Publish combinations with the combination method chosen on validation

Every two- and three-model combination now selects, per task, among
concatenation of prepared feature blocks and five training-free posterior
combiners, on validation macro-F1, and reads test once for the winner. That
admits the two strongest atomic contenders -- end-to-end GreBERTa and the
siamese encoder -- which emit predictions rather than stackable columns and
could not enter a concatenation at all.

Pooling wins 173 of 216 Sphragis cells and the geometric mean is the most
chosen method; the new best on one sentence is siamese with syntax rates and
lemma TF-IDF at 77.87, five points over the best atomic model. On the
source-held-out track concatenation still takes the top cells, so the choice
genuinely varies by track. Concatenation-only rows remain only for the
KainoBERT twins, which the method search did not cover. Rows containing the
authorial language models will be refreshed when their remake lands.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

.logs/publish-lb-2062343.err

@@ -0,0 +1,53 @@
+The following modules were not unloaded:
+  (Use "module --force purge" to unload all):
+
+  1) hpc_sysenv/.1   2) hpc/.2.1.0
+Traceback (most recent call last):
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 801, in <module>
+    main()
+    ~~~~^^
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 780, in main
+    sphragis_rows, metre_rows = make_rows(
+                                ~~~~~~~~~^
+        args.results_root, args.sphragis_data, args.metre_data
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 544, in make_rows
+    sphragis_rows = [
+                    ^
+    ...<109 lines>...
+    ]
+    ^
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 643, in <genexpr>
+    build_row(
+    ~~~~~~~~~^
+        model=pair_model_name(combo),
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    ...<5 lines>...
+        dataset_roots=roots,
+        ^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 123, in build_row
+    score, accuracy, confusion = diagnostics(
+                                 ~~~~~~~~~~~^
+        predictions_path, dataset_root, task, saved_macro_f1
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 60, in diagnostics
+    predictions = read_predictions(predictions_path)
+  File "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/scripts/publish_atomic_results.py", line 29, in read_predictions
+    rows = [json.loads(line) for line in path.read_text().splitlines() if line.strip()]
+                                         ~~~~~~~~~~~~~~^^
+  File "/software/sse2/el9_gh200/easybuild/prefix/software/Python/3.13.5-GCCcore-14.3.0/lib/python3.13/pathlib/_local.py", line 546, in read_text
+    return PathBase.read_text(self, encoding, errors, newline)
+           ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/software/sse2/el9_gh200/easybuild/prefix/software/Python/3.13.5-GCCcore-14.3.0/lib/python3.13/pathlib/_abc.py", line 632, in read_text
+    with self.open(mode='r', encoding=encoding, errors=errors, newline=newline) as f:
+         ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/software/sse2/el9_gh200/easybuild/prefix/software/Python/3.13.5-GCCcore-14.3.0/lib/python3.13/pathlib/_local.py", line 537, in open
+    return io.open(self, mode, buffering, encoding, errors, newline)
+           ~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+FileNotFoundError: [Errno 2] No such file or directory: 'results/rebuilt-2026-08-27/combination-method/alm-burrows/sphragis-main/sentence_5/predictions.jsonl'

.logs/publish-lb-2062418.err

@@ -0,0 +1,4 @@
+The following modules were not unloaded:
+  (Use "module --force purge" to unload all):
+
+  1) hpc_sysenv/.1   2) hpc/.2.1.0

.logs/publish-lb-2062418.out

@@ -0,0 +1 @@
+{"output": "/nobackup/proj/flash/dionysus/personal/cleland/sphragis-leaderboard/src/lib/atomicModelResults.ts", "sphragis_rows": 105, "metre_rows": 96, "validated_tasks": 842}

404.html

@@ -4,11 +4,11 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1" />
     <meta name="theme-color" content="#f7f5ef" />
-    <link href="/_app/immutable/entry/start.CWAvbtCY.js" rel="modulepreload">
+    <link href="/_app/immutable/entry/start.CVIQ-jrx.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/Dnoir60M.js" rel="modulepreload">
-		<link href="/_app/immutable/chunks/7n4STY6M.js" rel="modulepreload">
+		<link href="/_app/immutable/chunks/BluVH81E.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CGlyOndm.js" rel="modulepreload">
-		<link href="/_app/immutable/entry/app.BV2I02n0.js" rel="modulepreload">
+		<link href="/_app/immutable/entry/app.g6Xc-uhS.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CLflRIDg.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/CTfzyGBz.js" rel="modulepreload">
 		<link href="/_app/immutable/chunks/7ar41ERE.js" rel="modulepreload">
@@ -21,15 +21,15 @@
     <div style="display: contents">
 			<script>
 				{
-					__sveltekit_1ez977 = {
+					__sveltekit_7v03y4 = {
 						base: ""
 					};
 
 					const element = document.currentScript.parentElement;
 
 					Promise.all([
-						import("/_app/immutable/entry/start.CWAvbtCY.js"),
-						import("/_app/immutable/entry/app.BV2I02n0.js")
+						import("/_app/immutable/entry/start.CVIQ-jrx.js"),
+						import("/_app/immutable/entry/app.g6Xc-uhS.js")
 					]).then(([kit, app]) => {
 						kit.start(app, element);
 					});

_app/immutable/chunks/{7n4STY6M.js → BluVH81E.js}

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_app/immutable/entry/{app.BV2I02n0.js → app.g6Xc-uhS.js}

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_app/immutable/nodes/{1.DVVp9ljg.js → 1.CyMVKWdX.js}

@@ -1 +1 @@
-import{a as g,f as c}from"../chunks/CTfzyGBz.js";import{p as h,l as v,x as l,o as x,v as e,w as s,n as _}from"../chunks/Dnoir60M.js";import{s as o}from"../chunks/CLflRIDg.js";import{p}from"../chunks/7n4STY6M.js";const d={get error(){return p.error},get status(){return p.status}},m=d;var $=c("<h1> </h1> <p> </p>",1);function k(n,f){h(f,!0);var t=$(),r=v(t),i=e(r,!0);s(r);var a=_(r,2),u=e(a,!0);s(a),l(()=>{o(i,m.status),o(u,m.error?.message)}),g(n,t),x()}export{k as component};
+import{a as g,f as c}from"../chunks/CTfzyGBz.js";import{p as h,l as v,x as l,o as x,v as e,w as s,n as _}from"../chunks/Dnoir60M.js";import{s as o}from"../chunks/CLflRIDg.js";import{p}from"../chunks/BluVH81E.js";const d={get error(){return p.error},get status(){return p.status}},m=d;var $=c("<h1> </h1> <p> </p>",1);function k(n,f){h(f,!0);var t=$(),r=v(t),i=e(r,!0);s(r);var a=_(r,2),u=e(a,!0);s(a),l(()=>{o(i,m.status),o(u,m.error?.message)}),g(n,t),x()}export{k as component};

_app/version.json

@@ -1 +1 @@
-{"version":"1788339231393"}
+{"version":"1788621838584"}

scripts/publish_atomic_results.py

@@ -259,6 +259,42 @@ def pair_specs(root: Path, pair: str, family: str, tasks: tuple[str, ...]):
     return specs, scalings, counts
 
 
+def method_specs(root: Path, family: str, tasks: tuple[str, ...]) -> dict[str, tuple]:
+    """Specs for every combination whose method was settled on validation.
+
+    ``scripts/run_method_search.py`` scores concatenation and five posterior
+    combiners on validation, keeps the best, and reads test once for it. Each
+    cell records the winning method and where that method's predictions live:
+    a pooling win writes its own, a concatenation win points back into the pair
+    grid. Returns {combination: (specs, methods)} keyed by the arms joined with
+    a hyphen, in the search's sorted order.
+    """
+    out: dict[str, tuple] = {}
+    for combo_dir in sorted((root / "combination-method").glob("*")):
+        family_dir = combo_dir / family
+        if not family_dir.is_dir():
+            continue
+        specs, methods = [], []
+        for task in tasks:
+            selection_path = family_dir / task / "selection.json"
+            if not selection_path.is_file():
+                continue
+            selection = read_json(selection_path)
+            if selection.get("test_loaded") is not True or not selection.get("predictions_path"):
+                raise ValueError(f"combination not finalized: {selection_path}")
+            methods.append(str(selection["selected"]["method"]))
+            predictions = Path(selection["predictions_path"])
+            if not predictions.is_absolute():
+                # The search was run from the models repository root with a
+                # relative output root; resolve against that root.
+                predictions = root.parent.parent / predictions
+            specs.append((task, predictions, selection_path,
+                          float(selection["test_metrics"]["macro_f1"])))
+        if specs:
+            out[combo_dir.name] = (specs, methods)
+    return out
+
+
 def dimensions_for_specs(specs: list[tuple[str, Path, Path, float]]) -> list[int]:
     return [feature_count(read_json(metrics_path)) for _, _, metrics_path, _ in specs]
 
@@ -349,6 +385,17 @@ def make_rows(results_root: Path, sphragis_data: Path, metre_data: Path):
         "kainobert-syntax", "kainobert-char-tfidf", "kainobert-tfidf-alm",
         "kainobert-burrows-alm", "kainobert-burrows-char", "kainobert-char-alm",
     )
+    method_main = method_specs(results_root, "sphragis-main", MAIN_TASKS)
+    method_source = method_specs(results_root, "sphragis-source-held-out", SOURCE_TASKS)
+    method_sphragis: dict[str, tuple] = {}
+    for combo in sorted(set(method_main) | set(method_source)):
+        a = method_main.get(combo, ([], [])); b = method_source.get(combo, ([], []))
+        method_sphragis[combo] = (a[0] + b[0], a[1] + b[1])
+    method_metre = method_specs(results_root, "sphragis-metre", VERSE_TASKS)
+    settled = {frozenset(c.split("-")) for c in list(method_sphragis) + list(method_metre)}
+    # A concatenation-only row stays only where the method search never ran,
+    # which today means the KainoBERT twins.
+    pairs = tuple(p for p in pairs if frozenset(p.split("-")) not in settled)
     pair_data: dict[str, tuple] = {}
     for pair in pairs:
         main = pair_specs(results_root, pair, "sphragis-main", MAIN_TASKS)
@@ -368,6 +415,7 @@ def make_rows(results_root: Path, sphragis_data: Path, metre_data: Path):
         "burrows-char-tfidf",
     )
     metre_pair_data: dict[str, tuple] = {}
+    metre_pairs = tuple(p for p in metre_pairs if frozenset(p.split("-")) not in settled)
     for pair in metre_pairs:
         try:
             metre_pair_data[pair] = pair_specs(results_root, pair, "sphragis-metre", VERSE_TASKS)
@@ -398,8 +446,33 @@ def make_rows(results_root: Path, sphragis_data: Path, metre_data: Path):
         "burrows": "Burrows z-scores",
         "alm": "ALM",
         "metre": "metrical line features",
+        "greberta_e2e": "GreBERTa (end-to-end)",
+        "siamese": "Siamese GreBERTa",
+    }
+    METHOD_NAMES = {
+        "concatenation": "concatenation", "vote_hard": "hard vote", "vote_soft": "soft vote",
+        "logit_mean": "geometric mean", "rank_mean": "mean rank",
+        "vote_weighted": "validation-weighted vote",
     }
 
+    def method_classifier(methods: list[str]) -> str:
+        counts = Counter(METHOD_NAMES[m] for m in methods)
+        return "Validation-selected per task: " + ", ".join(
+            f"{name} \u00d7{n}" if n > 1 else name for name, n in counts.most_common())
+
+    def method_status(combo: str) -> str:
+        arms = combo.split("-")
+        note = ""
+        if "alm" in arms:
+            note = "; the authorial language models are being remade and this row will be refreshed"
+        return (
+            f"{len(arms)} atomic models combined; the combination method is a hyperparameter "
+            "chosen per task on validation macro-F1 among concatenation of prepared feature "
+            "blocks and five training-free posterior combiners (hard vote, soft vote, geometric "
+            "mean, mean rank, validation-weighted vote); test evaluated once for the chosen "
+            "method" + note
+        )
+
     def pair_model_name(pair: str) -> str:
         return " + ".join(ARM_NAMES[arm] for arm in pair.split("-"))
 
@@ -571,6 +644,18 @@ def make_rows(results_root: Path, sphragis_data: Path, metre_data: Path):
             )
             for pair in pairs
         ),
+        *(
+            build_row(
+                model=pair_model_name(combo),
+                url="https://github.com/Urdatorn/sphragis_models",
+                dimensionality=f"{len(combo.split('-'))} arms",
+                classifier=method_classifier(method_sphragis[combo][1]),
+                status=method_status(combo),
+                task_specs=list(method_sphragis[combo][0]),
+                dataset_roots=roots,
+            )
+            for combo in method_sphragis
+        ),
     ]
     metre_rows = [
         build_row(
@@ -674,6 +759,18 @@ def make_rows(results_root: Path, sphragis_data: Path, metre_data: Path):
             )
             for pair in metre_pairs if pair in metre_pair_data
         ),
+        *(
+            build_row(
+                model=pair_model_name(combo),
+                url="https://github.com/Urdatorn/sphragis_models",
+                dimensionality=f"{len(combo.split('-'))} arms",
+                classifier=method_classifier(method_metre[combo][1]),
+                status=method_status(combo),
+                task_specs=list(method_metre[combo][0]),
+                dataset_roots=roots,
+            )
+            for combo in method_metre
+        ),
     ]
     return sphragis_rows, metre_rows