Refactor tests for Tweedie distribution: improve readability and incr…

…ease sample size in test_sample

pygam/tests/test_tweedie.py

@@ -3,10 +3,12 @@
 from pygam.distributions import TweedieDist
 from pygam import GAM, s
 
+
 @pytest.fixture
 def tweedie_dist():
     return TweedieDist(power=1.5, scale=1.0)
 
+
 def test_log_pdf(tweedie_dist):
     mu = np.array([1.0, 2.0, 3.0])
     y = np.array([1.5, 2.5, 3.5])
@@ -15,34 +17,40 @@ def test_log_pdf(tweedie_dist):
     assert log_pdf.shape == y.shape
     assert np.all(np.isfinite(log_pdf)), "Log PDF contains non-finite values."
 
+
 def test_deviance(tweedie_dist):
     mu = np.array([1.0, 2.0, 3.0])
     y = np.array([1.5, 2.5, 3.5])
     deviance = tweedie_dist.deviance(y, mu, scaled=True)
     assert deviance.shape == y.shape
     assert np.all(deviance >= 0), "Deviance contains negative values."
 
+
 def test_sample(tweedie_dist):
     mu = np.array([1.0, 2.0, 3.0])
     # Generate 1000 samples for each mu
-    samples = np.array([tweedie_dist.sample(mu) for _ in range(100)])
+    samples = np.array([tweedie_dist.sample(mu) for _ in range(1000)])
     sample_mean = np.mean(samples)
     expected_mean = np.mean(mu)
     # Adjust the tolerance based on the variance
     tolerance = 0.1 * expected_mean
-    assert abs(sample_mean - expected_mean) < tolerance, "Sample mean is not within the expected range."
+    assert (
+        abs(sample_mean - expected_mean) < tolerance
+    ), "Sample mean is not within the expected range."
 
 
 def test_invalid_power():
     with pytest.raises(ValueError):
         TweedieDist(power=0.5, scale=1.0)  # Power less than 1 is invalid
 
+
 def test_not_implemented_power():
     dist = TweedieDist(power=3.0, scale=1.0)
     mu = np.array([1.0, 2.0, 3.0])
     with pytest.raises(NotImplementedError):
         dist.sample(mu)
 
+
 def test_gam_tweedie_fit():
     # Generate synthetic data
     np.random.seed(0)
@@ -67,20 +75,29 @@ def test_gam_tweedie_fit():
     # Optionally, check if the mean of predictions is close to the mean of y
     sample_mean = np.mean(y_pred)
     expected_mean = np.mean(y)
-    assert abs(sample_mean - expected_mean) < 1.0, "Sample mean is not within the expected range."
+    assert (
+        abs(sample_mean - expected_mean) < 1.0
+    ), "Sample mean is not within the expected range."
+
 
 def test_variance_function(tweedie_dist):
     mu = np.array([1.0, 2.0, 3.0])
     variance = tweedie_dist.V(mu)
-    expected_variance = mu ** tweedie_dist.power
-    assert np.allclose(variance, expected_variance), "Variance function V(mu) is incorrect."
+    expected_variance = mu**tweedie_dist.power
+    assert np.allclose(
+        variance, expected_variance
+    ), "Variance function V(mu) is incorrect."
+
 
 def test_zero_targets(tweedie_dist):
     mu = np.array([1.0, 2.0, 3.0])
     y = np.array([0.0, 0.0, 0.0])
     log_pdf = tweedie_dist.log_pdf(y, mu)
     assert log_pdf.shape == y.shape
-    assert np.all(np.isfinite(log_pdf)), "Log PDF with zero targets contains non-finite values."
+    assert np.all(
+        np.isfinite(log_pdf)
+    ), "Log PDF with zero targets contains non-finite values."
+
 
 def test_negative_inputs(tweedie_dist):
     mu = np.array([-1.0, 2.0, 3.0])
@@ -90,21 +107,27 @@ def test_negative_inputs(tweedie_dist):
     with pytest.raises(ValueError):
         tweedie_dist.deviance(y, mu)
 
+
 def test_sample_with_zero_mu(tweedie_dist):
     mu = np.array([0.0, 0.0, 0.0])
     samples = tweedie_dist.sample(mu)
     assert np.all(samples == 0), "Samples with zero mu should be zeros."
 
+
 def test_boundary_power_values():
     mu = np.array([1.0, 2.0, 3.0])
     y = np.array([1.0, 2.0, 3.0])
 
     # Power approaching 1
     tweedie_dist = TweedieDist(power=1.0001, scale=1.0)
     log_pdf = tweedie_dist.log_pdf(y, mu)
-    assert np.all(np.isfinite(log_pdf)), "Log PDF near power=1 contains non-finite values."
+    assert np.all(
+        np.isfinite(log_pdf)
+    ), "Log PDF near power=1 contains non-finite values."
 
     # Power approaching 2
     tweedie_dist = TweedieDist(power=1.9999, scale=1.0)
     log_pdf = tweedie_dist.log_pdf(y, mu)
-    assert np.all(np.isfinite(log_pdf)), "Log PDF near power=2 contains non-finite values."
+    assert np.all(
+        np.isfinite(log_pdf)
+    ), "Log PDF near power=2 contains non-finite values."