split errors in two

evalml/pipelines/components/transformers/encoders/ordinal_encoder.py

@@ -154,14 +154,16 @@ def fit(self, X, y=None):
         elif self.parameters["categories"] is not None:
             # Categories specified - make sure they match the ordinal columns
             input_categories = self.parameters["categories"]
-            if len(input_categories) != len(self.features_to_encode) or not isinstance(
-                input_categories[0],
-                list,
-            ):
+
+            if len(input_categories) != len(self.features_to_encode):
                 raise ValueError(
-                    "Categories argument must contain a list of categories for each ordinal feature",
+                    "Categories argument must contain as many elements as there are Ordinal features.",
                 )
 
+            if not all(isinstance(cats, list) for cats in input_categories):
+                raise ValueError(
+                    "Each element of the categories argument must be a list.",
+                )
             # Categories, as they're passed into SKOrdinalEncoder should be in the same order
             # as the data's Ordinal.order categories even if it's a subset
             for i, col_categories in enumerate(input_categories):
@@ -210,7 +212,7 @@ def transform(self, X, y=None):
 
         Returns:
             pd.DataFrame: Transformed data, where each ordinal feature has been encoded into
-            a numerical column using where of ordinal integers represent
+            a numerical column where ordinal integers represent
             the relative order of categories.
         """
         X = infer_feature_types(X)

evalml/tests/component_tests/test_ordinal_encoder.py

@@ -5,7 +5,6 @@
 
 from evalml.exceptions import ComponentNotYetFittedError
 from evalml.pipelines.components import OrdinalEncoder
-from evalml.utils import get_random_seed
 
 
 def set_first_three_columns_to_ordinal_with_categories(X, categories):
@@ -68,16 +67,12 @@ def test_invalid_inputs():
         [["a", "b", "c", "d"], ["a", "b", "c"], ["a"]],
     )
     encoder = OrdinalEncoder(categories=[["a", "b"], ["a", "c"]])
-    error_msg = (
-        "Categories argument must contain a list of categories for each ordinal feature"
-    )
+    error_msg = "Categories argument must contain as many elements as there are Ordinal features."
     with pytest.raises(ValueError, match=error_msg):
         encoder.fit(X)
 
-    encoder = OrdinalEncoder(categories=["a", "b", "c"])
-    error_msg = (
-        "Categories argument must contain a list of categories for each ordinal feature"
-    )
+    encoder = OrdinalEncoder(categories=[["a", "b"], 1, ["a"]])
+    error_msg = "Each element of the categories argument must be a list."
     with pytest.raises(ValueError, match=error_msg):
         encoder.fit(X)