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: oxygen_delivery_score = _safe_float(row.get("oxygen_delivery_score")) buffer_capacity_index = _safe_float(row.get("buffer_capacity_index")) latent_coupling_pressure = _safe_float(row.get("latent_coupling_pressure")) compensation_fatigue_score = _safe_float(row.get("compensation_fatigue_score")) drift_gradient = _safe_float(row.get("drift_gradient")) coherence_stability_score = _safe_float(row.get("coherence_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 - oxygen_delivery_score) * 1.1
score += max(0.0, 1.0 - buffer_capacity_index) * 1.2
score += latent_coupling_pressure * 1.3
score += compensation_fatigue_score * 1.1
score += max(0.0, drift_gradient) * 1.4
score += max(0.0, 1.0 - coherence_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))