⚡️ Speed up function mse by 15%

[codeflash-ai]

📄 15% (0.15x) speedup for mse in keras/src/metrics/reduction_metrics_test.py

⏱️ Runtime : 12.0 microseconds → 10.5 microseconds (best of 29 runs)

📝 Explanation and details

Here’s an optimized version of your mse function, preserving the interface, return value, and comments.
I’ll employ NumPy for element-wise vectorized computation, which is much faster for array/tensor data, as is typical in Keras, and crucial for performance over large batches.

If y_true and y_pred are already NumPy arrays or tensors (usual in Keras), the difference and exponentiation will be efficient. However, **2 can sometimes be slower than explicit elementwise multiplication (for some NumPy versions), so I’ll use that. No other changes are needed.

Key optimizations:

• Uses diff * diff instead of diff ** 2, which is marginally faster for numerical arrays.
• Avoids temporary Python object creation for the exponent operation, improving performance for large data.

If you know the inputs will always be NumPy arrays or similar tensor types, this is the fastest idiomatic way.
For float inputs, the performance gain is negligible, but for arrays, this approach will be much faster and more memory efficient.
No change to function signature or registration; all comments preserved.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 17 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests Details

import pytest  # used for our unit tests
from keras.src.metrics.reduction_metrics_test import mse
# function to test
# How users would register a custom function or class to use with
# MeanMetricWrapper.
from keras.src.saving import register_keras_serializable

# unit tests

# 1. Basic Test Cases

def test_mse_scalar_equal():
    # Both scalars, equal values
    codeflash_output = mse(2, 2)

def test_mse_scalar_unequal():
    # Both scalars, unequal values
    codeflash_output = mse(3, 1)
    codeflash_output = mse(-1, 2)











def test_mse_non_numeric():
    # Non-numeric input should raise TypeError
    with pytest.raises(TypeError):
        mse(['a'], ['b'])
    with pytest.raises(TypeError):
        mse([None], [None])

# 3. Large Scale Test Cases







import pytest
from keras.src.metrics.reduction_metrics_test import mse
# function to test
from keras.src.saving import register_keras_serializable

# unit tests

# 1. Basic Test Cases

def test_mse_scalar_zero():
    # Identical values should yield zero error
    codeflash_output = mse(0, 0)
    codeflash_output = mse(5, 5)

def test_mse_scalar_nonzero():
    # Known squared error for scalars
    codeflash_output = mse(2, 5)
    codeflash_output = mse(-3, 1)
    codeflash_output = mse(1.5, 0.5)









def test_mse_raises_on_type_mismatch():
    # Should raise TypeError if one input is a list and the other is a scalar
    with pytest.raises(TypeError):
        mse([1, 2], 1)
    with pytest.raises(TypeError):
        mse(1, [1, 2])



def test_mse_non_numeric_input():
    # Should raise TypeError on non-numeric input
    with pytest.raises(TypeError):
        mse("a", "b")
    with pytest.raises(TypeError):
        mse([1, 2], ["a", "b"])

def test_mse_zero_and_negative_zero():
    # Test 0.0 and -0.0 are treated the same
    codeflash_output = mse(0.0, -0.0)

# 3. Large Scale Test Cases



def test_mse_large_random_lists():
    # Large lists with random values, check output is correct
    import random
    random.seed(42)
    size = 1000
    y_true = [random.uniform(-100, 100) for _ in range(size)]
    y_pred = [random.uniform(-100, 100) for _ in range(size)]
    manual = sum((a-b)**2 for a, b in zip(y_true, y_pred)) / size


def test_mse_large_lists_with_negatives():
    # Large lists with negative values
    size = 1000
    y_true = [-i for i in range(size)]
    y_pred = [i for i in range(size)]
    manual = sum((a-b)**2 for a, b in zip(y_true, y_pred)) / size
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-mse-max5pv5b and push.