Fix explicit single-level boolean categories

patsy/categorical.py

@@ -360,7 +360,7 @@ def categorical_to_int(data, levels, NA_action, origin=None):
     # fastpath to avoid doing an item-by-item iteration over boolean arrays,
     # as requested by #44
     if hasattr(data, "dtype") and safe_issubdtype(data.dtype, np.bool_):
-        if level_to_int[False] == 0 and level_to_int[True] == 1:
+        if level_to_int.get(False) == 0 and level_to_int.get(True) == 1:
             return data.astype(np.int_)
     out = np.empty(len(data), dtype=int)
     for i, value in enumerate(data):

patsy/test_regressions.py

@@ -6,6 +6,9 @@
 # else)
 
 from patsy import EvalEnvironment, dmatrix, build_design_matrices, PatsyError, Origin
+import numpy as np
+import pytest
+from patsy.util import have_pandas
 
 
 def test_issue_11():
@@ -22,3 +25,45 @@ def test_issue_11():
         assert e.origin == Origin(formula, 0, 4)
     else:
         assert False
+
+
+@pytest.mark.parametrize("level", [False, True])
+def test_single_boolean_level_array(level):
+    matrix = dmatrix(
+        "0 + C(x, levels=levels)",
+        {"x": np.array([level, level]), "levels": [level]},
+    )
+    np.testing.assert_array_equal(matrix, [[1], [1]])
+    info = matrix.design_info
+    assert next(iter(info.factor_infos.values())).categories == (level,)
+    prediction = build_design_matrices(
+        [info], {"x": np.array([level]), "levels": [level]}
+    )[0]
+    np.testing.assert_array_equal(prediction, [[1]])
+
+
+@pytest.mark.parametrize("level", [False, True])
+def test_single_boolean_level_prediction(level):
+    matrix = dmatrix("0 + C(x, levels=levels)", {"x": [level], "levels": [level]})
+    prediction = build_design_matrices(
+        [matrix.design_info], {"x": np.array([level]), "levels": [level]}
+    )[0]
+    np.testing.assert_array_equal(prediction, [[1]])
+    with pytest.raises(PatsyError, match="does not match any of the expected levels"):
+        build_design_matrices(
+            [matrix.design_info], {"x": np.array([not level]), "levels": [level]}
+        )
+
+
+@pytest.mark.skipif(not have_pandas, reason="pandas is not available")
+@pytest.mark.parametrize("level", [False, True])
+def test_single_boolean_level_series(level):
+    import pandas
+
+    matrix = dmatrix(
+        "0 + C(x, levels=levels)",
+        {"x": pandas.Series([level, level], index=[10, 20]), "levels": [level]},
+        return_type="dataframe",
+    )
+    np.testing.assert_array_equal(matrix, [[1], [1]])
+    assert list(matrix.index) == [10, 20]