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

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


Classes
-------

.. autoapisummary::

   pdstools.valuefinder.ValueFinder.ValueFinder


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

.. py:class:: ValueFinder(df: polars.LazyFrame, *, query: Optional[pdstools.utils.types.QUERY] = None, n_customers: Optional[int] = None, threshold: Optional[float] = 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: Optional[str] = None, base_path: Union[os.PathLike, str] = '.', *, query: Optional[pdstools.utils.types.QUERY] = None, n_customers: Optional[int] = None, threshold: Optional[float] = None)
      :classmethod:



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



   .. py:method:: set_threshold(new_threshold: Optional[float] = None)


   .. py:property:: threshold


   .. py:method:: save_data(path: Union[os.PathLike, str] = '.') -> Optional[pathlib.Path]

      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])