91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
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import numpy as np
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from keras_core import testing
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from keras_core.metrics import reduction_metrics
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class SumTest(testing.TestCase):
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def test_config(self):
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sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
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self.assertEqual(sum_obj.name, "sum")
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self.assertEqual(len(sum_obj.variables), 1)
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self.assertEqual(sum_obj._dtype, "float32")
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# TODO: Check save and restore config
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def test_unweighted(self):
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sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
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sum_obj.update_state([1, 3, 5, 7])
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result = sum_obj.result()
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self.assertAllClose(result, 16.0, atol=1e-3)
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def test_weighted(self):
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sum_obj = reduction_metrics.Sum(name="sum", dtype="float32")
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sum_obj.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
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result = sum_obj.result()
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self.assertAllClose(result, 4.0, atol=1e-3)
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class MeanTest(testing.TestCase):
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def test_config(self):
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mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
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self.assertEqual(mean_obj.name, "mean")
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self.assertEqual(len(mean_obj.variables), 2)
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self.assertEqual(mean_obj._dtype, "float32")
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# TODO: Check save and restore config
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def test_unweighted(self):
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mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
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mean_obj.update_state([1, 3, 5, 7])
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result = mean_obj.result()
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self.assertAllClose(result, 4.0, atol=1e-3)
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def test_weighted(self):
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mean_obj = reduction_metrics.Mean(name="mean", dtype="float32")
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mean_obj.update_state([1, 3, 5, 7], sample_weight=[1, 1, 0, 0])
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result = mean_obj.result()
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self.assertAllClose(result, 2.0, atol=1e-3)
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def mse(y_true, y_pred):
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return (y_true - y_pred) ** 2
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class MetricWrapperTest(testing.TestCase):
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def test_config(self):
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mse_obj = reduction_metrics.MeanMetricWrapper(
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fn=mse, name="mse", dtype="float32"
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)
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self.assertEqual(mse_obj.name, "mse")
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self.assertEqual(len(mse_obj.variables), 2)
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self.assertEqual(mse_obj._dtype, "float32")
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# TODO: Check save and restore config
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def test_unweighted(self):
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mse_obj = reduction_metrics.MeanMetricWrapper(
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fn=mse, name="mse", dtype="float32"
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)
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y_true = np.array(
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[[0, 1, 0, 1, 0], [0, 0, 1, 1, 1], [1, 1, 1, 1, 0], [0, 0, 0, 0, 1]]
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)
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y_pred = np.array(
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[[0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 0, 1, 0], [1, 1, 1, 1, 1]]
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)
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mse_obj.update_state(y_true, y_pred)
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result = mse_obj.result()
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self.assertAllClose(0.5, result, atol=1e-5)
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def test_weighted(self):
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mse_obj = reduction_metrics.MeanMetricWrapper(
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fn=mse, name="mse", dtype="float32"
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)
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y_true = np.array(
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[[0, 1, 0, 1, 0], [0, 0, 1, 1, 1], [1, 1, 1, 1, 0], [0, 0, 0, 0, 1]]
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)
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y_pred = np.array(
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[[0, 0, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 0, 1, 0], [1, 1, 1, 1, 1]]
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)
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sample_weight = np.array([1.0, 1.5, 2.0, 2.5])
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result = mse_obj(y_true, y_pred, sample_weight=sample_weight)
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self.assertAllClose(0.54285, result, atol=1e-5)
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