameters:

None

Returns:

Psparse CSC matrix containing the model penalties in quadratic form

compile(X, verbose=False)

Method to validate and prepare data-dependent parameters.

Parameters:

Xarray-like

Input dataset

verbosebool

whether to show warnings

Returns:

None

get_params(deep=False)

Returns a dict of all of the object’s user-facing parameters.

Parameters:

deepboolean, default: False

when True, also gets non-user-facing parameters

Returns:

dict

set_params(deep=False, force=False, **parameters)

Sets an object’s parameters.

Parameters:

deepboolean, default: False

when True, also sets non-user-facing parameters

forceboolean, default: False

when True, also sets parameters that the object does not already have

**parametersparameters to set

Returns:

self