Spaces:
Paused
Paused
File size: 5,720 Bytes
5e0b58b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | """Clean-process productive-learning orchestration and DATA binding."""
from __future__ import annotations
import hashlib
import json
import subprocess
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any, cast
from .experiment_recorder import ExperimentRecorder
REPO_ROOT = Path(__file__).resolve().parents[2]
CONDITIONS = ("learning_on", "learning_off", "sham_replay")
CODE_FILES = (
"src/experiments/learning_lab.py",
"src/research/experiment_suite.py",
"src/research/productive_learning.py",
"src/research/productive_learning_worker.py",
)
@dataclass(frozen=True, slots=True)
class ProductiveLearningArtifact:
"""Paths and digests for one clean-process DATA publication."""
experiment_id: str
output_dir: Path
data_path: Path
manifest_path: Path
run_count: int
code_digest: str
config_digest: str
prompt_digest: str
data_digest: str
source_freeze_sha: str
def _sha256(payload: bytes) -> str:
return hashlib.sha256(payload).hexdigest()
def _json_digest(value: object) -> str:
return _sha256(
json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8")
)
def _code_digest() -> str:
files = [
(relative, _sha256((REPO_ROOT / relative).read_bytes()))
for relative in CODE_FILES
]
return _json_digest(files)
def _run_worker(config_path: Path, seed: int, condition: str) -> dict[str, Any]:
completed = subprocess.run(
[
sys.executable,
"-m",
"src.research.productive_learning_worker",
"--config",
str(config_path),
"--seed",
str(seed),
"--condition",
condition,
],
cwd=REPO_ROOT,
capture_output=True,
text=True,
check=False,
)
if completed.returncode != 0:
raise RuntimeError(
f"clean productive-learning worker failed for seed={seed}, "
f"condition={condition}: {completed.stderr.strip()}"
)
try:
payload_raw: object = json.loads(completed.stdout)
except json.JSONDecodeError as exc:
raise RuntimeError("clean worker returned invalid JSON") from exc
if not isinstance(payload_raw, dict):
raise RuntimeError("clean worker returned an invalid result envelope")
payload = cast(dict[str, Any], payload_raw)
if not isinstance(payload.get("result"), dict):
raise RuntimeError("clean worker returned an invalid result envelope")
return payload
def run_clean_process_repeats(
config_path: Path,
output_dir: Path,
*,
seeds: tuple[int, ...] = (42, 43, 44),
) -> ProductiveLearningArtifact:
"""Run each seed/condition in a fresh process and publish DATA + manifest."""
if not seeds or len(set(seeds)) != len(seeds):
raise ValueError("seeds must be non-empty and unique")
config_path = config_path.resolve()
if not config_path.is_file():
raise FileNotFoundError(config_path)
records: list[dict[str, Any]] = []
for seed in seeds:
for condition in CONDITIONS:
payload = _run_worker(config_path, seed, condition)
records.append(
{
"experiment_id": "EXP-STDP-0002",
"condition": condition,
"seed": seed,
"clean_process": True,
"process_id": payload["process_id"],
"metrics": payload["result"],
}
)
output_dir.mkdir(parents=True, exist_ok=True)
data_dir = output_dir / "DATA"
data_dir.mkdir(parents=True, exist_ok=True)
data_path = data_dir / "runs.jsonl"
with data_path.open("w", encoding="utf-8") as handle:
for record in records:
handle.write(json.dumps(record, sort_keys=True) + "\n")
code_digest = _code_digest()
config_digest = _sha256(config_path.read_bytes())
prompt_digest = _sha256(b"NO_PROMPT")
data_digest = _sha256(data_path.read_bytes())
provenance = {
"code": code_digest,
"config": config_digest,
"prompt": prompt_digest,
"data": data_digest,
}
source_freeze_sha = _json_digest(provenance)
recorder = ExperimentRecorder("EXP-STDP-0002", output_dir=output_dir)
recorder.record_config(str(config_path), config_digest)
recorder.record_research_links(
research_questions=["RQ-STDP-001"], hypotheses=["H-STDP-001-A"]
)
recorder.record_simulation_params(
protocol_id="PROTOCOL-STDP-0002",
protocol_version=1,
seeds=list(seeds),
conditions=list(CONDITIONS),
clean_process=True,
)
for partition in ("DEVELOPMENT", "VALIDATION", "SCIENTIFIC_HOLDOUT"):
recorder.record_data_partition(partition)
recorder.record_artifact("data", str(data_path.relative_to(output_dir)))
recorder.record_results(
run_count=len(records),
clean_process=True,
source_freeze_sha=source_freeze_sha,
)
recorder.record_provenance_digests(
code_digest=code_digest,
config_digest=config_digest,
prompt_digest=prompt_digest,
data_digest=data_digest,
)
manifest_path = recorder.mark_completed().save()
return ProductiveLearningArtifact(
experiment_id="EXP-STDP-0002",
output_dir=output_dir,
data_path=data_path,
manifest_path=manifest_path,
run_count=len(records),
code_digest=code_digest,
config_digest=config_digest,
prompt_digest=prompt_digest,
data_digest=data_digest,
source_freeze_sha=source_freeze_sha,
)
|