Add some more tests for the evaluation mixin

Author: Marius1311

tests/test_evaluate.py

@@ -1,3 +1,5 @@
+import numpy as np
+import pandas as pd
 import pytest
 
 
@@ -15,3 +17,34 @@ def test_evaluate_expression_transfer_layers_and_methods(self, cmap, eval_layer,
         assert cmap.query.var[f"metric_{method}"] is not None
         if groupby == "batch":
             assert cmap.query.varm[f"metric_{method}"] is not None
+
+    @pytest.mark.parametrize(
+        "log,percentile",
+        [
+            (False, (0, 100)),
+            (True, (0, 100)),
+            (False, (5, 95)),
+            (True, (1, 99)),
+        ],
+    )
+    def test_presence_score_overall(self, cmap, log, percentile):
+        cmap.estimate_presence_score(log=log, percentile=percentile)
+        assert "presence_score" in cmap.ref.obs
+        scores = cmap.ref.obs["presence_score"]
+        assert isinstance(scores, pd.Series | np.ndarray)
+        assert np.all((scores >= 0) & (scores <= 1))
+        assert not np.all(scores == 0)  # Should not be all zeros
+
+    @pytest.mark.parametrize("groupby", ["batch", "modality"])
+    def test_presence_score_groupby(self, cmap, groupby):
+        cmap.estimate_presence_score(groupby=groupby)
+        # Overall score should always be present in .obs
+        assert "presence_score" in cmap.ref.obs
+        # Per-group scores should be present in .obsm
+        assert "presence_score" in cmap.ref.obsm
+        df = cmap.ref.obsm["presence_score"]
+        assert isinstance(df, pd.DataFrame)
+        assert all(np.all((df[col] >= 0) & (df[col] <= 1)) for col in df.columns)
+        # Columns should match group names
+        groups = cmap.query.obs[groupby].unique()
+        assert set(df.columns) == set(groups)