def test_make_df_from_expectations_with_categories_expression_validation():
study = StudyDefinition(
population=patients.all(),
category=patients.categorised_as(
{
"A": "sex = 'F'",
"B": "sex = 'M'"
},
sex=patients.sex(),
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {
"ratios": {
"A": 0.3,
"B": 0.6,
"C": 0.1
}
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
)
population_size = 10000
with pytest.raises(ValueError):
study.make_df_from_expectations(population_size)
def test_make_df_from_expectations_with_categories_in_codelist_validation():
categorised_codelist = codelist([("X", "Y")], system="ctv3")
categorised_codelist.has_categories = True
study = StudyDefinition(
population=patients.all(),
ethnicity=patients.with_these_clinical_events(
categorised_codelist,
returning="category",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {
"ratios": {
"A": 0.3,
"B": 0.7
}
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
find_last_match_in_period=True,
include_date_of_match=False,
),
)
population_size = 10000
with pytest.raises(ValueError):
study.make_df_from_expectations(population_size)
def test_make_df_no_categories_validation_when_no_categories_in_definition():
study = StudyDefinition(
population=patients.all(),
sex=patients.sex(
return_expectations={
"rate": "universal",
"category": {"ratios": {"M": 0.49, "F": 0.51}},
}
),
)
population_size = 10000
# Just ensuring no exception is raised
study.make_df_from_expectations(population_size)
def test_make_df_from_expectations_with_distribution_and_date():
study = StudyDefinition(
population=patients.all(),
bmi=patients.most_recent_bmi(
on_or_after="2010-02-01",
minimum_age_at_measurement=16,
include_measurement_date=True,
include_month=True,
return_expectations={
"rate": "exponential_increase",
"incidence": 0.6,
"float": {
"distribution": "normal",
"mean": 35,
"stddev": 10
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert list(sorted(result.columns)) == ["bmi", "bmi_date_measured"]
# Check that the null-valued rows are aligned with each other
assert (result["bmi"][pd.isnull(
result["bmi"])].fillna(0) == result["bmi_date_measured"][pd.isnull(
result["bmi_date_measured"])].fillna(0)).all()
def test_make_df_from_expectations_with_date_filter():
study = StudyDefinition(
population=patients.all(),
asthma_condition=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
between=["2001-12-01", "2002-06-01"],
returning="date",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
find_first_match_in_period=True,
include_month=True,
include_day=True,
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result.columns == ["asthma_condition"]
assert result[~pd.isnull(result["asthma_condition"])].max(
)[0] <= "2002-06-01"
def test_make_df_from_expectations_with_categories_expression():
study = StudyDefinition(
population=patients.all(),
category=patients.categorised_as(
{
"A": "sex = 'F'",
"B": "sex = 'M'"
},
sex=patients.sex(),
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {
"ratios": {
"A": 0.3,
"B": 0.7
}
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
value_counts = result.category.value_counts()
assert value_counts["A"] < value_counts["B"]
def test_make_df_from_expectations_with_categories():
categorised_codelist = codelist([("1", "A"), ("2", "B")], system="ctv3")
categorised_codelist.has_categories = True
study = StudyDefinition(
population=patients.all(),
ethnicity=patients.with_these_clinical_events(
categorised_codelist,
returning="category",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {
"ratios": {
"A": 0.3,
"B": 0.7
}
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
find_last_match_in_period=True,
include_date_of_match=False,
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result.columns == ["ethnicity"]
category_counts = result.reset_index().groupby("ethnicity").count()
assert category_counts.loc["A", :][0] < category_counts.loc["B", :][0]
def test_make_df_from_expectations_doesnt_alter_date_defaults():
study = StudyDefinition(
default_expectations={
"rate": "exponential_increase",
"incidence": 1.0,
"date": {"earliest": "1900-01-01", "latest": "today"},
"category": {"ratios": {"M": 0.5, "F": 0.5}},
},
population=patients.all(),
with_different_incidence=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
return_expectations={"incidence": 0.2},
include_day=True,
),
with_different_date=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
return_expectations={"date": {"earliest": "2015-01-01", "latest": "today"}},
include_day=True,
),
with_defaults=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
return_expectations={"date": {}},
include_day=True,
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
# Regression test: make sure defaults are respected even when they've been overridden
assert result.with_defaults.min() < "2015-01-01"
assert len(result[pd.isnull(result.with_defaults)]) == 0
def test_make_df_from_expectations_partial_default_overrides():
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
"incidence": 0.2,
},
population=patients.all(),
asthma_condition=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
find_first_match_in_period=True,
date_format="YYYY",
return_expectations={"date": {
"latest": "2000-01-01"
}},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result.asthma_condition.astype("float").max() == 2000
def test_make_df_from_expectations_with_number_of_episodes():
study = StudyDefinition(
population=patients.all(),
episode_count=patients.with_these_clinical_events(
codelist(["A", "B", "C"], system="ctv3"),
ignore_days_where_these_codes_occur=codelist(["D", "E"],
system="ctv3"),
returning="number_of_episodes",
episode_defined_as="series of events each <= 14 days apart",
return_expectations={
"int": {
"distribution": "normal",
"mean": 4,
"stddev": 2
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"incidence": 0.2,
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result.columns == ["episode_count"]
def test_make_df_from_expectations_doesnt_alter_defaults():
study = StudyDefinition(
default_expectations={
"rate": "exponential_increase",
"incidence": 1.0,
"category": {
"ratios": {
"M": 0.5,
"F": 0.5
}
},
},
population=patients.all(),
sex_altered=patients.sex(return_expectations={
"incidence": 0.1,
"category": {
"ratios": {
"M": 0.5,
"F": 0.5
}
},
}),
sex_default=patients.sex(
return_expectations={"category": {
"ratios": {
"M": 0.5,
"F": 0.5
}
}}),
)
population_size = 10000
# Just ensuring no exception is raised
result = study.make_df_from_expectations(population_size)
assert len(result[pd.isnull(result.sex_default)]) == 0
def test_make_df_from_binary_default_outcome():
study = StudyDefinition(
population=patients.all(),
died=patients.died_from_any_cause(return_expectations={"incidence": 0.1}),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert len(result[~pd.isnull(result.died)]) == 0.1 * population_size
def test_make_df_from_expectations_with_mean_recorded_value():
study = StudyDefinition(
population=patients.all(),
drug_x=patients.mean_recorded_value(
codelist(["X"], system="ctv3"),
on_most_recent_day_of_measurement=True,
return_expectations={
"rate": "exponential_increase",
"incidence": 0.6,
"float": {"distribution": "normal", "mean": 35, "stddev": 10},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert abs(35 - int(result["drug_x"].mean())) < 5
def test_make_df_from_expectations_returning_date_using_defaults():
study = StudyDefinition(
default_expectations={
"date": {"earliest": "1900-01-01", "latest": "today"},
"rate": "exponential_increase",
},
population=patients.all(),
asthma_condition=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
return_expectations={"incidence": 0.2},
find_first_match_in_period=True,
date_format="YYYY-MM-DD",
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result[~pd.isnull(result["asthma_condition"])].min()[0] < "1960-01-01"
def test_make_df_from_expectations_with_care_home_status():
study = StudyDefinition(
population=patients.all(),
is_in_care_home=patients.care_home_status_as_of(
"2020-01-01",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.3,
"date": {
"earliest": "1900-01-01",
"latest": "2020-01-01"
},
"bool": True,
},
),
care_home_type=patients.care_home_status_as_of(
"2020-01-01",
categorised_as={
"PN": "IsPotentialCareHome AND LocationRequiresNursing='Y'",
"PC": "IsPotentialCareHome",
"U": "DEFAULT",
},
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"category": {
"ratios": {
"PN": 0.1,
"PC": 0.2,
"U": 0.7
}
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
value_counts = result.care_home_type.value_counts()
assert value_counts["PN"] < value_counts["U"]
def test_make_df_from_expectations_with_satisfying():
study = StudyDefinition(
population=patients.all(),
has_condition=patients.satisfying(
"condition_a OR condition_b",
condition_a=patients.with_these_clinical_events(
codelist(["A", "B", "C"], system="ctv3")),
condition_b=patients.with_these_clinical_events(
codelist(["X", "Y", "Z"], system="ctv3")),
return_expectations={
"date": {
"earliest": "2001-01-01",
"latest": "2020-03-01"
},
"incidence": 0.95,
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
assert result.columns == ["has_condition"]