INNER CODE UNIT · Python

latency_ms

google/fully-homomorphic-encryption · demos/cc_fraud/cleartext/evaluate_cleartext.py:102

  latency_ms = (end_time - start_time) * 1000.0
  pred = int(torch.argmax(probs, dim=1).item())
  fraud_prob = probs[0, 1].item()
  is_correct = pred == label

  print(f"\nEvaluating Credit Card Fraud Sample Index: {sample_idx}")
  print(f"True Label:      {label} ({'FRAUD' if label == 1 else 'LEGITIMATE'})")
  print(f"Predicted Label: {pred} ({'FRAUD' if pred == 1 else 'LEGITIMATE'})")
  print(f"Fraud Probability: {fraud_prob:.6f}")
  print(f"Result:          {'CORRECT' if is_correct else 'INCORRECT'}")
  print(f"Latency:         {latency_ms:.4f} ms")


def main():
  parser = argparse.ArgumentParser(
      description=(
          "Evaluate cleartext credit card fraud model on a single sample."
      )

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