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_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_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