def tests_no_metrics_to_protobuf_classification():
mod_met = ModelMetrics(model_type=ModelType.CLASSIFICATION)
assert mod_met.model_type == ModelType.CLASSIFICATION
message = mod_met.to_protobuf()
model_metrics = ModelMetrics.from_protobuf(message)
assert model_metrics.model_type == ModelType.CLASSIFICATION
def tests_no_metrics_to_protobuf_regression():
mod_met = ModelMetrics(model_type=ModelType.REGRESSION)
assert mod_met.model_type == ModelType.REGRESSION
message = mod_met.to_protobuf()
model_metrics = ModelMetrics.from_protobuf(message)
assert model_metrics.model_type == ModelType.REGRESSION
def from_protobuf(cls, message: ModelProfileMessage):
# convert google.protobuf.pyext._message.RepeatedScalarContainer to a list
output_fields = [f for f in message.output_fields]
return ModelProfile(
output_fields=output_fields,
metrics=ModelMetrics.from_protobuf(message.metrics),
)
def tests_model_metrics():
mod_met = ModelMetrics(model_type=ModelType.CLASSIFICATION)
targets_1 = ["cat", "dog", "pig"]
predictions_1 = ["cat", "dog", "dog"]
scores_1 = [0.1, 0.2, 0.4]
expected_1 = [[1, 0, 0], [0, 1, 1], [0, 0, 0]]
mod_met.compute_confusion_matrix(predictions_1, targets_1, scores_1)
assert mod_met.model_type == ModelType.CLASSIFICATION
for idx, value in enumerate(mod_met.confusion_matrix.labels):
for jdx, value_2 in enumerate(mod_met.confusion_matrix.labels):
print(idx, jdx)
assert mod_met.confusion_matrix.confusion_matrix[idx, jdx].floats.count == expected_1[idx][jdx]
def __init__(self, output_fields=None, metrics: ModelMetrics = None):
super().__init__()
if output_fields is None:
output_fields = []
self.output_fields = output_fields
if metrics is None:
metrics = ModelMetrics()
self.metrics = metrics
def test_merge_metrics_with_none_confusion_matrix():
metrics = ModelMetrics()
other = ModelMetrics()
other.confusion_matrix = None
other.regression_metrics = None
new_metrics = metrics.merge(other)
assert new_metrics.model_type == ModelType.UNKNOWN
def tests_model_metrics_to_protobuf_regression():
regression_model = ModelMetrics(model_type=ModelType.REGRESSION)
targets_1 = [0.1, 0.3, 0.4]
predictions_1 = [0.5, 0.5, 0.5]
regression_model.compute_regression_metrics(predictions_1, targets_1)
regression_message = regression_model.to_protobuf()
model_metrics_from_message = ModelMetrics.from_protobuf(regression_message)
assert model_metrics_from_message.model_type == ModelType.REGRESSION
def tests_model_metrics_to_protobuf():
mod_met = ModelMetrics()
targets_1 = ["cat", "dog", "pig"]
predictions_1 = ["cat", "dog", "dog"]
scores_1 = [0.1, 0.2, 0.4]
expected_1 = [[1, 0, 0], [0, 1, 1], [0, 0, 0]]
mod_met.compute_confusion_matrix(predictions_1, targets_1, scores_1)
message = mod_met.to_protobuf()
ModelMetrics.from_protobuf(message)
def tests_model_metrics_to_protobuf_classification():
mod_met = ModelMetrics(model_type=ModelType.CLASSIFICATION)
targets_1 = ["cat", "dog", "pig"]
predictions_1 = ["cat", "dog", "dog"]
scores_1 = [0.1, 0.2, 0.4]
mod_met.compute_confusion_matrix(predictions_1, targets_1, scores_1)
message = mod_met.to_protobuf()
model_metrics = ModelMetrics.from_protobuf(message)
assert model_metrics.model_type == ModelType.CLASSIFICATION
assert model_metrics.confusion_matrix.labels == ["cat", "dog", "pig"]
def test_merge_metrics_model():
metrics = ModelMetrics()
other = ModelMetrics(model_type=ModelType.REGRESSION)
other.regression_metrics = None
new_metrics = metrics.merge(other)
assert new_metrics.model_type == ModelType.REGRESSION
assert new_metrics.confusion_matrix is None
# keep initial model type during merge
metrics = ModelMetrics(model_type=ModelType.REGRESSION)
other = ModelMetrics(model_type=ModelType.CLASSIFICATION)
other.regression_metrics = None
new_metrics = metrics.merge(other)
assert new_metrics.model_type == ModelType.REGRESSION
assert new_metrics.confusion_matrix is None
def test_merge_metrics_with_none_confusion_matrix():
metrics = ModelMetrics()
other = ModelMetrics()
other.confusion_matrix = None
metrics.merge(other)
def test_merge_none():
metrics = ModelMetrics()
assert metrics.merge(None) == metrics
def test_model_metrics_init():
reg_met = RegressionMetrics()
conf_ma = ConfusionMatrix()
with pytest.raises(NotImplementedError):
metrics = ModelMetrics(confusion_matrix=conf_ma, regression_metrics=reg_met)
def test_merge_metrics_with_none_regression_matrix():
metrics = ModelMetrics()
other = ModelMetrics(model_type=ModelType.REGRESSION)
other.regression_metrics = None
new_metrics = metrics.merge(other)
assert new_metrics.model_type == ModelType.REGRESSION
def test_merge_none():
metrics = ModelMetrics()
metrics.merge(None)
