Update isolated_labels.py

scib/metrics/isolated_labels.py

@@ -1,8 +1,7 @@
 import pandas as pd
-from sklearn.metrics import f1_score
+from sklearn.metrics import f1_score, silhouette_samples
 
 from .clustering import cluster_optimal_resolution
-from .silhouette import silhouette
 
 
 def isolated_labels_f1(
@@ -227,7 +226,10 @@ def max_f1(adata, label_key, cluster_key, label, argmax=False):
     else:
         # AWS score between isolated label vs rest
         adata.obs[iso_label_key] = adata.obs[label_key] == isolated_label
-        score = silhouette(adata, iso_label_key, embed)
+        adata.obs["silhouette_temp"] = silhouette_samples(
+            adata.obsm[embed], adata.obs[iso_label_key]
+        )
+        score = adata.obs[adata.obs[iso_label_key]].silhouette_temp.mean()
 
     if verbose:
         print(f"{isolated_label}: {score}")

tests/metrics/test_isolated_label.py

@@ -43,7 +43,7 @@ def test_isolated_labels_ASW(adata_pca):
         verbose=True,
     )
     LOGGER.info(f"score: {score}")
-    assert_near_exact(score, 0.6101431176066399, diff=1e-3)
+    assert_near_exact(score, 0.1938440054655075, diff=1e-3)
 
 
 def test_isolated_labels_perfect(adata_pca):

tests/metrics/test_silhouette_metrics.py

@@ -53,4 +53,4 @@ def test_isolated_labels_silhouette(adata_pca):
         verbose=True,
     )
     LOGGER.info(f"score: {score}")
-    assert_near_exact(score, 0.6101431176066399, diff=1e-3)
+    assert_near_exact(score, 0.1938440054655075, diff=1e-3)