Correctly create missing category from model_spec

src/tabmat/formula.py

@@ -425,6 +425,7 @@ def from_categorical(
         reduced_rank: bool,
         missing_method: str = "fail",
         missing_name: str = "(MISSING)",
+        force_convert: bool = False,
     ) -> "_InteractableCategoricalVector":
         """Create an interactable categorical vector from a pandas categorical."""
         categories = list(cat.categories)
@@ -441,7 +442,7 @@ def from_categorical(
                 "if [cat_]missing_method='fail'."
             )
 
-        if missing_method == "convert" and -1 in codes:
+        if missing_method == "convert" and (-1 in codes or force_convert):
             codes[codes == -1] = len(categories)
             categories.append(missing_name)
 
@@ -718,14 +719,17 @@ def encode_contrasts(
             order to avoid spanning the intercept.
     """
     levels = levels if levels is not None else _state.get("categories")
+    force_convert = _state.get("force_convert", False)
     cat = pandas.Categorical(data._values, categories=levels)
     _state["categories"] = cat.categories
+    _state["force_convert"] = missing_method == "convert" and cat.isna().any()
 
     return _InteractableCategoricalVector.from_categorical(
         cat,
         reduced_rank=reduced_rank,
         missing_method=missing_method,
         missing_name=missing_name,
+        force_convert=force_convert,
     )
 
 

tests/test_formula.py

@@ -690,6 +690,10 @@ def test_cat_missing_C():
 
     assert result.column_names == expected_names
     assert result.model_spec.get_model_matrix(df).column_names == expected_names
+    np.testing.assert_equal(result.model_spec.get_model_matrix(df).A, result.A)
+    np.testing.assert_equal(
+        result.model_spec.get_model_matrix(df[:2]).A, result.A[:2, :]
+    )
 
 
 @pytest.mark.parametrize(