# -*- coding: utf-8 -*-
"""
Created on Sun May 10 08:23:48 2015
Author: Josef Perktold
License: BSD-3
"""
import numpy as np
from ._penalties import NonePenalty
from statsmodels.tools.numdiff import approx_fprime_cs, approx_fprime
class PenalizedMixin(object):
"""Mixin class for Maximum Penalized Likelihood
Parameters
----------
args and kwds for the model super class
penal : None or instance of Penalized function class
If penal is None, then NonePenalty is used.
pen_weight : float or None
factor for weighting the penalization term.
If None, then pen_weight is set to nobs.
TODO: missing **kwds or explicit keywords
TODO: do we adjust the inherited docstrings?
We would need templating to add the penalization parameters
"""
def __init__(self, *args, **kwds):
# pop extra kwds before calling super
self.penal = kwds.pop('penal', None)
self.pen_weight = kwds.pop('pen_weight', None)
super(PenalizedMixin, self).__init__(*args, **kwds)
# TODO: define pen_weight as average pen_weight? i.e. per observation
# I would have prefered len(self.endog) * kwds.get('pen_weight', 1)
# or use pen_weight_factor in signature
if self.pen_weight is None:
self.pen_weight = len(self.endog)
if self.penal is None:
# unpenalized by default
self.penal = NonePenalty()
self.pen_weight = 0
self._init_keys.extend(['penal', 'pen_weight'])
self._null_drop_keys = getattr(self, '_null_drop_keys', [])
self._null_drop_keys.extend(['penal'])
def _handle_scale(self, params, scale=None, **kwds):
if scale is None:
# special handling for GLM
if hasattr(self, 'scaletype'):
mu = self.predict(params)
scale = self.estimate_scale(mu)
else:
scale = 1
return scale
def loglike(self, params, pen_weight=None, **kwds):
"""
Log-likelihodo of model at params
"""
if pen_weight is None:
pen_weight = self.pen_weight
llf = super(PenalizedMixin, self).loglike(params, **kwds)
if pen_weight != 0:
scale = self._handle_scale(params, **kwds)
llf -= 1/scale * pen_weight * self.penal.func(params)
return llf
def loglikeobs(self, params, pen_weight=None, **kwds):
"""
Log-likelihood of model observations at params
"""
if pen_weight is None:
pen_weight = self.pen_weight
llf = super(PenalizedMixin, self).loglikeobs(params, **kwds)
nobs_llf = float(llf.shape[0])
if pen_weight != 0:
scale = self._handle_scale(params, **kwds)
llf -= 1/scale * pen_weight / nobs_llf * self.penal.func(params)
return llf
def score_numdiff(self, params, pen_weight=None, method='fd', **kwds):
"""score based on finite difference derivative
"""
if pen_weight is None:
pen_weight = self.pen_weight
loglike = lambda p: self.loglike(p, pen_weight=pen_weight, **kwds)
if method == 'cs':
return approx_fprime_cs(params, loglike)
elif method == 'fd':
return approx_fprime(params, loglike, centered=True)
else:
raise ValueError('method not recognized, should be "fd" or "cs"')
def score(self, params, pen_weight=None, **kwds):
"""
Gradient of model at params
"""
if pen_weight is None:
pen_weight = self.pen_weight
sc = super(PenalizedMixin, self).score(params, **kwds)
if pen_weight != 0:
scale = self._handle_scale(params, **kwds)
sc -= 1/scale * pen_weight * self.penal.deriv(params)
return sc
def score_obs(self, params, pen_weight=None, **kwds):
"""
Gradient of model observations at params
"""
if pen_weight is None:
pen_weight = self.pen_weight
sc = super(PenalizedMixin, self).score_obs(params, **kwds)
nobs_sc = float(sc.shape[0])
if pen_weight != 0:
scale = self._handle_scale(params, **kwds)
sc -= 1/scale * pen_weight / nobs_sc * self.penal.deriv(params)
return sc
def hessian_numdiff(self, params, pen_weight=None, **kwds):
"""hessian based on finite difference derivative
"""
if pen_weight is None:
pen_weight = self.pen_weight
loglike = lambda p: self.loglike(p, pen_weight=pen_weight, **kwds)
from statsmodels.tools.numdiff import approx_hess
return approx_hess(params, loglike)
def hessian(self, params, pen_weight=None, **kwds):
"""
Hessian of model at params
"""
if pen_weight is None:
pen_weight = self.pen_weight
hess = super(PenalizedMixin, self).hessian(params, **kwds)
if pen_weight != 0:
scale = self._handle_scale(params, **kwds)
h = self.penal.deriv2(params)
if h.ndim == 1:
hess -= 1/scale * np.diag(pen_weight * h)
else:
hess -= 1/scale * pen_weight * h
return hess
def fit(self, method=None, trim=None, **kwds):
"""minimize negative penalized log-likelihood
Parameters
----------
method : None or str
Method specifies the scipy optimizer as in nonlinear MLE models.
trim : Boolean or float
Default is False or None, which uses no trimming.
If trim is True or a float, then small parameters are set to zero.
If True, then a default threshold is used. If trim is a float, then
it will be used as threshold.
The default threshold is currently 1e-4, but it will change in
future and become penalty function dependent.
kwds : extra keyword arguments
This keyword arguments are treated in the same way as in the
fit method of the underlying model class.
Specifically, additional optimizer keywords and cov_type related
keywords can be added.
"""
# If method is None, then we choose a default method ourselves
# TODO: temporary hack, need extra fit kwds
# we need to rule out fit methods in a model that will not work with
# penalization
if hasattr(self, 'family'): # assume this identifies GLM
kwds.update({'max_start_irls' : 0})
# currently we use `bfgs` by default
if method is None:
method = 'bfgs'
if trim is None:
trim = False
res = super(PenalizedMixin, self).fit(method=method, **kwds)
if trim is False:
# note boolean check for "is False", not "False_like"
return res
else:
if trim is True:
trim = 1e-4 # trim threshold
# TODO: make it penal function dependent
# temporary standin, only checked for Poisson and GLM,
# and is computationally inefficient
drop_index = np.nonzero(np.abs(res.params) < trim)[0]
keep_index = np.nonzero(np.abs(res.params) > trim)[0]
if drop_index.any():
# TODO: do we need to add results attributes?
res_aux = self._fit_zeros(keep_index, **kwds)
return res_aux
else:
return res
