def test_include_in_monthly_metrics_after_end_date(self):
calculation_month_upper_bound = date(2000, 1, 31)
include = calculator_utils.include_in_output(
year=2000,
month=2,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=None,
)
self.assertFalse(include)
def test_include_in_monthly_metrics(self):
calculation_month_upper_bound = date(2000, 1, 31)
include = calculator_utils.include_in_output(
year=1999,
month=11,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=None,
)
self.assertTrue(include)
def test_include_in_monthly_metrics_one_month_run_exclude(self):
calculation_month_upper_bound = date(1999, 12, 31)
calculation_month_lower_bound = date(1999, 12, 1)
include = calculator_utils.include_in_output(
year=2000,
month=1,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound,
)
self.assertFalse(include)
def test_include_in_monthly_metrics_before_lower_bound(self):
calculation_month_upper_bound = date(2000, 1, 31)
calculation_month_lower_bound = date(1999, 10, 1)
include = calculator_utils.include_in_output(
year=1990,
month=4,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound,
)
self.assertFalse(include)
def test_include_in_monthly_metrics_month_of_end_date(self):
calculation_month_upper_bound = date(2000, 1, 31)
calculation_month_lower_bound = date(1999, 10, 1)
include = calculator_utils.include_in_output(
year=calculation_month_upper_bound.year,
month=calculation_month_upper_bound.month,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound,
)
self.assertTrue(include)
def map_supervision_combinations(
person: StatePerson,
supervision_time_buckets: List[SupervisionTimeBucket],
metric_inclusions: Dict[SupervisionMetricType, bool],
calculation_end_month: Optional[str],
calculation_month_count: int,
person_metadata: PersonMetadata,
) -> List[Tuple[Dict[str, Any], Any]]:
"""Transforms SupervisionTimeBuckets and a StatePerson into metric combinations.
Takes in a StatePerson and all of her SupervisionTimeBuckets and returns an array of "supervision combinations".
These are key-value pairs where the key represents a specific metric and the value represents whether or not
the person should be counted as a positive instance of that metric.
This translates a particular time on supervision into many different supervision population metrics.
Args:
person: the StatePerson
supervision_time_buckets: A list of SupervisionTimeBuckets for the given StatePerson.
metric_inclusions: A dictionary where the keys are each SupervisionMetricType, and the values are boolean
flags for whether or not to include that metric type in the calculations
calculation_end_month: The year and month in YYYY-MM format of the last month for which metrics should be
calculated. If unset, ends with the current month.
calculation_month_count: The number of months (including the month of the calculation_end_month) to
limit the monthly calculation output to. If set to -1, does not limit the calculations.
person_metadata: Contains information about the StatePerson that is necessary for the metrics.
Returns:
A list of key-value tuples representing specific metric combinations and the value corresponding to that metric.
"""
metrics: List[Tuple[Dict[str, Any], Any]] = []
supervision_time_buckets.sort(key=attrgetter("year", "month"))
calculation_month_upper_bound = get_calculation_month_upper_bound_date(
calculation_end_month)
calculation_month_lower_bound = get_calculation_month_lower_bound_date(
calculation_month_upper_bound, calculation_month_count)
for supervision_time_bucket in supervision_time_buckets:
event_date = supervision_time_bucket.event_date
if (isinstance(supervision_time_bucket,
NonRevocationReturnSupervisionTimeBucket)
and supervision_time_bucket.case_compliance):
event_date = supervision_time_bucket.case_compliance.date_of_evaluation
event_year = event_date.year
event_month = event_date.month
if not include_in_output(
event_year,
event_month,
calculation_month_upper_bound,
calculation_month_lower_bound,
):
continue
applicable_metric_types = BUCKET_TO_METRIC_TYPES.get(
type(supervision_time_bucket))
if not applicable_metric_types:
raise ValueError(
"No metric types mapped to supervision_time_bucket of type {}".
format(type(supervision_time_bucket)))
for metric_type in applicable_metric_types:
if not metric_inclusions[metric_type]:
continue
metric_class = METRIC_TYPE_TO_CLASS.get(metric_type)
if not metric_class:
raise ValueError(
"No metric class for metric type {}".format(metric_type))
if include_event_in_metric(supervision_time_bucket, metric_type):
characteristic_combo = characteristics_dict_builder(
pipeline="supervision",
event=supervision_time_bucket,
metric_class=metric_class,
person=person,
event_date=event_date,
person_metadata=person_metadata,
)
metric_combo = augmented_combo_for_calculations(
characteristic_combo,
supervision_time_bucket.state_code,
metric_type,
event_year,
event_month,
)
value = value_for_metric_combo(supervision_time_bucket,
metric_type)
metrics.append((metric_combo, value))
return metrics
def produce_metrics(
self,
person: StatePerson,
identifier_events: List[SupervisionEvent],
metric_inclusions: Dict[SupervisionMetricType, bool],
person_metadata: PersonMetadata,
pipeline_type: PipelineType,
pipeline_job_id: str,
calculation_end_month: Optional[str] = None,
calculation_month_count: int = -1,
) -> List[SupervisionMetric]:
"""Transforms SupervisionEvents and a StatePerson into SuperviisonMetrics.
Takes in a StatePerson and all of their SupervisionEvents and returns an array
of SupervisionMetrics.
Args:
person: the StatePerson
identifier_events: A list of SupervisionEvents for the given StatePerson.
metric_inclusions: A dictionary where the keys are each SupervisionMetricType, and the values are boolean
flags for whether or not to include that metric type in the calculations
calculation_end_month: The year and month in YYYY-MM format of the last month for which metrics should be
calculated. If unset, ends with the current month.
calculation_month_count: The number of months (including the month of the calculation_end_month) to
limit the monthly calculation output to. If set to -1, does not limit the calculations.
person_metadata: Contains information about the StatePerson that is necessary for the metrics.
pipeline_job_id: The job_id of the pipeline that is currently running.
Returns:
A list of SupervisionMetrics.
"""
metrics: List[SupervisionMetric] = []
identifier_events.sort(key=attrgetter("year", "month"))
calculation_month_upper_bound = get_calculation_month_upper_bound_date(
calculation_end_month)
calculation_month_lower_bound = get_calculation_month_lower_bound_date(
calculation_month_upper_bound, calculation_month_count)
for event in identifier_events:
event_date = event.event_date
if (isinstance(
event,
SupervisionPopulationEvent,
) and event.case_compliance):
event_date = event.case_compliance.date_of_evaluation
event_year = event_date.year
event_month = event_date.month
if not include_in_output(
event_year,
event_month,
calculation_month_upper_bound,
calculation_month_lower_bound,
):
continue
applicable_metric_types = self.event_to_metric_types.get(
type(event))
if not applicable_metric_types:
raise ValueError(
"No metric types mapped to event of type {}".format(
type(event)))
for metric_type in applicable_metric_types:
if not metric_inclusions[metric_type]:
continue
metric_class = self.metric_type_to_class.get(metric_type)
if not metric_class:
raise ValueError(
"No metric class for metric type {}".format(
metric_type))
if self.include_event_in_metric(event, metric_type):
additional_attributes: Dict[str, Any] = {}
if (isinstance(
event,
ProjectedSupervisionCompletionEvent,
) and metric_type == SupervisionMetricType.
SUPERVISION_SUCCESSFUL_SENTENCE_DAYS_SERVED):
additional_attributes[
"days_served"] = event.sentence_days_served
metric = build_metric(
pipeline=pipeline_type.value.lower(),
event=event,
metric_class=metric_class,
person=person,
event_date=event_date,
person_metadata=person_metadata,
pipeline_job_id=pipeline_job_id,
additional_attributes=additional_attributes,
)
if not isinstance(metric, SupervisionMetric):
raise ValueError(
f"Unexpected metric type {type(metric)}. "
"All metrics should be SupervisionMetric.")
metrics.append(metric)
return metrics
