def test_upsample_agebin_raises_if_data_not_edge_aligned(self):
# not lower-edge aligned
age_bin = AgeBin.from_string('[6:10)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
# not upper-edge aligned
age_bin = AgeBin.from_string('[5:14)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
# not lower or upper edge aligned
age_bin = AgeBin.from_string('[6:14)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
def test_upsample_agebin_raises_if_data_does_not_contain_requested_agebin(
self):
# overlap lower age
age_bin = AgeBin.from_string('[0:10)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
# overlap upper age
age_bin = AgeBin.from_string('[10:18)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
# overlap both lower and upper ages (requesting too large an age range on both sides)
age_bin = AgeBin.from_string('[4:16)')
self.assertRaises(NotUpsampleable,
self.grouped_data.apply,
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column)
def test_contains_works_with_AgeBin_and_string(self):
big_ab = AgeBin(10, 99)
ab = "[10, 15)"
self.assertTrue(big_ab.contains(ab))
ab = AgeBin.from_string(ab)
self.assertTrue(big_ab.contains(ab))
def test_merge_works_with_AgeBin_and_string(self):
expected_ab = AgeBin(10, 99)
ab1 = AgeBin(10, 15)
ab2 = "[15:99)"
self.assertEqual(ab1.merge(ab2), expected_ab)
ab2 = AgeBin.from_string(ab2)
self.assertEqual(ab1.merge(ab2), expected_ab)
def test_upsample_agebin_works(self):
age_bin = AgeBin.from_string('[5:10)')
result = self.grouped_data.apply(
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column).sort_values(
'Gender').reset_index(drop=True).sort_index()
expected_result = [{
'Gender': 'Male',
'AgeBin': '[5:10)',
'Prevalence': 0.1,
'Sim_Prevalence': 0.4,
'Count': 5
}, {
'Gender': 'Female',
'AgeBin': '[5:10)',
'Prevalence': 0.3,
'Sim_Prevalence': 0.2,
'Count': 20
}]
expected_result = pd.DataFrame(expected_result).sort_values(
'Gender').reset_index(drop=True).sort_index()
self.assertTrue(result.equals(expected_result))
age_bin = AgeBin.from_string('[10:15)')
result = self.grouped_data.apply(
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column).sort_values(
'Gender').reset_index(drop=True).sort_index()
expected_result = [{
'Gender': 'Male',
'AgeBin': '[10:15)',
'Prevalence': 0.2,
'Sim_Prevalence': 0.3,
'Count': 15
}, {
'Gender': 'Female',
'AgeBin': '[10:15)',
'Prevalence': 0.4,
'Sim_Prevalence': 0.1,
'Count': 20
}]
expected_result = pd.DataFrame(expected_result).sort_values(
'Gender').reset_index(drop=True).sort_index()
self.assertTrue(result.equals(expected_result))
age_bin = AgeBin.from_string('[5:15)')
result = self.grouped_data.apply(
upsample_agebin,
age_bin=age_bin,
aggregated_cols=self.aggregation_columns,
weighted_cols=self.weighted_columns,
weighting_col=self.weighting_column).sort_values(
'Gender').reset_index(drop=True).sort_index()
expected_result = [{
'Gender': 'Male',
'AgeBin': '[5:15)',
'Prevalence': 0.175,
'Sim_Prevalence': 0.325,
'Count': 20
}, {
'Gender': 'Female',
'AgeBin': '[5:15)',
'Prevalence': 0.35,
'Sim_Prevalence': 0.15,
'Count': 40
}]
expected_result = pd.DataFrame(expected_result).sort_values(
'Gender').reset_index(drop=True).sort_index()
numerical_cols = ['Prevalence', 'Sim_Prevalence', 'Count']
other_cols = ['Gender', 'AgeBin']
# checking that numerical values are REALLY close; off a bit due to division in algorithm
self.assertTrue(
np.allclose(result[numerical_cols],
expected_result[numerical_cols],
atol=1e-16,
rtol=0))
# checking non-numerical values are EXACT
self.assertTrue(result[other_cols].equals(expected_result[other_cols]))
def test_from_string_works_properly(self):
ab_string = '[0:;:99)'
ab = AgeBin.from_string(ab_string)
self.assertEqual(ab.start, 0)
self.assertEqual(ab.end, 99)
self.assertEqual(ab.delimiter, ':;:')