User API¶
Generalized Additive Model Classes¶
Terms¶
Linear Term¶

pygam.terms.
l
(feature, lam=0.6, penalties='auto', verbose=False)¶ creates an instance of a LinearTerm
 feature : int
 Index of the feature to use for the feature function.
 lam : float 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.
 n_coefs : int
 Number of coefficients contributed by the term to the model
 istensor : bool
 whether the term is a tensor product of subterms
 isintercept : bool
 whether the term is an intercept
 hasconstraint : bool
 whether the term has any constraints
 info : dict
 contains dict with the sufficient information to duplicate the term
See also
LinearTerm()
 for developer details
Spline Term¶

pygam.terms.
s
(feature, n_splines=20, spline_order=3, lam=0.6, penalties='auto', constraints=None, dtype='numerical', basis='ps', by=None, edge_knots=None, verbose=False)¶ creates an instance of a SplineTerm
 feature : int
 Index of the feature to use for the feature function.
 n_splines : int
 Number of splines to use for the feature function. Must be nonnegative.
 spline_order : int
 Order of spline to use for the feature function. Must be nonnegative.
 lam : float 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.
 constraints : {None, ‘convex’, ‘concave’, ‘monotonic_inc’, ‘monotonic_dec’}
or callable or iterable
Type of constraint to apply to the term.
If an iterable is used, multiple penalties are applied to the term.
 dtype : {‘numerical’, ‘categorical’}
 String describing the datatype of the feature.
 basis : {‘ps’, ‘cp’}
Type of basis function to use in the term.
‘ps’ : pspline basis
 ‘cp’ : cyclic pspline basis, useful for building periodic functions.
by default, the maximum and minimum of the feature values are used to determine the function’s period.
to specify a custom period use argument edge_knots
edge_knots : optional, arraylike of floats of length 2
these values specify minimum and maximum domain of the spline function.
in the case that spline_basis=”cp”, edge_knots determines the period of the cyclic function.
when edge_knots=None these values are inferred from the data.
default: None
 by : int, optional
Feature to use as a byvariable in the term.
For example, if feature = 2 by = 0, then the term will produce: x0 * f(x2)
 n_coefs : int
 Number of coefficients contributed by the term to the model
 istensor : bool
 whether the term is a tensor product of subterms
 isintercept : bool
 whether the term is an intercept
 hasconstraint : bool
 whether the term has any constraints
 info : dict
 contains dict with the sufficient information to duplicate the term
See also
SplineTerm()
 for developer details
Factor Term¶

pygam.terms.
f
(feature, lam=0.6, penalties='auto', coding='onehot', verbose=False)¶ creates an instance of a FactorTerm
 feature : int
 Index of the feature to use for the feature function.
 lam : float 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.
 coding : {‘onehot’} type of contrast encoding to use.
 currently, only ‘onehot’ encoding has been developed. this means that we fit one coefficient per category.
 n_coefs : int
 Number of coefficients contributed by the term to the model
 istensor : bool
 whether the term is a tensor product of subterms
 isintercept : bool
 whether the term is an intercept
 hasconstraint : bool
 whether the term has any constraints
 info : dict
 contains dict with the sufficient information to duplicate the term
See also
FactorTerm()
 for developer details
Tensor Term¶

pygam.terms.
te
(*args, **kwargs)¶ creates an instance of a TensorTerm
This is useful for creating interactions between features, or other terms.
*args : marginal Terms to combine into a tensor product
 feature : list of integers
 Indices of the features to use for the marginal terms.
 n_splines : list of integers
 Number of splines to use for each marginal term. Must be of same length as feature.
 spline_order : list of integers
 Order of spline to use for the feature function. Must be of same length as feature.
 lam : float 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.
 constraints : {None, ‘convex’, ‘concave’, ‘monotonic_inc’, ‘monotonic_dec’}
or callable or iterable
Type of constraint to apply to the term.
If an iterable is used, multiple penalties are applied to the term.
 dtype : list of {‘numerical’, ‘categorical’}
String describing the datatype of the feature.
Must be of same length as feature.
 basis : list of {‘ps’}
Type of basis function to use in the term.
‘ps’ : pspline basis
NotImplemented: ‘cp’ : cyclic pspline basis
Must be of same length as feature.
 by : int, optional
Feature to use as a byvariable in the term.
For example, if feature = [1, 2] by = 0, then the term will produce: x0 * te(x1, x2)
 n_coefs : int
 Number of coefficients contributed by the term to the model
 istensor : bool
 whether the term is a tensor product of subterms
 isintercept : bool
 whether the term is an intercept
 hasconstraint : bool
 whether the term has any constraints
 info : dict
 contains dict with the sufficient information to duplicate the term
See also
TensorTerm()
 for developer details