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'",
"": "DEFAULT"
},
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,
),
)
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",
"date": {"earliest": "1900-01-01", "latest": "today"},
"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_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_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", 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_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,
return_expectations={
"rate": "exponential_increase",
"incidence": 0.6,
"float": {
"distribution": "normal",
"mean": 35,
"stddev": 10
},
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
bmi_date_measured=patients.date_of(
"bmi",
date_format="YYYY-MM",
),
)
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"] == 0.0) == pd.isnull(
result["bmi_date_measured"])).all()
def test_make_df_from_expectations_with_categories_expression():
study = StudyDefinition(
population=patients.all(),
category=patients.categorised_as(
{
"A": "sex = 'F'",
"B": "sex = 'M'",
"": "DEFAULT"
},
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_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_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,
),
)
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_with_using_dates_as_categories():
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
"incidence": 0.2,
},
population=patients.all(),
eligible_date=patients.categorised_as(
{
"2020-04-14": "age >= 80",
"2020-06-16": "age >= 70 AND age < 80",
"2020-08-18": "DEFAULT",
},
age=patients.age_as_of("2020-01-01"),
return_expectations={
"category": {
"ratios": {
"2020-04-14": 0.25,
"2020-06-16": 0.25,
"2020-08-18": 0.5,
}
},
"incidence": 1,
},
),
)
population_size = 100
result = study.make_df_from_expectations(population_size)
assert set(result.eligible_date) == set(
["2020-08-18", "2020-06-16", "2020-04-14"])
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_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",
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"incidence": 0.6,
"float": {
"distribution": "normal",
"mean": 35,
"stddev": 10
},
},
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
nonzero_results = result[result["drug_x"] != 0.0]
assert abs(35 - int(nonzero_results["drug_x"].mean())) < 5
def test_column_refs_in_date_expressions_do_not_trigger_errors():
# Further down the road we want to actually interpret these expressions and
# generate appopriate dates, but for now we just need to not blow up when
# we encounter them
study = StudyDefinition(
population=patients.all(),
copd_exacerbation=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": "1990-01-01",
"latest": "today"
},
},
find_last_match_in_period=True,
date_format="YYYY-MM-DD",
),
drug_after_exacerbation=patients.with_these_medications(
codelist(["Y"], system="snomed"),
between=["copd_exacerbation", "copd_exacerbation + 3 months"],
returning="date",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"date": {
"earliest": "1990-01-01",
"latest": "today"
},
},
find_first_match_in_period=True,
date_format="YYYY-MM-DD",
),
)
population_size = 10000
# Just ensure no exception is raised
study.make_df_from_expectations(population_size)
def test_make_df_from_binary_default_outcome():
study = StudyDefinition(
population=patients.all(),
died=patients.died_from_any_cause(
return_expectations={
"date": {"earliest": "1900-01-01", "latest": "today"},
"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_returning_date_using_defaults():
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-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_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,
date_format="YYYY-MM-DD",
),
)
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_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"]
def test_make_df_from_expectations_with_deregistration_date():
study = StudyDefinition(
population=patients.all(),
dereg_date=patients.date_deregistered_from_all_supported_practices(
on_or_before="2018-02-01",
date_format="YYYY-MM",
return_expectations={
"incidence": 0.1,
"date": {
"earliest": "1980-01-01",
"latest": "2018-02-01"
},
},
),
)
population_size = 1000
result = study.make_df_from_expectations(population_size)
dates = result.dereg_date.dropna()
assert dates.max() <= "2018-02"
assert dates.min() >= "1980-01"
def test_make_df_from_expectations_with_covid_therapeutics():
study = StudyDefinition(
population=patients.all(),
therapeutic_approved=patients.with_covid_therapeutics(
with_these_statuses=["Approved", "Treatment Complete"],
returning="therapeutic",
return_expectations={
"rate": "universal",
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"category": {
"ratios": {
"Approved": 0.25,
"Treatment Complete": 0.25,
"Treatment Not Started": 0.25,
"Treatment Stopped": 0.25,
}
},
},
),
start_date=patients.with_covid_therapeutics(
with_these_statuses=["Approved", "Treatment Complete"],
returning="date",
return_expectations={
"rate": "exponential_increase",
"incidence": 0.2,
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
},
),
)
population_size = 1000
# Just ensuring no exception is raised
result = study.make_df_from_expectations(population_size)
assert len(result[pd.isnull(result.therapeutic_approved)]) == 0
assert len(result[pd.isnull(result.start_date)]) == 800
def test_make_df_from_expectations_doesnt_alter_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(),
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_expectations_with_aggregate_of():
# aggregate of variables defined in their own right
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
"incidence": 0.2,
},
population=patients.all(),
date_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_min=patients.minimum_of(
"date_1",
"date_2",
),
date_max=patients.maximum_of(
"date_1",
"date_2",
),
date_min_fixed=patients.minimum_of(
"date_1",
"1980-10-20",
),
int_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_min=patients.minimum_of("int_1", "int_2"),
int_max=patients.maximum_of("int_1", "int_2"),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
for _, row in result.iterrows():
print(row)
dates = [
d for d in [row["date_1"], row["date_2"]] if isinstance(d, str)
]
if dates:
date_min = min(dates)
date_max = max(dates)
else:
date_min = float("nan")
date_max = float("nan")
assert_nan_equal(row["date_min"], date_min)
assert_nan_equal(row["date_max"], date_max)
ints = [i for i in [row["int_1"], row["int_2"]] if isinstance(i, int)]
if ints:
int_min = min(ints)
int_max = max(ints)
else:
int_min = float("nan")
int_max = float("nan")
assert_nan_equal(row["int_min"], int_min)
assert_nan_equal(row["int_max"], int_max)
# aggregate of variables defined only within aggregate function
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
"incidence": 1,
},
# We use an expression here (never mind that it's a trivial and
# pointless one) as that triggers a bug which we want to ensure we've
# fixed
population=patients.satisfying("foo OR bar",
foo=patients.all(),
bar=patients.all()),
date_min=patients.maximum_of(
date_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
),
date_max=patients.maximum_of(
date_3=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_4=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
),
int_min=patients.minimum_of(
int_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
),
int_max=patients.maximum_of(
int_3=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_4=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
for _, row in result.iterrows():
print(row)
assert pd.notna(row["date_min"])
assert pd.notna(row["date_max"])
assert pd.notna(row["int_min"])
assert pd.notna(row["int_max"])
# aggregate of variables defined both inside and outside aggregation
study = StudyDefinition(
default_expectations={
"date": {
"earliest": "1900-01-01",
"latest": "today"
},
"rate": "exponential_increase",
"incidence": 0.2,
},
population=patients.all(),
date_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
date_min=patients.minimum_of(
"date_1",
"date_2",
date_3=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
),
date_max=patients.maximum_of(
"date_1",
"date_2",
date_4=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="date",
date_format="YYYY-MM-DD",
),
),
int_1=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_2=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
int_min=patients.minimum_of(
"int_1",
"int_2",
int_3=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
),
int_max=patients.maximum_of(
"int_1",
"int_2",
int_4=patients.with_these_clinical_events(
codelist(["X"], system="ctv3"),
returning="number_of_matches_in_period",
return_expectations={
"int": {
"distribution": "normal",
"mean": 25,
"stddev": 5
},
"incidence": 0.5,
},
),
),
)
population_size = 10000
result = study.make_df_from_expectations(population_size)
for _, row in result.iterrows():
print(row)
dates = [
d for d in [row["date_1"], row["date_2"]] if isinstance(d, str)
]
if dates:
date_min = min(dates)
date_max = max(dates)
else:
date_min = float("nan")
date_max = float("nan")
assert_nan_equal(row["date_min"], date_min)
assert_nan_equal(row["date_max"], date_max)
ints = [i for i in [row["int_1"], row["int_2"]] if isinstance(i, int)]
if ints:
int_min = min(ints)
int_max = max(ints)
else:
int_min = float("nan")
int_max = float("nan")
assert_nan_equal(row["int_min"], int_min)
assert_nan_equal(row["int_max"], int_max)
