Remove unused lines in composition module

src/elementembeddings/composition.py

@@ -145,11 +145,6 @@ def num_atoms(self) -> float:
         """Total number of atoms in Composition."""
         return self._natoms
 
-    @property
-    def embedding_dim(self) -> int:
-        """Dimension of the embedding."""
-        return self.embedding.dim
-
     def as_dict(self) -> dict:
         # TO-DO: Need to create a dict representation for the embedding class
         """Return the CompositionalEmbedding class as a dict."""
@@ -158,9 +153,6 @@ def as_dict(self) -> dict:
             "composition": self.composition,
             "fractional_composition": self.fractional_composition,
         }
-        # Se
-
-        # Set an attribute
 
     def _mean_feature_vector(self) -> np.ndarray:
         """Compute a weighted mean feature vector based of the embedding.
@@ -241,12 +233,6 @@ def feature_vector(self, stats: Union[str, list] = "mean"):
         ]
         if isinstance(stats, str):
             stats = [stats]
-        if not isinstance(stats, list):
-            msg = "Stats argument must be a list of strings"
-            raise ValueError(msg)
-        if not all(isinstance(s, str) for s in stats):
-            msg = "Stats argument must be a list of strings"
-            raise ValueError(msg)
         if not all(s in implemented_stats for s in stats):
             msg = (
                 f" {[stat for stat in stats if stat not in implemented_stats]} "

src/elementembeddings/tests/test_composition.py

@@ -182,3 +182,13 @@ def test_composition_distance(self):
             )
             == 0
         )
+
+        assert self.valid_magpie_compositions[0].distance(
+            self.formulas[1],
+            stats=["mean", "variance"],
+            distance_metric="cosine_distance",
+        ) == self.valid_magpie_compositions[1].distance(
+            self.valid_magpie_compositions[0],
+            stats=["mean", "variance"],
+            distance_metric="cosine_distance",
+        )