chore: solve issues with formatting

Author: DiogoRibeiro7

tests/test_aft.py

@@ -1,6 +1,7 @@
 """
 Tests for Accelerated Failure Time (AFT) models.
 """
+
 import os
 import sys
 import pandas as pd
@@ -20,7 +21,7 @@ def test_gen_aft_log_logistic_runs():
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
     assert isinstance(df, pd.DataFrame)
     assert not df.empty
@@ -32,38 +33,45 @@ def test_gen_aft_log_logistic_runs():
 
 
 def test_gen_aft_log_logistic_invalid_shape():
-    """Test that the Log-Logistic AFT generator raises error for invalid shape."""
+    """Test that the Log-Logistic AFT generator raises error
+    for invalid shape."""
     with pytest.raises(ValueError, match="shape parameter must be positive"):
         gen_aft_log_logistic(
             n=10,
             beta=[0.5, -0.2],
             shape=-1.0,  # Invalid negative shape
             scale=2.0,
             model_cens="uniform",
-            cens_par=5.0
+            cens_par=5.0,
         )
 
 
 def test_gen_aft_log_logistic_invalid_scale():
-    """Test that the Log-Logistic AFT generator raises error for invalid scale."""
+    """Test that the Log-Logistic AFT generator raises error
+    for invalid scale."""
     with pytest.raises(ValueError, match="scale parameter must be positive"):
         gen_aft_log_logistic(
             n=10,
             beta=[0.5, -0.2],
             shape=1.5,
             scale=0.0,  # Invalid zero scale
             model_cens="uniform",
-            cens_par=5.0
+            cens_par=5.0,
         )
 
 
-
 @given(
     n=st.integers(min_value=1, max_value=20),
-    shape=st.floats(min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False),
-    scale=st.floats(min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False),
-    cens_par=st.floats(min_value=0.1, max_value=10.0, allow_nan=False, allow_infinity=False),
-    seed=st.integers(min_value=0, max_value=1000)
+    shape=st.floats(
+        min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False
+    ),
+    scale=st.floats(
+        min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False
+    ),
+    cens_par=st.floats(
+        min_value=0.1, max_value=10.0, allow_nan=False, allow_infinity=False
+    ),
+    seed=st.integers(min_value=0, max_value=1000),
 )
 def test_gen_aft_log_logistic_properties(n, shape, scale, cens_par, seed):
     """Property-based test for the Log-Logistic AFT generator."""
@@ -74,7 +82,7 @@ def test_gen_aft_log_logistic_properties(n, shape, scale, cens_par, seed):
         scale=scale,
         model_cens="uniform",
         cens_par=cens_par,
-        seed=seed
+        seed=seed,
     )
     assert df.shape[0] == n
     assert set(df["status"].unique()).issubset({0, 1})
@@ -83,52 +91,48 @@ def test_gen_aft_log_logistic_properties(n, shape, scale, cens_par, seed):
 
 
 def test_gen_aft_log_logistic_reproducibility():
-    """Test that the Log-Logistic AFT generator is reproducible with the same seed."""
+    """Test that the Log-Logistic AFT generator is reproducible
+    with the same seed."""
     df1 = gen_aft_log_logistic(
         n=10,
         beta=[0.5, -0.2],
         shape=1.5,
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
-    
+
     df2 = gen_aft_log_logistic(
         n=10,
         beta=[0.5, -0.2],
         shape=1.5,
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
-    
+
     pd.testing.assert_frame_equal(df1, df2)
-    
+
     df3 = gen_aft_log_logistic(
         n=10,
         beta=[0.5, -0.2],
         shape=1.5,
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=43  # Different seed
+        seed=43,  # Different seed
     )
-    
+
     with pytest.raises(AssertionError):
         pd.testing.assert_frame_equal(df1, df3)
 
 
 def test_gen_aft_log_normal_runs():
     """Test that the log-normal AFT generator runs without errors."""
     df = gen_aft_log_normal(
-        n=10,
-        beta=[0.5, -0.2],
-        sigma=1.0,
-        model_cens="uniform",
-        cens_par=5.0,
-        seed=42
+        n=10, beta=[0.5, -0.2], sigma=1.0, model_cens="uniform", cens_par=5.0, seed=42
     )
     assert isinstance(df, pd.DataFrame)
     assert not df.empty
@@ -148,7 +152,7 @@ def test_gen_aft_weibull_runs():
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
     assert isinstance(df, pd.DataFrame)
     assert not df.empty
@@ -168,7 +172,7 @@ def test_gen_aft_weibull_invalid_shape():
             shape=-1.0,  # Invalid negative shape
             scale=2.0,
             model_cens="uniform",
-            cens_par=5.0
+            cens_par=5.0,
         )
 
 
@@ -181,29 +185,37 @@ def test_gen_aft_weibull_invalid_scale():
             shape=1.5,
             scale=0.0,  # Invalid zero scale
             model_cens="uniform",
-            cens_par=5.0
+            cens_par=5.0,
         )
 
 
 def test_gen_aft_weibull_invalid_cens_model():
     """Test that the Weibull AFT generator raises error for invalid censoring model."""
-    with pytest.raises(ValueError, match="model_cens must be 'uniform' or 'exponential'"):
+    with pytest.raises(
+        ValueError, match="model_cens must be 'uniform' or 'exponential'"
+    ):
         gen_aft_weibull(
             n=10,
             beta=[0.5, -0.2],
             shape=1.5,
             scale=2.0,
             model_cens="invalid",  # Invalid censoring model
-            cens_par=5.0
+            cens_par=5.0,
         )
 
 
 @given(
     n=st.integers(min_value=1, max_value=20),
-    shape=st.floats(min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False),
-    scale=st.floats(min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False),
-    cens_par=st.floats(min_value=0.1, max_value=10.0, allow_nan=False, allow_infinity=False),
-    seed=st.integers(min_value=0, max_value=1000)
+    shape=st.floats(
+        min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False
+    ),
+    scale=st.floats(
+        min_value=0.1, max_value=5.0, allow_nan=False, allow_infinity=False
+    ),
+    cens_par=st.floats(
+        min_value=0.1, max_value=10.0, allow_nan=False, allow_infinity=False
+    ),
+    seed=st.integers(min_value=0, max_value=1000),
 )
 def test_gen_aft_weibull_properties(n, shape, scale, cens_par, seed):
     """Property-based test for the Weibull AFT generator."""
@@ -214,7 +226,7 @@ def test_gen_aft_weibull_properties(n, shape, scale, cens_par, seed):
         scale=scale,
         model_cens="uniform",
         cens_par=cens_par,
-        seed=seed
+        seed=seed,
     )
     assert df.shape[0] == n
     assert set(df["status"].unique()).issubset({0, 1})
@@ -231,30 +243,30 @@ def test_gen_aft_weibull_reproducibility():
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
-    
+
     df2 = gen_aft_weibull(
         n=10,
         beta=[0.5, -0.2],
         shape=1.5,
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=42
+        seed=42,
     )
-    
+
     pd.testing.assert_frame_equal(df1, df2)
-    
+
     df3 = gen_aft_weibull(
         n=10,
         beta=[0.5, -0.2],
         shape=1.5,
         scale=2.0,
         model_cens="uniform",
         cens_par=5.0,
-        seed=43  # Different seed
+        seed=43,  # Different seed
     )
-    
+
     with pytest.raises(AssertionError):
         pd.testing.assert_frame_equal(df1, df3)