def bse(self):
params = self.params
if not fast_kalman or self.model.method == "css":
if len(params) == 1: # can't take an inverse
return np.sqrt(-1.0 / approx_hess_cs(params, self.model.loglike, epsilon=1e-5))
return np.sqrt(np.diag(-inv(approx_hess_cs(params, self.model.loglike, epsilon=1e-5))))
else:
if len(params) == 1:
return np.sqrt(-1.0 / approx_hess(params, self.model.loglike, epsilon=1e-3)[0])
return np.sqrt(np.diag(-inv(approx_hess(params, self.model.loglike, epsilon=1e-3)[0])))
def bse(self):
params = self.params
if not fast_kalman or self.model.method == "css":
if len(params) == 1: # can't take an inverse
return np.sqrt(-1./approx_hess_cs(params,
self.model.loglike, epsilon=1e-5))
return np.sqrt(np.diag(-inv(approx_hess_cs(params,
self.model.loglike, epsilon=1e-5))))
else:
if len(params) == 1:
return np.sqrt(-1./approx_hess(params,
self.model.loglike, epsilon=1e-3)[0])
return np.sqrt(np.diag(-inv(approx_hess(params,
self.model.loglike, epsilon=1e-3)[0])))
def cov_params(self): # add scale argument?
func = self.model.loglike
x0 = self.params
if not fast_kalman or self.model.method == "css":
return -inv(approx_hess_cs(x0, func))
else:
return -inv(approx_hess(x0, func, epsilon=1e-3)[0])
def cov_params(self): # add scale argument?
func = self.model.loglike
x0 = self.params
if not fast_kalman or self.model.method == "css":
return -inv(approx_hess_cs(x0, func))
else:
return -inv(approx_hess(x0, func, epsilon=1e-3)[0])
def bse(self): # allow user to specify?
if self.model.method == "cmle": # uses different scale/sigma definition
resid = self.resid
ssr = np.dot(resid,resid)
ols_scale = ssr/(self.nobs - self.k_ar - self.k_trend)
return np.sqrt(np.diag(self.cov_params(scale=ols_scale)))
else:
hess = approx_hess(self.params, self.model.loglike)
return np.sqrt(np.diag(-np.linalg.inv(hess[0])))
def bse(self): # allow user to specify?
if self.model.method == "cmle": # uses different scale/sigma definition
resid = self.resid
ssr = np.dot(resid, resid)
ols_scale = ssr / (self.nobs - self.k_ar - self.k_trend)
return np.sqrt(np.diag(self.cov_params(scale=ols_scale)))
else:
hess = approx_hess(self.params, self.model.loglike)
return np.sqrt(np.diag(-np.linalg.inv(hess[0])))
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
if self.transparams:
params = self._invtransparams(params)
# return approx_hess_cs(params, loglike, epsilon=1e-5)
return approx_hess(params, loglike, epsilon=1e-5)
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
if self.transparams:
params = self._invtransparams(params)
# return approx_hess_cs(params, loglike, epsilon=1e-5)
return approx_hess(params, loglike, epsilon=1e-5)
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
if not fast_kalman or self.method == "css":
return approx_hess_cs(params, loglike, epsilon=1e-5)
else:
return approx_hess(params, self.loglike, epsilon=1e-3)[0]
def hessian(self, params):
"""
Compute the Hessian at params,
Notes
-----
This is a numerical approximation.
"""
loglike = self.loglike
#if self.transparams:
# params = self._invtransparams(params)
if not fast_kalman or self.method == "css":
return approx_hess_cs(params, loglike, epsilon=1e-5)
else:
return approx_hess(params, self.loglike, epsilon=1e-3)[0]
yn = y + 0.2 * np.random.normal(size=len(x))
popt, pcov = curve_fit(func, x, yn)
print 'curve_fit results:'
print 'values:', popt
print 'errors:', np.sqrt(pcov.diagonal())
"""And here is how to compute the fit parameter values and errors
using one of the other optimizers (exemplified with fmin) and
a method to compute the Hesse matrix"""
def chi2(pars):
chi = yn - func(x, *pars)
return (chi ** 2).sum()
popt = fmin(chi2, p0, disp=False)
from numpy.dual import inv
from scikits.statsmodels.sandbox.regression.numdiff import approx_hess3 as approx_hess
phess = approx_hess(popt, chi2)
def approx_covar(hess, red_chi2):
return red_chi2 * inv(phess / 2.)
red_chi2 = chi2(popt) / (len(x) - len(p0))
pcov = approx_covar(popt, red_chi2)
print 'fmin and approx_hess results:'
print 'values:', popt
print 'errors:', np.sqrt(pcov.diagonal())
"""Just to check, here is what Minuit has to say"""
from minuit import Minuit
def chi2(a, b, c):
chi = yn - func(x, a, b, c)
return (chi ** 2).sum()
m = Minuit(chi2, a=2.5, b=1.3, c=0.5)
data_exog = sm.add_constant(rvs) xbeta = 0.9 + 0.1 * rvs.sum(1) data_endog = xbeta + 0.1 * np.random.standard_t(5, size=nobs) #print data_endog modp = MyT(data_endog, data_exog) modp.start_value = np.ones(data_exog.shape[1] + 2) modp.start_value[-2] = 10 modp.start_params = modp.start_value resp = modp.fit(start_params=modp.start_value) print resp.params print resp.bse from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess hb = -approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4)[0] tmp = modp.loglike(modp.start_value) print tmp.shape ''' >>> tmp = modp.loglike(modp.start_value) 8 >>> tmp.shape (100,) >>> tmp.sum(0) -24220.877108016182 >>> tmp = modp.nloglikeobs(modp.start_value) 8 >>> tmp.shape (100, 100) >>> np.dot(modp.exog, beta).shape Traceback (most recent call last):
print '\nResults with TLinearModel'
print '-------------------------'
resp = modp.fit(start_params=modp.start_params,
disp=1,
method='nm',
maxfun=10000,
maxiter=5000) #'newton')
#resp = modp.fit(start_params = modp.start_params, disp=1, method='newton')
print 'using Nelder-Mead'
print resp.params
print resp.bse
resp2 = modp.fit(start_params=resp.params, method='Newton')
print 'using Newton'
print resp2.params
print resp2.bse
from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess
hb = -approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)[0]
tmp = modp.loglike(modp.start_params)
print tmp.shape
print 'eigenvalues of numerical Hessian'
print np.linalg.eigh(np.linalg.inv(hb))[0]
#store_params is only available in original test script
##pp=np.array(store_params)
##print pp.min(0)
##print pp.max(0)
return approx_fprime1(params, self.nloglike)
File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s
tatsmodels\sandbox\regression\numdiff.py", line 81, in approx_fprime1
nobs = np.size(f0) #len(f0)
TypeError: object of type 'numpy.float64' has no len()
'''
res_bfgs = mod_norm2.fit(start_params=start_params,
method="bfgs",
fprime=None,
maxiter=500,
retall=0)
from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess
hb = -approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)[0]
hf = -approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)[0]
hh = (hf + hb) / 2.
print np.linalg.eigh(hh)
grad = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
print grad
gradb = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
gradf = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
print(gradb + gradf) / 2.
print res_norm3.model.score(res_norm3.params)
print res_norm3.model.score(start_params)
mod_norm2.loglike(start_params / 2.)
print np.linalg.inv(-1 * mod_norm2.hessian(res_norm3.params))
print np.sqrt(np.diag(res_bfgs.cov_params()))
score = lambda params: -self.score(params)
File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s
tatsmodels\model.py", line 480, in score
return approx_fprime1(params, self.nloglike)
File "c:\josef\eclipsegworkspace\statsmodels-josef-experimental-gsoc\scikits\s
tatsmodels\sandbox\regression\numdiff.py", line 81, in approx_fprime1
nobs = np.size(f0) #len(f0)
TypeError: object of type 'numpy.float64' has no len()
'''
res_bfgs = mod_norm2.fit(start_params=start_params, method="bfgs", fprime=None,
maxiter = 500, retall=0)
from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess
hb=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)[0]
hf=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)[0]
hh = (hf+hb)/2.
print np.linalg.eigh(hh)
grad = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
print grad
gradb = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
gradf = -approx_fprime1(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
print (gradb+gradf)/2.
print res_norm3.model.score(res_norm3.params)
print res_norm3.model.score(start_params)
mod_norm2.loglike(start_params/2.)
print np.linalg.inv(-1*mod_norm2.hessian(res_norm3.params))
print np.sqrt(np.diag(res_bfgs.cov_params()))
print '\nResults with TLinearModel'
print '-------------------------'
resp = modp.fit(start_params = modp.start_params, disp=1, method='nm',
maxfun=10000, maxiter=5000)#'newton')
#resp = modp.fit(start_params = modp.start_params, disp=1, method='newton')
print 'using Nelder-Mead'
print resp.params
print resp.bse
resp2 = modp.fit(start_params = resp.params, method='Newton')
print 'using Newton'
print resp2.params
print resp2.bse
from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess
hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)[0]
tmp = modp.loglike(modp.start_params)
print tmp.shape
print 'eigenvalues of numerical Hessian'
print np.linalg.eigh(np.linalg.inv(hb))[0]
#store_params is only available in original test script
##pp=np.array(store_params)
##print pp.min(0)
##print pp.max(0)
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)[0]
def hessian(self, params):
"""
Returns numerical hessian for now.
"""
loglike = self.loglike
return approx_hess(params, loglike)[0]
def hessian(self, params):
"""
Returns numerical hessian for now.
"""
loglike = self.loglike
return approx_hess(params, loglike)[0]
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)[0]
data_exog = sm.add_constant(rvs) xbeta = 0.9 + 0.1*rvs.sum(1) data_endog = xbeta + 0.1*np.random.standard_t(5, size=nobs) #print data_endog modp = MyT(data_endog, data_exog) modp.start_value = np.ones(data_exog.shape[1]+2) modp.start_value[-2] = 10 modp.start_params = modp.start_value resp = modp.fit(start_params = modp.start_value) print resp.params print resp.bse from scikits.statsmodels.sandbox.regression.numdiff import approx_fprime1, approx_hess hb=-approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4)[0] tmp = modp.loglike(modp.start_value) print tmp.shape ''' >>> tmp = modp.loglike(modp.start_value) 8 >>> tmp.shape (100,) >>> tmp.sum(0) -24220.877108016182 >>> tmp = modp.nloglikeobs(modp.start_value) 8 >>> tmp.shape (100, 100)
