def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.assessment_types_patcher = mock.patch(
'recidiviz.calculator.pipeline.program.identifier.assessment_utils.'
'_assessment_types_of_class_for_state')
self.mock_assessment_types = self.assessment_types_patcher.start()
self.mock_assessment_types.return_value = [StateAssessmentType.ORAS]
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.fake_bq_sink_factory = FakeWriteToBigQueryFactory(
FakeWriteToBigQuery)
self.violation_delegate_patcher = mock.patch(
"recidiviz.calculator.pipeline.violation.identifier.get_state_specific_violation_delegate"
)
self.mock_violation_delegate = self.violation_delegate_patcher.start()
self.mock_violation_delegate.return_value = UsXxViolationDelegate()
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
class TestProgramPipeline(unittest.TestCase):
"""Tests the entire program pipeline."""
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
@staticmethod
def build_data_dict(fake_person_id: int, fake_supervision_period_id: int):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(person_race_id=111,
state_code='CA',
race=Race.BLACK,
person_id=fake_person_id)
race_2 = schema.StatePersonRace(person_race_id=111,
state_code='ND',
race=Race.WHITE,
person_id=fake_person_id)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(person_ethnicity_id=111,
state_code='CA',
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
program_assignment = schema.StateProgramAssignment(
state_code='CA',
program_assignment_id=123,
referral_date=date(2015, 5, 10),
person_id=fake_person_id)
assessment = schema.StateAssessment(assessment_id=298374,
assessment_date=date(2015, 3, 19),
assessment_type='LSIR',
person_id=fake_person_id)
supervision_period = schema.StateSupervisionPeriod(
supervision_period_id=fake_supervision_period_id,
state_code='CA',
county_code='124',
start_date=date(2015, 3, 14),
termination_date=date(2016, 12, 29),
supervision_type=StateSupervisionType.PROBATION,
person_id=fake_person_id)
program_assignment_data = [
normalized_database_base_dict(program_assignment)
]
assessment_data = [normalized_database_base_dict(assessment)]
supervision_periods_data = [
normalized_database_base_dict(supervision_period)
]
supervision_violation_response = \
database_test_utils.generate_test_supervision_violation_response(
fake_person_id)
supervision_violation_response_data = [
normalized_database_base_dict(supervision_violation_response)
]
data_dict = {
schema.StatePerson.__tablename__: persons_data,
schema.StatePersonRace.__tablename__: races_data,
schema.StatePersonEthnicity.__tablename__: ethnicity_data,
schema.StateSupervisionViolationResponse.__tablename__:
supervision_violation_response_data,
schema.StateSupervisionPeriod.__tablename__:
supervision_periods_data,
schema.StateProgramAssignment.__tablename__:
program_assignment_data,
schema.StateAssessment.__tablename__: assessment_data,
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
}
return data_dict
def testProgramPipeline(self):
"""Tests the program pipeline."""
fake_person_id = 12345
fake_supervision_period_id = 12345
data_dict = self.build_data_dict(fake_person_id,
fake_supervision_period_id)
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(dataset, fake_supervision_period_id)
def testProgramPipelineWithFilterSet(self):
"""Tests the program pipeline."""
fake_person_id = 12345
fake_supervision_period_id = 12345
data_dict = self.build_data_dict(fake_person_id,
fake_supervision_period_id)
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(
dataset,
fake_supervision_period_id,
unifying_id_field_filter_set={fake_person_id})
def run_test_pipeline(
self,
dataset: str,
fake_supervision_period_id: int,
unifying_id_field_filter_set: Optional[Set[int]] = None,
metric_types_filter: Optional[Set[str]] = None):
"""Runs a test version of the program pipeline."""
test_pipeline = TestPipeline()
# Get StatePersons
persons = (
test_pipeline
| 'Load Persons' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StatePerson,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True))
# Get StateProgramAssignments
program_assignments = (
test_pipeline
| 'Load Program Assignments' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateProgramAssignment,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True,
unifying_id_field_filter_set=unifying_id_field_filter_set))
# Get StateAssessments
assessments = (
test_pipeline
| 'Load Assessments' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateAssessment,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=False,
unifying_id_field_filter_set=unifying_id_field_filter_set))
# Get StateSupervisionPeriods
supervision_periods = (
test_pipeline
| 'Load SupervisionPeriods' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateSupervisionPeriod,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=False,
unifying_id_field_filter_set=unifying_id_field_filter_set))
supervision_period_to_agent_map = {
'agent_id': 1010,
'agent_external_id': 'OFFICER0009',
'district_external_id': '10',
'supervision_period_id': fake_supervision_period_id
}
supervision_period_to_agent_associations = (
test_pipeline
| 'Create SupervisionPeriod to Agent table' >> beam.Create(
[supervision_period_to_agent_map]))
supervision_period_to_agent_associations_as_kv = (
supervision_period_to_agent_associations
| 'Convert SupervisionPeriod to Agent table to KV tuples' >>
beam.ParDo(pipeline.ConvertDictToKVTuple(),
'supervision_period_id'))
# Group each StatePerson with their other entities
persons_entities = ({
'person': persons,
'program_assignments': program_assignments,
'assessments': assessments,
'supervision_periods': supervision_periods
}
|
'Group StatePerson to StateProgramAssignments and'
>> beam.CoGroupByKey())
# Identify ProgramEvents from the StatePerson's
# StateProgramAssignments
person_program_events = (
persons_entities
| beam.ParDo(
pipeline.ClassifyProgramAssignments(),
AsDict(supervision_period_to_agent_associations_as_kv)))
# Get pipeline job details for accessing job_id
all_pipeline_options = PipelineOptions().get_all_options()
# Add timestamp for local jobs
job_timestamp = datetime.datetime.now().strftime(
'%Y-%m-%d_%H_%M_%S.%f')
all_pipeline_options['job_timestamp'] = job_timestamp
metric_types = metric_types_filter if metric_types_filter else {'ALL'}
# Get program metrics
program_metrics = (
person_program_events
| 'Get Program Metrics' >> # type: ignore
pipeline.GetProgramMetrics(pipeline_options=all_pipeline_options,
metric_types=metric_types,
calculation_end_month=None,
calculation_month_count=-1))
assert_that(program_metrics, AssertMatchers.validate_pipeline_test())
test_pipeline.run()
def testProgramPipelineNoReferrals(self):
"""Tests the program pipeline where one person does not have any
program assignment entities."""
fake_person_id = 12345
fake_person_id_2 = 9876
fake_person = schema.StatePerson(
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
fake_person_2 = schema.StatePerson(
person_id=fake_person_id_2,
gender=Gender.MALE,
birthdate=date(1974, 3, 12),
residency_status=ResidencyStatus.PERMANENT)
persons_data = normalized_database_base_dict_list(
[fake_person, fake_person_2])
race_1 = schema.StatePersonRace(person_race_id=111,
state_code='CA',
race=Race.BLACK,
person_id=fake_person_id)
race_2 = schema.StatePersonRace(person_race_id=111,
state_code='ND',
race=Race.WHITE,
person_id=fake_person_id)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(person_ethnicity_id=111,
state_code='CA',
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
# Program assignment for a different person
program_assignment = schema.StateProgramAssignment(
state_code='CA',
program_assignment_id=123,
referral_date=date(2015, 5, 10),
person_id=fake_person_id_2)
assessment = schema.StateAssessment(assessment_id=298374,
assessment_date=date(2015, 3, 19),
assessment_type='LSIR',
person_id=fake_person_id)
supervision_period = schema.StateSupervisionPeriod(
supervision_period_id=1111,
state_code='CA',
county_code='124',
start_date=date(2015, 3, 14),
termination_date=date(2016, 12, 29),
supervision_type=StateSupervisionType.PROBATION,
person_id=fake_person_id)
program_assignment_data = [
normalized_database_base_dict(program_assignment)
]
assessment_data = [normalized_database_base_dict(assessment)]
supervision_periods_data = [
normalized_database_base_dict(supervision_period)
]
supervision_violation_response = \
database_test_utils.generate_test_supervision_violation_response(
fake_person_id)
supervision_violation_response_data = [
normalized_database_base_dict(supervision_violation_response)
]
data_dict = {
schema.StatePerson.__tablename__: persons_data,
schema.StatePersonRace.__tablename__: races_data,
schema.StatePersonEthnicity.__tablename__: ethnicity_data,
schema.StateSupervisionViolationResponse.__tablename__:
supervision_violation_response_data,
schema.StateSupervisionPeriod.__tablename__:
supervision_periods_data,
schema.StateProgramAssignment.__tablename__:
program_assignment_data,
schema.StateAssessment.__tablename__: assessment_data,
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
}
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(dataset,
supervision_period.supervision_period_id)
class TestProgramPipeline(unittest.TestCase):
"""Tests the entire program pipeline."""
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.fake_bq_sink_factory = FakeWriteToBigQueryFactory(FakeWriteToBigQuery)
self.assessment_types_patcher = mock.patch(
"recidiviz.calculator.pipeline.program.identifier.assessment_utils."
"_assessment_types_of_class_for_state"
)
self.mock_assessment_types = self.assessment_types_patcher.start()
self.mock_assessment_types.return_value = [StateAssessmentType.ORAS]
def tearDown(self) -> None:
self.assessment_types_patcher.stop()
@staticmethod
def build_data_dict(fake_person_id: int, fake_supervision_period_id: int):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.BLACK,
person_id=fake_person_id,
)
race_2 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.WHITE,
person_id=fake_person_id,
)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(
person_ethnicity_id=111,
state_code="US_XX",
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id,
)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
program_assignment = schema.StateProgramAssignment(
state_code="US_XX",
program_assignment_id=123,
referral_date=date(2015, 5, 10),
person_id=fake_person_id,
participation_status=StateProgramAssignmentParticipationStatus.IN_PROGRESS,
)
assessment = schema.StateAssessment(
assessment_id=298374,
state_code="US_XX",
assessment_date=date(2015, 3, 19),
assessment_type="LSIR",
person_id=fake_person_id,
)
supervision_period = schema.StateSupervisionPeriod(
supervision_period_id=fake_supervision_period_id,
state_code="US_XX",
county_code="124",
start_date=date(2015, 3, 14),
termination_date=date(2016, 12, 29),
supervision_type=StateSupervisionType.PROBATION,
person_id=fake_person_id,
status=StateSupervisionPeriodStatus.PRESENT_WITHOUT_INFO,
)
program_assignment_data = [normalized_database_base_dict(program_assignment)]
assessment_data = [normalized_database_base_dict(assessment)]
supervision_periods_data = [normalized_database_base_dict(supervision_period)]
supervision_violation_response = (
database_test_utils.generate_test_supervision_violation_response(
fake_person_id
)
)
supervision_violation_response_data = [
normalized_database_base_dict(supervision_violation_response)
]
supervision_period_to_agent_data = [
{
"agent_id": 1010,
"person_id": fake_person_id,
"state_code": "US_XX",
"agent_external_id": "OFFICER0009",
"supervision_period_id": fake_supervision_period_id,
}
]
state_race_ethnicity_population_count_data = [
{
"state_code": "US_XX",
"race_or_ethnicity": "BLACK",
"population_count": 1,
"representation_priority": 1,
}
]
data_dict = {
schema.StatePerson.__tablename__: persons_data,
schema.StatePersonRace.__tablename__: races_data,
schema.StatePersonEthnicity.__tablename__: ethnicity_data,
schema.StateSupervisionViolationResponse.__tablename__: supervision_violation_response_data,
schema.StateSupervisionPeriod.__tablename__: supervision_periods_data,
schema.StateProgramAssignment.__tablename__: program_assignment_data,
schema.StateAssessment.__tablename__: assessment_data,
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
"supervision_period_to_agent_association": supervision_period_to_agent_data,
"state_race_ethnicity_population_counts": state_race_ethnicity_population_count_data,
}
return data_dict
def testProgramPipeline(self):
"""Tests the program pipeline."""
fake_person_id = 12345
fake_supervision_period_id = 12345
data_dict = self.build_data_dict(fake_person_id, fake_supervision_period_id)
dataset = "recidiviz-123.state"
self.run_test_pipeline(dataset, data_dict)
def testProgramPipelineWithFilterSet(self):
"""Tests the program pipeline."""
fake_person_id = 12345
fake_supervision_period_id = 12345
data_dict = self.build_data_dict(fake_person_id, fake_supervision_period_id)
dataset = "recidiviz-123.state"
self.run_test_pipeline(
dataset, data_dict, unifying_id_field_filter_set={fake_person_id}
)
def run_test_pipeline(
self,
dataset: str,
data_dict: DataTablesDict,
unifying_id_field_filter_set: Optional[Set[int]] = None,
metric_types_filter: Optional[Set[str]] = None,
):
"""Runs a test version of the program pipeline."""
expected_metric_types = {
ProgramMetricType.PROGRAM_REFERRAL,
}
read_from_bq_constructor = (
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict
)
)
write_to_bq_constructor = (
self.fake_bq_sink_factory.create_fake_bq_sink_constructor(
dataset,
expected_output_metric_types=expected_metric_types,
)
)
run_test_pipeline(
pipeline_module=pipeline,
state_code="US_XX",
dataset=dataset,
read_from_bq_constructor=read_from_bq_constructor,
write_to_bq_constructor=write_to_bq_constructor,
unifying_id_field_filter_set=unifying_id_field_filter_set,
metric_types_filter=metric_types_filter,
)
def testProgramPipelineNoReferrals(self):
"""Tests the program pipeline where one person does not have any
program assignment entities."""
fake_person_id = 12345
fake_person_id_2 = 9876
fake_person = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
fake_person_2 = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id_2,
gender=Gender.MALE,
birthdate=date(1974, 3, 12),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = normalized_database_base_dict_list([fake_person, fake_person_2])
race_1 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.BLACK,
person_id=fake_person_id,
)
race_2 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.WHITE,
person_id=fake_person_id,
)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(
person_ethnicity_id=111,
state_code="US_XX",
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id,
)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
# Program assignment for a different person
program_assignment = schema.StateProgramAssignment(
state_code="US_XX",
program_assignment_id=123,
referral_date=date(2015, 5, 10),
person_id=fake_person_id_2,
participation_status=StateProgramAssignmentParticipationStatus.DENIED,
)
assessment = schema.StateAssessment(
assessment_id=298374,
state_code="US_XX",
assessment_date=date(2015, 3, 19),
assessment_type="LSIR",
person_id=fake_person_id,
)
supervision_period = schema.StateSupervisionPeriod(
supervision_period_id=1111,
state_code="US_XX",
county_code="124",
start_date=date(2015, 3, 14),
termination_date=date(2016, 12, 29),
supervision_type=StateSupervisionType.PROBATION,
person_id=fake_person_id,
status=StateSupervisionPeriodStatus.PRESENT_WITHOUT_INFO,
)
program_assignment_data = [normalized_database_base_dict(program_assignment)]
assessment_data = [normalized_database_base_dict(assessment)]
supervision_periods_data = [normalized_database_base_dict(supervision_period)]
supervision_violation_response = (
database_test_utils.generate_test_supervision_violation_response(
fake_person_id
)
)
supervision_violation_response_data = [
normalized_database_base_dict(supervision_violation_response)
]
supervision_period_to_agent_data = [
{
"agent_id": 1010,
"person_id": fake_person_id,
"state_code": "US_XX",
"agent_external_id": "OFFICER0009",
"supervision_period_id": supervision_period.supervision_period_id,
}
]
state_race_ethnicity_population_count_data = [
{
"state_code": "US_XX",
"race_or_ethnicity": "BLACK",
"population_count": 1,
"representation_priority": 1,
}
]
data_dict = {
schema.StatePerson.__tablename__: persons_data,
schema.StatePersonRace.__tablename__: races_data,
schema.StatePersonEthnicity.__tablename__: ethnicity_data,
schema.StateSupervisionViolationResponse.__tablename__: supervision_violation_response_data,
schema.StateSupervisionPeriod.__tablename__: supervision_periods_data,
schema.StateProgramAssignment.__tablename__: program_assignment_data,
schema.StateAssessment.__tablename__: assessment_data,
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
"supervision_period_to_agent_association": supervision_period_to_agent_data,
"state_race_ethnicity_population_counts": state_race_ethnicity_population_count_data,
}
dataset = "recidiviz-123.state"
self.run_test_pipeline(dataset, data_dict)
class TestIncarcerationPipeline(unittest.TestCase):
"""Tests the entire incarceration pipeline."""
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
@staticmethod
def _default_data_dict():
return {
schema.StatePerson.__tablename__: [],
schema.StatePersonRace.__tablename__: [],
schema.StatePersonEthnicity.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
schema.StateIncarcerationSentence.__tablename__: [],
schema.StateSupervisionSentence.__tablename__: [],
schema.StateIncarcerationPeriod.__tablename__: [],
schema.state_incarceration_sentence_incarceration_period_association_table.name:
[],
schema.state_supervision_sentence_incarceration_period_association_table.name:
[],
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateAssessment.__tablename__: [],
schema.StateProgramAssignment.__tablename__: [],
schema.StateFine.__tablename__: [],
schema.StateCharge.__tablename__: [],
schema.StateSupervisionPeriod.__tablename__: [],
schema.StateEarlyDischarge.__tablename__: [],
schema.state_charge_incarceration_sentence_association_table.name:
[],
schema.state_charge_supervision_sentence_association_table.name:
[],
schema.state_incarceration_sentence_supervision_period_association_table.name:
[],
schema.state_supervision_sentence_supervision_period_association_table.name:
[],
}
def build_incarceration_pipeline_data_dict(self,
fake_person_id: int,
state_code: str = 'US_XX'):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code=state_code,
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(person_race_id=111,
state_code=state_code,
race=Race.BLACK,
person_id=fake_person_id)
race_2 = schema.StatePersonRace(person_race_id=111,
state_code=state_code,
race=Race.WHITE,
person_id=fake_person_id)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(person_ethnicity_id=111,
state_code=state_code,
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
sentence_group = schema.StateSentenceGroup(sentence_group_id=111,
person_id=fake_person_id)
initial_incarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
first_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=2222,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2011, 4, 5),
release_date=date(2014, 4, 14),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id)
subsequent_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=3333,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2017, 1, 4),
person_id=fake_person_id)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code=state_code,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[
initial_incarceration, first_reincarceration,
subsequent_reincarceration
],
person_id=fake_person_id)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code=state_code,
person_id=fake_person_id)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(initial_incarceration),
normalized_database_base_dict(first_reincarceration),
normalized_database_base_dict(subsequent_reincarceration)
]
state_incarceration_sentence_incarceration_period_association = [
{
'incarceration_period_id':
initial_incarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
first_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
subsequent_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
}
data_dict.update(data_dict_overrides)
return data_dict
def testIncarcerationPipeline(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(fake_person_id,
_STATE_CODE,
dataset,
expected_metric_types=ALL_METRIC_TYPES_SET)
def testIncarcerationPipelineFilterMetrics(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = 'recidiviz-123.state'
expected_metric_types = {
IncarcerationMetricType.INCARCERATION_ADMISSION
}
metric_types_filter = {
IncarcerationMetricType.INCARCERATION_ADMISSION.value
}
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(fake_person_id,
_STATE_CODE,
dataset,
expected_metric_types=expected_metric_types,
metric_types_filter=metric_types_filter)
def testIncarcerationPipelineUsMo(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id, state_code='US_MO')
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(fake_person_id,
'US_MO',
dataset,
expected_metric_types=ALL_METRIC_TYPES_SET)
def testIncarcerationPipelineWithFilterSet(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = 'recidivz-staging.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(
fake_person_id,
_STATE_CODE,
dataset,
unifying_id_field_filter_set={fake_person_id},
expected_metric_types=ALL_METRIC_TYPES_SET)
# TODO(#4375): Update tests to run actual pipeline code and only mock BQ I/O
@staticmethod
def run_test_pipeline(
fake_person_id: int,
state_code: str,
dataset: str,
expected_metric_types: Set[IncarcerationMetricType],
allow_empty: bool = False,
unifying_id_field_filter_set: Optional[Set[int]] = None,
metric_types_filter: Optional[Set[str]] = None):
"""Runs a test version of the incarceration pipeline."""
test_pipeline = TestPipeline()
# Get StatePersons
persons = (
test_pipeline
| 'Load Persons' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StatePerson,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True))
# Get StateSentenceGroups
sentence_groups = (
test_pipeline
| 'Load StateSentenceGroups' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateSentenceGroup,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True,
unifying_id_field_filter_set=unifying_id_field_filter_set))
# Get StateIncarcerationSentences
incarceration_sentences = (
test_pipeline
| 'Load StateIncarcerationSentences' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateIncarcerationSentence,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True,
unifying_id_field_filter_set=unifying_id_field_filter_set))
# Get StateSupervisionSentences
supervision_sentences = (
test_pipeline | 'Load StateSupervisionSentences' >> # type: ignore
extractor_utils.BuildRootEntity(
dataset=dataset,
root_entity_class=entities.StateSupervisionSentence,
unifying_id_field=entities.StatePerson.get_class_id_name(),
build_related_entities=True,
unifying_id_field_filter_set=unifying_id_field_filter_set))
us_mo_sentence_status_rows: List[Dict[str, Any]] = [{
'person_id':
fake_person_id,
'sentence_external_id':
'XXX',
'sentence_status_external_id':
'YYY',
'status_code':
'ZZZ',
'status_date':
'not_a_date',
'status_description':
'XYZ'
}]
us_mo_sentence_statuses = (test_pipeline
| 'Create MO sentence statuses' >>
beam.Create(us_mo_sentence_status_rows))
us_mo_sentence_status_rankings_as_kv = (
us_mo_sentence_statuses
| 'Convert sentence status ranking table to KV tuples' >>
beam.ParDo(ConvertDictToKVTuple(), 'person_id'))
sentences_and_statuses = (
{
'incarceration_sentences': incarceration_sentences,
'supervision_sentences': supervision_sentences,
'sentence_statuses': us_mo_sentence_status_rankings_as_kv
}
| 'Group sentences to the sentence statuses for that person' >>
beam.CoGroupByKey())
sentences_converted = (
sentences_and_statuses
| 'Convert to state-specific sentences' >> beam.ParDo(
ConvertSentencesToStateSpecificType()).with_outputs(
'incarceration_sentences', 'supervision_sentences'))
sentences_and_sentence_groups = (
{
'sentence_groups': sentence_groups,
'incarceration_sentences':
sentences_converted.incarceration_sentences,
'supervision_sentences':
sentences_converted.supervision_sentences
}
| 'Group sentences to sentence groups' >> beam.CoGroupByKey())
sentence_groups_with_hydrated_sentences = (
sentences_and_sentence_groups
| 'Set hydrated sentences on sentence groups' >> beam.ParDo(
SetSentencesOnSentenceGroup()))
# Identify IncarcerationEvents events from the StatePerson's
# StateIncarcerationPeriods
fake_person_id_to_county_query_result = [{
'person_id':
fake_person_id,
'county_of_residence':
_COUNTY_OF_RESIDENCE
}]
person_id_to_county_kv = (
test_pipeline
| "Read person id to county associations from BigQuery" >>
beam.Create(fake_person_id_to_county_query_result)
| "Convert person_id to counties to KV" >> beam.ParDo(
ConvertDictToKVTuple(), 'person_id'))
incarceration_period_judicial_district_association_row = \
{'person_id': fake_person_id, 'incarceration_period_id': 123, 'judicial_district_code': 'NW'}
ip_to_judicial_district_kv = (
test_pipeline
|
"Read incarceration_period to judicial_district associations from BigQuery"
>> beam.Create(
[incarceration_period_judicial_district_association_row])
| "Convert ips to judicial districts to KV" >> beam.ParDo(
ConvertDictToKVTuple(), 'person_id'))
state_race_ethnicity_population_count = {
'state_code': state_code,
'race_or_ethnicity': 'BLACK',
'population_count': 1,
'representation_priority': 1
}
state_race_ethnicity_population_counts = (
test_pipeline
| 'Create state_race_ethnicity_population_count table' >>
beam.Create([state_race_ethnicity_population_count]))
# Group each StatePerson with their related entities
person_entities = (
{
'person':
persons,
'sentence_groups':
sentence_groups_with_hydrated_sentences,
'incarceration_period_judicial_district_association':
ip_to_judicial_district_kv
}
| 'Group StatePerson to SentenceGroups' >> beam.CoGroupByKey())
# Identify IncarcerationEvents events from the StatePerson's StateIncarcerationPeriods
person_incarceration_events = (
person_entities | 'Classify Incarceration Events' >> beam.ParDo(
pipeline.ClassifyIncarcerationEvents(),
AsDict(person_id_to_county_kv)))
person_metadata = (
persons
| "Build the person_metadata dictionary" >> beam.ParDo(
BuildPersonMetadata(),
AsList(state_race_ethnicity_population_counts)))
person_incarceration_events_with_metadata = (
{
'person_events': person_incarceration_events,
'person_metadata': person_metadata
}
| 'Group IncarcerationEvents with person-level metadata' >>
beam.CoGroupByKey()
|
'Organize StatePerson, PersonMetadata and IncarcerationEvents for calculations'
>> beam.ParDo(ExtractPersonEventsMetadata()))
# Get pipeline job details for accessing job_id
all_pipeline_options = PipelineOptions().get_all_options()
# Add timestamp for local jobs
job_timestamp = datetime.datetime.now().strftime(
'%Y-%m-%d_%H_%M_%S.%f')
all_pipeline_options['job_timestamp'] = job_timestamp
metric_types = metric_types_filter if metric_types_filter else {'ALL'}
# Get IncarcerationMetrics
incarceration_metrics = (
person_incarceration_events_with_metadata
| 'Get Incarceration Metrics' >> # type: ignore
pipeline.GetIncarcerationMetrics(
pipeline_options=all_pipeline_options,
metric_types=metric_types,
calculation_end_month=None,
calculation_month_count=-1))
assert_that(
incarceration_metrics,
AssertMatchers.validate_metric_type(allow_empty=allow_empty),
'Assert that all metrics are of the expected type.')
assert_that(
incarceration_metrics,
AssertMatchers.validate_pipeline_test(expected_metric_types),
'Assert the type of metrics produced are expected')
test_pipeline.run()
def build_incarceration_pipeline_data_dict_no_incarceration(
self, fake_person_id: int):
"""Builds a data_dict for a run of the pipeline where the person has no incarceration."""
fake_person_1 = schema.StatePerson(
state_code='US_XX',
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
fake_person_id_2 = 6789
fake_person_2 = schema.StatePerson(
state_code='US_XX',
person_id=fake_person_id_2,
gender=Gender.FEMALE,
birthdate=date(1990, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [
normalized_database_base_dict(fake_person_1),
normalized_database_base_dict(fake_person_2)
]
sentence_group = schema.StateSentenceGroup(sentence_group_id=111,
person_id=fake_person_id)
incarceration_period = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code='US_XX',
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[incarceration_period],
person_id=fake_person_id)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123, person_id=fake_person_id)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(incarceration_period)
]
state_incarceration_sentence_incarceration_period_association = [
{
'incarceration_period_id':
incarceration_period.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
}
data_dict.update(data_dict_overrides)
return data_dict
def testIncarcerationPipelineNoIncarceration(self):
"""Tests the incarceration pipeline when a person doesn't have any
incarceration periods."""
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict_no_incarceration(
fake_person_id)
dataset = 'recidiviz-123.state'
with patch(
'recidiviz.calculator.pipeline.utils.extractor_utils.ReadFromBigQuery',
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict)):
self.run_test_pipeline(fake_person_id,
_STATE_CODE,
dataset,
expected_metric_types=set(),
allow_empty=True)
class TestViolationPipeline(unittest.TestCase):
"""Tests the entire violation pipeline."""
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.fake_bq_sink_factory = FakeWriteToBigQueryFactory(
FakeWriteToBigQuery)
self.violation_delegate_patcher = mock.patch(
"recidiviz.calculator.pipeline.violation.identifier.get_state_specific_violation_delegate"
)
self.mock_violation_delegate = self.violation_delegate_patcher.start()
self.mock_violation_delegate.return_value = UsXxViolationDelegate()
def tearDown(self) -> None:
self.violation_delegate_patcher.stop()
@staticmethod
def build_data_dict(
fake_person_id: int,
fake_supervision_violation_id: int) -> Dict[str, List[Any]]:
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id,
gender=Gender.FEMALE,
birthdate=date(1985, 2, 1),
)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.ASIAN,
person_id=fake_person_id,
)
race_2 = schema.StatePersonRace(
person_race_id=111,
state_code="US_XX",
race=Race.AMERICAN_INDIAN_ALASKAN_NATIVE,
person_id=fake_person_id,
)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(
person_ethnicity_id=111,
state_code="US_XX",
ethnicity=Ethnicity.NOT_HISPANIC,
person_id=fake_person_id,
)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
violation_type = schema.StateSupervisionViolationTypeEntry(
state_code="US_XX",
violation_type=StateSupervisionViolationType.FELONY,
person_id=fake_person_id,
)
incomplete_response = schema.StateSupervisionViolationResponse(
state_code="US_XX",
supervision_violation_response_id=1234,
response_type=entities.StateSupervisionViolationResponseType.
VIOLATION_REPORT,
response_date=date(2021, 1, 4),
is_draft=False,
person_id=fake_person_id,
)
violation = schema.StateSupervisionViolation(
state_code="US_XX",
supervision_violation_id=fake_supervision_violation_id,
violation_date=date(2021, 1, 1),
is_violent=False,
is_sex_offense=False,
supervision_violation_types=[violation_type],
supervision_violation_responses=[incomplete_response],
person_id=fake_person_id,
)
incomplete_response.supervision_violation_id = fake_supervision_violation_id
violation_type.supervision_violation_id = fake_supervision_violation_id
violation_decision = schema.StateSupervisionViolationResponseDecisionEntry(
state_code="US_XX",
decision=StateSupervisionViolationResponseDecision.
SHOCK_INCARCERATION,
person_id=fake_person_id,
supervision_violation_response_decision_entry_id=234,
supervision_violation_response_id=1234,
)
complete_violation_response = schema.StateSupervisionViolationResponse(
state_code="US_XX",
supervision_violation_response_id=1234,
response_type=entities.StateSupervisionViolationResponseType.
VIOLATION_REPORT,
response_date=date(2021, 1, 4),
is_draft=False,
supervision_violation_response_decisions=[violation_decision],
person_id=fake_person_id,
)
complete_violation_response.supervision_violation_id = (
fake_supervision_violation_id)
violations_data = [normalized_database_base_dict(violation)]
violation_responses_data = [
normalized_database_base_dict(complete_violation_response)
]
violation_types_data = [normalized_database_base_dict(violation_type)]
violation_decisions_data = [
normalized_database_base_dict(violation_decision)
]
state_race_ethnicity_population_count_data = [{
"state_code":
"US_XX",
"race_or_ethnicity":
"ASIAN",
"population_count":
1,
"representation_priority":
1,
}]
data_dict: Dict[str, List[Any]] = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSupervisionViolation.__tablename__:
violations_data,
schema.StateSupervisionViolationResponse.__tablename__:
violation_responses_data,
schema.StateSupervisionViolationTypeEntry.__tablename__:
violation_types_data,
schema.StateSupervisionViolatedConditionEntry.__tablename__: [],
schema.StateSupervisionViolationResponseDecisionEntry.__tablename__:
violation_decisions_data,
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateAssessment.__tablename__: [],
schema.StateProgramAssignment.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
"state_race_ethnicity_population_counts":
state_race_ethnicity_population_count_data,
}
return data_dict
def run_test_pipeline(
self,
dataset: str,
data_dict: DataTablesDict,
expected_metric_types: Set[ViolationMetricType],
unifying_id_field_filter_set: Optional[Set[int]] = None,
metric_types_filter: Optional[Set[str]] = None,
) -> None:
"""Runs a test version of the violation pipeline."""
read_from_bq_constructor = (
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict))
write_to_bq_constructor = (
self.fake_bq_sink_factory.create_fake_bq_sink_constructor(
dataset, expected_output_metric_types=expected_metric_types))
run_test_pipeline(
pipeline=ViolationPipeline(),
state_code="US_XX",
dataset=dataset,
read_from_bq_constructor=read_from_bq_constructor,
write_to_bq_constructor=write_to_bq_constructor,
unifying_id_field_filter_set=unifying_id_field_filter_set,
metric_types_filter=metric_types_filter,
)
def testViolationPipeline(self) -> None:
"""Tests the violaitons pipeline."""
data_dict = self.build_data_dict(fake_person_id=12345,
fake_supervision_violation_id=23456)
dataset = "recidiviz-123.state"
self.run_test_pipeline(
dataset,
data_dict,
expected_metric_types={ViolationMetricType.VIOLATION})
def testViolationPipelineWithFilterSet(self) -> None:
"""Tests the violation pipeline with a proper filter set."""
data_dict = self.build_data_dict(fake_person_id=12345,
fake_supervision_violation_id=23456)
dataset = "recidiviz-123.state"
self.run_test_pipeline(
dataset,
data_dict,
expected_metric_types={ViolationMetricType.VIOLATION},
unifying_id_field_filter_set={12345},
)
def testViolationPipelineWithNoViolations(self) -> None:
"""Tests the violation pipeline when a person does not have any violations."""
data_dict = self.build_data_dict(fake_person_id=12345,
fake_supervision_violation_id=23456)
data_dict[schema.StateSupervisionViolation.__tablename__] = []
data_dict[schema.StateSupervisionViolationResponse.__tablename__] = []
data_dict[schema.StateSupervisionViolationTypeEntry.__tablename__] = []
data_dict[schema.StateSupervisionViolationResponseDecisionEntry.
__tablename__] = []
dataset = "recidiviz-123.state"
self.run_test_pipeline(
dataset,
data_dict,
expected_metric_types=set(),
)
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.fake_bq_sink_factory = FakeWriteToBigQueryFactory(
FakeWriteToBigQuery)
class TestIncarcerationPipeline(unittest.TestCase):
"""Tests the entire incarceration pipeline."""
def setUp(self) -> None:
self.fake_bq_source_factory = FakeReadFromBigQueryFactory()
self.fake_bq_sink_factory = FakeWriteToBigQueryFactory(
FakeWriteToBigQuery)
@staticmethod
def _default_data_dict():
return {
schema.StatePerson.__tablename__: [],
schema.StatePersonRace.__tablename__: [],
schema.StatePersonEthnicity.__tablename__: [],
schema.StateSentenceGroup.__tablename__: [],
schema.StateIncarcerationSentence.__tablename__: [],
schema.StateSupervisionSentence.__tablename__: [],
schema.StateIncarcerationPeriod.__tablename__: [],
schema.state_incarceration_sentence_incarceration_period_association_table.name:
[],
schema.state_supervision_sentence_incarceration_period_association_table.name:
[],
schema.StatePersonExternalId.__tablename__: [],
schema.StatePersonAlias.__tablename__: [],
schema.StateAssessment.__tablename__: [],
schema.StateProgramAssignment.__tablename__: [],
schema.StateFine.__tablename__: [],
schema.StateCharge.__tablename__: [],
schema.StateSupervisionPeriod.__tablename__: [],
schema.StateEarlyDischarge.__tablename__: [],
schema.state_charge_incarceration_sentence_association_table.name:
[],
schema.state_charge_supervision_sentence_association_table.name:
[],
schema.state_incarceration_sentence_supervision_period_association_table.name:
[],
schema.state_supervision_sentence_supervision_period_association_table.name:
[],
}
def build_incarceration_pipeline_data_dict(self,
fake_person_id: int,
state_code: str = "US_XX"):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code=state_code,
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(
person_race_id=111,
state_code=state_code,
race=Race.BLACK,
person_id=fake_person_id,
)
race_2 = schema.StatePersonRace(
person_race_id=111,
state_code=state_code,
race=Race.WHITE,
person_id=fake_person_id,
)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(
person_ethnicity_id=111,
state_code=state_code,
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id,
)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
sentence_group = schema.StateSentenceGroup(
sentence_group_id=98765,
state_code=state_code,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
person_id=fake_person_id,
)
initial_incarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
first_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=2222,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2011, 4, 5),
release_date=date(2014, 4, 14),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
subsequent_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=3333,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2017, 1, 4),
person_id=fake_person_id,
)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code=state_code,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[
initial_incarceration,
first_reincarceration,
subsequent_reincarceration,
],
person_id=fake_person_id,
)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code=state_code,
sentence_group_id=sentence_group.sentence_group_id,
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group.supervision_sentences = [supervision_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(initial_incarceration),
normalized_database_base_dict(first_reincarceration),
normalized_database_base_dict(subsequent_reincarceration),
]
state_incarceration_sentence_incarceration_period_association = [
{
"incarceration_period_id":
initial_incarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
{
"incarceration_period_id":
first_reincarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
{
"incarceration_period_id":
subsequent_reincarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
]
fake_person_id_to_county_query_result = [{
"state_code":
state_code,
"person_id":
fake_person_id,
"county_of_residence":
_COUNTY_OF_RESIDENCE,
}]
us_mo_sentence_status_data: List[Dict[str, Any]] = [{
"state_code":
"US_MO",
"person_id":
fake_person_id,
"sentence_external_id":
"XXX",
"sentence_status_external_id":
"YYY",
"status_code":
"ZZZ",
"status_date":
"not_a_date",
"status_description":
"XYZ",
}]
incarceration_period_judicial_district_association_data = [{
"state_code":
state_code,
"person_id":
fake_person_id,
"incarceration_period_id":
123,
"judicial_district_code":
"NW",
}]
state_race_ethnicity_population_count_data = [{
"state_code":
state_code,
"race_or_ethnicity":
"BLACK",
"population_count":
1,
"representation_priority":
1,
}]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
"persons_to_recent_county_of_residence":
fake_person_id_to_county_query_result,
"incarceration_period_judicial_district_association":
incarceration_period_judicial_district_association_data,
"state_race_ethnicity_population_counts":
state_race_ethnicity_population_count_data,
"us_mo_sentence_statuses":
us_mo_sentence_status_data,
}
data_dict.update(data_dict_overrides)
return data_dict
@freeze_time("2015-01-31")
def testIncarcerationPipeline(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = "recidiviz-123.state"
self.run_test_pipeline(
state_code=_STATE_CODE,
dataset=dataset,
data_dict=data_dict,
expected_metric_types=ALL_METRIC_TYPES_SET,
)
@freeze_time("2015-01-31")
def testIncarcerationPipelineFilterMetrics(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = "recidiviz-123.state"
expected_metric_types = {
IncarcerationMetricType.INCARCERATION_ADMISSION
}
metric_types_filter = {
IncarcerationMetricType.INCARCERATION_ADMISSION.value
}
self.run_test_pipeline(
state_code=_STATE_CODE,
dataset=dataset,
data_dict=data_dict,
expected_metric_types=expected_metric_types,
metric_types_filter=metric_types_filter,
)
def testIncarcerationPipelineUsMo(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id, state_code="US_MO")
dataset = "recidiviz-123.state"
self.run_test_pipeline(
state_code="US_MO",
dataset=dataset,
data_dict=data_dict,
expected_metric_types=ALL_METRIC_TYPES_SET,
)
def testIncarcerationPipelineWithFilterSet(self):
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict(
fake_person_id=fake_person_id)
dataset = "recidivz-staging.state"
self.run_test_pipeline(
state_code=_STATE_CODE,
dataset=dataset,
data_dict=data_dict,
expected_metric_types=ALL_METRIC_TYPES_SET,
unifying_id_field_filter_set={fake_person_id},
)
def run_test_pipeline(
self,
state_code: str,
dataset: str,
data_dict: Dict[str, List[Dict]],
expected_metric_types: Set[IncarcerationMetricType],
unifying_id_field_filter_set: Optional[Set[int]] = None,
metric_types_filter: Optional[Set[str]] = None,
) -> None:
"""Runs a test version of the supervision pipeline."""
read_from_bq_constructor = (
self.fake_bq_source_factory.create_fake_bq_source_constructor(
dataset, data_dict))
write_to_bq_constructor = (
self.fake_bq_sink_factory.create_fake_bq_sink_constructor(
dataset,
expected_output_metric_types=expected_metric_types,
))
with patch(
f"{INCARCERATION_PIPELINE_PACKAGE_NAME}.ReadFromBigQuery",
read_from_bq_constructor,
):
run_test_pipeline(
pipeline_module=pipeline,
state_code=state_code,
dataset=dataset,
read_from_bq_constructor=read_from_bq_constructor,
write_to_bq_constructor=write_to_bq_constructor,
unifying_id_field_filter_set=unifying_id_field_filter_set,
metric_types_filter=metric_types_filter,
)
def build_incarceration_pipeline_data_dict_no_incarceration(
self, fake_person_id: int):
"""Builds a data_dict for a run of the pipeline where the person has no incarceration."""
fake_person_1 = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
fake_person_id_2 = 6789
fake_person_2 = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id_2,
gender=Gender.FEMALE,
birthdate=date(1990, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = [
normalized_database_base_dict(fake_person_1),
normalized_database_base_dict(fake_person_2),
]
sentence_group = schema.StateSentenceGroup(
sentence_group_id=111,
state_code="US_XX",
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
person_id=fake_person_id,
)
incarceration_period = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code="US_XX",
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code="US_XX",
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[incarceration_period],
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code="US_XX",
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(incarceration_period)
]
state_incarceration_sentence_incarceration_period_association = [
{
"incarceration_period_id":
incarceration_period.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
]
fake_person_id_to_county_query_result = [{
"state_code":
"US_XX",
"person_id":
fake_person_id,
"county_of_residence":
_COUNTY_OF_RESIDENCE,
}]
us_mo_sentence_status_data: List[Dict[str, Any]] = [{
"state_code":
"US_MO",
"person_id":
fake_person_id,
"sentence_external_id":
"XXX",
"sentence_status_external_id":
"YYY",
"status_code":
"ZZZ",
"status_date":
"not_a_date",
"status_description":
"XYZ",
}]
incarceration_period_judicial_district_association_data = [{
"state_code":
"US_XX",
"person_id":
fake_person_id,
"incarceration_period_id":
123,
"judicial_district_code":
"NW",
}]
state_race_ethnicity_population_count_data = [{
"state_code":
"US_XX",
"race_or_ethnicity":
"BLACK",
"population_count":
1,
"representation_priority":
1,
}]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
"persons_to_recent_county_of_residence":
fake_person_id_to_county_query_result,
"incarceration_period_judicial_district_association":
incarceration_period_judicial_district_association_data,
"state_race_ethnicity_population_counts":
state_race_ethnicity_population_count_data,
"us_mo_sentence_statuses":
us_mo_sentence_status_data,
}
data_dict.update(data_dict_overrides)
return data_dict
def testIncarcerationPipelineNoIncarceration(self):
"""Tests the incarceration pipeline when a person doesn't have any
incarceration periods."""
fake_person_id = 12345
data_dict = self.build_incarceration_pipeline_data_dict_no_incarceration(
fake_person_id)
dataset = "recidiviz-123.state"
self.run_test_pipeline(_STATE_CODE,
dataset,
data_dict,
expected_metric_types=set())
