DRConfig#

class datarobotx.DRConfig(Data=None, Target=None, Featurization=None, Partitioning=None, Modeling=None, Metadata=None)#

DataRobot configuration.

Parameters that default to ‘None’ (or are omitted by the user) are overridden to server-side defaults at runtime. Consult the DataRobot REST API and GUI documentation for additional information on each parameter.

Parameters:

Data (dict or DataConfig) – Row and column selection configuration
Target (dict or TargetConfig) – Target configuration
Featurization (dict or FeaturesConfig) – Featurization configuration
Partitioning (dict or PartitioningConfig) – Partitioning configuration
Modeling (dict or ModelingConfig) – Modeling configuration
Metadata (dict or MetadataConfig) – DataRobot metadata and worker configuration

Examples

Direct configuration object construction

>>> from datarobotx import DRConfig
>>> config_1 = DRConfig()

Configuration object construction via abstraction get_params()

>>> from datarobotx.models.automl import AutoMLModel
>>> model_1 = AutoMLModel(name='elated-varahamihira')
>>> config_2 = model.get_params()

Dictionary representation of configuration

>>> config_2
{'project_description': 'my_description', 'project_name': 'elated-varahamihira'}

Object specification through nested attributes (useful with autocomplete discovery)

>>> config_2.Metadata.project_description = 'my_description'

Configuration of abstractions using a config object, dictionary, or keyword arguments

>>> model_1.set_params(**config_2).get_params()
{'project_description': 'my_description', 'project_name': 'elated-varahamihira'}
>>> my_dict = {'project_description': 'my_new_description'}
>>> model_1.set_params(**my_dict).get_params()
{'project_description': 'my_new_description', 'project_name': 'elated-varahamihira'}
>>> model_1.set_params(project_name='my_new_name').get_params()
{'project_description': 'my_new_description', 'project_name': 'my_new_name'}

Construction of abstractions using a configuration object

>>> from datarobotx.models.automl import AutoMLModel
>>> model_2 = AutoMLModel(**config_2)
>>> model_2.get_params()
{'project_description': 'my_description', 'project_name': 'elated-varahamihira'}

Attributes:

`Data`	Row and column selection configuration.
`Featurization`	Featurization configuration.
`Metadata`	DataRobot metadata and worker configuration.
`Modeling`	Modeling configuration.
`Partitioning`	Partitioning configuration.
`Target`	Target configuration.

Inherited methods:

keys()

rtype:: Collection[str]

to_dict()

Return configuration as a dict.

property Data: DataConfig#

Row and column selection configuration.

Notes

Data : dict or DataConfig

property Featurization: FeaturesConfig#

Featurization configuration.

Notes

Featurization : dict or FeaturesConfig

property Metadata: MetadataConfig#

DataRobot metadata and worker configuration.

Notes

Metadata : dict or MetadataConfig

property Modeling: ModelingConfig#

Modeling configuration.

Notes

Modeling : dict or ModelingConfig

property Partitioning: PartitioningConfig#

Partitioning configuration.

Notes

Partitioning : dict or PartitioningConfig

property Target: TargetConfig#

Target configuration.

Notes

Target : dict or TargetConfig

to_dict()#

Return configuration as a dict.

Return type:: Dict[str, Any]