def __init__(self,
endog,
exog,
exog_infl=None,
offset=None,
exposure=None,
inflation='logit',
p=2,
missing='none',
**kwargs):
super(ZeroInflatedGeneralizedPoisson,
self).__init__(endog,
exog,
offset=offset,
inflation=inflation,
exog_infl=exog_infl,
exposure=exposure,
missing=missing,
**kwargs)
self.model_main = GeneralizedPoisson(self.endog,
self.exog,
offset=offset,
exposure=exposure,
p=p)
self.distribution = zigenpoisson
self.k_exog += 1
self.k_extra += 1
self.exog_names.append("alpha")
self.result_class = ZeroInflatedGeneralizedPoissonResults
self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper
self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults
self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper
def setup_class(cls):
cls.klass = GeneralizedPoisson
mod1 = GeneralizedPoisson(endog, exog)
res1 = mod1.fit(method="newton")
cls.res1 = res1
cls.res2 = resp.results_nb_docvis
cls.pred_kwds_mean = {}
cls.pred_kwds_6 = {}
cls.k_infl = 0
cls.rtol = 1e-8
def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
inflation='logit', p=2, missing='none', **kwargs):
super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog,
offset=offset,
inflation=inflation,
exog_infl=exog_infl,
exposure=exposure,
missing=missing, **kwargs)
self.model_main = GeneralizedPoisson(self.endog, self.exog,
offset=offset, exposure=exposure, p=p)
self.distribution = zigenpoisson
self.k_exog += 1
self.k_extra += 1
self.exog_names.append("alpha")
self.result_class = ZeroInflatedGeneralizedPoissonResults
self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper
self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults
self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper
def _make_model(self, X, y, n):
if self.model_type == 'auto':
per_zero = (X == 0).sum() / X.shape[0]
if per_zero > 0.9:
model_type = 'zeroinflated'
else:
model_type = 'glm'
logger.info(
'Auto-selected model type {} for neuron {} with {}% zeros'.
format(model_type, n, per_zero))
else:
model_type = self.model_type
if model_type == 'zeroinflated':
return ZeroInflatedPoisson(X, y)
elif model_type == 'generalized':
return GeneralizedPoisson(X, y)
else:
return sm.GLM(X, y, family=sm.families.Poisson(), missing='raise')
class ZeroInflatedGeneralizedPoisson(GenericZeroInflated):
__doc__ = """
Zero Inflated Generalized Poisson model for count data
%(params)s
%(extra_params)s
Attributes
----------
endog : array
A reference to the endogenous response variable
exog : array
A reference to the exogenous design.
exog_infl: array
A reference to the zero-inflated exogenous design.
p: scalar
P denotes parametrizations for ZIGP regression.
""" % {'params' : base._model_params_doc,
'extra_params' : _doc_zi_params +
"""p : float
dispersion power parameter for the GeneralizedPoisson model. p=1 for
ZIGP-1 and p=2 for ZIGP-2. Default is p=2
""" + base._missing_param_doc}
def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
inflation='logit', p=2, missing='none', **kwargs):
super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog,
offset=offset,
inflation=inflation,
exog_infl=exog_infl,
exposure=exposure,
missing=missing, **kwargs)
self.model_main = GeneralizedPoisson(self.endog, self.exog,
offset=offset, exposure=exposure, p=p)
self.distribution = zigenpoisson
self.k_exog += 1
self.k_extra += 1
self.exog_names.append("alpha")
self.result_class = ZeroInflatedGeneralizedPoissonResults
self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper
self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults
self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper
def _get_init_kwds(self):
kwds = super(ZeroInflatedGeneralizedPoisson, self)._get_init_kwds()
kwds['p'] = self.model_main.parameterization + 1
return kwds
def _predict_prob(self, params, exog, exog_infl, exposure, offset):
params_infl = params[:self.k_inflate]
params_main = params[self.k_inflate:]
p = self.model_main.parameterization
counts = np.atleast_2d(np.arange(0, np.max(self.endog)+1))
if len(exog_infl.shape) < 2:
transform = True
w = np.atleast_2d(
self.model_infl.predict(params_infl, exog_infl))[:, None]
else:
transform = False
w = self.model_infl.predict(params_infl, exog_infl)[:, None]
w[w == 1.] = np.nextafter(1, 0)
mu = self.model_main.predict(params_main, exog,
exposure=exposure, offset=offset)[:, None]
result = self.distribution.pmf(counts, mu, params_main[-1], p, w)
return result[0] if transform else result
def _get_start_params(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=ConvergenceWarning)
start_params = ZeroInflatedPoisson(self.endog, self.exog,
exog_infl=self.exog_infl).fit(disp=0).params
start_params = np.append(start_params, 0.1)
return start_params
class ZeroInflatedGeneralizedPoisson(GenericZeroInflated):
__doc__ = """
Zero Inflated Generalized Poisson Model
%(params)s
%(extra_params)s
Attributes
----------
endog : ndarray
A reference to the endogenous response variable
exog : ndarray
A reference to the exogenous design.
exog_infl : ndarray
A reference to the zero-inflated exogenous design.
p : scalar
P denotes parametrizations for ZIGP regression.
""" % {'params' : base._model_params_doc,
'extra_params' : _doc_zi_params +
"""p : float
dispersion power parameter for the GeneralizedPoisson model. p=1 for
ZIGP-1 and p=2 for ZIGP-2. Default is p=2
""" + base._missing_param_doc}
def __init__(self, endog, exog, exog_infl=None, offset=None, exposure=None,
inflation='logit', p=2, missing='none', **kwargs):
super(ZeroInflatedGeneralizedPoisson, self).__init__(endog, exog,
offset=offset,
inflation=inflation,
exog_infl=exog_infl,
exposure=exposure,
missing=missing, **kwargs)
self.model_main = GeneralizedPoisson(self.endog, self.exog,
offset=offset, exposure=exposure, p=p)
self.distribution = zigenpoisson
self.k_exog += 1
self.k_extra += 1
self.exog_names.append("alpha")
self.result_class = ZeroInflatedGeneralizedPoissonResults
self.result_class_wrapper = ZeroInflatedGeneralizedPoissonResultsWrapper
self.result_class_reg = L1ZeroInflatedGeneralizedPoissonResults
self.result_class_reg_wrapper = L1ZeroInflatedGeneralizedPoissonResultsWrapper
def _get_init_kwds(self):
kwds = super(ZeroInflatedGeneralizedPoisson, self)._get_init_kwds()
kwds['p'] = self.model_main.parameterization + 1
return kwds
def _predict_prob(self, params, exog, exog_infl, exposure, offset,
y_values=None):
params_infl = params[:self.k_inflate]
params_main = params[self.k_inflate:]
p = self.model_main.parameterization + 1
if y_values is None:
y_values = np.atleast_2d(np.arange(0, np.max(self.endog)+1))
if len(exog_infl.shape) < 2:
transform = True
w = np.atleast_2d(
self.model_infl.predict(params_infl, exog_infl))[:, None]
else:
transform = False
w = self.model_infl.predict(params_infl, exog_infl)[:, None]
w[w == 1.] = np.nextafter(1, 0)
mu = self.model_main.predict(params_main, exog,
exposure=exposure, offset=offset)[:, None]
result = self.distribution.pmf(y_values, mu, params_main[-1], p, w)
return result[0] if transform else result
def _predict_var(self, params, mu, prob_infl):
"""predict values for conditional variance V(endog | exog)
Parameters
----------
params : array_like
The model parameters. This is only used to extract extra params
like dispersion parameter.
mu : array_like
Array of mean predictions for main model.
prob_inlf : array_like
Array of predicted probabilities of zero-inflation `w`.
Returns
-------
Predicted conditional variance.
"""
alpha = params[-1]
w = prob_infl
p = self.model_main.parameterization
var_ = (1 - w) * mu * ((1 + alpha * mu**p)**2 + w * mu)
return var_
def _get_start_params(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=ConvergenceWarning)
start_params = ZeroInflatedPoisson(self.endog, self.exog,
exog_infl=self.exog_infl).fit(disp=0).params
start_params = np.append(start_params, 0.1)
return start_params
@Appender(ZeroInflatedPoisson.get_distribution.__doc__)
def get_distribution(self, params, exog=None, exog_infl=None,
exposure=None, offset=None):
p = self.model_main.parameterization + 1
mu = self.predict(params, exog=exog, exog_infl=exog_infl,
exposure=exposure, offset=offset, which="mean-main")
w = self.predict(params, exog=exog, exog_infl=exog_infl,
exposure=exposure, offset=offset, which="prob-main")
distr = self.distribution(mu[:, None], params[-1], p, 1 - w[:, None])
return distr