fix(deps): update dependency statsmodels to ^0.14.0 (#93)

* fix(deps): update dependency statsmodels to ^0.14.0

* Update poetry.lock

* specify statsmodel version

* fix tests

* remove 3.7 from ci tests

---------

Co-authored-by: renovate[bot] <29139614+renovate[bot]@users.noreply.github.com>
Co-authored-by: Xinyu Zou <[email protected]>
Co-authored-by: vjohnson1-godaddy <[email protected]>

.github/workflows/ci.yml

@@ -13,7 +13,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.8', '3.9', '3.10']
         include:
           - os: ubuntu-latest
           - os: windows-latest

pyproject.toml

@@ -9,8 +9,8 @@ maintainers = ["GoDaddy <[email protected]>"]
 keywords = ["sample size", "experimentation", "power analysis"]
 
 [tool.poetry.dependencies]
-python = ">=3.7.1,<3.11"
-statsmodels = "^0.13.1"
+python = ">=3.8,<3.11"
+statsmodels = "^0.14.0"
 jsonschema = "^4.5.1"
 
 [tool.poetry.dev-dependencies]

sample_size/metrics.py

@@ -104,7 +104,7 @@ def _generate_alt_p_values(
         z_alt = stats.norm.rvs(loc=effect_size, size=size, random_state=random_state)
         p_values: npt.NDArray[np.float_] = stats.norm.sf(np.abs(z_alt))
         if self.alternative == "two-sided":
-            p_values *= 2
+            return 2 * p_values
         return p_values
 
 
@@ -136,7 +136,7 @@ def _generate_alt_p_values(
         p_values: npt.NDArray[np.float_] = stats.t.sf(np.abs(t_alt), 2 * (sample_size - 1))
         # Todo: use accurate p-value calculation due to nct's asymmetric distribution
         if self.alternative == "two-sided":
-            p_values *= 2
+            return 2 * p_values
         return p_values
 
 
@@ -186,5 +186,5 @@ def _generate_alt_p_values(
         z_alt = stats.norm.rvs(loc=effect_size, size=size, random_state=random_state)
         p_values: npt.NDArray[np.float_] = stats.norm.sf(np.abs(z_alt))
         if self.alternative == "two-sided":
-            p_values *= 2
+            return 2 * p_values
         return p_values

sample_size/multiple_testing.py

@@ -101,7 +101,7 @@ def fdr_bh(a: npt.NDArray[np.float_]) -> npt.NDArray[np.bool_]:
             for i, m in enumerate(metrics):
                 p_values.append(m.generate_p_values(true_alt[i], sample_size, random_state))
 
-            rejected = np.apply_along_axis(fdr_bh, 0, np.array(p_values))  # type: ignore[no-untyped-call]
+            rejected = np.apply_along_axis(fdr_bh, 0, np.array(p_values))
 
             true_discoveries = rejected & true_alt
 

tests/sample_size/test_metrics.py

@@ -124,7 +124,7 @@ def test_boolean_metric_get_probability_too_small(self):
     @patch("scipy.stats.norm")
     def test_boolean__generate_alt_p_values(self, size, sample_size, alternative, mock_norm, mock_variance):
         p_value_generator = mock_norm.sf
-        p_values = MagicMock()
+        p_values = ["🏝️", "🏜️", "🌋"]
         mock_norm.rvs.return_value = -ord("🌮")
         p_value_generator.return_value = p_values
         mock_variance.__get__ = MagicMock(return_value=self.DEFAULT_MOCK_VARIANCE)
@@ -159,7 +159,7 @@ def test_numeric_metric_constructor_sets_params(self):
     @patch("scipy.stats.t")
     def test_numeric__generate_alt_p_values(self, size, sample_size, alternative, mock_t, mock_nct, mock_variance):
         p_value_generator = mock_t.sf
-        p_values = MagicMock()
+        p_values = ["🏝️", "🏜️", "🌋"]
         mock_nct.rvs.return_value = -ord("🌮")
         p_value_generator.return_value = p_values
         mock_variance.__get__ = MagicMock(return_value=self.DEFAULT_VARIANCE)
@@ -226,7 +226,7 @@ def test_ratio_metric_variance(self):
     @patch("scipy.stats.norm")
     def test_ratio__generate_alt_p_values(self, size, sample_size, alternative, mock_norm, mock_variance):
         p_value_generator = mock_norm.sf
-        p_values = MagicMock()
+        p_values = ["🏝️", "🏜️", "🌋"]
         mock_norm.rvs.return_value = -ord("🌮")
         p_value_generator.return_value = p_values
         mock_variance.__get__ = MagicMock(return_value=self.DEFAULT_VARIANCE)
@@ -238,7 +238,7 @@ def test_ratio__generate_alt_p_values(self, size, sample_size, alternative, mock
             self.DEFAULT_DENOMINATOR_VARIANCE,
             self.DEFAULT_COVARIANCE,
             self.DEFAULT_MDE,
-            self.DEFAULT_ALTERNATIVE,
+            alternative,
         )
 
         p = metric._generate_alt_p_values(size, sample_size, RANDOM_STATE)