Intercept

class pygam.terms.Intercept(verbose=False)

Bases: Term

Creates an instance of an Intercept term.

Parameters:

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