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DECNET/tests/profiler/behave_shell/test_calibration_grid.py

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Python

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