pdstools.valuefinder.ValueFinder

Classes

ValueFinder

Analyze the Value Finder dataset for detailed insights

Module Contents

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

Parameters:

  • df (polars.LazyFrame)

  • query (pdstools.utils.types.QUERY | None)

  • n_customers (int | None)

  • threshold (float | None)

df: polars.LazyFrame
n_customers: int
nbad_stages = ['Eligibility', 'Applicability', 'Suitability', 'Arbitration']
aggregates
plot
classmethod 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)
Parameters:

  • filename (str | None)

  • base_path (os.PathLike | str)

  • query (pdstools.utils.types.QUERY | None)

  • n_customers (int | None)

  • threshold (float | None)

classmethod 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')
Parameters:
set_threshold(new_threshold: float | None = None)
Parameters:

new_threshold (float | None)

property threshold
save_data(path: os.PathLike | str = '.') → pathlib.Path | None

Cache the pyValueFinder dataset to a Parquet file

Parameters:

path (str) – Where to place the file

Returns:

The paths to the model and predictor data files

Return type:

(Optional[Path], Optional[Path])