Auto-format with Black

Author: actions-user

tests/test_labelshift.py

@@ -1,6 +1,7 @@
 import numpy as np
 from ppi_py import *
 
+
 def test_ppi_distribution_label_shift_ci_sorted():
     """Test ppi_distribution_label_shift_ci with various sorting scenarios."""
     n = 1000
@@ -14,28 +15,38 @@ def test_ppi_distribution_label_shift_ci_sorted():
 
     # Determine K and nu
     unique_Yhat = np.unique(Yhat)
-    K = len(unique_Yhat)  # Should be equal to num_classes if all classes are present in Yhat
+    K = len(
+        unique_Yhat
+    )  # Should be equal to num_classes if all classes are present in Yhat
 
     # Set nu (example: vector of ones)
     nu = np.ones(K)
 
     # Test 1: Unsorted Yhat_unlabeled (implicit in the current implementation)
-    ci_unsorted = ppi_distribution_label_shift_ci(Y, Yhat, Yhat_unlabeled, K, nu, alpha=0.1)
+    ci_unsorted = ppi_distribution_label_shift_ci(
+        Y, Yhat, Yhat_unlabeled, K, nu, alpha=0.1
+    )
 
     # Test 2: Explicitly sorted Yhat_unlabeled (to demonstrate no dependence on order)
     uq, uq_counts = np.unique(Yhat_unlabeled, return_counts=True)
     sort_indices = np.argsort(uq)
-    Yhat_unlabeled_sorted = Yhat_unlabeled[np.argsort(np.argsort(Yhat_unlabeled))] # Stable sort to mimic previous behavior.
-    ci_sorted = ppi_distribution_label_shift_ci(Y, Yhat, Yhat_unlabeled_sorted, K, nu, alpha=0.1)
-
+    Yhat_unlabeled_sorted = Yhat_unlabeled[
+        np.argsort(np.argsort(Yhat_unlabeled))
+    ]  # Stable sort to mimic previous behavior.
+    ci_sorted = ppi_distribution_label_shift_ci(
+        Y, Yhat, Yhat_unlabeled_sorted, K, nu, alpha=0.1
+    )
 
     # Test 3: Reverse sorted Yhat_unlabeled
     uq, uq_counts = np.unique(Yhat_unlabeled, return_counts=True)
     sort_indices = np.argsort(uq)[::-1]
-    Yhat_unlabeled_reverse_sorted = Yhat_unlabeled[np.argsort(np.argsort(Yhat_unlabeled))[::-1]]
-    ci_reverse_sorted = ppi_distribution_label_shift_ci(Y, Yhat, Yhat_unlabeled_reverse_sorted, K, nu, alpha=0.1)
-
+    Yhat_unlabeled_reverse_sorted = Yhat_unlabeled[
+        np.argsort(np.argsort(Yhat_unlabeled))[::-1]
+    ]
+    ci_reverse_sorted = ppi_distribution_label_shift_ci(
+        Y, Yhat, Yhat_unlabeled_reverse_sorted, K, nu, alpha=0.1
+    )
 
     # The confidence intervals should be identical regardless of the sorting of Yhat_unlabeled
     np.testing.assert_allclose(ci_unsorted, ci_sorted)
-    np.testing.assert_allclose(ci_unsorted, ci_reverse_sorted)
+    np.testing.assert_allclose(ci_unsorted, ci_reverse_sorted)