def test_vaidate_targets(self):
data = pd.read_csv(self.path_data_exA)
engine = WeightEngine(data)
targets_gender = [45.6, 54.4]
targets_locality = [10, 15, 20, 25, 30]
weight_targets = [{
'gender':
{code: prop
for code, prop in enumerate(targets_gender, start=1)}
}, {
'locality': {
code: prop
for code, prop in enumerate(targets_locality, start=1)
}
}]
scheme = Rim('missing_data')
scheme.set_targets(weight_targets)
engine.add_scheme(scheme, key='unique_id', verbose=False)
validate_df = scheme.validate()
self.assertTrue(validate_df.columns.tolist() ==
['missing', 'mean', 'mode', 'median'])
self.assertTrue(validate_df.index.tolist() == ['gender', 'locality'])
self.assertTrue(validate_df.values.tolist() ==
[[0.0, 2.0, 2.0, 2.0], [177.0, 2.0, 1.0, 2.0]])
def test_group_targets(self):
data = pd.read_csv(self.path_data_B)
meta = json.load(open(self.path_meta_B))
weight = '_'.join(['weights', self.scheme_name_A3])
# Run weights for scheme_A3
engine_B = WeightEngine(data=data, meta=meta)
engine_B.add_scheme(scheme=self.scheme_A3,
key='identity',
verbose=False)
engine_B.run()
data_A3 = engine_B.dataframe("scheme_name_A3")
# check identical weighted column frequencies
df = data_A3.pivot_table(values=[weight],
index=['profile_gender'],
columns=['age_group'],
aggfunc='sum')
for column in df.columns.tolist():
self.assertTrue(
numpy.allclose(df[column].values, numpy.array([1.645, 1.855])))
# check weighted group frequencies have equal proportions
values = data_A3.pivot_table(values=[weight],
index=['age_group'],
aggfunc='sum').values
self.assertTrue(numpy.allclose(values, 3.5))
def test_wdf_structure(self):
data = pd.read_csv(self.path_data_exA)
engine = WeightEngine(data)
targets_gender = [45.6, 54.4]
targets_locality = [10, 15, 20, 25, 30]
weight_targets = [{
'gender':
{code: prop
for code, prop in enumerate(targets_gender, start=1)}
}, {
'locality': {
code: prop
for code, prop in enumerate(targets_locality, start=1)
}
}]
scheme = Rim('complex_filter')
scheme.add_group(name='W1, male',
filter_def='Wave==1 & religion==1',
targets=weight_targets)
scheme.add_group(name='W2, female',
filter_def='Wave==2 & religion==2',
targets=weight_targets)
engine.add_scheme(scheme, key='unique_id', verbose=False)
engine.run()
wdf = engine.dataframe('complex_filter')
self.assertEqual(
sorted(wdf.columns.tolist()),
sorted([
'unique_id', 'gender', 'locality', 'weights_complex_filter',
'Wave', 'religion'
]))
self.assertTrue(len(wdf.index) == 596)
class TestEngine(unittest.TestCase):
def setUp(self):
''' Simple engine without meta - engine_A
'''
self.path = './tests/'
# self.path = ''
name_data_A = 'engine_A'
self.path_data_A = '{}{}_data.csv'.format(self.path, name_data_A)
name_data_B = 'engine_B'
self.path_meta_B = '{}{}_meta.json'.format(self.path, name_data_B)
self.path_data_B = '{}{}_data.csv'.format(self.path, name_data_B)
name_data_exA = 'Example Data (A)'
self.path_meta_exA = '{}{}.json'.format(self.path, name_data_exA)
self.path_data_exA = '{}{}.csv'.format(self.path, name_data_exA)
# Setup engine_A
data = pd.read_csv(self.path_data_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.target_cols = ['column1', 'column2']
self.scheme_A1.add_group(
name='Senior Type 1',
filter_def='column3==1',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([32.00, 31.00, 37.00], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04, 13.3
],
start=1)
}
}])
self.scheme_A1.add_group(
name='Senior Type 2',
filter_def='column3==1',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([33.40, 33.40, 33.20], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
11.11, 11.11, 11.11, 11.11, 11.11, 11.11, 11.11, 11.11,
11.12
],
start=1)
}
}])
self.scheme_A1.add_group(
name='Senior Type 3',
filter_def='column3==3',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([33.2, 29.7, 37.1], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04, 13.3
],
start=1)
}
}])
self.scheme_A1.add_group(
name='Senior Type 4',
filter_def='column3==4',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([33.2, 29.7, 37.1], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 32.34,
12.00
],
start=1)
}
}])
self.scheme_A2 = Rim(self.scheme_name_A2)
self.scheme_A2.target_cols = ['column1', 'column2']
self.scheme_A2.add_group(
name='Senior Type 1',
filter_def='column3==1',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([37.00, 32.00, 31.00], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
13.3, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04
],
start=1)
}
}])
self.scheme_A2.add_group(
name='Senior Type 2',
filter_def='column3==1',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([33.2, 33.40, 33.40], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
11.11, 11.11, 11.11, 11.11, 11.11, 11.11, 11.11, 11.11,
11.12
],
start=1)
}
}])
self.scheme_A2.add_group(
name='Senior Type 3',
filter_def='column3==3',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([37.1, 33.2, 29.7], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
13.3, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47, 31.04
],
start=1)
}
}])
self.scheme_A2.add_group(
name='Senior Type 4',
filter_def='column3==4',
targets=[{
'column1': {
code: prop
for code, prop in enumerate([37.1, 33.2, 29.7], start=1)
}
}, {
'column2': {
code: prop
for code, prop in enumerate([
12.00, 23.13, 14.32, 4.78, 4.70, 2.65, 2.61, 3.47,
32.34
],
start=1)
}
}])
self.scheme_A3 = Rim(self.scheme_name_A3)
self.scheme_A3.target_cols = ['profile_gender']
self.scheme_A3.targets = [{'profile_gender': {1: 47, 2: 53}}]
self.scheme_A3.add_group(name='11-19',
filter_def='age_group==2',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='31-39',
filter_def='age_group==4',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='41-49',
filter_def='age_group==5',
targets=self.scheme_A3.targets)
self.scheme_A3.add_group(name='51-59',
filter_def='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
'''
data = pd.read_csv(self.path_data_B)
meta = json.load(open(self.path_meta_B))
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.target_cols = ['profile_gender', 'age_group']
# self.scheme_B1.set_targets()
def test_constructor(self):
data = pd.read_csv(self.path_data_B)
meta = json.load(open(self.path_meta_B))
engine_B = WeightEngine(data=data, meta=meta)
self.assertIsNotNone(engine_B._df)
self.assertTrue(engine_B.dropna)
self.assertEqual(engine_B.schemes, {})
self.assertIsInstance(engine_B.schemes, dict)
def test_add_scheme_and_dataframe(self):
#A list of scheme names used in setUp used for comparison
scheme_names = [self.scheme_name_A1, self.scheme_name_A2]
self.engine_A.add_scheme(scheme=self.scheme_A2,
key='identity',
verbose=False)
# Should now contain a dict with scheme_name_A2 as the first key
self.assertEqual(self.engine_A.schemes.keys()[0], self.scheme_name_A2)
self.engine_A.add_scheme(scheme=self.scheme_A1,
key='identity',
verbose=False)
# Should now contain a dict with scheme_name_A2 and scheme_name_A1 as keys
for key in self.engine_A.schemes:
self.assertIn(key, scheme_names)
self.assertIn('identity', self.engine_A.schemes[key]['key'])
# Sets weights_scheme_name_A1 and weights_scheme_name_A2 to ones
self.engine_A._df[self.scheme_A1._weight_name()] = pd.np.ones(
len(self.engine_A._df))
self.engine_A._df[self.scheme_A2._weight_name()] = pd.np.ones(
len(self.engine_A._df))
for key in self.engine_A.schemes:
weight_scheme = self.engine_A._df['weights_' + key]
boolean_vector = (weight_scheme == pd.np.ones(len(weight_scheme)))
self.assertTrue(boolean_vector.all())
self.engine_A.run(schemes=[key])
boolean_vector = (weight_scheme == pd.np.ones(len(weight_scheme)))
self.assertFalse(boolean_vector.all())
def test_add_scheme_no_key(self):
self.engine_A.add_scheme(scheme=self.scheme_A1,
key='identity',
verbose=False)
self.assertIsNotNone(self.engine_A.schemes[self.scheme_name_A1]['key'])
def test_weight_lazy(self):
return
self.engine_A.add_scheme(scheme=self.scheme_A2,
key='identity',
verbose=False)
self.engine_A.add_scheme(scheme=self.scheme_A1,
key='identity',
verbose=False)
self.assertNotIn('weights_scheme_name_A2', self.engine_A._df.columns)
self.engine_A.weight()
self.assertIn('weights_%s' % self.scheme_name_A1,
self.engine_A._df.columns)
self.assertIn('weights_%s' % self.scheme_name_A2,
self.engine_A._df.columns)
def test_group_targets(self):
data = pd.read_csv(self.path_data_B)
meta = json.load(open(self.path_meta_B))
weight = '_'.join(['weights', self.scheme_name_A3])
# Run weights for scheme_A3
engine_B = WeightEngine(data=data, meta=meta)
engine_B.add_scheme(scheme=self.scheme_A3,
key='identity',
verbose=False)
engine_B.run()
data_A3 = engine_B.dataframe("scheme_name_A3")
# check identical weighted column frequencies
df = data_A3.pivot_table(values=[weight],
index=['profile_gender'],
columns=['age_group'],
aggfunc='sum')
for column in df.columns.tolist():
self.assertTrue(
numpy.allclose(df[column].values, numpy.array([1.645, 1.855])))
# check weighted group frequencies have equal proportions
values = data_A3.pivot_table(values=[weight],
index=['age_group'],
aggfunc='sum').values
self.assertTrue(numpy.allclose(values, 3.5))
def test_vaidate_targets(self):
data = pd.read_csv(self.path_data_exA)
engine = WeightEngine(data)
targets_gender = [45.6, 54.4]
targets_locality = [10, 15, 20, 25, 30]
weight_targets = [{
'gender':
{code: prop
for code, prop in enumerate(targets_gender, start=1)}
}, {
'locality': {
code: prop
for code, prop in enumerate(targets_locality, start=1)
}
}]
scheme = Rim('missing_data')
scheme.set_targets(weight_targets)
engine.add_scheme(scheme, key='unique_id', verbose=False)
validate_df = scheme.validate()
self.assertTrue(validate_df.columns.tolist() ==
['missing', 'mean', 'mode', 'median'])
self.assertTrue(validate_df.index.tolist() == ['gender', 'locality'])
self.assertTrue(validate_df.values.tolist() ==
[[0.0, 2.0, 2.0, 2.0], [177.0, 2.0, 1.0, 2.0]])
def test_wdf_structure(self):
data = pd.read_csv(self.path_data_exA)
engine = WeightEngine(data)
targets_gender = [45.6, 54.4]
targets_locality = [10, 15, 20, 25, 30]
weight_targets = [{
'gender':
{code: prop
for code, prop in enumerate(targets_gender, start=1)}
}, {
'locality': {
code: prop
for code, prop in enumerate(targets_locality, start=1)
}
}]
scheme = Rim('complex_filter')
scheme.add_group(name='W1, male',
filter_def='Wave==1 & religion==1',
targets=weight_targets)
scheme.add_group(name='W2, female',
filter_def='Wave==2 & religion==2',
targets=weight_targets)
engine.add_scheme(scheme, key='unique_id', verbose=False)
engine.run()
wdf = engine.dataframe('complex_filter')
self.assertTrue(wdf.columns.tolist() == [
'unique_id', 'gender', 'locality', 'weights_complex_filter',
'religion', 'Wave'
])
self.assertTrue(len(wdf.index) == 596)