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Merge pull request #199 from dswah/easy-plural
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term are easier to broadcast
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@dswah
dswah authored Sep 13, 2018
2 parents 05d7fac + 1092e06 commit 75701bc
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2 changes: 1 addition & 1 deletion pygam/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,4 +21,4 @@
__all__ = ['GAM', 'LinearGAM', 'LogisticGAM', 'GammaGAM', 'PoissonGAM',
'InvGaussGAM', 'ExpectileGAM', 'l', 's', 'f', 'te', 'intercept']

__version__ = '0.6.0'
__version__ = '0.6.1'
175 changes: 65 additions & 110 deletions pygam/pygam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -104,14 +104,6 @@ class GAM(Core, MetaTermMixin):
link : str or Link object, default: 'identity'
Link function to use in the model.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -156,10 +148,10 @@ class GAM(Core, MetaTermMixin):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
distribution='normal', link='identity',
callbacks=['deviance', 'diffs'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):

self.max_iter = max_iter
self.tol = tol
Expand All @@ -169,32 +161,36 @@ def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
self.verbose = verbose
self.terms = TermList(terms) if isinstance(terms, Term) else terms
self.fit_intercept = fit_intercept
self.lam = lam

for k, v in kwargs.items():
if k not in self._plural:
raise TypeError('__init__() got an unexpected keyword argument {}'.format(k))
setattr(self, k, v)

# internal settings
self._constraint_lam = 1e9 # regularization intensity for constraints
self._constraint_l2 = 1e-3 # diagononal loading to improve conditioning
self._constraint_l2_max = 1e-1 # maximum loading
# self._opt = 0 # use 0 for numerically stable optimizer, 1 for naive
self._term_location = 'terms' # for locating sub terms
self._include = ['lam']
# self._include = ['lam']

# call super and exclude any variables
super(GAM, self).__init__()

@property
def lam(self):
if self._has_terms():
return self.terms.lam
else:
return self._lam

@lam.setter
def lam(self, value):
if self._has_terms():
self.terms.lam = value
else:
self._lam = value
# @property
# def lam(self):
# if self._has_terms():
# return self.terms.lam
# else:
# return self._lam
#
# @lam.setter
# def lam(self, value):
# if self._has_terms():
# self.terms.lam = value
# else:
# self._lam = value

@property
def _is_fitted(self):
Expand Down Expand Up @@ -281,7 +277,6 @@ def _validate_data_dep_params(self, X):
if self.terms is 'auto':
# one numerical spline per feature
self.terms = TermList(*[SplineTerm(feat, verbose=self.verbose) for feat in range(m_features)])
self.terms.lam = self._lam

elif self.terms is None:
# no terms
Expand All @@ -298,6 +293,15 @@ def _validate_data_dep_params(self, X):
if len(self.terms) == 0:
raise ValueError('At least 1 term must be specified')

# copy over things from plural
remove = []
for k, v in self.__dict__.items():
if k in self._plural:
setattr(self.terms, k, v)
remove.append(k)
for k in remove:
delattr(self, k)

self.terms.compile(X)

def loglikelihood(self, X, y, weights=None):
Expand Down Expand Up @@ -1709,6 +1713,7 @@ def gridsearch(self, X, y, weights=None, return_scores=False,
# check if model fitted
if not self._is_fitted:
self._validate_params()
self._validate_data_dep_params(X)

y = check_y(y, self.link, self.distribution, verbose=self.verbose)
X = check_X(X, verbose=self.verbose)
Expand Down Expand Up @@ -1801,23 +1806,21 @@ def gridsearch(self, X, y, weights=None, return_scores=False,

# loop through candidate model params
for param_grid in pbar(param_grid_list):

# define new model
gam = deepcopy(self)
gam.set_params(self.get_params())
gam.set_params(**param_grid)

# warm start with parameters from previous build
if models:
coef = models[-1].coef_
gam.set_params(coef_=coef, force=True, verbose=False)

try:
# try fitting
# define new model
gam = deepcopy(self)
gam.set_params(self.get_params())
gam.set_params(**param_grid)

# warm start with parameters from previous build
if models:
coef = models[-1].coef_
gam.set_params(coef_=coef, force=True, verbose=False)
gam.fit(X, y, weights)

except ValueError as error:
msg = str(error) + '\non model:\n' + str(gam)
msg = str(error) + '\non model with params:\n' + str(param_grid)
msg += '\nskipping...\n'
if self.verbose:
warnings.warn(msg)
Expand Down Expand Up @@ -2125,14 +2128,6 @@ class LinearGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -2181,18 +2176,18 @@ class LinearGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):
self.scale = scale
super(LinearGAM, self).__init__(terms=terms,
distribution=NormalDist(scale=self.scale),
link='identity',
lam=lam,
max_iter=max_iter,
tol=tol,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)

self._exclude += ['distribution', 'link']

Expand Down Expand Up @@ -2258,14 +2253,6 @@ class LogisticGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -2310,20 +2297,20 @@ class LogisticGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
callbacks=['deviance', 'diffs', 'accuracy'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):

# call super
super(LogisticGAM, self).__init__(terms=terms,
distribution='binomial',
link='logit',
lam=lam,
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)
# ignore any variables
self._exclude += ['distribution', 'link']

Expand Down Expand Up @@ -2414,14 +2401,6 @@ class PoissonGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -2466,20 +2445,20 @@ class PoissonGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
callbacks=['deviance', 'diffs'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):

# call super
super(PoissonGAM, self).__init__(terms=terms,
distribution='poisson',
link='log',
lam=lam,
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)
# ignore any variables
self._exclude += ['distribution', 'link']

Expand Down Expand Up @@ -2769,14 +2748,6 @@ class GammaGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -2825,19 +2796,19 @@ class GammaGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):
self.scale = scale
super(GammaGAM, self).__init__(terms=terms,
distribution=GammaDist(scale=self.scale),
link='log',
lam=lam,
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)

self._exclude += ['distribution', 'link']

Expand Down Expand Up @@ -2890,14 +2861,6 @@ class InvGaussGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -2946,19 +2909,19 @@ class InvGaussGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
fit_intercept=True, verbose=False):
fit_intercept=True, verbose=False, **kwargs):
self.scale = scale
super(InvGaussGAM, self).__init__(terms=terms,
distribution=InvGaussDist(scale=self.scale),
link='log',
lam=lam,
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)

self._exclude += ['distribution', 'link']

Expand Down Expand Up @@ -3004,14 +2967,6 @@ class ExpectileGAM(GAM):
default: ['deviance', 'diffs']
Names of callback objects to call during the optimization loop.
lam : float or iterable of floats > 0, default: 0.6
Smoothing strength; must be a positive float, or one positive float
per feature.
Larger values enforce stronger smoothing.
If only one float is specified, then it is copied for all features.
fit_intercept : bool, default: True
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function.
Expand Down Expand Up @@ -3057,20 +3012,20 @@ class ExpectileGAM(GAM):
International Biometric Society: A Crash Course on P-splines
http://www.ibschannel2015.nl/project/userfiles/Crash_course_handout.pdf
"""
def __init__(self, terms='auto', lam=0.6, max_iter=100, tol=1e-4,
def __init__(self, terms='auto', max_iter=100, tol=1e-4,
scale=None, callbacks=['deviance', 'diffs'],
fit_intercept=True, expectile=0.5, verbose=False):
fit_intercept=True, expectile=0.5, verbose=False, **kwargs):
self.scale = scale
self.expectile = expectile
super(ExpectileGAM, self).__init__(terms=terms,
distribution=NormalDist(scale=self.scale),
link='identity',
lam=lam,
max_iter=max_iter,
tol=tol,
callbacks=callbacks,
fit_intercept=fit_intercept,
verbose=verbose)
verbose=verbose,
**kwargs)

self._exclude += ['distribution', 'link']

Expand Down
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