def test_conditional_sampling_dataframe():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10
})
model = TVAE(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_conditional_sampling_dict():
data = pd.DataFrame({
'column1': [1.0, 0.5, 2.5] * 10,
'column2': ['a', 'b', 'c'] * 10
})
model = TVAE(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 = TVAE(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_dict():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10
})
model = TVAE(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 = TVAE(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_dataframe():
data = pd.DataFrame({
"column1": [1.0, 0.5, 2.5] * 10,
"column2": ["a", "b", "c"] * 10
})
model = TVAE(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_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 = TVAE(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_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 = TVAE(epochs=1)
model.fit(data)
conditions = {
"column1": 1.0,
}
sampled = model.sample(5, conditions=conditions)
assert list(sampled.column1) == [1.0] * 5
def test_tvae():
users = load_demo(metadata=False)['users']
tvae = TVAE(primary_key='user_id', epochs=1)
tvae.fit(users)
sampled = tvae.sample(len(users))
# 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)))
expected_metadata = {
'fields': {
'user_id': {
'type': 'id',
'subtype': 'integer',
'transformer': 'integer',
},
'country': {
'type': 'categorical',
'transformer': None,
},
'gender': {
'type': 'categorical',
'transformer': None,
},
'age': {
'type': 'numerical',
'subtype': 'integer',
'transformer': 'integer',
}
},
'primary_key': 'user_id',
'constraints': [],
'sequence_index': None,
'context_columns': [],
'entity_columns': [],
'model_kwargs': {},
'name': None
}
assert tvae.get_metadata().to_dict() == expected_metadata
def test__init__passes_correct_parameters(metadata_mock):
"""
Tests the ``BaseTabularModel.__init__`` method.
The method should pass the parameters to the ``Table``
class.
Input:
- rounding set to an int
- max_value set to an int
- min_value set to an int
Side Effects:
- ``instance._metadata`` should receive the correct parameters
"""
# Run
GaussianCopula(rounding=-1, max_value=100, min_value=-50)
CTGAN(epochs=1, rounding=-1, max_value=100, min_value=-50)
TVAE(epochs=1, rounding=-1, max_value=100, min_value=-50)
CopulaGAN(epochs=1, rounding=-1, max_value=100, min_value=-50)
# Asserts
assert len(metadata_mock.mock_calls) == 5
expected_calls = [
call(field_names=None,
primary_key=None,
field_types=None,
field_transformers=None,
anonymize_fields=None,
constraints=None,
dtype_transformers={'O': 'one_hot_encoding'},
rounding=-1,
max_value=100,
min_value=-50),
call(field_names=None,
primary_key=None,
field_types=None,
field_transformers=None,
anonymize_fields=None,
constraints=None,
dtype_transformers={'O': None},
rounding=-1,
max_value=100,
min_value=-50),
call(field_names=None,
primary_key=None,
field_types=None,
field_transformers=None,
anonymize_fields=None,
constraints=None,
dtype_transformers={'O': None},
rounding=-1,
max_value=100,
min_value=-50),
call(field_names=None,
primary_key=None,
field_types=None,
field_transformers=None,
anonymize_fields=None,
constraints=None,
dtype_transformers={'O': None},
rounding=-1,
max_value=100,
min_value=-50)
]
metadata_mock.assert_has_calls(expected_calls, any_order=True)
from unittest.mock import Mock, call, patch
import pandas as pd
import pytest
from sdv.metadata.table import Table
from sdv.tabular.base import COND_IDX
from sdv.tabular.copulagan import CopulaGAN
from sdv.tabular.copulas import GaussianCopula
from sdv.tabular.ctgan import CTGAN, TVAE
from tests.utils import DataFrameMatcher
MODELS = [
CTGAN(epochs=1),
TVAE(epochs=1),
GaussianCopula(),
CopulaGAN(epochs=1),
]
class TestBaseTabularModel:
def test_sample_no_transformed_columns(self):
"""Test the ``BaseTabularModel.sample`` method with no transformed columns.
When the transformed conditions DataFrame has no columns, expect that sample
does not pass through any conditions when conditionally sampling.
Setup:
- Mock the ``_make_conditions_df`` method to return a dataframe representing
the expected conditions, and the ``get_fields`` method to return metadata
fields containing the expected conditioned column.
def test_recreate():
data = load_demo(metadata=False)['users']
# If distribution is non parametric, get_parameters fails
model = TVAE(epochs=1)
model.fit(data)
sampled = model.sample(len(data))
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()
# Metadata
model_meta = TVAE(epochs=1, table_metadata=model.get_metadata())
model_meta.fit(data)
sampled = model_meta.sample(len(data))
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()
# Metadata dict
model_meta_dict = TVAE(epochs=1,
table_metadata=model.get_metadata().to_dict())
model_meta_dict.fit(data)
sampled = model_meta_dict.sample(len(data))
assert sampled.shape == data.shape
assert (sampled.dtypes == data.dtypes).all()
assert (sampled.notnull().sum(axis=1) != 0).all()
from unittest.mock import patch
import pandas as pd
import pytest
from copulas.multivariate.gaussian import GaussianMultivariate
from sdv.constraints import Unique, UniqueCombinations
from sdv.constraints.tabular import GreaterThan
from sdv.tabular.copulagan import CopulaGAN
from sdv.tabular.copulas import GaussianCopula
from sdv.tabular.ctgan import CTGAN, TVAE
MODELS = [
pytest.param(CTGAN(epochs=1), id='CTGAN'),
pytest.param(TVAE(epochs=1), id='TVAE'),
pytest.param(GaussianCopula(), id='GaussianCopula'),
pytest.param(CopulaGAN(epochs=1), id='CopulaGAN'),
]
@pytest.mark.parametrize('model', MODELS)
def test_conditional_sampling_graceful_reject_sampling_True_dict(model):
data = pd.DataFrame({
'column1': list(range(100)),
'column2': list(range(100)),
'column3': list(range(100))
})
model.fit(data)
conditions = {'column1': 28, 'column2': 37, 'column3': 93}