def test_mae_on_interval_with_unsorted_values(self):
annotations = [
RegressionAnnotation('identifier', -1),
RegressionAnnotation('identifier', 2),
RegressionAnnotation('identifier', 1)
]
predictions = [
RegressionPrediction('identifier', 1),
RegressionPrediction('identifier', 3),
RegressionPrediction('identifier', 1)
]
config = [{'type': 'mae_on_interval', 'intervals': [2.0, 0.0, 4.0]}]
expected = EvaluationResult(
pytest.approx([0.0, 0.0, 1.0, 0.0]),
None,
'mae_on_interval',
'mae_on_interval',
None,
None,
{
'postfix': ' ', 'scale': 1,
'names': ['mean: <= 0.0 < 2.0', 'std: <= 0.0 < 2.0', 'mean: <= 2.0 < 4.0', 'std: <= 2.0 < 4.0'],
'orig_names': ['mean: <= 0.0 < 2.0', 'std: <= 0.0 < 2.0', 'mean: <= 2.0 < 4.0', 'std: <= 2.0 < 4.0'],
'calculate_mean': False,
'target': 'higher-worse'
},
None
)
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_classification_accuracy_result_for_batch_2_with_not_ordered_ids(
self):
annotations = [
ClassificationAnnotation('identifier', 3),
ClassificationAnnotation('identifier1', 1)
]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0]),
ClassificationPrediction('identifier2', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1}], None)
metric_result = dispatcher.update_metrics_on_batch([42, 17],
annotations,
predictions)
expected_metric_result = [
PerImageMetricResult('accuracy', 'accuracy', 1.0, 'higher-better'),
PerImageMetricResult('accuracy', 'accuracy', 0.0, 'higher-better')
]
assert len(metric_result) == 2
assert 42 in metric_result
assert len(metric_result[42]) == 1
assert metric_result[42][0] == expected_metric_result[0]
assert 17 in metric_result
assert len(metric_result[17]) == 1
assert metric_result[17][0] == expected_metric_result[1]
def test_classification_per_class_accuracy_particual_prediction(self):
annotation = [
ClassificationAnnotation('identifier_1', 1),
ClassificationAnnotation('identifier_2', 0),
ClassificationAnnotation('identifier_3', 0)
]
prediction = [
ClassificationPrediction('identifier_1', [1.0, 2.0]),
ClassificationPrediction('identifier_2', [2.0, 1.0]),
ClassificationPrediction('identifier_3', [1.0, 5.0])
]
config = {
'annotation': 'mocked',
'metrics': [{
'type': 'accuracy_per_class',
'top_k': 1
}]
}
dataset = DummyDataset(label_map={0: '0', 1: '1'})
dispatcher = MetricsExecutor(config, dataset)
dispatcher.update_metrics_on_batch(annotation, prediction)
for _, evaluation_result in dispatcher.iterate_metrics(
annotation, prediction):
assert evaluation_result.name == 'accuracy_per_class'
assert len(evaluation_result.evaluated_value) == 2
assert evaluation_result.evaluated_value[0] == pytest.approx(0.5)
assert evaluation_result.evaluated_value[1] == pytest.approx(1.0)
assert evaluation_result.reference_value is None
assert evaluation_result.threshold is None
def test_mae_with_positive_diff_between_annotation_and_prediction(self):
annotations = [
RegressionAnnotation('identifier', 3),
RegressionAnnotation('identifier2', 1)
]
predictions = [
RegressionPrediction('identifier', 1),
RegressionPrediction('identifier2', -3)
]
config = [{'type': 'mae'}]
expected = EvaluationResult(
pytest.approx([3.0, 1.0]), None, 'mae', 'mae', None, {
'postfix': ' ',
'scale': 1,
'names': ['mean', 'std'],
'calculate_mean': False,
'target': 'higher-worse'
})
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(range(len(annotations)),
annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(
annotations, predictions):
assert evaluation_result == expected
def test_accuracy_with_wrong_annotation_type_raise_config_error_exception(self):
annotations = [DetectionAnnotation('identifier', 3)]
predictions = [ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1}], None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_classification_per_class_accuracy_prediction_top3(self):
annotation = [
ClassificationAnnotation('identifier_1', 1),
ClassificationAnnotation('identifier_2', 1)
]
prediction = [
ClassificationPrediction('identifier_1', [1.0, 2.0, 3.0, 4.0]),
ClassificationPrediction('identifier_2', [2.0, 1.0, 3.0, 4.0])
]
dataset = DummyDataset(label_map={0: '0', 1: '1', 2: '2', 3: '3'})
dispatcher = MetricsExecutor([{
'type': 'accuracy_per_class',
'top_k': 3
}], dataset)
dispatcher.update_metrics_on_batch(range(len(annotation)), annotation,
prediction)
for _, evaluation_result in dispatcher.iterate_metrics(
annotation, prediction):
assert evaluation_result.name == 'accuracy_per_class'
assert len(evaluation_result.evaluated_value) == 4
assert evaluation_result.evaluated_value[0] == pytest.approx(0.0)
assert evaluation_result.evaluated_value[1] == pytest.approx(0.5)
assert evaluation_result.evaluated_value[2] == pytest.approx(0.0)
assert evaluation_result.evaluated_value[3] == pytest.approx(0.0)
assert evaluation_result.reference_value is None
assert evaluation_result.threshold is None
def test_complete_accuracy_with_container_sources(self):
annotations = [
ContainerAnnotation(
{'a': ClassificationAnnotation('identifier', 3)})
]
predictions = [
ContainerPrediction({
'p':
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
})
]
config = [{
'type': 'accuracy',
'top_k': 1,
'annotation_source': 'a',
'prediction_source': 'p'
}]
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(range(len(annotations)),
annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(
annotations, predictions):
assert evaluation_result.name == 'accuracy'
assert evaluation_result.evaluated_value == pytest.approx(1.0)
assert evaluation_result.reference_value is None
assert evaluation_result.threshold is None
def test_classification_accuracy_result_for_batch_1_with_2_metrics(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [
ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
]
dispatcher = MetricsExecutor([{
'name': 'top1',
'type': 'accuracy',
'top_k': 1
}, {
'name': 'top3',
'type': 'accuracy',
'top_k': 3
}], None)
metric_result = dispatcher.update_metrics_on_batch(
range(len(annotations)), annotations, predictions)
expected_metric_result = [
PerImageMetricResult('top1', 'accuracy', 1.0, 'higher-better'),
PerImageMetricResult('top3', 'accuracy', 1.0, 'higher-better')
]
assert len(metric_result) == 1
assert 0 in metric_result
assert len(metric_result[0]) == 2
assert metric_result[0][0] == expected_metric_result[0]
assert metric_result[0][1] == expected_metric_result[1]
def test_update_metric_result(self):
dataset = multi_class_dataset()
annotations = make_segmentation_representation(np.array([[1, 2, 3, 2, 3], [0, 0, 0, 0, 0]]), True)
predictions = make_segmentation_representation(np.array([[1, 0, 3, 0, 0], [0, 0, 0, 0, 0]]), False)
dispatcher = MetricsExecutor(create_config(self.name), dataset)
metric_result, _ = dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
assert metric_result[0][0].result == 0.5125
def test_accuracy_with_unsupported_annotation_type_as_annotation_source_for_container_raises_config_error(self):
annotations = [ContainerAnnotation({'annotation': DetectionAnnotation('identifier', 3)})]
predictions = [ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1, 'annotation_source': 'annotation'}], None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_mae_on_interval_default_all_not_in_range_not_ignore_out_of_range(self):
annotations = [RegressionAnnotation('identifier', -1), RegressionAnnotation('identifier', 2)]
predictions = [RegressionPrediction('identifier', 1), RegressionPrediction('identifier', 2)]
expected = EvaluationResult(
pytest.approx([2.0, 0.0, 0.0, 0.0]),
None,
'mae_on_interval',
'mae_on_interval',
None,
None,
{
'postfix': ' ',
'scale': 1,
'names': ['mean: < 0.0', 'std: < 0.0', 'mean: > 1.0', 'std: > 1.0'],
'calculate_mean': False,
'target': 'higher-worse',
'orig_names': ['mean: < 0.0', 'std: < 0.0', 'mean: <= 0.0 < 1.0', 'std: <= 0.0 < 1.0', 'mean: > 1.0', 'std: > 1.0']
},
None
)
config = [{'type': 'mae_on_interval', 'end': 1, 'ignore_values_not_in_interval': False}]
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_mae_on_interval_values_in_range(self):
annotations = [
RegressionAnnotation('identifier', 0.5),
RegressionAnnotation('identifier', 0.5)
]
predictions = [
RegressionPrediction('identifier', 1),
RegressionPrediction('identifier', 0.25)
]
config = [{'type': 'mae_on_interval', 'end': 1}]
expected = EvaluationResult(
pytest.approx([0.375, 0.125]), None, 'mae_on_interval',
'mae_on_interval', None, {
'postfix': ' ',
'scale': 1,
'names': ['mean: <= 0.0 < 1.0', 'std: <= 0.0 < 1.0'],
'calculate_mean': False
})
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(
annotations, predictions):
assert evaluation_result == expected
def test_multi_class_not_matched(self):
annotations = make_segmentation_representation(np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]), True)
predictions = make_segmentation_representation(np.array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]), False)
dispatcher = MetricsExecutor(create_config(self.name), multi_class_dataset())
dispatcher.update_metrics_on_batch(annotations, predictions)
expected = generate_expected_result(0.0, self.name)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_multi_class(self):
annotations = make_segmentation_representation(np.array([[1, 0, 3, 0, 0], [0, 0, 0, 0, 0]]), True)
predictions = make_segmentation_representation(np.array([[1, 2, 3, 2, 3], [0, 0, 0, 0, 0]]), False)
dispatcher = MetricsExecutor(create_config(self.name), multi_class_dataset())
dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
expected = generate_expected_result((5.0+1.0+1.0)/(8.0+1.0+1.0), self.name)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_accuracy_on_annotation_container_with_several_suitable_representations_config_value_error_exception(self):
annotations = [ContainerAnnotation({'annotation1': ClassificationAnnotation('identifier', 3), 'annotation2': ClassificationAnnotation('identifier', 3)})]
predictions = [ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])]
config = {'annotation': 'mocked', 'metrics': [{'type': 'accuracy', 'top_k': 1}]}
dispatcher = MetricsExecutor(config, None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_accuracy_on_container_with_wrong_annotation_source_name_raise_config_error_exception(self):
annotations = [ContainerAnnotation({'annotation': ClassificationAnnotation('identifier', 3)})]
predictions = [ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])]
config = {'annotation': 'mocked', 'metrics': [{'type': 'accuracy', 'top_k': 1, 'annotation_source': 'a'}]}
dispatcher = MetricsExecutor(config, None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_accuracy_with_unsupported_prediction_in_container_raise_config_error_exception(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ContainerPrediction({'prediction': DetectionPrediction('identifier', [1.0, 1.0, 1.0, 4.0])})]
config = {'annotation': 'mocked', 'metrics': [{'type': 'accuracy', 'top_k': 1}]}
dispatcher = MetricsExecutor(config, None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_update_mse_metric_result(self):
annotations = [RegressionAnnotation('identifier', 3), RegressionAnnotation('identifier2', 1)]
predictions = [RegressionPrediction('identifier', 5), RegressionPrediction('identifier2', 5)]
config = [{'type': 'mse'}]
dispatcher = MetricsExecutor(config, None)
metric_result, _ = dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
assert metric_result[0][0].result == 4
assert metric_result[1][0].result == 16
def test_update_normed_error(self):
config = [{'type': 'normed_error'}]
annotations = [FacialLandmarksAnnotation('identifier', np.array([1, 1, 1, 1, 1]), np.array([1, 1, 1, 1, 1]))]
annotations[0].metadata.update({'left_eye': 0, 'right_eye': 1})
predictions = [FacialLandmarksPrediction('identifier', np.array([1, 1, 1, 1, 1]), np.array([1, 1, 1, 1, 1]))]
dispatcher = MetricsExecutor(config, None)
metric_result, _ = dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
assert metric_result[0][0].result == 0
def test_update_rmse_on_interval_metric(self):
config = [{'type': 'rmse_on_interval', 'intervals': [0.0, 2.0, 4.0]}]
annotations = [RegressionAnnotation('identifier', 3), RegressionAnnotation('identifier2', 1)]
predictions = [RegressionPrediction('identifier', 5), RegressionPrediction('identifier2', 5)]
dispatcher = MetricsExecutor(config, None)
metric_result, _ = dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
assert metric_result[0][0].result == 2
assert metric_result[1][0].result == 4
def test_multi_class(self):
dataset = multi_class_dataset()
annotations = make_segmentation_representation(np.array([[1, 2, 3, 2, 3], [0, 0, 0, 0, 0]]), True)
predictions = make_segmentation_representation(np.array([[1, 0, 3, 0, 0], [0, 0, 0, 0, 0]]), False)
dispatcher = MetricsExecutor(create_config(self.name), dataset)
dispatcher.update_metrics_on_batch(annotations, predictions)
expected = generate_expected_result([1.0, 1.0, 0.0, 0.5], self.name, dataset.labels)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_config_vector_presenter(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])]
config = [{'type': 'accuracy', 'top_k': 1, 'presenter': 'print_vector'}]
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
for presenter, _ in dispatcher.iterate_metrics(annotations, predictions):
assert isinstance(presenter, VectorPrintPresenter)
def test_one_class(self):
annotations = make_segmentation_representation(np.array([[0, 0], [0, 0]]), True)
predictions = make_segmentation_representation(np.array([[0, 0], [0, 0]]), False)
dataset = single_class_dataset()
dispatcher = MetricsExecutor(create_config(self.name), dataset)
dispatcher.update_metrics_on_batch(range(len(annotations)), annotations, predictions)
expected = generate_expected_result([1.0], self.name, {0: dataset.labels[0]})
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_mse_with_positive_diff_bitween_annotation_and_prediction(self):
annotations = [RegressionAnnotation('identifier', 3), RegressionAnnotation('identifier2', 1)]
predictions = [RegressionPrediction('identifier', 1), RegressionPrediction('identifier2', -3)]
config = {'annotation': 'mocked', 'metrics': [{'type': 'mse'}]}
expected = EvaluationResult(pytest.approx([10.0, 6.0]), None, 'mse', None, {'postfix': ' ', 'scale': 1, 'names': ['mean', 'std'], 'calculate_mean': False})
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(annotations, predictions)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result == expected
def test_one_class_update_metric_result(self):
annotations = make_segmentation_representation(
np.array([[0, 0], [0, 0]]), True)
predictions = make_segmentation_representation(
np.array([[0, 0], [0, 0]]), False)
dispatcher = MetricsExecutor(create_config(self.name),
single_class_dataset())
metric_result = dispatcher.update_metrics_on_batch(
range(len(annotations)), annotations, predictions)
assert metric_result[0][0].result == 1
def test_accuracy_on_prediction_container_with_several_suitable_representations_raise_config_error_exception(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ContainerPrediction({
'prediction1': ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0]),
'prediction2': ClassificationPrediction('identifier', [1.0, 1.0, 1.0, 4.0])
})]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 1}], None)
with pytest.raises(ConfigError):
dispatcher.update_metrics_on_batch(annotations, predictions)
def test_threshold_is_10_by_config(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ClassificationPrediction('identifier', [5.0, 3.0, 4.0, 1.0])]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 3, 'threshold': 10}], None)
for _, evaluation_result in dispatcher.iterate_metrics([annotations], [predictions]):
assert evaluation_result.name == 'accuracy'
assert evaluation_result.evaluated_value == 0.0
assert evaluation_result.reference_value is None
assert evaluation_result.threshold == 10
def test_zero_accuracy_top_3(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ClassificationPrediction('identifier', [5.0, 3.0, 4.0, 1.0])]
dispatcher = MetricsExecutor([{'type': 'accuracy', 'top_k': 3}], None)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result.name == 'accuracy'
assert evaluation_result.evaluated_value == 0.0
assert evaluation_result.reference_value is None
assert evaluation_result.threshold is None
def test_mae_on_interval_default_all_missed(self):
annotations = [RegressionAnnotation('identifier', -2)]
predictions = [RegressionPrediction('identifier', 1)]
config = {'annotation': 'mocked', 'metrics': [{'type': 'mae_on_interval', 'end': 1}]}
expected = EvaluationResult(pytest.approx([0.0]), None, 'mae_on_interval', None, {'postfix': ' ', 'scale': 1, 'names': [], 'calculate_mean': False})
dispatcher = MetricsExecutor(config, None)
dispatcher.update_metrics_on_batch(annotations, predictions)
with pytest.warns(UserWarning) as warnings:
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert len(warnings) == 1
assert evaluation_result == expected
def test_reference_is_10_by_config(self):
annotations = [ClassificationAnnotation('identifier', 3)]
predictions = [ClassificationPrediction('identifier', [5.0, 3.0, 4.0, 1.0])]
config = {'annotation': 'mocked', 'metrics': [{'type': 'accuracy', 'top_k': 3, 'reference': 10}]}
dispatcher = MetricsExecutor(config, None)
for _, evaluation_result in dispatcher.iterate_metrics(annotations, predictions):
assert evaluation_result.name == 'accuracy'
assert evaluation_result.evaluated_value == 0.0
assert evaluation_result.reference_value == 10
assert evaluation_result.threshold is None
