[BUG] fix scipy scale mapping for HalfNormal/HalfLogistic with regression tests (towards #22)

skpro/distributions/halfcauchy.py

@@ -67,7 +67,7 @@ def _get_scipy_object(self) -> rv_continuous:
 
     def _get_scipy_param(self):
         beta = self._bc_params["beta"]
-        return [beta], {}
+        return [], {"scale": beta}
 
     @classmethod
     def get_test_params(cls, parameter_set="default"):

skpro/distributions/halflogistic.py

@@ -68,7 +68,7 @@ def _get_scipy_object(self) -> rv_continuous:
 
     def _get_scipy_param(self):
         beta = self._bc_params["beta"]
-        return [beta], {}
+        return [], {"scale": beta}
 
     @classmethod
     def get_test_params(cls, parameter_set="default"):

skpro/distributions/halfnormal.py

@@ -63,7 +63,7 @@ def _get_scipy_object(self) -> rv_continuous:
 
     def _get_scipy_param(self):
         sigma = self._bc_params["sigma"]
-        return [sigma], {}
+        return [], {"scale": sigma}
 
     @classmethod
     def get_test_params(cls, parameter_set="default"):

skpro/distributions/tests/test_half_distributions.py

@@ -0,0 +1,110 @@
+"""Regression tests for half-distribution scipy parameter mappings."""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+
+import numpy as np
+import pandas as pd
+import pytest
+from numpy.testing import assert_allclose
+from scipy.stats import halflogistic, halfnorm
+
+from skpro.distributions.halflogistic import HalfLogistic
+from skpro.distributions.halfnormal import HalfNormal
+from skpro.tests.test_switch import run_test_module_changed
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halfnormal_scalar_matches_scipy_scale():
+    """HalfNormal scalar pdf/cdf/ppf should match scipy with scale=sigma."""
+    sigma = 2.5
+    dist = HalfNormal(sigma=sigma)
+
+    x = 1.7
+    p = 0.8
+
+    assert_allclose(dist.pdf(x), halfnorm.pdf(x, scale=sigma), rtol=1e-12)
+    assert_allclose(dist.cdf(x), halfnorm.cdf(x, scale=sigma), rtol=1e-12)
+    assert_allclose(dist.ppf(p), halfnorm.ppf(p, scale=sigma), rtol=1e-12)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halflogistic_scalar_matches_scipy_scale():
+    """HalfLogistic scalar pdf/cdf/ppf should match scipy with scale=beta."""
+    beta = 1.8
+    dist = HalfLogistic(beta=beta)
+
+    x = 1.3
+    p = 0.7
+
+    assert_allclose(dist.pdf(x), halflogistic.pdf(x, scale=beta), rtol=1e-12)
+    assert_allclose(dist.cdf(x), halflogistic.cdf(x, scale=beta), rtol=1e-12)
+    assert_allclose(dist.ppf(p), halflogistic.ppf(p, scale=beta), rtol=1e-12)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halfnormal_broadcast_and_shape():
+    """HalfNormal should preserve broadcasted shape/index/columns."""
+    sigma = [[1.0, 2.0], [3.0, 4.0]]
+    dist = HalfNormal(sigma=sigma)
+
+    x = pd.DataFrame([[0.5, 1.0], [2.0, 3.0]], index=dist.index, columns=dist.columns)
+    p = pd.DataFrame([[0.1, 0.2], [0.7, 0.9]], index=dist.index, columns=dist.columns)
+
+    pdf = dist.pdf(x)
+    cdf = dist.cdf(x)
+    ppf = dist.ppf(p)
+
+    assert pdf.shape == dist.shape
+    assert cdf.shape == dist.shape
+    assert ppf.shape == dist.shape
+    assert pdf.index.equals(dist.index)
+    assert cdf.index.equals(dist.index)
+    assert ppf.index.equals(dist.index)
+    assert pdf.columns.equals(dist.columns)
+    assert cdf.columns.equals(dist.columns)
+    assert ppf.columns.equals(dist.columns)
+
+    sigma_np = np.asarray(sigma)
+    assert_allclose(pdf.to_numpy(), halfnorm.pdf(x.to_numpy(), scale=sigma_np), rtol=1e-12)
+    assert_allclose(cdf.to_numpy(), halfnorm.cdf(x.to_numpy(), scale=sigma_np), rtol=1e-12)
+    assert_allclose(ppf.to_numpy(), halfnorm.ppf(p.to_numpy(), scale=sigma_np), rtol=1e-12)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halflogistic_broadcast_and_shape():
+    """HalfLogistic should preserve broadcasted shape/index/columns."""
+    beta = [[1.0, 2.0], [3.0, 4.0]]
+    dist = HalfLogistic(beta=beta)
+
+    x = pd.DataFrame([[0.5, 1.0], [2.0, 3.0]], index=dist.index, columns=dist.columns)
+    p = pd.DataFrame([[0.1, 0.2], [0.7, 0.9]], index=dist.index, columns=dist.columns)
+
+    pdf = dist.pdf(x)
+    cdf = dist.cdf(x)
+    ppf = dist.ppf(p)
+
+    assert pdf.shape == dist.shape
+    assert cdf.shape == dist.shape
+    assert ppf.shape == dist.shape
+    assert pdf.index.equals(dist.index)
+    assert cdf.index.equals(dist.index)
+    assert ppf.index.equals(dist.index)
+    assert pdf.columns.equals(dist.columns)
+    assert cdf.columns.equals(dist.columns)
+    assert ppf.columns.equals(dist.columns)
+
+    beta_np = np.asarray(beta)
+    assert_allclose(pdf.to_numpy(), halflogistic.pdf(x.to_numpy(), scale=beta_np), rtol=1e-12)
+    assert_allclose(cdf.to_numpy(), halflogistic.cdf(x.to_numpy(), scale=beta_np), rtol=1e-12)
+    assert_allclose(ppf.to_numpy(), halflogistic.ppf(p.to_numpy(), scale=beta_np), rtol=1e-12)

skpro/distributions/tests/test_halfcauchy.py

@@ -0,0 +1,71 @@
+"""Tests for HalfCauchy distribution."""
+# copyright: skpro developers, BSD-3-Clause License (see LICENSE file)
+
+import numpy as np
+import pandas as pd
+import pytest
+from numpy.testing import assert_allclose
+from scipy.stats import halfcauchy
+
+from skpro.distributions.halfcauchy import HalfCauchy
+from skpro.tests.test_switch import run_test_module_changed
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halfcauchy_scalar_matches_scipy():
+    """HalfCauchy scalar pdf/cdf/ppf should match scipy implementation."""
+    beta = 2.5
+    dist = HalfCauchy(beta=beta)
+
+    x = 1.7
+    p = 0.8
+
+    assert_allclose(dist.pdf(x), halfcauchy.pdf(x, scale=beta), rtol=1e-12)
+    assert_allclose(dist.cdf(x), halfcauchy.cdf(x, scale=beta), rtol=1e-12)
+    assert_allclose(dist.ppf(p), halfcauchy.ppf(p, scale=beta), rtol=1e-12)
+
+    # Check support behavior (x < 0 outside support)
+    assert_allclose(dist.pdf(-1.0), 0.0, atol=1e-15)
+    assert_allclose(dist.cdf(-1.0), 0.0, atol=1e-15)
+
+
+@pytest.mark.skipif(
+    not run_test_module_changed("skpro.distributions"),
+    reason="run only if skpro.distributions has been changed",
+)
+def test_halfcauchy_broadcast_and_shape():
+    """HalfCauchy should broadcast and preserve shape/index/columns."""
+    beta = [[1.0, 2.0], [3.0, 4.0]]
+    dist = HalfCauchy(beta=beta)
+
+    x = pd.DataFrame([[0.5, 1.0], [2.0, 3.0]], index=dist.index, columns=dist.columns)
+    p = pd.DataFrame([[0.1, 0.2], [0.7, 0.9]], index=dist.index, columns=dist.columns)
+
+    pdf = dist.pdf(x)
+    cdf = dist.cdf(x)
+    ppf = dist.ppf(p)
+
+    assert isinstance(pdf, pd.DataFrame)
+    assert isinstance(cdf, pd.DataFrame)
+    assert isinstance(ppf, pd.DataFrame)
+
+    assert pdf.shape == dist.shape
+    assert cdf.shape == dist.shape
+    assert ppf.shape == dist.shape
+
+    assert pdf.index.equals(dist.index)
+    assert cdf.index.equals(dist.index)
+    assert ppf.index.equals(dist.index)
+
+    assert pdf.columns.equals(dist.columns)
+    assert cdf.columns.equals(dist.columns)
+    assert ppf.columns.equals(dist.columns)
+
+    beta_np = np.asarray(beta)
+
+    assert_allclose(pdf.to_numpy(), halfcauchy.pdf(x.to_numpy(), scale=beta_np), rtol=1e-12)
+    assert_allclose(cdf.to_numpy(), halfcauchy.cdf(x.to_numpy(), scale=beta_np), rtol=1e-12)
+    assert_allclose(ppf.to_numpy(), halfcauchy.ppf(p.to_numpy(), scale=beta_np), rtol=1e-12)