refactor(batch_runner): table-drive runner fields, banner helper, reasoning-turn predicate

batch_runner.py

@@ -46,6 +46,13 @@ ALL_POSSIBLE_TOOLS = set(TOOL_TO_TOOLSET_MAP.keys())
 DEFAULT_TOOL_STATS = {'count': 0, 'success': 0, 'failure': 0}
 _REASONING_KEYS = ("total_assistant_turns", "turns_with_reasoning", "turns_without_reasoning")
 
+# BatchRunner.__init__ parameters stored as same-named attributes.
+_RUNNER_FIELDS = (
+    "batch_size", "run_name", "distribution", "max_iterations", "base_url", "api_key", "model",
+    "num_workers", "verbose", "ephemeral_system_prompt", "log_prefix_chars", "providers_allowed",
+    "providers_ignored", "providers_order", "provider_sort", "openrouter_min_coding_score",
+    "max_tokens", "reasoning_config", "prefill_messages", "max_samples",
+)
 # BatchRunner attributes forwarded verbatim to every AIAgent in the worker config.
 _AGENT_PASSTHROUGH = (
     "base_url", "api_key", "ephemeral_system_prompt", "providers_allowed", "providers_ignored",
@@ -130,24 +137,18 @@ def _extract_tool_stats(messages: List[Dict[str, Any]]) -> Dict[str, Dict[str, i
     return tool_stats
 
 
+def _turn_has_reasoning(msg: Dict[str, Any]) -> bool:
+    """``<REASONING_SCRATCHPAD>`` in content, or a non-empty native ``reasoning`` field."""
+    if "<REASONING_SCRATCHPAD>" in (msg.get("content", "") or ""):
+        return True
+    return bool(msg.get("reasoning", "").strip()) if msg.get("reasoning") else False
+
+
 def _extract_reasoning_stats(messages: List[Dict[str, Any]]) -> Dict[str, int]:
-    """Count assistant turns with reasoning (``<REASONING_SCRATCHPAD>`` in content or a
-    non-empty native ``reasoning`` field) vs without."""
-    total = 0
-    with_reasoning = 0
-
-    for msg in messages:
-        if msg.get("role") != "assistant":
-            continue
-        total += 1
-
-        content = msg.get("content", "") or ""
-        has_scratchpad = "<REASONING_SCRATCHPAD>" in content
-        has_native_reasoning = bool(msg.get("reasoning", "").strip()) if msg.get("reasoning") else False
-
-        if has_scratchpad or has_native_reasoning:
-            with_reasoning += 1
-
+    """Count assistant turns with reasoning vs without."""
+    assistant_turns = [msg for msg in messages if msg.get("role") == "assistant"]
+    total = len(assistant_turns)
+    with_reasoning = sum(1 for msg in assistant_turns if _turn_has_reasoning(msg))
     return {
         "total_assistant_turns": total,
         "turns_with_reasoning": with_reasoning,
@@ -405,6 +406,12 @@ def _entry_prompt_text(entry: Dict) -> str:
     return ""
 
 
+def _banner(title: str) -> None:
+    print("\n" + "=" * 70)
+    print(title)
+    print("=" * 70)
+
+
 def _chunk(entries: List[Tuple[int, Dict[str, Any]]], size: int) -> List[List[Tuple[int, Dict[str, Any]]]]:
     """Split ``(index, entry)`` tuples into batches of *size*, preserving original indices."""
     return [entries[i:i + size] for i in range(0, len(entries), size)]
@@ -443,27 +450,10 @@ class BatchRunner:
         ``prefill_messages`` are prepended as few-shot context; Anthropic Sonnet/Opus 4.6+
         reject a trailing assistant-role prefill (400) — use user-role priming for those.
         """
+        params = dict(locals())
         self.dataset_file = Path(dataset_file)
-        self.batch_size = batch_size
-        self.run_name = run_name
-        self.distribution = distribution
-        self.max_iterations = max_iterations
-        self.base_url = base_url
-        self.api_key = api_key
-        self.model = model
-        self.num_workers = num_workers
-        self.verbose = verbose
-        self.ephemeral_system_prompt = ephemeral_system_prompt
-        self.log_prefix_chars = log_prefix_chars
-        self.providers_allowed = providers_allowed
-        self.providers_ignored = providers_ignored
-        self.providers_order = providers_order
-        self.provider_sort = provider_sort
-        self.openrouter_min_coding_score = openrouter_min_coding_score
-        self.max_tokens = max_tokens
-        self.reasoning_config = reasoning_config
-        self.prefill_messages = prefill_messages
-        self.max_samples = max_samples
+        for name in _RUNNER_FIELDS:
+            setattr(self, name, params[name])
 
         if not validate_distribution(distribution):
             raise ValueError(f"Unknown distribution: {distribution}. Available: {list(list_distributions().keys())}")
@@ -627,9 +617,7 @@ class BatchRunner:
 
         self.batches = _chunk(filtered_entries, self.batch_size)
 
-        print("\n" + "=" * 70)
-        print("📊 RESUME SUMMARY")
-        print("=" * 70)
+        _banner("📊 RESUME SUMMARY")
         print(f"   Original dataset size:     {len(self.dataset):,} prompts")
         print(f"   Already completed:         {len(skipped_indices):,} prompts")
         print("   ─────────────────────────────────────────")
@@ -781,9 +769,7 @@ class BatchRunner:
         return kept, batch_files_found
 
     def _print_summary(self, results, total_tool_stats, total_reasoning_stats, kept, batch_files_found, start_time) -> None:
-        print("\n" + "=" * 70)
-        print("📊 BATCH PROCESSING COMPLETE")
-        print("=" * 70)
+        _banner("📊 BATCH PROCESSING COMPLETE")
         print(f"✅ Prompts processed this run: {sum(r.get('processed', 0) for r in results)}")
         print(f"✅ Total trajectories in merged file: {kept}")
         print(f"✅ Total batch files merged: {batch_files_found}")
@@ -827,9 +813,7 @@ class BatchRunner:
 
     def run(self, resume: bool = False):
         """Run the batch pipeline; with *resume*, skip prompts already present in batch files."""
-        print("\n" + "=" * 70)
-        print("🚀 Starting Batch Processing")
-        print("=" * 70)
+        _banner("🚀 Starting Batch Processing")
 
         if resume and not self._apply_resume():
             return