def test_dist_table_sum_row(cps_subsample):
rec = Records.cps_constructor(data=cps_subsample)
calc = Calculator(policy=Policy(), records=rec)
calc.calc_all()
tb1 = create_distribution_table(calc.distribution_table_dataframe(),
'standard_income_bins', 'expanded_income')
tb2 = create_distribution_table(calc.distribution_table_dataframe(),
'soi_agi_bins', 'expanded_income')
assert np.allclose(tb1[-1:], tb2[-1:])
def test_dist_table_sum_row(cps_subsample):
rec = Records.cps_constructor(data=cps_subsample)
calc = Calculator(policy=Policy(), records=rec)
calc.calc_all()
tb1 = create_distribution_table(calc.distribution_table_dataframe(),
'standard_income_bins', 'expanded_income')
tb2 = create_distribution_table(calc.distribution_table_dataframe(),
'soi_agi_bins', 'expanded_income')
assert np.allclose(tb1[-1:], tb2[-1:])
def test_dist_table_sum_row(pit_subsample, cit_crosssample):
rec = Records(data=pit_subsample)
crec = CorpRecords(data=cit_crosssample)
calc = Calculator(policy=Policy(), records=rec, corprecords=crec)
calc.calc_all()
tb1 = create_distribution_table(calc.distribution_table_dataframe(),
'standard_income_bins', 'GTI')
tb2 = create_distribution_table(calc.distribution_table_dataframe(),
'weighted_deciles', 'GTI')
allrow1 = tb1[-1:]
allrow2 = tb2[-4:-3]
assert np.allclose(allrow1, allrow2)
def test_dist_table_sum_row(cps_subsample):
rec = Records.cps_constructor(data=cps_subsample)
calc = Calculator(policy=Policy(), records=rec)
calc.calc_all()
tb1 = create_distribution_table(calc.distribution_table_dataframe(),
groupby='small_income_bins',
income_measure='expanded_income',
result_type='weighted_sum')
tb2 = create_distribution_table(calc.distribution_table_dataframe(),
groupby='large_income_bins',
income_measure='expanded_income',
result_type='weighted_sum')
assert np.allclose(tb1[-1:], tb2[-1:])
tb3 = create_distribution_table(calc.distribution_table_dataframe(),
groupby='small_income_bins',
income_measure='expanded_income',
result_type='weighted_avg')
assert isinstance(tb3, pd.DataFrame)
def test_create_tables(cps_subsample):
# pylint: disable=too-many-statements,too-many-branches
# create a current-law Policy object and Calculator object calc1
rec = Records.cps_constructor(data=cps_subsample)
pol = Policy()
calc1 = Calculator(policy=pol, records=rec)
calc1.calc_all()
# create a policy-reform Policy object and Calculator object calc2
reform = {2013: {'_II_rt1': [0.15]}}
pol.implement_reform(reform)
calc2 = Calculator(policy=pol, records=rec)
calc2.calc_all()
test_failure = False
# test creating various difference tables
diff = create_difference_table(calc1.dataframe(DIFF_VARIABLES),
calc2.dataframe(DIFF_VARIABLES),
groupby='large_income_bins',
income_measure='expanded_income',
tax_to_diff='combined')
assert isinstance(diff, pd.DataFrame)
expected = [
np.nan, np.nan, -0.16, -0.57, -0.72, -0.69, -0.82, -0.80, -0.75, -0.65,
-0.18, -0.59
]
tabcol = 'pc_aftertaxinc'
if not np.allclose(
diff[tabcol].values, expected, atol=0.005, rtol=0.0,
equal_nan=True):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
diff = create_difference_table(calc1.dataframe(DIFF_VARIABLES),
calc2.dataframe(DIFF_VARIABLES),
groupby='standard_income_bins',
income_measure='expanded_income',
tax_to_diff='iitax')
assert isinstance(diff, pd.DataFrame)
expected = [
np.nan, np.nan, -0.16, -0.57, -0.72, -0.69, -0.82, -0.80, -0.75, -0.65,
-0.23, -0.09, -0.06, -0.59
]
tabcol = 'pc_aftertaxinc'
if not np.allclose(
diff[tabcol].values, expected, atol=0.005, rtol=0.0,
equal_nan=True):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
diff = create_difference_table(calc1.dataframe(DIFF_VARIABLES),
calc2.dataframe(DIFF_VARIABLES),
groupby='small_income_bins',
income_measure='expanded_income',
tax_to_diff='iitax')
assert isinstance(diff, pd.DataFrame)
expected = [
np.nan, np.nan, -0.30, -0.10, -0.24, -0.76, -0.67, -0.75, -0.69, -0.82,
-0.80, -0.75, -0.65, -0.23, -0.09, -0.08, -0.07, -0.05, -0.02, np.nan,
-0.59
]
tabcol = 'pc_aftertaxinc'
if not np.allclose(
diff[tabcol].values, expected, atol=0.005, rtol=0.0,
equal_nan=True):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
diff = create_difference_table(calc1.dataframe(DIFF_VARIABLES),
calc2.dataframe(DIFF_VARIABLES),
groupby='weighted_deciles',
income_measure='expanded_income',
tax_to_diff='combined')
assert isinstance(diff, pd.DataFrame)
expected = [
0, 0, 1219678, 15503037, 25922077, 35000592, 48336897, 62637728,
79750078, 93136108, 116996252, 102458801, 580961247, 63156380,
33664610, 5637811
]
tabcol = 'tot_change'
if not np.allclose(diff[tabcol].values, expected, atol=0.51, rtol=0.0):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.0f},'.format(val))
expected = [
0.00, 0.00, 0.21, 2.67, 4.46, 6.02, 8.32, 10.78, 13.73, 16.03, 20.14,
17.64, 100.00, 10.87, 5.79, 0.97
]
tabcol = 'share_of_change'
if not np.allclose(diff[tabcol].values, expected, atol=0.005, rtol=0.0):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
expected = [
np.nan, np.nan, -0.15, -0.62, -0.70, -0.73, -0.78, -0.80, -0.80, -0.74,
-0.71, -0.30, -0.59, -0.55, -0.25, -0.06
]
tabcol = 'pc_aftertaxinc'
if not np.allclose(
diff[tabcol].values, expected, atol=0.005, rtol=0.0,
equal_nan=True):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
expected = [
np.nan, np.nan, -0.15, -0.62, -0.70, -0.73, -0.78, -0.80, -0.80, -0.74,
-0.71, -0.30, -0.59, -0.55, -0.25, -0.06
]
tabcol = 'pc_aftertaxinc'
if not np.allclose(
diff[tabcol].values, expected, atol=0.005, rtol=0.0,
equal_nan=True):
test_failure = True
print('diff', tabcol)
for val in diff[tabcol].values:
print('{:.2f},'.format(val))
# test creating various distribution tables
dvdf = calc2.distribution_table_dataframe()
dvdf = add_quantile_table_row_variable(dvdf,
'expanded_income',
num_quantiles=10,
decile_details=True)
dist = create_distribution_table(dvdf,
groupby='weighted_deciles',
income_measure='expanded_income',
result_type='weighted_sum')
assert isinstance(dist, pd.DataFrame)
expected = [
0, 0, -53644343, -65258622, -57617119, 37391333, 200879230, 329784586,
553827330, 1015854407, 1731283600, 7090603505, 10783103907, 1638192777,
2213960052, 3238450675
]
tabcol = 'iitax'
if not np.allclose(dist[tabcol].values, expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
expected = [
0, 0, 2561, 12610, 21936, 29172, 50890, 61563, 78247, 91823, 118523,
128886, 596211, 63986, 51634, 13266
]
tabcol = 'num_returns_ItemDed'
if not np.allclose(dist[tabcol].tolist(), expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
expected = [
0, 0, 835224673, 2639667638, 3940559051, 5286856071, 6972849344,
8881099529, 11467767759, 14761195525, 19832126806, 44213000235,
118830346631, 14399218059, 16868648076, 12945134101
]
tabcol = 'expanded_income'
if not np.allclose(dist[tabcol].tolist(), expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
expected = [
0, 0, 818813684, 2466000535, 3671150517, 4790979126, 6173998985,
7754183496, 9907604744, 12510477225, 16273592612, 33915377411,
98282178334, 11345456373, 13400757263, 9169163776
]
tabcol = 'aftertax_income'
if not np.allclose(dist[tabcol].tolist(), expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
dist = create_distribution_table(calc2.distribution_table_dataframe(),
groupby='standard_income_bins',
income_measure='expanded_income',
result_type='weighted_sum')
assert isinstance(dist, pd.DataFrame)
expected = [
0, 0, -42244205, -76727831, -62581860, 53797887, 217016689, 723516183,
1108097059, 3272479928, 2818979541, 950296405, 1820474110, 10783103907
]
tabcol = 'iitax'
if not np.allclose(dist[tabcol], expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
expected = [
0, 0, 1202, 13614, 27272, 34407, 48265, 117225, 103319, 181885, 61014,
5126, 2882, 596211
]
tabcol = 'num_returns_ItemDed'
if not np.allclose(dist[tabcol].tolist(), expected, atol=0.5, rtol=0.0):
test_failure = True
print('dist', tabcol)
for val in dist[tabcol].values:
print('{:.0f},'.format(val))
if test_failure:
assert 1 == 2
