pdstools.infinity.resources.prediction_studio.v24_2.model
=========================================================

.. py:module:: pdstools.infinity.resources.prediction_studio.v24_2.model


Classes
-------

.. autoapisummary::

   pdstools.infinity.resources.prediction_studio.v24_2.model._ModelV24_2Mixin
   pdstools.infinity.resources.prediction_studio.v24_2.model.Model
   pdstools.infinity.resources.prediction_studio.v24_2.model.AsyncModel


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

.. py:class:: _ModelV24_2Mixin

   v24.2 Model business logic — defined once.


   .. py:attribute:: model_id
      :type:  str


   .. py:method:: describe() -> pdstools.infinity.resources.prediction_studio.base.ModelAttributes
      :async:


      Fetches details about a model, including target labels, alternate labels, monitoring information, predictor information etc.

      :returns: An object containing information about the model.
      :rtype: ModelAttributes



.. py:class:: Model(client, *, modelId: str, label: str, modelType: str, status: str, componentName: str | None = None, source: str | None = None, lastUpdateTime: str | None = None, modelingTechnique: str | None = None, updatedBy: str | None = None)

   Bases: :py:obj:`_ModelV24_2Mixin`, :py:obj:`pdstools.infinity.resources.prediction_studio.base.Model`


   v24.2 Model business logic — defined once.


   .. py:method:: get_notifications(category: pdstools.infinity.resources.prediction_studio.types.NotificationCategory | None = None, return_df: Literal[False] = False) -> pdstools.infinity.internal._pagination.PaginatedList[pdstools.infinity.resources.prediction_studio.base.Notification]
                  get_notifications(category: pdstools.infinity.resources.prediction_studio.types.NotificationCategory | None = None, return_df: Literal[True] = True) -> polars.DataFrame

      Fetches a list of notifications for a specific model.

      This function retrieves notifications related to a model. You can filter these notifications by their category.
      Optionally, the notifications can be returned as a DataFrame for easier analysis and visualization.

      :param category: The category of notifications to retrieve. If not specified, all notifications are fetched.
      :type category: {"All", "Responses", "Performance", "Model approval", "Output", "Predictors", "Prediction deployment", "Generic"} or None, optional
      :param return_df: If True, returns the notifications as a DataFrame. Otherwise, returns a list.
      :type return_df: bool, default False

      :returns: A list of notifications or a DataFrame containing the notifications, depending on the value of `return_df`.
      :rtype: PaginatedList[Notification] or polars.DataFrame



.. py:class:: AsyncModel(client, *, modelId: str, label: str, modelType: str, status: str, componentName: str | None = None, source: str | None = None, lastUpdateTime: str | None = None, modelingTechnique: str | None = None, updatedBy: str | None = None)

   Bases: :py:obj:`_ModelV24_2Mixin`, :py:obj:`pdstools.infinity.resources.prediction_studio.base.AsyncModel`


   v24.2 Model business logic — defined once.


   .. py:method:: get_notifications(category: pdstools.infinity.resources.prediction_studio.types.NotificationCategory | None = None, return_df: bool = False) -> pdstools.infinity.internal._pagination.AsyncPaginatedList[pdstools.infinity.resources.prediction_studio.base.AsyncNotification] | polars.DataFrame
      :async:


      Fetches a list of notifications for a specific model.

      :param category: The category of notifications to retrieve. If not specified, all notifications are fetched.
      :type category: {"All", "Responses", "Performance", "Model approval", "Output", "Predictors", "Prediction deployment", "Generic"} or None, optional
      :param return_df: If True, returns the notifications as a DataFrame. Otherwise, returns a list.
      :type return_df: bool, default False

      :returns: A list of notifications or a DataFrame containing the notifications.
      :rtype: AsyncPaginatedList[AsyncNotification] or polars.DataFrame