2019/06/10 - Amazon Personalize - 36 new api methods
Changes Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications.
Describes the given dataset group. For more information on dataset groups, see CreateDatasetGroup.
See also: AWS API Documentation
Request Syntax
client.describe_dataset_group(
datasetGroupArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group to describe.
dict
Response Syntax
{
'datasetGroup': {
'name': 'string',
'datasetGroupArn': 'string',
'status': 'string',
'roleArn': 'string',
'kmsKeyArn': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
}
}
Response Structure
(dict) --
datasetGroup (dict) --
A listing of the dataset group's properties.
name (string) --
The name of the dataset group.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
status (string) --
The current status of the dataset group.
A dataset group can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING
roleArn (string) --
The ARN of the IAM role that has permissions to create the dataset group.
kmsKeyArn (string) --
The Amazon Resource Name (ARN) of the KMS key used to encrypt the datasets.
creationDateTime (datetime) --
The creation date and time (in Unix time) of the dataset group.
lastUpdatedDateTime (datetime) --
The last update date and time (in Unix time) of the dataset group.
failureReason (string) --
If creating a dataset group fails, provides the reason why.
Deletes a dataset group. Before you delete a dataset group, you must delete the following:
All associated event trackers.
All associated solutions.
All datasets in the dataset group.
See also: AWS API Documentation
Request Syntax
client.delete_dataset_group(
datasetGroupArn='string'
)
string
[REQUIRED]
The ARN of the dataset group to delete.
None
Deletes all versions of a solution and the Solution object itself. Before deleting a solution, you must delete all campaigns based on the solution. To determine what campaigns are using the solution, call ListCampaigns and supply the Amazon Resource Name (ARN) of the solution. You can't delete a solution if an associated SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on solutions, see CreateSolution.
See also: AWS API Documentation
Request Syntax
client.delete_solution(
solutionArn='string'
)
string
[REQUIRED]
The ARN of the solution to delete.
None
Deletes a dataset. You can't delete a dataset if an associated DatasetImportJob or SolutionVersion is in the CREATE PENDING or IN PROGRESS state. For more information on datasets, see CreateDataset.
See also: AWS API Documentation
Request Syntax
client.delete_dataset(
datasetArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset to delete.
None
Deletes a schema. Before deleting a schema, you must delete all datasets referencing the schema. For more information on schemas, see CreateSchema.
See also: AWS API Documentation
Request Syntax
client.delete_schema(
schemaArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the schema to delete.
None
Describes the given dataset. For more information on datasets, see CreateDataset.
See also: AWS API Documentation
Request Syntax
client.describe_dataset(
datasetArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset to describe.
dict
Response Syntax
{
'dataset': {
'name': 'string',
'datasetArn': 'string',
'datasetGroupArn': 'string',
'datasetType': 'string',
'schemaArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
dataset (dict) --
A listing of the dataset's properties.
name (string) --
The name of the dataset.
datasetArn (string) --
The Amazon Resource Name (ARN) of the dataset that you want metadata for.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
datasetType (string) --
One of the following values:
Interactions
Items
Users
schemaArn (string) --
The ARN of the associated schema.
status (string) --
The status of the dataset.
A dataset 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 dataset.
lastUpdatedDateTime (datetime) --
A time stamp that shows when the dataset was updated.
Returns the list of event trackers associated with the account. The response provides the properties for each event tracker, including the Amazon Resource Name (ARN) and tracking ID. For more information on event trackers, see CreateEventTracker.
See also: AWS API Documentation
Request Syntax
client.list_event_trackers(
datasetGroupArn='string',
nextToken='string',
maxResults=123
)
string
The ARN of a dataset group used to filter the response.
string
A token returned from the previous call to ListEventTrackers for getting the next set of event trackers (if they exist).
integer
The maximum number of event trackers to return.
dict
Response Syntax
{
'eventTrackers': [
{
'name': 'string',
'eventTrackerArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
eventTrackers (list) --
A list of event trackers.
(dict) --
Provides a summary of the properties of an event tracker. For a complete listing, call the DescribeEventTracker API.
name (string) --
The name of the event tracker.
eventTrackerArn (string) --
The Amazon Resource Name (ARN) of the event tracker.
status (string) --
The status of the event tracker.
An event tracker 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 date and time (in Unix time) that the event tracker was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the event tracker was last updated.
nextToken (string) --
A token for getting the next set of event trackers (if they exist).
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,
'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',
]
}
},
'autoMLResult': {
'bestRecipeArn': 'string'
},
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'latestSolutionVersion': {
'solutionVersionArn': 'string',
'status': 'string',
'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.
recipeArn (string) --
The ARN of the recipe used to create the solution.
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.
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). For use with the bring-your-own-recipe feature. Not used with Amazon Personalize predefined recipes.
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The data type of the metric.
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.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs.
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 hyperparameter names and ranges.
(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) --
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
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.
Creates an event tracker that you use when sending event data to the specified dataset group using the PutEvents API.
When Amazon Personalize creates an event tracker, it also creates an event-interactions dataset in the dataset group associated with the event tracker. The event-interactions dataset stores the event data from the PutEvents call. The contents of this dataset are not available to the user.
Note
Only one event tracker can be associated with a dataset group. You will get an error if you call CreateEventTracker using the same dataset group as an existing event tracker.
When you send event data you include your tracking ID. The tracking ID identifies the customer and authorizes the customer to send the data.
The event tracker 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 event tracker, call DescribeEventTracker.
Note
The event tracker must be in the ACTIVE state before using the tracking ID.
Related APIs
ListEventTrackers
DescribeEventTracker
DeleteEventTracker
See also: AWS API Documentation
Request Syntax
client.create_event_tracker(
name='string',
datasetGroupArn='string'
)
string
[REQUIRED]
The name for the event tracker.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group that receives the event data.
dict
Response Syntax
{
'eventTrackerArn': 'string',
'trackingId': 'string'
}
Response Structure
(dict) --
eventTrackerArn (string) --
The ARN of the event tracker.
trackingId (string) --
The ID of the event tracker. Include this ID in requests to the PutEvents API.
Removes a campaign by deleting the solution deployment. The solution that the campaign is based on is not deleted and can be redeployed when needed. A deleted campaign can no longer be specified in a GetRecommendations request. For more information on campaigns, see CreateCampaign.
See also: AWS API Documentation
Request Syntax
client.delete_campaign(
campaignArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the campaign to delete.
None
Describes an event tracker. The response includes the trackingId and status of the event tracker. For more information on event trackers, see CreateEventTracker.
See also: AWS API Documentation
Request Syntax
client.describe_event_tracker(
eventTrackerArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the event tracker to describe.
dict
Response Syntax
{
'eventTracker': {
'name': 'string',
'eventTrackerArn': 'string',
'accountId': 'string',
'trackingId': 'string',
'datasetGroupArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
eventTracker (dict) --
An object that describes the event tracker.
name (string) --
The name of the event tracker.
eventTrackerArn (string) --
The ARN of the event tracker.
accountId (string) --
The Amazon AWS account that owns the event tracker.
trackingId (string) --
The ID of the event tracker. Include this ID in requests to the PutEvents API.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that receives the event data.
status (string) --
The status of the event tracker.
An event tracker 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 date and time (in Unix format) that the event tracker was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the event tracker was last updated.
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': {
'solutionVersionArn': 'string',
'solutionArn': 'string',
'performHPO': True|False,
'performAutoML': 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',
]
}
},
'status': 'string',
'failureReason': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
solutionVersion (dict) --
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 performs a search for the most optimal recipe according to the solution configuration. When false (the default), Amazon Personalize uses recipeArn .
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). For use with the bring-your-own-recipe feature. Not used with Amazon Personalize predefined recipes.
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The data type of the metric.
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.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs.
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 hyperparameter names and ranges.
(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) --
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
failureReason (string) --
If training a solution version fails, the reason behind 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.
Describes the given algorithm.
See also: AWS API Documentation
Request Syntax
client.describe_algorithm(
algorithmArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the algorithm to describe.
dict
Response Syntax
{
'algorithm': {
'name': 'string',
'algorithmArn': 'string',
'algorithmImage': {
'name': 'string',
'dockerURI': 'string'
},
'defaultHyperParameters': {
'string': 'string'
},
'defaultHyperParameterRanges': {
'integerHyperParameterRanges': [
{
'name': 'string',
'minValue': 123,
'maxValue': 123,
'isTunable': True|False
},
],
'continuousHyperParameterRanges': [
{
'name': 'string',
'minValue': 123.0,
'maxValue': 123.0,
'isTunable': True|False
},
],
'categoricalHyperParameterRanges': [
{
'name': 'string',
'values': [
'string',
],
'isTunable': True|False
},
]
},
'defaultResourceConfig': {
'string': 'string'
},
'trainingInputMode': 'string',
'roleArn': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
algorithm (dict) --
A listing of the properties of the algorithm.
name (string) --
The name of the algorithm.
algorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm.
algorithmImage (dict) --
The URI of the Docker container for the algorithm image.
name (string) --
The name of the algorithm image.
dockerURI (string) --
The URI of the Docker container for the algorithm image.
defaultHyperParameters (dict) --
Specifies the default hyperparameters.
(string) --
(string) --
defaultHyperParameterRanges (dict) --
Specifies the default hyperparameters, their ranges, and whether they are tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
integerHyperParameterRanges (list) --
The integer-valued hyperparameters and their default ranges.
(dict) --
Provides the name and default range of a integer-valued hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
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.
isTunable (boolean) --
Indicates whether the hyperparameter is tunable.
continuousHyperParameterRanges (list) --
The continuous hyperparameters and their default ranges.
(dict) --
Provides the name and default range of a continuous hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
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.
isTunable (boolean) --
Whether the hyperparameter is tunable.
categoricalHyperParameterRanges (list) --
The categorical hyperparameters and their default ranges.
(dict) --
Provides the name and default range of a categorical hyperparameter and whether the hyperparameter is tunable. A tunable hyperparameter can have its value determined during hyperparameter optimization (HPO).
name (string) --
The name of the hyperparameter.
values (list) --
A list of the categories for the hyperparameter.
(string) --
isTunable (boolean) --
Whether the hyperparameter is tunable.
defaultResourceConfig (dict) --
Specifies the default maximum number of training jobs and parallel training jobs.
(string) --
(string) --
trainingInputMode (string) --
The training input mode.
roleArn (string) --
The Amazon Resource Name (ARN) of the role.
creationDateTime (datetime) --
The date and time (in Unix time) that the algorithm was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the algorithm was last updated.
Describes a schema. For more information on schemas, see CreateSchema.
See also: AWS API Documentation
Request Syntax
client.describe_schema(
schemaArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the schema to retrieve.
dict
Response Syntax
{
'schema': {
'name': 'string',
'schemaArn': 'string',
'schema': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
schema (dict) --
The requested schema.
name (string) --
The name of the schema.
schemaArn (string) --
The Amazon Resource Name (ARN) of the schema.
schema (string) --
The schema.
creationDateTime (datetime) --
The date and time (in Unix time) that the schema was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the schema was last updated.
Returns the list of datasets contained in the given dataset group. The response provides the properties for each dataset, including the Amazon Resource Name (ARN). For more information on datasets, see CreateDataset.
See also: AWS API Documentation
Request Syntax
client.list_datasets(
datasetGroupArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the dataset group that contains the datasets to list.
string
A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
integer
The maximum number of datasets to return.
dict
Response Syntax
{
'datasets': [
{
'name': 'string',
'datasetArn': 'string',
'datasetType': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
datasets (list) --
An array of Dataset objects. Each object provides metadata information.
(dict) --
Provides a summary of the properties of a dataset. For a complete listing, call the DescribeDataset API.
name (string) --
The name of the dataset.
datasetArn (string) --
The Amazon Resource Name (ARN) of the dataset.
datasetType (string) --
The dataset type. One of the following values:
Interactions
Items
Users
Event-Interactions
status (string) --
The status of the dataset.
A dataset 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 date and time (in Unix time) that the dataset was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the dataset was last updated.
nextToken (string) --
A token for getting the next set of datasets (if they exist).
Updates a campaign by either deploying a new solution or changing the value of the campaign's minProvisionedTPS parameter.
To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. Check the campaign status using the DescribeCampaign API.
Note
You must wait until the status of the updated campaign is ACTIVE before asking the campaign for recommendations.
For more information on campaigns, see CreateCampaign.
See also: AWS API Documentation
Request Syntax
client.update_campaign(
campaignArn='string',
solutionVersionArn='string',
minProvisionedTPS=123
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the campaign.
string
The ARN of a new solution version to deploy.
integer
Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
dict
Response Syntax
{
'campaignArn': 'string'
}
Response Structure
(dict) --
campaignArn (string) --
The same campaign ARN as given in the request.
Creates an empty dataset group. A dataset group contains related datasets that supply data for training a model. A dataset group can contain at most three datasets, one for each type of dataset:
Interactions
Items
Users
To train a model (create a solution), a dataset group that contains an Interactions dataset is required. Call CreateDataset to add a dataset to the group.
A dataset group can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING
To get the status of the dataset group, call DescribeDatasetGroup. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the creation failed.
Note
You must wait until the status of the dataset group is ACTIVE before adding a dataset to the group.
You can specify an AWS Key Management Service (KMS) key to encrypt the datasets in the group. If you specify a KMS key, you must also include an AWS Identity and Access Management (IAM) role that has permission to access the key.
APIs that require a dataset group ARN in the request
CreateDataset
CreateEventTracker
CreateSolution
Related APIs
ListDatasetGroups
DescribeDatasetGroup
DeleteDatasetGroup
See also: AWS API Documentation
Request Syntax
client.create_dataset_group(
name='string',
roleArn='string',
kmsKeyArn='string'
)
string
[REQUIRED]
The name for the new dataset group.
string
The ARN of the IAM role that has permissions to access the KMS key. Supplying an IAM role is only valid when also specifying a KMS key.
string
The Amazon Resource Name (ARN) of a KMS key used to encrypt the datasets.
dict
Response Syntax
{
'datasetGroupArn': 'string'
}
Response Structure
(dict) --
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the new dataset group.
Creates an Amazon Personalize schema from the specified schema string. The schema you create must be in Avro JSON format.
Amazon Personalize recognizes three schema variants. Each schema is associated with a dataset type and has a set of required field and keywords. You specify a schema when you call CreateDataset.
Related APIs
ListSchemas
DescribeSchema
DeleteSchema
See also: AWS API Documentation
Request Syntax
client.create_schema(
name='string',
schema='string'
)
string
[REQUIRED]
The name for the schema.
string
[REQUIRED]
A schema in Avro JSON format.
dict
Response Syntax
{
'schemaArn': 'string'
}
Response Structure
(dict) --
schemaArn (string) --
The Amazon Resource Name (ARN) of the created schema.
Trains or retrains an active solution. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion . A new version of the solution is created every time you call this operation.
Status
A solution version can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign .
If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.
Related APIs
ListSolutionVersions
DescribeSolutionVersion
ListSolutions
CreateSolution
DescribeSolution
DeleteSolution
See also: AWS API Documentation
Request Syntax
client.create_solution_version(
solutionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution containing the training configuration information.
dict
Response Syntax
{
'solutionVersionArn': 'string'
}
Response Structure
(dict) --
solutionVersionArn (string) --
The ARN of the new solution version.
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,
'status': 'string',
'failureReason': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'latestCampaignUpdate': {
'solutionVersionArn': 'string',
'minProvisionedTPS': 123,
'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 a specific version of the solution.
minProvisionedTPS (integer) --
Specifies the requested minimum provisioned transactions (recommendations) per second.
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.
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.
Describes a recipe.
A recipe contains three items:
An algorithm that trains a model.
Hyperparameters that govern the training.
Feature transformation information for modifying the input data before training.
Amazon Personalize provides a set of predefined recipes. You specify a recipe when you create a solution with the CreateSolution API. CreateSolution trains a model by using the algorithm in the specified recipe and a training dataset. The solution, when deployed as a campaign, can provide recommendations using the GetRecommendations API.
See also: AWS API Documentation
Request Syntax
client.describe_recipe(
recipeArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the recipe to describe.
dict
Response Syntax
{
'recipe': {
'name': 'string',
'recipeArn': 'string',
'algorithmArn': 'string',
'featureTransformationArn': 'string',
'status': 'string',
'description': 'string',
'creationDateTime': datetime(2015, 1, 1),
'recipeType': 'string',
'lastUpdatedDateTime': datetime(2015, 1, 1)
}
}
Response Structure
(dict) --
recipe (dict) --
An object that describes the recipe.
name (string) --
The name of the recipe.
recipeArn (string) --
The Amazon Resource Name (ARN) of the recipe.
algorithmArn (string) --
The Amazon Resource Name (ARN) of the algorithm that Amazon Personalize uses to train the model.
featureTransformationArn (string) --
The ARN of the FeatureTransformation object.
status (string) --
The status of the recipe.
description (string) --
The description of the recipe.
creationDateTime (datetime) --
The date and time (in Unix format) that the recipe was created.
recipeType (string) --
One of the following values:
SEARCH_PERSONALIZATION
RELATED_ITEMS
USER_PERSONALIZATION
lastUpdatedDateTime (datetime) --
The date and time (in Unix format) that the recipe was last updated.
Returns the list of schemas associated with the account. The response provides the properties for each schema, including the Amazon Resource Name (ARN). For more information on schemas, see CreateSchema.
See also: AWS API Documentation
Request Syntax
client.list_schemas(
nextToken='string',
maxResults=123
)
string
A token returned from the previous call to ListSchemas for getting the next set of schemas (if they exist).
integer
The maximum number of schemas to return.
dict
Response Syntax
{
'schemas': [
{
'name': 'string',
'schemaArn': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
schemas (list) --
A list of schemas.
(dict) --
Provides a summary of the properties of a dataset schema. For a complete listing, call the DescribeSchema API.
name (string) --
The name of the schema.
schemaArn (string) --
The Amazon Resource Name (ARN) of the schema.
creationDateTime (datetime) --
The date and time (in Unix time) that the schema was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the schema was last updated.
nextToken (string) --
A token used to get the next set of schemas (if they exist).
Returns a list of available recipes. The response provides the properties for each recipe, including the recipe's Amazon Resource Name (ARN).
See also: AWS API Documentation
Request Syntax
client.list_recipes(
recipeProvider='SERVICE',
nextToken='string',
maxResults=123
)
string
The default is SERVICE .
string
A token returned from the previous call to ListRecipes for getting the next set of recipes (if they exist).
integer
The maximum number of recipes to return.
dict
Response Syntax
{
'recipes': [
{
'name': 'string',
'recipeArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
recipes (list) --
The list of available recipes.
(dict) --
Provides a summary of the properties of a recipe. For a complete listing, call the DescribeRecipe API.
name (string) --
The name of the recipe.
recipeArn (string) --
The Amazon Resource Name (ARN) of the recipe.
status (string) --
The status of the recipe.
creationDateTime (datetime) --
The date and time (in Unix time) that the recipe was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the recipe was last updated.
nextToken (string) --
A token for getting the next set of recipes.
Creates an empty dataset and adds it to the specified dataset group. Use CreateDatasetImportJob to import your training data to a dataset.
There are three types of datasets:
Interactions
Items
Users
Each dataset type has an associated schema with required field types. Only the Interactions dataset is required in order to train a model (also referred to as creating a solution).
A dataset 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 dataset, call DescribeDataset.
Related APIs
CreateDatasetGroup
ListDatasets
DescribeDataset
DeleteDataset
See also: AWS API Documentation
Request Syntax
client.create_dataset(
name='string',
schemaArn='string',
datasetGroupArn='string',
datasetType='string'
)
string
[REQUIRED]
The name for the dataset.
string
[REQUIRED]
The ARN of the schema to associate with the dataset. The schema defines the dataset fields.
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset group to add the dataset to.
string
[REQUIRED]
The type of dataset.
One of the following (case insensitive) values:
Interactions
Items
Users
dict
Response Syntax
{
'datasetArn': 'string'
}
Response Structure
(dict) --
datasetArn (string) --
The ARN of the dataset.
Describes the dataset import job created by CreateDatasetImportJob, including the import job status.
See also: AWS API Documentation
Request Syntax
client.describe_dataset_import_job(
datasetImportJobArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the dataset import job to describe.
dict
Response Syntax
{
'datasetImportJob': {
'jobName': 'string',
'datasetImportJobArn': 'string',
'datasetArn': 'string',
'dataSource': {
'dataLocation': 'string'
},
'roleArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
}
}
Response Structure
(dict) --
datasetImportJob (dict) --
Information about the dataset import job, including the status.
The status is one of the following values:
CREATE PENDING
CREATE IN_PROGRESS
ACTIVE
CREATE FAILED
jobName (string) --
The name of the import job.
datasetImportJobArn (string) --
The ARN of the dataset import job.
datasetArn (string) --
The Amazon Resource Name (ARN) of the dataset that receives the imported data.
dataSource (dict) --
The Amazon S3 bucket that contains the training data to import.
dataLocation (string) --
The path to the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For example:
s3://bucket-name/training-data.csv
roleArn (string) --
The ARN of the AWS Identity and Access Management (IAM) role that has permissions to read from the Amazon S3 data source.
status (string) --
The status of the dataset import job.
A dataset import job can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
creationDateTime (datetime) --
The creation date and time (in Unix time) of the dataset import job.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) the dataset was last updated.
failureReason (string) --
If a dataset import job fails, provides the reason why.
Creates a campaign by deploying 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
A transaction is a single GetRecommendations or GetPersonalizedRanking call. Transactions per second (TPS) is the throughput and unit of billing for Amazon Personalize. The minimum provisioned TPS ( minProvisionedTPS ) specifies the baseline throughput provisioned by Amazon Personalize, and thus, the minimum billing charge. If your TPS increases beyond minProvisionedTPS , Amazon Personalize auto-scales the provisioned capacity up and down, but never below minProvisionedTPS , to maintain a 70% utilization. There's a short time delay while the capacity is increased that might cause loss of transactions. It's recommended to start with a low minProvisionedTPS , track your usage using Amazon CloudWatch metrics, and then increase the minProvisionedTPS as necessary.
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.
Note
Wait until the status of the campaign is ACTIVE before asking the campaign for recommendations.
Related APIs
ListCampaigns
DescribeCampaign
UpdateCampaign
DeleteCampaign
See also: AWS API Documentation
Request Syntax
client.create_campaign(
name='string',
solutionVersionArn='string',
minProvisionedTPS=123
)
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 solution version to deploy.
integer
[REQUIRED]
Specifies the requested minimum provisioned transactions (recommendations) per second that Amazon Personalize will support.
dict
Response Syntax
{
'campaignArn': 'string'
}
Response Structure
(dict) --
campaignArn (string) --
The Amazon Resource Name (ARN) of the campaign.
Returns a list of dataset groups. The response provides the properties for each dataset group, including the Amazon Resource Name (ARN). For more information on dataset groups, see CreateDatasetGroup.
See also: AWS API Documentation
Request Syntax
client.list_dataset_groups(
nextToken='string',
maxResults=123
)
string
A token returned from the previous call to ListDatasetGroups for getting the next set of dataset groups (if they exist).
integer
The maximum number of dataset groups to return.
dict
Response Syntax
{
'datasetGroups': [
{
'name': 'string',
'datasetGroupArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
datasetGroups (list) --
The list of your dataset groups.
(dict) --
Provides a summary of the properties of a dataset group. For a complete listing, call the DescribeDatasetGroup API.
name (string) --
The name of the dataset group.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group.
status (string) --
The status of the dataset group.
A dataset group can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING
creationDateTime (datetime) --
The date and time (in Unix time) that the dataset group was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the dataset group was last updated.
failureReason (string) --
If creating a dataset group fails, the reason behind the failure.
nextToken (string) --
A token for getting the next set of dataset groups (if they exist).
Returns a list of campaigns that use the given solution. When a solution is not specified, all the campaigns associated with the account are listed. The response provides the properties for each campaign, including the Amazon Resource Name (ARN). For more information on campaigns, see CreateCampaign.
See also: AWS API Documentation
Request Syntax
client.list_campaigns(
solutionArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the solution to list the campaigns for. When a solution is not specified, all the campaigns associated with the account are listed.
string
A token returned from the previous call to ListCampaigns for getting the next set of campaigns (if they exist).
integer
The maximum number of campaigns to return.
dict
Response Syntax
{
'campaigns': [
{
'name': 'string',
'campaignArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
campaigns (list) --
A list of the campaigns.
(dict) --
Provides a summary of the properties of a campaign. For a complete listing, call the DescribeCampaign API.
name (string) --
The name of the campaign.
campaignArn (string) --
The Amazon Resource Name (ARN) of the campaign.
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
creationDateTime (datetime) --
The date and time (in Unix time) that the campaign was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the campaign was last updated.
failureReason (string) --
If a campaign fails, the reason behind the failure.
nextToken (string) --
A token for getting the next set of campaigns (if they exist).
Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset. To allow Amazon Personalize to import the training data, you must specify an AWS Identity and Access Management (IAM) role that has permission to read from the data source.
Warning
The dataset import job replaces any previous data in the dataset.
Status
A dataset import job can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of the import job, call DescribeDatasetImportJob, providing the Amazon Resource Name (ARN) of the dataset import job. The dataset import is complete when the status shows as ACTIVE. If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.
Note
Importing takes time. You must wait until the status shows as ACTIVE before training a model using the dataset.
Related APIs
ListDatasetImportJobs
DescribeDatasetImportJob
See also: AWS API Documentation
Request Syntax
client.create_dataset_import_job(
jobName='string',
datasetArn='string',
dataSource={
'dataLocation': 'string'
},
roleArn='string'
)
string
[REQUIRED]
The name for the dataset import job.
string
[REQUIRED]
The ARN of the dataset that receives the imported data.
dict
[REQUIRED]
The Amazon S3 bucket that contains the training data to import.
dataLocation (string) --
The path to the Amazon S3 bucket where the data that you want to upload to your dataset is stored. For example:
s3://bucket-name/training-data.csv
string
[REQUIRED]
The ARN of the IAM role that has permissions to read from the Amazon S3 data source.
dict
Response Syntax
{
'datasetImportJobArn': 'string'
}
Response Structure
(dict) --
datasetImportJobArn (string) --
The ARN of the dataset import job.
Gets the metrics for the specified solution version.
See also: AWS API Documentation
Request Syntax
client.get_solution_metrics(
solutionVersionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution version for which to get metrics.
dict
Response Syntax
{
'solutionVersionArn': 'string',
'metrics': {
'string': 123.0
}
}
Response Structure
(dict) --
solutionVersionArn (string) --
The same solution version ARN as specified in the request.
metrics (dict) --
The metrics for the solution version.
(string) --
(float) --
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.
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. Wait until the status shows as ACTIVE before calling CreateSolutionVersion .
Related APIs
ListSolutions
CreateSolutionVersion
DescribeSolution
DeleteSolution
ListSolutionVersions
DescribeSolutionVersion
See also: AWS API Documentation
Request Syntax
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',
]
}
}
)
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.
string
The ARN of the recipe to use for model training. Only specified when performAutoML is false.
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.
dict
The configuration to use with 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). For use with the bring-your-own-recipe feature. Not used with Amazon Personalize predefined recipes.
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The data type of the metric.
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.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs.
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 hyperparameter names and ranges.
(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) --
dict
Response Syntax
{
'solutionArn': 'string'
}
Response Structure
(dict) --
solutionArn (string) --
The ARN of the solution.
Deletes the event tracker. Does not delete the event-interactions dataset from the associated dataset group. For more information on event trackers, see CreateEventTracker.
See also: AWS API Documentation
Request Syntax
client.delete_event_tracker(
eventTrackerArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the event tracker to delete.
None
Returns a list of solutions that use the given dataset group. When a dataset group is not specified, all the solutions associated with the account are listed. The response provides the properties for each solution, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.
See also: AWS API Documentation
Request Syntax
client.list_solutions(
datasetGroupArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the dataset group.
string
A token returned from the previous call to ListSolutions for getting the next set of solutions (if they exist).
integer
The maximum number of solutions to return.
dict
Response Syntax
{
'solutions': [
{
'name': 'string',
'solutionArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1)
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
solutions (list) --
A list of the current solutions.
(dict) --
Provides a summary of the properties of a solution. For a complete listing, call the DescribeSolution API.
name (string) --
The name of the solution.
solutionArn (string) --
The Amazon Resource Name (ARN) of the solution.
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 date and time (in Unix time) that the solution was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution was last updated.
nextToken (string) --
A token for getting the next set of solutions (if they exist).
Returns a list of dataset import jobs that use the given dataset. When a dataset is not specified, all the dataset import jobs associated with the account are listed. The response provides the properties for each dataset import job, including the Amazon Resource Name (ARN). For more information on dataset import jobs, see CreateDatasetImportJob. For more information on datasets, see CreateDataset.
See also: AWS API Documentation
Request Syntax
client.list_dataset_import_jobs(
datasetArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the dataset to list the dataset import jobs for.
string
A token returned from the previous call to ListDatasetImportJobs for getting the next set of dataset import jobs (if they exist).
integer
The maximum number of dataset import jobs to return.
dict
Response Syntax
{
'datasetImportJobs': [
{
'datasetImportJobArn': 'string',
'jobName': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
datasetImportJobs (list) --
The list of dataset import jobs.
(dict) --
Provides a summary of the properties of a dataset import job. For a complete listing, call the DescribeDatasetImportJob API.
datasetImportJobArn (string) --
The Amazon Resource Name (ARN) of the dataset import job.
jobName (string) --
The name of the dataset import job.
status (string) --
The status of the dataset import job.
A dataset import job can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
creationDateTime (datetime) --
The date and time (in Unix time) that the dataset import job was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the dataset was last updated.
failureReason (string) --
If a dataset import job fails, the reason behind the failure.
nextToken (string) --
A token for getting the next set of dataset import jobs (if they exist).
Returns a list of solution versions for the given solution. When a solution is not specified, all the solution versions associated with the account are listed. The response provides the properties for each solution version, including the Amazon Resource Name (ARN). For more information on solutions, see CreateSolution.
See also: AWS API Documentation
Request Syntax
client.list_solution_versions(
solutionArn='string',
nextToken='string',
maxResults=123
)
string
The Amazon Resource Name (ARN) of the solution.
string
A token returned from the previous call to ListSolutionVersions for getting the next set of solution versions (if they exist).
integer
The maximum number of solution versions to return.
dict
Response Syntax
{
'solutionVersions': [
{
'solutionVersionArn': 'string',
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
],
'nextToken': 'string'
}
Response Structure
(dict) --
solutionVersions (list) --
A list of solution versions describing the version properties.
(dict) --
Provides a summary of the properties of a solution version. For a complete listing, call the DescribeSolutionVersion API.
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
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.
nextToken (string) --
A token for getting the next set of solution versions (if they exist).
Describes the given feature transformation.
See also: AWS API Documentation
Request Syntax
client.describe_feature_transformation(
featureTransformationArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the feature transformation to describe.
dict
Response Syntax
{
'featureTransformation': {
'name': 'string',
'featureTransformationArn': 'string',
'defaultParameters': {
'string': 'string'
},
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'status': 'string'
}
}
Response Structure
(dict) --
featureTransformation (dict) --
A listing of the FeatureTransformation properties.
name (string) --
The name of the feature transformation.
featureTransformationArn (string) --
The Amazon Resource Name (ARN) of the FeatureTransformation object.
defaultParameters (dict) --
Provides the default parameters for feature transformation.
(string) --
(string) --
creationDateTime (datetime) --
The creation date and time (in Unix time) of the feature transformation.
lastUpdatedDateTime (datetime) --
The last update date and time (in Unix time) of the feature transformation.
status (string) --
The status of the feature transformation.
A feature transformation can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED