File size: 6,649 Bytes
3751f09 | 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 | from __future__ import annotations
import argparse
import os
import re
import sys
import pandas as pd
def validate_submission(submission_path: str) -> bool:
"""
Run all format validation checks on submission.csv.
Returns True if all checks pass, False if any fail.
Prints detailed output for each check.
"""
errors = []
warnings = []
print("=" * 60)
print("SUBMISSION VALIDATOR")
print(f"File: {submission_path}")
print("=" * 60)
# file existence
if not os.path.isfile(submission_path):
print(f"\n[FAIL] File not found: {submission_path}")
return False
try:
df = pd.read_csv(submission_path, dtype={"candidate_id": str, "reasoning": str})
except Exception as e:
print(f"\n[FAIL] Cannot parse CSV: {e}")
return False
print(f"\nParsed: {len(df)} rows × {len(df.columns)} columns")
required_cols = ["candidate_id", "rank", "score", "reasoning"]
if list(df.columns) != required_cols:
missing = set(required_cols) - set(df.columns)
extra = set(df.columns) - set(required_cols)
wrong_order = set(df.columns) == set(required_cols) and list(df.columns) != required_cols
if missing:
errors.append(f"Missing columns: {sorted(missing)}")
if extra:
errors.append(f"Extra columns (not allowed): {sorted(extra)}")
if wrong_order:
errors.append(
f"Column order wrong. Expected: {required_cols}, "
f"Got: {list(df.columns)}"
)
if errors:
for e in errors:
print(f"[FAIL] {e}")
return False
if len(df) != 100:
errors.append(f"Expected exactly 100 rows, got {len(df)}")
try:
ranks = df["rank"].tolist()
rank_set = set(int(r) for r in ranks)
if rank_set != set(range(1, 101)):
missing_ranks = set(range(1, 101)) - rank_set
extra_ranks = rank_set - set(range(1, 101))
if missing_ranks:
errors.append(f"Missing ranks: {sorted(missing_ranks)[:10]}")
if extra_ranks:
errors.append(f"Invalid ranks (out of 1–100): {sorted(extra_ranks)[:10]}")
if len(ranks) != len(set(ranks)):
errors.append("Duplicate ranks found")
except (TypeError, ValueError) as e:
errors.append(f"Rank column contains non-integer values: {e}")
try:
scores = pd.to_numeric(df["score"], errors="raise")
if scores.isna().any():
errors.append("Score column contains NaN values")
else:
if scores.min() < 0:
errors.append(f"Score below 0: min={scores.min():.6f}")
if scores.max() > 1.0001:
errors.append(f"Score above 1: max={scores.max():.6f}")
except ValueError as e:
errors.append(f"Score column contains non-numeric values: {e}")
try:
df_sorted = df.copy()
df_sorted["rank_int"] = pd.to_numeric(df_sorted["rank"], errors="coerce")
df_sorted = df_sorted.sort_values("rank_int")
score_vals = pd.to_numeric(df_sorted["score"], errors="coerce").values
violations = []
for i in range(1, len(score_vals)):
if score_vals[i] > score_vals[i - 1] + 1e-9:
violations.append(
f"rank {i} → {i+1}: {score_vals[i-1]:.6f} → {score_vals[i]:.6f}"
)
if violations:
errors.append(
f"Monotonicity violated at {len(violations)} positions: "
f"{violations[:3]}"
)
except Exception as e:
errors.append(f"Could not check monotonicity: {e}")
if df["candidate_id"].isna().any():
errors.append("candidate_id column contains NaN values")
else:
if df["candidate_id"].duplicated().any():
dups = df[df["candidate_id"].duplicated()]["candidate_id"].tolist()
errors.append(f"Duplicate candidate_ids: {dups[:5]}")
bad_format = [
cid for cid in df["candidate_id"]
if not re.match(r'^(CAND_\d{7}|SYNTH_[A-Z_]+)$', str(cid))
]
if bad_format:
warnings.append(
f"{len(bad_format)} candidate_ids don't match CAND_XXXXXXX format: "
f"{bad_format[:3]}"
)
if df["reasoning"].isna().any():
errors.append(f"{df['reasoning'].isna().sum()} reasoning fields are null")
empty_reasoning = df["reasoning"].fillna("").str.strip() == ""
if empty_reasoning.any():
errors.append(f"{empty_reasoning.sum()} reasoning fields are empty")
# check reasonable length (warn if very short)
short_reasoning = df["reasoning"].fillna("").str.len() < 20
if short_reasoning.any():
warnings.append(
f"{short_reasoning.sum()} reasoning fields are very short (<20 chars)"
)
stripped = df["candidate_id"].str.strip()
if (stripped != df["candidate_id"]).any():
errors.append("Some candidate_ids have leading/trailing whitespace")
print()
if errors:
print(f"RESULT: FAIL ({len(errors)} error(s), {len(warnings)} warning(s))\n")
for e in errors:
print(f" [FAIL] {e}")
for w in warnings:
print(f" [WARN] {w}")
return False
else:
print(f"RESULT: PASS (0 errors, {len(warnings)} warning(s))\n")
df_sorted = df.sort_values("rank")
scores = pd.to_numeric(df_sorted["score"])
print(f" Rows: {len(df)}")
print(f" Ranks: 1–{int(df['rank'].max())}")
print(f" Score range: [{scores.min():.6f}, {scores.max():.6f}]")
print(f" Avg reasoning length: {df['reasoning'].str.len().mean():.0f} chars")
print(f" Distinct candidate_ids: {df['candidate_id'].nunique()}")
for w in warnings:
print(f"\n [WARN] {w}")
print("\nSAFE TO SUBMIT [PASS]")
return True
def main():
parser = argparse.ArgumentParser(
description="Validate submission.csv against the Redrob spec checklist"
)
parser.add_argument(
"--submission",
default="./CTRL_COFFEE_REPEAT.csv",
help="Path to CTRL_COFFEE_REPEAT.csv to validate",
)
args = parser.parse_args()
passed = validate_submission(os.path.abspath(args.submission))
sys.exit(0 if passed else 1)
if __name__ == "__main__":
main()
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