abacusai.algorithm
==================

.. py:module:: abacusai.algorithm


Classes
-------

.. autoapisummary::

   abacusai.algorithm.Algorithm


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

.. py:class:: Algorithm(client, name=None, problemType=None, createdAt=None, updatedAt=None, isDefaultEnabled=None, trainingInputMappings=None, trainFunctionName=None, predictFunctionName=None, predictManyFunctionName=None, initializeFunctionName=None, configOptions=None, algorithmId=None, useGpu=None, algorithmTrainingConfig=None, onlyOfflineDeployable=None, codeSource={})

   Bases: :py:obj:`abacusai.return_class.AbstractApiClass`


   Customer created algorithm

   :param client: An authenticated API Client instance
   :type client: ApiClient
   :param name: The name of the algorithm
   :type name: str
   :param problemType: The type of the problem this algorithm will work on
   :type problemType: str
   :param createdAt: When the algorithm was created
   :type createdAt: str
   :param updatedAt: When the algorithm was last updated
   :type updatedAt: str
   :param isDefaultEnabled: Whether train with the algorithm by default
   :type isDefaultEnabled: bool
   :param trainingInputMappings: The mappings for train function parameters' names, e.g. names for training data, name for training config
   :type trainingInputMappings: dict
   :param trainFunctionName: Name of the function found in the source code that will be executed to train the model. It is not executed when this function is run.
   :type trainFunctionName: str
   :param predictFunctionName: Name of the function found in the source code that will be executed run predictions through model. It is not executed when this function is run.
   :type predictFunctionName: str
   :param predictManyFunctionName: Name of the function found in the source code that will be executed for batch prediction of the model. It is not executed when this function is run.
   :type predictManyFunctionName: str
   :param initializeFunctionName: Name of the function found in the source code to initialize the trained model before using it to make predictions using the model
   :type initializeFunctionName: str
   :param configOptions: Map dataset types and configs to train function parameter names
   :type configOptions: dict
   :param algorithmId: The unique identifier of the algorithm
   :type algorithmId: str
   :param useGpu: Whether to use gpu for model training
   :type useGpu: bool
   :param algorithmTrainingConfig: The algorithm specific training config
   :type algorithmTrainingConfig: dict
   :param onlyOfflineDeployable: Whether or not the algorithm is only allowed to be deployed offline
   :type onlyOfflineDeployable: bool
   :param codeSource: Info about the source code of the algorithm
   :type codeSource: CodeSource


   .. py:attribute:: name
      :value: None



   .. py:attribute:: problem_type
      :value: None



   .. py:attribute:: created_at
      :value: None



   .. py:attribute:: updated_at
      :value: None



   .. py:attribute:: is_default_enabled
      :value: None



   .. py:attribute:: training_input_mappings
      :value: None



   .. py:attribute:: train_function_name
      :value: None



   .. py:attribute:: predict_function_name
      :value: None



   .. py:attribute:: predict_many_function_name
      :value: None



   .. py:attribute:: initialize_function_name
      :value: None



   .. py:attribute:: config_options
      :value: None



   .. py:attribute:: algorithm_id
      :value: None



   .. py:attribute:: use_gpu
      :value: None



   .. py:attribute:: algorithm_training_config
      :value: None



   .. py:attribute:: only_offline_deployable
      :value: None



   .. py:attribute:: code_source


   .. py:attribute:: deprecated_keys


   .. py:method:: __repr__()


   .. py:method:: to_dict()

      Get a dict representation of the parameters in this class

      :returns: The dict value representation of the class parameters
      :rtype: dict