keras/keras_core/metrics/regression_metrics_test.py

import numpy as np

from keras_core import testing
from keras_core.metrics import regression_metrics


class MeanSquaredErrorTest(testing.TestCase):
    def test_config(self):
        # TODO
        pass

    def test_unweighted(self):
        mse_obj = regression_metrics.MeanSquaredError()
        y_true = np.array(
            [[0, 1, 0, 1, 0], [0, 0, 1, 1, 1], [1, 1, 1, 1, 0], [0, 0, 0, 0, 1]]
        )
        y_pred = np.array(
            [[0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 0, 1, 0], [1, 1, 1, 1, 1]]
        )

        mse_obj.update_state(y_true, y_pred)
        result = mse_obj.result()
        self.assertAllClose(0.5, result, atol=1e-5)

    def test_weighted(self):
        mse_obj = regression_metrics.MeanSquaredError()
        y_true = np.array(
            [[0, 1, 0, 1, 0], [0, 0, 1, 1, 1], [1, 1, 1, 1, 0], [0, 0, 0, 0, 1]]
        )
        y_pred = np.array(
            [[0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 0, 1, 0], [1, 1, 1, 1, 1]]
        )
        sample_weight = np.array([1.0, 1.5, 2.0, 2.5])
        result = mse_obj(y_true, y_pred, sample_weight=sample_weight)
        self.assertAllClose(0.54285, result, atol=1e-5)