def test_unique_combination_constraint():
employees = load_tabular_demo()
unique_company_department_constraint = UniqueCombinations(
columns=['company', 'department'], handling_strategy='transform')
model = CopulaGAN(constraints=[unique_company_department_constraint])
model.fit(employees)
model.sample(10)
def test_conditional_sampling_dict():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = {"column2": "b"}
sampled = model.sample(30, conditions=conditions)
assert sampled.shape == data.shape
assert set(sampled["column2"].unique()) == set(["b"])
def test_conditional_sampling_two_conditions():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10,
"column3": ["d", "e", "f"] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = {"column2": "b", "column3": "f"}
samples = model.sample(5, conditions=conditions)
assert list(samples.column2) == ['b'] * 5
assert list(samples.column3) == ['f'] * 5
def test_conditional_sampling_dict():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = [Condition({'column2': 'b'}, num_rows=30)]
sampled = model.sample_conditions(conditions=conditions)
assert sampled.shape == data.shape
assert set(sampled['column2'].unique()) == set(['b'])
def test_conditional_sampling_dataframe():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = pd.DataFrame({"column2": ["b", "b", "b", "c", "c"]})
sampled = model.sample(conditions=conditions)
assert sampled.shape[0] == len(conditions["column2"])
assert (sampled["column2"] == np.array(["b", "b", "b", "c", "c"])).all()
def test_unique_combination_constraint():
# Setup
employees = load_tabular_demo()
fixed_company_department_constraint = FixedCombinations(
column_names=['company', 'department'])
model = CopulaGAN(constraints=[fixed_company_department_constraint])
# Run
model.fit(employees)
sampled = model.sample(10)
# Assert
assert all(fixed_company_department_constraint.is_valid(sampled))
def test_conditional_sampling_two_conditions():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10,
'column3': ['d', 'e', 'f'] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = [Condition({'column2': 'b', 'column3': 'f'}, num_rows=5)]
samples = model.sample_conditions(conditions=conditions)
assert list(samples.column2) == ['b'] * 5
assert list(samples.column3) == ['f'] * 5
def test_conditional_sampling_dataframe():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = pd.DataFrame({'column2': ['b', 'b', 'b', 'c', 'c']})
sampled = model.sample_remaining_columns(conditions)
assert sampled.shape[0] == len(conditions['column2'])
assert (sampled['column2'] == np.array(['b', 'b', 'b', 'c', 'c'])).all()
def test_copulagan():
users = load_demo(metadata=False)['users']
model = CopulaGAN(
primary_key='user_id',
epochs=1,
field_distributions={
'age': 'beta'
},
default_distribution='bounded'
)
model.fit(users)
sampled = model.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)))
assert model.get_metadata().to_dict() == {
'fields': {
'user_id': {
'type': 'id',
'subtype': 'integer',
'transformer': 'integer',
},
'country': {
'type': 'categorical',
'transformer': 'label_encoding',
},
'gender': {
'type': 'categorical',
'transformer': 'label_encoding',
},
'age': {
'type': 'numerical',
'subtype': 'integer',
'transformer': 'integer',
}
},
'primary_key': 'user_id',
'constraints': [],
'sequence_index': None,
'context_columns': [],
'entity_columns': [],
'model_kwargs': {},
'name': None
}
def test_conditional_sampling_numerical():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10,
"column3": ["d", "e", "f"] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = {
"column1": 1.0,
}
sampled = model.sample(5, conditions=conditions)
assert list(sampled.column1) == [1.0] * 5
def test_conditional_sampling_numerical():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10,
'column3': ['d', 'e', 'f'] * 10
})
model = CopulaGAN(epochs=1)
model.fit(data)
conditions = [Condition({
'column1': 1.0,
}, num_rows=5)]
sampled = model.sample_conditions(conditions=conditions)
assert list(sampled.column1) == [1.0] * 5
def test_recreate():
data = load_demo(metadata=False)['users']
# If distribution is non parametric, get_parameters fails
model = CopulaGAN(epochs=1)
model.fit(data)
sampled = model.sample()
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()
# Metadata
model_meta = CopulaGAN(epochs=1, table_metadata=model.get_metadata())
model_meta.fit(data)
sampled = model_meta.sample()
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()
# Metadata dict
model_meta_dict = CopulaGAN(epochs=1,
table_metadata=model.get_metadata().to_dict())
model_meta_dict.fit(data)
sampled = model_meta_dict.sample()
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()