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Fix pyGAM to work with SkLearn GridSearchCV #267

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Fix pyGAM to work with SkLearn GridSearchCV #267

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31 changes: 21 additions & 10 deletions pygam/pygam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -97,6 +97,7 @@ class GAM(Core, MetaTermMixin):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs']` will be used.

distribution : str or Distribution object, optional
Distribution to use in the model.
Expand Down Expand Up @@ -149,14 +150,14 @@ class GAM(Core, MetaTermMixin):
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
distribution='normal', link='identity',
callbacks=['deviance', 'diffs'],
callbacks=None,
fit_intercept=True, verbose=False, **kwargs):

self.max_iter = max_iter
self.tol = tol
self.distribution = distribution
self.link = link
self.callbacks = callbacks
self.callbacks = callbacks or ['deviance', 'diffs']
self.verbose = verbose
self.terms = TermList(terms) if isinstance(terms, Term) else terms
self.fit_intercept = fit_intercept
Expand Down Expand Up @@ -2234,6 +2235,7 @@ class LinearGAM(GAM):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs']` will be used.

fit_intercept : bool, optional
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -2279,14 +2281,16 @@ class LinearGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
scale=None, callbacks=None,
fit_intercept=True, verbose=False, **kwargs):
callbacks = callbacks or ['deviance', 'diffs']
self.scale = scale
super(LinearGAM, self).__init__(terms=terms,
distribution=NormalDist(scale=self.scale),
link='identity',
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose,
**kwargs)
Expand Down Expand Up @@ -2354,6 +2358,7 @@ class LogisticGAM(GAM):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs', 'accuracy']` will be used.

fit_intercept : bool, optional
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -2399,9 +2404,9 @@ class LogisticGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
callbacks=['deviance', 'diffs', 'accuracy'],
callbacks=None,
fit_intercept=True, verbose=False, **kwargs):

self.callbacks = callbacks or ['deviance', 'diffs', 'accuracy']
# call super
super(LogisticGAM, self).__init__(terms=terms,
distribution='binomial',
Expand Down Expand Up @@ -2565,9 +2570,9 @@ class PoissonGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
callbacks=['deviance', 'diffs'],
callbacks=None,
fit_intercept=True, verbose=False, **kwargs):

callbacks = callbacks or ['deviance', 'diffs', 'accuracy']
# call super
super(PoissonGAM, self).__init__(terms=terms,
distribution='poisson',
Expand Down Expand Up @@ -2868,6 +2873,7 @@ class GammaGAM(GAM):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs']` will be used.

fit_intercept : bool, optional
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -2913,8 +2919,9 @@ class GammaGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
scale=None, callbacks=None,
fit_intercept=True, verbose=False, **kwargs):
callbacks = callbacks or ['deviance', 'diffs']
self.scale = scale
super(GammaGAM, self).__init__(terms=terms,
distribution=GammaDist(scale=self.scale),
Expand Down Expand Up @@ -2976,6 +2983,7 @@ class InvGaussGAM(GAM):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs']` will be used.

fit_intercept : bool, optional
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -3021,8 +3029,9 @@ class InvGaussGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
scale=None, callbacks=None,
fit_intercept=True, verbose=False, **kwargs):
callbacks = callbacks or ['deviance', 'diffs']
self.scale = scale
super(InvGaussGAM, self).__init__(terms=terms,
distribution=InvGaussDist(scale=self.scale),
Expand Down Expand Up @@ -3074,6 +3083,7 @@ class ExpectileGAM(GAM):

callbacks : list of str or list of CallBack objects, optional
Names of callback objects to call during the optimization loop.
By default `['deviance', 'diffs']` will be used.

fit_intercept : bool, optional
Specifies if a constant (a.k.a. bias or intercept) should be
Expand Down Expand Up @@ -3119,8 +3129,9 @@ class ExpectileGAM(GAM):
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
scale=None, callbacks=None,
fit_intercept=True, expectile=0.5, verbose=False, **kwargs):
callbacks = callbacks or ['deviance', 'diffs']
self.scale = scale
self.expectile = expectile
super(ExpectileGAM, self).__init__(terms=terms,
Expand Down