dataeval.workflows.SufficiencyOutput
====================================

.. py:class:: dataeval.workflows.SufficiencyOutput



   Output class for :class:`Sufficiency` workflow

   .. attribute:: steps

      Array of sample sizes

      :type: NDArray

   .. attribute:: params

      Inverse power curve coefficients for the line of best fit for each measure

      :type: Dict[str, NDArray]

   .. attribute:: measures

      Average of values observed for each sample size step for each measure

      :type: Dict[str, NDArray]


   .. py:method:: inv_project(targets)

      Calculate training samples needed to achieve target model metric values.

      :param targets: Mapping of target metric scores (from 0.0 to 1.0) that we want
                      to achieve, where the key is the name of the metric.
      :type targets: Mapping[str, ArrayLike]

      :returns: List of the number of training samples needed to achieve each
                corresponding entry in targets
      :rtype: dict[str, NDArray]



   .. py:method:: plot(class_names = None)

      Plotting function for data :term:`sufficience<Sufficiency>` tasks

      :param class_names: List of class names
      :type class_names: Sequence[str] | None, default None

      :returns: List of Figures for each measure
      :rtype: list[Figure]

      :raises ValueError: If the length of data points in the measures do not match



   .. py:method:: project(projection)

      Projects the measures for each value of X

      :param projection: Step or steps to project
      :type projection: int | Iterable[int]

      :returns: Dataclass containing the projected measures per projection
      :rtype: SufficiencyOutput

      :raises ValueError: If the length of data points in the measures do not match
          If `projection` is not numerical