2025/12/01 - Amazon Personalize - 13 updated api methods
Changes This release adds support for includedDatasetColumns and performIncrementalUpdate in solution APIs, and rankingInfluence in campaign and batch inference APIs.
{'batchInferenceJobConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}}}
Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket.
To generate batch recommendations, specify the ARN of a solution version and an Amazon S3 URI for the input and output data. For user personalization, popular items, and personalized ranking solutions, the batch inference job generates a list of recommended items for each user ID in the input file. For related items solutions, the job generates a list of recommended items for each item ID in the input file.
For more information, see Creating a batch inference job.
If you use the Similar-Items recipe, Amazon Personalize can add descriptive themes to batch recommendations. To generate themes, set the job's mode to THEME_GENERATION and specify the name of the field that contains item names in the input data.
For more information about generating themes, see Batch recommendations with themes from Content Generator.
You can't get batch recommendations with the Trending-Now or Next-Best-Action recipes.
See also: AWS API Documentation
Request Syntax
client.create_batch_inference_job(
jobName='string',
solutionVersionArn='string',
filterArn='string',
numResults=123,
jobInput={
's3DataSource': {
'path': 'string',
'kmsKeyArn': 'string'
}
},
jobOutput={
's3DataDestination': {
'path': 'string',
'kmsKeyArn': 'string'
}
},
roleArn='string',
batchInferenceJobConfig={
'itemExplorationConfig': {
'string': 'string'
},
'rankingInfluence': {
'string': 123.0
}
},
tags=[
{
'tagKey': 'string',
'tagValue': 'string'
},
],
batchInferenceJobMode='BATCH_INFERENCE'|'THEME_GENERATION',
themeGenerationConfig={
'fieldsForThemeGeneration': {
'itemName': 'string'
}
}
)
string
[REQUIRED]
The name of the batch inference job to create.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution version that will be used to generate the batch inference recommendations.
string
The ARN of the filter to apply to the batch inference job. For more information on using filters, see Filtering batch recommendations.
integer
The number of recommendations to retrieve.
dict
[REQUIRED]
The Amazon S3 path that leads to the input file to base your recommendations on. The input material must be in JSON format.
s3DataSource (dict) -- [REQUIRED]
The URI of the Amazon S3 location that contains your input data. The Amazon S3 bucket must be in the same region as the API endpoint you are calling.
path (string) -- [REQUIRED]
The file path of the Amazon S3 bucket.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the Key Management Service (KMS) key that Amazon Personalize uses to encrypt or decrypt the input and output files.
dict
[REQUIRED]
The path to the Amazon S3 bucket where the job's output will be stored.
s3DataDestination (dict) -- [REQUIRED]
Information on the Amazon S3 bucket in which the batch inference job's output is stored.
path (string) -- [REQUIRED]
The file path of the Amazon S3 bucket.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the Key Management Service (KMS) key that Amazon Personalize uses to encrypt or decrypt the input and output files.
string
[REQUIRED]
The ARN of the Amazon Identity and Access Management role that has permissions to read and write to your input and output Amazon S3 buckets respectively.
dict
The configuration details of a batch inference job.
itemExplorationConfig (dict) --
A string to string map specifying the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. See User-Personalization.
(string) --
(string) --
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
list
A list of tags to apply to the batch inference job.
(dict) --
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 Amazon Personalize resources.
tagKey (string) -- [REQUIRED]
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.
tagValue (string) -- [REQUIRED]
The optional part of a key-value pair that makes up a tag. A value acts as a descriptor within a tag category (key).
string
The mode of the batch inference job. To generate descriptive themes for groups of similar items, set the job mode to THEME_GENERATION. If you don't want to generate themes, use the default BATCH_INFERENCE.
When you get batch recommendations with themes, you will incur additional costs. For more information, see Amazon Personalize pricing.
dict
For theme generation jobs, specify the name of the column in your Items dataset that contains each item's name.
fieldsForThemeGeneration (dict) -- [REQUIRED]
Fields used to generate descriptive themes for a batch inference job.
itemName (string) -- [REQUIRED]
The name of the Items dataset column that stores the name of each item in the dataset.
dict
Response Syntax
{
'batchInferenceJobArn': 'string'
}
Response Structure
(dict) --
batchInferenceJobArn (string) --
The ARN of the batch inference job.
{'campaignConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}}}
Creates a campaign that deploys a solution version. When a client calls the GetRecommendations and GetPersonalizedRanking APIs, a campaign is specified in the request.
Minimum Provisioned TPS and Auto-Scaling
When you create an Amazon Personalize campaign, you can specify the minimum provisioned transactions per second ( minProvisionedTPS) for the campaign. This is the baseline transaction throughput for the campaign provisioned by Amazon Personalize. It sets the minimum billing charge for the campaign while it is active. A transaction is a single GetRecommendations or GetPersonalizedRanking request. The default minProvisionedTPS is 1.
If your TPS increases beyond the minProvisionedTPS, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS. There's a short time delay while the capacity is increased that might cause loss of transactions. When your traffic reduces, capacity returns to the minProvisionedTPS.
You are charged for the the minimum provisioned TPS or, if your requests exceed the minProvisionedTPS, the actual TPS. The actual TPS is the total number of recommendation requests you make. We recommend starting with a low minProvisionedTPS, track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary.
For more information about campaign costs, see Amazon Personalize pricing.
Status
A campaign can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
To get the campaign status, call DescribeCampaign.
Related APIs
See also: AWS API Documentation
Request Syntax
client.create_campaign(
name='string',
solutionVersionArn='string',
minProvisionedTPS=123,
campaignConfig={
'itemExplorationConfig': {
'string': 'string'
},
'enableMetadataWithRecommendations': True|False,
'syncWithLatestSolutionVersion': True|False,
'rankingInfluence': {
'string': 123.0
}
},
tags=[
{
'tagKey': 'string',
'tagValue': 'string'
},
]
)
string
[REQUIRED]
A name for the new campaign. The campaign name must be unique within your account.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the trained model to deploy with the campaign. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.
To deploy a model that isn't the latest solution version of your solution, specify the ARN of the solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
integer
Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
dict
The configuration details of a campaign.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your solution uses the User-Personalization recipe.
(string) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the campaign. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
syncWithLatestSolutionVersion (boolean) --
Whether the campaign automatically updates to use the latest solution version (trained model) of a solution. If you specify True, you must specify the ARN of your solution for the SolutionVersionArn parameter. It must be in SolutionArn/$LATEST format. The default is False and you must manually update the campaign to deploy the latest solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
list
A list of tags to apply to the campaign.
(dict) --
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 Amazon Personalize resources.
tagKey (string) -- [REQUIRED]
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.
tagValue (string) -- [REQUIRED]
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
{
'campaignArn': 'string'
}
Response Structure
(dict) --
campaignArn (string) --
The Amazon Resource Name (ARN) of the campaign.
{'recommenderConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}
Creates a recommender with the recipe (a Domain dataset group use case) you specify. You create recommenders for a Domain dataset group and specify the recommender's Amazon Resource Name (ARN) when you make a GetRecommendations request.
Minimum recommendation requests per second
When you create a recommender, you can configure the recommender's minimum recommendation requests per second. The minimum recommendation requests per second ( minRecommendationRequestsPerSecond) specifies the baseline recommendation request throughput provisioned by Amazon Personalize. The default minRecommendationRequestsPerSecond is 1. A recommendation request is a single GetRecommendations operation. Request throughput is measured in requests per second and Amazon Personalize uses your requests per second to derive your requests per hour and the price of your recommender usage.
If your requests per second increases beyond minRecommendationRequestsPerSecond, Amazon Personalize auto-scales the provisioned capacity up and down, but never below minRecommendationRequestsPerSecond. There's a short time delay while the capacity is increased that might cause loss of requests.
Your bill is the greater of either the minimum requests per hour (based on minRecommendationRequestsPerSecond) or the actual number of requests. The actual request throughput used is calculated as the average requests/second within a one-hour window. We recommend starting with the default minRecommendationRequestsPerSecond, track your usage using Amazon CloudWatch metrics, and then increase the minRecommendationRequestsPerSecond as necessary.
Status
A recommender can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
DELETE PENDING > DELETE IN_PROGRESS
To get the recommender status, call DescribeRecommender.
Related APIs
See also: AWS API Documentation
Request Syntax
client.create_recommender(
name='string',
datasetGroupArn='string',
recipeArn='string',
recommenderConfig={
'itemExplorationConfig': {
'string': 'string'
},
'minRecommendationRequestsPerSecond': 123,
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'enableMetadataWithRecommendations': True|False
},
tags=[
{
'tagKey': 'string',
'tagValue': 'string'
},
]
)
string
[REQUIRED]
The name of the recommender.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the destination domain dataset group for the recommender.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the recipe that the recommender will use. For a recommender, a recipe is a Domain dataset group use case. Only Domain dataset group use cases can be used to create a recommender. For information about use cases see Choosing recommender use cases.
dict
The configuration details of the recommender.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
(string) --
(string) --
minRecommendationRequestsPerSecond (integer) --
Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a domain recommender.
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
list
A list of tags to apply to the recommender.
(dict) --
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 Amazon Personalize resources.
tagKey (string) -- [REQUIRED]
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.
tagValue (string) -- [REQUIRED]
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
{
'recommenderArn': 'string'
}
Response Structure
(dict) --
recommenderArn (string) --
The Amazon Resource Name (ARN) of the recommender.
{'performIncrementalUpdate': 'boolean',
'solutionConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}
Creates the configuration for training a model (creating a solution version). This configuration includes the recipe to use for model training and optional training configuration, such as columns to use in training and feature transformation parameters. For more information about configuring a solution, see Creating and configuring a solution.
By default, new solutions use automatic training to create solution versions every 7 days. You can change the training frequency. Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information, see Configuring automatic training.
To turn off automatic training, set performAutoTraining to false. If you turn off automatic training, you must manually create a solution version by calling the CreateSolutionVersion operation.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
After training completes you can evaluate model accuracy by calling GetSolutionMetrics. When you are satisfied with the solution version, you deploy it using CreateCampaign. The campaign provides recommendations to a client through the GetRecommendations API.
Status
A solution can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
To get the status of the solution, call DescribeSolution. If you use manual training, the status must be ACTIVE before you call CreateSolutionVersion.
Related APIs
See also: AWS API Documentation
Request Syntax
client.create_solution(
name='string',
performHPO=True|False,
performAutoML=True|False,
performAutoTraining=True|False,
performIncrementalUpdate=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',
]
},
'eventsConfig': {
'eventParametersList': [
{
'eventType': 'string',
'eventValueThreshold': 123.0,
'weight': 123.0
},
]
},
'optimizationObjective': {
'itemAttribute': 'string',
'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
},
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
},
tags=[
{
'tagKey': 'string',
'tagValue': 'string'
},
]
)
string
[REQUIRED]
The name for the solution.
boolean
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.
boolean
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.
boolean
Whether the solution uses automatic training to create new solution versions (trained models). The default is True and the solution automatically creates new solution versions every 7 days. You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration. For more information about automatic training, see Configuring automatic training.
Automatic solution version creation starts within one hour after the solution is ACTIVE. If you manually create a solution version within the hour, the solution skips the first automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
boolean
Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.
string
The Amazon Resource Name (ARN) of the recipe to use for model training. This is required when performAutoML is false. For information about different Amazon Personalize recipes and their ARNs, see Choosing a recipe.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group that provides the training data.
string
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.
dict
The configuration properties for the solution. When performAutoML is set to true, Amazon Personalize only evaluates the autoMLConfig section of the solution configuration.
eventValueThreshold (string) --
Only events with a value greater than or equal to this threshold are used for training a model.
hpoConfig (dict) --
Describes the properties for hyperparameter optimization (HPO).
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The type of the metric. Valid values are Maximize and Minimize.
metricName (string) --
The name of the metric.
metricRegex (string) --
A regular expression for finding the metric in the training job logs.
hpoResourceConfig (dict) --
Describes the resource configuration for HPO.
maxNumberOfTrainingJobs (string) --
The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10.
algorithmHyperParameterRanges (dict) --
The hyperparameters and their allowable ranges.
integerHyperParameterRanges (list) --
The integer-valued hyperparameters and their ranges.
(dict) --
Provides the name and range of an integer-valued hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (integer) --
The minimum allowable value for the hyperparameter.
maxValue (integer) --
The maximum allowable value for the hyperparameter.
continuousHyperParameterRanges (list) --
The continuous hyperparameters and their ranges.
(dict) --
Provides the name and range of a continuous hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (float) --
The minimum allowable value for the hyperparameter.
maxValue (float) --
The maximum allowable value for the hyperparameter.
categoricalHyperParameterRanges (list) --
The categorical hyperparameters and their ranges.
(dict) --
Provides the name and range of a categorical hyperparameter.
name (string) --
The name of the hyperparameter.
values (list) --
A list of the categories for the hyperparameter.
(string) --
algorithmHyperParameters (dict) --
Lists the algorithm hyperparameters and their values.
(string) --
(string) --
featureTransformationParameters (dict) --
Lists the feature transformation parameters.
(string) --
(string) --
autoMLConfig (dict) --
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
metricName (string) --
The metric to optimize.
recipeList (list) --
The list of candidate recipes.
(string) --
eventsConfig (dict) --
Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
eventParametersList (list) --
A list of event parameters, which includes event types and their event value thresholds and weights.
(dict) --
Describes the parameters of events, which are used in solution creation.
eventType (string) --
The name of the event type to be considered for solution creation.
eventValueThreshold (float) --
The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
weight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
optimizationObjective (dict) --
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
itemAttribute (string) --
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).
objectiveSensitivity (string) --
Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a custom solution version (trained model).
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
autoTrainingConfig (dict) --
Specifies the automatic training configuration to use.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
list
A list of tags to apply to the solution.
(dict) --
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 Amazon Personalize resources.
tagKey (string) -- [REQUIRED]
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.
tagValue (string) -- [REQUIRED]
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.
{'batchInferenceJob': {'batchInferenceJobConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}}}}
Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations.
See also: AWS API Documentation
Request Syntax
client.describe_batch_inference_job(
batchInferenceJobArn='string'
)
string
[REQUIRED]
The ARN of the batch inference job to describe.
dict
Response Syntax
{
'batchInferenceJob': {
'jobName': 'string',
'batchInferenceJobArn': 'string',
'filterArn': 'string',
'failureReason': 'string',
'solutionVersionArn': 'string',
'numResults': 123,
'jobInput': {
's3DataSource': {
'path': 'string',
'kmsKeyArn': 'string'
}
},
'jobOutput': {
's3DataDestination': {
'path': 'string',
'kmsKeyArn': 'string'
}
},
'batchInferenceJobConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'rankingInfluence': {
'string': 123.0
}
},
'roleArn': 'string',
'batchInferenceJobMode': 'BATCH_INFERENCE'|'THEME_GENERATION',
'themeGenerationConfig': {
'fieldsForThemeGeneration': {
'itemName': 'string'
}
},
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
batchInferenceJob (dict) --
Information on the specified batch inference job.
jobName (string) --
The name of the batch inference job.
batchInferenceJobArn (string) --
The Amazon Resource Name (ARN) of the batch inference job.
filterArn (string) --
The ARN of the filter used on the batch inference job.
failureReason (string) --
If the batch inference job failed, the reason for the failure.
solutionVersionArn (string) --
The Amazon Resource Name (ARN) of the solution version from which the batch inference job was created.
numResults (integer) --
The number of recommendations generated by the batch inference job. This number includes the error messages generated for failed input records.
jobInput (dict) --
The Amazon S3 path that leads to the input data used to generate the batch inference job.
s3DataSource (dict) --
The URI of the Amazon S3 location that contains your input data. The Amazon S3 bucket must be in the same region as the API endpoint you are calling.
path (string) --
The file path of the Amazon S3 bucket.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the Key Management Service (KMS) key that Amazon Personalize uses to encrypt or decrypt the input and output files.
jobOutput (dict) --
The Amazon S3 bucket that contains the output data generated by the batch inference job.
s3DataDestination (dict) --
Information on the Amazon S3 bucket in which the batch inference job's output is stored.
path (string) --
The file path of the Amazon S3 bucket.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the Key Management Service (KMS) key that Amazon Personalize uses to encrypt or decrypt the input and output files.
batchInferenceJobConfig (dict) --
A string to string map of the configuration details of a batch inference job.
itemExplorationConfig (dict) --
A string to string map specifying the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. See User-Personalization.
(string) --
(string) --
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
roleArn (string) --
The ARN of the Amazon Identity and Access Management (IAM) role that requested the batch inference job.
batchInferenceJobMode (string) --
The job's mode.
themeGenerationConfig (dict) --
The job's theme generation settings.
fieldsForThemeGeneration (dict) --
Fields used to generate descriptive themes for a batch inference job.
itemName (string) --
The name of the Items dataset column that stores the name of each item in the dataset.
status (string) --
The status of the batch inference job. The status is one of the following values:
PENDING
IN PROGRESS
ACTIVE
CREATE FAILED
creationDateTime (datetime) --
The time at which the batch inference job was created.
lastUpdatedDateTime (datetime) --
The time at which the batch inference job was last updated.
{'campaign': {'campaignConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}},
'latestCampaignUpdate': {'campaignConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}}}}}
Describes the given campaign, including its status.
A campaign can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
When the status is CREATE FAILED, the response includes the failureReason key, which describes why.
For more information on campaigns, see CreateCampaign.
See also: AWS API Documentation
Request Syntax
client.describe_campaign(
campaignArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the campaign.
dict
Response Syntax
{
'campaign': {
'name': 'string',
'campaignArn': 'string',
'solutionVersionArn': 'string',
'minProvisionedTPS': 123,
'campaignConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'enableMetadataWithRecommendations': True|False,
'syncWithLatestSolutionVersion': True|False,
'rankingInfluence': {
'string': 123.0
}
},
'status': 'string',
'failureReason': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'latestCampaignUpdate': {
'solutionVersionArn': 'string',
'minProvisionedTPS': 123,
'campaignConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'enableMetadataWithRecommendations': True|False,
'syncWithLatestSolutionVersion': True|False,
'rankingInfluence': {
'string': 123.0
}
},
'status': 'string',
'failureReason': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
}
Response Structure
(dict) --
campaign (dict) --
The properties of the campaign.
name (string) --
The name of the campaign.
campaignArn (string) --
The Amazon Resource Name (ARN) of the campaign.
solutionVersionArn (string) --
The Amazon Resource Name (ARN) of the solution version the campaign uses.
minProvisionedTPS (integer) --
Specifies the requested minimum provisioned transactions (recommendations) per second. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
campaignConfig (dict) --
The configuration details of a campaign.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your solution uses the User-Personalization recipe.
(string) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the campaign. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
syncWithLatestSolutionVersion (boolean) --
Whether the campaign automatically updates to use the latest solution version (trained model) of a solution. If you specify True, you must specify the ARN of your solution for the SolutionVersionArn parameter. It must be in SolutionArn/$LATEST format. The default is False and you must manually update the campaign to deploy the latest solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
status (string) --
The status of the campaign.
A campaign can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
failureReason (string) --
If a campaign fails, the reason behind the failure.
creationDateTime (datetime) --
The date and time (in Unix format) that the campaign was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix format) that the campaign was last updated.
latestCampaignUpdate (dict) --
Provides a summary of the properties of a campaign update. For a complete listing, call the DescribeCampaign API.
solutionVersionArn (string) --
The Amazon Resource Name (ARN) of the deployed solution version.
minProvisionedTPS (integer) --
Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
campaignConfig (dict) --
The configuration details of a campaign.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your solution uses the User-Personalization recipe.
(string) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the campaign. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
syncWithLatestSolutionVersion (boolean) --
Whether the campaign automatically updates to use the latest solution version (trained model) of a solution. If you specify True, you must specify the ARN of your solution for the SolutionVersionArn parameter. It must be in SolutionArn/$LATEST format. The default is False and you must manually update the campaign to deploy the latest solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
status (string) --
The status of the campaign update.
A campaign update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
failureReason (string) --
If a campaign update fails, the reason behind the failure.
creationDateTime (datetime) --
The date and time (in Unix time) that the campaign update was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the campaign update was last updated.
{'recommender': {'latestRecommenderUpdate': {'recommenderConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}},
'recommenderConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}}
Describes the given recommender, including its status.
A recommender can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
DELETE PENDING > DELETE IN_PROGRESS
When the status is CREATE FAILED, the response includes the failureReason key, which describes why.
The modelMetrics key is null when the recommender is being created or deleted.
For more information on recommenders, see CreateRecommender.
See also: AWS API Documentation
Request Syntax
client.describe_recommender(
recommenderArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the recommender to describe.
dict
Response Syntax
{
'recommender': {
'recommenderArn': 'string',
'datasetGroupArn': 'string',
'name': 'string',
'recipeArn': 'string',
'recommenderConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'minRecommendationRequestsPerSecond': 123,
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'enableMetadataWithRecommendations': True|False
},
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'status': 'string',
'failureReason': 'string',
'latestRecommenderUpdate': {
'recommenderConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'minRecommendationRequestsPerSecond': 123,
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'enableMetadataWithRecommendations': True|False
},
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'status': 'string',
'failureReason': 'string'
},
'modelMetrics': {
'string': 123.0
}
}
}
Response Structure
(dict) --
recommender (dict) --
The properties of the recommender.
recommenderArn (string) --
The Amazon Resource Name (ARN) of the recommender.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.
name (string) --
The name of the recommender.
recipeArn (string) --
The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for.
recommenderConfig (dict) --
The configuration details of the recommender.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
(string) --
(string) --
minRecommendationRequestsPerSecond (integer) --
Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a domain recommender.
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
creationDateTime (datetime) --
The date and time (in Unix format) that the recommender was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix format) that the recommender was last updated.
status (string) --
The status of the recommender.
A recommender can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
DELETE PENDING > DELETE IN_PROGRESS
failureReason (string) --
If a recommender fails, the reason behind the failure.
latestRecommenderUpdate (dict) --
Provides a summary of the latest updates to the recommender.
recommenderConfig (dict) --
The configuration details of the recommender update.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
(string) --
(string) --
minRecommendationRequestsPerSecond (integer) --
Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a domain recommender.
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
creationDateTime (datetime) --
The date and time (in Unix format) that the recommender update was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the recommender update was last updated.
status (string) --
The status of the recommender update. A recommender update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
failureReason (string) --
If a recommender update fails, the reason behind the failure.
modelMetrics (dict) --
Provides evaluation metrics that help you determine the performance of a recommender. For more information, see Evaluating a recommender.
(string) --
(float) --
{'solution': {'latestSolutionUpdate': {'performIncrementalUpdate': 'boolean'},
'performIncrementalUpdate': 'boolean',
'solutionConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}}
Describes a solution. For more information on solutions, see CreateSolution.
See also: AWS API Documentation
Request Syntax
client.describe_solution(
solutionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution to describe.
dict
Response Syntax
{
'solution': {
'name': 'string',
'solutionArn': 'string',
'performHPO': True|False,
'performAutoML': True|False,
'performAutoTraining': True|False,
'performIncrementalUpdate': 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',
]
},
'eventsConfig': {
'eventParametersList': [
{
'eventType': 'string',
'eventValueThreshold': 123.0,
'weight': 123.0
},
]
},
'optimizationObjective': {
'itemAttribute': 'string',
'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
},
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
},
'autoMLResult': {
'bestRecipeArn': 'string'
},
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'latestSolutionVersion': {
'solutionVersionArn': 'string',
'status': 'string',
'trainingMode': 'FULL'|'UPDATE'|'AUTOTRAIN',
'trainingType': 'AUTOMATIC'|'MANUAL',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
'latestSolutionUpdate': {
'solutionUpdateConfig': {
'autoTrainingConfig': {
'schedulingExpression': 'string'
},
'eventsConfig': {
'eventParametersList': [
{
'eventType': 'string',
'eventValueThreshold': 123.0,
'weight': 123.0
},
]
}
},
'status': 'string',
'performAutoTraining': True|False,
'performIncrementalUpdate': True|False,
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
}
}
}
Response Structure
(dict) --
solution (dict) --
An object that describes the solution.
name (string) --
The name of the solution.
solutionArn (string) --
The ARN of the solution.
performHPO (boolean) --
Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false.
performAutoML (boolean) --
When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration ( recipeArn must not be specified). When false (the default), Amazon Personalize uses recipeArn for training.
performAutoTraining (boolean) --
Specifies whether the solution automatically creates solution versions. The default is True and the solution automatically creates new solution versions every 7 days.
For more information about auto training, see Creating and configuring a solution.
performIncrementalUpdate (boolean) --
A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe
recipeArn (string) --
The ARN of the recipe used to create the solution. This is required when performAutoML is false.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that provides the training data.
eventType (string) --
The event type (for example, 'click' or 'like') that is used for training the model. If no eventType is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.
solutionConfig (dict) --
Describes the configuration properties for the solution.
eventValueThreshold (string) --
Only events with a value greater than or equal to this threshold are used for training a model.
hpoConfig (dict) --
Describes the properties for hyperparameter optimization (HPO).
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The type of the metric. Valid values are Maximize and Minimize.
metricName (string) --
The name of the metric.
metricRegex (string) --
A regular expression for finding the metric in the training job logs.
hpoResourceConfig (dict) --
Describes the resource configuration for HPO.
maxNumberOfTrainingJobs (string) --
The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10.
algorithmHyperParameterRanges (dict) --
The hyperparameters and their allowable ranges.
integerHyperParameterRanges (list) --
The integer-valued hyperparameters and their ranges.
(dict) --
Provides the name and range of an integer-valued hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (integer) --
The minimum allowable value for the hyperparameter.
maxValue (integer) --
The maximum allowable value for the hyperparameter.
continuousHyperParameterRanges (list) --
The continuous hyperparameters and their ranges.
(dict) --
Provides the name and range of a continuous hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (float) --
The minimum allowable value for the hyperparameter.
maxValue (float) --
The maximum allowable value for the hyperparameter.
categoricalHyperParameterRanges (list) --
The categorical hyperparameters and their ranges.
(dict) --
Provides the name and range of a categorical hyperparameter.
name (string) --
The name of the hyperparameter.
values (list) --
A list of the categories for the hyperparameter.
(string) --
algorithmHyperParameters (dict) --
Lists the algorithm hyperparameters and their values.
(string) --
(string) --
featureTransformationParameters (dict) --
Lists the feature transformation parameters.
(string) --
(string) --
autoMLConfig (dict) --
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
metricName (string) --
The metric to optimize.
recipeList (list) --
The list of candidate recipes.
(string) --
eventsConfig (dict) --
Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
eventParametersList (list) --
A list of event parameters, which includes event types and their event value thresholds and weights.
(dict) --
Describes the parameters of events, which are used in solution creation.
eventType (string) --
The name of the event type to be considered for solution creation.
eventValueThreshold (float) --
The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
weight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
optimizationObjective (dict) --
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
itemAttribute (string) --
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).
objectiveSensitivity (string) --
Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a custom solution version (trained model).
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
autoTrainingConfig (dict) --
Specifies the automatic training configuration to use.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
autoMLResult (dict) --
When performAutoML is true, specifies the best recipe found.
bestRecipeArn (string) --
The Amazon Resource Name (ARN) of the best recipe.
status (string) --
The status of the solution.
A solution can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
creationDateTime (datetime) --
The creation date and time (in Unix time) of the solution.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution was last updated.
latestSolutionVersion (dict) --
Describes the latest version of the solution, including the status and the ARN.
solutionVersionArn (string) --
The Amazon Resource Name (ARN) of the solution version.
status (string) --
The status of the solution version.
A solution version can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
trainingMode (string) --
The scope of training to be performed when creating the solution version. A FULL training considers all of the data in your dataset group. An UPDATE processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use UPDATE.
trainingType (string) --
Whether the solution version was created automatically or manually.
creationDateTime (datetime) --
The date and time (in Unix time) that this version of a solution was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution version was last updated.
failureReason (string) --
If a solution version fails, the reason behind the failure.
latestSolutionUpdate (dict) --
Provides a summary of the latest updates to the solution.
solutionUpdateConfig (dict) --
The configuration details of the solution.
autoTrainingConfig (dict) --
The automatic training configuration to use when performAutoTraining is true.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
eventsConfig (dict) --
Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
eventParametersList (list) --
A list of event parameters, which includes event types and their event value thresholds and weights.
(dict) --
Describes the parameters of events, which are used in solution creation.
eventType (string) --
The name of the event type to be considered for solution creation.
eventValueThreshold (float) --
The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
weight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
status (string) --
The status of the solution update. A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
performAutoTraining (boolean) --
Whether the solution automatically creates solution versions.
performIncrementalUpdate (boolean) --
A Boolean value that indicates whether incremental training updates are performed on the model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.
creationDateTime (datetime) --
The date and time (in Unix format) that the solution update was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution update was last updated.
failureReason (string) --
If a solution update fails, the reason behind the failure.
{'solutionVersion': {'performIncrementalUpdate': 'boolean',
'solutionConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}}
Describes a specific version of a solution. For more information on solutions, see CreateSolution
See also: AWS API Documentation
Request Syntax
client.describe_solution_version(
solutionVersionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution version.
dict
Response Syntax
{
'solutionVersion': {
'name': 'string',
'solutionVersionArn': 'string',
'solutionArn': 'string',
'performHPO': True|False,
'performAutoML': True|False,
'performIncrementalUpdate': True|False,
'recipeArn': 'string',
'eventType': 'string',
'datasetGroupArn': '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',
]
},
'eventsConfig': {
'eventParametersList': [
{
'eventType': 'string',
'eventValueThreshold': 123.0,
'weight': 123.0
},
]
},
'optimizationObjective': {
'itemAttribute': 'string',
'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
},
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
},
'trainingHours': 123.0,
'trainingMode': 'FULL'|'UPDATE'|'AUTOTRAIN',
'tunedHPOParams': {
'algorithmHyperParameters': {
'string': 'string'
}
},
'status': 'string',
'failureReason': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'trainingType': 'AUTOMATIC'|'MANUAL'
}
}
Response Structure
(dict) --
solutionVersion (dict) --
The solution version.
name (string) --
The name of the solution version.
solutionVersionArn (string) --
The ARN of the solution version.
solutionArn (string) --
The ARN of the solution.
performHPO (boolean) --
Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false.
performAutoML (boolean) --
When true, Amazon Personalize searches for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses recipeArn.
performIncrementalUpdate (boolean) --
Whether the solution version should perform an incremental update. When set to true, the training will process only the data that has changed since the latest training, similar to when trainingMode is set to UPDATE. This can only be used with solution versions that use the User-Personalization recipe.
recipeArn (string) --
The ARN of the recipe used in the solution.
eventType (string) --
The event type (for example, 'click' or 'like') that is used for training the model.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group providing the training data.
solutionConfig (dict) --
Describes the configuration properties for the solution.
eventValueThreshold (string) --
Only events with a value greater than or equal to this threshold are used for training a model.
hpoConfig (dict) --
Describes the properties for hyperparameter optimization (HPO).
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The type of the metric. Valid values are Maximize and Minimize.
metricName (string) --
The name of the metric.
metricRegex (string) --
A regular expression for finding the metric in the training job logs.
hpoResourceConfig (dict) --
Describes the resource configuration for HPO.
maxNumberOfTrainingJobs (string) --
The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10.
algorithmHyperParameterRanges (dict) --
The hyperparameters and their allowable ranges.
integerHyperParameterRanges (list) --
The integer-valued hyperparameters and their ranges.
(dict) --
Provides the name and range of an integer-valued hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (integer) --
The minimum allowable value for the hyperparameter.
maxValue (integer) --
The maximum allowable value for the hyperparameter.
continuousHyperParameterRanges (list) --
The continuous hyperparameters and their ranges.
(dict) --
Provides the name and range of a continuous hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (float) --
The minimum allowable value for the hyperparameter.
maxValue (float) --
The maximum allowable value for the hyperparameter.
categoricalHyperParameterRanges (list) --
The categorical hyperparameters and their ranges.
(dict) --
Provides the name and range of a categorical hyperparameter.
name (string) --
The name of the hyperparameter.
values (list) --
A list of the categories for the hyperparameter.
(string) --
algorithmHyperParameters (dict) --
Lists the algorithm hyperparameters and their values.
(string) --
(string) --
featureTransformationParameters (dict) --
Lists the feature transformation parameters.
(string) --
(string) --
autoMLConfig (dict) --
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
metricName (string) --
The metric to optimize.
recipeList (list) --
The list of candidate recipes.
(string) --
eventsConfig (dict) --
Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
eventParametersList (list) --
A list of event parameters, which includes event types and their event value thresholds and weights.
(dict) --
Describes the parameters of events, which are used in solution creation.
eventType (string) --
The name of the event type to be considered for solution creation.
eventValueThreshold (float) --
The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
weight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
optimizationObjective (dict) --
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
itemAttribute (string) --
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).
objectiveSensitivity (string) --
Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a custom solution version (trained model).
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
autoTrainingConfig (dict) --
Specifies the automatic training configuration to use.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
trainingHours (float) --
The time used to train the model. You are billed for the time it takes to train a model. This field is visible only after Amazon Personalize successfully trains a model.
trainingMode (string) --
The scope of training to be performed when creating the solution version. A FULL training considers all of the data in your dataset group. An UPDATE processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use UPDATE.
tunedHPOParams (dict) --
If hyperparameter optimization was performed, contains the hyperparameter values of the best performing model.
algorithmHyperParameters (dict) --
A list of the hyperparameter values of the best performing model.
(string) --
(string) --
status (string) --
The status of the solution version.
A solution version can be in one of the following states:
CREATE PENDING
CREATE IN_PROGRESS
ACTIVE
CREATE FAILED
CREATE STOPPING
CREATE STOPPED
failureReason (string) --
If training a solution version fails, the reason for the failure.
creationDateTime (datetime) --
The date and time (in Unix time) that this version of the solution was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution was last updated.
trainingType (string) --
Whether the solution version was created automatically or manually.
{'recommenders': {'recommenderConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}}
Returns a list of recommenders in a given Domain dataset group. When a Domain dataset group is not specified, all the recommenders associated with the account are listed. The response provides the properties for each recommender, including the Amazon Resource Name (ARN). For more information on recommenders, see CreateRecommender.
See also: AWS API Documentation
Request Syntax
client.list_recommenders(
datasetGroupArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the Domain dataset group to list the recommenders for. When a Domain dataset group is not specified, all the recommenders associated with the account are listed.
string
A token returned from the previous call to ListRecommenders for getting the next set of recommenders (if they exist).
integer
The maximum number of recommenders to return.
dict
Response Syntax
{
'recommenders': [
{
'name': 'string',
'recommenderArn': 'string',
'datasetGroupArn': 'string',
'recipeArn': 'string',
'recommenderConfig': {
'itemExplorationConfig': {
'string': 'string'
},
'minRecommendationRequestsPerSecond': 123,
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'enableMetadataWithRecommendations': True|False
},
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
recommenders (list) --
A list of the recommenders.
(dict) --
Provides a summary of the properties of the recommender.
name (string) --
The name of the recommender.
recommenderArn (string) --
The Amazon Resource Name (ARN) of the recommender.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the Domain dataset group that contains the recommender.
recipeArn (string) --
The Amazon Resource Name (ARN) of the recipe (Domain dataset group use case) that the recommender was created for.
recommenderConfig (dict) --
The configuration details of the recommender.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
(string) --
(string) --
minRecommendationRequestsPerSecond (integer) --
Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a domain recommender.
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
status (string) --
The status of the recommender. A recommender can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > START IN_PROGRESS > ACTIVE
DELETE PENDING > DELETE IN_PROGRESS
creationDateTime (datetime) --
The date and time (in Unix format) that the recommender was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix format) that the recommender was last updated.
nextToken (string) --
A token for getting the next set of recommenders (if they exist).
{'campaignConfig': {'rankingInfluence': {'POPULARITY | FRESHNESS': 'double'}}}
Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration. For example, you can set enableMetadataWithRecommendations to true for an existing campaign.
To update a campaign to start automatically using the latest solution version, specify the following:
For the SolutionVersionArn parameter, specify the Amazon Resource Name (ARN) of your solution in SolutionArn/$LATEST format.
In the campaignConfig, set syncWithLatestSolutionVersion to true.
To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign operation.
For more information about updating a campaign, including code samples, see Updating a campaign. For more information about campaigns, see Creating a campaign.
See also: AWS API Documentation
Request Syntax
client.update_campaign(
campaignArn='string',
solutionVersionArn='string',
minProvisionedTPS=123,
campaignConfig={
'itemExplorationConfig': {
'string': 'string'
},
'enableMetadataWithRecommendations': True|False,
'syncWithLatestSolutionVersion': True|False,
'rankingInfluence': {
'string': 123.0
}
}
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the campaign.
string
The Amazon Resource Name (ARN) of a new model to deploy. To specify the latest solution version of your solution, specify the ARN of your solution in SolutionArn/$LATEST format. You must use this format if you set syncWithLatestSolutionVersion to True in the CampaignConfig.
To deploy a model that isn't the latest solution version of your solution, specify the ARN of the solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
integer
Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support. A high minProvisionedTPS will increase your bill. We recommend starting with 1 for minProvisionedTPS (the default). Track your usage using Amazon CloudWatch metrics, and increase the minProvisionedTPS as necessary.
dict
The configuration details of a campaign.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your solution uses the User-Personalization recipe.
(string) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the campaign. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a campaign, see Enabling metadata in recommendations for a campaign.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
syncWithLatestSolutionVersion (boolean) --
Whether the campaign automatically updates to use the latest solution version (trained model) of a solution. If you specify True, you must specify the ARN of your solution for the SolutionVersionArn parameter. It must be in SolutionArn/$LATEST format. The default is False and you must manually update the campaign to deploy the latest solution version.
For more information about automatic campaign updates, see Enabling automatic campaign updates.
rankingInfluence (dict) --
A map of ranking influence values for POPULARITY and FRESHNESS. For each key, specify a numerical value between 0.0 and 1.0 that determines how much influence that ranking factor has on the final recommendations. A value closer to 1.0 gives more weight to the factor, while a value closer to 0.0 reduces its influence.
(string) --
(float) --
dict
Response Syntax
{
'campaignArn': 'string'
}
Response Structure
(dict) --
campaignArn (string) --
The same campaign ARN as given in the request.
{'recommenderConfig': {'trainingDataConfig': {'includedDatasetColumns': {'string': ['string']}}}}
Updates the recommender to modify the recommender configuration. If you update the recommender to modify the columns used in training, Amazon Personalize automatically starts a full retraining of the models backing your recommender. While the update completes, you can still get recommendations from the recommender. The recommender uses the previous configuration until the update completes. To track the status of this update, use the latestRecommenderUpdate returned in the DescribeRecommender operation.
See also: AWS API Documentation
Request Syntax
client.update_recommender(
recommenderArn='string',
recommenderConfig={
'itemExplorationConfig': {
'string': 'string'
},
'minRecommendationRequestsPerSecond': 123,
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
},
'includedDatasetColumns': {
'string': [
'string',
]
}
},
'enableMetadataWithRecommendations': True|False
}
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the recommender to modify.
dict
[REQUIRED]
The configuration details of the recommender.
itemExplorationConfig (dict) --
Specifies the exploration configuration hyperparameters, including explorationWeight and explorationItemAgeCutOff, you want to use to configure the amount of item exploration Amazon Personalize uses when recommending items. Provide itemExplorationConfig data only if your recommenders generate personalized recommendations for a user (not popular items or similar items).
(string) --
(string) --
minRecommendationRequestsPerSecond (integer) --
Specifies the requested minimum provisioned recommendation requests per second that Amazon Personalize will support. A high minRecommendationRequestsPerSecond will increase your bill. We recommend starting with 1 for minRecommendationRequestsPerSecond (the default). Track your usage using Amazon CloudWatch metrics, and increase the minRecommendationRequestsPerSecond as necessary.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a domain recommender.
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
includedDatasetColumns (dict) --
A map that specifies which columns to include from each dataset during training. The map can contain up to 3 entries, where each key is a dataset name (maximum length of 256 characters, must contain only letters and underscores) and each value is an array of up to 50 column names. Column names can be up to 150 characters long, must start with a letter or underscore, and can contain only letters, numbers, and underscores.
(string) --
(list) --
(string) --
enableMetadataWithRecommendations (boolean) --
Whether metadata with recommendations is enabled for the recommender. If enabled, you can specify the columns from your Items dataset in your request for recommendations. Amazon Personalize returns this data for each item in the recommendation response. For information about enabling metadata for a recommender, see Enabling metadata in recommendations for a recommender.
If you enable metadata in recommendations, you will incur additional costs. For more information, see Amazon Personalize pricing.
dict
Response Syntax
{
'recommenderArn': 'string'
}
Response Structure
(dict) --
recommenderArn (string) --
The same recommender Amazon Resource Name (ARN) as given in the request.
{'performIncrementalUpdate': 'boolean'}
Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution.
A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of a solution update, call the DescribeSolution API operation and find the status in the latestSolutionUpdate.
See also: AWS API Documentation
Request Syntax
client.update_solution(
solutionArn='string',
performAutoTraining=True|False,
performIncrementalUpdate=True|False,
solutionUpdateConfig={
'autoTrainingConfig': {
'schedulingExpression': 'string'
},
'eventsConfig': {
'eventParametersList': [
{
'eventType': 'string',
'eventValueThreshold': 123.0,
'weight': 123.0
},
]
}
}
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution to update.
boolean
Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration.
If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see Configuring automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
boolean
Whether to perform incremental training updates on your model. When enabled, this allows the model to learn from new data more frequently without requiring full retraining, which enables near real-time personalization. This parameter is supported only for solutions that use the semantic-similarity recipe.
dict
The new configuration details of the solution.
autoTrainingConfig (dict) --
The automatic training configuration to use when performAutoTraining is true.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
eventsConfig (dict) --
Describes the configuration of an event, which includes a list of event parameters. You can specify up to 10 event parameters. Events are used in solution creation.
eventParametersList (list) --
A list of event parameters, which includes event types and their event value thresholds and weights.
(dict) --
Describes the parameters of events, which are used in solution creation.
eventType (string) --
The name of the event type to be considered for solution creation.
eventValueThreshold (float) --
The threshold of the event type. Only events with a value greater or equal to this threshold will be considered for solution creation.
weight (float) --
The weight of the event type. A higher weight means higher importance of the event type for the created solution.
dict
Response Syntax
{
'solutionArn': 'string'
}
Response Structure
(dict) --
solutionArn (string) --
The same solution Amazon Resource Name (ARN) as given in the request.