def test_include_assessment_in_metric(self):
pipeline = 'supervision'
state_code = 'US_MO'
assessment_type = StateAssessmentType.ORAS_COMMUNITY_SUPERVISION
include_assessment = assessment_utils.include_assessment_in_metric(pipeline, state_code, assessment_type)
self.assertTrue(include_assessment)
assessment_type = StateAssessmentType.ORAS_COMMUNITY_SUPERVISION_SCREENING
include_assessment = assessment_utils.include_assessment_in_metric(pipeline, state_code, assessment_type)
self.assertTrue(include_assessment)
def test_include_assessment_in_metric_unsupported_state_code(self):
pipeline = 'supervision'
state_code = 'US_XX'
assessment_type = StateAssessmentType.ORAS_MISDEMEANOR_ASSESSMENT
include_assessment = assessment_utils.include_assessment_in_metric(pipeline, state_code, assessment_type)
self.assertFalse(include_assessment)
def characteristics_dict(person: StatePerson,
program_event: ProgramEvent) -> Dict[str, Any]:
"""Builds a dictionary that describes the characteristics of the person and event.
Args:
person: the StatePerson we are picking characteristics from
program_event: the ProgramEvent we are picking characteristics from
Returns:
A dictionary populated with all relevant characteristics.
"""
characteristics: Dict[str, Any] = {}
event_date = program_event.event_date
if isinstance(program_event, ProgramReferralEvent):
if program_event.supervision_type:
characteristics['supervision_type'] = program_event.supervision_type
if program_event.assessment_score and program_event.assessment_type:
assessment_bucket = assessment_score_bucket(
assessment_score=program_event.assessment_score,
assessment_level=None,
assessment_type=program_event.assessment_type)
if assessment_bucket and include_assessment_in_metric(
'program', program_event.state_code, program_event.assessment_type):
characteristics['assessment_score_bucket'] = assessment_bucket
characteristics['assessment_type'] = program_event.assessment_type
if program_event.participation_status:
characteristics['participation_status'] = program_event.participation_status
if program_event.supervising_officer_external_id:
characteristics['supervising_officer_external_id'] = program_event.supervising_officer_external_id
if program_event.supervising_district_external_id:
characteristics['supervising_district_external_id'] = program_event.supervising_district_external_id
elif isinstance(program_event, ProgramParticipationEvent):
characteristics['date_of_participation'] = event_date
if program_event.supervision_type:
characteristics['supervision_type'] = program_event.supervision_type
if program_event.program_location_id:
characteristics['program_location_id'] = program_event.program_location_id
if program_event.program_id:
characteristics['program_id'] = program_event.program_id
characteristics = add_demographic_characteristics(characteristics, person, event_date)
characteristics_with_person_details = characteristics_with_person_id_fields(
characteristics, program_event.state_code, person, 'program')
return characteristics_with_person_details
def characteristics_dict(person: StatePerson,
supervision_time_bucket: SupervisionTimeBucket,
metric_type: SupervisionMetricType) -> Dict[str, Any]:
"""Builds a dictionary that describes the characteristics of the person and supervision_time_bucket.
Args:
person: the StatePerson we are picking characteristics from
supervision_time_bucket: the SupervisionTimeBucket we are picking characteristics from
metric_type: The SupervisionMetricType provided determines which fields should be added to the characteristics
dictionary
Returns:
A dictionary populated with all relevant characteristics.
"""
characteristics: Dict[str, Any] = {}
include_revocation_dimensions = _include_revocation_dimensions_for_metric(
metric_type)
include_assessment_dimensions = _include_assessment_dimensions_for_metric(
metric_type)
include_demographic_dimensions = _include_demographic_dimensions_for_metric(
metric_type)
include_person_level_dimensions = _include_person_level_dimensions_for_metric(
metric_type)
if (metric_type == SupervisionMetricType.POPULATION
and isinstance(supervision_time_bucket,
(RevocationReturnSupervisionTimeBucket,
NonRevocationReturnSupervisionTimeBucket))):
if supervision_time_bucket.most_severe_violation_type:
characteristics[
'most_severe_violation_type'] = supervision_time_bucket.most_severe_violation_type
if supervision_time_bucket.most_severe_violation_type_subtype:
characteristics['most_severe_violation_type_subtype'] = \
supervision_time_bucket.most_severe_violation_type_subtype
if supervision_time_bucket.response_count is not None:
characteristics[
'response_count'] = supervision_time_bucket.response_count
if include_revocation_dimensions and \
isinstance(supervision_time_bucket,
RevocationReturnSupervisionTimeBucket):
if supervision_time_bucket.revocation_type:
characteristics[
'revocation_type'] = supervision_time_bucket.revocation_type
if supervision_time_bucket.source_violation_type:
characteristics[
'source_violation_type'] = supervision_time_bucket.source_violation_type
if metric_type in [
SupervisionMetricType.REVOCATION_ANALYSIS,
SupervisionMetricType.REVOCATION_VIOLATION_TYPE_ANALYSIS
]:
if supervision_time_bucket.most_severe_violation_type:
characteristics[
'most_severe_violation_type'] = supervision_time_bucket.most_severe_violation_type
if supervision_time_bucket.most_severe_violation_type_subtype:
characteristics['most_severe_violation_type_subtype'] = \
supervision_time_bucket.most_severe_violation_type_subtype
if metric_type in [SupervisionMetricType.REVOCATION_ANALYSIS]:
if supervision_time_bucket.most_severe_response_decision:
characteristics['most_severe_response_decision'] = \
supervision_time_bucket.most_severe_response_decision
if supervision_time_bucket.response_count is not None:
characteristics[
'response_count'] = supervision_time_bucket.response_count
if isinstance(supervision_time_bucket, SupervisionTerminationBucket):
if supervision_time_bucket.termination_reason:
characteristics[
'termination_reason'] = supervision_time_bucket.termination_reason
if supervision_time_bucket.supervision_type:
characteristics[
'supervision_type'] = supervision_time_bucket.supervision_type
if supervision_time_bucket.case_type:
characteristics['case_type'] = supervision_time_bucket.case_type
if not include_revocation_dimensions and supervision_time_bucket.supervision_level:
characteristics[
'supervision_level'] = supervision_time_bucket.supervision_level
if include_assessment_dimensions:
# TODO(2853): Figure out more robust solution for not assessed people. Here we don't set assessment_type when
# someone is not assessed. This only works as desired because BQ doesn't rely on assessment_type at all.
characteristics['assessment_score_bucket'] = 'NOT_ASSESSED'
if supervision_time_bucket.assessment_score and supervision_time_bucket.assessment_type:
assessment_bucket = assessment_score_bucket(
supervision_time_bucket.assessment_score,
supervision_time_bucket.assessment_level,
supervision_time_bucket.assessment_type)
if assessment_bucket and include_assessment_in_metric(
'supervision', supervision_time_bucket.state_code,
supervision_time_bucket.assessment_type):
characteristics['assessment_score_bucket'] = assessment_bucket
characteristics[
'assessment_type'] = supervision_time_bucket.assessment_type
if supervision_time_bucket.supervising_officer_external_id:
characteristics[
'supervising_officer_external_id'] = supervision_time_bucket.supervising_officer_external_id
if supervision_time_bucket.supervising_district_external_id:
characteristics[
'supervising_district_external_id'] = supervision_time_bucket.supervising_district_external_id
if isinstance(supervision_time_bucket,
RevocationReturnSupervisionTimeBucket):
event_date = supervision_time_bucket.revocation_admission_date
elif isinstance(supervision_time_bucket, SupervisionTerminationBucket):
event_date = supervision_time_bucket.termination_date
else:
year = supervision_time_bucket.year
month = supervision_time_bucket.month
event_date = date(year, month, 1)
if include_demographic_dimensions:
characteristics = add_demographic_characteristics(
characteristics, person, event_date)
if include_person_level_dimensions:
characteristics = characteristics_with_person_id_fields(
characteristics, person, 'supervision')
if not include_revocation_dimensions and supervision_time_bucket.supervision_level_raw_text:
characteristics['supervision_level_raw_text'] = \
supervision_time_bucket.supervision_level_raw_text
if metric_type == SupervisionMetricType.POPULATION:
if isinstance(supervision_time_bucket,
(RevocationReturnSupervisionTimeBucket,
NonRevocationReturnSupervisionTimeBucket)):
characteristics['is_on_supervision_last_day_of_month'] = \
supervision_time_bucket.is_on_supervision_last_day_of_month
if metric_type == SupervisionMetricType.REVOCATION_ANALYSIS:
if isinstance(supervision_time_bucket, RevocationReturnSupervisionTimeBucket) \
and supervision_time_bucket.violation_history_description:
characteristics['violation_history_description'] = \
supervision_time_bucket.violation_history_description
if include_revocation_dimensions and isinstance(
supervision_time_bucket,
RevocationReturnSupervisionTimeBucket):
characteristics['revocation_admission_date'] = \
supervision_time_bucket.revocation_admission_date
if metric_type == SupervisionMetricType.ASSESSMENT_CHANGE:
if isinstance(supervision_time_bucket, SupervisionTerminationBucket) \
and supervision_time_bucket.termination_date:
characteristics[
'termination_date'] = supervision_time_bucket.termination_date
return characteristics
def characteristic_combinations(person: StatePerson,
supervision_time_bucket: SupervisionTimeBucket,
inclusions: Dict[str, bool],
metric_type: SupervisionMetricType) -> \
List[Dict[str, Any]]:
"""Calculates all supervision metric combinations.
Returns the list of all combinations of the metric characteristics, of all sizes, given the StatePerson and
SupervisionTimeBucket. That is, this returns a list of dictionaries where each dictionary is a combination of 0
to n unique elements of characteristics applicable to the given person and supervision_time_bucket.
Args:
person: the StatePerson we are picking characteristics from
supervision_time_bucket: the SupervisionTimeBucket we are picking characteristics from
inclusions: A dictionary containing the following keys that correspond
to characteristic dimensions:
- age_bucket
- ethnicity
- gender
- race
Where the values are boolean flags indicating whether to include
the dimension in the calculations.
metric_type: The SupervisionMetricType provided determines which fields should be added to the characteristics
dictionary
Returns:
A list of dictionaries containing all unique combinations of
characteristics.
"""
characteristics: Dict[str, Any] = {}
include_revocation_dimensions = _include_revocation_dimensions_for_metric(
metric_type)
include_assessment_dimensions = _include_assessment_dimensions_for_metric(
metric_type)
include_person_level_dimensions = _include_person_level_dimensions_for_metric(
metric_type)
if (metric_type == SupervisionMetricType.POPULATION
and isinstance(supervision_time_bucket,
(RevocationReturnSupervisionTimeBucket,
NonRevocationReturnSupervisionTimeBucket))):
if supervision_time_bucket.most_severe_violation_type:
characteristics[
'most_severe_violation_type'] = supervision_time_bucket.most_severe_violation_type
if supervision_time_bucket.most_severe_violation_type_subtype:
characteristics['most_severe_violation_type_subtype'] = \
supervision_time_bucket.most_severe_violation_type_subtype
if supervision_time_bucket.response_count is not None:
characteristics[
'response_count'] = supervision_time_bucket.response_count
if include_revocation_dimensions and \
isinstance(supervision_time_bucket,
RevocationReturnSupervisionTimeBucket):
if supervision_time_bucket.revocation_type:
characteristics[
'revocation_type'] = supervision_time_bucket.revocation_type
if supervision_time_bucket.source_violation_type:
characteristics[
'source_violation_type'] = supervision_time_bucket.source_violation_type
if metric_type in [
SupervisionMetricType.REVOCATION_ANALYSIS,
SupervisionMetricType.REVOCATION_VIOLATION_TYPE_ANALYSIS
]:
if supervision_time_bucket.most_severe_violation_type:
characteristics[
'most_severe_violation_type'] = supervision_time_bucket.most_severe_violation_type
if supervision_time_bucket.most_severe_violation_type_subtype:
characteristics['most_severe_violation_type_subtype'] = \
supervision_time_bucket.most_severe_violation_type_subtype
if metric_type in [SupervisionMetricType.REVOCATION_ANALYSIS]:
if supervision_time_bucket.most_severe_response_decision:
characteristics['most_severe_response_decision'] = \
supervision_time_bucket.most_severe_response_decision
if supervision_time_bucket.response_count is not None:
characteristics[
'response_count'] = supervision_time_bucket.response_count
if isinstance(supervision_time_bucket, SupervisionTerminationBucket):
if supervision_time_bucket.termination_reason:
characteristics[
'termination_reason'] = supervision_time_bucket.termination_reason
if supervision_time_bucket.supervision_type:
characteristics[
'supervision_type'] = supervision_time_bucket.supervision_type
if supervision_time_bucket.case_type:
characteristics['case_type'] = supervision_time_bucket.case_type
if not include_revocation_dimensions and supervision_time_bucket.supervision_level:
characteristics[
'supervision_level'] = supervision_time_bucket.supervision_level
if include_assessment_dimensions:
# TODO(2853): Figure out more robust solution for not assessed people. Here we don't set assessment_type when
# someone is not assessed. This only works as desired because BQ doesn't rely on assessment_type at all.
characteristics['assessment_score_bucket'] = 'NOT_ASSESSED'
if supervision_time_bucket.assessment_score and supervision_time_bucket.assessment_type:
assessment_bucket = assessment_score_bucket(
supervision_time_bucket.assessment_score,
supervision_time_bucket.assessment_level,
supervision_time_bucket.assessment_type)
if assessment_bucket and include_assessment_in_metric(
'supervision', supervision_time_bucket.state_code,
supervision_time_bucket.assessment_type):
characteristics['assessment_score_bucket'] = assessment_bucket
characteristics[
'assessment_type'] = supervision_time_bucket.assessment_type
if supervision_time_bucket.supervising_officer_external_id:
characteristics[
'supervising_officer_external_id'] = supervision_time_bucket.supervising_officer_external_id
if supervision_time_bucket.supervising_district_external_id:
characteristics[
'supervising_district_external_id'] = supervision_time_bucket.supervising_district_external_id
if inclusions.get('age_bucket'):
year = supervision_time_bucket.year
month = supervision_time_bucket.month
start_of_bucket = date(year, month, 1)
entry_age = age_at_date(person, start_of_bucket)
entry_age_bucket = age_bucket(entry_age)
if entry_age_bucket is not None:
characteristics['age_bucket'] = entry_age_bucket
if inclusions.get('gender'):
if person.gender is not None:
characteristics['gender'] = person.gender
if person.races or person.ethnicities:
if inclusions.get('race'):
races = person.races
else:
races = []
if inclusions.get('ethnicity'):
ethnicities = person.ethnicities
else:
ethnicities = []
all_combinations = for_characteristics_races_ethnicities(
races, ethnicities, characteristics)
else:
all_combinations = for_characteristics(characteristics)
if include_person_level_dimensions:
characteristics_with_person_details = characteristics_with_person_id_fields(
characteristics, person, 'supervision')
if not include_revocation_dimensions and supervision_time_bucket.supervision_level_raw_text:
characteristics_with_person_details['supervision_level_raw_text'] = \
supervision_time_bucket.supervision_level_raw_text
if metric_type == SupervisionMetricType.POPULATION:
if isinstance(supervision_time_bucket,
(RevocationReturnSupervisionTimeBucket,
NonRevocationReturnSupervisionTimeBucket)):
characteristics_with_person_details['is_on_supervision_last_day_of_month'] = \
supervision_time_bucket.is_on_supervision_last_day_of_month
if metric_type == SupervisionMetricType.REVOCATION_ANALYSIS:
# Only include violation history descriptions on person-level metrics
if isinstance(supervision_time_bucket, RevocationReturnSupervisionTimeBucket) \
and supervision_time_bucket.violation_history_description:
characteristics_with_person_details['violation_history_description'] = \
supervision_time_bucket.violation_history_description
all_combinations.append(characteristics_with_person_details)
return all_combinations
