Create baseline_heuristic.py

baseline_heuristic.py

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+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:
+    intervention_intensity = _safe_float(row.get("intervention_intensity"))
+    physiologic_instability_index = _safe_float(row.get("physiologic_instability_index"))
+    recovery_signal_strength = _safe_float(row.get("recovery_signal_strength"))
+    overshoot_risk_score = _safe_float(row.get("overshoot_risk_score"))
+    drift_gradient = _safe_float(row.get("drift_gradient"))
+    stop_point_margin = _safe_float(row.get("stop_point_margin"))
+    intervention_taper_alignment = _safe_float(row.get("intervention_taper_alignment"))
+    coordination_stability_score = _safe_float(row.get("coordination_stability_score"))
+
+    score = 0.0
+    score += intervention_intensity * 1.0
+    score += physiologic_instability_index * 1.2
+    score += overshoot_risk_score * 1.5
+    score += max(0.0, drift_gradient) * 1.4
+    score += max(0.0, 0.20 - stop_point_margin) * 2.0
+    score -= recovery_signal_strength * 1.0
+    score -= intervention_taper_alignment * 0.9
+    score -= coordination_stability_score * 0.8
+
+    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))