pdstools.valuefinder¶

Submodules¶

Classes¶

Analyze the Value Finder dataset for detailed insights

Package 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 = '', cache_directory: os.PathLike | str = 'cache')¶

Parameters:

files (collections.abc.Iterable[str] | str)
query (pdstools.utils.types.QUERY | None)
n_customers (int | None)
threshold (float | None)
cache_file_prefix (str)
cache_directory (os.PathLike | str)

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])