def setUp(self):
self.analog_scheme = Rim('analog')
def test_groups(self):
scheme = Rim('digital')
self.assertIsInstance(scheme.groups, dict)
self.assertIsNone(scheme.groups['__default_group_name__']['report'])
self.assertIsNone(scheme.groups['__default_group_name__']['filters'])
scheme.lists = [
'gender', 'column1c', 'q06', 'sta_wo', 'abschluss', 'q04'
]
scheme.add_group(name='Apple', filter='ownership==1')
self.assertIn('Apple', scheme.groups)
scheme.add_group(name='Samsung', filter={'ownership': 2})
self.assertIn('Samsung', scheme.groups)
# Try an invalid filter
# It should not contain any filters since it was called incorrectly
with self.assertRaises(Exception):
scheme.add_group(name='Motorola', filter='ownership3')
# Check to see if the available methods to add filter are equal
self.assertEqual(scheme.groups['Samsung']['filters']['ownership'], 2)
self.assertEqual(scheme.groups['Apple']['filters']['ownership'], 1)
# Test for changes to valid filter
scheme.group_filter(group_name='Apple', filter='ownership==8')
self.assertEqual(scheme.groups['Apple']['filters']['ownership'], 8)
# Test for changes to an non-existing filter, NOTE: the group_filter function actually creates the group motorola
scheme.group_filter(group_name='Motorola', filter='ownership==12')
self.assertEqual(scheme.groups['Motorola']['filters']['ownership'], 12)
#Test for invalid filter type
#Test for invalid group with dictionary
with self.assertRaises(ValueError):
scheme.group_filter(group_name='Motorola', filter=[])
with self.assertRaises(ValueError):
scheme.group_filter(group_name='Ferrari', filter={'ownership': 3})
# Try to change a filter for invalid group
self.assertNotIn('doesnotexist', scheme.groups.keys())
# Try to make illegal changes to filter in a valid group
self.assertNotEqual(scheme.groups['Apple']['filters']['ownership'], 1)
with self.assertRaises(Exception):
scheme.group_filter(group_name='Apple', filter='ownership1')
#The targets should be empty lists
for key in scheme.groups['Apple']['targets']:
self.assertEqual(scheme.groups['Apple']['targets'][key], [])
#Test for incorrect target change
with self.assertRaises(ValueError):
scheme.set_targets(group_name='Apple', targets={'gender': 1234})
#Test for setting targets to a list that wasn't given
# with self.assertRaises(ValueError):
# scheme.set_targets(group_name='Apple', targets={'doesnotexist': [80, 20]})
#Set valid targets
valid_targets = {
'gender': [50, 50],
'column1c': [20, 18, 25, 21, 16],
'q04': [20, 55, 12.5, 12.5],
'sta_wo': [50, 50],
'abschluss': [60, 40],
'q06': [20, 20, 20, 20, 20]
}
scheme.set_targets(group_name='Apple', targets=valid_targets)
#Test that only the most recently set targets are still in the scheme
self.assertNotIn('doesnotexist',
scheme.groups['Apple']['targets'].keys())
#Test that the targets were applied to the lists corrected
for key in scheme.groups['Apple']['targets']:
self.assertEqual(scheme.groups['Apple']['targets'][key],
valid_targets[key])
#add group_targets
scheme.group_targets({"Apple": 30, "Samsung": 40, "Motorola": 30})
self.assertItemsEqual(scheme._group_targets.keys(),
['Motorola', 'Apple', 'Samsung'])
self.assertItemsEqual(scheme._group_targets.values(), [0.3, 0.3, 0.4])
def setUp(self):
''' Simple engine without meta - engine_A
'''
path_data = 'tests/engine_A_data.csv'
data = pd.read_csv(path_data)
# Setup engine_A
self.engine_A = WeightEngine(data=data)
self.scheme_name_A1 = 'scheme_name_A1'
self.scheme_name_A2 = 'scheme_name_A2'
self.scheme_name_A3 = 'scheme_name_A3'
# Setup schemes to use in tests
self.scheme_A1 = Rim(self.scheme_name_A1)
self.scheme_A1.lists = ['column1', 'column2']
self.scheme_A1.add_group(
name='Senior Type 1',
filter='column3==1',
targets={
'column1': [32.00, 31.00, 37.00],
'column2':
[23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04, 13.3]
})
self.scheme_A1.add_group(name='Senior Type 2',
filter='column3==1',
targets={
'column1': [33.40, 33.40, 33.20],
'column2': [
11.11, 11.11, 11.11, 11.11, 11.11,
11.11, 11.11, 11.11, 11.11
]
})
self.scheme_A1.add_group(
name='Senior Type 3',
filter='column3==3',
targets={
'column1': [33.2, 29.7, 37.1],
'column2':
[23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04, 13.3]
})
self.scheme_A1.add_group(
name='Senior Type 4',
filter='column3==4',
targets={
'column1': [33.2, 29.7, 37.1],
'column2':
[23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 32.34, 12.00]
})
self.scheme_A2 = Rim(self.scheme_name_A2)
self.scheme_A2.lists = ['column1', 'column2']
self.scheme_A2.add_group(
name='Senior Type 1',
filter='column3==1',
targets={
'column1': [37.00, 32.00, 31.00],
'column2':
[13.3, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04]
})
self.scheme_A2.add_group(name='Senior Type 2',
filter='column3==1',
targets={
'column1': [33.2, 33.40, 33.40],
'column2': [
11.11, 11.11, 11.11, 11.11, 11.11,
11.11, 11.11, 11.11, 11.11
]
})
self.scheme_A2.add_group(
name='Senior Type 3',
filter='column3==3',
targets={
'column1': [37.1, 33.2, 29.7],
'column2':
[13.3, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04]
})
self.scheme_A2.add_group(
name='Senior Type 4',
filter='column3==4',
targets={
'column1': [37.1, 33.2, 29.7],
'column2':
[12.00, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 32.34]
})
self.scheme_A3 = Rim(self.scheme_name_A3)
self.scheme_A3.lists = ['profile_gender']
self.scheme_A3.targets = {'profile_gender': [47, 53]}
self.scheme_A3.add_group(name='11-19',
filter='age_group=2',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='31-39',
filter='age_group=4',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='41-49',
filter='age_group=5',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='51-59',
filter='age_group=6',
targets=self.scheme_A3.targets)
self.scheme_A3.group_targets({
'11-19': 25,
'31-39': 25,
'41-49': 25,
'51-59': 25
})
''' Complex engine with meta - engine_B
'''
path_data = 'tests/engine_B_data.csv'
path_meta = 'tests/engine_B_meta.json'
data = pd.read_csv(path_data)
meta = json.load(file(path_meta))
self.scheme_name_B1 = 'scheme_name_B1'
engine_B = WeightEngine(data=data, meta=meta)
# Setup schemes to use in tests
self.scheme_B1 = Rim(self.scheme_name_B1)
self.scheme_B1.lists = ['profile_gender', 'age_group']
