def process(self, element, calculation_month_limit, inclusions):
"""Produces various program metric combinations.
Sends the calculator the StatePerson entity and their corresponding ProgramEvents for mapping all program
combinations.
Args:
element: Tuple containing a StatePerson and their ProgramEvents
calculation_month_limit: The number of months to limit the monthly calculation output to.
inclusions: This should be a dictionary with values for the
following keys:
- age_bucket
- gender
- race
- ethnicity
Yields:
Each program metric combination, tagged by metric type.
"""
person, program_events = element
# Calculate program metric combinations for this person and their program events
metric_combinations = \
calculator.map_program_combinations(person=person,
program_events=program_events,
inclusions=inclusions,
calculation_month_limit=calculation_month_limit)
# Return each of the program metric combinations
for metric_combination in metric_combinations:
metric_key, value = metric_combination
metric_type = metric_key.get('metric_type')
# Converting the metric key to a JSON string so it is hashable
serializable_dict = json_serializable_metric_key(metric_key)
json_key = json.dumps(serializable_dict, sort_keys=True)
if metric_type == MetricType.REFERRAL.value:
yield beam.pvalue.TaggedOutput('referrals', (json_key, value))
def test_map_program_combinations_calculation_month_count_1(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='US_ND',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='US_ND', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
included_event = ProgramReferralEvent(state_code='US_ND',
event_date=date(2012, 11, 10),
program_id='XXX')
not_included_event = ProgramReferralEvent(state_code='US_ND',
event_date=date(2000, 2, 2),
program_id='ZZZ')
program_events = [included_event, not_included_event]
program_combinations = calculator.map_program_combinations(
person,
program_events,
ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=1)
expected_combinations_count = expected_metric_combos_count(
[included_event], len(calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(program_combinations))
assert all(value == 1 for _combination, value in program_combinations)
def test_map_program_combinations(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='US_ND',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='US_ND', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
program_events = [
ProgramReferralEvent(state_code='US_ND',
event_date=date(2019, 10, 10),
program_id='XXX'),
ProgramParticipationEvent(state_code='US_ND',
event_date=date(2019, 2, 2),
program_id='ZZZ')
]
program_combinations = calculator.map_program_combinations(
person,
program_events,
ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1)
expected_combinations_count = expected_metric_combos_count(
program_events)
self.assertEqual(expected_combinations_count,
len(program_combinations))
assert all(value == 1 for _combination, value in program_combinations)
def test_map_program_combinations_relevant_periods_multiple_supervisions(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='US_ND',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='US_ND',
ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
program_events = [
ProgramReferralEvent(
state_code='US_ND',
event_date=date(2007, 1, 7),
program_id='XXX',
supervision_type=StateSupervisionType.PAROLE,
assessment_score=22,
assessment_type=StateAssessmentType.LSIR,
supervising_officer_external_id='OFFICERZ',
supervising_district_external_id='135'
),
ProgramReferralEvent(
state_code='US_ND',
event_date=date(2007, 1, 11),
program_id='XXX',
supervision_type=StateSupervisionType.PROBATION,
assessment_score=22,
assessment_type=StateAssessmentType.LSIR,
supervising_officer_external_id='OFFICERZ',
supervising_district_external_id='135'
)
]
program_combinations = calculator.map_program_combinations(
person, program_events, ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA
)
relevant_periods = [36, 12]
expected_combinations_count = expected_metric_combos_count(
program_events, len(relevant_periods),
duplicated_months_different_supervision_types=True)
self.assertEqual(expected_combinations_count, len(program_combinations))
assert all(value == 1 for _combination, value in program_combinations)
parole_combos = 0
probation_combos = 0
for combo, _ in program_combinations:
if combo.get('supervision_type') \
and combo_has_enum_value_for_key(
combo, 'methodology', MetricMethodologyType.PERSON):
# Ensure that all person-based metrics are of type parole
if combo.get('supervision_type') == \
StateSupervisionType.PAROLE:
parole_combos += 1
elif combo.get('supervision_type') == \
StateSupervisionType.PROBATION:
probation_combos += 1
# Assert that there are the same number of parole and probation
# person-based combinations
assert parole_combos == probation_combos
def test_map_program_combinations_multiple_supervision_types(self):
person = StatePerson.new_with_defaults(
state_code="US_ND",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_ND",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_ND", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
event_date = date(2009, 10, 7)
program_events = [
ProgramReferralEvent(
state_code="US_ND",
event_date=event_date,
program_id="XXX",
supervision_type=StateSupervisionType.PAROLE,
assessment_score=22,
assessment_type=StateAssessmentType.LSIR,
supervising_officer_external_id="OFFICERZ",
supervising_district_external_id="135",
),
ProgramReferralEvent(
state_code="US_ND",
event_date=date(2009, 10, 7),
program_id="XXX",
supervision_type=StateSupervisionType.PROBATION,
assessment_score=22,
assessment_type=StateAssessmentType.LSIR,
supervising_officer_external_id="OFFICERZ",
supervising_district_external_id="135",
),
ProgramParticipationEvent(
state_code="US_ND",
event_date=event_date,
program_id="XXX",
program_location_id="YYY",
supervision_type=StateSupervisionType.PAROLE,
),
ProgramParticipationEvent(
state_code="US_ND",
event_date=event_date,
program_id="XXX",
program_location_id="YYY",
supervision_type=StateSupervisionType.PROBATION,
),
]
program_combinations = calculator.map_program_combinations(
person,
program_events,
ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month="2009-10",
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
program_events)
self.assertEqual(expected_combinations_count,
len(program_combinations))
assert all(value == 1 for _combination, value in program_combinations)
