caikit.interfaces.ts.data_model.backends.base
=============================================

.. py:module:: caikit.interfaces.ts.data_model.backends.base

.. autoapi-nested-parse::

   Base classes to share between data model backends



Attributes
----------

.. autoapisummary::

   caikit.interfaces.ts.data_model.backends.base.log
   caikit.interfaces.ts.data_model.backends.base.error


Classes
-------

.. autoapisummary::

   caikit.interfaces.ts.data_model.backends.base.UncachedBackendMixin
   caikit.interfaces.ts.data_model.backends.base.StrictFieldBackendMixin
   caikit.interfaces.ts.data_model.backends.base.TimeSeriesBackendBase
   caikit.interfaces.ts.data_model.backends.base.MultiTimeSeriesBackendBase


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

.. py:data:: log

.. py:data:: error

.. py:class:: UncachedBackendMixin

   Bases: :py:obj:`caikit.core.data_model.data_backends.DataModelBackendBase`


   Intermediate base class that disables attribute caching


   .. py:method:: cache_attribute(*_, **__) -> bool

      Never cache attributes



.. py:class:: StrictFieldBackendMixin

   Bases: :py:obj:`caikit.core.data_model.data_backends.DataModelBackendBase`


   Intermediate base class that raises attribute errors for unknown fields


   .. py:method:: get_attribute(data_model_class: Type, name: str) -> Any

      Base implementation that raises an AttributeError on bad attr names.
      It should be called after object-specific logic.



.. py:class:: TimeSeriesBackendBase

   Bases: :py:obj:`UncachedBackendMixin`, :py:obj:`StrictFieldBackendMixin`


   Abstract base class for all backends of the central TimeSeries data model
   type


   .. py:method:: as_pandas() -> Tuple[pandas.DataFrame, str, Iterable[str]]
      :abstractmethod:


      All backends must implement the ability to coerce their underlying
      data into a pandas DataFrame and provide the pointers to the timeseries
      source and value source(s)

      Returns:
          df:  pd.DataFrame
              The data frame itself
          timestamp_source:  str
              The column name (or None) indicating where the
              timestamp sequence can be found
          value_source:  Iterable[str]
              The names of the columns holding value sequences



.. py:class:: MultiTimeSeriesBackendBase

   Bases: :py:obj:`UncachedBackendMixin`, :py:obj:`StrictFieldBackendMixin`


   Abstract base class for all backends of the central MultiTimeSeries data model
   type


   .. py:method:: as_pandas() -> Tuple[pandas.DataFrame, Iterable[str], str, Iterable[str]]
      :abstractmethod:


      All backends must implement the ability to coerce their underlying
      data into a pandas DataFrame and provide the pointers to the timeseries
      source and value source(s)

      Returns:
          df:  pd.DataFrame
              The data frame itself
          key_source: Iterable[str]
              the names of the columns holding key values
          timestamp_source:  str
              The column name (or None) indicating where the
              timestamp sequence can be found
          value_source:  Iterable[str]
              The names of the columns holding value sequences