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 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 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 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 do not have # the profile Hessian matrix and we do not 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