def test_gaussian_copula():
users = load_demo(metadata=False)['users']
field_types = {
'age': {
'type': 'numerical',
'subtype': 'integer',
},
'country': {
'type': 'categorical'
}
}
anonymize_fields = {'country': 'country_code'}
gc = GaussianCopula(
field_names=['user_id', 'country', 'gender', 'age'],
field_types=field_types,
primary_key='user_id',
anonymize_fields=anonymize_fields,
categorical_transformer='one_hot_encoding',
)
gc.fit(users)
parameters = gc.get_parameters()
new_gc = GaussianCopula(
table_metadata=gc.get_metadata(),
categorical_transformer='one_hot_encoding',
)
new_gc.set_parameters(parameters)
sampled = new_gc.sample()
# test shape is right
assert sampled.shape == users.shape
# test user_id has been generated as an ID field
assert list(sampled['user_id']) == list(range(0, len(users)))
# country codes have been replaced with new ones
assert set(sampled.country.unique()) != set(users.country.unique())
metadata = gc.get_metadata().to_dict()
assert metadata['fields'] == {
'user_id': {
'type': 'id',
'subtype': 'integer'
},
'country': {
'type': 'categorical'
},
'gender': {
'type': 'categorical'
},
'age': {
'type': 'numerical',
'subtype': 'integer'
}
}
assert 'model_kwargs' in metadata
def test_get_parameters_non_parametric(self):
"""Test the ``get_parameters`` method when model is parametric.
If there is at least one distributions in the model that is not
parametric, a NonParametricError should be raised.
Setup:
- ``self._model`` is set to a ``GaussianMultivariate`` that
uses ``GaussianKDE`` as its ``distribution``.
Side Effects:
- A NonParametricError is raised.
"""
# Setup
gm = GaussianMultivariate(distribution=GaussianKDE())
data = pd.DataFrame([1, 1, 1])
gm.fit(data)
gc = Mock()
gc._model = gm
# Run, Assert
with pytest.raises(NonParametricError):
GaussianCopula.get_parameters(gc)
def test_gaussian_copula():
users = load_demo(metadata=False)['users']
field_types = {
'age': {
'type': 'numerical',
'subtype': 'integer',
},
'country': {
'type': 'categorical'
}
}
anonymize_fields = {'country': 'country_code'}
# If distribution is non parametric, get_parameters fails
gc = GaussianCopula(
field_names=['user_id', 'country', 'gender', 'age'],
field_types=field_types,
primary_key='user_id',
anonymize_fields=anonymize_fields,
field_distributions={'age': 'gamma'},
default_distribution='gaussian_kde',
)
gc.fit(users)
with pytest.raises(NonParametricError):
parameters = gc.get_parameters()
# If distribution is parametric, copula can be recreated
gc = GaussianCopula(
field_names=['user_id', 'country', 'gender', 'age'],
field_types=field_types,
primary_key='user_id',
anonymize_fields=anonymize_fields,
field_distributions={'age': 'gamma'},
default_distribution='bounded',
)
gc.fit(users)
parameters = gc.get_parameters()
new_gc = GaussianCopula(table_metadata=gc.get_metadata(), )
new_gc.set_parameters(parameters)
# Validate sampled dat
sampled = new_gc.sample()
# test shape is right
assert sampled.shape == users.shape
# test user_id has been generated as an ID field
assert list(sampled['user_id']) == list(range(0, len(users)))
# country codes have been replaced with new ones
assert set(sampled.country.unique()) != set(users.country.unique())
# Validate metadata
metadata = gc.get_metadata().to_dict()
assert metadata['fields'] == {
'user_id': {
'type': 'id',
'subtype': 'integer',
'transformer': 'integer',
},
'country': {
'type': 'categorical',
'pii': True,
'pii_category': 'country_code',
'transformer': 'one_hot_encoding',
},
'gender': {
'type': 'categorical',
'transformer': 'one_hot_encoding',
},
'age': {
'type': 'numerical',
'subtype': 'integer',
'transformer': 'integer',
}
}
assert 'model_kwargs' in metadata
assert 'GaussianCopula' in metadata['model_kwargs']
