def test__sample_valid_rows_fitted(self):
"""sample valid rows with model fitted"""
# Setup
pk_keys_mock = Mock()
pk_keys_mock.return_value = 'pk_name', [1, 2, 3, 4]
synthesized_mock = pd.DataFrame({'foo': [0, 1.1], 'bar': [1, 0]})
sample_model_mock = Mock()
sample_model_mock.return_value = synthesized_mock
missing_valid_rows_mock = Mock()
missing_valid_rows_mock.side_effect = [(True, {}), (False, {})]
missing_valid_rows_mock.return_value = False, {}
dn_mock = Mock()
dn_mock.get_meta_data.return_value = {
'fields': {
'foo': {
'type': 'categorical',
},
'bar': {
'type': 'numeric'
}
}
}
tables = {'DEMO': pd.DataFrame({'a_field': [1, 0], 'b_field': [0, 1]})}
# Run
sampler_mock = Mock()
sampler_mock._get_primary_keys = pk_keys_mock
sampler_mock._sample_model = sample_model_mock
sampler_mock._get_missing_valid_rows = missing_valid_rows_mock
sampler_mock.modeler.tables = tables
sampler_mock.dn = dn_mock
model_mock = Mock()
model_mock.fitted = True
Sampler._sample_valid_rows(sampler_mock, model_mock, 5, 'DEMO')
# Asserts
assert missing_valid_rows_mock.call_count == 2
assert sample_model_mock.call_count == 2
def test__sample_valid_rows_raises_unfitted_model(self):
"""_sample_valid_rows raise an exception for invalid models."""
# Setup
data_navigator = MagicMock(spec=DataNavigator)
modeler = MagicMock(spec=Modeler)
sampler = Sampler(data_navigator, modeler)
data_navigator.get_parents.return_value = set()
num_rows = 5
table_name = 'table_name'
model = None
# Run
with self.assertRaises(ValueError):
sampler._sample_valid_rows(model, num_rows, table_name)
# Check
modeler.assert_not_called()
assert len(modeler.method_calls) == 0
data_navigator.assert_not_called()
data_navigator.get_parents.assert_called_once_with('table_name')
def test__sample_valid_rows_respect_categorical_values(self):
"""_sample_valid_rows will return rows with valid values for categorical columns."""
# Setup
data_navigator = MagicMock(spec=DataNavigator)
modeler = MagicMock(spec=Modeler)
sampler = Sampler(data_navigator, modeler)
data = pd.DataFrame(columns=['field_A', 'field_B'])
modeler.tables = {
'table_name': data,
}
data_navigator.meta = {
'tables': [{
'name':
'table_name',
'fields': [{
'name': 'field_A',
'type': 'categorical'
}, {
'name': 'field_B',
'type': 'categorical'
}]
}]
}
num_rows = 5
table_name = 'table_name'
model = MagicMock(spec=GaussianMultivariate)
model.fitted = True
sample_dataframe = pd.DataFrame([
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 1.5
}, # Invalid field_B
{
'field_A': 1.5,
'field_B': 0.5
}, # Invalid field_A
])
model.sample.side_effect = lambda x: sample_dataframe.iloc[:x].copy()
expected_model_call_args_list = [((5, ), {}), ((2, ), {})]
expected_result = pd.DataFrame([
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
{
'field_A': 0.5,
'field_B': 0.5
},
])
# Run
result = sampler._sample_valid_rows(model, num_rows, table_name)
# Check
assert result.equals(expected_result)
modeler.assert_not_called()
assert len(modeler.method_calls) == 0
data_navigator.assert_not_called()
assert len(data_navigator.method_calls) == 0
assert model.sample.call_args_list == expected_model_call_args_list