feat(learning-harness): round zero and the per-family noise floor

Task 3.3. Before anything learns, every family gets the same detector and is
measured against itself. Running a pair several times at the same settings does
not give the same answer, because the chat provider offers no seed, so the
spread between runs is real and unavoidable. That spread is the family's noise
floor and it becomes the budget a later round must stay inside before a
cross-family change counts as a regression. Choosing a budget instead of
measuring one would be a guess dressed as a threshold.

A pair the corpus already marked unscorable is never run: spending model calls
on a row that cannot be scored buys nothing and would put its noise into a floor
where it does not belong.

Artifacts are keyed by detector model, so a second model is baselined by
changing the model and running again, and the comparison table reads both side
by side. A model swap never touches a configuration, which is what makes the two
numbers comparable and reconsidering the model cheap. The web slices are the
default selection and the memory-safety family is measured with its floor marked
as never gating a round.

Co-Authored-By: Claude Opus 5 (1M context) <[email protected]>
Claude-Session: https://claude.ai/code/session_01T7XD7x5KonMdmGRkp63EQG

src/openultrasast/learning/rounds.py

@@ -0,0 +1,227 @@
+"""Round zero and the per-family noise floor (learning-harness, Req 8).
+
+Before anything learns, every family gets the same detector and is measured against itself. Running a
+pair several times at the same settings does not give the same answer: the chat provider offers no seed,
+so the spread between runs is real and unavoidable. That spread is the family's noise floor, and it
+becomes the budget a later round must stay inside before a cross-family change counts as a regression.
+Choosing a budget instead of measuring one would be a guess dressed as a threshold.
+
+Artifacts are keyed by detector model, so a second model is baselined by changing the model and running
+again. Configurations are never touched by a model swap, which is what makes the two numbers comparable
+and what makes reconsidering the model cheap.
+"""
+
+from __future__ import annotations
+
+import json
+import math
+import re
+import tempfile
+from collections.abc import Callable, Mapping, Sequence
+from dataclasses import dataclass, field
+from pathlib import Path
+
+from ..findings import StaticFinding
+from ..pairs import PairCase
+from .classify import classify_pair
+from .detectors import write_default_configs
+from .families import FamilyTaxonomy
+from .scoring import FamilyMetrics, PairFamilyScore, aggregate, score_pair_family
+
+DEFAULT_SLICES = ("vibe-py", "agent-vfc")  # the web families, where real-world code and dangerous fruit coincide
+NON_GATING_FAMILIES = frozenset({"memory"})  # measured on the vendored slice, never allowed to fail a round
+DEFAULT_K_RUNS = 5
+Scan = Callable[[Path], list[StaticFinding]]
+
+
+@dataclass(frozen=True)
+class NoiseFloor:
+    """How much a family disagrees with itself between runs, and the budget that follows from it."""
+
+    family: str
+    k_runs: int
+    pairs: int
+    flaky_pairs: int
+    negative_flip_rate: float
+    budget_flips: int
+    gates: bool = True
+
+    def to_dict(self) -> dict[str, object]:
+        return {
+            "family": self.family,
+            "k_runs": self.k_runs,
+            "pairs": self.pairs,
+            "flaky_pairs": self.flaky_pairs,
+            "negative_flip_rate": self.negative_flip_rate,
+            "budget_flips": self.budget_flips,
+            "gates": self.gates,
+        }
+
+
+@dataclass(frozen=True)
+class BaselineReport:
+    model: str
+    k_runs: int
+    taxonomy_version: str
+    slices: tuple[str, ...]
+    floors: dict[str, NoiseFloor] = field(default_factory=dict)
+    metrics: dict[str, dict[str, object]] = field(default_factory=dict)
+    scores: tuple[PairFamilyScore, ...] = ()
+
+    def to_dict(self) -> dict[str, object]:
+        return {
+            "model": self.model,
+            "k_runs": self.k_runs,
+            "taxonomy_version": self.taxonomy_version,
+            "slices": list(self.slices),
+            "floors": {name: floor.to_dict() for name, floor in sorted(self.floors.items())},
+            "metrics": {name: dict(block) for name, block in sorted(self.metrics.items())},
+        }
+
+
+def run_baseline(
+    cases: Sequence[PairCase],
+    *,
+    taxonomy: FamilyTaxonomy,
+    configs_dir: Path,
+    scan: Scan,
+    model: str,
+    out_dir: Path,
+    slices: Sequence[str] = DEFAULT_SLICES,
+    k_runs: int = DEFAULT_K_RUNS,
+    prompt: str | None = None,
+) -> BaselineReport:
+    """Clone one detector into every family, measure each family against itself, and write the artifacts."""
+    from ..tool_hunter import _SYSTEM_PROMPT
+
+    if not configs_dir.exists():
+        write_default_configs(configs_dir, taxonomy, prompt=prompt or _SYSTEM_PROMPT, version="0")
+    selected = [case for case in cases if case.slice in set(slices)]
+    scores = [score_case(case, scan, taxonomy=taxonomy, runs=k_runs) for case in selected]
+    metrics = aggregate(scores, taxonomy=taxonomy)
+    floors = {name: _floor(name, block, [item for item in scores if item.family == name], k_runs) for name, block in metrics.items()}
+    report = BaselineReport(
+        model=model,
+        k_runs=k_runs,
+        taxonomy_version=taxonomy.version,
+        slices=tuple(slices),
+        floors=floors,
+        metrics={name: block.to_dict() for name, block in metrics.items()},
+        scores=tuple(scores),
+    )
+    _write(out_dir, report)
+    return report
+
+
+def score_case(case: PairCase, scan: Scan, *, taxonomy: FamilyTaxonomy, runs: int, family: str | None = None) -> PairFamilyScore:
+    """Run one detector over both sides of one pair ``runs`` times and score it for its family.
+
+    A pair the corpus already marked unscorable is never run: spending model calls on a row that cannot
+    be scored buys nothing and would put its noise into a floor it does not belong in.
+    """
+    from ..pairs import _materialize, _overlay_scan
+
+    name = family or _family_of(case, taxonomy)
+    if case.unscorable:
+        return PairFamilyScore(
+            pair=case.name,
+            family=name,
+            runs=(),
+            outcome="unscorable",
+            slice=case.slice,
+            unscorable_reason=case.unscorable,
+        )
+    with tempfile.TemporaryDirectory(prefix="ousast-round-") as scratch:
+        vuln_root = _materialize(Path(scratch) / "vuln", case.vuln_file, case.relpath)
+        fix_root = _materialize(Path(scratch) / "fixed", case.fixed_file, case.relpath)
+        ranges = _overlay_scan(vuln_root).ranges
+        vuln_runs = [scan(vuln_root) for _ in range(max(runs, 1))]
+        fix_runs = [scan(fix_root) for _ in range(max(runs, 1))]
+    return score_pair_family(case, name, vuln_runs, fix_runs, ranges=ranges, taxonomy=taxonomy, parse_ok=bool(ranges))
+
+
+def compare_baselines(out_dir: Path) -> dict[str, dict[str, dict[str, object]]]:
+    """Every baseline written under ``out_dir``, model by model, so two detectors can be read side by side."""
+    root = out_dir / "baseline"
+    table: dict[str, dict[str, dict[str, object]]] = {}
+    for directory in sorted(path for path in root.iterdir() if path.is_dir()) if root.is_dir() else []:
+        report = directory / "report.json"
+        if not report.is_file():
+            continue
+        payload = json.loads(report.read_text(encoding="utf-8"))
+        table[str(payload.get("model", directory.name))] = {family: dict(block) for family, block in (payload.get("metrics") or {}).items()}
+    return table
+
+
+def _floor(family: str, metrics: FamilyMetrics, scores: Sequence[PairFamilyScore], k_runs: int) -> NoiseFloor:
+    """A family's own disagreement between runs, as a rate and as a whole number of pairs."""
+    scorable = [item for item in scores if item.outcome != "unscorable"]
+    flaky = sum(1 for item in scorable if item.flips)
+    rate = flaky / len(scorable) if scorable else 0.0
+    return NoiseFloor(
+        family=family,
+        k_runs=k_runs,
+        pairs=metrics.scorable,
+        flaky_pairs=flaky,
+        negative_flip_rate=rate,
+        budget_flips=math.ceil(rate * metrics.scorable) if metrics.scorable else 0,
+        gates=family not in NON_GATING_FAMILIES,
+    )
+
+
+def _family_of(case: PairCase, taxonomy: FamilyTaxonomy) -> str:
+    for row in case.expected:
+        if row.family:
+            return str(row.family)
+    answer = classify_pair(case, taxonomy, client=None)
+    return answer.families[0] if answer.families else "unknown"
+
+
+def _write(out_dir: Path, report: BaselineReport) -> None:
+    directory = out_dir / "baseline" / _slug(report.model)
+    directory.mkdir(parents=True, exist_ok=True)
+    payload = report.to_dict()
+    (directory / "noise-floors.json").write_text(
+        json.dumps({"model": report.model, "k_runs": report.k_runs, "floors": payload["floors"]}, indent=2, sort_keys=True) + "\n",
+        encoding="utf-8",
+    )
+    (directory / "report.json").write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+
+def _slug(model: str) -> str:
+    return re.sub(r"[^A-Za-z0-9._-]+", "-", model).strip("-") or "unnamed"
+
+
+def load_noise_floors(out_dir: Path, model: str) -> dict[str, NoiseFloor]:
+    """The budgets a later round must stay inside, as round zero measured them for this model."""
+    path = out_dir / "baseline" / _slug(model) / "noise-floors.json"
+    if not path.is_file():
+        return {}
+    payload = json.loads(path.read_text(encoding="utf-8"))
+    floors: dict[str, NoiseFloor] = {}
+    for name, block in (payload.get("floors") or {}).items():
+        if isinstance(block, Mapping):
+            floors[str(name)] = NoiseFloor(
+                family=str(block.get("family", name)),
+                k_runs=int(block.get("k_runs", DEFAULT_K_RUNS) or DEFAULT_K_RUNS),
+                pairs=int(block.get("pairs", 0) or 0),
+                flaky_pairs=int(block.get("flaky_pairs", 0) or 0),
+                negative_flip_rate=float(block.get("negative_flip_rate", 0.0) or 0.0),
+                budget_flips=int(block.get("budget_flips", 0) or 0),
+                gates=bool(block.get("gates", True)),
+            )
+    return floors
+
+
+__all__ = [
+    "DEFAULT_K_RUNS",
+    "DEFAULT_SLICES",
+    "NON_GATING_FAMILIES",
+    "BaselineReport",
+    "NoiseFloor",
+    "Scan",
+    "compare_baselines",
+    "load_noise_floors",
+    "run_baseline",
+    "score_case",
+]