scorer.py

__version__ = "1.0.0"
__scorer_id__ = "clarus-f1-thermal-load-v1"

import csv, hashlib, json, sys
from datetime import datetime, timezone

def _find_label_column(fields):
    for f in fields:
        if f.startswith("label_"):
            return f
    raise ValueError("No label column")

def _norm(v):
    v = str(v).strip().lower()
    return 1 if v in {"1","true","yes"} else 0

def _safe(n,d): return n/d if d else 0.0

def _read(p):
    with open(p,"r") as f:
        return list(csv.DictReader(f))

def _hash(p):
    h=hashlib.sha256()
    with open(p,"rb") as f:
        h.update(f.read())
    return h.hexdigest()

def score(ref, pred):
    r = _read(ref)
    p = _read(pred)

    label = _find_label_column(r[0].keys())
    y_true = [_norm(x[label]) for x in r]
    y_pred = [_norm(x["prediction"]) for x in p]

    tp = sum(1 for a,b in zip(y_true,y_pred) if a==1 and b==1)
    tn = sum(1 for a,b in zip(y_true,y_pred) if a==0 and b==0)
    fp = sum(1 for a,b in zip(y_true,y_pred) if a==0 and b==1)
    fn = sum(1 for a,b in zip(y_true,y_pred) if a==1 and b==0)

    precision = _safe(tp,tp+fp)
    recall = _safe(tp,tp+fn)
    f1 = _safe(2*precision*recall, precision+recall)

    return {
        "accuracy": _safe(tp+tn,len(y_true)),
        "precision": precision,
        "recall": recall,
        "f1": f1,
        "false_activation_rate": _safe(fp,fp+tn),
        "missed_latent_activation_rate": _safe(fn,fn+tp),
        "hash_ref": _hash(ref),
        "hash_pred": _hash(pred)
    }

if __name__ == "__main__":
    print(json.dumps(score(sys.argv[1], sys.argv[2]), indent=2))