:py:mod:`rhino_health.lib.metrics.base_metric`
==============================================

.. py:module:: rhino_health.lib.metrics.base_metric


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   rhino_health.lib.metrics.base_metric.DataFilter
   rhino_health.lib.metrics.base_metric.GroupingData
   rhino_health.lib.metrics.base_metric.MetricResponse
   rhino_health.lib.metrics.base_metric.KaplanMeierMetricResponse
   rhino_health.lib.metrics.base_metric.BaseMetric
   rhino_health.lib.metrics.base_metric.JoinMode




Attributes
~~~~~~~~~~

.. autoapisummary::

   rhino_health.lib.metrics.base_metric.MetricResultDataType


.. py:class:: DataFilter(/, **data: Any)


   Bases: :py:obj:`pydantic.BaseModel`

   
   A filter to be applied on the entire Dataset
















   ..
       !! processed by numpydoc !!
   .. py:attribute:: filter_column
      :type: str

      
      The column in the remote dataset df to check against
















      ..
          !! processed by numpydoc !!

   .. py:attribute:: filter_value
      :type: Union[Any, rhino_health.lib.metrics.filter_variable.FilterBetweenRange]

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
















      ..
          !! processed by numpydoc !!

   .. py:attribute:: filter_type
      :type: Optional[rhino_health.lib.metrics.filter_variable.FilterType]

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
















      ..
          !! processed by numpydoc !!

   .. py:attribute:: filter_dataset
      :type: Optional[str]

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
















      ..
          !! processed by numpydoc !!


.. py:class:: GroupingData(/, **data: Any)


   Bases: :py:obj:`pydantic.BaseModel`

   
   Configuration for grouping metric results










   .. seealso::

       
       :obj:`pandas.groupby`
           Implementation used for grouping. `See documentation <https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html>`_
       
       





   ..
       !! processed by numpydoc !!
   .. py:attribute:: groupings
      :type: List[str]
      :value: []

      
      A list of columns to group metric results by
















      ..
          !! processed by numpydoc !!

   .. py:attribute:: dropna
      :type: Optional[bool]
      :value: True

      
      Should na values be dropped if in a grouping key
















      ..
          !! processed by numpydoc !!


.. py:data:: MetricResultDataType

   
   Dict[str, Any]
















   ..
       !! processed by numpydoc !!

.. py:class:: MetricResponse(**data)


   Bases: :py:obj:`pydantic.BaseModel`

   
   Standardized response from querying metrics against a Dataset
















   ..
       !! processed by numpydoc !!
   .. py:attribute:: output
      :type: MetricResultDataType

      

   .. py:attribute:: metric_configuration_dict
      :type: Optional[Dict[str, Any]]

      

   .. py:attribute:: dataset_uids
      :type: Optional[List[str]]

      

   .. py:attribute:: session
      :type: Any

      


.. py:class:: KaplanMeierMetricResponse(**data)


   Bases: :py:obj:`MetricResponse`

   
   Standardized response from querying metrics against a Dataset
















   ..
       !! processed by numpydoc !!
   .. py:attribute:: time_variable
      :type: str

      

   .. py:attribute:: event_variable
      :type: str

      

   .. py:method:: surv_func_right_model(group=None)

      
      Creates a survival function model for the metric response
















      ..
          !! processed by numpydoc !!


.. py:class:: BaseMetric(/, **data: Any)


   Bases: :py:obj:`pydantic.BaseModel`

   
   Parameters available for every metric
















   ..
       !! processed by numpydoc !!
   .. py:property:: metric_response


   .. py:attribute:: data_filters
      :type: Optional[List[DataFilter]]
      :value: []

      

   .. py:attribute:: group_by
      :type: Optional[GroupingData]

      

   .. py:attribute:: timeout_seconds
      :type: Optional[float]
      :value: 600.0

      

   .. py:attribute:: count_variable_name
      :type: str
      :value: 'variable'

      

   .. py:method:: data()



.. py:class:: JoinMode


   Bases: :py:obj:`str`, :py:obj:`enum.Enum`

   

   The mode we are performing the FederatedJoin
















   ..
       !! processed by numpydoc !!
   .. py:attribute:: INTERSECTION
      :value: 'intersection'

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
















      ..
          !! processed by numpydoc !!

   .. py:attribute:: UNION
      :value: 'union'

      
       Returns values where rows with the same identifiers are deduplicated.
















      ..
          !! processed by numpydoc !!