%% WARNING: DO NOT EDIT, AUTO-GENERATED CODE! %% See https://github.com/aws-beam/aws-codegen for more details. %% @doc Amazon Personalize is a machine learning service that makes it easy %% to add individualized recommendations to customers. -module(aws_personalize). -export([create_batch_inference_job/2, create_batch_inference_job/3, create_batch_segment_job/2, create_batch_segment_job/3, create_campaign/2, create_campaign/3, create_dataset/2, create_dataset/3, create_dataset_export_job/2, create_dataset_export_job/3, create_dataset_group/2, create_dataset_group/3, create_dataset_import_job/2, create_dataset_import_job/3, create_event_tracker/2, create_event_tracker/3, create_filter/2, create_filter/3, create_metric_attribution/2, create_metric_attribution/3, create_recommender/2, create_recommender/3, create_schema/2, create_schema/3, create_solution/2, create_solution/3, create_solution_version/2, create_solution_version/3, delete_campaign/2, delete_campaign/3, delete_dataset/2, delete_dataset/3, delete_dataset_group/2, delete_dataset_group/3, delete_event_tracker/2, delete_event_tracker/3, delete_filter/2, delete_filter/3, delete_metric_attribution/2, delete_metric_attribution/3, delete_recommender/2, delete_recommender/3, delete_schema/2, delete_schema/3, delete_solution/2, delete_solution/3, describe_algorithm/2, describe_algorithm/3, describe_batch_inference_job/2, describe_batch_inference_job/3, describe_batch_segment_job/2, describe_batch_segment_job/3, describe_campaign/2, describe_campaign/3, describe_dataset/2, describe_dataset/3, describe_dataset_export_job/2, describe_dataset_export_job/3, describe_dataset_group/2, describe_dataset_group/3, describe_dataset_import_job/2, describe_dataset_import_job/3, describe_event_tracker/2, describe_event_tracker/3, describe_feature_transformation/2, describe_feature_transformation/3, describe_filter/2, describe_filter/3, describe_metric_attribution/2, describe_metric_attribution/3, describe_recipe/2, describe_recipe/3, describe_recommender/2, describe_recommender/3, describe_schema/2, describe_schema/3, describe_solution/2, describe_solution/3, describe_solution_version/2, describe_solution_version/3, get_solution_metrics/2, get_solution_metrics/3, list_batch_inference_jobs/2, list_batch_inference_jobs/3, list_batch_segment_jobs/2, list_batch_segment_jobs/3, list_campaigns/2, list_campaigns/3, list_dataset_export_jobs/2, list_dataset_export_jobs/3, list_dataset_groups/2, list_dataset_groups/3, list_dataset_import_jobs/2, list_dataset_import_jobs/3, list_datasets/2, list_datasets/3, list_event_trackers/2, list_event_trackers/3, list_filters/2, list_filters/3, list_metric_attribution_metrics/2, list_metric_attribution_metrics/3, list_metric_attributions/2, list_metric_attributions/3, list_recipes/2, list_recipes/3, list_recommenders/2, list_recommenders/3, list_schemas/2, list_schemas/3, list_solution_versions/2, list_solution_versions/3, list_solutions/2, list_solutions/3, list_tags_for_resource/2, list_tags_for_resource/3, start_recommender/2, start_recommender/3, stop_recommender/2, stop_recommender/3, stop_solution_version_creation/2, stop_solution_version_creation/3, tag_resource/2, tag_resource/3, untag_resource/2, untag_resource/3, update_campaign/2, update_campaign/3, update_metric_attribution/2, update_metric_attribution/3, update_recommender/2, update_recommender/3]). -include_lib("hackney/include/hackney_lib.hrl"). %%==================================================================== %% API %%==================================================================== %% @doc Creates a batch inference job. %% %% The operation can handle up to 50 million records and the input file must %% be in JSON format. For more information, see Creating a batch inference %% job. create_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_inference_job(Client, Input, []). create_batch_inference_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateBatchInferenceJob">>, Input, Options). %% @doc Creates a batch segment job. %% %% The operation can handle up to 50 million records and the input file must %% be in JSON format. For more information, see Getting batch recommendations %% and user segments. create_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_segment_job(Client, Input, []). create_batch_segment_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateBatchSegmentJob">>, Input, Options). %% @doc 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 %% %% 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'. There's a short time delay while the capacity is %% increased that might cause loss of transactions. %% %% The actual TPS used is calculated as the average requests/second within a %% 5-minute window. You pay for maximum of either the minimum provisioned TPS %% or the actual TPS. We recommend starting 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: %% %% To get the campaign status, call DescribeCampaign. %% %% Wait until the `status' of the campaign is `ACTIVE' before asking the %% campaign for recommendations. %% %% == Related APIs == %% %% create_campaign(Client, Input) when is_map(Client), is_map(Input) -> create_campaign(Client, Input, []). create_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateCampaign">>, Input, Options). %% @doc 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: %% %% 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: %% %% To get the status of the dataset, call DescribeDataset. %% %% == Related APIs == %% %% create_dataset(Client, Input) when is_map(Client), is_map(Input) -> create_dataset(Client, Input, []). create_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDataset">>, Input, Options). %% @doc Creates a job that exports data from your dataset to an Amazon S3 %% bucket. %% %% To allow Amazon Personalize to export the training data, you must specify %% an service-linked IAM role that gives Amazon Personalize `PutObject' %% permissions for your Amazon S3 bucket. For information, see Exporting a %% dataset in the Amazon Personalize developer guide. %% %% Status %% %% A dataset export job can be in one of the following states: %% %% To get the status of the export job, call %% DescribeDatasetExportJob, and specify the Amazon Resource Name (ARN) of %% the dataset export job. The dataset export 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. create_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_export_job(Client, Input, []). create_dataset_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetExportJob">>, Input, Options). %% @doc Creates an empty dataset group. %% %% A dataset group is a container for Amazon Personalize resources. A dataset %% group can contain at most three datasets, one for each type of dataset: %% %% A dataset group can be a Domain dataset group, where you %% specify a domain and use pre-configured resources like recommenders, or a %% Custom dataset group, where you use custom resources, such as a solution %% with a solution version, that you deploy with a campaign. If you start %% with a Domain dataset group, you can still add custom resources such as %% solutions and solution versions trained with recipes for custom use cases %% and deployed with campaigns. %% %% A dataset group can be in one of the following states: %% %% 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. %% %% You must wait until the `status' of the dataset group is `ACTIVE' before %% adding a dataset to the group. %% %% You can specify an Key Management Service (KMS) key to encrypt the %% datasets in the group. If you specify a KMS key, you must also include an %% Identity and Access Management (IAM) role that has permission to access %% the key. %% %% == APIs that require a dataset group ARN in the request == %% %% == Related APIs == %% %% create_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_group(Client, Input, []). create_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetGroup">>, Input, Options). %% @doc 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 IAM service role that has permission to read from the data source, as %% Amazon Personalize makes a copy of your data and processes it internally. %% For information on granting access to your Amazon S3 bucket, see Giving %% Amazon Personalize Access to Amazon S3 Resources. %% %% By default, a dataset import job replaces any existing data in the dataset %% that you imported in bulk. To add new records without replacing existing %% data, specify INCREMENTAL for the import mode in the %% CreateDatasetImportJob operation. %% %% Status %% %% A dataset import job can be in one of the following states: %% %% 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. %% %% Importing takes time. You must wait until the status shows as ACTIVE %% before training a model using the dataset. %% %% == Related APIs == %% %% create_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_import_job(Client, Input, []). create_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDatasetImportJob">>, Input, Options). %% @doc Creates an event tracker that you use when adding event data to a %% specified dataset group using the PutEvents API. %% %% 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 create an event tracker, the response includes a tracking ID, %% which you pass as a parameter when you use the PutEvents operation. Amazon %% Personalize then appends the event data to the Interactions dataset of the %% dataset group you specify in your event tracker. %% %% The event tracker can be in one of the following states: %% %% To get the status of the event tracker, call %% DescribeEventTracker. %% %% The event tracker must be in the ACTIVE state before using the tracking %% ID. %% %% == Related APIs == %% %% create_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> create_event_tracker(Client, Input, []). create_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateEventTracker">>, Input, Options). %% @doc Creates a recommendation filter. %% %% For more information, see Filtering recommendations and user segments. create_filter(Client, Input) when is_map(Client), is_map(Input) -> create_filter(Client, Input, []). create_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateFilter">>, Input, Options). %% @doc Creates a metric attribution. %% %% A metric attribution creates reports on the data that you import into %% Amazon Personalize. Depending on how you imported the data, you can view %% reports in Amazon CloudWatch or Amazon S3. For more information, see %% Measuring impact of recommendations. create_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> create_metric_attribution(Client, Input, []). create_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateMetricAttribution">>, Input, Options). %% @doc 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: %% %% To get the recommender status, call DescribeRecommender. %% %% Wait until the `status' of the recommender is `ACTIVE' before asking the %% recommender for recommendations. %% %% == Related APIs == %% %% create_recommender(Client, Input) when is_map(Client), is_map(Input) -> create_recommender(Client, Input, []). create_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateRecommender">>, Input, Options). %% @doc 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. If you are creating a schema for a dataset in a Domain dataset %% group, you provide the domain of the Domain dataset group. You specify a %% schema when you call CreateDataset. %% %% == Related APIs == %% %% create_schema(Client, Input) when is_map(Client), is_map(Input) -> create_schema(Client, Input, []). create_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSchema">>, Input, Options). %% @doc 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. %% %% Amazon Personalize doesn't support configuring the `hpoObjective' for %% solution hyperparameter optimization at this time. %% %% Status %% %% A solution can be in one of the following states: %% %% To get the status of the solution, call DescribeSolution. Wait %% until the status shows as ACTIVE before calling `CreateSolutionVersion'. %% %% == Related APIs == %% %% create_solution(Client, Input) when is_map(Client), is_map(Input) -> create_solution(Client, Input, []). create_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSolution">>, Input, Options). %% @doc Trains or retrains an active solution in a Custom dataset group. %% %% 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: %% %% 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 == %% %% create_solution_version(Client, Input) when is_map(Client), is_map(Input) -> create_solution_version(Client, Input, []). create_solution_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSolutionVersion">>, Input, Options). %% @doc 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 information on creating campaigns, see %% CreateCampaign. delete_campaign(Client, Input) when is_map(Client), is_map(Input) -> delete_campaign(Client, Input, []). delete_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteCampaign">>, Input, Options). %% @doc 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. delete_dataset(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset(Client, Input, []). delete_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDataset">>, Input, Options). %% @doc Deletes a dataset group. %% %% Before you delete a dataset group, you must delete the following: %% %% delete_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset_group(Client, Input, []). delete_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteDatasetGroup">>, Input, Options). %% @doc Deletes the event tracker. %% %% Does not delete the event-interactions dataset from the associated dataset %% group. For more information on event trackers, see CreateEventTracker. delete_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> delete_event_tracker(Client, Input, []). delete_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteEventTracker">>, Input, Options). %% @doc Deletes a filter. delete_filter(Client, Input) when is_map(Client), is_map(Input) -> delete_filter(Client, Input, []). delete_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteFilter">>, Input, Options). %% @doc Deletes a metric attribution. delete_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> delete_metric_attribution(Client, Input, []). delete_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteMetricAttribution">>, Input, Options). %% @doc Deactivates and removes a recommender. %% %% A deleted recommender can no longer be specified in a GetRecommendations %% request. delete_recommender(Client, Input) when is_map(Client), is_map(Input) -> delete_recommender(Client, Input, []). delete_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteRecommender">>, Input, Options). %% @doc Deletes a schema. %% %% Before deleting a schema, you must delete all datasets referencing the %% schema. For more information on schemas, see CreateSchema. delete_schema(Client, Input) when is_map(Client), is_map(Input) -> delete_schema(Client, Input, []). delete_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteSchema">>, Input, Options). %% @doc 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. delete_solution(Client, Input) when is_map(Client), is_map(Input) -> delete_solution(Client, Input, []). delete_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DeleteSolution">>, Input, Options). %% @doc Describes the given algorithm. describe_algorithm(Client, Input) when is_map(Client), is_map(Input) -> describe_algorithm(Client, Input, []). describe_algorithm(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeAlgorithm">>, Input, Options). %% @doc 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. describe_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_inference_job(Client, Input, []). describe_batch_inference_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeBatchInferenceJob">>, Input, Options). %% @doc Gets the properties of a batch segment job including name, Amazon %% Resource Name (ARN), status, input and output configurations, and the ARN %% of the solution version used to generate segments. describe_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_segment_job(Client, Input, []). describe_batch_segment_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeBatchSegmentJob">>, Input, Options). %% @doc Describes the given campaign, including its status. %% %% A campaign can be in one of the following states: %% %% When the `status' is `CREATE FAILED', the response includes %% the `failureReason' key, which describes why. %% %% For more information on campaigns, see CreateCampaign. describe_campaign(Client, Input) when is_map(Client), is_map(Input) -> describe_campaign(Client, Input, []). describe_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeCampaign">>, Input, Options). %% @doc Describes the given dataset. %% %% For more information on datasets, see CreateDataset. describe_dataset(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset(Client, Input, []). describe_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDataset">>, Input, Options). %% @doc Describes the dataset export job created by CreateDatasetExportJob, %% including the export job status. describe_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_export_job(Client, Input, []). describe_dataset_export_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetExportJob">>, Input, Options). %% @doc Describes the given dataset group. %% %% For more information on dataset groups, see CreateDatasetGroup. describe_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_group(Client, Input, []). describe_dataset_group(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetGroup">>, Input, Options). %% @doc Describes the dataset import job created by CreateDatasetImportJob, %% including the import job status. describe_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_import_job(Client, Input, []). describe_dataset_import_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDatasetImportJob">>, Input, Options). %% @doc Describes an event tracker. %% %% The response includes the `trackingId' and `status' of the event tracker. %% For more information on event trackers, see CreateEventTracker. describe_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> describe_event_tracker(Client, Input, []). describe_event_tracker(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeEventTracker">>, Input, Options). %% @doc Describes the given feature transformation. describe_feature_transformation(Client, Input) when is_map(Client), is_map(Input) -> describe_feature_transformation(Client, Input, []). describe_feature_transformation(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFeatureTransformation">>, Input, Options). %% @doc Describes a filter's properties. describe_filter(Client, Input) when is_map(Client), is_map(Input) -> describe_filter(Client, Input, []). describe_filter(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeFilter">>, Input, Options). %% @doc Describes a metric attribution. describe_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> describe_metric_attribution(Client, Input, []). describe_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeMetricAttribution">>, Input, Options). %% @doc Describes a recipe. %% %% A recipe contains three items: %% %% 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. describe_recipe(Client, Input) when is_map(Client), is_map(Input) -> describe_recipe(Client, Input, []). describe_recipe(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeRecipe">>, Input, Options). %% @doc Describes the given recommender, including its status. %% %% A recommender can be in one of the following states: %% %% 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. describe_recommender(Client, Input) when is_map(Client), is_map(Input) -> describe_recommender(Client, Input, []). describe_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeRecommender">>, Input, Options). %% @doc Describes a schema. %% %% For more information on schemas, see CreateSchema. describe_schema(Client, Input) when is_map(Client), is_map(Input) -> describe_schema(Client, Input, []). describe_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSchema">>, Input, Options). %% @doc Describes a solution. %% %% For more information on solutions, see CreateSolution. describe_solution(Client, Input) when is_map(Client), is_map(Input) -> describe_solution(Client, Input, []). describe_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSolution">>, Input, Options). %% @doc Describes a specific version of a solution. %% %% For more information on solutions, see CreateSolution describe_solution_version(Client, Input) when is_map(Client), is_map(Input) -> describe_solution_version(Client, Input, []). describe_solution_version(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeSolutionVersion">>, Input, Options). %% @doc Gets the metrics for the specified solution version. get_solution_metrics(Client, Input) when is_map(Client), is_map(Input) -> get_solution_metrics(Client, Input, []). get_solution_metrics(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"GetSolutionMetrics">>, Input, Options). %% @doc Gets a list of the batch inference jobs that have been performed off %% of a solution version. list_batch_inference_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_inference_jobs(Client, Input, []). list_batch_inference_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListBatchInferenceJobs">>, Input, Options). %% @doc Gets a list of the batch segment jobs that have been performed off of %% a solution version that you specify. list_batch_segment_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_segment_jobs(Client, Input, []). list_batch_segment_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListBatchSegmentJobs">>, Input, Options). %% @doc 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. list_campaigns(Client, Input) when is_map(Client), is_map(Input) -> list_campaigns(Client, Input, []). list_campaigns(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListCampaigns">>, Input, Options). %% @doc Returns a list of dataset export jobs that use the given dataset. %% %% When a dataset is not specified, all the dataset export jobs associated %% with the account are listed. The response provides the properties for each %% dataset export job, including the Amazon Resource Name (ARN). For more %% information on dataset export jobs, see CreateDatasetExportJob. For more %% information on datasets, see CreateDataset. list_dataset_export_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_export_jobs(Client, Input, []). list_dataset_export_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetExportJobs">>, Input, Options). %% @doc 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. list_dataset_groups(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_groups(Client, Input, []). list_dataset_groups(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetGroups">>, Input, Options). %% @doc 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. list_dataset_import_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_import_jobs(Client, Input, []). list_dataset_import_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasetImportJobs">>, Input, Options). %% @doc 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. list_datasets(Client, Input) when is_map(Client), is_map(Input) -> list_datasets(Client, Input, []). list_datasets(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDatasets">>, Input, Options). %% @doc 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. list_event_trackers(Client, Input) when is_map(Client), is_map(Input) -> list_event_trackers(Client, Input, []). list_event_trackers(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListEventTrackers">>, Input, Options). %% @doc Lists all filters that belong to a given dataset group. list_filters(Client, Input) when is_map(Client), is_map(Input) -> list_filters(Client, Input, []). list_filters(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListFilters">>, Input, Options). %% @doc Lists the metrics for the metric attribution. list_metric_attribution_metrics(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attribution_metrics(Client, Input, []). list_metric_attribution_metrics(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMetricAttributionMetrics">>, Input, Options). %% @doc Lists metric attributions. list_metric_attributions(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attributions(Client, Input, []). list_metric_attributions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListMetricAttributions">>, Input, Options). %% @doc Returns a list of available recipes. %% %% The response provides the properties for each recipe, including the %% recipe's Amazon Resource Name (ARN). list_recipes(Client, Input) when is_map(Client), is_map(Input) -> list_recipes(Client, Input, []). list_recipes(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListRecipes">>, Input, Options). %% @doc 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. list_recommenders(Client, Input) when is_map(Client), is_map(Input) -> list_recommenders(Client, Input, []). list_recommenders(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListRecommenders">>, Input, Options). %% @doc 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. list_schemas(Client, Input) when is_map(Client), is_map(Input) -> list_schemas(Client, Input, []). list_schemas(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSchemas">>, Input, Options). %% @doc 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). list_solution_versions(Client, Input) when is_map(Client), is_map(Input) -> list_solution_versions(Client, Input, []). list_solution_versions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSolutionVersions">>, Input, Options). %% @doc 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. list_solutions(Client, Input) when is_map(Client), is_map(Input) -> list_solutions(Client, Input, []). list_solutions(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListSolutions">>, Input, Options). %% @doc Get a list of tags attached to a resource. list_tags_for_resource(Client, Input) when is_map(Client), is_map(Input) -> list_tags_for_resource(Client, Input, []). list_tags_for_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListTagsForResource">>, Input, Options). %% @doc Starts a recommender that is INACTIVE. %% %% Starting a recommender does not create any new models, but resumes billing %% and automatic retraining for the recommender. start_recommender(Client, Input) when is_map(Client), is_map(Input) -> start_recommender(Client, Input, []). start_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StartRecommender">>, Input, Options). %% @doc Stops a recommender that is ACTIVE. %% %% Stopping a recommender halts billing and automatic retraining for the %% recommender. stop_recommender(Client, Input) when is_map(Client), is_map(Input) -> stop_recommender(Client, Input, []). stop_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopRecommender">>, Input, Options). %% @doc Stops creating a solution version that is in a state of %% CREATE_PENDING or CREATE IN_PROGRESS. %% %% Depending on the current state of the solution version, the solution %% version state changes as follows: %% %% You are billed for all of the training completed up until you %% stop the solution version creation. You cannot resume creating a solution %% version once it has been stopped. stop_solution_version_creation(Client, Input) when is_map(Client), is_map(Input) -> stop_solution_version_creation(Client, Input, []). stop_solution_version_creation(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"StopSolutionVersionCreation">>, Input, Options). %% @doc Add a list of tags to a resource. tag_resource(Client, Input) when is_map(Client), is_map(Input) -> tag_resource(Client, Input, []). tag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"TagResource">>, Input, Options). %% @doc Remove tags that are attached to a resource. untag_resource(Client, Input) when is_map(Client), is_map(Input) -> untag_resource(Client, Input, []). untag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UntagResource">>, Input, Options). %% @doc 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 operation. %% %% You can still get recommendations from a campaign while an update is in %% progress. The campaign will use the previous solution version and campaign %% configuration to generate recommendations until the latest campaign update %% status is `Active'. %% %% For more information on campaigns, see CreateCampaign. update_campaign(Client, Input) when is_map(Client), is_map(Input) -> update_campaign(Client, Input, []). update_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateCampaign">>, Input, Options). %% @doc Updates a metric attribution. update_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> update_metric_attribution(Client, Input, []). update_metric_attribution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateMetricAttribution">>, Input, Options). %% @doc Updates the recommender to modify the recommender configuration. update_recommender(Client, Input) when is_map(Client), is_map(Input) -> update_recommender(Client, Input, []). update_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateRecommender">>, Input, Options). %%==================================================================== %% Internal functions %%==================================================================== -spec request(aws_client:aws_client(), binary(), map(), list()) -> {ok, Result, {integer(), list(), hackney:client()}} | {error, Error, {integer(), list(), hackney:client()}} | {error, term()} when Result :: map() | undefined, Error :: map(). request(Client, Action, Input, Options) -> RequestFun = fun() -> do_request(Client, Action, Input, Options) end, aws_request:request(RequestFun, Options). do_request(Client, Action, Input0, Options) -> Client1 = Client#{service => <<"personalize">>}, Host = build_host(<<"personalize">>, Client1), URL = build_url(Host, Client1), Headers = [ {<<"Host">>, Host}, {<<"Content-Type">>, <<"application/x-amz-json-1.1">>}, {<<"X-Amz-Target">>, <<"AmazonPersonalize.", Action/binary>>} ], Input = Input0, Payload = jsx:encode(Input), SignedHeaders = aws_request:sign_request(Client1, <<"POST">>, URL, Headers, Payload), Response = hackney:request(post, URL, SignedHeaders, Payload, Options), handle_response(Response). handle_response({ok, 200, ResponseHeaders, Client}) -> case hackney:body(Client) of {ok, <<>>} -> {ok, undefined, {200, ResponseHeaders, Client}}; {ok, Body} -> Result = jsx:decode(Body), {ok, Result, {200, ResponseHeaders, Client}} end; handle_response({ok, StatusCode, ResponseHeaders, Client}) -> {ok, Body} = hackney:body(Client), Error = jsx:decode(Body), {error, Error, {StatusCode, ResponseHeaders, Client}}; handle_response({error, Reason}) -> {error, Reason}. build_host(_EndpointPrefix, #{region := <<"local">>, endpoint := Endpoint}) -> Endpoint; build_host(_EndpointPrefix, #{region := <<"local">>}) -> <<"localhost">>; build_host(EndpointPrefix, #{region := Region, endpoint := Endpoint}) -> aws_util:binary_join([EndpointPrefix, Region, Endpoint], <<".">>). build_url(Host, Client) -> Proto = maps:get(proto, Client), Port = maps:get(port, Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).