pdstools.prediction.Prediction
==============================

.. py:module:: pdstools.prediction.Prediction


Attributes
----------

.. autoapisummary::

   pdstools.prediction.Prediction.logger
   pdstools.prediction.Prediction.COLORSCALE_TYPES
   pdstools.prediction.Prediction.Figure


Classes
-------

.. autoapisummary::

   pdstools.prediction.Prediction.PredictionPlots
   pdstools.prediction.Prediction.Prediction


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

.. py:data:: logger

.. py:data:: COLORSCALE_TYPES

.. py:data:: Figure

.. py:class:: PredictionPlots(prediction)

   Bases: :py:obj:`pdstools.utils.namespaces.LazyNamespace`


   .. py:attribute:: dependencies
      :value: ['plotly']



   .. py:attribute:: prediction


   .. py:method:: _prediction_trend(period: str, query: Optional[pdstools.utils.types.QUERY], return_df: bool, metric: str, title: str, facet_row: str = None, facet_col: str = None, bar_mode: bool = False)


   .. py:method:: performance_trend(period: str = '1d', *, query: Optional[pdstools.utils.types.QUERY] = None, return_df: bool = False)


   .. py:method:: lift_trend(period: str = '1d', *, query: Optional[pdstools.utils.types.QUERY] = None, return_df: bool = False)


   .. py:method:: ctr_trend(period: str = '1d', facetting=False, *, query: Optional[pdstools.utils.types.QUERY] = None, return_df: bool = False)


   .. py:method:: responsecount_trend(period: str = '1d', facetting=False, *, query: Optional[pdstools.utils.types.QUERY] = None, return_df: bool = False)


.. py:class:: Prediction(df: polars.LazyFrame)

   Monitor Pega Prediction Studio Predictions


   .. py:attribute:: predictions
      :type:  polars.LazyFrame


   .. py:attribute:: plot
      :type:  PredictionPlots


   .. py:attribute:: prediction_validity_expr


   .. py:attribute:: cdh_guidelines


   .. py:method:: from_mock_data(days=70)
      :staticmethod:



   .. py:property:: is_available
      :type: bool



   .. py:property:: is_valid
      :type: bool



   .. py:method:: summary_by_channel(custom_predictions: Optional[List[List]] = None, by_period: str = None) -> polars.LazyFrame

      Summarize prediction per channel

      :param custom_predictions: Optional list with custom prediction name to channel mappings. Defaults to None.
      :type custom_predictions: Optional[List[CDH_Guidelines.NBAD_Prediction]], optional
      :param by_period: Optional grouping by time period. Format string as in polars.Expr.dt.truncate (https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.dt.truncate.html), for example "1mo", "1w", "1d" for calendar month, week day. If provided, creates a new Period column with the truncated date/time. Defaults to None.
      :type by_period: str, optional

      :returns: Dataframe with prediction summary (validity, numbers in test, control etc.)
      :rtype: pl.LazyFrame



   .. py:method:: overall_summary(custom_predictions: Optional[List[List]] = None, by_period: str = None) -> polars.LazyFrame

      Overall prediction summary. Only valid prediction data is included.

      :param custom_predictions: Optional list with custom prediction name to channel mappings. Defaults to None.
      :type custom_predictions: Optional[List[CDH_Guidelines.NBAD_Prediction]], optional
      :param by_period: Optional grouping by time period. Format string as in polars.Expr.dt.truncate (https://docs.pola.rs/api/python/stable/reference/expressions/api/polars.Expr.dt.truncate.html), for example "1mo", "1w", "1d" for calendar month, week day. If provided, creates a new Period column with the truncated date/time. Defaults to None.
      :type by_period: str, optional

      :returns: Summary across all valid predictions as a dataframe
      :rtype: pl.LazyFrame