rhino_health.lib.metrics.base_metric

Module Contents

Classes

DataFilter	A filter to be applied on the entire Dataset
GroupingData	Configuration for grouping metric results
MetricResponse	Standardized response from querying metrics against a Dataset
KaplanMeierMetricResponse	Standardized response from querying metrics against a Dataset
BaseMetric	Parameters available for every metric
JoinMode	The mode we are performing the FederatedJoin

Attributes

MetricResultDataType

Dict[str, Any]

class rhino_health.lib.metrics.base_metric.DataFilter(**data: Any)

Bases: pydantic.BaseModel

A filter to be applied on the entire Dataset

filter_column: str

The column in the remote dataset df to check against

filter_value: Any | rhino_health.lib.metrics.filter_variable.FilterBetweenRange

The value to match against or a FilterBetweenRange if filter_type is FilterType.BETWEEN

filter_type: rhino_health.lib.metrics.filter_variable.FilterType | None

The type of filtering to perform. Defaults to FilterType.EQUAL

filter_dataset: str | None

The dataset to perform the filter on if there are multiple datasets for Federated Join. If unspecified will be all datasets

class rhino_health.lib.metrics.base_metric.GroupingData(**data: Any)

Bases: pydantic.BaseModel

Configuration for grouping metric results

See also

pandas.groupby

Implementation used for grouping. See documentation

groupings: List[str] = []

A list of columns to group metric results by

dropna: bool | None = True

Should na values be dropped if in a grouping key

rhino_health.lib.metrics.base_metric.MetricResultDataType

Dict[str, Any]

class rhino_health.lib.metrics.base_metric.MetricResponse(**data)

Bases: pydantic.BaseModel

Standardized response from querying metrics against a Dataset

output: MetricResultDataType

metric_configuration_dict: Dict[str, Any] | None

dataset_uids: List[str] | None

session: Any

class rhino_health.lib.metrics.base_metric.KaplanMeierMetricResponse(**data)

Bases: MetricResponse

Standardized response from querying metrics against a Dataset

time_variable: str

event_variable: str

surv_func_right_model(group=None)

Creates a survival function model for the metric response

class rhino_health.lib.metrics.base_metric.BaseMetric(**data: Any)

Bases: pydantic.BaseModel

Parameters available for every metric

property metric_response

data_filters: List[DataFilter] | None = []

group_by: GroupingData | None

timeout_seconds: float | None = 600.0

count_variable_name: str = 'variable'

data()

class rhino_health.lib.metrics.base_metric.JoinMode

Bases: str, enum.Enum

The mode we are performing the FederatedJoin

INTERSECTION = 'intersection'

Return values where the identifiers are found in both the filter and query datasets.

UNION = 'union'

Returns values where rows with the same identifiers are deduplicated.