Fix plot_tree crash on truncated and multi-output SGTRegressor nodes

.test-checker/ledger.jsonl

@@ -2079,3 +2079,16 @@
 {"ts":"2026-10-02T20:30:43.618Z","run":"r-20261002-162723-b8b5","type":"classified","exec":1,"counts":{"sgtlearn-ensemble-random-sgforest-regressor-py--randomsgforestregressor-check-x-y":{"accepted":47}},"queued":0}
 {"ts":"2026-10-02T20:30:43.673Z","run":"r-20261002-162723-b8b5","type":"run_reported","likely_bugs":0,"needs_decision":0}
 {"ts":"2026-10-02T20:30:43.740Z","run":"r-20261002-162723-b8b5","type":"promoted","files":["tests/regression/test_sgtlearn_ensemble_random_sgforest_regressor_py__randomsgforestregressor_check_x_y_1.py"],"verified":["sgtlearn/ensemble/random_sgforest_regressor.py"]}
+{"ts":"2026-10-03T01:57:33.139Z","run":"r-20261002-215733-b7b8","type":"run_started","mode":"bugfix","revisions":{"buggy":"53a5c148d71076030fca81ed23bf8df64780e3d2","fixed":"676c746e8e2c52431adb7bb362fb3c2428833a4f"},"given":{"buggy":"HEAD","fixed":"WORKTREE"}}
+{"ts":"2026-10-03T01:57:36.583Z","run":"r-20261002-215733-b7b8","type":"target_added","target":"sgtlearn-export-py--draw-leaf-text","file":"sgtlearn/_export.py","symbol":"_draw_leaf_text","lines":[685,765],"rev":"fixed","body_sha":"faf7044cbc68687fcfa0ea5b13ea538a606b5779d860a71be10b8c0c9ccb09a1"}
+{"ts":"2026-10-03T01:58:04.295Z","run":"r-20261002-215733-b7b8","type":"context_set","target":"sgtlearn-export-py--draw-leaf-text","shingle_ratio":0}
+{"ts":"2026-10-03T01:58:04.349Z","run":"r-20261002-215733-b7b8","type":"payload_emitted","payload":"p-e471ca8a","role":"spec","sha256":"6d27fc85bb8d000d2c86b86805d3ec975f42a1fda3dba494d8f92f355edf3aa9","target":"sgtlearn-export-py--draw-leaf-text"}
+{"ts":"2026-10-03T01:59:28.987Z","run":"r-20261002-215733-b7b8","type":"spec_saved","target":"sgtlearn-export-py--draw-leaf-text","shingle_ratio":0}
+{"ts":"2026-10-03T01:59:29.046Z","run":"r-20261002-215733-b7b8","type":"bundle_frozen","target":"sgtlearn-export-py--draw-leaf-text","bundle":"b-b7b8-sgtlearn-export-py--draw-leaf-text"}
+{"ts":"2026-10-03T01:59:34.750Z","run":"r-20261002-215733-b7b8","type":"payload_emitted","payload":"p-c938749f","role":"writer","sha256":"2bb722170802873f243950a6b3c462a56963a47bfb1eb760125e1fc085d7860b","target":"sgtlearn-export-py--draw-leaf-text"}
+{"ts":"2026-10-03T01:59:42.323Z","run":"r-20261002-215733-b7b8","type":"payload_emitted","payload":"p-4bd33228","role":"writer","sha256":"325fe67e3a0cfc9e93e785cb01f65540cddf10cd4c2a63ba227634458617b724","target":"sgtlearn-export-py--draw-leaf-text"}
+{"ts":"2026-10-03T02:00:21.771Z","run":"r-20261002-215733-b7b8","type":"tests_submitted","target":"sgtlearn-export-py--draw-leaf-text","round":0,"role":"writer","payload":"p-4bd33228","files":["test_sgtlearn_export_py__draw_leaf_text_1.py"]}
+{"ts":"2026-10-03T02:02:09.212Z","run":"r-20261002-215733-b7b8","type":"exec_completed","exec":1,"labels":{"buggy":{"failure":9,"pass":30,"load_errors":0},"fixed":{"pass":39,"load_errors":0}}}
+{"ts":"2026-10-03T02:02:11.890Z","run":"r-20261002-215733-b7b8","type":"classified","exec":1,"counts":{"sgtlearn-export-py--draw-leaf-text":{"effective":9,"neutral":30}},"queued":0}
+{"ts":"2026-10-03T02:02:15.743Z","run":"r-20261002-215733-b7b8","type":"run_reported","likely_bugs":0,"needs_decision":0}
+{"ts":"2026-10-03T02:02:15.813Z","run":"r-20261002-215733-b7b8","type":"promoted","files":["tests/regression/test_sgtlearn_export_py__draw_leaf_text_1.py"],"verified":["sgtlearn/_export.py"]}

.test-checker/reports/r-20261002-215733-b7b8.json

@@ -0,0 +1,41 @@
+{
+  "run": "r-20261002-215733-b7b8",
+  "mode": "bugfix",
+  "revisions": {
+    "buggy": {
+      "given": "HEAD",
+      "commit": "53a5c148d71076030fca81ed23bf8df64780e3d2"
+    },
+    "fixed": {
+      "given": "WORKTREE",
+      "commit": "676c746e8e2c52431adb7bb362fb3c2428833a4f"
+    }
+  },
+  "date": "2026-10-03T02:02:15.741Z",
+  "harness": "claude-code",
+  "blind_isolation": "strong",
+  "summary": [
+    {
+      "target": "sgtlearn-export-py--draw-leaf-text",
+      "effective": 9,
+      "misguided": 0,
+      "neutral": 30,
+      "broken": 0,
+      "flaky": 0,
+      "dropped": 0
+    }
+  ],
+  "likely_bugs": [],
+  "suspicions": [],
+  "needs_decision": [],
+  "misguided": [],
+  "dropped": [],
+  "promotable": [
+    {
+      "file": "tests/tcheck/test_sgtlearn_export_py__draw_leaf_text_1.py",
+      "dest": "tests/regression/test_sgtlearn_export_py__draw_leaf_text_1.py",
+      "tests": 39
+    }
+  ],
+  "not_promotable": []
+}

.test-checker/reports/r-20261002-215733-b7b8.md

@@ -0,0 +1,38 @@
+# test-checker report r-20261002-215733-b7b8
+
+- Mode: bugfix
+- buggy: HEAD → `53a5c148d7`
+- fixed: WORKTREE → `676c746e8e`
+- Date: 2026-10-03T02:02:15.741Z
+- Harness: claude-code
+- Blind isolation: strong
+
+## Summary
+
+| target | effective | misguided | neutral | broken | flaky | dropped |
+|---|---|---|---|---|---|---|
+| sgtlearn-export-py--draw-leaf-text | 9 | 0 | 30 | 0 | 0 | 0 |
+
+## Likely bugs
+
+_none_
+
+## Suspicions
+
+_none_
+
+## Needs your decision
+
+_none_
+
+## Misguided tests and what happened to them
+
+_none_
+
+## Dropped
+
+_none_
+
+## Promotable tests
+
+- tests/tcheck/test_sgtlearn_export_py__draw_leaf_text_1.py → `tests/regression/test_sgtlearn_export_py__draw_leaf_text_1.py` (39 test(s))

.test-checker/state.json

@@ -32,6 +32,11 @@
       "sha": "b2ad723f36c9520fda0013c7951e257e4907ba73",
       "run": "r-20261002-162723-b8b5",
       "at": "2026-10-02T20:30:43.728Z"
+    },
+    "sgtlearn/_export.py": {
+      "sha": "9af0b3c1a90f76795010d09eac3d2257bab5af37",
+      "run": "r-20261002-215733-b7b8",
+      "at": "2026-10-03T02:02:15.801Z"
     }
   },
   "protected": {
@@ -715,6 +720,50 @@
         "sgtlearn-ensemble-random-sgforest-regressor-py--randomsgforestregressor-check-x-y::tests.tcheck.test_sgtlearn_ensemble_random_sgforest_regressor_py__randomsgforestregressor_check_x_y_1::test_float32_overflow_in_sample_weight_is_rejected"
       ],
       "run": "r-20261002-162723-b8b5"
+    },
+    "tests/regression/test_sgtlearn_export_py__draw_leaf_text_1.py": {
+      "test_ids": [
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_single_output_shows_majority_class_name",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_tie_picks_lowest_class_index",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_all_equal_counts_picks_first_class",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_multi_output_shows_bracketed_comma_separated_names",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_multi_output_uses_per_output_class_names",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_empty_class_counts_shows_empty_string",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_classifier_missing_class_counts_shows_empty_string",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_none_value_shows_ellipsis",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_scalar_value_formatted_to_precision",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_scalar_value_respects_different_precision",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_scalar_value_zero_precision",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_multi_output_shows_bracketed_formatted_values",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_multi_output_tuple_sequence",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_regressor_ignores_class_names",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_is_bold",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_positioned_at_xy",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_uses_axes_transform",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_centred_horizontally_and_vertically",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_uses_given_fontsize",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_default_fontsize_when_none",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_uses_given_color",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_primary_line_is_added_to_host_axes",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_none_returns_single_text_artist",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_other_label_value_returns_single_text_artist",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_all_returns_two_text_artists",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_all_primary_artist_first",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_none_does_not_require_n_samples",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_shows_n_samples_without_impurity",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_includes_impurity_line_when_requested",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_for_classifier_node",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_is_not_bold",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_placed_below_primary",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_anchored_top_and_centred",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_uses_axes_transform",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_fontsize_one_smaller_than_integer_fontsize",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_subtitle_default_fontsize_when_fontsize_none",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_all_missing_n_samples_raises_key_error",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_all_with_impurity_missing_impurity_raises_key_error",
+        "sgtlearn-export-py--draw-leaf-text::tests.tcheck.test_sgtlearn_export_py__draw_leaf_text_1::test_label_all_without_impurity_does_not_need_impurity_key"
+      ],
+      "run": "r-20261002-215733-b7b8"
     }
   },
   "quarantined": [],

sgtlearn/_export.py

@@ -83,12 +83,6 @@ def group_label(cols: Sequence[int]) -> str:
             prefer_logical_name=feature_names is None,
         )
 
-    def per_output(items: Sequence[str]) -> str:
-        return items[0] if len(items) == 1 else "[" + ", ".join(items) + "]"
-
-    def fmt_list(values: Sequence[float]) -> str:
-        return "[" + ", ".join(f"{v:.{decimals}f}" for v in values) + "]"
-
     names: Any = class_names
     if names is None:
         names = getattr(estimator, "classes_", None)
@@ -98,14 +92,15 @@ def leaf_text(node: dict) -> str:
         if not isinstance(estimator, SGTClassifier):
             value = node["value"]
             if isinstance(value, (list, tuple)):
-                return f"value: {fmt_list(value)}"
+                return f"value: {_fmt_list(value, decimals)}"
             return f"value: {value:.{decimals}f}"
         counts = node["class_counts"]
-        text = "class: " + per_output(
+        text = "class: " + _per_output(
             [str(names_per_output[o][int(np.argmax(c))]) for o, c in enumerate(counts)]
         )
         if show_weights:
-            text = f"weights: {per_output([fmt_list(c) for c in counts])} {text}"
+            fmt = [_fmt_list(c, decimals) for c in counts]
+            text = f"weights: {_per_output(fmt)} {text}"
         return text
 
     lines: list[str] = []
@@ -126,6 +121,15 @@ def walk(nid: int, depth: int) -> None:
     return "\n".join(lines) + "\n"
 
 
+def _per_output(items: Sequence[str]) -> str:
+    """One item as-is; several (one per output) as ``[a, b]``."""
+    return items[0] if len(items) == 1 else "[" + ", ".join(items) + "]"
+
+
+def _fmt_list(values: Sequence[float], decimals: int) -> str:
+    return "[" + ", ".join(f"{v:.{decimals}f}" for v in values) + "]"
+
+
 def _check_fitted_sgt(estimator: Any, caller: str) -> None:
     if not isinstance(estimator, (SGTClassifier, SGTRegressor)):
         raise TypeError(
@@ -687,7 +691,7 @@ def _draw_leaf_text(
     node: dict,
     *,
     is_classifier: bool,
-    class_names: list[str] | None,
+    class_names: list[list[str]] | None,
     criterion: str,
     precision: int,
     fontsize: int | None,
@@ -697,19 +701,33 @@ def _draw_leaf_text(
 ) -> list:
     """Render a leaf as bold colored text (no box).
 
+    ``node`` may also be an internal node cut off by ``max_depth``. Regressor
+    internals carry no ``value``, so they render as ``"…"``; classifier
+    internals still carry ``class_counts`` and show their majority class.
+    Multi-output nodes show one entry per output, as ``[a, b]``.
+
     Returns a list of Text artists (one bold class/value line, optional
     subtitle lines for ``label='all'``).
     """
     artists: list = []
 
     if is_classifier:
-        counts_by_output = list(node.get("class_counts", []))
-        counts = list(counts_by_output[0]) if counts_by_output else []
-        if counts:
-            arg = max(range(len(counts)), key=lambda i: counts[i])
-            primary = class_names[arg] if class_names is not None else str(arg)
-        else:
-            primary = ""
+        assert class_names is not None
+        counts_by_output = node.get("class_counts") or []
+        primary = (
+            _per_output(
+                [
+                    class_names[o][int(np.argmax(counts))]
+                    for o, counts in enumerate(counts_by_output)
+                ]
+            )
+            if counts_by_output
+            else ""
+        )
+    elif node["value"] is None:
+        primary = "…"
+    elif isinstance(node["value"], (list, tuple)):
+        primary = _fmt_list(node["value"], precision)
     else:
         primary = f"{node['value']:.{precision}f}"
 
@@ -1395,11 +1413,15 @@ def plot_tree(
     class_names : list of str, bool, or None, default=None
         Class labels shown on classifier leaves, in ``classes_`` order.
         ``True`` uses ``classes_``; ``None`` or ``False`` shows the encoded
-        class index (``0``, ``1``, ...).
+        class index (``0``, ``1``, ...). For multi-output classifiers, pass
+        one list per output.
     label : str, default="feature"
         Sample-count annotations: ``"feature"`` shows ``n=`` on internal
         nodes, ``"all"`` also adds each leaf's ``n`` (and impurity when
-        ``impurity=True``), and ``"none"`` hides them.
+        ``impurity=True``), and ``"none"`` hides them. The ``n=`` counts are
+        the rounded weighted sample counts for classifiers (so they reflect
+        ``sample_weight`` and ``class_weight``) and unweighted training
+        sample counts for regressors.
     impurity : bool, default=False
         Whether to display leaf impurity; only used with ``label="all"``.
     proportion : bool, default=False
@@ -1462,19 +1484,20 @@ class index (``0``, ``1``, ...).
     palette = _build_palette(cmap, tree["num_partitions"])
 
     is_classifier = isinstance(estimator, SGTClassifier)
-    resolved_class_names: list[str] | None
+    # One list of class labels per output.
+    resolved_class_names: list[list[str]] | None
     if not is_classifier:
         resolved_class_names = None
-    elif class_names is True:
-        clf_estimator: SGTClassifier = estimator  # type: ignore[assignment]
-        classes: Any = (
-            clf_estimator.classes_ if clf_estimator.classes_ is not None else []
-        )
-        resolved_class_names = [str(c) for c in classes]
     elif class_names in (None, False):
-        resolved_class_names = [str(c) for c in range(tree["num_classes"][0])]
+        resolved_class_names = [[str(c) for c in range(k)] for k in tree["num_classes"]]
     else:
-        resolved_class_names = list(class_names)  # type: ignore[arg-type]
+        names: Any = (
+            estimator.classes_  # type: ignore[union-attr]
+            if class_names is True
+            else class_names
+        )
+        names_per_output = [names] if tree["num_outputs"] == 1 else names
+        resolved_class_names = [[str(c) for c in n] for n in names_per_output]
 
     feat_names = _resolve_feature_names(estimator, feature_names)