ModelingModeConfig

class datarobotx.ModelingModeConfig(metric=None, mode=None, use_time_series=None, unsupervised_mode=None, unsupervised_type=None, shap_only_mode=None, quantile_level=None, accuracy_optimized_mb=None, only_include_monotonic_blueprints=None, monotonic_increasing_featurelist_id=None, monotonic_decreasing_featurelist_id=None)

Autopilot modes and metric(s).

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:

• metric (str) – The metric to use to select the best models. See /api/v2/projects/(projectId)/features/metrics/ for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column.

• mode ({'4', 'manual', '2', '0', 'auto', 'comprehensive', '3', 'quick'}) – The autopilot mode to use. Either ‘quick’, ‘auto’, ‘manual’ or ‘comprehensive’

• use_time_series (bool) – A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.

• unsupervised_mode (bool) – If True, unsupervised project (without target) will be created. target cannot be specified if unsupervisedMode is True.

• unsupervised_type ({'anomaly', 'clustering'}) – The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly.

• shap_only_mode (bool) – Keep only models that support SHAP values during Autopilot run. Use SHAP- based insights wherever possible.

• quantile_level (float) – The quantile level between 0.01 and 0.99 for specifying the Quantile metric.

• accuracy_optimized_mb (bool) – Include additional, longer-running models that will be run by the autopilot and available to run manually.

• only_include_monotonic_blueprints (bool) – When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.

• monotonic_increasing_featurelist_id (str) – The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.

• monotonic_decreasing_featurelist_id (str) – The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.

See also

DRConfig

Configuration object for DataRobot project and autopilot settings, also includes detailed examples of usage

Attributes:

accuracy_optimized_mb	Include additional, longer-running models that will be run by the autopilot and available to run manually.
metric	The metric to use to select the best models.
mode	The autopilot mode to use.
monotonic_decreasing_featurelist_id	The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target.
monotonic_increasing_featurelist_id	The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target.
only_include_monotonic_blueprints	When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.
quantile_level	The quantile level between 0.01 and 0.99 for specifying the Quantile metric.
shap_only_mode	Keep only models that support SHAP values during Autopilot run.
unsupervised_mode	If True, unsupervised project (without target) will be created.
unsupervised_type	The type of unsupervised project.
use_time_series	A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.

Inherited methods:

keys()	rtype: Collection[str]
to_dict()	Return configuration as a dict.

property accuracy_optimized_mb: bool

Include additional, longer-running models that will be run by the autopilot and available to run manually.

Notes

accuracy_optimized_mb : bool

property metric: str

The metric to use to select the best models. See /api/v2/projects/(projectId)/features/metrics/ for the metrics that may be valid for a potential target. Note that weighted metrics must be used with a weights column.

Notes

metric : str

property mode: str

The autopilot mode to use. Either ‘quick’, ‘auto’, ‘manual’ or ‘comprehensive’.

Notes

mode : {‘4’, ‘manual’, ‘2’, ‘0’, ‘auto’, ‘comprehensive’, ‘3’, ‘quick’}

property monotonic_decreasing_featurelist_id: str

The ID of the featurelist that defines the set of features with a monotonically decreasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.

Notes

monotonic_decreasing_featurelist_id : str

property monotonic_increasing_featurelist_id: str

The ID of the featurelist that defines the set of features with a monotonically increasing relationship to the target. If null, no such constraints are enforced. When specified, this will set a default for the project that can be overridden at model submission time if desired.

Notes

monotonic_increasing_featurelist_id : str

property only_include_monotonic_blueprints: bool

When true, only blueprints that support enforcing montonic constraints will be available in the project or selected for autopilot.

Notes

only_include_monotonic_blueprints : bool

property quantile_level: float

The quantile level between 0.01 and 0.99 for specifying the Quantile metric.

Notes

quantile_level : float

property shap_only_mode: bool

Keep only models that support SHAP values during Autopilot run. Use SHAP-based insights wherever possible.

Notes

shap_only_mode : bool

to_dict()

Return configuration as a dict.

Return type:

Dict[str, Any]

property unsupervised_mode: bool

If True, unsupervised project (without target) will be created. target cannot be specified if unsupervisedMode is True.

Notes

unsupervised_mode : bool

property unsupervised_type: str

The type of unsupervised project. Only valid when unsupervisedMode is true. If unsupervisedMode, defaults to anomaly.

Notes

unsupervised_type : {‘anomaly’, ‘clustering’}

property use_time_series: bool

A boolean value indicating whether a time series project should be created instead of a regular project which uses datetime partitioning.

Notes

use_time_series : bool