%% 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_data_deletion_job/2, create_data_deletion_job/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_data_deletion_job/2, describe_data_deletion_job/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_data_deletion_jobs/2, list_data_deletion_jobs/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_dataset/2, update_dataset/3, update_metric_attribution/2, update_metric_attribution/3, update_recommender/2, update_recommender/3, update_solution/2, update_solution/3]). -include_lib("hackney/include/hackney_lib.hrl"). %% Example: %% hyper_parameter_ranges() :: #{ %% <<"categoricalHyperParameterRanges">> => list(categorical_hyper_parameter_range()), %% <<"continuousHyperParameterRanges">> => list(continuous_hyper_parameter_range()), %% <<"integerHyperParameterRanges">> => list(integer_hyper_parameter_range()) %% } -type hyper_parameter_ranges() :: #{binary() => any()}. %% Example: %% data_deletion_job() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"dataDeletionJobArn">> => string(), %% <<"dataSource">> => data_source(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"numDeleted">> => integer(), %% <<"roleArn">> => string(), %% <<"status">> => string() %% } -type data_deletion_job() :: #{binary() => any()}. %% Example: %% create_event_tracker_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"name">> := string(), %% <<"tags">> => list(tag()) %% } -type create_event_tracker_request() :: #{binary() => any()}. %% Example: %% describe_event_tracker_request() :: #{ %% <<"eventTrackerArn">> := string() %% } -type describe_event_tracker_request() :: #{binary() => any()}. %% Example: %% delete_metric_attribution_request() :: #{ %% <<"metricAttributionArn">> := string() %% } -type delete_metric_attribution_request() :: #{binary() => any()}. %% Example: %% describe_solution_response() :: #{ %% <<"solution">> => solution() %% } -type describe_solution_response() :: #{binary() => any()}. %% Example: %% describe_metric_attribution_request() :: #{ %% <<"metricAttributionArn">> := string() %% } -type describe_metric_attribution_request() :: #{binary() => any()}. %% Example: %% update_solution_request() :: #{ %% <<"performAutoTraining">> => boolean(), %% <<"performIncrementalUpdate">> => boolean(), %% <<"solutionArn">> := string(), %% <<"solutionUpdateConfig">> => solution_update_config() %% } -type update_solution_request() :: #{binary() => any()}. %% Example: %% list_batch_segment_jobs_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"solutionVersionArn">> => string() %% } -type list_batch_segment_jobs_request() :: #{binary() => any()}. %% Example: %% describe_campaign_response() :: #{ %% <<"campaign">> => campaign() %% } -type describe_campaign_response() :: #{binary() => any()}. %% Example: %% update_campaign_response() :: #{ %% <<"campaignArn">> => string() %% } -type update_campaign_response() :: #{binary() => any()}. %% Example: %% describe_batch_inference_job_response() :: #{ %% <<"batchInferenceJob">> => batch_inference_job() %% } -type describe_batch_inference_job_response() :: #{binary() => any()}. %% Example: %% create_batch_inference_job_request() :: #{ %% <<"batchInferenceJobConfig">> => batch_inference_job_config(), %% <<"batchInferenceJobMode">> => list(any()), %% <<"filterArn">> => string(), %% <<"jobInput">> := batch_inference_job_input(), %% <<"jobName">> := string(), %% <<"jobOutput">> := batch_inference_job_output(), %% <<"numResults">> => integer(), %% <<"roleArn">> := string(), %% <<"solutionVersionArn">> := string(), %% <<"tags">> => list(tag()), %% <<"themeGenerationConfig">> => theme_generation_config() %% } -type create_batch_inference_job_request() :: #{binary() => any()}. %% Example: %% filter_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"filterArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type filter_summary() :: #{binary() => any()}. %% Example: %% tag_resource_request() :: #{ %% <<"resourceArn">> := string(), %% <<"tags">> := list(tag()) %% } -type tag_resource_request() :: #{binary() => any()}. %% Example: %% list_batch_inference_jobs_response() :: #{ %% <<"batchInferenceJobs">> => list(batch_inference_job_summary()), %% <<"nextToken">> => string() %% } -type list_batch_inference_jobs_response() :: #{binary() => any()}. %% Example: %% list_data_deletion_jobs_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_data_deletion_jobs_request() :: #{binary() => any()}. %% Example: %% create_schema_request() :: #{ %% <<"domain">> => list(any()), %% <<"name">> := string(), %% <<"schema">> := string() %% } -type create_schema_request() :: #{binary() => any()}. %% Example: %% campaign() :: #{ %% <<"campaignArn">> => string(), %% <<"campaignConfig">> => campaign_config(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"latestCampaignUpdate">> => campaign_update_summary(), %% <<"minProvisionedTPS">> => integer(), %% <<"name">> => string(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string() %% } -type campaign() :: #{binary() => any()}. %% Example: %% list_recipes_response() :: #{ %% <<"nextToken">> => string(), %% <<"recipes">> => list(recipe_summary()) %% } -type list_recipes_response() :: #{binary() => any()}. %% Example: %% recommender_update_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"recommenderConfig">> => recommender_config(), %% <<"status">> => string() %% } -type recommender_update_summary() :: #{binary() => any()}. %% Example: %% create_event_tracker_response() :: #{ %% <<"eventTrackerArn">> => string(), %% <<"trackingId">> => string() %% } -type create_event_tracker_response() :: #{binary() => any()}. %% Example: %% create_recommender_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"name">> := string(), %% <<"recipeArn">> := string(), %% <<"recommenderConfig">> => recommender_config(), %% <<"tags">> => list(tag()) %% } -type create_recommender_request() :: #{binary() => any()}. %% Example: %% default_integer_hyper_parameter_range() :: #{ %% <<"isTunable">> => boolean(), %% <<"maxValue">> => integer(), %% <<"minValue">> => integer(), %% <<"name">> => string() %% } -type default_integer_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% update_recommender_response() :: #{ %% <<"recommenderArn">> => string() %% } -type update_recommender_response() :: #{binary() => any()}. %% Example: %% describe_schema_request() :: #{ %% <<"schemaArn">> := string() %% } -type describe_schema_request() :: #{binary() => any()}. %% Example: %% auto_training_config() :: #{ %% <<"schedulingExpression">> => string() %% } -type auto_training_config() :: #{binary() => any()}. %% Example: %% update_dataset_response() :: #{ %% <<"datasetArn">> => string() %% } -type update_dataset_response() :: #{binary() => any()}. %% Example: %% default_categorical_hyper_parameter_range() :: #{ %% <<"isTunable">> => boolean(), %% <<"name">> => string(), %% <<"values">> => list(string()) %% } -type default_categorical_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% describe_solution_version_request() :: #{ %% <<"solutionVersionArn">> := string() %% } -type describe_solution_version_request() :: #{binary() => any()}. %% Example: %% continuous_hyper_parameter_range() :: #{ %% <<"maxValue">> => float(), %% <<"minValue">> => float(), %% <<"name">> => string() %% } -type continuous_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% create_solution_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"eventType">> => string(), %% <<"name">> := string(), %% <<"performAutoML">> => boolean(), %% <<"performAutoTraining">> => boolean(), %% <<"performHPO">> => boolean(), %% <<"performIncrementalUpdate">> => boolean(), %% <<"recipeArn">> => string(), %% <<"solutionConfig">> => solution_config(), %% <<"tags">> => list(tag()) %% } -type create_solution_request() :: #{binary() => any()}. %% Example: %% create_metric_attribution_response() :: #{ %% <<"metricAttributionArn">> => string() %% } -type create_metric_attribution_response() :: #{binary() => any()}. %% Example: %% untag_resource_response() :: #{ %% } -type untag_resource_response() :: #{binary() => any()}. %% Example: %% delete_schema_request() :: #{ %% <<"schemaArn">> := string() %% } -type delete_schema_request() :: #{binary() => any()}. %% Example: %% create_data_deletion_job_response() :: #{ %% <<"dataDeletionJobArn">> => string() %% } -type create_data_deletion_job_response() :: #{binary() => any()}. %% Example: %% metric_attribution_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"metricAttributionArn">> => string(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type metric_attribution_summary() :: #{binary() => any()}. %% Example: %% resource_in_use_exception() :: #{ %% <<"message">> => string() %% } -type resource_in_use_exception() :: #{binary() => any()}. %% Example: %% batch_segment_job_output() :: #{ %% <<"s3DataDestination">> => s3_data_config() %% } -type batch_segment_job_output() :: #{binary() => any()}. %% Example: %% describe_dataset_import_job_response() :: #{ %% <<"datasetImportJob">> => dataset_import_job() %% } -type describe_dataset_import_job_response() :: #{binary() => any()}. %% Example: %% solution() :: #{ %% <<"autoMLResult">> => auto_ml_result(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"eventType">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"latestSolutionUpdate">> => solution_update_summary(), %% <<"latestSolutionVersion">> => solution_version_summary(), %% <<"name">> => string(), %% <<"performAutoML">> => boolean(), %% <<"performAutoTraining">> => boolean(), %% <<"performHPO">> => boolean(), %% <<"performIncrementalUpdate">> => boolean(), %% <<"recipeArn">> => string(), %% <<"solutionArn">> => string(), %% <<"solutionConfig">> => solution_config(), %% <<"status">> => string() %% } -type solution() :: #{binary() => any()}. %% Example: %% create_batch_segment_job_response() :: #{ %% <<"batchSegmentJobArn">> => string() %% } -type create_batch_segment_job_response() :: #{binary() => any()}. %% Example: %% start_recommender_response() :: #{ %% <<"recommenderArn">> => string() %% } -type start_recommender_response() :: #{binary() => any()}. %% Example: %% dataset_schema() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"domain">> => list(any()), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"schema">> => string(), %% <<"schemaArn">> => string() %% } -type dataset_schema() :: #{binary() => any()}. %% Example: %% algorithm() :: #{ %% <<"algorithmArn">> => string(), %% <<"algorithmImage">> => algorithm_image(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"defaultHyperParameterRanges">> => default_hyper_parameter_ranges(), %% <<"defaultHyperParameters">> => map(), %% <<"defaultResourceConfig">> => map(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"roleArn">> => string(), %% <<"trainingInputMode">> => string() %% } -type algorithm() :: #{binary() => any()}. %% Example: %% recipe_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"domain">> => list(any()), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"recipeArn">> => string(), %% <<"status">> => string() %% } -type recipe_summary() :: #{binary() => any()}. %% Example: %% list_datasets_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_datasets_request() :: #{binary() => any()}. %% Example: %% list_campaigns_response() :: #{ %% <<"campaigns">> => list(campaign_summary()), %% <<"nextToken">> => string() %% } -type list_campaigns_response() :: #{binary() => any()}. %% Example: %% event_parameters() :: #{ %% <<"eventType">> => string(), %% <<"eventValueThreshold">> => float(), %% <<"weight">> => float() %% } -type event_parameters() :: #{binary() => any()}. %% Example: %% create_filter_response() :: #{ %% <<"filterArn">> => string() %% } -type create_filter_response() :: #{binary() => any()}. %% Example: %% theme_generation_config() :: #{ %% <<"fieldsForThemeGeneration">> => fields_for_theme_generation() %% } -type theme_generation_config() :: #{binary() => any()}. %% Example: %% default_hyper_parameter_ranges() :: #{ %% <<"categoricalHyperParameterRanges">> => list(default_categorical_hyper_parameter_range()), %% <<"continuousHyperParameterRanges">> => list(default_continuous_hyper_parameter_range()), %% <<"integerHyperParameterRanges">> => list(default_integer_hyper_parameter_range()) %% } -type default_hyper_parameter_ranges() :: #{binary() => any()}. %% Example: %% describe_schema_response() :: #{ %% <<"schema">> => dataset_schema() %% } -type describe_schema_response() :: #{binary() => any()}. %% Example: %% delete_campaign_request() :: #{ %% <<"campaignArn">> := string() %% } -type delete_campaign_request() :: #{binary() => any()}. %% Example: %% describe_campaign_request() :: #{ %% <<"campaignArn">> := string() %% } -type describe_campaign_request() :: #{binary() => any()}. %% Example: %% list_metric_attribution_metrics_request() :: #{ %% <<"maxResults">> => integer(), %% <<"metricAttributionArn">> => string(), %% <<"nextToken">> => string() %% } -type list_metric_attribution_metrics_request() :: #{binary() => any()}. %% Example: %% list_solutions_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_solutions_request() :: #{binary() => any()}. %% Example: %% describe_algorithm_request() :: #{ %% <<"algorithmArn">> := string() %% } -type describe_algorithm_request() :: #{binary() => any()}. %% Example: %% create_schema_response() :: #{ %% <<"schemaArn">> => string() %% } -type create_schema_response() :: #{binary() => any()}. %% Example: %% untag_resource_request() :: #{ %% <<"resourceArn">> := string(), %% <<"tagKeys">> := list(string()) %% } -type untag_resource_request() :: #{binary() => any()}. %% Example: %% batch_inference_job_input() :: #{ %% <<"s3DataSource">> => s3_data_config() %% } -type batch_inference_job_input() :: #{binary() => any()}. %% Example: %% batch_inference_job_config() :: #{ %% <<"itemExplorationConfig">> => map(), %% <<"rankingInfluence">> => map() %% } -type batch_inference_job_config() :: #{binary() => any()}. %% Example: %% get_solution_metrics_request() :: #{ %% <<"solutionVersionArn">> := string() %% } -type get_solution_metrics_request() :: #{binary() => any()}. %% Example: %% campaign_config() :: #{ %% <<"enableMetadataWithRecommendations">> => boolean(), %% <<"itemExplorationConfig">> => map(), %% <<"rankingInfluence">> => map(), %% <<"syncWithLatestSolutionVersion">> => boolean() %% } -type campaign_config() :: #{binary() => any()}. %% Example: %% describe_data_deletion_job_response() :: #{ %% <<"dataDeletionJob">> => data_deletion_job() %% } -type describe_data_deletion_job_response() :: #{binary() => any()}. %% Example: %% list_data_deletion_jobs_response() :: #{ %% <<"dataDeletionJobs">> => list(data_deletion_job_summary()), %% <<"nextToken">> => string() %% } -type list_data_deletion_jobs_response() :: #{binary() => any()}. %% Example: %% list_event_trackers_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_event_trackers_request() :: #{binary() => any()}. %% Example: %% create_dataset_group_request() :: #{ %% <<"domain">> => list(any()), %% <<"kmsKeyArn">> => string(), %% <<"name">> := string(), %% <<"roleArn">> => string(), %% <<"tags">> => list(tag()) %% } -type create_dataset_group_request() :: #{binary() => any()}. %% Example: %% too_many_tag_keys_exception() :: #{ %% <<"message">> => string() %% } -type too_many_tag_keys_exception() :: #{binary() => any()}. %% Example: %% solution_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"recipeArn">> => string(), %% <<"solutionArn">> => string(), %% <<"status">> => string() %% } -type solution_summary() :: #{binary() => any()}. %% Example: %% list_schemas_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_schemas_request() :: #{binary() => any()}. %% Example: %% recommender_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"recipeArn">> => string(), %% <<"recommenderArn">> => string(), %% <<"recommenderConfig">> => recommender_config(), %% <<"status">> => string() %% } -type recommender_summary() :: #{binary() => any()}. %% Example: %% describe_solution_request() :: #{ %% <<"solutionArn">> := string() %% } -type describe_solution_request() :: #{binary() => any()}. %% Example: %% update_recommender_request() :: #{ %% <<"recommenderArn">> := string(), %% <<"recommenderConfig">> := recommender_config() %% } -type update_recommender_request() :: #{binary() => any()}. %% Example: %% create_metric_attribution_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"metrics">> := list(metric_attribute()), %% <<"metricsOutputConfig">> := metric_attribution_output(), %% <<"name">> := string() %% } -type create_metric_attribution_request() :: #{binary() => any()}. %% Example: %% batch_inference_job() :: #{ %% <<"batchInferenceJobArn">> => string(), %% <<"batchInferenceJobConfig">> => batch_inference_job_config(), %% <<"batchInferenceJobMode">> => list(any()), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"filterArn">> => string(), %% <<"jobInput">> => batch_inference_job_input(), %% <<"jobName">> => string(), %% <<"jobOutput">> => batch_inference_job_output(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"numResults">> => integer(), %% <<"roleArn">> => string(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string(), %% <<"themeGenerationConfig">> => theme_generation_config() %% } -type batch_inference_job() :: #{binary() => any()}. %% Example: %% dataset() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetArn">> => string(), %% <<"datasetGroupArn">> => string(), %% <<"datasetType">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"latestDatasetUpdate">> => dataset_update_summary(), %% <<"name">> => string(), %% <<"schemaArn">> => string(), %% <<"status">> => string(), %% <<"trackingId">> => string() %% } -type dataset() :: #{binary() => any()}. %% Example: %% describe_dataset_export_job_response() :: #{ %% <<"datasetExportJob">> => dataset_export_job() %% } -type describe_dataset_export_job_response() :: #{binary() => any()}. %% Example: %% create_dataset_response() :: #{ %% <<"datasetArn">> => string() %% } -type create_dataset_response() :: #{binary() => any()}. %% Example: %% list_campaigns_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"solutionArn">> => string() %% } -type list_campaigns_request() :: #{binary() => any()}. %% Example: %% batch_segment_job_summary() :: #{ %% <<"batchSegmentJobArn">> => string(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string() %% } -type batch_segment_job_summary() :: #{binary() => any()}. %% Example: %% describe_batch_segment_job_response() :: #{ %% <<"batchSegmentJob">> => batch_segment_job() %% } -type describe_batch_segment_job_response() :: #{binary() => any()}. %% Example: %% campaign_update_summary() :: #{ %% <<"campaignConfig">> => campaign_config(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"minProvisionedTPS">> => integer(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string() %% } -type campaign_update_summary() :: #{binary() => any()}. %% Example: %% batch_inference_job_output() :: #{ %% <<"s3DataDestination">> => s3_data_config() %% } -type batch_inference_job_output() :: #{binary() => any()}. %% Example: %% dataset_import_job() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"dataSource">> => data_source(), %% <<"datasetArn">> => string(), %% <<"datasetImportJobArn">> => string(), %% <<"failureReason">> => string(), %% <<"importMode">> => list(any()), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"publishAttributionMetricsToS3">> => boolean(), %% <<"roleArn">> => string(), %% <<"status">> => string() %% } -type dataset_import_job() :: #{binary() => any()}. %% Example: %% metric_attribute() :: #{ %% <<"eventType">> => string(), %% <<"expression">> => string(), %% <<"metricName">> => string() %% } -type metric_attribute() :: #{binary() => any()}. %% Example: %% solution_version() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"eventType">> => string(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"performAutoML">> => boolean(), %% <<"performHPO">> => boolean(), %% <<"performIncrementalUpdate">> => boolean(), %% <<"recipeArn">> => string(), %% <<"solutionArn">> => string(), %% <<"solutionConfig">> => solution_config(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string(), %% <<"trainingHours">> => float(), %% <<"trainingMode">> => list(any()), %% <<"trainingType">> => list(any()), %% <<"tunedHPOParams">> => tuned_h_p_o_params() %% } -type solution_version() :: #{binary() => any()}. %% Example: %% dataset_export_job_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetExportJobArn">> => string(), %% <<"failureReason">> => string(), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"status">> => string() %% } -type dataset_export_job_summary() :: #{binary() => any()}. %% Example: %% fields_for_theme_generation() :: #{ %% <<"itemName">> => string() %% } -type fields_for_theme_generation() :: #{binary() => any()}. %% Example: %% resource_not_found_exception() :: #{ %% <<"message">> => string() %% } -type resource_not_found_exception() :: #{binary() => any()}. %% Example: %% list_dataset_import_jobs_request() :: #{ %% <<"datasetArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_dataset_import_jobs_request() :: #{binary() => any()}. %% Example: %% delete_event_tracker_request() :: #{ %% <<"eventTrackerArn">> := string() %% } -type delete_event_tracker_request() :: #{binary() => any()}. %% Example: %% create_filter_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"filterExpression">> := string(), %% <<"name">> := string(), %% <<"tags">> => list(tag()) %% } -type create_filter_request() :: #{binary() => any()}. %% Example: %% recommender() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"latestRecommenderUpdate">> => recommender_update_summary(), %% <<"modelMetrics">> => map(), %% <<"name">> => string(), %% <<"recipeArn">> => string(), %% <<"recommenderArn">> => string(), %% <<"recommenderConfig">> => recommender_config(), %% <<"status">> => string() %% } -type recommender() :: #{binary() => any()}. %% Example: %% create_solution_version_request() :: #{ %% <<"name">> => string(), %% <<"solutionArn">> := string(), %% <<"tags">> => list(tag()), %% <<"trainingMode">> => list(any()) %% } -type create_solution_version_request() :: #{binary() => any()}. %% Example: %% metric_attribution() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"metricAttributionArn">> => string(), %% <<"metricsOutputConfig">> => metric_attribution_output(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type metric_attribution() :: #{binary() => any()}. %% Example: %% describe_solution_version_response() :: #{ %% <<"solutionVersion">> => solution_version() %% } -type describe_solution_version_response() :: #{binary() => any()}. %% Example: %% describe_batch_segment_job_request() :: #{ %% <<"batchSegmentJobArn">> := string() %% } -type describe_batch_segment_job_request() :: #{binary() => any()}. %% Example: %% dataset_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetArn">> => string(), %% <<"datasetType">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type dataset_summary() :: #{binary() => any()}. %% Example: %% tuned_h_p_o_params() :: #{ %% <<"algorithmHyperParameters">> => map() %% } -type tuned_h_p_o_params() :: #{binary() => any()}. %% Example: %% tag() :: #{ %% <<"tagKey">> => string(), %% <<"tagValue">> => string() %% } -type tag() :: #{binary() => any()}. %% Example: %% batch_segment_job_input() :: #{ %% <<"s3DataSource">> => s3_data_config() %% } -type batch_segment_job_input() :: #{binary() => any()}. %% Example: %% create_solution_version_response() :: #{ %% <<"solutionVersionArn">> => string() %% } -type create_solution_version_response() :: #{binary() => any()}. %% Example: %% create_batch_inference_job_response() :: #{ %% <<"batchInferenceJobArn">> => string() %% } -type create_batch_inference_job_response() :: #{binary() => any()}. %% Example: %% solution_version_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string(), %% <<"trainingMode">> => list(any()), %% <<"trainingType">> => list(any()) %% } -type solution_version_summary() :: #{binary() => any()}. %% Example: %% delete_recommender_request() :: #{ %% <<"recommenderArn">> := string() %% } -type delete_recommender_request() :: #{binary() => any()}. %% Example: %% invalid_next_token_exception() :: #{ %% <<"message">> => string() %% } -type invalid_next_token_exception() :: #{binary() => any()}. %% Example: %% describe_recipe_response() :: #{ %% <<"recipe">> => recipe() %% } -type describe_recipe_response() :: #{binary() => any()}. %% Example: %% list_metric_attributions_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_metric_attributions_request() :: #{binary() => any()}. %% Example: %% list_dataset_groups_response() :: #{ %% <<"datasetGroups">> => list(dataset_group_summary()), %% <<"nextToken">> => string() %% } -type list_dataset_groups_response() :: #{binary() => any()}. %% Example: %% recipe() :: #{ %% <<"algorithmArn">> => string(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"description">> => string(), %% <<"featureTransformationArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"recipeArn">> => string(), %% <<"recipeType">> => string(), %% <<"status">> => string() %% } -type recipe() :: #{binary() => any()}. %% Example: %% batch_segment_job() :: #{ %% <<"batchSegmentJobArn">> => string(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"filterArn">> => string(), %% <<"jobInput">> => batch_segment_job_input(), %% <<"jobName">> => string(), %% <<"jobOutput">> => batch_segment_job_output(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"numResults">> => integer(), %% <<"roleArn">> => string(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string() %% } -type batch_segment_job() :: #{binary() => any()}. %% Example: %% optimization_objective() :: #{ %% <<"itemAttribute">> => string(), %% <<"objectiveSensitivity">> => list(any()) %% } -type optimization_objective() :: #{binary() => any()}. %% Example: %% list_metric_attribution_metrics_response() :: #{ %% <<"metrics">> => list(metric_attribute()), %% <<"nextToken">> => string() %% } -type list_metric_attribution_metrics_response() :: #{binary() => any()}. %% Example: %% list_dataset_groups_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_dataset_groups_request() :: #{binary() => any()}. %% Example: %% invalid_input_exception() :: #{ %% <<"message">> => string() %% } -type invalid_input_exception() :: #{binary() => any()}. %% Example: %% start_recommender_request() :: #{ %% <<"recommenderArn">> := string() %% } -type start_recommender_request() :: #{binary() => any()}. %% Example: %% stop_recommender_response() :: #{ %% <<"recommenderArn">> => string() %% } -type stop_recommender_response() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_response() :: #{ %% <<"tags">> => list(tag()) %% } -type list_tags_for_resource_response() :: #{binary() => any()}. %% Example: %% events_config() :: #{ %% <<"eventParametersList">> => list(event_parameters()) %% } -type events_config() :: #{binary() => any()}. %% Example: %% integer_hyper_parameter_range() :: #{ %% <<"maxValue">> => integer(), %% <<"minValue">> => integer(), %% <<"name">> => string() %% } -type integer_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% categorical_hyper_parameter_range() :: #{ %% <<"name">> => string(), %% <<"values">> => list(string()) %% } -type categorical_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% describe_recommender_request() :: #{ %% <<"recommenderArn">> := string() %% } -type describe_recommender_request() :: #{binary() => any()}. %% Example: %% list_batch_segment_jobs_response() :: #{ %% <<"batchSegmentJobs">> => list(batch_segment_job_summary()), %% <<"nextToken">> => string() %% } -type list_batch_segment_jobs_response() :: #{binary() => any()}. %% Example: %% update_dataset_request() :: #{ %% <<"datasetArn">> := string(), %% <<"schemaArn">> := string() %% } -type update_dataset_request() :: #{binary() => any()}. %% Example: %% list_solutions_response() :: #{ %% <<"nextToken">> => string(), %% <<"solutions">> => list(solution_summary()) %% } -type list_solutions_response() :: #{binary() => any()}. %% Example: %% delete_solution_request() :: #{ %% <<"solutionArn">> := string() %% } -type delete_solution_request() :: #{binary() => any()}. %% Example: %% filter() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"filterArn">> => string(), %% <<"filterExpression">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type filter() :: #{binary() => any()}. %% Example: %% describe_feature_transformation_request() :: #{ %% <<"featureTransformationArn">> := string() %% } -type describe_feature_transformation_request() :: #{binary() => any()}. %% Example: %% dataset_update_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"schemaArn">> => string(), %% <<"status">> => string() %% } -type dataset_update_summary() :: #{binary() => any()}. %% Example: %% describe_algorithm_response() :: #{ %% <<"algorithm">> => algorithm() %% } -type describe_algorithm_response() :: #{binary() => any()}. %% Example: %% describe_recipe_request() :: #{ %% <<"recipeArn">> := string() %% } -type describe_recipe_request() :: #{binary() => any()}. %% Example: %% describe_filter_request() :: #{ %% <<"filterArn">> := string() %% } -type describe_filter_request() :: #{binary() => any()}. %% Example: %% h_p_o_objective() :: #{ %% <<"metricName">> => string(), %% <<"metricRegex">> => string(), %% <<"type">> => string() %% } -type h_p_o_objective() :: #{binary() => any()}. %% Example: %% create_data_deletion_job_request() :: #{ %% <<"dataSource">> := data_source(), %% <<"datasetGroupArn">> := string(), %% <<"jobName">> := string(), %% <<"roleArn">> := string(), %% <<"tags">> => list(tag()) %% } -type create_data_deletion_job_request() :: #{binary() => any()}. %% Example: %% create_recommender_response() :: #{ %% <<"recommenderArn">> => string() %% } -type create_recommender_response() :: #{binary() => any()}. %% Example: %% describe_recommender_response() :: #{ %% <<"recommender">> => recommender() %% } -type describe_recommender_response() :: #{binary() => any()}. %% Example: %% list_batch_inference_jobs_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"solutionVersionArn">> => string() %% } -type list_batch_inference_jobs_request() :: #{binary() => any()}. %% Example: %% dataset_export_job() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetArn">> => string(), %% <<"datasetExportJobArn">> => string(), %% <<"failureReason">> => string(), %% <<"ingestionMode">> => list(any()), %% <<"jobName">> => string(), %% <<"jobOutput">> => dataset_export_job_output(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"roleArn">> => string(), %% <<"status">> => string() %% } -type dataset_export_job() :: #{binary() => any()}. %% Example: %% auto_ml_config() :: #{ %% <<"metricName">> => string(), %% <<"recipeList">> => list(string()) %% } -type auto_ml_config() :: #{binary() => any()}. %% Example: %% stop_recommender_request() :: #{ %% <<"recommenderArn">> := string() %% } -type stop_recommender_request() :: #{binary() => any()}. %% Example: %% describe_batch_inference_job_request() :: #{ %% <<"batchInferenceJobArn">> := string() %% } -type describe_batch_inference_job_request() :: #{binary() => any()}. %% Example: %% dataset_import_job_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetImportJobArn">> => string(), %% <<"failureReason">> => string(), %% <<"importMode">> => list(any()), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"status">> => string() %% } -type dataset_import_job_summary() :: #{binary() => any()}. %% Example: %% create_batch_segment_job_request() :: #{ %% <<"filterArn">> => string(), %% <<"jobInput">> := batch_segment_job_input(), %% <<"jobName">> := string(), %% <<"jobOutput">> := batch_segment_job_output(), %% <<"numResults">> => integer(), %% <<"roleArn">> := string(), %% <<"solutionVersionArn">> := string(), %% <<"tags">> => list(tag()) %% } -type create_batch_segment_job_request() :: #{binary() => any()}. %% Example: %% data_deletion_job_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"dataDeletionJobArn">> => string(), %% <<"datasetGroupArn">> => string(), %% <<"failureReason">> => string(), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"status">> => string() %% } -type data_deletion_job_summary() :: #{binary() => any()}. %% Example: %% list_dataset_import_jobs_response() :: #{ %% <<"datasetImportJobs">> => list(dataset_import_job_summary()), %% <<"nextToken">> => string() %% } -type list_dataset_import_jobs_response() :: #{binary() => any()}. %% Example: %% h_p_o_resource_config() :: #{ %% <<"maxNumberOfTrainingJobs">> => string(), %% <<"maxParallelTrainingJobs">> => string() %% } -type h_p_o_resource_config() :: #{binary() => any()}. %% Example: %% event_tracker_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"eventTrackerArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type event_tracker_summary() :: #{binary() => any()}. %% Example: %% delete_dataset_group_request() :: #{ %% <<"datasetGroupArn">> := string() %% } -type delete_dataset_group_request() :: #{binary() => any()}. %% Example: %% solution_update_config() :: #{ %% <<"autoTrainingConfig">> => auto_training_config(), %% <<"eventsConfig">> => events_config() %% } -type solution_update_config() :: #{binary() => any()}. %% Example: %% tag_resource_response() :: #{ %% } -type tag_resource_response() :: #{binary() => any()}. %% Example: %% create_campaign_request() :: #{ %% <<"campaignConfig">> => campaign_config(), %% <<"minProvisionedTPS">> => integer(), %% <<"name">> := string(), %% <<"solutionVersionArn">> := string(), %% <<"tags">> => list(tag()) %% } -type create_campaign_request() :: #{binary() => any()}. %% Example: %% list_dataset_export_jobs_response() :: #{ %% <<"datasetExportJobs">> => list(dataset_export_job_summary()), %% <<"nextToken">> => string() %% } -type list_dataset_export_jobs_response() :: #{binary() => any()}. %% Example: %% event_tracker() :: #{ %% <<"accountId">> => string(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"eventTrackerArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string(), %% <<"trackingId">> => string() %% } -type event_tracker() :: #{binary() => any()}. %% Example: %% create_dataset_group_response() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"domain">> => list(any()) %% } -type create_dataset_group_response() :: #{binary() => any()}. %% Example: %% batch_inference_job_summary() :: #{ %% <<"batchInferenceJobArn">> => string(), %% <<"batchInferenceJobMode">> => list(any()), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"jobName">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"solutionVersionArn">> => string(), %% <<"status">> => string() %% } -type batch_inference_job_summary() :: #{binary() => any()}. %% Example: %% describe_feature_transformation_response() :: #{ %% <<"featureTransformation">> => feature_transformation() %% } -type describe_feature_transformation_response() :: #{binary() => any()}. %% Example: %% describe_event_tracker_response() :: #{ %% <<"eventTracker">> => event_tracker() %% } -type describe_event_tracker_response() :: #{binary() => any()}. %% Example: %% list_recommenders_response() :: #{ %% <<"nextToken">> => string(), %% <<"recommenders">> => list(recommender_summary()) %% } -type list_recommenders_response() :: #{binary() => any()}. %% Example: %% describe_metric_attribution_response() :: #{ %% <<"metricAttribution">> => metric_attribution() %% } -type describe_metric_attribution_response() :: #{binary() => any()}. %% Example: %% describe_dataset_response() :: #{ %% <<"dataset">> => dataset() %% } -type describe_dataset_response() :: #{binary() => any()}. %% Example: %% recommender_config() :: #{ %% <<"enableMetadataWithRecommendations">> => boolean(), %% <<"itemExplorationConfig">> => map(), %% <<"minRecommendationRequestsPerSecond">> => integer(), %% <<"trainingDataConfig">> => training_data_config() %% } -type recommender_config() :: #{binary() => any()}. %% Example: %% auto_ml_result() :: #{ %% <<"bestRecipeArn">> => string() %% } -type auto_ml_result() :: #{binary() => any()}. %% Example: %% stop_solution_version_creation_request() :: #{ %% <<"solutionVersionArn">> := string() %% } -type stop_solution_version_creation_request() :: #{binary() => any()}. %% Example: %% list_tags_for_resource_request() :: #{ %% <<"resourceArn">> := string() %% } -type list_tags_for_resource_request() :: #{binary() => any()}. %% Example: %% create_dataset_request() :: #{ %% <<"datasetGroupArn">> := string(), %% <<"datasetType">> := string(), %% <<"name">> := string(), %% <<"schemaArn">> := string(), %% <<"tags">> => list(tag()) %% } -type create_dataset_request() :: #{binary() => any()}. %% Example: %% describe_dataset_group_request() :: #{ %% <<"datasetGroupArn">> := string() %% } -type describe_dataset_group_request() :: #{binary() => any()}. %% Example: %% list_metric_attributions_response() :: #{ %% <<"metricAttributions">> => list(metric_attribution_summary()), %% <<"nextToken">> => string() %% } -type list_metric_attributions_response() :: #{binary() => any()}. %% Example: %% list_event_trackers_response() :: #{ %% <<"eventTrackers">> => list(event_tracker_summary()), %% <<"nextToken">> => string() %% } -type list_event_trackers_response() :: #{binary() => any()}. %% Example: %% feature_transformation() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"defaultParameters">> => map(), %% <<"featureTransformationArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type feature_transformation() :: #{binary() => any()}. %% Example: %% create_dataset_import_job_request() :: #{ %% <<"dataSource">> := data_source(), %% <<"datasetArn">> := string(), %% <<"importMode">> => list(any()), %% <<"jobName">> := string(), %% <<"publishAttributionMetricsToS3">> => boolean(), %% <<"roleArn">> => string(), %% <<"tags">> => list(tag()) %% } -type create_dataset_import_job_request() :: #{binary() => any()}. %% Example: %% solution_config() :: #{ %% <<"algorithmHyperParameters">> => map(), %% <<"autoMLConfig">> => auto_ml_config(), %% <<"autoTrainingConfig">> => auto_training_config(), %% <<"eventValueThreshold">> => string(), %% <<"eventsConfig">> => events_config(), %% <<"featureTransformationParameters">> => map(), %% <<"hpoConfig">> => h_p_o_config(), %% <<"optimizationObjective">> => optimization_objective(), %% <<"trainingDataConfig">> => training_data_config() %% } -type solution_config() :: #{binary() => any()}. %% Example: %% list_solution_versions_response() :: #{ %% <<"nextToken">> => string(), %% <<"solutionVersions">> => list(solution_version_summary()) %% } -type list_solution_versions_response() :: #{binary() => any()}. %% Example: %% dataset_schema_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"domain">> => list(any()), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"schemaArn">> => string() %% } -type dataset_schema_summary() :: #{binary() => any()}. %% Example: %% describe_dataset_import_job_request() :: #{ %% <<"datasetImportJobArn">> := string() %% } -type describe_dataset_import_job_request() :: #{binary() => any()}. %% Example: %% limit_exceeded_exception() :: #{ %% <<"message">> => string() %% } -type limit_exceeded_exception() :: #{binary() => any()}. %% Example: %% update_metric_attribution_request() :: #{ %% <<"addMetrics">> => list(metric_attribute()), %% <<"metricAttributionArn">> => string(), %% <<"metricsOutputConfig">> => metric_attribution_output(), %% <<"removeMetrics">> => list(string()) %% } -type update_metric_attribution_request() :: #{binary() => any()}. %% Example: %% s3_data_config() :: #{ %% <<"kmsKeyArn">> => string(), %% <<"path">> => string() %% } -type s3_data_config() :: #{binary() => any()}. %% Example: %% create_solution_response() :: #{ %% <<"solutionArn">> => string() %% } -type create_solution_response() :: #{binary() => any()}. %% Example: %% metric_attribution_output() :: #{ %% <<"roleArn">> => string(), %% <<"s3DataDestination">> => s3_data_config() %% } -type metric_attribution_output() :: #{binary() => any()}. %% Example: %% update_metric_attribution_response() :: #{ %% <<"metricAttributionArn">> => string() %% } -type update_metric_attribution_response() :: #{binary() => any()}. %% Example: %% describe_dataset_group_response() :: #{ %% <<"datasetGroup">> => dataset_group() %% } -type describe_dataset_group_response() :: #{binary() => any()}. %% Example: %% default_continuous_hyper_parameter_range() :: #{ %% <<"isTunable">> => boolean(), %% <<"maxValue">> => float(), %% <<"minValue">> => float(), %% <<"name">> => string() %% } -type default_continuous_hyper_parameter_range() :: #{binary() => any()}. %% Example: %% get_solution_metrics_response() :: #{ %% <<"metrics">> => map(), %% <<"solutionVersionArn">> => string() %% } -type get_solution_metrics_response() :: #{binary() => any()}. %% Example: %% solution_update_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"performAutoTraining">> => boolean(), %% <<"performIncrementalUpdate">> => boolean(), %% <<"solutionUpdateConfig">> => solution_update_config(), %% <<"status">> => string() %% } -type solution_update_summary() :: #{binary() => any()}. %% Example: %% update_campaign_request() :: #{ %% <<"campaignArn">> := string(), %% <<"campaignConfig">> => campaign_config(), %% <<"minProvisionedTPS">> => integer(), %% <<"solutionVersionArn">> => string() %% } -type update_campaign_request() :: #{binary() => any()}. %% Example: %% describe_dataset_request() :: #{ %% <<"datasetArn">> := string() %% } -type describe_dataset_request() :: #{binary() => any()}. %% Example: %% delete_dataset_request() :: #{ %% <<"datasetArn">> := string() %% } -type delete_dataset_request() :: #{binary() => any()}. %% Example: %% list_datasets_response() :: #{ %% <<"datasets">> => list(dataset_summary()), %% <<"nextToken">> => string() %% } -type list_datasets_response() :: #{binary() => any()}. %% Example: %% algorithm_image() :: #{ %% <<"dockerURI">> => string(), %% <<"name">> => string() %% } -type algorithm_image() :: #{binary() => any()}. %% Example: %% resource_already_exists_exception() :: #{ %% <<"message">> => string() %% } -type resource_already_exists_exception() :: #{binary() => any()}. %% Example: %% list_dataset_export_jobs_request() :: #{ %% <<"datasetArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_dataset_export_jobs_request() :: #{binary() => any()}. %% Example: %% h_p_o_config() :: #{ %% <<"algorithmHyperParameterRanges">> => hyper_parameter_ranges(), %% <<"hpoObjective">> => h_p_o_objective(), %% <<"hpoResourceConfig">> => h_p_o_resource_config() %% } -type h_p_o_config() :: #{binary() => any()}. %% Example: %% data_source() :: #{ %% <<"dataLocation">> => string() %% } -type data_source() :: #{binary() => any()}. %% Example: %% update_solution_response() :: #{ %% <<"solutionArn">> => string() %% } -type update_solution_response() :: #{binary() => any()}. %% Example: %% describe_data_deletion_job_request() :: #{ %% <<"dataDeletionJobArn">> := string() %% } -type describe_data_deletion_job_request() :: #{binary() => any()}. %% Example: %% dataset_export_job_output() :: #{ %% <<"s3DataDestination">> => s3_data_config() %% } -type dataset_export_job_output() :: #{binary() => any()}. %% Example: %% create_campaign_response() :: #{ %% <<"campaignArn">> => string() %% } -type create_campaign_response() :: #{binary() => any()}. %% Example: %% list_schemas_response() :: #{ %% <<"nextToken">> => string(), %% <<"schemas">> => list(dataset_schema_summary()) %% } -type list_schemas_response() :: #{binary() => any()}. %% Example: %% create_dataset_export_job_response() :: #{ %% <<"datasetExportJobArn">> => string() %% } -type create_dataset_export_job_response() :: #{binary() => any()}. %% Example: %% create_dataset_export_job_request() :: #{ %% <<"datasetArn">> := string(), %% <<"ingestionMode">> => list(any()), %% <<"jobName">> := string(), %% <<"jobOutput">> := dataset_export_job_output(), %% <<"roleArn">> := string(), %% <<"tags">> => list(tag()) %% } -type create_dataset_export_job_request() :: #{binary() => any()}. %% Example: %% training_data_config() :: #{ %% <<"excludedDatasetColumns">> => map(), %% <<"includedDatasetColumns">> => map() %% } -type training_data_config() :: #{binary() => any()}. %% Example: %% list_recipes_request() :: #{ %% <<"domain">> => list(any()), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"recipeProvider">> => list(any()) %% } -type list_recipes_request() :: #{binary() => any()}. %% Example: %% list_solution_versions_request() :: #{ %% <<"maxResults">> => integer(), %% <<"nextToken">> => string(), %% <<"solutionArn">> => string() %% } -type list_solution_versions_request() :: #{binary() => any()}. %% Example: %% list_filters_response() :: #{ %% <<"Filters">> => list(filter_summary()), %% <<"nextToken">> => string() %% } -type list_filters_response() :: #{binary() => any()}. %% Example: %% describe_filter_response() :: #{ %% <<"filter">> => filter() %% } -type describe_filter_response() :: #{binary() => any()}. %% Example: %% dataset_group() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"domain">> => list(any()), %% <<"failureReason">> => string(), %% <<"kmsKeyArn">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"roleArn">> => string(), %% <<"status">> => string() %% } -type dataset_group() :: #{binary() => any()}. %% Example: %% campaign_summary() :: #{ %% <<"campaignArn">> => string(), %% <<"creationDateTime">> => non_neg_integer(), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type campaign_summary() :: #{binary() => any()}. %% Example: %% describe_dataset_export_job_request() :: #{ %% <<"datasetExportJobArn">> := string() %% } -type describe_dataset_export_job_request() :: #{binary() => any()}. %% Example: %% too_many_tags_exception() :: #{ %% <<"message">> => string() %% } -type too_many_tags_exception() :: #{binary() => any()}. %% Example: %% delete_filter_request() :: #{ %% <<"filterArn">> := string() %% } -type delete_filter_request() :: #{binary() => any()}. %% Example: %% list_recommenders_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_recommenders_request() :: #{binary() => any()}. %% Example: %% create_dataset_import_job_response() :: #{ %% <<"datasetImportJobArn">> => string() %% } -type create_dataset_import_job_response() :: #{binary() => any()}. %% Example: %% list_filters_request() :: #{ %% <<"datasetGroupArn">> => string(), %% <<"maxResults">> => integer(), %% <<"nextToken">> => string() %% } -type list_filters_request() :: #{binary() => any()}. %% Example: %% dataset_group_summary() :: #{ %% <<"creationDateTime">> => non_neg_integer(), %% <<"datasetGroupArn">> => string(), %% <<"domain">> => list(any()), %% <<"failureReason">> => string(), %% <<"lastUpdatedDateTime">> => non_neg_integer(), %% <<"name">> => string(), %% <<"status">> => string() %% } -type dataset_group_summary() :: #{binary() => any()}. -type create_batch_inference_job_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_batch_segment_job_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_campaign_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_data_deletion_job_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_dataset_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_dataset_export_job_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_dataset_group_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception(). -type create_dataset_import_job_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_event_tracker_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_filter_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception(). -type create_metric_attribution_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_recommender_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_schema_errors() :: resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception(). -type create_solution_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type create_solution_version_errors() :: too_many_tags_exception() | resource_already_exists_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_campaign_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_dataset_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_dataset_group_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_event_tracker_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_filter_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_metric_attribution_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_recommender_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_schema_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type delete_solution_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type describe_algorithm_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_batch_inference_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_batch_segment_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_campaign_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_data_deletion_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_export_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_group_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_dataset_import_job_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_event_tracker_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_feature_transformation_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_filter_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_metric_attribution_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_recipe_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_recommender_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_schema_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_solution_errors() :: invalid_input_exception() | resource_not_found_exception(). -type describe_solution_version_errors() :: invalid_input_exception() | resource_not_found_exception(). -type get_solution_metrics_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type list_batch_inference_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_batch_segment_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_campaigns_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_data_deletion_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_dataset_export_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_dataset_groups_errors() :: invalid_next_token_exception(). -type list_dataset_import_jobs_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_datasets_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_event_trackers_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_filters_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_metric_attribution_metrics_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_metric_attributions_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_recipes_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_recommenders_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_schemas_errors() :: invalid_next_token_exception(). -type list_solution_versions_errors() :: invalid_input_exception() | invalid_next_token_exception() | resource_not_found_exception(). -type list_solutions_errors() :: invalid_input_exception() | invalid_next_token_exception(). -type list_tags_for_resource_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type start_recommender_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type stop_recommender_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type stop_solution_version_creation_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type tag_resource_errors() :: too_many_tags_exception() | limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type untag_resource_errors() :: invalid_input_exception() | resource_not_found_exception() | too_many_tag_keys_exception() | resource_in_use_exception(). -type update_campaign_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type update_dataset_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type update_metric_attribution_errors() :: resource_already_exists_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type update_recommender_errors() :: invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). -type update_solution_errors() :: limit_exceeded_exception() | invalid_input_exception() | resource_not_found_exception() | resource_in_use_exception(). %%==================================================================== %% API %%==================================================================== %% @doc Generates batch recommendations based on a list of items or users %% stored in Amazon S3 %% and exports the recommendations to an Amazon S3 bucket. %% %% To generate batch recommendations, specify the ARN of a solution version %% and an Amazon S3 URI for the input and output data. %% For user personalization, popular items, and personalized ranking %% solutions, the batch inference job generates a list of %% recommended items for each user ID in the input file. For related items %% solutions, the job generates a list of recommended %% items for each item ID in the input file. %% %% For more information, see Creating a batch inference job %% : %% https://docs.aws.amazon.com/personalize/latest/dg/getting-batch-recommendations.html. %% %% If you use the Similar-Items recipe, Amazon Personalize can add %% descriptive themes to batch recommendations. %% To generate themes, set the job's mode to %% `THEME_GENERATION' and specify the name of the field that contains %% item names in the %% input data. %% %% For more information about generating themes, see Batch recommendations %% with themes from Content Generator %% : %% https://docs.aws.amazon.com/personalize/latest/dg/themed-batch-recommendations.html. %% %% You can't get batch recommendations with the Trending-Now or %% Next-Best-Action recipes. -spec create_batch_inference_job(aws_client:aws_client(), create_batch_inference_job_request()) -> {ok, create_batch_inference_job_response(), tuple()} | {error, any()} | {error, create_batch_inference_job_errors(), tuple()}. create_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_inference_job(Client, Input, []). -spec create_batch_inference_job(aws_client:aws_client(), create_batch_inference_job_request(), proplists:proplist()) -> {ok, create_batch_inference_job_response(), tuple()} | {error, any()} | {error, create_batch_inference_job_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/recommendations-batch.html. -spec create_batch_segment_job(aws_client:aws_client(), create_batch_segment_job_request()) -> {ok, create_batch_segment_job_response(), tuple()} | {error, any()} | {error, create_batch_segment_job_errors(), tuple()}. create_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> create_batch_segment_job(Client, Input, []). -spec create_batch_segment_job(aws_client:aws_client(), create_batch_segment_job_request(), proplists:proplist()) -> {ok, create_batch_segment_job_response(), tuple()} | {error, any()} | {error, create_batch_segment_job_errors(), tuple()}. create_batch_segment_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateBatchSegmentJob">>, Input, Options). %% @doc %% You incur campaign costs while it is active. %% %% To avoid unnecessary costs, make sure to delete the campaign when you are %% finished. For information about campaign %% costs, see Amazon Personalize pricing: %% https://aws.amazon.com/personalize/pricing/. %% %% Creates a campaign that deploys a solution version. When a client calls %% the %% GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% and %% GetPersonalizedRanking: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetPersonalizedRanking.html %% APIs, a campaign is specified in the request. %% %% Minimum Provisioned TPS and Auto-Scaling %% %% A high `minProvisionedTPS' will increase your cost. We recommend %% starting with 1 for `minProvisionedTPS' (the default). Track %% your usage using Amazon CloudWatch metrics, and increase the %% `minProvisionedTPS' %% as necessary. %% %% When you create an Amazon Personalize campaign, you can specify the %% minimum provisioned transactions per second %% (`minProvisionedTPS') for the campaign. This is the baseline %% transaction throughput for the campaign provisioned by %% Amazon Personalize. It sets the minimum billing charge for the campaign %% while it is active. A transaction is a single `GetRecommendations' or %% `GetPersonalizedRanking' request. The default `minProvisionedTPS' %% is 1. %% %% If your TPS increases beyond the `minProvisionedTPS', Amazon %% Personalize auto-scales the provisioned capacity up %% and down, but never below `minProvisionedTPS'. %% There's a short time delay while the capacity is increased %% that might cause loss of transactions. When your traffic reduces, capacity %% returns to the `minProvisionedTPS'. %% %% You are charged for the %% the minimum provisioned TPS or, if your requests exceed the %% `minProvisionedTPS', the actual TPS. %% The actual TPS is the total number of recommendation requests you make. %% We recommend starting with a low `minProvisionedTPS', track %% your usage using Amazon CloudWatch metrics, and then increase the %% `minProvisionedTPS' as necessary. %% %% For more information about campaign costs, see Amazon Personalize pricing: %% https://aws.amazon.com/personalize/pricing/. %% %% Status %% %% A campaign can be in one of the following states: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED %% %% DELETE PENDING > DELETE IN_PROGRESS %% %% To get the campaign status, call DescribeCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeCampaign.html. %% %% Wait until the `status' of the campaign %% is `ACTIVE' before asking the campaign for recommendations. %% %% == Related APIs == %% %% ListCampaigns: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListCampaigns.html %% %% DescribeCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeCampaign.html %% %% UpdateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateCampaign.html %% %% DeleteCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteCampaign.html -spec create_campaign(aws_client:aws_client(), create_campaign_request()) -> {ok, create_campaign_response(), tuple()} | {error, any()} | {error, create_campaign_errors(), tuple()}. create_campaign(Client, Input) when is_map(Client), is_map(Input) -> create_campaign(Client, Input, []). -spec create_campaign(aws_client:aws_client(), create_campaign_request(), proplists:proplist()) -> {ok, create_campaign_response(), tuple()} | {error, any()} | {error, create_campaign_errors(), tuple()}. create_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateCampaign">>, Input, Options). %% @doc Creates a batch job that deletes all %% references to specific users from an Amazon Personalize dataset group in %% batches. %% %% You specify the users to delete in a CSV file of userIds in %% an Amazon S3 bucket. After a job completes, Amazon Personalize no longer %% trains %% on the users’ data and no longer considers the users when generating user %% segments. %% For more information about creating a data deletion job, see Deleting %% users: %% https://docs.aws.amazon.com/personalize/latest/dg/delete-records.html. %% %% Your input file must be a CSV file with a single USER_ID column that lists %% the users IDs. For more information %% about preparing the CSV file, see Preparing your data deletion file and %% uploading it to Amazon S3: %% https://docs.aws.amazon.com/personalize/latest/dg/prepare-deletion-input-file.html. %% %% To give Amazon Personalize permission to access your input CSV file of %% userIds, you must specify an IAM service role that has permission to %% read from the data source. This role %% needs `GetObject' and `ListBucket' permissions for the bucket and %% its content. %% These permissions are the same as importing data. For information on %% granting access to your Amazon S3 %% bucket, see Giving %% Amazon Personalize Access to Amazon S3 Resources: %% https://docs.aws.amazon.com/personalize/latest/dg/granting-personalize-s3-access.html. %% %% After you create a job, it can take up to a day to delete all references %% to the users from datasets and models. Until the job completes, %% Amazon Personalize continues to use the data when training. And if you use %% a User Segmentation recipe, the users might appear in user segments. %% %% Status %% %% A data deletion job can have one of the following statuses: %% %% PENDING > IN_PROGRESS > COMPLETED -or- FAILED %% %% To get the status of the data deletion job, call DescribeDataDeletionJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDataDeletionJob.html %% API operation and specify the Amazon Resource Name %% (ARN) of the job. If the status is FAILED, the response %% includes a `failureReason' key, which describes why the job %% failed. %% %% == Related APIs == %% %% ListDataDeletionJobs: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListDataDeletionJobs.html %% %% DescribeDataDeletionJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDataDeletionJob.html -spec create_data_deletion_job(aws_client:aws_client(), create_data_deletion_job_request()) -> {ok, create_data_deletion_job_response(), tuple()} | {error, any()} | {error, create_data_deletion_job_errors(), tuple()}. create_data_deletion_job(Client, Input) when is_map(Client), is_map(Input) -> create_data_deletion_job(Client, Input, []). -spec create_data_deletion_job(aws_client:aws_client(), create_data_deletion_job_request(), proplists:proplist()) -> {ok, create_data_deletion_job_response(), tuple()} | {error, any()} | {error, create_data_deletion_job_errors(), tuple()}. create_data_deletion_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateDataDeletionJob">>, Input, Options). %% @doc Creates an empty dataset and adds it to the specified dataset group. %% %% Use CreateDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html %% to import your training data to a %% dataset. %% %% There are 5 types of datasets: %% %% Item interactions %% %% Items %% %% Users %% %% Action interactions %% %% Actions %% %% Each dataset type has an associated schema with required field types. %% Only the `Item 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDataset.html. %% %% == Related APIs == %% %% CreateDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetGroup.html %% %% ListDatasets: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListDatasets.html %% %% DescribeDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDataset.html %% %% DeleteDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteDataset.html -spec create_dataset(aws_client:aws_client(), create_dataset_request()) -> {ok, create_dataset_response(), tuple()} | {error, any()} | {error, create_dataset_errors(), tuple()}. create_dataset(Client, Input) when is_map(Client), is_map(Input) -> create_dataset(Client, Input, []). -spec create_dataset(aws_client:aws_client(), create_dataset_request(), proplists:proplist()) -> {ok, create_dataset_response(), tuple()} | {error, any()} | {error, create_dataset_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/export-data.html in the %% Amazon Personalize developer guide. %% %% Status %% %% A dataset export job can be in one of the following states: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE %% FAILED %% %% To get the status of the export job, call DescribeDatasetExportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetExportJob.html, %% 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. -spec create_dataset_export_job(aws_client:aws_client(), create_dataset_export_job_request()) -> {ok, create_dataset_export_job_response(), tuple()} | {error, any()} | {error, create_dataset_export_job_errors(), tuple()}. create_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_export_job(Client, Input, []). -spec create_dataset_export_job(aws_client:aws_client(), create_dataset_export_job_request(), proplists:proplist()) -> {ok, create_dataset_export_job_response(), tuple()} | {error, any()} | {error, create_dataset_export_job_errors(), tuple()}. 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: %% %% Item interactions %% %% Items %% %% Users %% %% Actions %% %% Action interactions %% %% 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: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE %% FAILED %% %% DELETE PENDING %% %% To get the status of the dataset group, call DescribeDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetGroup.html. %% 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 == %% %% CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html %% %% CreateEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html %% %% CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% %% == Related APIs == %% %% ListDatasetGroups: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListDatasetGroups.html %% %% DescribeDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetGroup.html %% %% DeleteDatasetGroup: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteDatasetGroup.html -spec create_dataset_group(aws_client:aws_client(), create_dataset_group_request()) -> {ok, create_dataset_group_response(), tuple()} | {error, any()} | {error, create_dataset_group_errors(), tuple()}. create_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_group(Client, Input, []). -spec create_dataset_group(aws_client:aws_client(), create_dataset_group_request(), proplists:proplist()) -> {ok, create_dataset_group_response(), tuple()} | {error, any()} | {error, create_dataset_group_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/granting-personalize-s3-access.html. %% %% If you already created a recommender or deployed a custom solution version %% with a campaign, how new bulk records %% influence recommendations depends on the domain use case or recipe that %% you use. For more information, see How new data influences %% real-time recommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/how-new-data-influences-recommendations.html. %% %% 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: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE %% FAILED %% %% To get the status of the import job, call DescribeDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetImportJob.html, %% 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 == %% %% ListDatasetImportJobs: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListDatasetImportJobs.html %% %% DescribeDatasetImportJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeDatasetImportJob.html -spec create_dataset_import_job(aws_client:aws_client(), create_dataset_import_job_request()) -> {ok, create_dataset_import_job_response(), tuple()} | {error, any()} | {error, create_dataset_import_job_errors(), tuple()}. create_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> create_dataset_import_job(Client, Input, []). -spec create_dataset_import_job(aws_client:aws_client(), create_dataset_import_job_request(), proplists:proplist()) -> {ok, create_dataset_import_job_response(), tuple()} | {error, any()} | {error, create_dataset_import_job_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UBS_PutEvents.html %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UBS_PutEvents.html %% operation. %% Amazon Personalize then appends the event data to the Item interactions %% dataset of the dataset group you specify %% in your event tracker. %% %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeEventTracker.html. %% %% The event tracker must be in the ACTIVE state before using the tracking %% ID. %% %% == Related APIs == %% %% ListEventTrackers: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListEventTrackers.html %% %% DescribeEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeEventTracker.html %% %% DeleteEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteEventTracker.html -spec create_event_tracker(aws_client:aws_client(), create_event_tracker_request()) -> {ok, create_event_tracker_response(), tuple()} | {error, any()} | {error, create_event_tracker_errors(), tuple()}. create_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> create_event_tracker(Client, Input, []). -spec create_event_tracker(aws_client:aws_client(), create_event_tracker_request(), proplists:proplist()) -> {ok, create_event_tracker_response(), tuple()} | {error, any()} | {error, create_event_tracker_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/filter.html. -spec create_filter(aws_client:aws_client(), create_filter_request()) -> {ok, create_filter_response(), tuple()} | {error, any()} | {error, create_filter_errors(), tuple()}. create_filter(Client, Input) when is_map(Client), is_map(Input) -> create_filter(Client, Input, []). -spec create_filter(aws_client:aws_client(), create_filter_request(), proplists:proplist()) -> {ok, create_filter_response(), tuple()} | {error, any()} | {error, create_filter_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/measuring-recommendation-impact.html. -spec create_metric_attribution(aws_client:aws_client(), create_metric_attribution_request()) -> {ok, create_metric_attribution_response(), tuple()} | {error, any()} | {error, create_metric_attribution_errors(), tuple()}. create_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> create_metric_attribution(Client, Input, []). -spec create_metric_attribution(aws_client:aws_client(), create_metric_attribution_request(), proplists:proplist()) -> {ok, create_metric_attribution_response(), tuple()} | {error, any()} | {error, create_metric_attribution_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. %% %% Minimum recommendation requests per second %% %% A high `minRecommendationRequestsPerSecond' will increase your bill. %% We recommend starting with 1 for `minRecommendationRequestsPerSecond' %% (the default). Track %% your usage using Amazon CloudWatch metrics, and increase the %% `minRecommendationRequestsPerSecond' %% as necessary. %% %% When you create a recommender, you can configure the recommender's %% minimum recommendation requests per second. The minimum recommendation %% requests per second %% (`minRecommendationRequestsPerSecond') specifies the baseline %% recommendation request throughput provisioned by %% Amazon Personalize. The default minRecommendationRequestsPerSecond is %% `1'. A recommendation request is a single `GetRecommendations' %% operation. %% Request throughput is measured in requests per second and Amazon %% Personalize uses your requests per second to derive %% your requests per hour and the price of your recommender usage. %% %% If your requests per second increases beyond %% `minRecommendationRequestsPerSecond', Amazon Personalize auto-scales %% the provisioned capacity up and down, %% but never below `minRecommendationRequestsPerSecond'. %% There's a short time delay while the capacity is increased that might %% cause loss of %% requests. %% %% Your bill is the greater of either the minimum requests per hour (based on %% minRecommendationRequestsPerSecond) %% or the actual number of requests. The actual request throughput used is %% calculated as the average requests/second within a one-hour window. %% %% We recommend starting with the default %% `minRecommendationRequestsPerSecond', track %% your usage using Amazon CloudWatch metrics, and then increase the %% `minRecommendationRequestsPerSecond' %% as necessary. %% %% Status %% %% A recommender can be in one of the following states: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED %% %% STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > %% START IN_PROGRESS > ACTIVE %% %% DELETE PENDING > DELETE IN_PROGRESS %% %% To get the recommender status, call DescribeRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeRecommender.html. %% %% Wait until the `status' of the recommender %% is `ACTIVE' before asking the recommender for recommendations. %% %% == Related APIs == %% %% ListRecommenders: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListRecommenders.html %% %% DescribeRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeRecommender.html %% %% UpdateRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateRecommender.html %% %% DeleteRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteRecommender.html -spec create_recommender(aws_client:aws_client(), create_recommender_request()) -> {ok, create_recommender_response(), tuple()} | {error, any()} | {error, create_recommender_errors(), tuple()}. create_recommender(Client, Input) when is_map(Client), is_map(Input) -> create_recommender(Client, Input, []). -spec create_recommender(aws_client:aws_client(), create_recommender_request(), proplists:proplist()) -> {ok, create_recommender_response(), tuple()} | {error, any()} | {error, create_recommender_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. %% %% == Related APIs == %% %% ListSchemas: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSchemas.html %% %% DescribeSchema: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSchema.html %% %% DeleteSchema: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSchema.html -spec create_schema(aws_client:aws_client(), create_schema_request()) -> {ok, create_schema_response(), tuple()} | {error, any()} | {error, create_schema_errors(), tuple()}. create_schema(Client, Input) when is_map(Client), is_map(Input) -> create_schema(Client, Input, []). -spec create_schema(aws_client:aws_client(), create_schema_request(), proplists:proplist()) -> {ok, create_schema_response(), tuple()} | {error, any()} | {error, create_schema_errors(), tuple()}. create_schema(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"CreateSchema">>, Input, Options). %% @doc %% By default, all new solutions use automatic training. %% %% With automatic training, you incur training costs while %% your solution is active. To avoid unnecessary costs, when you are finished %% you can %% update the solution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html %% to turn off automatic training. %% For information about training %% costs, see Amazon Personalize pricing: %% https://aws.amazon.com/personalize/pricing/. %% %% Creates the configuration for training a model (creating a solution %% version). This configuration %% includes the recipe to use for model training and optional training %% configuration, such as columns to use %% in training and feature transformation parameters. For more information %% about configuring a solution, see Creating and configuring a solution: %% https://docs.aws.amazon.com/personalize/latest/dg/customizing-solution-config.html. %% %% By default, new solutions use automatic training to create solution %% versions every 7 days. You can change the training frequency. %% Automatic solution version creation starts within one hour after the %% solution is ACTIVE. If you manually create a solution version within %% the hour, the solution skips the first automatic training. For more %% information, %% see Configuring automatic training: %% https://docs.aws.amazon.com/personalize/latest/dg/solution-config-auto-training.html. %% %% To turn off automatic training, set `performAutoTraining' to false. If %% you turn off automatic training, you must manually create a solution %% version %% by calling the CreateSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html %% operation. %% %% After training starts, you can %% get the solution version's Amazon Resource Name (ARN) with the %% ListSolutionVersions: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html %% API operation. %% To get its status, use the DescribeSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html. %% %% After training completes you can evaluate model accuracy by calling %% GetSolutionMetrics: %% https://docs.aws.amazon.com/personalize/latest/dg/API_GetSolutionMetrics.html. %% When you are satisfied with the solution version, you %% deploy it using CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. %% The campaign provides recommendations %% to a client through the %% GetRecommendations: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% API. %% %% 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: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED %% %% DELETE PENDING > DELETE IN_PROGRESS %% %% To get the status of the solution, call DescribeSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html. %% If you use %% manual training, the status must be ACTIVE before you call %% `CreateSolutionVersion'. %% %% == Related APIs == %% %% UpdateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_UpdateSolution.html %% %% ListSolutions: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html %% %% CreateSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolutionVersion.html %% %% DescribeSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html %% %% DeleteSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html %% %% ListSolutionVersions: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html %% %% DescribeSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html -spec create_solution(aws_client:aws_client(), create_solution_request()) -> {ok, create_solution_response(), tuple()} | {error, any()} | {error, create_solution_errors(), tuple()}. create_solution(Client, Input) when is_map(Client), is_map(Input) -> create_solution(Client, Input, []). -spec create_solution(aws_client:aws_client(), create_solution_request(), proplists:proplist()) -> {ok, create_solution_response(), tuple()} | {error, any()} | {error, create_solution_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% 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 %% %% CREATE FAILED %% %% CREATE STOPPING %% %% CREATE STOPPED %% %% To get the status of the version, call DescribeSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html. %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html %% %% DescribeSolutionVersion: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html %% %% ListSolutions: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html %% %% CreateSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% %% DescribeSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html %% %% DeleteSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html -spec create_solution_version(aws_client:aws_client(), create_solution_version_request()) -> {ok, create_solution_version_response(), tuple()} | {error, any()} | {error, create_solution_version_errors(), tuple()}. create_solution_version(Client, Input) when is_map(Client), is_map(Input) -> create_solution_version(Client, Input, []). -spec create_solution_version(aws_client:aws_client(), create_solution_version_request(), proplists:proplist()) -> {ok, create_solution_version_response(), tuple()} | {error, any()} | {error, create_solution_version_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. %% For information on creating campaigns, see CreateCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. -spec delete_campaign(aws_client:aws_client(), delete_campaign_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_campaign_errors(), tuple()}. delete_campaign(Client, Input) when is_map(Client), is_map(Input) -> delete_campaign(Client, Input, []). -spec delete_campaign(aws_client:aws_client(), delete_campaign_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_campaign_errors(), tuple()}. 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 about deleting %% datasets, %% see Deleting a dataset: %% https://docs.aws.amazon.com/personalize/latest/dg/delete-dataset.html. -spec delete_dataset(aws_client:aws_client(), delete_dataset_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_errors(), tuple()}. delete_dataset(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset(Client, Input, []). -spec delete_dataset(aws_client:aws_client(), delete_dataset_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_errors(), tuple()}. 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: %% %% All associated event trackers. %% %% All associated solutions. %% %% All datasets in the dataset group. -spec delete_dataset_group(aws_client:aws_client(), delete_dataset_group_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_group_errors(), tuple()}. delete_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> delete_dataset_group(Client, Input, []). -spec delete_dataset_group(aws_client:aws_client(), delete_dataset_group_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_dataset_group_errors(), tuple()}. 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 dataset from %% the dataset group. For more %% information on event trackers, see CreateEventTracker: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. -spec delete_event_tracker(aws_client:aws_client(), delete_event_tracker_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_event_tracker_errors(), tuple()}. delete_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> delete_event_tracker(Client, Input, []). -spec delete_event_tracker(aws_client:aws_client(), delete_event_tracker_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_event_tracker_errors(), tuple()}. 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. -spec delete_filter(aws_client:aws_client(), delete_filter_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_filter_errors(), tuple()}. delete_filter(Client, Input) when is_map(Client), is_map(Input) -> delete_filter(Client, Input, []). -spec delete_filter(aws_client:aws_client(), delete_filter_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_filter_errors(), tuple()}. 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. -spec delete_metric_attribution(aws_client:aws_client(), delete_metric_attribution_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_metric_attribution_errors(), tuple()}. delete_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> delete_metric_attribution(Client, Input, []). -spec delete_metric_attribution(aws_client:aws_client(), delete_metric_attribution_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_metric_attribution_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% request. -spec delete_recommender(aws_client:aws_client(), delete_recommender_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_recommender_errors(), tuple()}. delete_recommender(Client, Input) when is_map(Client), is_map(Input) -> delete_recommender(Client, Input, []). -spec delete_recommender(aws_client:aws_client(), delete_recommender_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_recommender_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. -spec delete_schema(aws_client:aws_client(), delete_schema_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_schema_errors(), tuple()}. delete_schema(Client, Input) when is_map(Client), is_map(Input) -> delete_schema(Client, Input, []). -spec delete_schema(aws_client:aws_client(), delete_schema_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_schema_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_ListCampaigns.html %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. -spec delete_solution(aws_client:aws_client(), delete_solution_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_solution_errors(), tuple()}. delete_solution(Client, Input) when is_map(Client), is_map(Input) -> delete_solution(Client, Input, []). -spec delete_solution(aws_client:aws_client(), delete_solution_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, delete_solution_errors(), tuple()}. 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. -spec describe_algorithm(aws_client:aws_client(), describe_algorithm_request()) -> {ok, describe_algorithm_response(), tuple()} | {error, any()} | {error, describe_algorithm_errors(), tuple()}. describe_algorithm(Client, Input) when is_map(Client), is_map(Input) -> describe_algorithm(Client, Input, []). -spec describe_algorithm(aws_client:aws_client(), describe_algorithm_request(), proplists:proplist()) -> {ok, describe_algorithm_response(), tuple()} | {error, any()} | {error, describe_algorithm_errors(), tuple()}. 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. -spec describe_batch_inference_job(aws_client:aws_client(), describe_batch_inference_job_request()) -> {ok, describe_batch_inference_job_response(), tuple()} | {error, any()} | {error, describe_batch_inference_job_errors(), tuple()}. describe_batch_inference_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_inference_job(Client, Input, []). -spec describe_batch_inference_job(aws_client:aws_client(), describe_batch_inference_job_request(), proplists:proplist()) -> {ok, describe_batch_inference_job_response(), tuple()} | {error, any()} | {error, describe_batch_inference_job_errors(), tuple()}. 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. -spec describe_batch_segment_job(aws_client:aws_client(), describe_batch_segment_job_request()) -> {ok, describe_batch_segment_job_response(), tuple()} | {error, any()} | {error, describe_batch_segment_job_errors(), tuple()}. describe_batch_segment_job(Client, Input) when is_map(Client), is_map(Input) -> describe_batch_segment_job(Client, Input, []). -spec describe_batch_segment_job(aws_client:aws_client(), describe_batch_segment_job_request(), proplists:proplist()) -> {ok, describe_batch_segment_job_response(), tuple()} | {error, any()} | {error, describe_batch_segment_job_errors(), tuple()}. 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: %% %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. -spec describe_campaign(aws_client:aws_client(), describe_campaign_request()) -> {ok, describe_campaign_response(), tuple()} | {error, any()} | {error, describe_campaign_errors(), tuple()}. describe_campaign(Client, Input) when is_map(Client), is_map(Input) -> describe_campaign(Client, Input, []). -spec describe_campaign(aws_client:aws_client(), describe_campaign_request(), proplists:proplist()) -> {ok, describe_campaign_response(), tuple()} | {error, any()} | {error, describe_campaign_errors(), tuple()}. describe_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeCampaign">>, Input, Options). %% @doc Describes the data deletion job created by CreateDataDeletionJob: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataDeletionJob.html, %% including the job status. -spec describe_data_deletion_job(aws_client:aws_client(), describe_data_deletion_job_request()) -> {ok, describe_data_deletion_job_response(), tuple()} | {error, any()} | {error, describe_data_deletion_job_errors(), tuple()}. describe_data_deletion_job(Client, Input) when is_map(Client), is_map(Input) -> describe_data_deletion_job(Client, Input, []). -spec describe_data_deletion_job(aws_client:aws_client(), describe_data_deletion_job_request(), proplists:proplist()) -> {ok, describe_data_deletion_job_response(), tuple()} | {error, any()} | {error, describe_data_deletion_job_errors(), tuple()}. describe_data_deletion_job(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"DescribeDataDeletionJob">>, Input, Options). %% @doc Describes the given dataset. %% %% For more information on datasets, see %% CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. -spec describe_dataset(aws_client:aws_client(), describe_dataset_request()) -> {ok, describe_dataset_response(), tuple()} | {error, any()} | {error, describe_dataset_errors(), tuple()}. describe_dataset(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset(Client, Input, []). -spec describe_dataset(aws_client:aws_client(), describe_dataset_request(), proplists:proplist()) -> {ok, describe_dataset_response(), tuple()} | {error, any()} | {error, describe_dataset_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetExportJob.html, %% including the export job status. -spec describe_dataset_export_job(aws_client:aws_client(), describe_dataset_export_job_request()) -> {ok, describe_dataset_export_job_response(), tuple()} | {error, any()} | {error, describe_dataset_export_job_errors(), tuple()}. describe_dataset_export_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_export_job(Client, Input, []). -spec describe_dataset_export_job(aws_client:aws_client(), describe_dataset_export_job_request(), proplists:proplist()) -> {ok, describe_dataset_export_job_response(), tuple()} | {error, any()} | {error, describe_dataset_export_job_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetGroup.html. -spec describe_dataset_group(aws_client:aws_client(), describe_dataset_group_request()) -> {ok, describe_dataset_group_response(), tuple()} | {error, any()} | {error, describe_dataset_group_errors(), tuple()}. describe_dataset_group(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_group(Client, Input, []). -spec describe_dataset_group(aws_client:aws_client(), describe_dataset_group_request(), proplists:proplist()) -> {ok, describe_dataset_group_response(), tuple()} | {error, any()} | {error, describe_dataset_group_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html, %% including the import job status. -spec describe_dataset_import_job(aws_client:aws_client(), describe_dataset_import_job_request()) -> {ok, describe_dataset_import_job_response(), tuple()} | {error, any()} | {error, describe_dataset_import_job_errors(), tuple()}. describe_dataset_import_job(Client, Input) when is_map(Client), is_map(Input) -> describe_dataset_import_job(Client, Input, []). -spec describe_dataset_import_job(aws_client:aws_client(), describe_dataset_import_job_request(), proplists:proplist()) -> {ok, describe_dataset_import_job_response(), tuple()} | {error, any()} | {error, describe_dataset_import_job_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. -spec describe_event_tracker(aws_client:aws_client(), describe_event_tracker_request()) -> {ok, describe_event_tracker_response(), tuple()} | {error, any()} | {error, describe_event_tracker_errors(), tuple()}. describe_event_tracker(Client, Input) when is_map(Client), is_map(Input) -> describe_event_tracker(Client, Input, []). -spec describe_event_tracker(aws_client:aws_client(), describe_event_tracker_request(), proplists:proplist()) -> {ok, describe_event_tracker_response(), tuple()} | {error, any()} | {error, describe_event_tracker_errors(), tuple()}. 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. -spec describe_feature_transformation(aws_client:aws_client(), describe_feature_transformation_request()) -> {ok, describe_feature_transformation_response(), tuple()} | {error, any()} | {error, describe_feature_transformation_errors(), tuple()}. describe_feature_transformation(Client, Input) when is_map(Client), is_map(Input) -> describe_feature_transformation(Client, Input, []). -spec describe_feature_transformation(aws_client:aws_client(), describe_feature_transformation_request(), proplists:proplist()) -> {ok, describe_feature_transformation_response(), tuple()} | {error, any()} | {error, describe_feature_transformation_errors(), tuple()}. 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. -spec describe_filter(aws_client:aws_client(), describe_filter_request()) -> {ok, describe_filter_response(), tuple()} | {error, any()} | {error, describe_filter_errors(), tuple()}. describe_filter(Client, Input) when is_map(Client), is_map(Input) -> describe_filter(Client, Input, []). -spec describe_filter(aws_client:aws_client(), describe_filter_request(), proplists:proplist()) -> {ok, describe_filter_response(), tuple()} | {error, any()} | {error, describe_filter_errors(), tuple()}. 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. -spec describe_metric_attribution(aws_client:aws_client(), describe_metric_attribution_request()) -> {ok, describe_metric_attribution_response(), tuple()} | {error, any()} | {error, describe_metric_attribution_errors(), tuple()}. describe_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> describe_metric_attribution(Client, Input, []). -spec describe_metric_attribution(aws_client:aws_client(), describe_metric_attribution_request(), proplists:proplist()) -> {ok, describe_metric_attribution_response(), tuple()} | {error, any()} | {error, describe_metric_attribution_errors(), tuple()}. 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: %% %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html %% 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_RS_GetRecommendations.html %% API. -spec describe_recipe(aws_client:aws_client(), describe_recipe_request()) -> {ok, describe_recipe_response(), tuple()} | {error, any()} | {error, describe_recipe_errors(), tuple()}. describe_recipe(Client, Input) when is_map(Client), is_map(Input) -> describe_recipe(Client, Input, []). -spec describe_recipe(aws_client:aws_client(), describe_recipe_request(), proplists:proplist()) -> {ok, describe_recipe_response(), tuple()} | {error, any()} | {error, describe_recipe_errors(), tuple()}. 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: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED %% %% STOP PENDING > STOP IN_PROGRESS > INACTIVE > START PENDING > %% START IN_PROGRESS > ACTIVE %% %% DELETE PENDING > DELETE IN_PROGRESS %% %% When the `status' is `CREATE FAILED', the response includes the %% `failureReason' key, which describes why. %% %% The `modelMetrics' key is null when %% the recommender is being created or deleted. %% %% For more information on recommenders, see CreateRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateRecommender.html. -spec describe_recommender(aws_client:aws_client(), describe_recommender_request()) -> {ok, describe_recommender_response(), tuple()} | {error, any()} | {error, describe_recommender_errors(), tuple()}. describe_recommender(Client, Input) when is_map(Client), is_map(Input) -> describe_recommender(Client, Input, []). -spec describe_recommender(aws_client:aws_client(), describe_recommender_request(), proplists:proplist()) -> {ok, describe_recommender_response(), tuple()} | {error, any()} | {error, describe_recommender_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. -spec describe_schema(aws_client:aws_client(), describe_schema_request()) -> {ok, describe_schema_response(), tuple()} | {error, any()} | {error, describe_schema_errors(), tuple()}. describe_schema(Client, Input) when is_map(Client), is_map(Input) -> describe_schema(Client, Input, []). -spec describe_schema(aws_client:aws_client(), describe_schema_request(), proplists:proplist()) -> {ok, describe_schema_response(), tuple()} | {error, any()} | {error, describe_schema_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. -spec describe_solution(aws_client:aws_client(), describe_solution_request()) -> {ok, describe_solution_response(), tuple()} | {error, any()} | {error, describe_solution_errors(), tuple()}. describe_solution(Client, Input) when is_map(Client), is_map(Input) -> describe_solution(Client, Input, []). -spec describe_solution(aws_client:aws_client(), describe_solution_request(), proplists:proplist()) -> {ok, describe_solution_response(), tuple()} | {error, any()} | {error, describe_solution_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html -spec describe_solution_version(aws_client:aws_client(), describe_solution_version_request()) -> {ok, describe_solution_version_response(), tuple()} | {error, any()} | {error, describe_solution_version_errors(), tuple()}. describe_solution_version(Client, Input) when is_map(Client), is_map(Input) -> describe_solution_version(Client, Input, []). -spec describe_solution_version(aws_client:aws_client(), describe_solution_version_request(), proplists:proplist()) -> {ok, describe_solution_version_response(), tuple()} | {error, any()} | {error, describe_solution_version_errors(), tuple()}. 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. -spec get_solution_metrics(aws_client:aws_client(), get_solution_metrics_request()) -> {ok, get_solution_metrics_response(), tuple()} | {error, any()} | {error, get_solution_metrics_errors(), tuple()}. get_solution_metrics(Client, Input) when is_map(Client), is_map(Input) -> get_solution_metrics(Client, Input, []). -spec get_solution_metrics(aws_client:aws_client(), get_solution_metrics_request(), proplists:proplist()) -> {ok, get_solution_metrics_response(), tuple()} | {error, any()} | {error, get_solution_metrics_errors(), tuple()}. 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. -spec list_batch_inference_jobs(aws_client:aws_client(), list_batch_inference_jobs_request()) -> {ok, list_batch_inference_jobs_response(), tuple()} | {error, any()} | {error, list_batch_inference_jobs_errors(), tuple()}. list_batch_inference_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_inference_jobs(Client, Input, []). -spec list_batch_inference_jobs(aws_client:aws_client(), list_batch_inference_jobs_request(), proplists:proplist()) -> {ok, list_batch_inference_jobs_response(), tuple()} | {error, any()} | {error, list_batch_inference_jobs_errors(), tuple()}. 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. -spec list_batch_segment_jobs(aws_client:aws_client(), list_batch_segment_jobs_request()) -> {ok, list_batch_segment_jobs_response(), tuple()} | {error, any()} | {error, list_batch_segment_jobs_errors(), tuple()}. list_batch_segment_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_batch_segment_jobs(Client, Input, []). -spec list_batch_segment_jobs(aws_client:aws_client(), list_batch_segment_jobs_request(), proplists:proplist()) -> {ok, list_batch_segment_jobs_response(), tuple()} | {error, any()} | {error, list_batch_segment_jobs_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateCampaign.html. -spec list_campaigns(aws_client:aws_client(), list_campaigns_request()) -> {ok, list_campaigns_response(), tuple()} | {error, any()} | {error, list_campaigns_errors(), tuple()}. list_campaigns(Client, Input) when is_map(Client), is_map(Input) -> list_campaigns(Client, Input, []). -spec list_campaigns(aws_client:aws_client(), list_campaigns_request(), proplists:proplist()) -> {ok, list_campaigns_response(), tuple()} | {error, any()} | {error, list_campaigns_errors(), tuple()}. 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 data deletion jobs for a dataset group ordered by %% creation time, %% with the most recent first. %% %% When %% a dataset group is not specified, all the data deletion jobs associated %% with %% the account are listed. The response provides the properties for each %% job, including the Amazon Resource Name (ARN). For more %% information on data deletion jobs, see Deleting users: %% https://docs.aws.amazon.com/personalize/latest/dg/delete-records.html. -spec list_data_deletion_jobs(aws_client:aws_client(), list_data_deletion_jobs_request()) -> {ok, list_data_deletion_jobs_response(), tuple()} | {error, any()} | {error, list_data_deletion_jobs_errors(), tuple()}. list_data_deletion_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_data_deletion_jobs(Client, Input, []). -spec list_data_deletion_jobs(aws_client:aws_client(), list_data_deletion_jobs_request(), proplists:proplist()) -> {ok, list_data_deletion_jobs_response(), tuple()} | {error, any()} | {error, list_data_deletion_jobs_errors(), tuple()}. list_data_deletion_jobs(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"ListDataDeletionJobs">>, 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetExportJob.html. %% For more information on datasets, see %% CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. -spec list_dataset_export_jobs(aws_client:aws_client(), list_dataset_export_jobs_request()) -> {ok, list_dataset_export_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_export_jobs_errors(), tuple()}. list_dataset_export_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_export_jobs(Client, Input, []). -spec list_dataset_export_jobs(aws_client:aws_client(), list_dataset_export_jobs_request(), proplists:proplist()) -> {ok, list_dataset_export_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_export_jobs_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetGroup.html. -spec list_dataset_groups(aws_client:aws_client(), list_dataset_groups_request()) -> {ok, list_dataset_groups_response(), tuple()} | {error, any()} | {error, list_dataset_groups_errors(), tuple()}. list_dataset_groups(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_groups(Client, Input, []). -spec list_dataset_groups(aws_client:aws_client(), list_dataset_groups_request(), proplists:proplist()) -> {ok, list_dataset_groups_response(), tuple()} | {error, any()} | {error, list_dataset_groups_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDatasetImportJob.html. %% For more information on datasets, see %% CreateDataset: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. -spec list_dataset_import_jobs(aws_client:aws_client(), list_dataset_import_jobs_request()) -> {ok, list_dataset_import_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_import_jobs_errors(), tuple()}. list_dataset_import_jobs(Client, Input) when is_map(Client), is_map(Input) -> list_dataset_import_jobs(Client, Input, []). -spec list_dataset_import_jobs(aws_client:aws_client(), list_dataset_import_jobs_request(), proplists:proplist()) -> {ok, list_dataset_import_jobs_response(), tuple()} | {error, any()} | {error, list_dataset_import_jobs_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateDataset.html. -spec list_datasets(aws_client:aws_client(), list_datasets_request()) -> {ok, list_datasets_response(), tuple()} | {error, any()} | {error, list_datasets_errors(), tuple()}. list_datasets(Client, Input) when is_map(Client), is_map(Input) -> list_datasets(Client, Input, []). -spec list_datasets(aws_client:aws_client(), list_datasets_request(), proplists:proplist()) -> {ok, list_datasets_response(), tuple()} | {error, any()} | {error, list_datasets_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateEventTracker.html. -spec list_event_trackers(aws_client:aws_client(), list_event_trackers_request()) -> {ok, list_event_trackers_response(), tuple()} | {error, any()} | {error, list_event_trackers_errors(), tuple()}. list_event_trackers(Client, Input) when is_map(Client), is_map(Input) -> list_event_trackers(Client, Input, []). -spec list_event_trackers(aws_client:aws_client(), list_event_trackers_request(), proplists:proplist()) -> {ok, list_event_trackers_response(), tuple()} | {error, any()} | {error, list_event_trackers_errors(), tuple()}. 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. -spec list_filters(aws_client:aws_client(), list_filters_request()) -> {ok, list_filters_response(), tuple()} | {error, any()} | {error, list_filters_errors(), tuple()}. list_filters(Client, Input) when is_map(Client), is_map(Input) -> list_filters(Client, Input, []). -spec list_filters(aws_client:aws_client(), list_filters_request(), proplists:proplist()) -> {ok, list_filters_response(), tuple()} | {error, any()} | {error, list_filters_errors(), tuple()}. 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. -spec list_metric_attribution_metrics(aws_client:aws_client(), list_metric_attribution_metrics_request()) -> {ok, list_metric_attribution_metrics_response(), tuple()} | {error, any()} | {error, list_metric_attribution_metrics_errors(), tuple()}. list_metric_attribution_metrics(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attribution_metrics(Client, Input, []). -spec list_metric_attribution_metrics(aws_client:aws_client(), list_metric_attribution_metrics_request(), proplists:proplist()) -> {ok, list_metric_attribution_metrics_response(), tuple()} | {error, any()} | {error, list_metric_attribution_metrics_errors(), tuple()}. 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. -spec list_metric_attributions(aws_client:aws_client(), list_metric_attributions_request()) -> {ok, list_metric_attributions_response(), tuple()} | {error, any()} | {error, list_metric_attributions_errors(), tuple()}. list_metric_attributions(Client, Input) when is_map(Client), is_map(Input) -> list_metric_attributions(Client, Input, []). -spec list_metric_attributions(aws_client:aws_client(), list_metric_attributions_request(), proplists:proplist()) -> {ok, list_metric_attributions_response(), tuple()} | {error, any()} | {error, list_metric_attributions_errors(), tuple()}. 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). -spec list_recipes(aws_client:aws_client(), list_recipes_request()) -> {ok, list_recipes_response(), tuple()} | {error, any()} | {error, list_recipes_errors(), tuple()}. list_recipes(Client, Input) when is_map(Client), is_map(Input) -> list_recipes(Client, Input, []). -spec list_recipes(aws_client:aws_client(), list_recipes_request(), proplists:proplist()) -> {ok, list_recipes_response(), tuple()} | {error, any()} | {error, list_recipes_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateRecommender.html. -spec list_recommenders(aws_client:aws_client(), list_recommenders_request()) -> {ok, list_recommenders_response(), tuple()} | {error, any()} | {error, list_recommenders_errors(), tuple()}. list_recommenders(Client, Input) when is_map(Client), is_map(Input) -> list_recommenders(Client, Input, []). -spec list_recommenders(aws_client:aws_client(), list_recommenders_request(), proplists:proplist()) -> {ok, list_recommenders_response(), tuple()} | {error, any()} | {error, list_recommenders_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSchema.html. -spec list_schemas(aws_client:aws_client(), list_schemas_request()) -> {ok, list_schemas_response(), tuple()} | {error, any()} | {error, list_schemas_errors(), tuple()}. list_schemas(Client, Input) when is_map(Client), is_map(Input) -> list_schemas(Client, Input, []). -spec list_schemas(aws_client:aws_client(), list_schemas_request(), proplists:proplist()) -> {ok, list_schemas_response(), tuple()} | {error, any()} | {error, list_schemas_errors(), tuple()}. 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). -spec list_solution_versions(aws_client:aws_client(), list_solution_versions_request()) -> {ok, list_solution_versions_response(), tuple()} | {error, any()} | {error, list_solution_versions_errors(), tuple()}. list_solution_versions(Client, Input) when is_map(Client), is_map(Input) -> list_solution_versions(Client, Input, []). -spec list_solution_versions(aws_client:aws_client(), list_solution_versions_request(), proplists:proplist()) -> {ok, list_solution_versions_response(), tuple()} | {error, any()} | {error, list_solution_versions_errors(), tuple()}. 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 in a 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: %% https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html. -spec list_solutions(aws_client:aws_client(), list_solutions_request()) -> {ok, list_solutions_response(), tuple()} | {error, any()} | {error, list_solutions_errors(), tuple()}. list_solutions(Client, Input) when is_map(Client), is_map(Input) -> list_solutions(Client, Input, []). -spec list_solutions(aws_client:aws_client(), list_solutions_request(), proplists:proplist()) -> {ok, list_solutions_response(), tuple()} | {error, any()} | {error, list_solutions_errors(), tuple()}. 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: %% https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html %% attached to a resource. -spec list_tags_for_resource(aws_client:aws_client(), list_tags_for_resource_request()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. list_tags_for_resource(Client, Input) when is_map(Client), is_map(Input) -> list_tags_for_resource(Client, Input, []). -spec list_tags_for_resource(aws_client:aws_client(), list_tags_for_resource_request(), proplists:proplist()) -> {ok, list_tags_for_resource_response(), tuple()} | {error, any()} | {error, list_tags_for_resource_errors(), tuple()}. 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. -spec start_recommender(aws_client:aws_client(), start_recommender_request()) -> {ok, start_recommender_response(), tuple()} | {error, any()} | {error, start_recommender_errors(), tuple()}. start_recommender(Client, Input) when is_map(Client), is_map(Input) -> start_recommender(Client, Input, []). -spec start_recommender(aws_client:aws_client(), start_recommender_request(), proplists:proplist()) -> {ok, start_recommender_response(), tuple()} | {error, any()} | {error, start_recommender_errors(), tuple()}. 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. -spec stop_recommender(aws_client:aws_client(), stop_recommender_request()) -> {ok, stop_recommender_response(), tuple()} | {error, any()} | {error, stop_recommender_errors(), tuple()}. stop_recommender(Client, Input) when is_map(Client), is_map(Input) -> stop_recommender(Client, Input, []). -spec stop_recommender(aws_client:aws_client(), stop_recommender_request(), proplists:proplist()) -> {ok, stop_recommender_response(), tuple()} | {error, any()} | {error, stop_recommender_errors(), tuple()}. 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: %% %% CREATE_PENDING > CREATE_STOPPED %% %% or %% %% CREATE_IN_PROGRESS > CREATE_STOPPING > CREATE_STOPPED %% %% 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. -spec stop_solution_version_creation(aws_client:aws_client(), stop_solution_version_creation_request()) -> {ok, undefined, tuple()} | {error, any()} | {error, stop_solution_version_creation_errors(), tuple()}. stop_solution_version_creation(Client, Input) when is_map(Client), is_map(Input) -> stop_solution_version_creation(Client, Input, []). -spec stop_solution_version_creation(aws_client:aws_client(), stop_solution_version_creation_request(), proplists:proplist()) -> {ok, undefined, tuple()} | {error, any()} | {error, stop_solution_version_creation_errors(), tuple()}. 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. -spec tag_resource(aws_client:aws_client(), tag_resource_request()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, Input) when is_map(Client), is_map(Input) -> tag_resource(Client, Input, []). -spec tag_resource(aws_client:aws_client(), tag_resource_request(), proplists:proplist()) -> {ok, tag_resource_response(), tuple()} | {error, any()} | {error, tag_resource_errors(), tuple()}. tag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"TagResource">>, Input, Options). %% @doc Removes the specified tags that are attached to a resource. %% %% For more information, see Removing tags from Amazon Personalize resources: %% https://docs.aws.amazon.com/personalize/latest/dg/tags-remove.html. -spec untag_resource(aws_client:aws_client(), untag_resource_request()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, Input) when is_map(Client), is_map(Input) -> untag_resource(Client, Input, []). -spec untag_resource(aws_client:aws_client(), untag_resource_request(), proplists:proplist()) -> {ok, untag_resource_response(), tuple()} | {error, any()} | {error, untag_resource_errors(), tuple()}. untag_resource(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UntagResource">>, Input, Options). %% @doc %% Updates a campaign to deploy a retrained solution version with an existing %% campaign, change your campaign's `minProvisionedTPS', %% or modify your campaign's configuration. %% %% For example, you can set `enableMetadataWithRecommendations' to true %% for an existing campaign. %% %% To update a campaign to start automatically using the latest solution %% version, specify the following: %% %% For the `SolutionVersionArn' parameter, specify the Amazon Resource %% Name (ARN) of your solution in %% `SolutionArn/$LATEST' format. %% %% In the `campaignConfig', set `syncWithLatestSolutionVersion' to %% `true'. %% %% To update a campaign, the campaign status must be ACTIVE or CREATE FAILED. %% Check the campaign status using the DescribeCampaign: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeCampaign.html %% 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 about updating a campaign, including code samples, %% see Updating a campaign: %% https://docs.aws.amazon.com/personalize/latest/dg/update-campaigns.html. %% For more information about campaigns, see Creating a campaign: %% https://docs.aws.amazon.com/personalize/latest/dg/campaigns.html. -spec update_campaign(aws_client:aws_client(), update_campaign_request()) -> {ok, update_campaign_response(), tuple()} | {error, any()} | {error, update_campaign_errors(), tuple()}. update_campaign(Client, Input) when is_map(Client), is_map(Input) -> update_campaign(Client, Input, []). -spec update_campaign(aws_client:aws_client(), update_campaign_request(), proplists:proplist()) -> {ok, update_campaign_response(), tuple()} | {error, any()} | {error, update_campaign_errors(), tuple()}. update_campaign(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateCampaign">>, Input, Options). %% @doc Update a dataset to replace its schema with a new or existing one. %% %% For more information, see Replacing a dataset's schema: %% https://docs.aws.amazon.com/personalize/latest/dg/updating-dataset-schema.html. -spec update_dataset(aws_client:aws_client(), update_dataset_request()) -> {ok, update_dataset_response(), tuple()} | {error, any()} | {error, update_dataset_errors(), tuple()}. update_dataset(Client, Input) when is_map(Client), is_map(Input) -> update_dataset(Client, Input, []). -spec update_dataset(aws_client:aws_client(), update_dataset_request(), proplists:proplist()) -> {ok, update_dataset_response(), tuple()} | {error, any()} | {error, update_dataset_errors(), tuple()}. update_dataset(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateDataset">>, Input, Options). %% @doc Updates a metric attribution. -spec update_metric_attribution(aws_client:aws_client(), update_metric_attribution_request()) -> {ok, update_metric_attribution_response(), tuple()} | {error, any()} | {error, update_metric_attribution_errors(), tuple()}. update_metric_attribution(Client, Input) when is_map(Client), is_map(Input) -> update_metric_attribution(Client, Input, []). -spec update_metric_attribution(aws_client:aws_client(), update_metric_attribution_request(), proplists:proplist()) -> {ok, update_metric_attribution_response(), tuple()} | {error, any()} | {error, update_metric_attribution_errors(), tuple()}. 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. %% %% If you update the recommender to modify the columns used in training, %% Amazon Personalize automatically starts a full retraining of %% the models backing your recommender. While the update completes, you can %% still get recommendations from the recommender. The recommender %% uses the previous configuration until the update completes. %% To track the status of this update, %% use the `latestRecommenderUpdate' returned in the DescribeRecommender: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeRecommender.html %% operation. -spec update_recommender(aws_client:aws_client(), update_recommender_request()) -> {ok, update_recommender_response(), tuple()} | {error, any()} | {error, update_recommender_errors(), tuple()}. update_recommender(Client, Input) when is_map(Client), is_map(Input) -> update_recommender(Client, Input, []). -spec update_recommender(aws_client:aws_client(), update_recommender_request(), proplists:proplist()) -> {ok, update_recommender_response(), tuple()} | {error, any()} | {error, update_recommender_errors(), tuple()}. update_recommender(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateRecommender">>, Input, Options). %% @doc Updates an Amazon Personalize solution to use a different automatic %% training configuration. %% %% When you update a solution, %% you can change whether the solution uses %% automatic training, and you can change the training frequency. For more %% information about updating a solution, see %% Updating a solution: %% https://docs.aws.amazon.com/personalize/latest/dg/updating-solution.html. %% %% A solution update can be in one of the %% following states: %% %% CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED %% %% To get the status of a solution update, call the %% DescribeSolution: %% https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html %% API operation and find the status %% in the `latestSolutionUpdate'. -spec update_solution(aws_client:aws_client(), update_solution_request()) -> {ok, update_solution_response(), tuple()} | {error, any()} | {error, update_solution_errors(), tuple()}. update_solution(Client, Input) when is_map(Client), is_map(Input) -> update_solution(Client, Input, []). -spec update_solution(aws_client:aws_client(), update_solution_request(), proplists:proplist()) -> {ok, update_solution_response(), tuple()} | {error, any()} | {error, update_solution_errors(), tuple()}. update_solution(Client, Input, Options) when is_map(Client), is_map(Input), is_list(Options) -> request(Client, <<"UpdateSolution">>, 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 = aws_client:proto(Client), Port = aws_client:port(Client), aws_util:binary_join([Proto, <<"://">>, Host, <<":">>, Port, <<"/">>], <<"">>).