: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.TwoByTwoTableMetricResponse
   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
















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   .. py:attribute:: filter_column
      :type: str

      
      The column in the remote dataset df to check against
















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   .. 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
















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

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
















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   .. 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
















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.. 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>`_
       
       





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   .. py:attribute:: groupings
      :type: List[str]
      :value: []

      
      A list of columns to group metric results by
















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

      
      Should na values be dropped if in a grouping key
















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.. py:data:: MetricResultDataType

   
   Dict[str, Any]
















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


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

   
   Standardized response from querying metrics against a Dataset
















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   .. py:attribute:: output
      :type: MetricResultDataType

      

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

      


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


   Bases: :py:obj:`MetricResponse`

   
   Standardized response from querying metrics against a Dataset
















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   .. 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
















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


   Bases: :py:obj:`MetricResponse`

   
   Standardized response from querying metrics against a Dataset
















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   .. py:method:: as_table()

      
      Display the 2X2 table metric response as a dict representing a table. Use pd.DataFrame(as_table_result)
      to visualize the table.
      The data provided should represent a 2X2 table meaning the
      dict is of length 4, the keys should be tuples of length 2 representing the possible combination of values.
      The "detected" values are the columns and the "exposed" values are the rows.
      If the data is boolean, the table order is (true, false) for both columns and rows.
      If not, the order is alphabetical.
















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   .. py:method:: get_ordered_dict_table(table_data: dict)
      :staticmethod:

      
      Returns the table data as an ordered dict representing a table.
      The data provided should represent a 2X2 table meaning the
      dict is of length 4, the keys should be tuples of length 2 representing the possible combination of values.
      The "detected" values are the columns and the "exposed" values are the rows.
      If the data is boolean, the table order is (true, false) for both columns and rows.
      If not, the order is alphabetical.
















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


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

   
   Parameters available for every metric
















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   .. 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
















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   .. py:attribute:: INTERSECTION
      :value: 'intersection'

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
















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

      
       Returns values where rows with the same identifiers are deduplicated.
















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