# Sample labels (1=spam, 0=not spam)
actual    = [1, 0, 1, 1, 0, 0, 1, 0, 1, 0]
predicted = [1, 0, 1, 0, 0, 1, 1, 0, 0, 0]

# Compute confusion matrix entries
TP = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 1)
FP = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 1)
TN = sum(1 for a, p in zip(actual, predicted) if a == 0 and p == 0)
FN = sum(1 for a, p in zip(actual, predicted) if a == 1 and p == 0)

print(f"TP: {TP}, FP: {FP}, TN: {TN}, FN: {FN}")

# Now calculate the metrics
precision = TP / (TP + FP) if (TP + FP) else 0
recall = TP / (TP + FN) if (TP + FN) else 0
f1 = 2 * precision * recall / (precision + recall) if (precision + recall) else 0

print(f"Precision: {precision:.2f}")
print(f"Recall: {recall:.2f}")
print(f"F1 Score: {f1:.2f}")