data_science

data_science

DomoDataflow Data Science Actions

Machine learning and data science actions in Magic ETL v2. These correspond to the "Data Science" category in the Domo ETL UI sidebar.

DomoDataflow_Action_MLInferenceAction dataclass

DomoDataflow_Action_MLInferenceAction(
    id: str,
    action_type: str = None,
    tile_type: str | None = None,
    name: str = None,
    depends_on: list[str] = None,
    disabled: bool = False,
    gui: dict = None,
    settings: dict = None,
    raw: dict = None,
    parent_actions: list[DomoDataflow_Action_Base] = None,
    ml_model_id: str = None,
    inference_column: str = None,
    inference_column_rename: str = None,
    inference_response: dict = None,
    include_input_data: bool = True,
    model_schema: dict = None,
    column_settings: dict = None,
    notes: str = None,
    input: str = None,
    tables: list[dict] = None,
)

Bases: DomoDataflow_Action_Base

ML Inference action - runs ML model predictions.

Attributes:

Name	Type	Description
ml_model_id	str	ID of the ML model to use
inference_column	str	Output column name for predictions
include_input_data	bool	Whether to include input columns in output

Example

ml_action = dataflow.get_action_objects("MLInferenceAction")[0] print(f"Model ID: {ml_action.ml_model_id}")

DomoDataflow_Action_UserDefined dataclass

DomoDataflow_Action_UserDefined(
    id: str,
    action_type: str = None,
    tile_type: str | None = None,
    name: str = None,
    depends_on: list[str] = None,
    disabled: bool = False,
    gui: dict = None,
    settings: dict = None,
    raw: dict = None,
    parent_actions: list[DomoDataflow_Action_Base] = None,
    action_definition_id: str = None,
    variables: dict = None,
    inputs: list[str] = None,
    additions: list[dict] = None,
    remove_by_default: bool = False,
    tables: list[dict] = None,
)

Bases: DomoDataflow_Action_Base

User Defined Action - custom reusable action from Action Library.

Attributes:

Name	Type	Description
action_definition_id	str	ID of the action definition
variables	dict	Variable values for the action
inputs	list[str]	List of input action IDs
additions	list[dict]	Output column definitions

Example

uda_action = dataflow.get_action_objects("UserDefinedAction")[0] print(f"Action Definition: {uda_action.action_definition_id}")