def test__unflatten_dict_raises_error_column_index(self):
"""Test unflatten dict raises error column_index"""
# Setup
sampler = Mock(spec=Sampler)
flat = {'foo__1__0': 'some value'}
# Run
with pytest.raises(ValueError):
Sampler._unflatten_dict(sampler, flat)
def test__unflatten_dict_raise_value_error_column_index(self):
"""Raises ValueError by column_index"""
# Setup
setdefault_mock = Mock()
setdefault_mock.return_value = [[1, 2, 3, 4]]
# Run and assert
sampler = Mock()
sampler._setdefault = setdefault_mock
flat = {'foo__1__1': 'foo'}
with self.assertRaises(ValueError):
Sampler._unflatten_dict(sampler, flat)
def test__unflatten_dict(self):
"""Test unflatten_dict"""
# Setup
sampler = Mock(spec=Sampler)
sampler._key_order = None
# Run
flat = {
'foo__0__foo': 'foo value',
'bar__0__0': 'bar value',
'tar': 'tar value'
}
result = Sampler._unflatten_dict(sampler, flat)
# Asserts
expected = {
'foo': {
0: {
'foo': 'foo value'
}
},
'bar': [['bar value']],
'tar': 'tar value',
}
assert result == expected
def test__unflatten_dict_respect_covariance_matrix(self):
"""unflatten_dict restructures the covariance matrix into an square matrix."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
sampler = Sampler(data_navigator, modeler)
def fake_values(i, j):
return '{}, {}'.format(i, j)
expected_result = {
'covariance':
np.array([[fake_values(i, j) for j in range(40)]
for i in range(40)]).tolist()
}
flat = {
'covariance__{}__{}'.format(i, j): fake_values(i, j)
for i in range(40) for j in range(40)
}
# Run
result = sampler._unflatten_dict(flat)
# Check
assert result == expected_result
def test__unflatten_dict_child_name(self):
"""unflatten_dict will respect the name of child tables."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
sampler = Sampler(data_navigator, modeler)
flat = {
'first_key__a__b': 1,
'first_key____CHILD_TABLE__model_param': 0,
'distribs____CHILD_TABLE__distribs__UNIT_PRICE__std__mean': 0
}
expected_result = {
'first_key': {
'a': {
'b': 1
},
'__CHILD_TABLE': {
'model_param': 0
}
},
'distribs': {
'__CHILD_TABLE__distribs__UNIT_PRICE__std': {
'mean': 0
}
}
}
# Run
result = sampler._unflatten_dict(flat)
# Check
assert result == expected_result
modeler.assert_not_called()
data_navigator.assert_not_called()
def test__unflatten_dict(self):
"""unflatten_dict restructure flatten dicts."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
sampler = Sampler(data_navigator, modeler)
flat = {
'a__first_key__a': 1,
'a__first_key__b': 2,
'b__second_key__x': 0
}
expected_result = {
'a': {
'first_key': {
'a': 1,
'b': 2
},
},
'b': {
'second_key': {
'x': 0
},
}
}
# Run
result = sampler._unflatten_dict(flat)
# Check
assert result == expected_result
data_navigator.assert_not_called()
modeler.assert_not_called()
def test__unflatten_dict_child_name(self):
"""unflatten_dict will respect the name of child tables."""
# Setup
data_navigator = MagicMock()
data_navigator.get_children.return_value = ['CHILD_TABLE']
modeler = MagicMock()
sampler = Sampler(data_navigator, modeler)
flat = {
'first_key__a': 1,
'first_key____CHILD_TABLE__model_param': 0,
'distribs____CHILD_TABLE__distribs__UNIT_PRICE__std__mean': 0
}
table_name = 'TABLE_NAME'
expected_result = {
'first_key': {
'a': 1,
'__CHILD_TABLE': {
'model_param': 0
}
},
'distribs': {
'__CHILD_TABLE__distribs__UNIT_PRICE__std': {
'mean': 0
}
}
}
# Run
result = sampler._unflatten_dict(flat, table_name)
# Check
assert result == expected_result
modeler.assert_not_called()
data_navigator.get_children.assert_called_once_with('TABLE_NAME')
def test__unflatten_dict_alrady_unflatted(self):
"""Already unflatted dict."""
# Setup
# Run
sampler = Mock()
flat = {'foo': 'bar'}
result = Sampler._unflatten_dict(sampler, flat)
# Asserts
exp_dict = {'foo': 'bar'}
assert result == exp_dict
def test__unflatten_dict_mixed_array(self):
"""unflatten_dict restructure arrays."""
# Setup
data_navigator = MagicMock()
modeler = MagicMock()
sampler = Sampler(data_navigator, modeler)
flat = {
'first_key__0__0': 1,
'first_key__0__1': 0,
'first_key__1__0': 0,
'first_key__1__1': 1,
'second_key__0__std': 0.5,
'second_key__0__mean': 0.5,
'second_key__1__std': 0.25,
'second_key__1__mean': 0.25
}
expected_result = {
'first_key': [
[1, 0],
[0, 1]
],
'second_key': [
{
'std': 0.5,
'mean': 0.5
},
{
'std': 0.25,
'mean': 0.25
}
]
}
# Run
result = sampler._unflatten_dict(flat)
# Check
assert result == expected_result
data_navigator.assert_not_called()
modeler.assert_not_called()