def multinomial_nb(
name,
alpha=None,
fit_prior=None,
):
def _name(msg):
return '%s.%s_%s' % (name, 'multinomial_nb', msg)
rval = scope.sklearn_MultinomialNB(
alpha=hp.quniform(_name('alpha'), 0, 1, 0.001)
if alpha is None else alpha,
fit_prior=hp.choice(_name('fit_prior'), [True, False])
if fit_prior is None else fit_prior,
)
return rval
def multinomial_nb(name,
alpha=None,
fit_prior=None,
class_prior=None,
):
def _name(msg):
return '%s.%s_%s' % (name, 'multinomial_nb', msg)
rval = scope.sklearn_MultinomialNB(
alpha=(hp.quniform(_name('alpha'), 0, 1, 0.001)
if alpha is None else alpha),
fit_prior=(hp_bool(_name('fit_prior'))
if fit_prior is None else fit_prior),
class_prior=class_prior
)
return rval
def multinomial_nb(name,
alpha=None,
fit_prior=None,
class_prior=None,
):
def _name(msg):
return '%s.%s_%s' % (name, 'multinomial_nb', msg)
rval = scope.sklearn_MultinomialNB(
alpha=(hp.quniform(_name('alpha'), 0, 1, 0.001)
if alpha is None else alpha),
fit_prior=(hp_bool(_name('fit_prior'))
if fit_prior is None else fit_prior),
class_prior=class_prior
)
return rval
def multinomial_nb(name,
alpha=None,
fit_prior=None,
):
def _name(msg):
return '%s.%s_%s' % (name, 'multinomial_nb', msg)
rval = scope.sklearn_MultinomialNB(
alpha=hp.quniform(
_name('alpha'),
0, 1, 0.001 ) if alpha is None else alpha,
fit_prior=hp.choice(
_name('fit_prior'),
[ True, False ] ) if fit_prior is None else fit_prior,
)
return rval
