164 lines
5.6 KiB
Python
164 lines
5.6 KiB
Python
"""Step 9: calibration grid lockdown — the Phase A gate.
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Runs the **pure engine** (``behave_shell.extract_session()``) against
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each of the five 2026-05-02 calibration shards. The shards live in
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``BEHAVE/prototype_extractors/shell/`` and are gitignored — fixture
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path is resolved via the ``BEHAVE_CALIBRATION_DIR`` env var; the test
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is skipped if that var is unset (CI provides it; local dev doesn't
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have to).
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The hard gate that this commit pins (and that all subsequent Phase
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B-G PRs must keep green): each shard must emit every Phase A
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primitive at least once across its sessions. Engine is allowed to
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emit *more* than required.
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Per-class expected values (the calibration **target**, not a hard
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gate yet — value-level pins land once cross-class thresholds are
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re-tuned with a wider corpus) are pinned in a softer cross-class
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discrimination check below.
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"""
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from __future__ import annotations
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import collections
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import json
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import os
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from pathlib import Path
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from typing import Any
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import pytest
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from decnet.profiler.behave_shell import extract_session
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from decnet.profiler.behave_shell._parse import parse_shard_line
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PHASE_ABC_PRIMITIVES: frozenset[str] = frozenset({
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# Phase A — calibration floor
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"motor.input_modality",
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"motor.paste_burst_rate",
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"cognitive.inter_command_latency_class",
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"cognitive.command_branch_diversity",
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"cognitive.feedback_loop_engagement",
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"cognitive.inter_command_consistency",
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# Phase B — motor.* completion (lands one primitive per commit)
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"motor.keystroke_cadence",
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"motor.motor_stability",
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"motor.error_correction",
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"motor.command_chunking",
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# Phase C — motor.shell_mastery.* (lands one primitive per commit)
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"motor.shell_mastery.tab_completion",
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"motor.shell_mastery.shortcut_usage",
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"motor.shell_mastery.pipe_chaining_depth",
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})
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# (shard filename, class label)
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SHARDS: list[tuple[str, str]] = [
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("sessions-2026-05-02.jsonl", "HUMAN"),
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("sessions-2026-05-02-with-llm.jsonl", "YOU-sim"),
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("sessions-2026-05-02-new.jsonl", "LW-sim"),
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("sessions-2026-05-02-with-claude.jsonl", "CLAUDE-FF"),
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("sessions-2026-05-02-closed-loop.jsonl", "CLAUDE-CL"),
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]
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def _calibration_dir() -> Path | None:
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raw = os.environ.get("BEHAVE_CALIBRATION_DIR")
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if not raw:
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return None
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p = Path(raw).expanduser()
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return p if p.is_dir() else None
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@pytest.fixture(scope="module")
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def calibration_dir() -> Path:
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d = _calibration_dir()
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if d is None:
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pytest.skip("BEHAVE_CALIBRATION_DIR unset or not a directory")
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return d
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def _sessions_in_shard(path: Path) -> dict[str, list[Any]]:
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"""Group raw events by sid, skipping headers and junk."""
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by_sid: dict[str, list[Any]] = collections.defaultdict(list)
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with path.open() as f:
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for line in f:
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try:
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rec = json.loads(line)
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except (json.JSONDecodeError, ValueError):
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continue
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sid = rec.get("sid") if isinstance(rec, dict) else None
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if not sid or "hdr" in rec:
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continue
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ev = parse_shard_line(line)
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if ev is not None:
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by_sid[sid].append(ev)
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return by_sid
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def _all_observations(path: Path) -> list:
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obs: list = []
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for sid, events in _sessions_in_shard(path).items():
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obs.extend(extract_session(events, sid=sid))
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return obs
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@pytest.mark.parametrize("shard_file,class_label", SHARDS, ids=[c for _, c in SHARDS])
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def test_shard_emits_all_phase_a_primitives(
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shard_file: str,
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class_label: str,
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calibration_dir: Path,
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) -> None:
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"""Hard gate: every Phase A primitive fires at least once per shard."""
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path = calibration_dir / shard_file
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if not path.is_file():
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pytest.skip(f"shard not present at {path}")
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obs = _all_observations(path)
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assert obs, f"{class_label}: extractor produced zero observations"
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seen = {o.primitive for o in obs}
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missing = PHASE_ABC_PRIMITIVES - seen
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assert not missing, (
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f"{class_label} ({shard_file}) missing primitives: "
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f"{sorted(missing)}"
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)
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def test_shards_are_discriminative_across_classes(
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calibration_dir: Path,
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) -> None:
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"""Smoke discrimination: at least one Phase A primitive must
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show different majority values across classes.
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A constant-output engine (every shard yields the same value for
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every primitive) would fail this check — that's the regression we
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care about. Tighter per-class value pinning lands as the corpus
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grows.
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"""
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by_class: dict[str, dict[str, str]] = {}
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for shard_file, label in SHARDS:
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path = calibration_dir / shard_file
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if not path.is_file():
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continue
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per_prim: dict[str, collections.Counter] = collections.defaultdict(
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collections.Counter
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)
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for o in _all_observations(path):
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per_prim[o.primitive][str(o.value)] += 1
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by_class[label] = {
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prim: ctr.most_common(1)[0][0] for prim, ctr in per_prim.items()
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}
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if len(by_class) < 2:
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pytest.skip("need at least two shards present to compare")
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# At least one primitive should produce different majority values
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# across the present classes.
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discriminative_primitives: list[str] = []
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for prim in PHASE_ABC_PRIMITIVES:
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values = {by_class[c].get(prim) for c in by_class if prim in by_class[c]}
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if len(values) >= 2:
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discriminative_primitives.append(prim)
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assert discriminative_primitives, (
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f"Engine emitted identical majority values for every Phase A "
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f"primitive across {sorted(by_class)} — likely a constant-output "
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f"regression. Class summaries: {by_class}"
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)
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