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 (Optional[pdstools.utils.types.QUERY])
n_customers (Optional[int])
threshold (Optional[float])

df: polars.LazyFrame¶

n_customers: int¶

nbad_stages = ['Eligibility', 'Applicability', 'Suitability', 'Arbitration']¶

aggregates¶

plot¶

Parameters:

filename (Optional[str])
base_path (Union[os.PathLike, str])
query (Optional[pdstools.utils.types.QUERY])
n_customers (Optional[int])
threshold (Optional[float])

classmethod from_dataflow_export(files: 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:

files (Union[Iterable[str], str])
query (Optional[pdstools.utils.types.QUERY])
n_customers (Optional[int])
threshold (Optional[float])
cache_file_prefix (str)
extension (Literal['json'])
compression (Literal['gzip'])
cache_directory (Union[os.PathLike, str])

set_threshold(new_threshold: float | None = None)¶

Parameters:: new_threshold (Optional[float])

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])