check save and restore config check save and restore config how users would register a custom function or class to use with meanmetricwrapper check save and restore config check save and restore config check save and restore config how users would register a custom function or class to use with meanmetricwrapper check save and restore config
import numpy as np

from keras import testing
from keras.metrics import reduction_metrics
from keras.saving import register_keras_serializable


class SumTest(testing.TestCase):
    def test_config(self):
        sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
        self.assertEqual(sum_obj.name, "sum")
        self.assertEqual(len(sum_obj.variables), 1)
        self.assertEqual(sum_obj._dtype, "float32")

        
        sum_obj2 = reduction_metrics.Sum.from_config(sum_obj.get_config())
        self.assertEqual(sum_obj2.name, "sum")
        self.assertEqual(len(sum_obj2.variables), 1)
        self.assertEqual(sum_obj2._dtype, "float32")

    def test_unweighted(self):
        sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
        sum_obj.update_state([1, 3, 5, 7])
        result = sum_obj.result()
        self.assertAllClose(result, 16.0, atol=1e-3)

    def test_weighted(self):
        sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
        sum_obj.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
        result = sum_obj.result()
        self.assertAllClose(result, 4.0, atol=1e-3)

    def test_weighted_nd(self):
        sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
        sum_obj.update_state([[1, 3], [5, 7]], sample_weight=[[1, 1], [1, 0]])
        result = sum_obj.result()
        self.assertAllClose(result, 9.0, atol=1e-3)


class MeanTest(testing.TestCase):
    def test_config(self):
        mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
        self.assertEqual(mean_obj.name, "mean")
        self.assertEqual(len(mean_obj.variables), 2)
        self.assertEqual(mean_obj._dtype, "float32")

        
        mean_obj2 = reduction_metrics.Mean.from_config(mean_obj.get_config())
        self.assertEqual(mean_obj2.name, "mean")
        self.assertEqual(len(mean_obj2.variables), 2)
        self.assertEqual(mean_obj2._dtype, "float32")

    def test_unweighted(self):
        mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
        mean_obj.update_state([1, 3, 5, 7])
        result = mean_obj.result()
        self.assertAllClose(result, 4.0, atol=1e-3)

    def test_weighted(self):
        mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
        mean_obj.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
        result = mean_obj.result()
        self.assertAllClose(result, 2.0, atol=1e-3)

    def test_weighted_nd(self):
        mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
        mean_obj.update_state([[1, 3], [5, 7]], sample_weight=[[1, 1], [1, 0]])
        result = mean_obj.result()
        self.assertAllClose(result, 3.0, atol=1e-3)




@register_keras_serializable(package="test", name="mse")
def mse(y_true, y_pred):
    return (y_true - y_pred) ** 2


class MetricWrapperTest(testing.TestCase):
    def test_config(self):
        mse_obj = reduction_metrics.MeanMetricWrapper(
            fn=mse, name="mse", dtype="float32"
        )
        self.assertEqual(mse_obj.name, "mse")
        self.assertEqual(len(mse_obj.variables), 2)
        self.assertEqual(mse_obj._dtype, "float32")
        
        mse_obj2 = reduction_metrics.MeanMetricWrapper.from_config(
            mse_obj.get_config()
        )
        self.assertEqual(mse_obj2.name, "mse")
        self.assertEqual(len(mse_obj2.variables), 2)
        self.assertEqual(mse_obj2._dtype, "float32")
        self.assertTrue("fn" in mse_obj2.get_config())

    def test_unweighted(self):
        mse_obj = reduction_metrics.MeanMetricWrapper(
            fn=mse, name="mse", dtype="float32"
        )
        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 = reduction_metrics.MeanMetricWrapper(
            fn=mse, name="mse", dtype="float32"
        )
        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)