reconnect moved files to git repo

venv/lib/python3.11/site-packages/statsmodels/distributions/tests/test_bernstein.py

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+"""
+Created on Wed Feb 17 23:44:18 2021
+
+Author: Josef Perktold
+License: BSD-3
+
+"""
+
+import numpy as np
+from numpy.testing import assert_allclose, assert_array_less
+from scipy import stats
+
+from statsmodels.distributions.copula.api import (
+        CopulaDistribution, ArchimedeanCopula)
+from statsmodels.distributions.copula.api import transforms as tra
+import statsmodels.distributions.tools as dt
+from statsmodels.distributions.bernstein import (
+    BernsteinDistribution, BernsteinDistributionBV, BernsteinDistributionUV)
+
+
+def test_bernstein_distribution_1d():
+    grid = dt._Grid([501])
+    loc = grid.x_flat == 0
+    grid.x_flat[loc] = grid.x_flat[~loc].min() / 2
+    grid.x_flat[grid.x_flat == 1] = 1 - grid.x_flat.min()
+
+    distr = stats.beta(3, 5)
+    cdf_g = distr.cdf(np.squeeze(grid.x_flat))
+    bpd = BernsteinDistribution(cdf_g)
+
+    cdf_bp = bpd.cdf(grid.x_flat)
+    assert_allclose(cdf_bp, cdf_g, atol=0.005)
+    assert_array_less(np.median(np.abs(cdf_bp - cdf_g)), 0.001)
+
+    pdfv = distr.pdf(np.squeeze(grid.x_flat))
+    pdf_bp = bpd.pdf(grid.x_flat)
+    assert_allclose(pdf_bp, pdfv, atol=0.02)
+    assert_array_less(np.median(np.abs(pdf_bp - pdfv)), 0.01)
+
+    # compare with UV class
+    xf = np.squeeze(grid.x_flat)  # UV returns column if x is column
+    bpd1 = BernsteinDistributionUV(cdf_g)
+    cdf_bp1 = bpd1.cdf(xf)
+    assert_allclose(cdf_bp1, cdf_bp, atol=1e-13)
+    pdf_bp1 = bpd1.pdf(xf)
+    assert_allclose(pdf_bp1, pdf_bp, atol=1e-13)
+
+    cdf_bp1 = bpd1.cdf(xf, method="beta")
+    assert_allclose(cdf_bp1, cdf_bp, atol=1e-13)
+    pdf_bp1 = bpd1.pdf(xf, method="beta")
+    assert_allclose(pdf_bp1, pdf_bp, atol=1e-13)
+
+    cdf_bp1 = bpd1.cdf(xf, method="bpoly")
+    assert_allclose(cdf_bp1, cdf_bp, atol=1e-13)
+    pdf_bp1 = bpd1.pdf(xf, method="bpoly")
+    assert_allclose(pdf_bp1, pdf_bp, atol=1e-13)
+
+    # check rvs
+    # currently smoke test
+    rvs = bpd.rvs(100)
+    assert len(rvs) == 100
+
+
+def test_bernstein_distribution_2d():
+    grid = dt._Grid([51, 51])
+
+    cop_tr = tra.TransfFrank
+    args = (2,)
+    ca = ArchimedeanCopula(cop_tr())
+    distr1 = stats.uniform
+    distr2 = stats.uniform
+    cad = CopulaDistribution(ca, [distr1, distr2], cop_args=args)
+    cdfv = cad.cdf(grid.x_flat, args)
+    cdf_g = cdfv.reshape(grid.k_grid)
+
+    bpd = BernsteinDistribution(cdf_g)
+
+    cdf_bp = bpd.cdf(grid.x_flat)
+    assert_allclose(cdf_bp, cdfv, atol=0.005)
+    assert_array_less(np.median(np.abs(cdf_bp - cdfv)), 0.001)
+
+    grid_eps = dt._Grid([51, 51], eps=1e-8)
+    pdfv = cad.pdf(grid_eps.x_flat)
+    pdf_bp = bpd.pdf(grid_eps.x_flat)
+    assert_allclose(pdf_bp, pdfv, atol=0.01, rtol=0.04)
+    assert_array_less(np.median(np.abs(pdf_bp - pdfv)), 0.05)
+
+    # check marginal cdfs
+    # get marginal cdf
+    xx = np.column_stack((np.linspace(0, 1, 5), np.ones(5)))
+    cdf_m1 = bpd.cdf(xx)
+    assert_allclose(cdf_m1, xx[:, 0], atol=1e-13)
+    xx = np.column_stack((np.ones(5), np.linspace(0, 1, 5)))
+    cdf_m2 = bpd.cdf(xx)
+    assert_allclose(cdf_m2, xx[:, 1], atol=1e-13)
+
+    xx_ = np.linspace(0, 1, 5)
+    xx = xx_[:, None]  # currently requires 2-dim
+    bpd_m1 = bpd.get_marginal(0)
+    cdf_m1 = bpd_m1.cdf(xx)
+    assert_allclose(cdf_m1, xx_, atol=1e-13)
+    pdf_m1 = bpd_m1.pdf(xx)
+    assert_allclose(pdf_m1, np.ones(len(xx)), atol=1e-13)
+
+    bpd_m = bpd.get_marginal(1)
+    cdf_m = bpd_m.cdf(xx)
+    assert_allclose(cdf_m, xx_, atol=1e-13)
+    pdf_m = bpd_m.pdf(xx)
+    assert_allclose(pdf_m, np.ones(len(xx)), atol=1e-13)
+
+
+class TestBernsteinBeta2d:
+
+    @classmethod
+    def setup_class(cls):
+        grid = dt._Grid([91, 101])
+
+        cop_tr = tra.TransfFrank
+        args = (2,)
+        ca = ArchimedeanCopula(cop_tr())
+        distr1 = stats.beta(4, 3)
+        distr2 = stats.beta(4, 4)  # (5, 2)
+        cad = CopulaDistribution(ca, [distr1, distr2], cop_args=args)
+        cdfv = cad.cdf(grid.x_flat, args)
+        cdf_g = cdfv.reshape(grid.k_grid)
+
+        cls.grid = grid
+        cls.cdfv = cdfv
+        cls.distr = cad
+        cls.bpd = BernsteinDistributionBV(cdf_g)
+
+    def test_basic(self):
+        bpd = self.bpd
+        grid = self.grid
+        cdfv = self.cdfv
+        distr = self.distr
+
+        if grid.x_flat.shape[0] < 51**2:
+            cdf_bp = bpd.cdf(grid.x_flat)
+            assert_allclose(cdf_bp, cdfv, atol=0.05)
+            assert_array_less(np.median(np.abs(cdf_bp - cdfv)), 0.01)
+
+        grid_eps = dt._Grid([51, 51], eps=1e-2)
+        cdfv = distr.cdf(grid_eps.x_flat)
+        cdf_bp = bpd.cdf(grid_eps.x_flat)
+        assert_allclose(cdf_bp, cdfv, atol=0.01, rtol=0.01)
+        assert_array_less(np.median(np.abs(cdf_bp - cdfv)), 0.05)
+
+        pdfv = distr.pdf(grid_eps.x_flat)
+        pdf_bp = bpd.pdf(grid_eps.x_flat)
+        assert_allclose(pdf_bp, pdfv, atol=0.06, rtol=0.1)
+        assert_array_less(np.median(np.abs(pdf_bp - pdfv)), 0.05)
+
+    def test_rvs(self):
+        # currently smoke test
+        rvs = self.bpd.rvs(100)
+        assert len(rvs) == 100
+
+
+class TestBernsteinBeta2dd(TestBernsteinBeta2d):
+
+    @classmethod
+    def setup_class(cls):
+        grid = dt._Grid([91, 101])
+
+        cop_tr = tra.TransfFrank
+        args = (2,)
+        ca = ArchimedeanCopula(cop_tr())
+        distr1 = stats.beta(4, 3)
+        distr2 = stats.beta(4, 4)  # (5, 2)
+        cad = CopulaDistribution(ca, [distr1, distr2], cop_args=args)
+        cdfv = cad.cdf(grid.x_flat, args)
+        cdf_g = cdfv.reshape(grid.k_grid)
+
+        cls.grid = grid
+        cls.cdfv = cdfv
+        cls.distr = cad
+        cls.bpd = BernsteinDistribution(cdf_g)