Object type mix: INTF, TABL, STRU, MSAG, exception tasks (harness G2, mutants, generator notes), balanced generator, kind-deficit job order, dashboard mix card

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>

harness/mix.py

@@ -0,0 +1,79 @@
+"""Object type mix of the training data (CLAUDE.md section 1, Opus review 2026-10-05).
+
+Target share of the accepted tasks (and of the accepted trajectories) per "kind":
+  CLAS/INTF 35 % (CLAS 28, INTF 7), CDS (DDLS) 25 %, FUNC 15 %, PROG 10 %, DDIC 10 % (TABL 8, STRU 2),
+  MSAG + exception 5 % (MSAG 2.5, EXC 2.5).
+A task has the kind of its main contract object; a CLAS whose reference inherits from CX_ is EXC.
+DTEL / DOMA tasks are not made yet (the DDIC share is covered by TABL and STRU).
+"""
+import json
+import os
+import re
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+POOL = os.path.join(ROOT, "tasks_gen", "train")
+TYPE_SHARE = {"CLAS": 28.0, "INTF": 7.0, "DDLS": 25.0, "FUNC": 15.0, "PROG": 10.0, "TABL": 8.0, "STRU": 2.0,
+              "MSAG": 2.5, "EXC": 2.5}
+# categories a kind supports (docs/faz1-tasarim.md 8): the generator picks the one with the biggest deficit
+KIND_CATEGORIES = {"CLAS": "ACDEFGHI", "FUNC": "ACDEFGHI", "PROG": "BCEGH", "DDLS": "BEFHI", "INTF": "A",
+                   "TABL": "B", "STRU": "B", "MSAG": "D", "EXC": "D"}
+CATEGORY_SHARE = {"A": 10, "B": 15, "C": 15, "D": 10, "E": 15, "F": 10, "G": 10, "I": 5, "H": 10}
+_CACHE = {}
+
+
+def kind_of_task_dir(task_id):
+    """Kind of an accepted training task (cached)."""
+    if task_id in _CACHE:
+        return _CACHE[task_id]
+    d = os.path.join(POOL, task_id)
+    try:
+        t = json.load(open(os.path.join(d, "task.json")))
+    except (OSError, ValueError):
+        return None
+    otype = t.get("object_type") or "CLAS"
+    for c in t.get("contract", []):   # K variants and old tasks: the contract object is the truth
+        otype = c.get("type", otype)
+        break
+    kind = otype
+    if otype == "CLAS":
+        for o in t.get("reference", []):
+            if o.get("file") and o.get("name") == (t.get("contract") or [{}])[0].get("name"):
+                try:
+                    src = open(os.path.join(d, o["file"])).read()
+                except OSError:
+                    src = ""
+                if re.search(r"INHERITING\s+FROM\s+\S*CX_", src, re.I):
+                    kind = "EXC"
+    _CACHE[task_id] = kind
+    return kind
+
+
+def deficit_pick(counts, share=TYPE_SHARE, allowed=None):
+    """Kind with the largest (target - actual) for the next item. counts: {kind: n}."""
+    total = sum(counts.values()) + 1
+    best, best_def = None, None
+    tot_share = sum(share.values())
+    for k, s in share.items():
+        if allowed and k not in allowed:
+            continue
+        d = total * s / tot_share - counts.get(k, 0)
+        if best_def is None or d > best_def:
+            best, best_def = k, d
+    return best
+
+
+def accepted_task_counts(logs_dir=None):
+    """{kind: accepted tasks} from the generation logs of the training pool (K variants count by their kind)."""
+    import glob
+    logs_dir = logs_dir or os.path.join(POOL, "_logs")
+    out = {}
+    for f in glob.glob(os.path.join(logs_dir, "G*.json")):
+        try:
+            l = json.load(open(f))
+        except (OSError, ValueError):
+            continue
+        if l.get("accepted"):
+            k = kind_of_task_dir(l["id"])
+            if k:
+                out[k] = out.get(k, 0) + 1
+    return out