def test_for_characteristics_races_ethnicities_one_race_multiple_ethnicities():
race_white = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
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], [ethnicity_hispanic, ethnicity_not_hispanic],
characteristics)
assert combinations == [{}, {
'age': '<25'
}, {
'race': Race.WHITE
}, {
'age': '<25',
'race': Race.WHITE
}, {
'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
}]
def test_add_person_characteristics_RaceEthnicity(self):
person = StatePerson.new_with_defaults(
state_code='US_XX',
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
races=[StatePersonRace.new_with_defaults(race=Race.ASIAN)],
ethnicities=[
StatePersonEthnicity.new_with_defaults(
ethnicity=Ethnicity.HISPANIC)
])
event_date = date(2010, 9, 1)
person_metadata = PersonMetadata(
prioritized_race_or_ethnicity='HISPANIC')
updated_characteristics = person_characteristics(
person, event_date, person_metadata, 'pipeline')
expected_output = {
'age_bucket': '25-29',
'race': [Race.ASIAN],
'gender': Gender.FEMALE,
'ethnicity': [Ethnicity.HISPANIC],
'person_id': person.person_id,
'prioritized_race_or_ethnicity': 'HISPANIC'
}
self.assertEqual(updated_characteristics, expected_output)
def test_add_person_characteristics_IncludeExternalId(self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
races=[StatePersonRace.new_with_defaults(state_code="US_XX", )],
ethnicities=[
StatePersonEthnicity.new_with_defaults(state_code="US_XX", )
],
external_ids=[
StatePersonExternalId.new_with_defaults(external_id="DOC1341",
id_type="US_XX_DOC",
state_code="US_XX"),
StatePersonExternalId.new_with_defaults(external_id="SID9889",
id_type="US_XX_SID",
state_code="US_XX"),
],
)
event_date = date(2010, 9, 1)
person_metadata = PersonMetadata()
updated_characteristics = person_characteristics(
person, event_date, person_metadata, "test_pipeline")
expected_output = {
"age_bucket": "25-29",
"gender": Gender.FEMALE,
"person_id": person.person_id,
"person_external_id": "DOC1341",
}
self.assertEqual(updated_characteristics, expected_output)
def test_characteristic_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='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2018, 9, 13),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
combinations = calculator.characteristic_combinations(
person, incarceration_event, ALL_INCLUSIONS_DICT,
IncarcerationMetricType.ADMISSION)
# 64 combinations of demographics + 1 person-level metric
assert len(combinations) == 65
def test_map_incarceration_combinations_two_stays_same_month_facility(
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, 31),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 31),
facility="FACILITY 18",
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
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_characteristic_combinations_no_county(self):
person = StatePerson.new_with_defaults(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_event = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2018, 9, 13),
facility='facility',
)
combinations = calculator.characteristic_combinations(
person, incarceration_event, ALL_INCLUSIONS_DICT,
IncarcerationMetricType.RELEASE)
# 32 combinations of demographics + 1 person-level metric
assert len(combinations) == 33
def test_add_demographic_characteristics_RaceEthnicity(self):
characteristics = {}
person = StatePerson.new_with_defaults(
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
races=[StatePersonRace.new_with_defaults(race=Race.ASIAN)],
ethnicities=[
StatePersonEthnicity.new_with_defaults(
ethnicity=Ethnicity.HISPANIC)
])
event_date = date(2010, 9, 1)
updated_characteristics = add_demographic_characteristics(
characteristics, person, event_date)
expected_output = {
'age_bucket': '25-29',
'race': [Race.ASIAN],
'gender': Gender.FEMALE,
'ethnicity': [Ethnicity.HISPANIC]
}
self.assertEqual(updated_characteristics, expected_output)
def test_for_characteristics_races_ethnicities_one_ethnicity():
ethnicity_hispanic = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.HISPANIC)
characteristics = {'gender': 'female', 'age': '<25'}
combinations = calculator_utils.for_characteristics_races_ethnicities(
[], [ethnicity_hispanic], characteristics)
assert combinations == [{}, {
'gender': 'female'
}, {
'age': '<25'
}, {
'gender': 'female',
'age': '<25'
}, {
'ethnicity': Ethnicity.HISPANIC
}, {
'ethnicity': Ethnicity.HISPANIC,
'gender': 'female'
}, {
'age': '<25',
'ethnicity': Ethnicity.HISPANIC
}, {
'age': '<25',
'ethnicity': Ethnicity.HISPANIC,
'gender': 'female'
}]
def test_build_person_metadata(self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_XX",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_XX", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
person_metadata = build_person_metadata(
person, self.state_race_ethnicity_population_counts)
expected_person_metadata = PersonMetadata(
prioritized_race_or_ethnicity=Race.WHITE.value)
self.assertEqual(expected_person_metadata, person_metadata)
def test_add_person_characteristics_IncludeExternalId(self):
person = StatePerson.new_with_defaults(
state_code='US_MO',
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
races=[StatePersonRace.new_with_defaults()],
ethnicities=[StatePersonEthnicity.new_with_defaults()],
external_ids=[
StatePersonExternalId.new_with_defaults(external_id='SID1341',
id_type='US_MO_DOC',
state_code='US_MO')
])
event_date = date(2010, 9, 1)
person_metadata = PersonMetadata()
updated_characteristics = person_characteristics(
person, event_date, person_metadata, 'supervision')
expected_output = {
'age_bucket': '25-29',
'gender': Gender.FEMALE,
'person_id': person.person_id,
'person_external_id': 'SID1341'
}
self.assertEqual(updated_characteristics, expected_output)
def test_map_incarceration_combinations_calculation_month_limit_exclude(
self):
person = StatePerson.new_with_defaults(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_event = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(1990, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=1)
self.assertEqual(0, len(incarceration_combinations))
def test_map_incarceration_combinations_all_types(self):
person = StatePerson.new_with_defaults(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_statute=_STATUTE,
),
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,
)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_produce_incarceration_metrics_multiple_overlapping_stays(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]
incarceration_events = [
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
]
metrics = self.metric_producer.produce_metrics(
person=person,
identifier_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = self.expected_metrics_count(incarceration_events)
self.assertEqual(expected_count, len(metrics))
def test_produce_incarceration_metrics_two_stays_same_month(self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_XX",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_XX", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
state_code="US_XX",
event_date=date(2010, 3, 31),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="US_XX",
event_date=date(2010, 3, 31),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
]
metrics = self.metric_producer.produce_metrics(
person=person,
identifier_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = self.expected_metrics_count(incarceration_events)
self.assertEqual(expected_count, len(metrics))
def test_produce_incarceration_metrics_secondary_person_external_id(self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
races=[
StatePersonRace.new_with_defaults(state_code="US_XX",
race=Race.WHITE)
],
ethnicities=[
StatePersonEthnicity.new_with_defaults(
state_code="US_XX", ethnicity=Ethnicity.NOT_HISPANIC)
],
external_ids=[
StatePersonExternalId.new_with_defaults(external_id="DOC1341",
id_type="US_XX_DOC",
state_code="US_XX"),
StatePersonExternalId.new_with_defaults(external_id="SID9889",
id_type="US_XX_SID",
state_code="US_XX"),
],
)
incarceration_events = [
IncarcerationCommitmentFromSupervisionAdmissionEvent(
state_code="US_XX",
event_date=date(2000, 3, 12),
facility="FACILITY X",
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
supervision_type=StateSupervisionPeriodSupervisionType.PAROLE,
),
]
metrics = self.metric_producer.produce_metrics(
person=person,
identifier_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = self.expected_metrics_count(incarceration_events)
self.assertEqual(expected_count, len(metrics))
for metric in metrics:
self.assertEqual("DOC1341", metric.person_external_id)
if isinstance(metric,
IncarcerationCommitmentFromSupervisionMetric):
self.assertEqual("SID9889",
metric.secondary_person_external_id)
def test_map_incarceration_combinations_calculation_month_count_include_one(
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_event_include = IncarcerationAdmissionEvent(
state_code="CA",
event_date=date(2000, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_event_exclude = IncarcerationAdmissionEvent(
state_code="CA",
event_date=date(1994, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [
incarceration_event_include,
incarceration_event_exclude,
]
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=36,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
[incarceration_event_include])
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get("year") == 2000
def test_produce_incarceration_metrics_calculation_month_count_include_one(
self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_XX",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_XX", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event_include = IncarcerationStandardAdmissionEvent(
state_code="US_XX",
event_date=date(2000, 3, 12),
facility="FACILITY X",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_event_exclude = IncarcerationStandardAdmissionEvent(
state_code="US_XX",
event_date=date(1994, 3, 12),
facility="FACILITY X",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [
incarceration_event_include,
incarceration_event_exclude,
]
metrics = self.metric_producer.produce_metrics(
person=person,
identifier_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=36,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = self.expected_metrics_count(
[incarceration_event_include])
self.assertEqual(expected_count, len(metrics))
for metric in metrics:
assert metric.year == 2000
def test_map_incarceration_combinations_relevant_periods_revocations(self):
person = StatePerson.new_with_defaults(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 = [
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2010, 10, 2),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION),
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2010, 10, 19),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.NEW_ADMISSION)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT,
len(calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2010
for combo, _ in incarceration_combinations:
if combo.get('admission_reason')\
and combo_has_enum_value_for_key(
combo, 'metric_type', MetricMethodologyType.PERSON):
# Ensure that all person-based metrics have the parole
# revocation admission reason on them
assert combo.get('admission_reason') == \
AdmissionReason.PAROLE_REVOCATION
def test_map_program_combinations_full_info(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, 31)
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",
),
ProgramParticipationEvent(
state_code="US_ND",
event_date=event_date,
program_id="XXX",
program_location_id="YYY",
supervision_type=StateSupervisionType.PAROLE,
),
]
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)
def test_produce_incarceration_metrics_commitment_from_supervision(self):
person = StatePerson.new_with_defaults(
state_code="US_XX",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_XX",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_XX", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationCommitmentFromSupervisionAdmissionEvent(
state_code="US_XX",
event_date=date(2000, 3, 12),
facility="FACILITY X",
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
supervision_type=StateSupervisionPeriodSupervisionType.PAROLE,
),
]
metrics = self.metric_producer.produce_metrics(
person=person,
identifier_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = self.expected_metrics_count(incarceration_events)
# Assert that the IncarcerationCommitmentFromSupervisionAdmissionEvent produces both an
# INCARCERATION_ADMISSION and INCARCERATION_COMMITMENT_FROM_SUPERVISION metric
self.assertTrue(IncarcerationMetricType.INCARCERATION_ADMISSION in
[metric.metric_type for metric in metrics])
self.assertTrue(
IncarcerationMetricType.INCARCERATION_COMMITMENT_FROM_SUPERVISION
in [metric.metric_type for metric in metrics])
self.assertEqual(expected_count, len(metrics))
def test_produce_program_metrics_full_info(self) -> None:
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, 31)
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",
),
ProgramParticipationEvent(
state_code="US_ND",
event_date=event_date,
program_id="XXX",
program_location_id="YYY",
supervision_type=StateSupervisionType.PAROLE,
),
]
metrics = self.metric_producer.produce_metrics(
person,
program_events,
ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month="2009-10",
calculation_month_count=1,
person_metadata=_DEFAULT_PERSON_METADATA,
pipeline_job_id=PIPELINE_JOB_ID,
pipeline_type=self.pipeline_config.pipeline_type,
)
expected_count = expected_metrics_count(program_events)
self.assertEqual(expected_count, len(metrics))
def test_map_incarceration_combinations_includes_statute_output(self):
person = StatePerson.new_with_defaults(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, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE),
IncarcerationStayEvent(state_code='CA',
event_date=date(2010, 4, 30),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE),
IncarcerationStayEvent(state_code='CA',
event_date=date(2010, 5, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
assert all(
combo.get('most_serious_offense_statute') is not None
for combo, value in incarceration_combinations
if combo.get('person_id') is not None)
def test_map_incarceration_combinations_calculation_month_limit_include_one(
self):
person = StatePerson.new_with_defaults(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_event_include = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_event_exclude = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(1994, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [
incarceration_event_include, incarceration_event_exclude
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=36)
expected_combinations_count = expected_metric_combos_count(
person, [incarceration_event_include],
ALL_INCLUSIONS_DICT,
num_relevant_periods_admissions=len(
calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2000
def test_map_incarceration_combinations_two_stays_same_month(self):
person = StatePerson.new_with_defaults(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, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text='PAROLE_REVOCATION',
),
IncarcerationStayEvent(
state_code='CA',
event_date=date(2010, 3, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text='PAROLE_REVOCATION',
)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations(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_event = IncarcerationAdmissionEvent(
state_code="CA",
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
admission_reason_raw_text="NEW_ADMISSION",
supervision_type_at_admission=StateSupervisionPeriodSupervisionType
.PROBATION,
event_date=date(2000, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month="2000-03",
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)
for combo, _ in incarceration_combinations:
assert combo.get("year") == 2000
def test_map_program_combinations_relevant_periods_duplicates(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, 12, 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, 12, 11),
program_id='XXX',
supervision_type=StateSupervisionType.PAROLE,
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
)
expected_combinations_count = expected_metric_combos_count(
program_events, 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_incarceration_combinations_two_releases_same_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 = [
IncarcerationReleaseEvent(
state_code="CA",
event_date=date(2010, 3, 12),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
),
IncarcerationReleaseEvent(
state_code="CA",
event_date=date(2010, 3, 24),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
),
]
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_full_info_probation(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]
event_date = date(2009, 10, 31)
program_events = [
ProgramReferralEvent(
state_code='US_ND',
event_date=event_date,
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.PROBATION,
)
]
program_combinations = calculator.map_program_combinations(
person,
program_events,
ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month='2009-10',
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 setUpClass(cls):
"""Initialize the test person for the characteristics dict."""
cls.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)
cls.person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='US_ND', ethnicity=Ethnicity.NOT_HISPANIC)
cls.person.ethnicities = [ethnicity]
def setUp(self) -> None:
self.person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race_white = StatePersonRace.new_with_defaults(state_code='US_MO', race=Race.WHITE)
race_black = StatePersonRace.new_with_defaults(state_code='US_MO', race=Race.BLACK)
self.person.races = [race_white, race_black]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='US_MO',
ethnicity=Ethnicity.NOT_HISPANIC)
self.person.ethnicities = [ethnicity]
