File size: 1,737 Bytes
6d35426 | 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 | import re
import sys
from pathlib import Path
from typing import Any
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
from constants import METRIC_COLUMNS
ALLOWED_MODEL_TYPES = {"text-conditioned", "action-conditioned"}
REQUIRED_KEYS = ["model_name", "model_link", "model_type", "total_m_score"]
SUMMARY_SCORE_FIELDS = [
"total_m_score",
"entity_score",
"environment_score",
"causal_score",
]
def _normalize_key(key: str) -> str:
return re.sub(r"[^a-z0-9]+", "_", key.lower()).strip("_")
def _is_present(value: Any) -> bool:
return value is not None and str(value).strip() != ""
def validate_submission_json(data: dict) -> tuple[bool, str]:
if not isinstance(data, dict):
return False, "Submission JSON must be an object."
normalized = {_normalize_key(str(key)): value for key, value in data.items()}
for key in REQUIRED_KEYS:
if key not in normalized or not _is_present(normalized[key]):
return False, f"Missing required field: {key}"
model_type = str(normalized["model_type"]).strip()
if model_type not in ALLOWED_MODEL_TYPES:
return (
False,
"model_type must be either text-conditioned or action-conditioned.",
)
score_fields = set(SUMMARY_SCORE_FIELDS)
score_fields.update(_normalize_key(column) for column in METRIC_COLUMNS)
for key, value in normalized.items():
if key in score_fields and _is_present(value):
try:
float(value)
except (TypeError, ValueError):
return False, f"Score field {key} must be convertible to float."
return True, "Submission JSON is valid."
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