def test_evaluate_preset(self, ground_truth, predictions, expected_precision):
tensor_shape = random_tensor_shape()
ground_truth_labels = np.tile(ground_truth, tensor_shape + [1])
predicted_labels = np.tile(predictions, tensor_shape + [1])
expected = np.tile(expected_precision, tensor_shape)
result = precision.evaluate(
ground_truth_labels, predicted_labels, classes=[1])
self.assertAllClose(expected, result)
def test_evaluate_preset_multiclass(self, ground_truth, predictions, classes,
reduce_average, expected_precision):
tensor_shape = random_tensor_shape()
ground_truth_labels = np.tile(ground_truth, tensor_shape + [1])
predicted_labels = np.tile(predictions, tensor_shape + [1])
expected = np.tile(expected_precision,
tensor_shape + ([1] if not reduce_average else []))
result = precision.evaluate(ground_truth_labels, predicted_labels, classes,
reduce_average)
self.assertAllClose(expected, result)
def binary_precision_function(ground_truth, predictions):
return precision.evaluate(ground_truth, predictions, classes=[1])