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Merged
SFJohnson24 merged 5 commits into from
Apr 3, 2026
Merged

#1652 wildcard replacement with domain on grouping_variables #1674

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ab9614a
#1652 wildcard replacement with domain on grouping_variables
alexfurmenkov Mar 25, 2026
30e395e
Merge branch 'main' into 1652-wildcard-on-grouping-variables
alexfurmenkov Mar 25, 2026
f8a0a17
#1652 renamed wildcard property to comply with new metadata structure
alexfurmenkov Mar 26, 2026
c72f285
extended test to check for wildcard replacement
alexfurmenkov Mar 27, 2026
35051cb
#1652 updated grouping test to cover one more cases
alexfurmenkov Apr 2, 2026
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4 changes: 4 additions & 0 deletions cdisc_rules_engine/models/actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -191,6 +191,10 @@ def generate_targeted_error_object( # noqa: C901
targets,
)

grouping_variables = [
x.replace("--", self.dataset_metadata.wildcard_replacement or "")
for x in grouping_variables
]
missing_grouping_vars = [
var for var in grouping_variables if var not in data.columns
]
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196 changes: 156 additions & 40 deletions tests/unit/test_actions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -105,11 +105,159 @@ def test_targeted_error_object_with_dataset_sensitivity():
assert error["value"] == {"TEST": 1, "MISSING": "Not in dataset"}


def test_targeted_error_object_with_group_sensitivity():
@pytest.mark.parametrize(
"grouping_variables, expected_column, df, expected_groups",
[
# Case 1: explicit column name, no wildcard
(
["SETCD"],
"SETCD",
pd.DataFrame(
{
"TXPARMCD": [
"PLANSUB",
"PLANSUB",
"PLANSUB",
"OTHER",
"OTHER",
"OTHER",
"OTHER",
"VALUE",
"VALUE",
],
"SETCD": [
"SET1",
"SET1",
"SET1",
"SET2",
"SET2",
"SET2",
"SET2",
"SET3",
"SET3",
],
"USUBJID": [
"001",
"001",
"001",
"002",
"002",
"002",
"002",
"003",
"003",
],
}
),
["SET1", "SET2", "SET3"],
),
# Case 2: wildcard "--PARMCD" → "TXPARMCD", grouped only on TXPARMCD
# Each distinct TXPARMCD value should produce one value
(
["--PARMCD"],
"TXPARMCD",
pd.DataFrame(
{
"TXPARMCD": [
"PLANSUB",
"PLANSUB",
"PLANSUB",
"OTHER",
"OTHER",
"OTHER",
"OTHER",
"VALUE",
"VALUE",
],
"SETCD": [
"SET1",
"SET1",
"SET1",
"SET2",
"SET2",
"SET2",
"SET2",
"SET3",
"SET3",
],
"USUBJID": [
"001",
"001",
"001",
"002",
"002",
"002",
"002",
"003",
"003",
],
}
),
["PLANSUB", "OTHER", "VALUE"],
),
# Case 3: grouped on both "--PARMCD" (→ "TXPARMCD") and "SETCD"
# TXPARMCD varies within SETCD groups to prove both columns contribute to grouping:
# SET1 has PLANSUB + OTHER → 2 groups
# SET2 has OTHER only → 1 group
# SET3 has VALUE + PLANSUB → 2 groups
# Total: 5 distinct (TXPARMCD, SETCD) combinations
(
["--PARMCD", "SETCD"],
"SETCD",
pd.DataFrame(
{
"TXPARMCD": [
"PLANSUB",
"PLANSUB",
"OTHER",
"OTHER",
"OTHER",
"OTHER",
"OTHER",
"VALUE",
"VALUE",
"PLANSUB",
"PLANSUB",
],
"SETCD": [
"SET1",
"SET1",
"SET1",
"SET1",
"SET2",
"SET2",
"SET2",
"SET3",
"SET3",
"SET3",
"SET3",
],
"USUBJID": [
"001",
"001",
"001",
"001",
"002",
"002",
"002",
"003",
"003",
"003",
"003",
],
}
),
["SET1", "SET1", "SET2", "SET3", "SET3"], # two SET1s and two SET3s
),
],
)
def test_targeted_error_object_with_group_sensitivity(
grouping_variables, expected_column, df, expected_groups
):
dummy_rule = {
"core_id": "FB4607",
"sensitivity": "Group",
"grouping_variables": ["SETCD"],
"grouping_variables": grouping_variables,
"conditions": {
"all": [
{
Expand All @@ -128,35 +276,6 @@ def test_targeted_error_object_with_group_sensitivity():
"output_variables": ["SETCD", "TXPARMCD"],
}

# Create test data with 3 groups (SETCD), multiple records each
df = pd.DataFrame.from_dict(
{
"TXPARMCD": [
"PLANSUB",
"PLANSUB",
"PLANSUB",
"OTHER",
"OTHER",
"OTHER",
"OTHER",
"VALUE",
"VALUE",
],
"SETCD": [
"SET1",
"SET1",
"SET1",
"SET2",
"SET2",
"SET2",
"SET2",
"SET3",
"SET3",
],
"USUBJID": ["001", "001", "001", "002", "002", "002", "002", "003", "003"],
}
)

variable = DatasetVariable(PandasDataset(df))
dataset_metadata = SDTMDatasetMetadata(
first_record={"DOMAIN": "TX"}, filename="tx.xpt"
Expand All @@ -168,21 +287,18 @@ def test_targeted_error_object_with_group_sensitivity():
targets, df, "Missing required TXPARMCD value per set"
)

# Should have exactly 3 errors (one per SETCD group)
assert len(result.errors) == 3
assert len(result.errors) == len(expected_groups)

# Extract SETCD values from errors
setcd_values = [
error.to_representation()["value"]["SETCD"] for error in result.errors
]
assert sorted(setcd_values) == ["SET1", "SET2", "SET3"]
actual_values = sorted(
error.to_representation()["value"][expected_column] for error in result.errors
)
assert actual_values == sorted(expected_groups)

# Each error should have row information from the first record in its group
for error in result.errors:
error_repr = error.to_representation()
assert "row" in error_repr
assert "SETCD" in error_repr["value"]
assert "TXPARMCD" in error_repr["value"]
assert expected_column in error_repr["value"]


def test_group_sensitivity_missing_grouping_variables():
Expand Down
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