Personalize.Client.create_solution(**kwargs)¶Creates the configuration for training a model. A trained model is known as a solution. After the configuration is created, you train the model (create a solution) by calling the CreateSolutionVersion operation. Every time you call CreateSolutionVersion , a new version of the solution is created.
After creating a solution version, you check its accuracy by calling GetSolutionMetrics. When you are satisfied with the version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.
To train a model, Amazon Personalize requires training data and a recipe. The training data comes from the dataset group that you provide in the request. A recipe specifies the training algorithm and a feature transformation. You can specify one of the predefined recipes provided by Amazon Personalize. Alternatively, you can specify performAutoML and Amazon Personalize will analyze your data and select the optimum USER_PERSONALIZATION recipe for you.
Note
Amazon Personalize doesn't support configuring thehpoObjectivefor solution hyperparameter optimization at this time.
Status
A solution can be in one of the following states:
To get the status of the solution, call DescribeSolution. Wait until the status shows as ACTIVE before calling CreateSolutionVersion .
Related APIs
See also: AWS API Documentation
Request Syntax
response = client.create_solution(
name='string',
performHPO=True|False,
performAutoML=True|False,
recipeArn='string',
datasetGroupArn='string',
eventType='string',
solutionConfig={
'eventValueThreshold': 'string',
'hpoConfig': {
'hpoObjective': {
'type': 'string',
'metricName': 'string',
'metricRegex': 'string'
},
'hpoResourceConfig': {
'maxNumberOfTrainingJobs': 'string',
'maxParallelTrainingJobs': 'string'
},
'algorithmHyperParameterRanges': {
'integerHyperParameterRanges': [
{
'name': 'string',
'minValue': 123,
'maxValue': 123
},
],
'continuousHyperParameterRanges': [
{
'name': 'string',
'minValue': 123.0,
'maxValue': 123.0
},
],
'categoricalHyperParameterRanges': [
{
'name': 'string',
'values': [
'string',
]
},
]
}
},
'algorithmHyperParameters': {
'string': 'string'
},
'featureTransformationParameters': {
'string': 'string'
},
'autoMLConfig': {
'metricName': 'string',
'recipeList': [
'string',
]
},
'optimizationObjective': {
'itemAttribute': 'string',
'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
}
},
tags=[
{
'tagKey': 'string',
'tagValue': 'string'
},
]
)
[REQUIRED]
The name for the solution.
Whether to perform hyperparameter optimization (HPO) on the specified or selected recipe. The default is false .
When performing AutoML, this parameter is always true and you should not set it to false .
Whether to perform automated machine learning (AutoML). The default is false . For this case, you must specify recipeArn .
When set to true , Amazon Personalize analyzes your training data and selects the optimal USER_PERSONALIZATION recipe and hyperparameters. In this case, you must omit recipeArn . Amazon Personalize determines the optimal recipe by running tests with different values for the hyperparameters. AutoML lengthens the training process as compared to selecting a specific recipe.
performAutoML is false.[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group that provides the training data.
When your have multiple event types (using an EVENT_TYPE schema field), this parameter specifies which event type (for example, 'click' or 'like') is used for training the model.
If you do not provide an eventType , Amazon Personalize will use all interactions for training with equal weight regardless of type.
The configuration to use with the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
Note
Amazon Personalize doesn't support configuring the hpoObjective at this time.
Only events with a value greater than or equal to this threshold are used for training a model.
Describes the properties for hyperparameter optimization (HPO).
The metric to optimize during HPO.
Note
Amazon Personalize doesn't support configuring the hpoObjective at this time.
The type of the metric. Valid values are Maximize and Minimize .
The name of the metric.
A regular expression for finding the metric in the training job logs.
Describes the resource configuration for HPO.
The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40 .
The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10 .
The hyperparameters and their allowable ranges.
The integer-valued hyperparameters and their ranges.
Provides the name and range of an integer-valued hyperparameter.
The name of the hyperparameter.
The minimum allowable value for the hyperparameter.
The maximum allowable value for the hyperparameter.
The continuous hyperparameters and their ranges.
Provides the name and range of a continuous hyperparameter.
The name of the hyperparameter.
The minimum allowable value for the hyperparameter.
The maximum allowable value for the hyperparameter.
The categorical hyperparameters and their ranges.
Provides the name and range of a categorical hyperparameter.
The name of the hyperparameter.
A list of the categories for the hyperparameter.
Lists the hyperparameter names and ranges.
Lists the feature transformation parameters.
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
The metric to optimize.
The list of candidate recipes.
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
The numerical metadata column in an Items dataset related to the optimization objective. For example, VIDEO_LENGTH (to maximize streaming minutes), or PRICE (to maximize revenue).
Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
A list of tags to apply to the solution.
The optional metadata that you apply to resources to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define. For more information see Tagging Personalize resources.
One part of a key-value pair that makes up a tag. A key is a general label that acts like a category for more specific tag values.
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
dict
Response Syntax
{
'solutionArn': 'string'
}
Response Structure
(dict) --
solutionArn (string) --
The ARN of the solution.
Exceptions
Personalize.Client.exceptions.InvalidInputExceptionPersonalize.Client.exceptions.ResourceAlreadyExistsExceptionPersonalize.Client.exceptions.ResourceNotFoundExceptionPersonalize.Client.exceptions.LimitExceededExceptionPersonalize.Client.exceptions.ResourceInUseExceptionPersonalize.Client.exceptions.TooManyTagsException