同一批分类结果，为什么能算出三个总分？

def report(matrix):
    # Rows are true labels; columns are predictions. Keep all three labels.
    def ratio(a, b):
        return a / b if b else 0.0  # Explicit zero_division=0 convention.

    labels = ["consult", "refund", "fault"]
    rows = []
    for i, label in enumerate(labels):
        tp = matrix[i][i]
        support = sum(matrix[i])
        predicted = sum(row[i] for row in matrix)
        fp, fn = predicted - tp, support - tp
        p, r = ratio(tp, tp + fp), ratio(tp, tp + fn)
        f1 = ratio(2 * tp, 2 * tp + fp + fn)
        rows.append((tp, fp, fn, support, f1))
        print(f"{label}: TP={tp} FP={fp} FN={fn} n={support} "
              f"P={p:.4f} R={r:.4f} F1={f1:.4f}")
    total = sum(row[3] for row in rows)
    if not total:
        print("aggregate: no data")
        return
    tp, fp, fn = (sum(row[i] for row in rows) for i in range(3))
    micro = ratio(2 * tp, 2 * tp + fp + fn)
    macro = sum(row[4] for row in rows) / len(rows)
    weighted = sum(row[3] * row[4] for row in rows) / total
    accuracy = tp / total
    print(f"micro={micro:.4f} macro={macro:.4f} "
          f"weighted={weighted:.4f} accuracy={accuracy:.4f}")


cases = {
    "all-consult": [[80, 0, 0], [15, 0, 0], [5, 0, 0]],
    "mixed": [[72, 6, 2], [4, 9, 2], [1, 1, 3]],
    "empty": [[0, 0, 0], [0, 0, 0], [0, 0, 0]],
}
for name, matrix in cases.items():
    print(name)
    report(matrix)