pdstools.valuefinder.ValueFinder
================================

.. py:module:: pdstools.valuefinder.ValueFinder


Classes
-------

.. autoapisummary::

   pdstools.valuefinder.ValueFinder.ValueFinder


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

.. py:class:: ValueFinder(df: polars.LazyFrame, *, query: pdstools.utils.types.QUERY | None = None, n_customers: int | None = None, threshold: float | None = None)

   Analyze the Value Finder dataset for detailed insights


   .. py:attribute:: df
      :type:  polars.LazyFrame


   .. py:attribute:: n_customers
      :type:  int


   .. py:attribute:: nbad_stages
      :value: ['Eligibility', 'Applicability', 'Suitability', 'Arbitration']



   .. py:attribute:: aggregates


   .. py:attribute:: plot


   .. py:method:: from_ds_export(filename: str | None = None, base_path: os.PathLike | str = '.', *, query: pdstools.utils.types.QUERY | None = None, n_customers: int | None = None, threshold: float | None = None)
      :classmethod:



   .. py:method:: from_dataflow_export(files: collections.abc.Iterable[str] | str, *, query: pdstools.utils.types.QUERY | None = None, n_customers: int | None = None, threshold: float | None = None, cache_file_prefix: str = '', extension: Literal['json'] = 'json', compression: Literal['gzip'] = 'gzip', cache_directory: os.PathLike | str = 'cache')
      :classmethod:



   .. py:method:: set_threshold(new_threshold: float | None = None)


   .. py:property:: threshold


   .. py:method:: save_data(path: os.PathLike | str = '.') -> pathlib.Path | None

      Cache the pyValueFinder dataset to a Parquet file

      :param path: Where to place the file
      :type path: str

      :returns: The paths to the model and predictor data files
      :rtype: (Optional[Path], Optional[Path])