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[Fix] Combining column relationship with constraints not working #2771

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Merged
frances-h merged 1 commit into from
Jan 7, 2026
Merged

[Fix] Combining column relationship with constraints not working #2771

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1 change: 1 addition & 0 deletions sdv/single_table/base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -474,6 +474,7 @@ def add_constraints(self, constraints):
except ConstraintNotMetError:
raise e

self.metadata.validate()
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@amontanez24 amontanez24 Jan 5, 2026

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why does this fix it?

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@frances-h frances-h Jan 5, 2026

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The data processor uses the _valid_column_relationships attribute here to iterate over column relationships. That attribute only gets set here when validating the column relationships.

When we add constraints at the single table level, we use the updated metadata from the constraints for the data processor, but it was missing the _valid_column_relationships attribute because the updated metadata hadn't been validated.

self._data_processor = DataProcessor(
metadata=self.metadata._convert_to_single_table(),
enforce_rounding=self.enforce_rounding,
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50 changes: 50 additions & 0 deletions tests/integration/single_table/test_constraints.py
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Expand Up @@ -37,6 +37,56 @@ def demo_metadata():
return DEMO_METADATA


def test_column_relationships_with_constraints():
"""Test constraints with column relationships. GH#2768"""
# Setup
metadata = Metadata.load_from_dict({
'tables': {
'table': {
'columns': {
'id': {'sdtype': 'id'},
'street': {'sdtype': 'street_address'},
'city': {'sdtype': 'city'},
'state': {'sdtype': 'administrative_unit'},
'zip': {'sdtype': 'postcode'},
'code': {'sdtype': 'categorical'},
'description': {'sdtype': 'categorical'},
}
},
},
})
metadata.add_column_relationship(
table_name='table',
relationship_type='address',
column_names=['street', 'city', 'state', 'zip'],
)
data = pd.DataFrame({
'id': [f'id_{i}' for i in range(6)],
'street': ['123 Street'] * 6,
'city': ['Boston', 'LA', 'Cambridge', 'San Francisco', 'Boston', 'LA'],
'state': ['MA', 'CA'] * 3,
'zip': ['72801'] * 6,
'code': ['000', '001', '002'] * 2,
'description': ['code 0', 'code 1', 'code 2'] * 2,
})
constraint = FixedCombinations(table_name='table', column_names=['code', 'description'])
synth = GaussianCopulaSynthesizer(metadata)

# Run
synth.add_constraints([constraint])
synth.fit(data)
samples = synth.sample(100)

# Assert
assert samples.columns.tolist() == data.columns.to_list()
expected_combinations = {('000', 'code 0'), ('001', 'code 1'), ('002', 'code 2')}
sampled_combinations = {
(code, description)
for code, description in samples[['code', 'description']].drop_duplicates().to_numpy()
}
assert expected_combinations == sampled_combinations


def test_conditional_sampling_with_constraints(demo_data, demo_metadata):
"""Test constraints with conditional sampling. GH#1737"""
# Setup
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