Datasets:
File size: 3,729 Bytes
7881c16 | 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 | import csv
import json
import sys
from typing import Dict, List
DEFAULT_INPUT_PATH = "data/tester.csv"
def _safe_float(value, default: float = 0.0) -> float:
try:
return float(value)
except (TypeError, ValueError):
return default
def _read_csv(path: str) -> List[Dict[str, str]]:
with open(path, "r", encoding="utf-8") as f:
return list(csv.DictReader(f))
def _detect_id_column(fieldnames: List[str]) -> str:
preferred = ["id", "row_id", "sample_id", "case_id", "record_id"]
for col in preferred:
if col in fieldnames:
return col
return ""
def heuristic_score(row: Dict[str, str]) -> float:
narrative_coherence_score = _safe_float(row.get("narrative_coherence_score"))
image_alignment_score = _safe_float(row.get("image_alignment_score"))
interpretive_distortion_index = _safe_float(row.get("interpretive_distortion_index"))
signal_fragmentation_score = _safe_float(row.get("signal_fragmentation_score"))
drift_gradient = _safe_float(row.get("drift_gradient"))
representation_stability_score = _safe_float(row.get("representation_stability_score"))
context_integrity_score = _safe_float(row.get("context_integrity_score"))
decision_readiness_score = _safe_float(row.get("decision_readiness_score"))
score = 0.0
score += max(0.0, 1.0 - narrative_coherence_score) * 1.1
score += max(0.0, 1.0 - image_alignment_score) * 1.0
score += interpretive_distortion_index * 1.3
score += signal_fragmentation_score * 1.1
score += max(0.0, drift_gradient) * 1.4
score += max(0.0, 1.0 - representation_stability_score) * 1.1
score += max(0.0, 1.0 - context_integrity_score) * 1.0
score += max(0.0, 1.0 - decision_readiness_score) * 1.0
if score < 0.0:
return 0.0
if score > 1.0:
return 1.0
return round(score, 6)
def generate_predictions(
input_path: str = DEFAULT_INPUT_PATH,
output_path: str = "predictions.csv",
threshold: float = 0.5,
) -> Dict[str, object]:
rows = _read_csv(input_path)
if not rows:
raise ValueError("Input file is empty.")
fieldnames = list(rows[0].keys())
id_col = _detect_id_column(fieldnames)
output_rows = []
positive_predictions = 0
for idx, row in enumerate(rows):
row_id = row[id_col] if id_col else str(idx)
pred_score = heuristic_score(row)
pred_label = 1 if pred_score >= threshold else 0
positive_predictions += pred_label
output_rows.append(
{
"id": row_id,
"prediction_score": pred_score,
"prediction": pred_label,
}
)
with open(output_path, "w", encoding="utf-8", newline="") as f:
writer = csv.DictWriter(f, fieldnames=["id", "prediction_score", "prediction"])
writer.writeheader()
writer.writerows(output_rows)
return {
"input_path": input_path,
"output_path": output_path,
"rows_processed": len(rows),
"threshold_used": threshold,
"predicted_positive_support": positive_predictions,
"predicted_negative_support": len(rows) - positive_predictions,
"note": "This is a dataset-specific baseline heuristic, not the canonical evaluation scorer.",
}
if __name__ == "__main__":
input_path = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_INPUT_PATH
output_path = sys.argv[2] if len(sys.argv) > 2 else "predictions.csv"
threshold = float(sys.argv[3]) if len(sys.argv) > 3 else 0.5
result = generate_predictions(
input_path=input_path,
output_path=output_path,
threshold=threshold,
)
print(json.dumps(result, indent=2)) |