def test__validate_data_meets_constraints_invalid_input(self):
"""Test the ``_validate_data_meets_constraint`` method.
Expect that the method raises an error when the constraint columns
are in the given data and the ``is_valid`` returns False for any row.
Input:
- Table data contains an invalid row
Output:
- None
Side Effects:
- A ``ConstraintsNotMetError`` is thrown
"""
# Setup
data = pd.DataFrame(
{
'a': [0, 1, 2, 3, 4, 5, 6, 7],
'b': [3, 4, 5, 6, 7, 8, 9, 10]
},
index=[0, 1, 2, 3, 4, 5, 6, 7])
constraint = Constraint()
constraint.constraint_columns = ['a', 'b']
is_valid_result = pd.Series(
[True, False, True, False, False, False, False, False])
constraint.is_valid = Mock(return_value=is_valid_result)
# Run / Assert
error_message = re.escape(
"Data is not valid for the 'Constraint' constraint:\n "
'a b\n1 1 4\n3 3 6\n4 4 7\n5 5 8\n6 6 9'
'\n+1 more')
with pytest.raises(ConstraintsNotMetError, match=error_message):
constraint._validate_data_meets_constraint(data)
def test_is_valid(self):
"""Test the ``Constraint.is_valid` method. This should be overwritten by all the
subclasses that have a way to decide which rows are valid and which are not.
The ``Constraint.is_valid`` method is expected to:
- Say whether the given table rows are valid.
Input:
- Table data (pandas.DataFrame)
Output:
- Series of ``True`` values (pandas.Series)
"""
# Setup
table_data = pd.DataFrame({'a': [1, 2, 3]})
# Run
instance = Constraint(handling_strategy='transform')
out = instance.is_valid(table_data)
# Assert
expected_out = pd.Series([True, True, True])
pd.testing.assert_series_equal(expected_out, out)
def test__validate_data_meets_constraints_missing_cols(self):
"""Test the ``_validate_data_meets_constraint`` method.
Expect that the method doesn't do anything when the columns are not in the given data.
Input:
- Table data that is missing a constraint column
Output:
- None
Side Effects:
- No error
"""
# Setup
data = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]}, index=[0, 1, 2])
constraint = Constraint()
constraint.constraint_columns = ['a', 'b', 'c']
constraint.is_valid = Mock()
# Run
constraint._validate_data_meets_constraint(data)
# Assert
assert not constraint.is_valid.called
def test__validate_data_meets_constraints(self):
"""Test the ``_validate_data_meets_constraint`` method.
Expect that the method calls ``is_valid`` when the constraint columns
are in the given data.
Input:
- Table data
Output:
- None
Side Effects:
- No error
"""
# Setup
data = pd.DataFrame({'a': [0, 1, 2], 'b': [3, 4, 5]}, index=[0, 1, 2])
constraint = Constraint()
constraint.constraint_columns = ['a', 'b']
constraint.is_valid = Mock()
# Run
constraint._validate_data_meets_constraint(data)
# Assert
constraint.is_valid.assert_called_once_with(data)