reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/discrete/tests/test_diagnostic.py

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+"""
+Created on Fri Sep 15 13:38:13 2017
+
+Author: Josef Perktold
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_equal
+import pytest
+
+from statsmodels.discrete.discrete_model import Poisson
+import statsmodels.discrete._diagnostics_count as dia
+from statsmodels.discrete.diagnostic import PoissonDiagnostic
+
+
+class TestCountDiagnostic:
+
+    @classmethod
+    def setup_class(cls):
+
+        expected_params = [1, 1, 0.5]
+        np.random.seed(987123)
+        nobs = 500
+        exog = np.ones((nobs, 2))
+        exog[:nobs//2, 1] = 0
+        # offset is used to create misspecification of the model
+        # for predicted probabilities conditional moment test
+        # offset = 0.5 * np.random.randn(nobs)
+        # range_mix = 0.5
+        # offset = -range_mix / 2 + range_mix * np.random.rand(nobs)
+        offset = 0
+        mu_true = np.exp(exog.dot(expected_params[:-1]) + offset)
+
+        endog_poi = np.random.poisson(mu_true / 5)
+        # endog3 = distr.zigenpoisson.rvs(mu_true, 0,
+        #                                2, 0.01, size=mu_true.shape)
+
+        model_poi = Poisson(endog_poi, exog)
+        res_poi = model_poi.fit(method='bfgs', maxiter=5000, disp=False)
+        cls.exog = exog
+        cls.endog = endog_poi
+        cls.res = res_poi
+        cls.nobs = nobs
+
+    def test_count(self):
+        # partially smoke
+        tzi1 = dia.test_poisson_zeroinflation_jh(self.res)
+
+        tzi2 = dia.test_poisson_zeroinflation_broek(self.res)
+        # compare two implementation in special case
+        assert_allclose(tzi1[:2], (tzi2[0]**2, tzi2[1]), rtol=1e-5)
+
+        tzi3 = dia.test_poisson_zeroinflation_jh(self.res, self.exog)
+
+        # regression test
+        tzi3_1 = (0.79863597832443878, 0.67077736750318928)
+        assert_allclose(tzi3, tzi3_1, rtol=5e-4)
+        assert_equal(tzi3.df, 2)
+
+    @pytest.mark.matplotlib
+    def test_probs(self, close_figures):
+        nobs = self.nobs
+        probs = self.res.predict_prob()
+        freq = np.bincount(self.endog) / nobs
+
+        tzi = dia.test_chisquare_prob(self.res, probs[:, :2])
+        # regression numbers
+        tzi1 = (0.387770845, 0.5334734738)
+        assert_allclose(tzi[:2], tzi1, rtol=5e-5)
+
+        # smoke test for plot
+        dia.plot_probs(freq, probs.mean(0))
+
+
+class TestPoissonDiagnosticClass():
+
+    @classmethod
+    def setup_class(cls):
+        np.random.seed(987125643)
+        nr = 1
+        n_groups = 2
+        labels = np.arange(n_groups)
+        x = np.repeat(labels, np.array([40, 60]) * nr)
+        nobs = x.shape[0]
+        exog = (x[:, None] == labels).astype(np.float64)
+        # reparameterize to explicit constant
+        # exog[:, 1] = 1
+        beta = np.array([0.1, 0.3], np.float64)
+
+        linpred = exog @ beta
+        mean = np.exp(linpred)
+        y = np.random.poisson(mean)
+
+        cls.endog = y
+        cls.exog = exog
+
+    def test_spec_tests(self):
+        # regression test, numbers similar to Monte Carlo simulation
+        res_dispersion = np.array([
+            [0.1396096387543, 0.8889684245877],
+            [0.1396096387543, 0.8889684245877],
+            [0.2977840351238, 0.7658680002106],
+            [0.1307899995877, 0.8959414342111],
+            [0.1307899995877, 0.8959414342111],
+            [0.1357101381056, 0.8920504328246],
+            [0.2776587511235, 0.7812743277372]
+            ])
+
+        res_zi = np.array([
+            [00.1389582826821, 0.7093188241734],
+            [-0.3727710861669, 0.7093188241734],
+            [-0.2496729648642, 0.8028402670888],
+            [00.0601651553909, 0.8062350958880],
+            ])
+
+        respoi = Poisson(self.endog, self.exog).fit(disp=0)
+        dia = PoissonDiagnostic(respoi)
+        t_disp = dia.test_dispersion()
+        res_disp = np.column_stack((t_disp.statistic, t_disp.pvalue))
+        assert_allclose(res_disp, res_dispersion, rtol=1e-8)
+
+        nobs = self.endog.shape[0]
+        t_zi_jh = dia.test_poisson_zeroinflation(method="broek",
+                                                 exog_infl=np.ones(nobs))
+        t_zib = dia.test_poisson_zeroinflation(method="broek")
+        t_zim = dia.test_poisson_zeroinflation(method="prob")
+        t_zichi2 = dia.test_chisquare_prob(bin_edges=np.arange(3))
+
+        t_zi = np.vstack([t_zi_jh[:2], t_zib[:2], t_zim[:2], t_zichi2[:2]])
+        assert_allclose(t_zi, res_zi, rtol=1e-8)
+
+        # test jansakul and hinde with exog_infl
+        t_zi_ex = dia.test_poisson_zeroinflation(method="broek",
+                                                 exog_infl=self.exog)
+        res_zi_ex = np.array([3.7813218150779, 0.1509719973257])
+        assert_allclose(t_zi_ex[:2], res_zi_ex, rtol=1e-8)