def test_suppression_doesnt_affect_later_calculations_on_the_same_data():
data = pandas.DataFrame({"fish": [2], "litres": [2]})
m1 = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
r1 = calculate(m1, data)
assert numpy.isnan(r1.iloc[0]["value"])
m2 = Measure("ignored-id", numerator="fish", denominator="litres")
r2 = calculate(m2, data)
assert r2.iloc[0]["value"] == 1.0
def test_reports_suppression_of_small_values():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({"fish": [1], "litres": [100]}, index=["bowl"])
reporter = RecordingReporter()
calculate(m, data, reporter)
assert "Suppressed small numbers in column fish" in reporter.msg
def test_suppresses_denominator_if_its_small_enough():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({"fish": [0], "litres": [4]}, index=["bag"])
result = calculate(m, data)
assert numpy.isnan(result.loc["bag"]["litres"])
assert numpy.isnan(result.loc["bag"]["value"])
def test_suppresses_small_numbers_in_the_numerator():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({"fish": [1], "litres": [100]}, index=["bowl"])
result = calculate(m, data)
assert numpy.isnan(result.loc["bowl"]["fish"])
assert numpy.isnan(result.loc["bowl"]["value"])
def test_throws_away_unused_columns():
m = Measure("ignored-id", numerator="fish", denominator="litres")
data = pandas.DataFrame({
"fish": [10],
"litres": [1],
"colour": ["green"],
"clothing": ["trousers"]
})
result = calculate(m, data)
assert "clothing" not in result.iloc[0]
m = Measure("ignored-id",
numerator="fish",
denominator="litres",
group_by="colour")
data = pandas.DataFrame({
"fish": [10],
"litres": [1],
"colour": ["green"],
"age": [12]
})
result = calculate(m, data)
assert "age" not in result.iloc[0]
def test_calculates_quotients():
m = Measure("ignored-id", numerator="fish", denominator="litres")
data = pandas.DataFrame(
{
"fish": [10, 20, 50],
"litres": [1, 2, 100]
},
index=["small bowl", "large bowl", "pond"],
)
result = calculate(m, data)
assert result.loc["small bowl"]["value"] == 10.0
assert result.loc["large bowl"]["value"] == 10.0
assert result.loc["pond"]["value"] == 0.5
def test_groups_into_multiple_buckets():
m = Measure("ignored-id",
numerator="fish",
denominator="litres",
group_by="colour")
data = pandas.DataFrame({
"fish": [10, 10],
"litres": [1, 2],
"colour": ["gold", "pink"]
})
result = calculate(m, data)
result.set_index("colour", inplace=True)
assert result.loc["gold"]["value"] == 10.0
assert result.loc["pink"]["value"] == 5.0
def test_reports_suppression_of_extra_values():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({
"fish": [2, 10, 8],
"litres": [10, 10, 10]
},
index=["a", "b", "c"])
reporter = RecordingReporter()
calculate(m, data, reporter)
assert "Additional suppression in column fish" in reporter.msg
def test_doesnt_suppress_zero_values():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({
"fish": [0, 1],
"litres": [100, 100]
},
index=["bowl", "bag"])
result = calculate(m, data)
assert result.loc["bowl"]["fish"] == 0
assert result.loc["bowl"]["value"] == 0
def test_suppresses_all_equal_extra_values_to_reach_threshold():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({
"fish": [1, 10, 10],
"litres": [10, 10, 10]
},
index=["a", "b", "c"])
result = calculate(m, data)
assert numpy.isnan(result.loc["a"]["fish"])
assert numpy.isnan(result.loc["b"]["fish"])
assert numpy.isnan(result.loc["c"]["fish"])
def test_suppresses_all_small_values_even_if_total_is_way_over_threshold():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame({
"fish": [2, 2, 2, 2],
"litres": [10, 10, 10, 10]
},
index=["a", "b", "c", "d"])
result = calculate(m, data)
assert numpy.isnan(result.loc["a"]["fish"])
assert numpy.isnan(result.loc["b"]["fish"])
assert numpy.isnan(result.loc["c"]["fish"])
assert numpy.isnan(result.loc["d"]["fish"])
def test_groups_data_together():
m = Measure("ignored-id",
numerator="fish",
denominator="litres",
group_by="colour")
data = pandas.DataFrame(
{
"fish": [10, 20],
"litres": [1, 2],
"colour": ["gold", "gold"]
},
index=["small bowl", "large bowl"],
)
result = calculate(m, data)
result.set_index("colour", inplace=True)
assert result.loc["gold"]["fish"] == 30
assert result.loc["gold"]["litres"] == 3
assert result.loc["gold"]["value"] == 10.0
def test_suppresses_small_numbers_after_grouping():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
group_by="colour",
small_number_suppression=True,
)
data = pandas.DataFrame({
"fish": [2, 2, 2, 2, 3, 3],
"litres": [2, 2, 2, 2, 3, 3],
"colour": ["gold", "gold", "bronze", "bronze", "pink", "pink"],
})
result = calculate(m, data)
result.set_index("colour", inplace=True)
assert numpy.isnan(result.loc["gold"]["value"])
assert numpy.isnan(result.loc["bronze"]["value"])
assert result.loc["pink"]["value"] == 1.0
def test_suppresses_small_numbers_at_threshold_in_the_numerator():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
small_number_suppression=True,
)
data = pandas.DataFrame(
{
"fish": [
measure.SMALL_NUMBER_THRESHOLD,
measure.SMALL_NUMBER_THRESHOLD,
measure.SMALL_NUMBER_THRESHOLD + 1,
],
"litres": [100, 100, measure.SMALL_NUMBER_THRESHOLD + 1],
},
index=["bowl", "box", "bag"],
)
result = calculate(m, data)
assert numpy.isnan(result.loc["bowl"]["fish"])
assert numpy.isnan(result.loc["box"]["fish"])
assert result.loc["bag"]["value"] == 1.0
def test_groups_by_multiple_columns():
m = Measure(
"ignored-id",
numerator="fish",
denominator="litres",
group_by=["colour", "nationality"],
)
data = pandas.DataFrame({
"fish": [10, 20, 40, 80],
"litres": [1, 1, 1, 1],
"colour": ["gold", "gold", "gold", "pink"],
"nationality": ["russian", "japanese", "russian", "french"],
})
result = calculate(m, data)
assert result.iloc[0]["colour"] == "gold"
assert result.iloc[0]["nationality"] == "japanese"
assert result.iloc[0]["fish"] == 20
assert result.iloc[1]["colour"] == "gold"
assert result.iloc[1]["nationality"] == "russian"
assert result.iloc[1]["fish"] == 50
assert result.iloc[2]["colour"] == "pink"
assert result.iloc[2]["nationality"] == "french"
assert result.iloc[2]["fish"] == 80
def test_stats_logging_generate_measures(mock_load, _mock_list, _mock_check,
logger, tmp_path):
import csv
from cohortextractor.measure import Measure
measures = [
Measure(
id="has_code",
numerator="has_code",
denominator="population",
),
Measure(
id="has_code_one_group",
numerator="has_code",
denominator="population",
group_by="population",
),
]
mock_load.return_value = measures
# initial stats
expected_initial_logs = [{"measures_count": 2}]
# set up an expected input file
input_filepath = tmp_path / "input_2020-01-01.csv"
with open(input_filepath, "w") as file_to_write:
writer = csv.writer(file_to_write)
writer.writerow(["patient_id", "has_code"])
writer.writerow([1, 1])
writer.writerow([2, 1])
writer.writerow([3, 1])
writer.writerow([4, 0])
generate_measures(output_dir=tmp_path)
stats_logs = get_stats_logs(logger.entries)
memory_logs = get_logs_by_key(stats_logs, "memory")
measure_date = "2020-01-01"
expected_timing_logs = [
dict(
description="generate_measures",
input_file="all",
study_definition="study_definition",
timing="start",
state="started",
),
dict(
description="generate_measures",
date=measure_date,
input_file=str(input_filepath),
study_definition="study_definition",
timing="start",
state="started",
),
dict(
description="Load patient dataframe for measures",
date=measure_date,
input_file=str(input_filepath),
timing="start",
state="started",
),
dict(
description="Load patient dataframe for measures",
date=measure_date,
input_file=str(input_filepath),
timing="stop",
state="ok",
),
dict(
description="Calculate measure",
measure_id="has_code",
date=measure_date,
timing="start",
state="started",
),
dict(
description="Calculate measure",
measure_id="has_code",
date=measure_date,
timing="stop",
state="ok",
),
dict(
description="Calculate measure",
measure_id="has_code_one_group",
date=measure_date,
timing="start",
state="started",
),
dict(
description="Calculate measure",
measure_id="has_code_one_group",
date=measure_date,
timing="stop",
state="ok",
),
dict(
description="generate_measures",
date=measure_date,
input_file=str(input_filepath),
study_definition="study_definition",
timing="stop",
state="ok",
),
dict(
description="generate_measures",
input_file="all",
study_definition="study_definition",
timing="stop",
state="ok",
),
]
assert_stats_logs(logger, expected_initial_logs + memory_logs,
expected_timing_logs)
expected_memory_logs = [
("patient_df", measure_date, "has_code"),
("measure_df", measure_date, "has_code"),
("measure_df", measure_date, "has_code_one_group"),
]
for i, memory_log in enumerate(memory_logs):
df, measure_date, measure_id = expected_memory_logs[i]
assert memory_log["dataframe"] == df
assert memory_log["date"] == measure_date
assert memory_log["measure_id"] == measure_id
