CfnSolutionPropsMixin

class aws_cdk.cfn_property_mixins.aws_personalize.CfnSolutionPropsMixin(props, *, strategy=None)

Bases: Mixin

By default, all new solutions use automatic training.

With automatic training, you incur training costs while your solution is active. To avoid unnecessary costs, when you are finished you can update the solution to turn off automatic training. For information about training costs, see Amazon Personalize pricing .

An object that provides information about a solution. A solution includes the custom recipe, customized parameters, and trained models (Solution Versions) that Amazon Personalize uses to generate recommendations.

After you create a solution, you can’t change its configuration. If you need to make changes, you can clone the solution with the Amazon Personalize console or create a new one.

see:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-personalize-solution.html

cloudformationResource:

AWS::Personalize::Solution

mixin:

true

exampleMetadata:

fixture=_generated

Example:
# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.cfn_property_mixins import aws_personalize as personalize
import aws_cdk as cdk

# auto_ml_config: Any
# hpo_config: Any
# merge_strategy: cdk.IMergeStrategy

cfn_solution_props_mixin = personalize.CfnSolutionPropsMixin(personalize.CfnSolutionMixinProps(
    dataset_group_arn="datasetGroupArn",
    event_type="eventType",
    name="name",
    perform_auto_ml=False,
    perform_hpo=False,
    recipe_arn="recipeArn",
    solution_config=personalize.CfnSolutionPropsMixin.SolutionConfigProperty(
        algorithm_hyper_parameters={
            "algorithm_hyper_parameters_key": "algorithmHyperParameters"
        },
        auto_ml_config=auto_ml_config,
        event_value_threshold="eventValueThreshold",
        feature_transformation_parameters={
            "feature_transformation_parameters_key": "featureTransformationParameters"
        },
        hpo_config=hpo_config
    )
),
    strategy=merge_strategy
)

Create a mixin to apply properties to AWS::Personalize::Solution.

Parameters:

props (Union[CfnSolutionMixinProps, Dict[str, Any]]) – L1 properties to apply.
strategy (Optional[IMergeStrategy]) – Strategy for merging nested properties. Default: - PropertyMergeStrategy.combine()

Methods

apply_to(construct)

Apply the mixin properties to the construct.

Parameters:: construct (IConstruct)
Return type:: None

supports(construct)

Check if this mixin supports the given construct.

Parameters:: construct (IConstruct)
Return type:: bool

Attributes

CFN_PROPERTY_KEYS = ['datasetGroupArn', 'eventType', 'name', 'performAutoMl', 'performHpo', 'recipeArn', 'solutionConfig']

Static Methods

classmethod is_mixin(x)

Checks if x is a Mixin.

Parameters:: x (Any) – Any object.
Return type:: bool
Returns:: true if x is an object created from a class which extends Mixin.

SolutionConfigProperty

class CfnSolutionPropsMixin.SolutionConfigProperty(*, algorithm_hyper_parameters=None, auto_ml_config=None, event_value_threshold=None, feature_transformation_parameters=None, hpo_config=None)

Bases: object

Describes the configuration properties for the solution.

Parameters:

algorithm_hyper_parameters (Union[Mapping[str, str], IResolvable, None]) – Lists the algorithm hyperparameters and their values.
auto_ml_config (Any) – The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
event_value_threshold (Optional[str]) – Only events with a value greater than or equal to this threshold are used for training a model.
feature_transformation_parameters (Union[Mapping[str, str], IResolvable, None]) – Lists the feature transformation parameters.
hpo_config (Any) – Describes the properties for hyperparameter optimization (HPO).

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-personalize-solution-solutionconfig.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk.cfn_property_mixins import aws_personalize as personalize

# auto_ml_config: Any
# hpo_config: Any

solution_config_property = personalize.CfnSolutionPropsMixin.SolutionConfigProperty(
    algorithm_hyper_parameters={
        "algorithm_hyper_parameters_key": "algorithmHyperParameters"
    },
    auto_ml_config=auto_ml_config,
    event_value_threshold="eventValueThreshold",
    feature_transformation_parameters={
        "feature_transformation_parameters_key": "featureTransformationParameters"
    },
    hpo_config=hpo_config
)