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Showing content from https://docs.aws.amazon.com/sdkforruby/api/Aws/ForecastService/Client.html below:

Client — AWS SDK for Ruby V2

You are viewing documentation for version 2 of the AWS SDK for Ruby. Version 3 documentation can be found here.

Class: Aws::ForecastService::Client Overview

An API client for Amazon Forecast Service. To construct a client, you need to configure a :region and :credentials.

forecastservice = Aws::ForecastService::Client.new(
  region: region_name,
  credentials: credentials,
  )

See #initialize for a full list of supported configuration options.

Region

You can configure a default region in the following locations:

Go here for a list of supported regions.

Credentials

Default credentials are loaded automatically from the following locations:

You can also construct a credentials object from one of the following classes:

Alternatively, you configure credentials with :access_key_id and :secret_access_key:

creds = YAML.load(File.read('/path/to/secrets'))

Aws::ForecastService::Client.new(
  access_key_id: creds['access_key_id'],
  secret_access_key: creds['secret_access_key']
)

Always load your credentials from outside your application. Avoid configuring credentials statically and never commit them to source control.

Attribute Summary collapse Instance Attribute Summary Attributes inherited from Seahorse::Client::Base

#config, #handlers

Constructor collapse API Operations collapse Instance Method Summary collapse Methods inherited from Seahorse::Client::Base

add_plugin, api, #build_request, clear_plugins, define, new, #operation, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Instance Method Details #create_dataset(options = {}) ⇒ Types::CreateDatasetResponse

Creates an Amazon Forecast dataset. The information about the dataset that you provide helps Forecast understand how to consume the data for model training. This includes the following:

After creating a dataset, you import your training data into it and add the dataset to a dataset group. You use the dataset group to create a predictor. For more information, see howitworks-datasets-groups.

To get a list of all your datasets, use the ListDatasets operation.

For example Forecast datasets, see the Amazon Forecast Sample GitHub repository.

The Status of a dataset must be ACTIVE before you can import training data. Use the DescribeDataset operation to get the status.

#create_dataset_group(options = {}) ⇒ Types::CreateDatasetGroupResponse

Creates a dataset group, which holds a collection of related datasets. You can add datasets to the dataset group when you create the dataset group, or later by using the UpdateDatasetGroup operation.

After creating a dataset group and adding datasets, you use the dataset group when you create a predictor. For more information, see howitworks-datasets-groups.

To get a list of all your datasets groups, use the ListDatasetGroups operation.

The Status of a dataset group must be ACTIVE before you can use the dataset group to create a predictor. To get the status, use the DescribeDatasetGroup operation.

#create_dataset_import_job(options = {}) ⇒ Types::CreateDatasetImportJobResponse

Imports your training data to an Amazon Forecast dataset. You provide the location of your training data in an Amazon Simple Storage Service (Amazon S3) bucket and the Amazon Resource Name (ARN) of the dataset that you want to import the data to.

You must specify a DataSource object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the data, as Amazon Forecast makes a copy of your data and processes it in an internal AWS system. For more information, see aws-forecast-iam-roles.

The training data must be in CSV format. The delimiter must be a comma (,).

You can specify the path to a specific CSV file, the S3 bucket, or to a folder in the S3 bucket. For the latter two cases, Amazon Forecast imports all files up to the limit of 10,000 files.

Because dataset imports are not aggregated, your most recent dataset import is the one that is used when training a predictor or generating a forecast. Make sure that your most recent dataset import contains all of the data you want to model off of, and not just the new data collected since the previous import.

To get a list of all your dataset import jobs, filtered by specified criteria, use the ListDatasetImportJobs operation.

#create_forecast(options = {}) ⇒ Types::CreateForecastResponse

Creates a forecast for each item in the TARGET_TIME_SERIES dataset that was used to train the predictor. This is known as inference. To retrieve the forecast for a single item at low latency, use the operation. To export the complete forecast into your Amazon Simple Storage Service (Amazon S3) bucket, use the CreateForecastExportJob operation.

The range of the forecast is determined by the ForecastHorizon value, which you specify in the CreatePredictor request. When you query a forecast, you can request a specific date range within the forecast.

To get a list of all your forecasts, use the ListForecasts operation.

The forecasts generated by Amazon Forecast are in the same time zone as the dataset that was used to create the predictor.

For more information, see howitworks-forecast.

The Status of the forecast must be ACTIVE before you can query or export the forecast. Use the DescribeForecast operation to get the status.

#create_forecast_export_job(options = {}) ⇒ Types::CreateForecastExportJobResponse

Exports a forecast created by the CreateForecast operation to your Amazon Simple Storage Service (Amazon S3) bucket. The forecast file name will match the following conventions:

<ForecastExportJobName><ExportTimestamp><PartNumber>

where the <ExportTimestamp> component is in Java SimpleDateFormat (yyyy-MM-ddTHH-mm-ssZ).

You must specify a DataDestination object that includes an AWS Identity and Access Management (IAM) role that Amazon Forecast can assume to access the Amazon S3 bucket. For more information, see aws-forecast-iam-roles.

For more information, see howitworks-forecast.

To get a list of all your forecast export jobs, use the ListForecastExportJobs operation.

The Status of the forecast export job must be ACTIVE before you can access the forecast in your Amazon S3 bucket. To get the status, use the DescribeForecastExportJob operation.

#create_predictor(options = {}) ⇒ Types::CreatePredictorResponse

Creates an Amazon Forecast predictor.

In the request, provide a dataset group and either specify an algorithm or let Amazon Forecast choose an algorithm for you using AutoML. If you specify an algorithm, you also can override algorithm-specific hyperparameters.

Amazon Forecast uses the algorithm to train a predictor using the latest version of the datasets in the specified dataset group. You can then generate a forecast using the CreateForecast operation.

To see the evaluation metrics, use the GetAccuracyMetrics operation.

You can specify a featurization configuration to fill and aggregate the data fields in the TARGET_TIME_SERIES dataset to improve model training. For more information, see FeaturizationConfig.

For RELATED_TIME_SERIES datasets, CreatePredictor verifies that the DataFrequency specified when the dataset was created matches the ForecastFrequency. TARGET_TIME_SERIES datasets don't have this restriction. Amazon Forecast also verifies the delimiter and timestamp format. For more information, see howitworks-datasets-groups.

By default, predictors are trained and evaluated at the 0.1 (P10), 0.5 (P50), and 0.9 (P90) quantiles. You can choose custom forecast types to train and evaluate your predictor by setting the ForecastTypes.

AutoML

If you want Amazon Forecast to evaluate each algorithm and choose the one that minimizes the objective function, set PerformAutoML to true. The objective function is defined as the mean of the weighted losses over the forecast types. By default, these are the p10, p50, and p90 quantile losses. For more information, see EvaluationResult.

When AutoML is enabled, the following properties are disallowed:

To get a list of all of your predictors, use the ListPredictors operation.

Before you can use the predictor to create a forecast, the Status of the predictor must be ACTIVE, signifying that training has completed. To get the status, use the DescribePredictor operation.

#delete_dataset(options = {}) ⇒ Struct

Deletes an Amazon Forecast dataset that was created using the CreateDataset operation. You can only delete datasets that have a status of ACTIVE or CREATE_FAILED. To get the status use the DescribeDataset operation.

Forecast does not automatically update any dataset groups that contain the deleted dataset. In order to update the dataset group, use the operation, omitting the deleted dataset's ARN.

#delete_dataset_group(options = {}) ⇒ Struct

Deletes a dataset group created using the CreateDatasetGroup operation. You can only delete dataset groups that have a status of ACTIVE, CREATE_FAILED, or UPDATE_FAILED. To get the status, use the DescribeDatasetGroup operation.

This operation deletes only the dataset group, not the datasets in the group.

#delete_dataset_import_job(options = {}) ⇒ Struct

Deletes a dataset import job created using the CreateDatasetImportJob operation. You can delete only dataset import jobs that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeDatasetImportJob operation.

#delete_forecast(options = {}) ⇒ Struct

Deletes a forecast created using the CreateForecast operation. You can delete only forecasts that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribeForecast operation.

You can't delete a forecast while it is being exported. After a forecast is deleted, you can no longer query the forecast.

#delete_forecast_export_job(options = {}) ⇒ Struct #delete_predictor(options = {}) ⇒ Struct

Deletes a predictor created using the CreatePredictor operation. You can delete only predictor that have a status of ACTIVE or CREATE_FAILED. To get the status, use the DescribePredictor operation.

#describe_dataset(options = {}) ⇒ Types::DescribeDatasetResponse

Describes an Amazon Forecast dataset created using the CreateDataset operation.

In addition to listing the parameters specified in the CreateDataset request, this operation includes the following dataset properties:

#describe_dataset_group(options = {}) ⇒ Types::DescribeDatasetGroupResponse

Describes a dataset group created using the CreateDatasetGroup operation.

In addition to listing the parameters provided in the CreateDatasetGroup request, this operation includes the following properties:

#describe_dataset_import_job(options = {}) ⇒ Types::DescribeDatasetImportJobResponse

Describes a dataset import job created using the CreateDatasetImportJob operation.

In addition to listing the parameters provided in the CreateDatasetImportJob request, this operation includes the following properties:

#describe_forecast(options = {}) ⇒ Types::DescribeForecastResponse

Describes a forecast created using the CreateForecast operation.

In addition to listing the properties provided in the CreateForecast request, this operation lists the following properties:

#describe_forecast_export_job(options = {}) ⇒ Types::DescribeForecastExportJobResponse

Describes a forecast export job created using the CreateForecastExportJob operation.

In addition to listing the properties provided by the user in the CreateForecastExportJob request, this operation lists the following properties:

#describe_predictor(options = {}) ⇒ Types::DescribePredictorResponse

Describes a predictor created using the CreatePredictor operation.

In addition to listing the properties provided in the CreatePredictor request, this operation lists the following properties:

#get_accuracy_metrics(options = {}) ⇒ Types::GetAccuracyMetricsResponse

Provides metrics on the accuracy of the models that were trained by the CreatePredictor operation. Use metrics to see how well the model performed and to decide whether to use the predictor to generate a forecast. For more information, see Predictor Metrics.

This operation generates metrics for each backtest window that was evaluated. The number of backtest windows (NumberOfBacktestWindows) is specified using the EvaluationParameters object, which is optionally included in the CreatePredictor request. If NumberOfBacktestWindows isn't specified, the number defaults to one.

The parameters of the filling method determine which items contribute to the metrics. If you want all items to contribute, specify zero. If you want only those items that have complete data in the range being evaluated to contribute, specify nan. For more information, see FeaturizationMethod.

Before you can get accuracy metrics, the Status of the predictor must be ACTIVE, signifying that training has completed. To get the status, use the DescribePredictor operation.

#list_dataset_groups(options = {}) ⇒ Types::ListDatasetGroupsResponse

Returns a list of dataset groups created using the CreateDatasetGroup operation. For each dataset group, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the dataset group ARN with the DescribeDatasetGroup operation.

#list_dataset_import_jobs(options = {}) ⇒ Types::ListDatasetImportJobsResponse

Returns a list of dataset import jobs created using the CreateDatasetImportJob operation. For each import job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the ARN with the DescribeDatasetImportJob operation. You can filter the list by providing an array of Filter objects.

#list_datasets(options = {}) ⇒ Types::ListDatasetsResponse

Returns a list of datasets created using the CreateDataset operation. For each dataset, a summary of its properties, including its Amazon Resource Name (ARN), is returned. To retrieve the complete set of properties, use the ARN with the DescribeDataset operation.

#list_forecast_export_jobs(options = {}) ⇒ Types::ListForecastExportJobsResponse

Returns a list of forecast export jobs created using the CreateForecastExportJob operation. For each forecast export job, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, use the ARN with the DescribeForecastExportJob operation. You can filter the list using an array of Filter objects.

#list_forecasts(options = {}) ⇒ Types::ListForecastsResponse

Returns a list of forecasts created using the CreateForecast operation. For each forecast, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). To retrieve the complete set of properties, specify the ARN with the DescribeForecast operation. You can filter the list using an array of Filter objects.

#list_predictors(options = {}) ⇒ Types::ListPredictorsResponse

Returns a list of predictors created using the CreatePredictor operation. For each predictor, this operation returns a summary of its properties, including its Amazon Resource Name (ARN). You can retrieve the complete set of properties by using the ARN with the DescribePredictor operation. You can filter the list using an array of Filter objects.

#tag_resource(options = {}) ⇒ Struct

Associates the specified tags to a resource with the specified resourceArn. If existing tags on a resource are not specified in the request parameters, they are not changed. When a resource is deleted, the tags associated with that resource are also deleted.

#untag_resource(options = {}) ⇒ Struct

Deletes the specified tags from a resource.

#update_dataset_group(options = {}) ⇒ Struct

Replaces the datasets in a dataset group with the specified datasets.

The Status of the dataset group must be ACTIVE before you can use the dataset group to create a predictor. Use the DescribeDatasetGroup operation to get the status.

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean

Waiters polls an API operation until a resource enters a desired state.

Basic Usage

Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.

# polls in a loop, sleeping between attempts client.waiter_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:

# poll for ~25 seconds
client.wait_until(...) do |w|
  w.max_attempts = 5
  w.delay = 5
end
Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(...) do |w|

  # disable max attempts
  w.max_attempts = nil

  # poll for 1 hour, instead of a number of attempts
  w.before_wait do |attempts, response|
    throw :failure if Time.now - started_at > 3600
  end

end
Handling Errors

When a waiter is successful, it returns true. When a waiter fails, it raises an error. All errors raised extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end
#waiter_names ⇒ Array<Symbol>

Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:

Waiter Name Client Method Default Delay: Default Max Attempts:

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