From 825a0532a0bd57f652465662a4cdb94d14803e0f Mon Sep 17 00:00:00 2001
From: ChrisW09 <50968720+ChrisW09@users.noreply.github.com>
Date: Mon, 27 Jul 2026 22:47:00 +0200
Subject: [PATCH] fix(core): report output_dim, not min_output_dim, when it is
 too small

``_resolve_output_bounds`` reported every floor violation against
``min_output_dim``, but ``lo`` is set to ``output_dim`` on the non-adaptive
branch. So setting ``output_dim=0`` blamed a parameter the caller had not
touched -- and one that is ignored entirely when ``adaptive`` is False:

    Preprocessor(numerical_method="ple", output_dim=0)
    -> min_output_dim must be >= 1, got 0.
       Fix: raise min_output_dim to at least the family minimum.

Following that advice would not have helped. Families that pre-validate
``output_dim`` themselves (cubicspline, bspline) already reported it correctly,
so the two halves of the package disagreed.

Name the parameter that produced the value.

    ple          output_dim=0 -> output_dim must be >= 1, got 0.
    cubicspline  output_dim=0 -> output_dim must be >= 3 for the cubic spline basis, got 0
    adaptive, min_output_dim=0 -> min_output_dim must be >= 1, got 0.

Closes #38

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 pretab/core/adaptive.py           |  9 +++++--
 tests/test_adaptive_resolution.py | 40 +++++++++++++++++++++++++++++++
 2 files changed, 47 insertions(+), 2 deletions(-)

diff --git a/pretab/core/adaptive.py b/pretab/core/adaptive.py
index 3015715..7a388ec 100644
--- a/pretab/core/adaptive.py
+++ b/pretab/core/adaptive.py
@@ -88,9 +88,14 @@ def _resolve_output_bounds(
 
         label = floor_label if floor_label is not None else str(floor)
         if lo < floor:
+            # ``lo`` is ``output_dim`` on the non-adaptive branch and whenever no
+            # explicit ``min_output_dim`` was supplied, so naming the parameter
+            # unconditionally pointed users at a knob they had not set -- and one
+            # that is ignored when ``adaptive`` is False.
+            name = "min_output_dim" if self.adaptive and min_req is not None else "output_dim"
             raise InvalidParamError(
-                f"min_output_dim must be >= {label}, got {lo}.\n"
-                "Fix: raise min_output_dim to at least the family minimum."
+                f"{name} must be >= {label}, got {lo}.\n"
+                f"Fix: raise {name} to at least the family minimum."
             )
         if ceil is not None and hi > ceil:
             raise InvalidParamError(
diff --git a/tests/test_adaptive_resolution.py b/tests/test_adaptive_resolution.py
index 7a5a10f..835974a 100644
--- a/tests/test_adaptive_resolution.py
+++ b/tests/test_adaptive_resolution.py
@@ -254,3 +254,43 @@ def test_preprocessor_adaptive_rbf_within_window(frame):
     out = pre.fit_transform(X, y, return_array=True)
     assert isinstance(out, np.ndarray)
     assert 2 * 3 <= out.shape[1] <= 2 * 9
+
+
+# --------------------------------------------------------------------------- #
+# The floor error must name the parameter the caller actually set.
+#
+# ``lo`` is ``output_dim`` on the non-adaptive branch, but the message always
+# said "min_output_dim" -- a knob the user had not touched, and one that is
+# ignored entirely when ``adaptive`` is False.
+# --------------------------------------------------------------------------- #
+def test_floor_error_names_output_dim_when_not_adaptive():
+    from pretab.core.exceptions import InvalidParamError
+
+    rng = np.random.default_rng(0)
+    frame = pd.DataFrame({"a": rng.normal(size=50)})
+
+    with pytest.raises(InvalidParamError, match="output_dim must be >= 1, got 0"):
+        Preprocessor(numerical_method="ple", output_dim=0).fit(frame, rng.normal(size=50))
+
+
+def test_floor_error_does_not_mention_min_output_dim_when_not_adaptive():
+    from pretab.core.exceptions import InvalidParamError
+
+    rng = np.random.default_rng(0)
+    frame = pd.DataFrame({"a": rng.normal(size=50)})
+
+    with pytest.raises(InvalidParamError) as excinfo:
+        Preprocessor(numerical_method="ple", output_dim=0).fit(frame, rng.normal(size=50))
+
+    assert "min_output_dim" not in str(excinfo.value)
+
+
+def test_floor_error_names_min_output_dim_when_it_was_set():
+    from pretab.core.exceptions import InvalidParamError
+    from pretab.transformers import PLETransformer
+
+    rng = np.random.default_rng(0)
+    X = rng.normal(size=(50, 1))
+
+    with pytest.raises(InvalidParamError, match="min_output_dim must be >= 1"):
+        PLETransformer(output_dim=5, adaptive=True, min_output_dim=0).fit(X, rng.normal(size=50))