def testCalculateIncarcerationMetricCombinations_NoIncarceration(self):
"""Tests the CalculateIncarcerationMetricCombinations when there are
no incarceration_events. This should never happen because any person
without incarceration events is dropped entirely from the pipeline."""
fake_person = StatePerson.new_with_defaults(
person_id=123,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
test_pipeline = TestPipeline()
inputs = [(self.fake_person_id, {
'person_incarceration_events': [(fake_person, [])],
'person_metadata': [self.person_metadata]
})]
output = (test_pipeline
| beam.Create(inputs)
| beam.ParDo(ExtractPersonEventsMetadata())
| 'Calculate Incarceration Metrics' >> beam.ParDo(
pipeline.CalculateIncarcerationMetricCombinations(),
None, -1, ALL_METRICS_INCLUSIONS_DICT))
assert_that(output, equal_to([]))
test_pipeline.run()
def testCalculateIncarcerationMetricCombinations(self):
"""Tests the CalculateIncarcerationMetricCombinations DoFn."""
fake_person = StatePerson.new_with_defaults(
person_id=123,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
incarceration_events = [
IncarcerationAdmissionEvent(
state_code='US_XX',
event_date=date(2001, 3, 16),
facility='SAN QUENTIN',
county_of_residence='county_of_residence',
admission_reason=StateIncarcerationPeriodAdmissionReason.
PROBATION_REVOCATION),
IncarcerationReleaseEvent(
state_code='US_XX',
event_date=date(2002, 5, 26),
facility='SAN QUENTIN',
county_of_residence='county_of_residence',
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED)
]
# One metric per methodology for each event
expected_metric_count = 2
expected_combination_counts = \
{'admissions': expected_metric_count,
'releases': expected_metric_count}
test_pipeline = TestPipeline()
inputs = [(self.fake_person_id, {
'person_events': [(fake_person, incarceration_events)],
'person_metadata': [self.person_metadata]
})]
output = (test_pipeline
| beam.Create(inputs)
| beam.ParDo(ExtractPersonEventsMetadata())
| 'Calculate Incarceration Metrics' >> beam.ParDo(
pipeline.CalculateIncarcerationMetricCombinations(),
None, -1, ALL_METRICS_INCLUSIONS_DICT))
assert_that(
output,
AssertMatchers.count_combinations(expected_combination_counts),
'Assert number of metrics is expected value')
test_pipeline.run()
def testCalculateIncarcerationMetricCombinations_NoInput(self):
"""Tests the CalculateIncarcerationMetricCombinations when there is
no input to the function."""
test_pipeline = TestPipeline()
output = (test_pipeline
| beam.Create([])
| 'Calculate Incarceration Metrics' >> beam.ParDo(
pipeline.CalculateIncarcerationMetricCombinations(),
None, -1, ALL_METRICS_INCLUSIONS_DICT))
assert_that(output, equal_to([]))
test_pipeline.run()
def testCalculateIncarcerationMetricCombinations(self):
"""Tests the CalculateIncarcerationMetricCombinations DoFn."""
fake_person = StatePerson.new_with_defaults(
person_id=123,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
incarceration_events = [
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2001, 3, 16),
facility='SAN QUENTIN',
county_of_residence='county_of_residence',
admission_reason=StateIncarcerationPeriodAdmissionReason.
PROBATION_REVOCATION),
IncarcerationReleaseEvent(
state_code='CA',
event_date=date(2002, 5, 26),
facility='SAN QUENTIN',
county_of_residence='county_of_residence',
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED)
]
# Get the number of combinations of person-event characteristics.
num_combinations = len(
calculator.characteristic_combinations(
fake_person, incarceration_events[0], ALL_INCLUSIONS_DICT,
IncarcerationMetricType.POPULATION))
assert num_combinations > 0
expected_metric_count = num_combinations * 2
expected_admission_combination_counts = \
{'admissions': expected_metric_count}
expected_releases_combination_counts = \
{'releases': expected_metric_count}
test_pipeline = TestPipeline()
output = (
test_pipeline
| beam.Create([(fake_person, incarceration_events)])
| 'Calculate Incarceration Metrics' >> beam.ParDo(
pipeline.CalculateIncarcerationMetricCombinations(), -1,
ALL_INCLUSIONS_DICT).with_outputs('admissions', 'releases'))
assert_that(
output.admissions,
AssertMatchers.count_combinations(
expected_admission_combination_counts),
'Assert number of admission metrics is expected value')
assert_that(
output.releases,
AssertMatchers.count_combinations(
expected_releases_combination_counts),
'Assert number of release metrics is expected value')
test_pipeline.run()
def testCalculateIncarcerationMetricCombinations(self):
"""Tests the CalculateIncarcerationMetricCombinations DoFn."""
fake_person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=123,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
incarceration_events = [
IncarcerationAdmissionEvent(
state_code="US_XX",
event_date=date(2001, 3, 16),
facility="SAN QUENTIN",
county_of_residence="county_of_residence",
admission_reason=StateIncarcerationPeriodAdmissionReason.
PROBATION_REVOCATION,
),
IncarcerationReleaseEvent(
state_code="US_XX",
event_date=date(2002, 5, 26),
facility="SAN QUENTIN",
county_of_residence="county_of_residence",
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
),
]
expected_metric_count = 1
expected_combination_counts = {
"admissions": expected_metric_count,
"releases": expected_metric_count,
}
test_pipeline = TestPipeline()
inputs = [(
self.fake_person_id,
{
"person_events": [(fake_person, incarceration_events)],
"person_metadata": [self.person_metadata],
},
)]
output = (test_pipeline
| beam.Create(inputs)
| beam.ParDo(ExtractPersonEventsMetadata())
| "Calculate Incarceration Metrics" >> beam.ParDo(
pipeline.CalculateIncarcerationMetricCombinations(),
None,
-1,
ALL_METRICS_INCLUSIONS_DICT,
))
assert_that(
output,
AssertMatchers.count_combinations(expected_combination_counts),
"Assert number of metrics is expected value",
)
test_pipeline.run()