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Parametrize `test_classifications`

Merged Parametrize `test_classifications`
parametrize-classifications-tests into master
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Merged Manon Blanco requested to merge parametrize-classifications-tests into master
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tests/test_elements_worker/test_classifications.py
+ 126
− 262
@@ -10,6 +10,8 @@ from arkindex_worker.models import Element
from . import BASE_API_CALLS
DELETE_PARAMETER = "DELETE_PARAMETER"
def test_get_ml_class_id_load_classes(responses, mock_elements_worker):
corpus_id = "11111111-1111-1111-1111-111111111111"
@@ -191,108 +193,79 @@ def test_retrieve_ml_class_not_in_cache(responses, mock_elements_worker):
@pytest.mark.parametrize(
("data", "error_message"),
("arg_name", "data", "error_message"),
[
# Wrong element
(
{
"element": None,
"ml_class": "a_class",
"confidence": 0.42,
"high_confidence": True,
},
"element",
None,
"element shouldn't be null and should be an Element or CachedElement",
),
(
{
"element": "not element type",
"ml_class": "a_class",
"confidence": 0.42,
"high_confidence": True,
},
"element",
"not element type",
"element shouldn't be null and should be an Element or CachedElement",
),
# Wrong ml_class
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": None,
"confidence": 0.42,
"high_confidence": True,
},
"ml_class",
None,
"ml_class shouldn't be null and should be of type str",
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": 1234,
"confidence": 0.42,
"high_confidence": True,
},
"ml_class",
1234,
"ml_class shouldn't be null and should be of type str",
),
# Wrong confidence
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": None,
"high_confidence": True,
},
"confidence",
None,
"confidence shouldn't be null and should be a float in [0..1] range",
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": "wrong confidence",
"high_confidence": True,
},
"confidence",
"wrong confidence",
"confidence shouldn't be null and should be a float in [0..1] range",
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": 0,
"high_confidence": True,
},
"confidence",
0,
"confidence shouldn't be null and should be a float in [0..1] range",
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": 2.00,
"high_confidence": True,
},
"confidence",
2.00,
"confidence shouldn't be null and should be a float in [0..1] range",
),
# Wrong high_confidence
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": 0.42,
"high_confidence": None,
},
"high_confidence",
None,
"high_confidence shouldn't be null and should be of type bool",
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": 0.42,
"high_confidence": "wrong high_confidence",
},
"high_confidence",
"wrong high_confidence",
"high_confidence shouldn't be null and should be of type bool",
),
],
)
def test_create_classification_wrong_data(data, error_message, mock_elements_worker):
def test_create_classification_wrong_data(
arg_name, data, error_message, mock_elements_worker
):
mock_elements_worker.classes = {"a_class": "0000"}
with pytest.raises(AssertionError, match=re.escape(error_message)):
mock_elements_worker.create_classification(**data)
mock_elements_worker.create_classification(
**{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"ml_class": "a_class",
"confidence": 0.42,
"high_confidence": True,
# Overwrite with wrong data
arg_name: data,
}
)
def test_create_classification_api_error(responses, mock_elements_worker):
@@ -498,262 +471,153 @@ def test_create_classification_duplicate_worker_run(responses, mock_elements_wor
@pytest.mark.parametrize(
("data", "error_message", "error_type"),
("arg_name", "data", "error_message"),
[
# Wrong element
(
{
"element": None,
"classifications": [
{
"ml_class_id": "uuid1",
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": "uuid2",
"confidence": 0.25,
"high_confidence": False,
},
],
},
"element",
None,
"element shouldn't be null and should be an Element or CachedElement",
AssertionError,
),
(
{
"element": "not element type",
"classifications": [
{
"ml_class_id": "uuid1",
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": "uuid2",
"confidence": 0.25,
"high_confidence": False,
},
],
},
"element",
"not element type",
"element shouldn't be null and should be an Element or CachedElement",
AssertionError,
),
# Wrong classifications
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": None,
},
"classifications",
None,
"classifications shouldn't be null and should be of type list",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": 1234,
},
"classifications",
1234,
"classifications shouldn't be null and should be of type list",
AssertionError,
),
# Wrong classifications > ml_class_id
(
{
],
)
def test_create_classifications_wrong_data(
arg_name, data, error_message, mock_elements_worker
):
with pytest.raises(AssertionError, match=error_message):
mock_elements_worker.create_classifications(
**{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"ml_class_id": "uuid1",
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": 0.25,
"ml_class_id": "uuid2",
"confidence": 0.25,
"high_confidence": False,
},
],
# Overwrite with wrong data
arg_name: data,
},
"Classification at index 1 in classifications: ml_class_id shouldn't be null and should be of type str",
)
@pytest.mark.parametrize(
("arg_name", "data", "error_message", "error_type"),
[
# Wrong classifications > ml_class_id
(
"ml_class_id",
DELETE_PARAMETER,
"ml_class_id shouldn't be null and should be of type str",
AssertionError,
),
), # Updated
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": None,
"confidence": 0.25,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: ml_class_id shouldn't be null and should be of type str",
"ml_class_id",
None,
"ml_class_id shouldn't be null and should be of type str",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": 1234,
"confidence": 0.25,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: ml_class_id shouldn't be null and should be of type str",
"ml_class_id",
1234,
"ml_class_id shouldn't be null and should be of type str",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": "not_an_uuid",
"confidence": 0.25,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: ml_class_id is not a valid uuid.",
"ml_class_id",
"not_an_uuid",
"ml_class_id is not a valid uuid.",
ValueError,
),
# Wrong classifications > confidence
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: confidence shouldn't be null and should be a float in [0..1] range",
"confidence",
DELETE_PARAMETER,
"confidence shouldn't be null and should be a float in [0..1] range",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": None,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: confidence shouldn't be null and should be a float in [0..1] range",
"confidence",
None,
"confidence shouldn't be null and should be a float in [0..1] range",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": "wrong confidence",
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: confidence shouldn't be null and should be a float in [0..1] range",
"confidence",
"wrong confidence",
"confidence shouldn't be null and should be a float in [0..1] range",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": 0,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: confidence shouldn't be null and should be a float in [0..1] range",
"confidence",
0,
"confidence shouldn't be null and should be a float in [0..1] range",
AssertionError,
),
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": 2.00,
"high_confidence": False,
},
],
},
"Classification at index 1 in classifications: confidence shouldn't be null and should be a float in [0..1] range",
"confidence",
2.00,
"confidence shouldn't be null and should be a float in [0..1] range",
AssertionError,
),
# Wrong classifications > high_confidence
(
{
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": 0.25,
"high_confidence": "wrong high_confidence",
},
],
},
"Classification at index 1 in classifications: high_confidence should be of type bool",
"high_confidence",
"wrong high_confidence",
"high_confidence should be of type bool",
AssertionError,
),
],
)
def test_create_classifications_wrong_data(
data, error_message, error_type, mock_elements_worker
def test_create_classifications_wrong_classifications_data(
arg_name, data, error_message, error_type, mock_elements_worker
):
with pytest.raises(error_type, match=re.escape(error_message)):
mock_elements_worker.create_classifications(**data)
all_data = {
"element": Element({"id": "12341234-1234-1234-1234-123412341234"}),
"classifications": [
{
"ml_class_id": str(uuid4()),
"confidence": 0.75,
"high_confidence": False,
},
{
"ml_class_id": str(uuid4()),
"confidence": 0.25,
"high_confidence": False,
# Overwrite with wrong data
arg_name: data,
},
],
}
if data == DELETE_PARAMETER:
del all_data["classifications"][1][arg_name]
with pytest.raises(
error_type,
match=re.escape(
f"Classification at index 1 in classifications: {error_message}"
),
):
mock_elements_worker.create_classifications(**all_data)
def test_create_classifications_api_error(responses, mock_elements_worker):