abacusai.algorithm — AbacusAI documentation

# `abacusai.algorithm`

## Module Contents

### Classes

`Algorithm` | Customer created algorithm

class abacusai.algorithm.Algorithm(client, name=None, problemType=None, externalProblemType=None, createdAt=None, updatedAt=None, isDefaultEnabled=None, trainingInputMappings=None, trainFunctionName=None, predictFunctionName=None, predictManyFunctionName=None, initializeFunctionName=None, configOptions=None, algorithmId=None, useGpu=None, codeSource={})

  Bases: `abacusai.return_class.AbstractApiClass`

  Customer created algorithm

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - name (str) – The name of the algorithm

    - problemType (str) – The type of the problem this algorithm will work on

    - externalProblemType (str) – The problem type name shown to external user

    - createdAt (str) – When the algorithm was created

    - updatedAt (str) – When the algorithm was last updated

    - isDefaultEnabled (bool) – Whether train with the algorithm by default

    - trainingInputMappings (dict) – The mappings for train function parameters’ names, e.g. names for training data, name for training config

    - trainFunctionName (str) – 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.

    - predictFunctionName (str) – 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.

    - predictManyFunctionName (str) – 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.

    - initializeFunctionName (str) – Name of the function found in the source code to initialize the trained model before using it to make predictions using the model

    - configOptions (dict) – Map dataset types and configs to train function parameter names

    - algorithmId (str) – The unique identifier of the algorithm

    - useGpu (bool) – Whether to use gpu for model training

    - codeSource (CodeSource) – Info about the source code of the algorithm

  __repr__()

    Return repr(self).

  to_dict()

    Get a dict representation of the parameters in this class

    Returns:

      The dict value representation of the class parameters

    Return type:

      dict