def test_map_incarceration_combinations_multiple_stays_one_month(self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 13),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 14),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 15),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations_all_types(self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
admission_reason=StateIncarcerationPeriodAdmissionReason.
PAROLE_REVOCATION,
admission_reason_raw_text="NEW_ADMISSION",
supervision_type_at_admission=
StateSupervisionPeriodSupervisionType.PAROLE,
state_code="CA",
event_date=date(2000, 3, 31),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationAdmissionEvent(
state_code="CA",
event_date=date(2000, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
supervision_type_at_admission=
StateSupervisionPeriodSupervisionType.PAROLE,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationReleaseEvent(
state_code="CA",
event_date=date(2003, 4, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
supervision_type_at_release=
StateSupervisionPeriodSupervisionType.PAROLE,
),
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_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)
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_for_characteristics_races_ethnicities_multiple_races_and_ethnicities(
):
race_white = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
race_black = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.BLACK)
ethnicity_hispanic = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.HISPANIC)
ethnicity_not_hispanic = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
characteristics = {'age': '<25'}
combinations = calculator_utils.for_characteristics_races_ethnicities(
[race_white, race_black], [ethnicity_hispanic, ethnicity_not_hispanic],
characteristics)
assert combinations == [{}, {
'age': '<25'
}, {
'race': Race.WHITE
}, {
'age': '<25',
'race': Race.WHITE
}, {
'race': Race.BLACK
}, {
'age': '<25',
'race': Race.BLACK
}, {
'ethnicity': Ethnicity.HISPANIC
}, {
'age': '<25',
'ethnicity': Ethnicity.HISPANIC
}, {
'ethnicity': Ethnicity.NOT_HISPANIC
}, {
'age': '<25',
'ethnicity': Ethnicity.NOT_HISPANIC
}, {
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC
}, {
'age': '<25',
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC
}, {
'race': Race.WHITE,
'ethnicity': Ethnicity.NOT_HISPANIC
}, {
'age': '<25',
'race': Race.WHITE,
'ethnicity': Ethnicity.NOT_HISPANIC
}, {
'race': Race.BLACK,
'ethnicity': Ethnicity.HISPANIC
}, {
'age': '<25',
'race': Race.BLACK,
'ethnicity': Ethnicity.HISPANIC
}, {
'race': Race.BLACK,
'ethnicity': Ethnicity.NOT_HISPANIC
}, {
'age': '<25',
'race': Race.BLACK,
'ethnicity': Ethnicity.NOT_HISPANIC
}]
def test_for_characteristics_races_ethnicities_one_race_one_ethnicity():
race_white = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
ethnicity_hispanic = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.HISPANIC)
characteristics = {'gender': 'female', 'age': '<25'}
combinations = calculator_utils.for_characteristics_races_ethnicities(
[race_white], [ethnicity_hispanic], characteristics)
assert combinations == [
{},
{
'gender': 'female'
},
{
'age': '<25'
},
{
'gender': 'female',
'age': '<25'
},
{
'race': Race.WHITE
},
{
'race': Race.WHITE,
'gender': 'female'
},
{
'age': '<25',
'race': Race.WHITE
},
{
'age': '<25',
'race': Race.WHITE,
'gender': 'female'
},
{
'ethnicity': Ethnicity.HISPANIC
},
{
'ethnicity': Ethnicity.HISPANIC,
'gender': 'female'
},
{
'age': '<25',
'ethnicity': Ethnicity.HISPANIC
},
{
'age': '<25',
'ethnicity': Ethnicity.HISPANIC,
'gender': 'female'
},
{
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC
},
{
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC,
'gender': 'female'
},
{
'age': '<25',
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC
},
{
'race': Race.WHITE,
'ethnicity': Ethnicity.HISPANIC,
'age': '<25',
'gender': 'female'
},
]
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)