617 lines
22 KiB
Python
617 lines
22 KiB
Python
|
"""
|
||
|
|
Created on Fri May 30 16:22:29 2014
|
||
|
|
|
||
|
|
Author: Josef Perktold
|
||
|
|
License: BSD-3
|
||
|
|
|
||
|
|
"""
|
||
|
|
from io import StringIO
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
from numpy.testing import assert_, assert_allclose, assert_equal
|
||
|
|
import pandas as pd
|
||
|
|
import patsy
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from statsmodels import datasets
|
||
|
|
from statsmodels.base._constraints import fit_constrained
|
||
|
|
from statsmodels.discrete.discrete_model import Poisson, Logit
|
||
|
|
from statsmodels.genmod import families
|
||
|
|
from statsmodels.genmod.generalized_linear_model import GLM
|
||
|
|
from statsmodels.tools.tools import add_constant
|
||
|
|
|
||
|
|
from .results import (
|
||
|
|
results_glm_logit_constrained as reslogit,
|
||
|
|
results_poisson_constrained as results,
|
||
|
|
)
|
||
|
|
|
||
|
|
spector_data = datasets.spector.load()
|
||
|
|
spector_data.endog = np.asarray(spector_data.endog)
|
||
|
|
spector_data.exog = np.asarray(spector_data.exog)
|
||
|
|
spector_data.exog = add_constant(spector_data.exog, prepend=False)
|
||
|
|
|
||
|
|
|
||
|
|
DEBUG = False
|
||
|
|
|
||
|
|
ss = '''\
|
||
|
|
agecat smokes deaths pyears
|
||
|
|
1 1 32 52407
|
||
|
|
2 1 104 43248
|
||
|
|
3 1 206 28612
|
||
|
|
4 1 186 12663
|
||
|
|
5 1 102 5317
|
||
|
|
1 0 2 18790
|
||
|
|
2 0 12 10673
|
||
|
|
3 0 28 5710
|
||
|
|
4 0 28 2585
|
||
|
|
5 0 31 1462'''
|
||
|
|
|
||
|
|
data = pd.read_csv(StringIO(ss), delimiter='\t')
|
||
|
|
data = data.astype(int)
|
||
|
|
data['logpyears'] = np.log(data['pyears'])
|
||
|
|
|
||
|
|
|
||
|
|
class CheckPoissonConstrainedMixin:
|
||
|
|
|
||
|
|
def test_basic(self):
|
||
|
|
res1 = self.res1
|
||
|
|
res2 = self.res2
|
||
|
|
assert_allclose(res1[0], res2.params[self.idx], rtol=1e-6)
|
||
|
|
# see below Stata has nan, we have zero
|
||
|
|
bse1 = np.sqrt(np.diag(res1[1]))
|
||
|
|
mask = (bse1 == 0) & np.isnan(res2.bse[self.idx])
|
||
|
|
assert_allclose(bse1[~mask], res2.bse[self.idx][~mask], rtol=1e-6)
|
||
|
|
|
||
|
|
def test_basic_method(self):
|
||
|
|
if hasattr(self, 'res1m'):
|
||
|
|
res1 = (self.res1m if not hasattr(self.res1m, '_results')
|
||
|
|
else self.res1m._results)
|
||
|
|
res2 = self.res2
|
||
|
|
assert_allclose(res1.params, res2.params[self.idx], rtol=1e-6)
|
||
|
|
|
||
|
|
# when a parameter is fixed, the Stata has bse=nan, we have bse=0
|
||
|
|
mask = (res1.bse == 0) & np.isnan(res2.bse[self.idx])
|
||
|
|
assert_allclose(res1.bse[~mask], res2.bse[self.idx][~mask],
|
||
|
|
rtol=1e-6)
|
||
|
|
|
||
|
|
tvalues = res2.params_table[self.idx, 2]
|
||
|
|
# when a parameter is fixed, the Stata has tvalue=nan,
|
||
|
|
# we have tvalue=inf
|
||
|
|
mask = np.isinf(res1.tvalues) & np.isnan(tvalues)
|
||
|
|
assert_allclose(res1.tvalues[~mask], tvalues[~mask], rtol=1e-6)
|
||
|
|
pvalues = res2.params_table[self.idx, 3]
|
||
|
|
# note most pvalues are very small
|
||
|
|
# examples so far agree at 8 or more decimal, but rtol is stricter
|
||
|
|
# see above
|
||
|
|
mask = (res1.pvalues == 0) & np.isnan(pvalues)
|
||
|
|
assert_allclose(res1.pvalues[~mask], pvalues[~mask], rtol=5e-5)
|
||
|
|
|
||
|
|
ci_low = res2.params_table[self.idx, 4]
|
||
|
|
ci_upp = res2.params_table[self.idx, 5]
|
||
|
|
ci = np.column_stack((ci_low, ci_upp))
|
||
|
|
# note most pvalues are very small
|
||
|
|
# examples so far agree at 8 or more decimal, but rtol is stricter
|
||
|
|
# see above: nan versus value
|
||
|
|
assert_allclose(res1.conf_int()[~np.isnan(ci)], ci[~np.isnan(ci)],
|
||
|
|
rtol=5e-5)
|
||
|
|
|
||
|
|
# other
|
||
|
|
assert_allclose(res1.llf, res2.ll, rtol=1e-6)
|
||
|
|
assert_equal(res1.df_model, res2.df_m)
|
||
|
|
# Stata does not have df_resid
|
||
|
|
df_r = res2.N - res2.df_m - 1
|
||
|
|
assert_equal(res1.df_resid, df_r)
|
||
|
|
else:
|
||
|
|
pytest.skip("not available yet")
|
||
|
|
|
||
|
|
def test_other(self):
|
||
|
|
# some results may not be valid or available for all models
|
||
|
|
if hasattr(self, 'res1m'):
|
||
|
|
res1 = self.res1m
|
||
|
|
res2 = self.res2
|
||
|
|
|
||
|
|
if hasattr(res2, 'll_0'):
|
||
|
|
assert_allclose(res1.llnull, res2.ll_0, rtol=1e-6)
|
||
|
|
else:
|
||
|
|
if DEBUG:
|
||
|
|
import warnings
|
||
|
|
message = ('test: ll_0 not available, llnull=%6.4F'
|
||
|
|
% res1.llnull)
|
||
|
|
warnings.warn(message)
|
||
|
|
|
||
|
|
else:
|
||
|
|
pytest.skip("not available yet")
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained1a(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_noexposure_constraint
|
||
|
|
# 2 is dropped baseline for categorical
|
||
|
|
cls.idx = [7, 3, 4, 5, 6, 0, 1]
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ logpyears + smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data)
|
||
|
|
# get start_params, example fails to converge on one CI run
|
||
|
|
k_vars = len(mod.exog_names)
|
||
|
|
start_params = np.zeros(k_vars)
|
||
|
|
start_params[0] = np.log(mod.endog.mean())
|
||
|
|
# if we need it, this is desired params
|
||
|
|
# p = np.array([-3.93478643, 1.37276214, 2.33077032, 2.71338891,
|
||
|
|
# 2.71338891, 0.57966535, 0.97254074])
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
start_params=start_params,
|
||
|
|
fit_kwds={'method': 'bfgs', 'disp': 0})
|
||
|
|
# TODO: Newton fails
|
||
|
|
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr, start_params=start_params,
|
||
|
|
method='bfgs', disp=0)
|
||
|
|
|
||
|
|
@pytest.mark.smoke
|
||
|
|
def test_summary(self):
|
||
|
|
# trailing text in summary, assumes it's the first extra string
|
||
|
|
# NOTE: see comment about convergence in llnull for self.res1m
|
||
|
|
summ = self.res1m.summary()
|
||
|
|
assert_('linear equality constraints' in summ.extra_txt)
|
||
|
|
|
||
|
|
@pytest.mark.smoke
|
||
|
|
def test_summary2(self):
|
||
|
|
# trailing text in summary, assumes it's the first extra string
|
||
|
|
# NOTE: see comment about convergence in llnull for self.res1m
|
||
|
|
summ = self.res1m.summary2()
|
||
|
|
assert_('linear equality constraints' in summ.extra_txt[0])
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained1b(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_constraint
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
exposure=data['pyears'].values)
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'method': 'newton',
|
||
|
|
'disp': 0})
|
||
|
|
cls.constraints = lc
|
||
|
|
# TODO: bfgs fails
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr, method='newton',
|
||
|
|
disp=0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained1c(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_constraint
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
offset=np.log(data['pyears'].values))
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'method': 'newton',
|
||
|
|
'disp': 0})
|
||
|
|
cls.constraints = lc
|
||
|
|
# TODO: bfgs fails
|
||
|
|
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr, method='newton', disp=0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonNoConstrained(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_noconstraint
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 1 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
offset=np.log(data['pyears'].values))
|
||
|
|
res1 = mod.fit(disp=0)._results
|
||
|
|
# res1 is duplicate check, so we can follow the same pattern
|
||
|
|
cls.res1 = (res1.params, res1.cov_params())
|
||
|
|
cls.res1m = res1
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained2a(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_noexposure_constraint2
|
||
|
|
# 2 is dropped baseline for categorical
|
||
|
|
cls.idx = [7, 3, 4, 5, 6, 0, 1]
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ logpyears + smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data)
|
||
|
|
|
||
|
|
# get start_params, example fails to converge on one CI run
|
||
|
|
k_vars = len(mod.exog_names)
|
||
|
|
start_params = np.zeros(k_vars)
|
||
|
|
start_params[0] = np.log(mod.endog.mean())
|
||
|
|
# if we need it, this is desired params
|
||
|
|
# p = np.array([-9.43762015, 1.52762442, 2.74155711, 3.58730007,
|
||
|
|
# 4.08730007, 1.15987869, 0.12111539])
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
start_params=start_params,
|
||
|
|
fit_kwds={'method': 'bfgs', 'disp': 0})
|
||
|
|
# TODO: Newton fails
|
||
|
|
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr, start_params=start_params,
|
||
|
|
method='bfgs', disp=0)
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained2b(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_constraint2
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
exposure=data['pyears'].values)
|
||
|
|
constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'method': 'newton',
|
||
|
|
'disp': 0})
|
||
|
|
cls.constraints = lc
|
||
|
|
# TODO: bfgs fails to converge. overflow somewhere?
|
||
|
|
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr, method='bfgs', disp=0,
|
||
|
|
start_params=cls.res1[0])
|
||
|
|
|
||
|
|
|
||
|
|
class TestPoissonConstrained2c(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_constraint2
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
offset=np.log(data['pyears'].values))
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.5] - C(agecat)[T.4] = 0.5'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'method': 'newton', 'disp': 0})
|
||
|
|
cls.constraints = lc
|
||
|
|
# TODO: bfgs fails
|
||
|
|
|
||
|
|
# test method of Poisson, not monkey patched
|
||
|
|
cls.res1m = mod.fit_constrained(constr,
|
||
|
|
method='bfgs', disp=0,
|
||
|
|
start_params=cls.res1[0])
|
||
|
|
|
||
|
|
|
||
|
|
class TestGLMPoissonConstrained1a(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
from statsmodels.base._constraints import fit_constrained
|
||
|
|
|
||
|
|
cls.res2 = results.results_noexposure_constraint
|
||
|
|
# 2 is dropped baseline for categorical
|
||
|
|
cls.idx = [7, 3, 4, 5, 6, 0, 1]
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula = 'deaths ~ logpyears + smokes + C(agecat)'
|
||
|
|
mod = GLM.from_formula(formula, data=data,
|
||
|
|
family=families.Poisson())
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'atol': 1e-10})
|
||
|
|
cls.constraints = lc
|
||
|
|
cls.res1m = mod.fit_constrained(constr, atol=1e-10)
|
||
|
|
|
||
|
|
|
||
|
|
class TestGLMPoissonConstrained1b(CheckPoissonConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
from statsmodels.base._constraints import fit_constrained
|
||
|
|
from statsmodels.genmod import families
|
||
|
|
from statsmodels.genmod.generalized_linear_model import GLM
|
||
|
|
|
||
|
|
cls.res2 = results.results_exposure_constraint
|
||
|
|
cls.idx = [6, 2, 3, 4, 5, 0] # 2 is dropped baseline for categorical
|
||
|
|
|
||
|
|
# example with offset
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = GLM.from_formula(formula, data=data,
|
||
|
|
family=families.Poisson(),
|
||
|
|
offset=np.log(data['pyears'].values))
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constr)
|
||
|
|
|
||
|
|
cls.res1 = fit_constrained(mod, lc.coefs, lc.constants,
|
||
|
|
fit_kwds={'atol': 1e-10})
|
||
|
|
cls.constraints = lc
|
||
|
|
cls.res1m = mod.fit_constrained(constr, atol=1e-10)._results
|
||
|
|
|
||
|
|
def test_compare_glm_poisson(self):
|
||
|
|
res1 = self.res1m
|
||
|
|
res2 = self.res2
|
||
|
|
|
||
|
|
formula = 'deaths ~ smokes + C(agecat)'
|
||
|
|
mod = Poisson.from_formula(formula, data=data,
|
||
|
|
exposure=data['pyears'].values)
|
||
|
|
|
||
|
|
constr = 'C(agecat)[T.4] = C(agecat)[T.5]'
|
||
|
|
res2 = mod.fit_constrained(constr, start_params=self.res1m.params,
|
||
|
|
method='newton', warn_convergence=False,
|
||
|
|
disp=0)
|
||
|
|
|
||
|
|
# we get high precision because we use the params as start_params
|
||
|
|
|
||
|
|
# basic, just as check that we have the same model
|
||
|
|
assert_allclose(res1.params, res2.params, rtol=1e-12)
|
||
|
|
assert_allclose(res1.bse, res2.bse, rtol=1e-11)
|
||
|
|
|
||
|
|
# check predict, fitted, ...
|
||
|
|
|
||
|
|
predicted = res1.predict()
|
||
|
|
assert_allclose(predicted, res2.predict(), rtol=1e-10)
|
||
|
|
assert_allclose(res1.mu, predicted, rtol=1e-10)
|
||
|
|
assert_allclose(res1.fittedvalues, predicted, rtol=1e-10)
|
||
|
|
assert_allclose(res2.predict(which="linear"),
|
||
|
|
res2.predict(which="linear"),
|
||
|
|
rtol=1e-10)
|
||
|
|
|
||
|
|
|
||
|
|
class CheckGLMConstrainedMixin(CheckPoissonConstrainedMixin):
|
||
|
|
# add tests for some GLM specific attributes
|
||
|
|
|
||
|
|
def test_glm(self):
|
||
|
|
res2 = self.res2 # reference results
|
||
|
|
res1 = self.res1m
|
||
|
|
|
||
|
|
# assert_allclose(res1.aic, res2.aic, rtol=1e-10) # far away
|
||
|
|
# Stata aic in ereturn and in estat ic are very different
|
||
|
|
# we have the same as estat ic
|
||
|
|
# see issue GH#1733
|
||
|
|
assert_allclose(res1.aic, res2.infocrit[4], rtol=1e-10)
|
||
|
|
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore", FutureWarning)
|
||
|
|
# FutureWarning for BIC changes
|
||
|
|
assert_allclose(res1.bic, res2.bic, rtol=1e-10)
|
||
|
|
# bic is deviance based
|
||
|
|
# assert_allclose(res1.bic, res2.infocrit[5], rtol=1e-10)
|
||
|
|
assert_allclose(res1.deviance, res2.deviance, rtol=1e-10)
|
||
|
|
# TODO: which chi2 are these
|
||
|
|
# assert_allclose(res1.pearson_chi2, res2.chi2, rtol=1e-10)
|
||
|
|
|
||
|
|
|
||
|
|
class TestGLMLogitConstrained1(CheckGLMConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
cls.idx = slice(None)
|
||
|
|
# params sequence same as Stata, but Stata reports param = nan
|
||
|
|
# and we have param = value = 0
|
||
|
|
|
||
|
|
cls.res2 = reslogit.results_constraint1
|
||
|
|
|
||
|
|
mod1 = GLM(spector_data.endog, spector_data.exog,
|
||
|
|
family=families.Binomial())
|
||
|
|
|
||
|
|
constr = 'x1 = 2.8'
|
||
|
|
cls.res1m = mod1.fit_constrained(constr)
|
||
|
|
|
||
|
|
R, q = cls.res1m.constraints
|
||
|
|
cls.res1 = fit_constrained(mod1, R, q)
|
||
|
|
|
||
|
|
|
||
|
|
class TestLogitConstrained1(CheckGLMConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
cls.idx = slice(None)
|
||
|
|
# params sequence same as Stata, but Stata reports param = nan
|
||
|
|
# and we have param = value = 0
|
||
|
|
|
||
|
|
# res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
|
||
|
|
cls.res2 = reslogit.results_constraint1
|
||
|
|
|
||
|
|
mod1 = Logit(spector_data.endog, spector_data.exog)
|
||
|
|
|
||
|
|
constr = 'x1 = 2.8'
|
||
|
|
# newton doesn't work, raises hessian singular
|
||
|
|
cls.res1m = mod1.fit_constrained(constr, method='bfgs')
|
||
|
|
|
||
|
|
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
|
||
|
|
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'method': 'bfgs'})
|
||
|
|
|
||
|
|
@pytest.mark.skip(reason='not a GLM')
|
||
|
|
def test_glm(self):
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
class TestGLMLogitConstrained2(CheckGLMConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
cls.idx = slice(None) # params sequence same as Stata
|
||
|
|
cls.res2 = reslogit.results_constraint2
|
||
|
|
|
||
|
|
mod1 = GLM(spector_data.endog, spector_data.exog,
|
||
|
|
family=families.Binomial())
|
||
|
|
|
||
|
|
constr = 'x1 - x3 = 0'
|
||
|
|
cls.res1m = mod1.fit_constrained(constr, atol=1e-10)
|
||
|
|
|
||
|
|
# patsy compatible constraints
|
||
|
|
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
|
||
|
|
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'atol': 1e-10})
|
||
|
|
cls.constraints_rq = (R, q)
|
||
|
|
|
||
|
|
def test_predict(self):
|
||
|
|
# results only available for this case
|
||
|
|
res2 = self.res2 # reference results
|
||
|
|
res1 = self.res1m
|
||
|
|
|
||
|
|
predicted = res1.predict()
|
||
|
|
assert_allclose(predicted, res2.predict_mu, atol=1e-7)
|
||
|
|
assert_allclose(res1.mu, predicted, rtol=1e-10)
|
||
|
|
assert_allclose(res1.fittedvalues, predicted, rtol=1e-10)
|
||
|
|
|
||
|
|
@pytest.mark.smoke
|
||
|
|
def test_summary(self):
|
||
|
|
# trailing text in summary, assumes it's the first extra string
|
||
|
|
summ = self.res1m.summary()
|
||
|
|
assert_('linear equality constraints' in summ.extra_txt)
|
||
|
|
|
||
|
|
lc_string = str(self.res1m.constraints)
|
||
|
|
assert lc_string == "x1 - x3 = 0.0"
|
||
|
|
|
||
|
|
@pytest.mark.smoke
|
||
|
|
def test_summary2(self):
|
||
|
|
# trailing text in summary, assumes it's the first extra string
|
||
|
|
import warnings
|
||
|
|
|
||
|
|
with warnings.catch_warnings():
|
||
|
|
warnings.simplefilter("ignore", FutureWarning)
|
||
|
|
# FutureWarning for BIC changes
|
||
|
|
summ = self.res1m.summary2()
|
||
|
|
|
||
|
|
assert_('linear equality constraints' in summ.extra_txt[0])
|
||
|
|
|
||
|
|
def test_fit_constrained_wrap(self):
|
||
|
|
# minimal test
|
||
|
|
res2 = self.res2 # reference results
|
||
|
|
|
||
|
|
from statsmodels.base._constraints import fit_constrained_wrap
|
||
|
|
res_wrap = fit_constrained_wrap(self.res1m.model, self.constraints_rq)
|
||
|
|
assert_allclose(res_wrap.params, res2.params, rtol=1e-6)
|
||
|
|
assert_allclose(res_wrap.params, res2.params, rtol=1e-6)
|
||
|
|
|
||
|
|
|
||
|
|
class TestGLMLogitConstrained2HC(CheckGLMConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
cls.idx = slice(None) # params sequence same as Stata
|
||
|
|
cls.res2 = reslogit.results_constraint2_robust
|
||
|
|
|
||
|
|
mod1 = GLM(spector_data.endog, spector_data.exog,
|
||
|
|
family=families.Binomial())
|
||
|
|
|
||
|
|
# not used to match Stata for HC
|
||
|
|
# nobs, k_params = mod1.exog.shape
|
||
|
|
# k_params -= 1 # one constraint
|
||
|
|
cov_type = 'HC0'
|
||
|
|
cov_kwds = {'scaling_factor': 32/31}
|
||
|
|
# looks like nobs / (nobs - 1) and not (nobs - 1.) / (nobs - k_params)}
|
||
|
|
constr = 'x1 - x3 = 0'
|
||
|
|
cls.res1m = mod1.fit_constrained(constr, cov_type=cov_type,
|
||
|
|
cov_kwds=cov_kwds, atol=1e-10)
|
||
|
|
|
||
|
|
R, q = cls.res1m.constraints
|
||
|
|
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'atol': 1e-10,
|
||
|
|
'cov_type': cov_type,
|
||
|
|
'cov_kwds': cov_kwds})
|
||
|
|
cls.constraints_rq = (R, q)
|
||
|
|
|
||
|
|
|
||
|
|
class TestLogitConstrained2HC(CheckGLMConstrainedMixin):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setup_class(cls):
|
||
|
|
cls.idx = slice(None) # params sequence same as Stata
|
||
|
|
# res1ul = Logit(data.endog, data.exog).fit(method="newton", disp=0)
|
||
|
|
cls.res2 = reslogit.results_constraint2_robust
|
||
|
|
|
||
|
|
mod1 = Logit(spector_data.endog, spector_data.exog)
|
||
|
|
|
||
|
|
# not used to match Stata for HC
|
||
|
|
# nobs, k_params = mod1.exog.shape
|
||
|
|
# k_params -= 1 # one constraint
|
||
|
|
cov_type = 'HC0'
|
||
|
|
cov_kwds = {'scaling_factor': 32/31}
|
||
|
|
# looks like nobs / (nobs - 1) and not (nobs - 1.) / (nobs - k_params)}
|
||
|
|
constr = 'x1 - x3 = 0'
|
||
|
|
cls.res1m = mod1.fit_constrained(constr, cov_type=cov_type,
|
||
|
|
cov_kwds=cov_kwds, tol=1e-10,
|
||
|
|
)
|
||
|
|
|
||
|
|
R, q = cls.res1m.constraints.coefs, cls.res1m.constraints.constants
|
||
|
|
cls.res1 = fit_constrained(mod1, R, q, fit_kwds={'tol': 1e-10,
|
||
|
|
'cov_type': cov_type,
|
||
|
|
'cov_kwds': cov_kwds})
|
||
|
|
cls.constraints_rq = (R, q)
|
||
|
|
|
||
|
|
@pytest.mark.skip(reason='not a GLM')
|
||
|
|
def test_glm(self):
|
||
|
|
return
|
||
|
|
|
||
|
|
|
||
|
|
def junk(): # FIXME: make this into a test, or move/remove
|
||
|
|
# Singular Matrix in mod1a.fit()
|
||
|
|
|
||
|
|
# same as Stata default
|
||
|
|
formula2 = 'deaths ~ C(agecat) + C(smokes) : C(agecat)'
|
||
|
|
|
||
|
|
mod = Poisson.from_formula(formula2, data=data,
|
||
|
|
exposure=data['pyears'].values)
|
||
|
|
|
||
|
|
mod.fit()
|
||
|
|
|
||
|
|
constraints = 'C(smokes)[T.1]:C(agecat)[3] = C(smokes)[T.1]:C(agec`at)[4]'
|
||
|
|
|
||
|
|
import patsy
|
||
|
|
lc = patsy.DesignInfo(mod.exog_names).linear_constraint(constraints)
|
||
|
|
R, q = lc.coefs, lc.constants
|
||
|
|
|
||
|
|
mod.fit_constrained(R, q, fit_kwds={'method': 'bfgs'})
|
||
|
|
|
||
|
|
# example without offset
|
||
|
|
formula1a = 'deaths ~ logpyears + smokes + C(agecat)'
|
||
|
|
mod1a = Poisson.from_formula(formula1a, data=data)
|
||
|
|
|
||
|
|
mod1a.fit()
|
||
|
|
lc_1a = patsy.DesignInfo(mod1a.exog_names).linear_constraint(
|
||
|
|
'C(agecat)[T.4] = C(agecat)[T.5]')
|
||
|
|
mod1a.fit_constrained(lc_1a.coefs, lc_1a.constants,
|
||
|
|
fit_kwds={'method': 'newton'})
|