abacusai.batch_prediction — AbacusAI documentation

# `abacusai.batch_prediction`

## Module Contents

### Classes

`BatchPrediction` | Batch predictions

class abacusai.batch_prediction.BatchPrediction(client, batchPredictionId=None, createdAt=None, name=None, deploymentId=None, fileConnectorOutputLocation=None, globalPredictionArgs=None, databaseConnectorId=None, databaseOutputConfiguration=None, explanations=None, fileOutputFormat=None, connectorType=None, legacyInputLocation=None, featureGroupTableName=None, summaryFeatureGroupTableName=None, csvInputPrefix=None, csvPredictionPrefix=None, csvExplanationsPrefix=None, outputIncludesMetadata=None, resultInputColumns=None, modelMonitorId=None, batchInputs={}, latestBatchPredictionVersion={}, refreshSchedules={})

  Bases: `abacusai.return_class.AbstractApiClass`

  Batch predictions

  Parameters:

    - client (ApiClient) – An authenticated API Client instance

    - batchPredictionId (str) – The unique identifier of the batch prediction request

    - createdAt (str) – When the batch prediction was created

    - name (str) – Name given to the batch prediction object

    - deploymentId (str) – The deployment used to make the predictions

    - fileConnectorOutputLocation (str) – Contains information about where the batch predictions are written to

    - globalPredictionArgs (dict) – Argument(s) passed to every prediction call

    - databaseConnectorId (str) – The database connector to write the results to

    - databaseOutputConfiguration (dict) – Contains information about where the batch predictions are written to

    - explanations (bool) – If true, explanations for each prediction were created

    - fileOutputFormat (str) – The format of the batch prediction output (CSV or JSON)

    - connectorType (str) – Null if writing to internal console, else FEATURE_GROUP | FILE_CONNECTOR | DATABASE_CONNECTOR

    - legacyInputLocation (str) – The location of the input data

    - featureGroupTableName (str) – The table name of the Batch Prediction feature group

    - summaryFeatureGroupTableName (str) – The table name of the metrics summary feature group output by Batch Prediction

    - csvInputPrefix (str) – A prefix to prepend to the input columns, only applies when output format is CSV

    - csvPredictionPrefix (str) – A prefix to prepend to the prediction columns, only applies when output format is CSV

    - csvExplanationsPrefix (str) – A prefix to prepend to the explanation columns, only applies when output format is CSV

    - outputIncludesMetadata (bool) – If true, output will contain columns including prediction start time, batch prediction version, and model version

    - resultInputColumns (list of string) – If present, will limit result files or feature groups to only include columns present in this list

    - modelMonitorId (str) –

    - batchInputs (PredictionInput) – Inputs to the batch prediction

    - latestBatchPredictionVersion (BatchPredictionVersion) – The latest batch prediction version

    - refreshSchedules (RefreshSchedule) – List of refresh schedules that dictate the next time the batch prediction will be run

  __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

  start()

    Creates a new batch prediction version job for a given batch prediction job description

    Parameters:

      batch_prediction_id (str) – The unique identifier of the batch prediction to create a new version of

    Returns:

      The batch prediction version started by this method call.

    Return type:

      BatchPredictionVersion

  refresh()

    Calls describe and refreshes the current object’s fields

    Returns:

      The current object

    Return type:

      BatchPrediction

  describe()

    Describes the batch prediction

    Parameters:

      batch_prediction_id (str) – The unique ID associated with the batch prediction.

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  list_versions(limit=100, start_after_version=None)

    Retrieves a list of versions of a given batch prediction

    Parameters:

      - limit (int) – The number of versions to list

      - start_after_version (str) – The version to start after

    Returns:

      A list of batch prediction versions.

    Return type:

      BatchPredictionVersion

  update(deployment_id=None, global_prediction_args=None, explanations=None, output_format=None, csv_input_prefix=None, csv_prediction_prefix=None, csv_explanations_prefix=None, output_includes_metadata=None, result_input_columns=None)

    Updates a batch prediction job description

    Parameters:

      - deployment_id (str) – The unique identifier to a deployment.

      - global_prediction_args (dict) – Argument(s) to pass on every prediction call.

      - explanations (bool) – If true, will provide SHAP Explanations for each prediction, if supported by the use case.

      - output_format (str) – If specified, sets the format of the batch prediction output (CSV or JSON).

      - csv_input_prefix (str) – A prefix to prepend to the input columns, only applies when output format is CSV

      - csv_prediction_prefix (str) – A prefix to prepend to the prediction columns, only applies when output format is CSV

      - csv_explanations_prefix (str) – A prefix to prepend to the explanation columns, only applies when output format is CSV

      - output_includes_metadata (bool) – If true, output will contain columns including prediction start time, batch prediction version, and model version

      - result_input_columns (list) – If present, will limit result files or feature groups to only include columns present in this list

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_file_connector_output(output_format=None, output_location=None)

    Updates the file connector output configuration of the batch prediction

    Parameters:

      - output_format (str) – If specified, sets the format of the batch prediction output (CSV or JSON).

      - output_location (str) – If specified, the location to write the prediction results. Otherwise, results will be stored in Abacus.AI.

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_database_connector_output(database_connector_id=None, database_output_config=None)

    Updates the database connector output configuration of the batch prediction

    Parameters:

      - database_connector_id (str) – The unique identifier of an Database Connection to write predictions to.

      - database_output_config (dict) – A key-value pair of columns/values to write to the database connector

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_feature_group_output(table_name)

    Creates a feature group and sets it to be the batch prediction output

    Parameters:

      table_name (str) – The name of the feature group table to create

    Returns:

      The batch prediction after the output has been applied

    Return type:

      BatchPrediction

  set_output_to_console()

    Sets the batch prediction output to the console, clearing both the file connector and database connector config

    Parameters:

      batch_prediction_id (str) – The unique identifier of the batch prediction

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_dataset(dataset_type, dataset_id=None)

    [Deprecated] Sets the batch prediction input dataset. Only applicable for legacy dataset-based projects

    Parameters:

      - dataset_type (str) – The dataset type to set

      - dataset_id (str) – The dataset to set

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_feature_group(feature_group_type, feature_group_id=None)

    Sets the batch prediction input feature group.

    Parameters:

      - feature_group_type (str) – The feature group type to set. The feature group type of the feature group. The type is based on the use case under which the feature group is being created. For example, Catalog Attributes can be a feature group type under personalized recommendation use case.

      - feature_group_id (str) – The feature group to set as input to the batch prediction

    Returns:

      The batch prediction description.

    Return type:

      BatchPrediction

  set_dataset_remap(dataset_id_remap)

    For the purpose of this batch prediction, will swap out datasets in the input feature groups

    Parameters:

      dataset_id_remap (dict) – Key/value pairs of dataset_ids to replace during batch predictions

    Returns:

      Batch Prediction object

    Return type:

      BatchPrediction

  delete()

    Deletes a batch prediction

    Parameters:

      batch_prediction_id (str) – The unique identifier of the batch prediction

  wait_for_predictions(timeout=86400)

    A waiting call until batch predictions are ready.

    Parameters:

      timeout (int, optional) – The waiting time given to the call to finish, if it doesn’t finish by the allocated time, the call is said to be timed out.

  wait_for_drift_monitor(timeout=86400)

    A waiting call until batch prediction drift monitor calculations are ready.

    Parameters:

      timeout (int, optional) – The waiting time given to the call to finish, if it doesn’t finish by the allocated time, the call is said to be timed out.

  get_status()

    Gets the status of the latest batch prediction version.

    Returns:

      A string describing the status of the latest batch prediction version e.g., pending, complete, etc.

    Return type:

      str

  create_refresh_policy(cron)

    To create a refresh policy for a batch prediction.

    Parameters:

      cron (str) – A cron style string to set the refresh time.

    Returns:

      The refresh policy object.

    Return type:

      RefreshPolicy

  list_refresh_policies()

    Gets the refresh policies in a list.

    Returns:

      A list of refresh policy objects.

    Return type:

      List[RefreshPolicy]

  describe_output_feature_group()

    Gets the results feature group for this batch prediction

    Returns:

      A feature group object.

    Return type:

      FeatureGroup