def hessian(self, params):
"""
Generic Zero Inflated model Hessian matrix of the loglikelihood
Parameters
----------
params : array-like
The parameters of the model
Returns
-------
hess : ndarray, (k_vars, k_vars)
The Hessian, second derivative of loglikelihood function,
evaluated at `params`
Notes
-----
"""
hess_arr_main = self._hessian_main(params)
hess_arr_infl = self._hessian_inflate(params)
if hess_arr_main is None or hess_arr_infl is None:
return approx_hess(params, self.loglike)
dim = self.k_exog + self.k_inflate
hess_arr = np.zeros((dim, dim))
hess_arr[:self.k_inflate,:] = hess_arr_infl
hess_arr[self.k_inflate:,self.k_inflate:] = hess_arr_main
tri_idx = np.triu_indices(self.k_exog + self.k_inflate, k=1)
hess_arr[tri_idx] = hess_arr.T[tri_idx]
return hess_arr
def hessian(self, params):
"""
Conditional logit Hessian matrix of the log-likelihood
Parameters
-----------
params : array-like
The parameters of the model
Returns
-------
hess : ndarray, (K, K)
The Hessian
Notes
-----
It is the second derivative with respect to the flattened parameters
of the loglikelihood function of the conditional logit model
evaluated at `params`.
.. math:: \\frac{\\partial^{2}\\ln L}{\\partial\\beta_{j}\\partial\\beta_{l}}=-\\sum_{i=1}^{n}\\frac{\\exp\\left(\\beta_{j}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\left[\\boldsymbol{1}\\left(j=l\\right)-\\frac{\\exp\\left(\\beta_{l}^{\\prime}x_{i}\\right)}{\\sum_{k=0}^{J}\\exp\\left(\\beta_{k}^{\\prime}x_{i}\\right)}\\right]x_{i}x_{l}^{\\prime}
where
:math:`\\boldsymbol{1}\\left(j=l\\right)` equals 1 if `j` = `l` and 0
otherwise.
"""
# TODO: analytical derivatives
from statsmodels.tools.numdiff import approx_hess
# need options for hess (epsilon)
return approx_hess(params, self.loglike)
def compute_param_cov(self, params, backcast=None, robust=True):
"""
Computes parameter covariances using numerical derivatives.
Parameters
----------
params : 1-d array
Model parameters
robust : bool, optional
Flag indicating whether to use robust standard errors (True) or
classic MLE (False)
"""
resids = self.resids(self.starting_values())
var_bounds = self.volatility.variance_bounds(resids)
nobs = resids.shape[0]
if backcast is None and self._backcast is None:
backcast = self.volatility.backcast(resids)
self._backcast = backcast
elif backcast is None:
backcast = self._backcast
kwargs = {"sigma2": np.zeros_like(resids), "backcast": backcast, "var_bounds": var_bounds, "individual": False}
hess = approx_hess(params, self._loglikelihood, kwargs=kwargs)
hess /= nobs
inv_hess = np.linalg.inv(hess)
if robust:
kwargs["individual"] = True
scores = approx_fprime(params, self._loglikelihood, kwargs=kwargs)
score_cov = np.cov(scores.T)
return inv_hess.dot(score_cov).dot(inv_hess) / nobs
else:
return inv_hess / nobs
def bse(self): # allow user to specify?
if self.model.method == "cmle": # uses different scale/sigma def.
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)))
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 test_derivatives(self):
pen = self.pen
x = self.params
ps = np.array([pen.deriv(np.atleast_1d(xi)) for xi in x])
psn = np.array([approx_fprime(np.atleast_1d(xi), pen.func) for xi in x])
assert_allclose(ps, psn, rtol=1e-7, atol=1e-8)
ph = np.array([pen.deriv2(np.atleast_1d(xi)) for xi in x])
phn = np.array([approx_hess(np.atleast_1d(xi), pen.func) for xi in x])
if ph.ndim == 2:
# SmoothedSCAD returns only diagonal if hessian if independent
# TODO should ww allow this also in L@?
ph = np.array([np.diag(phi) for phi in ph])
assert_allclose(ph, phn, rtol=1e-7, atol=1e-8)
def test_compute_covariance_without_hess_inverse(self):
fitmethod = "powell"
opt = scipy.optimize.minimize(self.lpost, self.t0,
method=fitmethod,
args=self.neg, tol=1.e-10)
optres = OptimizationResultsSubclassDummy(self.lpost, opt,
neg=True)
optres._compute_covariance(self.lpost, opt)
if comp_hessian:
phess = approx_hess(opt.x, self.lpost)
hess_inv = np.linalg.inv(phess)
assert np.all(optres.cov == hess_inv)
assert np.all(optres.err == np.sqrt(np.diag(np.abs(hess_inv))))
else:
assert optres.cov is None
assert optres.err is None
def _compute_covariance(self, lpost, res):
if hasattr(res, "hess_inv"):
if not isinstance(res.hess_inv, np.ndarray):
self.cov = np.asarray(res.hess_inv.todense())
else:
self.cov = res.hess_inv
self.err = np.sqrt(np.diag(self.cov))
else:
if comp_hessian:
# calculate Hessian approximating with finite differences
logging.info("Approximating Hessian with finite differences ...")
phess = approx_hess(self.p_opt, lpost)
self.cov = np.linalg.inv(phess)
self.err = np.sqrt(np.diag(np.abs(self.cov)))
else:
self.cov = None
self.err = None
def test_compare_numdiff(self, use_sqrt, reml, profile_fe):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(
endog,
exog_fe,
groups,
exog_re,
exog_vc=vc,
use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(
model, profile_fe=profile_fe, has_fe=not profile_fe)
try:
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2)**2
params = MixedLMParams.from_components(
fe_params, cov_re=cov_re, vcomp=vcomp)
params_vec = params.get_packed(
has_fe=not profile_fe, use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(
use_sqrt=False, has_fe=True)
loglike_h = loglike_function(
model, profile_fe=False, has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)
except AssertionError:
# See GH#5628; because this test fails unpredictably but only on
# OSX, we only xfail it there.
if PLATFORM_OSX:
pytest.xfail("fails on OSX due to unresolved "
"numerical differences")
else:
raise
def fit_from_data(self, data, disp=True):
"""
MLE estimator for the GARCH
"""
# Automatic re-scaling: 1. the solver has problems with positive derivative in linesearch.
# 2. the log has overflows using small values
n = np.floor(np.log10(np.abs(data.mean())))
R = data / 10**n
# initial guesses
a0 = 0.05
b0 = 0.9
g0 = 1 - a0 - b0
w0 = g0 * np.var(R)
# bounds and constraint
bounds = ((0, None), (0, 1), (0, 1))
def sum_small_1(x):
return 1 - x[1] - x[2]
cons = ({"fun": sum_small_1, "type": "ineq"})
def log_likely(x):
var = R[0]**2 # initial variance
N = len(R)
log_lik = 0
for i in range(1, N):
var = x[0] + x[1] * R[i - 1]**2 + x[2] * var # variance update
log_lik += -np.log(var) - (R[i]**2 / var)
return (-1) * log_lik
result = minimize(log_likely,
x0=[w0, a0, b0],
method='SLSQP',
bounds=bounds,
constraints=cons,
tol=1e-8,
options={"maxiter": 150})
print(result.message)
self.omega = result.x[0] * 10**(2 * n)
self.alpha, self.beta = result.x[1:]
self.gamma = 1 - self.alpha - self.beta
self.VL = self.omega / self.gamma
if disp == True:
hess = approx_hess(result.x,
log_likely) # hessian by finite differences
se = np.sqrt(np.diag(np.linalg.inv(hess))) # standard error
cv = ss.norm.ppf(1.0 - 0.05 / 2.0) # alpha=0.05
p_val = ss.norm.sf(np.abs(result.x / se)) # survival function
df = pd.DataFrame(index=["omega", "alpha", "beta"])
df["Params"] = result.x
df["SE"] = se
df["P-val"] = p_val
df["95% CI lower"] = result.x - cv * se
df["95% CI upper"] = result.x + cv * se
df.loc["omega",
["Params", "SE", "95% CI lower", "95% CI upper"]] *= 10**(
2 * n)
print(df)
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 statsmodels.tools.numdiff import approx_fprime, approx_hess
hb=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
hf=-approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
hh = (hf+hb)/2.
print(np.linalg.eigh(hh))
grad = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
print(grad)
gradb = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
gradf = -approx_fprime(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())))
modp.fixed_params = None
modp.fixed_paramsmask = None
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 statsmodels.tools.numdiff import approx_hess
hb=-approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)
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)
print('llf', res2.llf, res_norm3.llf)
bse = np.sqrt(np.diag(np.linalg.inv(res_norm3.model.hessian(
res_norm3.params))))
res_norm3.model.score(res_norm3.params)
#fprime in fit option cannot be overwritten, set to None, when score is defined
# exception is fixed, but I don't think score was supposed to be called
res_bfgs = mod_norm2.fit(start_params=start_params,
method="bfgs",
fprime=None,
maxiter=500,
retall=0)
hb = -approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
hf = -approx_hess(res_norm3.params, mod_norm2.loglike, epsilon=1e-4)
hh = (hf + hb) / 2.
print(np.linalg.eigh(hh))
grad = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
print(grad)
gradb = -approx_fprime(res_norm3.params, mod_norm2.loglike, epsilon=-1e-4)
gradf = -approx_fprime(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())))
data_exog = sm.add_constant(rvs, prepend=False) 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 statsmodels.tools.numdiff import approx_hess hb=-approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4) 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)
xk = np.array([1,2,3])
xk = np.array([1.,1.,1.])
#xk = np.zeros(3)
beta = xk
y = np.dot(x, beta) + 0.1*np.random.randn(nobs)
xkols = np.dot(np.linalg.pinv(x),y)
print(approx_fprime((1,2,3),fun,epsilon,x))
gradtrue = x.sum(0)
print(x.sum(0))
gradcs = approx_fprime_cs((1,2,3), fun, (x,), h=1.0e-20)
print(gradcs, maxabs(gradcs, gradtrue))
print(approx_hess_cs((1,2,3), fun, (x,), h=1.0e-20)) #this is correctly zero
print(approx_hess_cs((1,2,3), fun2, (y,x), h=1.0e-20)-2*np.dot(x.T, x))
print(numdiff.approx_hess(xk,fun2,1e-3, (y,x))[0] - 2*np.dot(x.T, x))
gt = (-x*2*(y-np.dot(x, [1,2,3]))[:,None])
g = approx_fprime_cs((1,2,3), fun1, (y,x), h=1.0e-20)#.T #this shouldn't be transposed
gd = numdiff.approx_fprime((1,2,3),fun1,epsilon,(y,x))
print(maxabs(g, gt))
print(maxabs(gd, gt))
import statsmodels.api as sm
data = sm.datasets.spector.load(as_pandas=False)
data.exog = sm.add_constant(data.exog, prepend=False)
#mod = sm.Probit(data.endog, data.exog)
mod = sm.Logit(data.endog, data.exog)
#res = mod.fit(method="newton")
def hessian(self, params):
"""
Returns numerical hessian for now.
"""
loglike = self.loglike
return approx_hess(params, loglike)
data_exog = sm.add_constant(rvs, prepend=False) 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 statsmodels.tools.numdiff import approx_fprime, approx_hess hb = -approx_hess(modp.start_value, modp.loglike, epsilon=-1e-4) 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):
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)
def hessian(self, AB_mask):
"""
Returns numerical hessian.
"""
loglike = self.loglike
return approx_hess(AB_mask, loglike)
def test_compare_numdiff(self, use_sqrt, reml, profile_fe):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(endog,
exog_fe,
groups,
exog_re,
exog_vc=vc,
use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(model,
profile_fe=profile_fe,
has_fe=not profile_fe)
try:
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2)**2
params = MixedLMParams.from_components(fe_params,
cov_re=cov_re,
vcomp=vcomp)
params_vec = params.get_packed(has_fe=not profile_fe,
use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(use_sqrt=False,
has_fe=True)
loglike_h = loglike_function(model,
profile_fe=False,
has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)
except AssertionError:
# See GH#5628; because this test fails unpredictably but only on
# OSX, we only xfail it there.
if PLATFORM_OSX:
pytest.xfail("fails on OSX due to unresolved "
"numerical differences")
else:
raise
def hessian(self, params):
"""
Returns numerical hessian for now.
"""
loglike = self.loglike
return approx_hess(params, loglike)
def test_compare_numdiff(self):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
for use_sqrt in False, True:
for reml in False, True:
for profile_fe in False, True:
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(endog, exog_fe, groups,
exog_re, exog_vc=vc, use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(
model, profile_fe=profile_fe, has_fe=not profile_fe)
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2) ** 2
params = MixedLMParams.from_components(
fe_params, cov_re=cov_re, vcomp=vcomp)
params_vec = params.get_packed(
has_fe=not profile_fe, use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(
use_sqrt=False, has_fe=True)
loglike_h = loglike_function(
model, profile_fe=False, has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)
modp.fixed_paramsmask = None
resp = modp.fit(start_params=modp.start_params, disp=1,
method='nm') #'newton')
#resp = modp.fit(start_params = modp.start_params, disp=1, method='newton')
print('\nestimation results t-dist')
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 statsmodels.tools.numdiff import approx_fprime, approx_hess
hb = -approx_hess(modp.start_params, modp.loglike, epsilon=-1e-4)
tmp = modp.loglike(modp.start_params)
print(tmp.shape)
#np.linalg.eigh(np.linalg.inv(hb))[0]
pp = np.array(store_params)
print(pp.min(0))
print(pp.max(0))
##################### Example: Pareto
# estimating scale doesn't work yet, a bug somewhere ?
# fit_ks works well, but no bse or other result statistics yet
#import for kstest based estimation
#should be replace
import statsmodels.sandbox.distributions.sppatch
def test_compare_numdiff(self):
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
for use_sqrt in False, True:
for reml in False, True:
for profile_fe in False, True:
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp * grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp * grpsize, k_re))
exog_re[:, 0] = 1
exog_vc = np.random.normal(size=(n_grp * grpsize, 3))
slopes = np.random.normal(size=(n_grp, k_re))
slopes[:, -1] *= 2
slopes = np.kron(slopes, np.ones((grpsize, 1)))
slopes_vc = np.random.normal(size=(n_grp, 3))
slopes_vc = np.kron(slopes_vc, np.ones((grpsize, 1)))
slopes_vc[:, -1] *= 2
re_values = (slopes * exog_re).sum(1)
vc_values = (slopes_vc * exog_vc).sum(1)
err = np.random.normal(size=n_grp * grpsize)
endog = exog_fe.sum(1) + re_values + vc_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
vc = {"a": {}, "b": {}}
for i in range(n_grp):
ix = np.flatnonzero(groups == i)
vc["a"][i] = exog_vc[ix, 0:2]
vc["b"][i] = exog_vc[ix, 2:3]
model = MixedLM(endog, exog_fe, groups, exog_re, exog_vc=vc, use_sqrt=use_sqrt)
rslt = model.fit(reml=reml)
loglike = loglike_function(model, profile_fe=profile_fe, has_fe=not profile_fe)
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
vcomp = np.random.normal(size=2) ** 2
params = MixedLMParams.from_components(fe_params, cov_re=cov_re, vcomp=vcomp)
params_vec = params.get_packed(has_fe=not profile_fe, use_sqrt=use_sqrt)
# Check scores
gr = -model.score(params, profile_fe=profile_fe)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-3)
# Check Hessian matrices at the MLE (we don't have
# the profile Hessian matrix and we don't care
# about the Hessian for the square root
# transformed parameter).
if (profile_fe is False) and (use_sqrt is False):
hess = -model.hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed(use_sqrt=False, has_fe=True)
loglike_h = loglike_function(model, profile_fe=False, has_fe=True)
nhess = nd.approx_hess(params_vec, loglike_h)
assert_allclose(hess, nhess, rtol=1e-3)
def _fitting(self, optfunc, ain, optfuncprime=None, neg=True, obs=True):
lenpower = float(len(self.y))
if neg == True:
if scipy.__version__ < "0.10.0":
args = [neg]
else:
args = (neg,)
else:
args = ()
### different commands for different fitting methods,
### at least until scipy 0.11 is out
funcval = 100.0
while funcval == 100 or funcval == 200 or funcval == 0.0 or funcval == np.inf or funcval == -np.inf:
## constrained minimization with truncated newton or constrained bfgs
## Newton conjugate gradient, which doesn't work
if self.fitmethod == scipy.optimize.fmin_ncg:
aopt = self.fitmethod(optfunc, ain, optfuncprime, disp=0, args=args)
### BFGS algorithm
elif self.fitmethod == scipy.optimize.fmin_bfgs:
aopt = self.fitmethod(optfunc, ain, disp=0, full_output=True, args=args)
warnflag = aopt[6]
if warnflag == 1:
print("*** ACHTUNG! Maximum number of iterations exceeded! ***")
# elif warnflag == 2:
# print("Gradient and/or function calls not changing!")
## all other methods: Simplex, Powell, Gradient
else:
aopt = self.fitmethod(optfunc, ain, disp=0, full_output=True, args=args)
funcval = aopt[1]
ain = np.array(ain) * ((np.random.rand(len(ain)) - 0.5) * 4.0)
### make a dictionary with best-fit parameters:
## popt: best fit parameters (list)
## result: value of ML function at minimum
## model: the model used
fitparams = {"popt": aopt[0], "result": aopt[1]}
### calculate model power spectrum from optimal parameters
# fitparams['mfit'] = func(self.x, *fitparams['popt'])
### degrees of freedom
fitparams["dof"] = lenpower - float(len(fitparams["popt"]))
### Akaike Information Criterion
fitparams["aic"] = fitparams["result"] + 2.0 * len(ain)
### Bayesian Information Criterion
fitparams["bic"] = fitparams["result"] + len(ain) * len(self.x)
### compute deviance
try:
fitparams["deviance"] = 2.0 * optfunc.loglikelihood(fitparams["popt"])
except AttributeError:
fitparams["deviance"] = 2.0 * optfunc(fitparams["popt"])
fitparams["sexp"] = 2.0 * len(self.x) * len(fitparams["popt"])
fitparams["ssd"] = np.sqrt(2.0 * fitparams["sexp"])
fitparams["smooth3"] = scipy.signal.wiener(self.y, 3)
fitparams["smooth5"] = scipy.signal.wiener(self.y, 5)
fitparams["smooth11"] = scipy.signal.wiener(self.y, 11)
### if this is an observation (not fake data), compute the covariance matrix
if obs == True:
### for BFGS, get covariance from algorithm output
if self.fitmethod == scipy.optimize.fmin_bfgs:
print("Approximating covariance from BFGS: ")
covar = aopt[3]
stderr = np.sqrt(np.diag(covar))
else:
### calculate Hessian approximating with finite differences
print("Approximating Hessian with finite differences ...")
if comp_hessian:
phess = approx_hess(aopt[0], optfunc, neg=args)
### covariance is the inverse of the Hessian
print("Hessian (empirical): " + str(phess))
covar = np.linalg.inv(phess)
stderr = np.sqrt(np.diag(covar))
else:
print("Cannot compute hessian! Use BFGS or install statsmodels!")
covar = None
stderr = None
print("Covariance (empirical): " + str(covar))
fitparams["cov"] = covar
### errors of parameters are on the diagonal of the covariance
### matrix; take square root to get standard deviation
fitparams["err"] = stderr
### Print results to screen
print("The best-fit model parameters plus errors are:")
for i, (x, y) in enumerate(zip(fitparams["popt"], stderr)):
print("Parameter " + str(i) + ": " + str(x) + " +/- " + str(y))
print("The Akaike Information Criterion of the power law model is: " + str(fitparams["aic"]) + ".")
return fitparams
def _fitting(self, optfunc, ain, optfuncprime=None, neg=True, obs=True):
lenpower = float(len(self.y))
if neg == True:
if scipy.__version__ < "0.10.0":
args = [neg]
else:
args = (neg, )
else:
args = ()
### different commands for different fitting methods,
### at least until scipy 0.11 is out
funcval = 100.0
while funcval == 100 or funcval == 200 or funcval == 0.0 or funcval == np.inf or funcval == -np.inf:
## constrained minimization with truncated newton or constrained bfgs
## Newton conjugate gradient, which doesn't work
if self.fitmethod == scipy.optimize.fmin_ncg:
aopt = self.fitmethod(optfunc,
ain,
optfuncprime,
disp=0,
args=args)
### BFGS algorithm
elif self.fitmethod == scipy.optimize.fmin_bfgs:
aopt = self.fitmethod(optfunc,
ain,
disp=0,
full_output=True,
args=args)
warnflag = aopt[6]
if warnflag == 1:
print(
"*** ACHTUNG! Maximum number of iterations exceeded! ***"
)
#elif warnflag == 2:
#print("Gradient and/or function calls not changing!")
## all other methods: Simplex, Powell, Gradient
else:
aopt = self.fitmethod(optfunc,
ain,
disp=0,
full_output=True,
args=args)
funcval = aopt[1]
ain = np.array(ain) * ((np.random.rand(len(ain)) - 0.5) * 4.0)
### make a dictionary with best-fit parameters:
## popt: best fit parameters (list)
## result: value of ML function at minimum
## model: the model used
fitparams = {'popt': aopt[0], 'result': aopt[1]}
### calculate model power spectrum from optimal parameters
#fitparams['mfit'] = func(self.x, *fitparams['popt'])
### degrees of freedom
fitparams['dof'] = lenpower - float(len(fitparams['popt']))
### Akaike Information Criterion
fitparams['aic'] = fitparams['result'] + 2.0 * len(ain)
### Bayesian Information Criterion
fitparams['bic'] = fitparams['result'] + len(ain) * len(self.x)
### compute deviance
try:
fitparams['deviance'] = 2.0 * optfunc.loglikelihood(
fitparams['popt'])
except AttributeError:
fitparams['deviance'] = 2.0 * optfunc(fitparams['popt'])
fitparams['sexp'] = 2.0 * len(self.x) * len(fitparams['popt'])
fitparams['ssd'] = np.sqrt(2.0 * fitparams['sexp'])
fitparams['smooth3'] = scipy.signal.wiener(self.y, 3)
fitparams['smooth5'] = scipy.signal.wiener(self.y, 5)
fitparams['smooth11'] = scipy.signal.wiener(self.y, 11)
### if this is an observation (not fake data), compute the covariance matrix
if obs == True:
### for BFGS, get covariance from algorithm output
if self.fitmethod == scipy.optimize.fmin_bfgs:
print("Approximating covariance from BFGS: ")
covar = aopt[3]
stderr = np.sqrt(np.diag(covar))
else:
### calculate Hessian approximating with finite differences
print("Approximating Hessian with finite differences ...")
if comp_hessian:
phess = approx_hess(aopt[0], optfunc, neg=args)
### covariance is the inverse of the Hessian
print("Hessian (empirical): " + str(phess))
covar = np.linalg.inv(phess)
stderr = np.sqrt(np.diag(covar))
else:
print(
"Cannot compute hessian! Use BFGS or install statsmodels!"
)
covar = None
stderr = None
print("Covariance (empirical): " + str(covar))
fitparams['cov'] = covar
### errors of parameters are on the diagonal of the covariance
### matrix; take square root to get standard deviation
fitparams['err'] = stderr
### Print results to screen
print("The best-fit model parameters plus errors are:")
for i, (x, y) in enumerate(zip(fitparams['popt'], stderr)):
print("Parameter " + str(i) + ": " + str(x) + " +/- " + str(y))
print(
"The Akaike Information Criterion of the power law model is: "
+ str(fitparams['aic']) + ".")
return fitparams
beta = xk
y = np.dot(x, beta) + 0.1 * np.random.randn(nobs)
xkols = np.dot(np.linalg.pinv(x), y)
print(approx_fprime((1, 2, 3), fun, epsilon, x))
gradtrue = x.sum(0)
print(x.sum(0))
gradcs = approx_fprime_cs((1, 2, 3), fun, (x, ), h=1.0e-20)
print(gradcs, maxabs(gradcs, gradtrue))
print(approx_hess_cs((1, 2, 3), fun, (x, ),
h=1.0e-20)) #this is correctly zero
print(
approx_hess_cs((1, 2, 3), fun2, (y, x), h=1.0e-20) -
2 * np.dot(x.T, x))
print(numdiff.approx_hess(xk, fun2, 1e-3, (y, x))[0] - 2 * np.dot(x.T, x))
gt = (-x * 2 * (y - np.dot(x, [1, 2, 3]))[:, None])
g = approx_fprime_cs((1, 2, 3), fun1, (y, x),
h=1.0e-20) #.T #this shouldn't be transposed
gd = numdiff.approx_fprime((1, 2, 3), fun1, epsilon, (y, x))
print(maxabs(g, gt))
print(maxabs(gd, gt))
import statsmodels.api as sm
data = sm.datasets.spector.load(as_pandas=False)
data.exog = sm.add_constant(data.exog, prepend=False)
#mod = sm.Probit(data.endog, data.exog)
mod = sm.Logit(data.endog, data.exog)
#res = mod.fit(method="newton")
def test_compare_numdiff(self):
import statsmodels.tools.numdiff as nd
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
for jl in 0,1:
for reml in False,True:
for cov_pen_wt in 0,10:
cov_pen = penalties.PSD(cov_pen_wt)
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp*grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp*grpsize, k_re))
exog_re[:, 0] = 1
slopes = np.random.normal(size=(n_grp, k_re))
slopes = np.kron(slopes, np.ones((grpsize,1)))
re_values = (slopes * exog_re).sum(1)
err = np.random.normal(size=n_grp*grpsize)
endog = exog_fe.sum(1) + re_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
if jl == 0:
md = MixedLM(endog, exog_fe, groups, exog_re)
score = lambda x: -md.score_sqrt(x)
hessian = lambda x : -md.hessian_sqrt(x)
else:
md = MixedLM(endog, exog_fe, groups, exog_re,
use_sqrt=False)
score = lambda x: -md.score_full(x)
hessian = lambda x: -md.hessian_full(x)
md.reml = reml
md.cov_pen = cov_pen
loglike = lambda x: -md.loglike(x)
rslt = md.fit()
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
params = MixedLMParams.from_components(fe_params,
cov_re)
if jl == 0:
params_vec = params.get_packed()
else:
params_vec = params.get_packed(use_sqrt=False)
# Check scores
gr = score(params)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-2)
# Hessian matrices don't agree well away from
# the MLE.
#if cov_pen_wt == 0:
# hess = hessian(params)
# nhess = nd.approx_hess(params_vec, loglike)
# assert_allclose(hess, nhess, rtol=1e-2)
# Check Hessian matrices at the MLE.
if cov_pen_wt == 0:
hess = hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed()
nhess = nd.approx_hess(params_vec, loglike)
assert_allclose(hess, nhess, rtol=1e-2)
def test_compare_numdiff(self):
import statsmodels.tools.numdiff as nd
n_grp = 200
grpsize = 5
k_fe = 3
k_re = 2
for jl in 0,1:
for reml in False,True:
for cov_pen_wt in 0,10:
cov_pen = penalties.PSD(cov_pen_wt)
np.random.seed(3558)
exog_fe = np.random.normal(size=(n_grp*grpsize, k_fe))
exog_re = np.random.normal(size=(n_grp*grpsize, k_re))
exog_re[:, 0] = 1
slopes = np.random.normal(size=(n_grp, k_re))
slopes = np.kron(slopes, np.ones((grpsize,1)))
re_values = (slopes * exog_re).sum(1)
err = np.random.normal(size=n_grp*grpsize)
endog = exog_fe.sum(1) + re_values + err
groups = np.kron(range(n_grp), np.ones(grpsize))
if jl == 0:
md = MixedLM(endog, exog_fe, groups, exog_re)
score = lambda x: -md.score_sqrt(x)
hessian = lambda x : -md.hessian_sqrt(x)
else:
md = MixedLM(endog, exog_fe, groups, exog_re, use_sqrt=False)
score = lambda x: -md.score_full(x)
hessian = lambda x: -md.hessian_full(x)
md.reml = reml
md.cov_pen = cov_pen
loglike = lambda x: -md.loglike(x)
rslt = md.fit()
# Test the score at several points.
for kr in range(5):
fe_params = np.random.normal(size=k_fe)
cov_re = np.random.normal(size=(k_re, k_re))
cov_re = np.dot(cov_re.T, cov_re)
params = MixedLMParams.from_components(fe_params, cov_re)
if jl == 0:
params_vec = params.get_packed()
else:
params_vec = params.get_packed(use_sqrt=False)
# Check scores
gr = score(params)
ngr = nd.approx_fprime(params_vec, loglike)
assert_allclose(gr, ngr, rtol=1e-2)
# Hessian matrices don't agree well away from
# the MLE.
#if cov_pen_wt == 0:
# hess = hessian(params)
# nhess = nd.approx_hess(params_vec, loglike)
# assert_allclose(hess, nhess, rtol=1e-2)
# Check Hessian matrices at the MLE.
if cov_pen_wt == 0:
hess = hessian(rslt.params_object)
params_vec = rslt.params_object.get_packed()
nhess = nd.approx_hess(params_vec, loglike)
assert_allclose(hess, nhess, rtol=1e-2)