File size: 3,252 Bytes
e69d4e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | """Unit tests for the apply_logic_response_criterion function."""
import copd
import numpy as np
import pandas as pd
import pytest
@pytest.fixture
def exacerbation_event():
"""Dataframe index (27) of the exacerbation event of interest."""
return 27
@pytest.fixture
def first_pro_response():
"""Dataframe index (8) of the first weekly PRO response."""
return 8
@pytest.fixture
def second_pro_response(first_pro_response):
"""Dataframe index of the second weekly PRO response. Seven days after first."""
return first_pro_response + 7
@pytest.fixture
def input_df(exacerbation_event):
"""Sample input dataframe template - specific cases to be added in each test.
This initial dataframe has no PRO responses between the initial exac and the event of
interest with DaysSinceLastExac=25. Interim PRO responses should be added in tests.
"""
df = pd.DataFrame({'PatientId': ['1'] * 31,
'DateOfEvent': pd.date_range('2022-01-01', '2022-01-31'),
'Q5Answered': [0] * 31,
'NegativeQ5': [np.nan] * 31,
'IsExac': [0] * 31,
'DaysSinceLastExac': [-1, -1, -1] + list(np.arange(1, 26)) +
list(np.arange(1, 4))})
# Add initial event to simulate DaysSinceLastExac restart from 1
df.loc[2, 'Q5Answered'] = 1
df.loc[2, 'NegativeQ5'] = 0
df.loc[2, 'IsExac'] = 1
# Add event of interest (DaysSinceLastExac = 25)
df.loc[exacerbation_event, 'Q5Answered'] = 1
df.loc[exacerbation_event, 'NegativeQ5'] = 0
df.loc[exacerbation_event, 'IsExac'] = 1
# Add a negative response after the event of interest (should not be counted)
df.loc[exacerbation_event + 2, 'Q5Answered'] = 1
df.loc[exacerbation_event + 2, 'NegativeQ5'] = 1
return df
def test_output_equals_expected_criterion_failed(input_df, exacerbation_event):
"""Test output is as expected for failed LOGIC response criterion."""
# Output should be same as input with additional RemoveExac column
expected_df = input_df.copy()
# Insert RemoveExac column with exac flagged for removal
expected_df['RemoveExac'] = np.nan
expected_df.loc[exacerbation_event, 'RemoveExac'] = 1
output_df = copd.apply_logic_response_criterion(input_df)
pd.testing.assert_frame_equal(output_df, expected_df)
def test_output_equals_expected_criterion_passed(
input_df, exacerbation_event, first_pro_response, second_pro_response):
"""Test output is as expected for passed LOGIC response criterion."""
# Add PRO responses needed to pass LOGIC criterion
input_df.loc[first_pro_response, 'Q5Answered'] = 1
input_df.loc[first_pro_response, 'NegativeQ5'] = 1
input_df.loc[second_pro_response, 'Q5Answered'] = 1
input_df.loc[second_pro_response, 'NegativeQ5'] = 1
# Output should be same as input with additional RemoveExac column
expected_df = input_df.copy()
# Insert RemoveExac column with exac flagged for removal
expected_df['RemoveExac'] = np.nan
expected_df.loc[exacerbation_event, 'RemoveExac'] = 0
output_df = copd.apply_logic_response_criterion(input_df)
pd.testing.assert_frame_equal(output_df, expected_df)
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