211 lines
7.7 KiB
Python
211 lines
7.7 KiB
Python
'''Testing GenericLikelihoodModel variations on Poisson
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'''
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import pytest
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import numpy as np
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from numpy.testing import assert_almost_equal
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from scipy import stats
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import statsmodels.api as sm
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from statsmodels.miscmodels.count import PoissonGMLE, PoissonOffsetGMLE, \
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PoissonZiGMLE
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from statsmodels.discrete.discrete_model import Poisson
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from statsmodels.tools.sm_exceptions import ValueWarning
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DEC = 4
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DEC4 = 4
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DEC5 = 5
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class CompareMixin:
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def test_params(self):
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assert_almost_equal(self.res.params, self.res_glm.params, DEC5)
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assert_almost_equal(self.res.params, self.res_discrete.params, DEC5)
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def test_cov_params(self):
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assert_almost_equal(self.res.bse, self.res_glm.bse, DEC5)
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assert_almost_equal(self.res.bse, self.res_discrete.bse, DEC5)
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#TODO check problem with the following, precision is low,
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#dof error? last t-value is 22, 23, error is around 1% for PoissonMLE
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#this was with constant=1,
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#now changed constant=0.1 to make it less significant and test passes
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#overall precision for tstat looks like 1%
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#assert_almost_equal(self.res.tval, self.res_glm.t(), DEC)
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assert_almost_equal(self.res.tvalues, self.res_discrete.tvalues, DEC4)
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#assert_almost_equal(self.res.params, self.res_discrete.params)
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assert_almost_equal(self.res.pvalues, self.res_discrete.pvalues, DEC)
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def test_ttest(self):
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tt = self.res.t_test(np.eye(len(self.res.params)))
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from scipy import stats
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pvalue = stats.norm.sf(np.abs(tt.tvalue)) * 2
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assert_almost_equal(tt.tvalue, self.res.tvalues, DEC)
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assert_almost_equal(pvalue, self.res.pvalues, DEC)
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def test_df(self):
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res = self.res
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k_extra = getattr(self, "k_extra", 0)
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nobs, k_vars = res.model.exog.shape
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assert res.df_resid == nobs - k_vars - k_extra
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assert res.df_model == k_vars - 1 # -1 for constant
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assert len(res.params) == k_vars + k_extra
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@pytest.mark.smoke
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def test_summary(self):
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self.res.summary()
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class TestPoissonMLE(CompareMixin):
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@classmethod
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def setup_class(cls):
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# generate artificial data
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np.random.seed(98765678)
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nobs = 200
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rvs = np.random.randn(nobs,6)
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data_exog = rvs
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data_exog = sm.add_constant(data_exog, prepend=False)
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xbeta = 0.1 + 0.1*rvs.sum(1)
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data_endog = np.random.poisson(np.exp(xbeta))
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#estimate discretemod.Poisson as benchmark
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cls.res_discrete = Poisson(data_endog, data_exog).fit(disp=0)
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mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
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cls.res_glm = mod_glm.fit()
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#estimate generic MLE
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cls.mod = PoissonGMLE(data_endog, data_exog)
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cls.res = cls.mod.fit(start_params=0.9 * cls.res_discrete.params,
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method='bfgs', disp=0)
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def test_predict_distribution(self):
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res = self.res
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model = self.mod
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with pytest.raises(ValueError):
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# No "result" attribute
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model.predict_distribution(model.exog)
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try:
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model.result = res
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dist = model.predict_distribution(model.exog)
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assert isinstance(dist, stats._distn_infrastructure.rv_frozen)
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assert_almost_equal(dist.mean(),
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np.exp(model.exog.dot(res.params)),
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15)
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finally:
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# leave the model object how we found it
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model.__delattr__("result")
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class TestPoissonOffset(CompareMixin):
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#this uses the first exog to construct an offset variable
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@classmethod
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def setup_class(cls):
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# generate artificial data
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np.random.seed(98765678)
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nobs = 200
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rvs = np.random.randn(nobs,6)
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data_exog = rvs
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data_exog = sm.add_constant(data_exog, prepend=False)
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xbeta = 1 + 0.1*rvs.sum(1)
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data_endog = np.random.poisson(np.exp(xbeta))
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mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
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cls.res_glm = mod_glm.fit()
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#estimate generic MLE
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#cls.mod = PoissonGMLE(data_endog, data_exog)
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#res = cls.mod.fit()
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#create offset variable based on first exog
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cls.res_discrete = Poisson(data_endog, data_exog).fit(disp=0)
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offset = cls.res_discrete.params[0] * data_exog[:,0] #1d ???
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#estimate discretemod.Poisson as benchmark, now has offset
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cls.res_discrete = Poisson(data_endog, data_exog[:,1:],
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offset=offset).fit(disp=0)
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mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
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cls.res_glm = mod_glm.fit()
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#cls.res = PoissonOffsetGMLE(data_endog, data_exog[:,1:], offset=offset).fit(start_params = np.ones(6)/2., method='nm')
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modo = PoissonOffsetGMLE(data_endog, data_exog[:,1:], offset=offset)
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cls.res = modo.fit(start_params = 0.9*cls.res_discrete.params,
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method='bfgs', disp=0)
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def test_params(self):
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assert_almost_equal(self.res.params, self.res_glm.params[1:], DEC)
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assert_almost_equal(self.res.params, self.res_discrete.params, DEC)
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def test_cov_params(self):
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assert_almost_equal(self.res.bse, self.res_glm.bse[1:], DEC-1)
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assert_almost_equal(self.res.bse, self.res_discrete.bse, DEC5)
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#precision of next is very low ???
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#assert_almost_equal(self.res.tval, self.res_glm.t()[1:], DEC)
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#assert_almost_equal(self.res.params, self.res_discrete.params)
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#DEC = DEC - 1
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class TestPoissonZi(CompareMixin):
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#this uses the first exog to construct an offset variable
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@classmethod
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def setup_class(cls):
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# generate artificial data
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np.random.seed(98765678)
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nobs = 200
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rvs = np.random.randn(nobs,6)
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data_exog = rvs
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data_exog = sm.add_constant(data_exog, prepend=False)
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xbeta = 1 + 0.1*rvs.sum(1)
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data_endog = np.random.poisson(np.exp(xbeta))
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cls.k_extra = 1
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mod_glm = sm.GLM(data_endog, data_exog, family=sm.families.Poisson())
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cls.res_glm = mod_glm.fit()
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#estimate generic MLE
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#cls.mod = PoissonGMLE(data_endog, data_exog)
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#res = cls.mod.fit()
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#create offset variable based on first exog
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cls.res_discrete = Poisson(data_endog, data_exog).fit(disp=0)
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offset = cls.res_discrete.params[0] * data_exog[:,0] #1d ???
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#estimate discretemod.Poisson as benchmark, now has offset
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cls.res_discrete = Poisson(data_endog, data_exog[:,1:], offset=offset).fit(disp=0)
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# Note : ZI has one extra parameter
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cls.res = PoissonZiGMLE(data_endog, data_exog[:,1:], offset=offset).fit(
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start_params=np.r_[0.9*cls.res_discrete.params,10],
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method='bfgs', disp=0)
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cls.decimal = 4
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def test_params(self):
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assert_almost_equal(self.res.params[:-1], self.res_glm.params[1:], self.decimal)
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assert_almost_equal(self.res.params[:-1], self.res_discrete.params, self.decimal)
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def test_cov_params(self):
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#skip until I have test with zero-inflated data
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#use bsejac for now since it seems to work
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assert_almost_equal(self.res.bsejac[:-1], self.res_glm.bse[1:], self.decimal-2)
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assert_almost_equal(self.res.bsejac[:-1], self.res_discrete.bse, self.decimal-2)
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#assert_almost_equal(self.res.tval[:-1], self.res_glm.t()[1:], self.decimal)
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def test_exog_names_warning(self):
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mod = self.res.model
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mod1 = PoissonOffsetGMLE(mod.endog, mod.exog, offset=mod.offset)
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from numpy.testing import assert_warns
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mod1.data.xnames = mod1.data.xnames * 2
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assert_warns(ValueWarning, mod1.fit, disp=0)
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