pdstools.decision_analyzer.utils
================================

.. py:module:: pdstools.decision_analyzer.utils


Attributes
----------

.. autoapisummary::

   pdstools.decision_analyzer.utils.NBADScope_Mapping


Functions
---------

.. autoapisummary::

   pdstools.decision_analyzer.utils.apply_filter
   pdstools.decision_analyzer.utils.filtered_action_counts
   pdstools.decision_analyzer.utils.area_under_curve
   pdstools.decision_analyzer.utils.gini_coefficient
   pdstools.decision_analyzer.utils.get_first_level_stats
   pdstools.decision_analyzer.utils.get_git_version_and_date
   pdstools.decision_analyzer.utils.find_lever_value
   pdstools.decision_analyzer.utils.determine_extract_type
   pdstools.decision_analyzer.utils.process
   pdstools.decision_analyzer.utils.get_table_definition
   pdstools.decision_analyzer.utils.get_schema


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

.. py:data:: NBADScope_Mapping

.. py:function:: apply_filter(df: polars.LazyFrame, filters: Optional[Union[polars.Expr, List[polars.Expr]]] = None)

   Apply a global set of filters. Kept outside of the DecisionData class as
   this is really more of a utility function, not bound to that class at all.


.. py:function:: filtered_action_counts(df: polars.LazyFrame, groupby_cols: list, propensityTH: float = None, priorityTH: float = None) -> polars.LazyFrame

   Returns a DataFrame with action counts filtered based on the given propensity and priority thresholds.

   :param df: The input dataframe.
   :type df: pl.LazyFrame
   :param groupby_cols: The list of column names to group by(["pxEngagementStage", "pxInteractionID"]).
   :type groupby_cols: list
   :param propensityTH: The propensity threshold.
   :type propensityTH: float
   :param priorityTH: The priority threshold.
   :type priorityTH: float

   :returns: A DataFrame with action counts filtered based on the given propensity and priority thresholds.
   :rtype: pl.LazyFrame


.. py:function:: area_under_curve(df: polars.DataFrame, col_x: str, col_y: str)

.. py:function:: gini_coefficient(df: polars.DataFrame, col_x: str, col_y: str)

.. py:function:: get_first_level_stats(interaction_data: polars.LazyFrame, filters: List[polars.Expr] = None)

   Returns some first level stats of a dataframe. Used to
   show effects of user data filters.


.. py:function:: get_git_version_and_date()

.. py:function:: find_lever_value(decision_data, action, target_win_percentage, win_rank, low=0, high=100, precision=0.01, ranking_stages=['Arbitration'])

   Binary search algorithm to find lever given a desired win percentage


.. py:function:: determine_extract_type(raw_data)

.. py:function:: process(df: polars.LazyFrame, table: Literal['decision_analyzer', 'explainability_extract'], subset: bool = True, include_cols: Optional[Iterable[str]] = None, drop_cols: Optional[Iterable[str]] = None, raise_on_unknown: bool = True) -> polars.LazyFrame

.. py:function:: get_table_definition(table: str)

.. py:function:: get_schema(df: polars.LazyFrame, table_definition: Dict[str, pdstools.decision_analyzer.table_definition.TableConfig], include_cols: Iterable[str], drop_cols: Iterable[str], subset: bool, raise_on_unknown: bool = True) -> Dict[str, Type[polars.DataType]]