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f4cc109 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | """ID-based submission validation.
Validation never relies on row order: a submission is checked against the set of
reference case IDs. See EVALUATION.md for the official
``missing_prediction_policy`` and why incomplete final submissions are rejected
before scoring rather than silently truncated.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from pathlib import Path
from typing import Dict, Iterable, List, Optional, Sequence
from .io import DataError, load_gold, load_inputs, load_predictions
from .schema import ParticipantPrediction
__all__ = ["ValidationReport", "validate_predictions", "validate_submission_file"]
#: Maximum number of individual IDs echoed in an error message.
_MAX_LISTED = 20
def _format_ids(ids: Sequence[str]) -> str:
listed = list(ids[:_MAX_LISTED])
suffix = "" if len(ids) <= _MAX_LISTED else f" ... (+{len(ids) - _MAX_LISTED} more)"
return ", ".join(listed) + suffix
@dataclass
class ValidationReport:
"""Outcome of validating one submission file."""
predictions_path: str
reference_path: str
num_reference: int = 0
num_predictions: int = 0
missing_ids: List[str] = field(default_factory=list)
unexpected_ids: List[str] = field(default_factory=list)
errors: List[str] = field(default_factory=list)
warnings: List[str] = field(default_factory=list)
@property
def ok(self) -> bool:
return not self.errors
def to_dict(self) -> Dict[str, object]:
return {
"ok": self.ok,
"predictions": self.predictions_path,
"reference": self.reference_path,
"num_reference": self.num_reference,
"num_predictions": self.num_predictions,
"num_missing": len(self.missing_ids),
"num_unexpected": len(self.unexpected_ids),
"missing_ids": self.missing_ids,
"unexpected_ids": self.unexpected_ids,
"errors": self.errors,
"warnings": self.warnings,
}
def render(self) -> str:
lines = [
"FinReason Cup 2026 - Task 3 Submission Validation",
"=" * 48,
"",
f"Predictions: {self.predictions_path}",
f"Reference: {self.reference_path}",
"",
f"Reference cases: {self.num_reference}",
f"Predictions found: {self.num_predictions}",
f"Missing predictions: {len(self.missing_ids)}",
f"Unexpected IDs: {len(self.unexpected_ids)}",
"",
]
for warning in self.warnings:
lines.append(f"WARNING: {warning}")
if self.warnings:
lines.append("")
if self.errors:
for error in self.errors:
lines.append(f"ERROR: {error}")
lines.append("")
lines.append(f"RESULT: INVALID ({len(self.errors)} error(s))")
else:
lines.append("RESULT: VALID")
return "\n".join(lines)
def validate_predictions(
predictions: Dict[str, ParticipantPrediction],
reference_ids: Iterable[str],
*,
load_errors: Optional[Sequence[str]] = None,
predictions_path: str = "<predictions>",
reference_path: str = "<reference>",
allow_empty_values: bool = False,
allow_missing: bool = False,
) -> ValidationReport:
"""Validate an already-loaded prediction mapping against reference IDs.
``allow_missing`` downgrades missing-prediction errors to warnings. It exists
for local development under a non-official ``missing_prediction_policy``;
official scoring always leaves it False, so an incomplete submission is
rejected before it is scored.
"""
reference = list(dict.fromkeys(str(i) for i in reference_ids))
reference_set = set(reference)
report = ValidationReport(
predictions_path=predictions_path,
reference_path=reference_path,
num_reference=len(reference),
num_predictions=len(predictions),
)
report.errors.extend(load_errors or [])
report.missing_ids = [cid for cid in reference if cid not in predictions]
report.unexpected_ids = sorted(set(predictions) - reference_set)
if report.missing_ids:
message = (
f"Missing prediction for {len(report.missing_ids)} case(s): "
f"{_format_ids(report.missing_ids)}"
)
(report.warnings if allow_missing else report.errors).append(message)
if report.unexpected_ids:
report.errors.append(
f"Unexpected id(s) not present in the reference set: "
f"{_format_ids(report.unexpected_ids)}"
)
if len(predictions) != len(reference) and not (report.missing_ids or report.unexpected_ids):
report.errors.append(
f"Expected {len(reference)} predictions but found {len(predictions)}"
)
if report.missing_ids and allow_missing:
report.warnings.append(
f"Scoring only the {len(predictions)} case(s) present. This is a development "
"convenience; the official rule rejects incomplete submissions."
)
empty_fields: List[str] = []
for case_id, prediction in predictions.items():
if case_id not in reference_set:
continue
# For an lm-eval record the raw model text IS the answer and is what the
# judge sees; empty parsed fields simply mean the text was not a valid
# answer object, which is a structural error to be scored, not a
# malformed submission to be rejected.
if getattr(prediction, "source_format", "native") == "lm-eval":
continue
for key in ("extracted_value", "calculated_value"):
if not str(getattr(prediction, key)).strip():
empty_fields.append(f"{case_id}.{key}")
if empty_fields:
message = (
f"Empty value in {len(empty_fields)} field(s): {_format_ids(empty_fields)}. "
"Emit \"0\" when your system cannot determine a value."
)
(report.warnings if allow_empty_values else report.errors).append(message)
return report
def validate_submission_file(
predictions_path: Path | str,
reference_path: Path | str,
*,
fmt: str = "native",
reference_kind: str = "auto",
allow_empty_values: bool = False,
allow_missing: bool = False,
) -> ValidationReport:
"""Validate ``predictions_path`` against an inputs or gold reference file.
``reference_kind`` is ``"inputs"``, ``"gold"`` or ``"auto"`` (detect by
probing the first record for an ``answer`` field).
"""
predictions_path = Path(predictions_path)
reference_path = Path(reference_path)
if reference_kind == "auto":
reference_kind = _detect_reference_kind(reference_path)
if reference_kind == "gold":
reference_ids = list(load_gold(reference_path))
elif reference_kind == "inputs":
reference_ids = list(load_inputs(reference_path))
else:
raise DataError(f"Unknown reference_kind {reference_kind!r}")
try:
predictions, load_errors = load_predictions(predictions_path, fmt=fmt)
except DataError as exc:
report = ValidationReport(
predictions_path=str(predictions_path),
reference_path=str(reference_path),
num_reference=len(reference_ids),
)
report.errors.append(str(exc))
return report
return validate_predictions(
predictions,
reference_ids,
load_errors=load_errors,
predictions_path=str(predictions_path),
reference_path=str(reference_path),
allow_empty_values=allow_empty_values,
allow_missing=allow_missing,
)
def _detect_reference_kind(path: Path) -> str:
from .io import iter_jsonl
for _, obj in iter_jsonl(path):
if isinstance(obj, dict) and "answer" in obj:
return "gold"
return "inputs"
raise DataError(f"{path}: contains no records")
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