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// THIS FILE IS AUTOMATICALLY GENERATED. DO NOT EDIT.
// Package machinelearning provides a client for Amazon Machine Learning.
package machinelearning
import (
"time"
"github.com/aws/aws-sdk-go/aws/awsutil"
"github.com/aws/aws-sdk-go/aws/request"
)
const opCreateBatchPrediction = "CreateBatchPrediction"
// CreateBatchPredictionRequest generates a request for the CreateBatchPrediction operation.
func (c *MachineLearning) CreateBatchPredictionRequest(input *CreateBatchPredictionInput) (req *request.Request, output *CreateBatchPredictionOutput) {
op := &request.Operation{
Name: opCreateBatchPrediction,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateBatchPredictionInput{}
}
req = c.newRequest(op, input, output)
output = &CreateBatchPredictionOutput{}
req.Data = output
return
}
// Generates predictions for a group of observations. The observations to process
// exist in one or more data files referenced by a DataSource. This operation
// creates a new BatchPrediction, and uses an MLModel and the data files referenced
// by the DataSource as information sources.
//
// CreateBatchPrediction is an asynchronous operation. In response to CreateBatchPrediction,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction
// status to PENDING. After the BatchPrediction completes, Amazon ML sets the
// status to COMPLETED.
//
// You can poll for status updates by using the GetBatchPrediction operation
// and checking the Status parameter of the result. After the COMPLETED status
// appears, the results are available in the location specified by the OutputUri
// parameter.
func (c *MachineLearning) CreateBatchPrediction(input *CreateBatchPredictionInput) (*CreateBatchPredictionOutput, error) {
req, out := c.CreateBatchPredictionRequest(input)
err := req.Send()
return out, err
}
const opCreateDataSourceFromRDS = "CreateDataSourceFromRDS"
// CreateDataSourceFromRDSRequest generates a request for the CreateDataSourceFromRDS operation.
func (c *MachineLearning) CreateDataSourceFromRDSRequest(input *CreateDataSourceFromRDSInput) (req *request.Request, output *CreateDataSourceFromRDSOutput) {
op := &request.Operation{
Name: opCreateDataSourceFromRDS,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateDataSourceFromRDSInput{}
}
req = c.newRequest(op, input, output)
output = &CreateDataSourceFromRDSOutput{}
req.Data = output
return
}
// Creates a DataSource object from an Amazon Relational Database Service (http://aws.amazon.com/rds/)
// (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel,
// CreateEvaluation, or CreateBatchPrediction operations.
//
// CreateDataSourceFromRDS is an asynchronous operation. In response to CreateDataSourceFromRDS,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
// status to PENDING. After the DataSource is created and ready for use, Amazon
// ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING
// status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction
// operations.
//
// If Amazon ML cannot accept the input source, it sets the Status parameter
// to FAILED and includes an error message in the Message attribute of the GetDataSource
// operation response.
func (c *MachineLearning) CreateDataSourceFromRDS(input *CreateDataSourceFromRDSInput) (*CreateDataSourceFromRDSOutput, error) {
req, out := c.CreateDataSourceFromRDSRequest(input)
err := req.Send()
return out, err
}
const opCreateDataSourceFromRedshift = "CreateDataSourceFromRedshift"
// CreateDataSourceFromRedshiftRequest generates a request for the CreateDataSourceFromRedshift operation.
func (c *MachineLearning) CreateDataSourceFromRedshiftRequest(input *CreateDataSourceFromRedshiftInput) (req *request.Request, output *CreateDataSourceFromRedshiftOutput) {
op := &request.Operation{
Name: opCreateDataSourceFromRedshift,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateDataSourceFromRedshiftInput{}
}
req = c.newRequest(op, input, output)
output = &CreateDataSourceFromRedshiftOutput{}
req.Data = output
return
}
// Creates a DataSource from Amazon Redshift (http://aws.amazon.com/redshift/).
// A DataSource references data that can be used to perform either CreateMLModel,
// CreateEvaluation or CreateBatchPrediction operations.
//
// CreateDataSourceFromRedshift is an asynchronous operation. In response to
// CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately
// returns and sets the DataSource status to PENDING. After the DataSource is
// created and ready for use, Amazon ML sets the Status parameter to COMPLETED.
// DataSource in COMPLETED or PENDING status can only be used to perform CreateMLModel,
// CreateEvaluation, or CreateBatchPrediction operations.
//
// If Amazon ML cannot accept the input source, it sets the Status parameter
// to FAILED and includes an error message in the Message attribute of the GetDataSource
// operation response.
//
// The observations should exist in the database hosted on an Amazon Redshift
// cluster and should be specified by a SelectSqlQuery. Amazon ML executes
// Unload (http://docs.aws.amazon.com/redshift/latest/dg/t_Unloading_tables.html)
// command in Amazon Redshift to transfer the result set of SelectSqlQuery to
// S3StagingLocation.
//
// After the DataSource is created, it's ready for use in evaluations and batch
// predictions. If you plan to use the DataSource to train an MLModel, the DataSource
// requires another item -- a recipe. A recipe describes the observation variables
// that participate in training an MLModel. A recipe describes how each input
// variable will be used in training. Will the variable be included or excluded
// from training? Will the variable be manipulated, for example, combined with
// another variable or split apart into word combinations? The recipe provides
// answers to these questions. For more information, see the Amazon Machine
// Learning Developer Guide.
func (c *MachineLearning) CreateDataSourceFromRedshift(input *CreateDataSourceFromRedshiftInput) (*CreateDataSourceFromRedshiftOutput, error) {
req, out := c.CreateDataSourceFromRedshiftRequest(input)
err := req.Send()
return out, err
}
const opCreateDataSourceFromS3 = "CreateDataSourceFromS3"
// CreateDataSourceFromS3Request generates a request for the CreateDataSourceFromS3 operation.
func (c *MachineLearning) CreateDataSourceFromS3Request(input *CreateDataSourceFromS3Input) (req *request.Request, output *CreateDataSourceFromS3Output) {
op := &request.Operation{
Name: opCreateDataSourceFromS3,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateDataSourceFromS3Input{}
}
req = c.newRequest(op, input, output)
output = &CreateDataSourceFromS3Output{}
req.Data = output
return
}
// Creates a DataSource object. A DataSource references data that can be used
// to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
//
// CreateDataSourceFromS3 is an asynchronous operation. In response to CreateDataSourceFromS3,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
// status to PENDING. After the DataSource is created and ready for use, Amazon
// ML sets the Status parameter to COMPLETED. DataSource in COMPLETED or PENDING
// status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction
// operations.
//
// If Amazon ML cannot accept the input source, it sets the Status parameter
// to FAILED and includes an error message in the Message attribute of the GetDataSource
// operation response.
//
// The observation data used in a DataSource should be ready to use; that is,
// it should have a consistent structure, and missing data values should be
// kept to a minimum. The observation data must reside in one or more CSV files
// in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema
// that describes the data items by name and type. The same schema must be used
// for all of the data files referenced by the DataSource.
//
// After the DataSource has been created, it's ready to use in evaluations
// and batch predictions. If you plan to use the DataSource to train an MLModel,
// the DataSource requires another item: a recipe. A recipe describes the observation
// variables that participate in training an MLModel. A recipe describes how
// each input variable will be used in training. Will the variable be included
// or excluded from training? Will the variable be manipulated, for example,
// combined with another variable, or split apart into word combinations? The
// recipe provides answers to these questions. For more information, see the
// Amazon Machine Learning Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).
func (c *MachineLearning) CreateDataSourceFromS3(input *CreateDataSourceFromS3Input) (*CreateDataSourceFromS3Output, error) {
req, out := c.CreateDataSourceFromS3Request(input)
err := req.Send()
return out, err
}
const opCreateEvaluation = "CreateEvaluation"
// CreateEvaluationRequest generates a request for the CreateEvaluation operation.
func (c *MachineLearning) CreateEvaluationRequest(input *CreateEvaluationInput) (req *request.Request, output *CreateEvaluationOutput) {
op := &request.Operation{
Name: opCreateEvaluation,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateEvaluationInput{}
}
req = c.newRequest(op, input, output)
output = &CreateEvaluationOutput{}
req.Data = output
return
}
// Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set
// of observations associated to a DataSource. Like a DataSource for an MLModel,
// the DataSource for an Evaluation contains values for the Target Variable.
// The Evaluation compares the predicted result for each observation to the
// actual outcome and provides a summary so that you know how effective the
// MLModel functions on the test data. Evaluation generates a relevant performance
// metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on
// the corresponding MLModelType: BINARY, REGRESSION or MULTICLASS.
//
// CreateEvaluation is an asynchronous operation. In response to CreateEvaluation,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation
// status to PENDING. After the Evaluation is created and ready for use, Amazon
// ML sets the status to COMPLETED.
//
// You can use the GetEvaluation operation to check progress of the evaluation
// during the creation operation.
func (c *MachineLearning) CreateEvaluation(input *CreateEvaluationInput) (*CreateEvaluationOutput, error) {
req, out := c.CreateEvaluationRequest(input)
err := req.Send()
return out, err
}
const opCreateMLModel = "CreateMLModel"
// CreateMLModelRequest generates a request for the CreateMLModel operation.
func (c *MachineLearning) CreateMLModelRequest(input *CreateMLModelInput) (req *request.Request, output *CreateMLModelOutput) {
op := &request.Operation{
Name: opCreateMLModel,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateMLModelInput{}
}
req = c.newRequest(op, input, output)
output = &CreateMLModelOutput{}
req.Data = output
return
}
// Creates a new MLModel using the data files and the recipe as information
// sources.
//
// An MLModel is nearly immutable. Users can only update the MLModelName and
// the ScoreThreshold in an MLModel without creating a new MLModel.
//
// CreateMLModel is an asynchronous operation. In response to CreateMLModel,
// Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel
// status to PENDING. After the MLModel is created and ready for use, Amazon
// ML sets the status to COMPLETED.
//
// You can use the GetMLModel operation to check progress of the MLModel during
// the creation operation.
//
// CreateMLModel requires a DataSource with computed statistics, which can
// be created by setting ComputeStatistics to true in CreateDataSourceFromRDS,
// CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
func (c *MachineLearning) CreateMLModel(input *CreateMLModelInput) (*CreateMLModelOutput, error) {
req, out := c.CreateMLModelRequest(input)
err := req.Send()
return out, err
}
const opCreateRealtimeEndpoint = "CreateRealtimeEndpoint"
// CreateRealtimeEndpointRequest generates a request for the CreateRealtimeEndpoint operation.
func (c *MachineLearning) CreateRealtimeEndpointRequest(input *CreateRealtimeEndpointInput) (req *request.Request, output *CreateRealtimeEndpointOutput) {
op := &request.Operation{
Name: opCreateRealtimeEndpoint,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &CreateRealtimeEndpointInput{}
}
req = c.newRequest(op, input, output)
output = &CreateRealtimeEndpointOutput{}
req.Data = output
return
}
// Creates a real-time endpoint for the MLModel. The endpoint contains the URI
// of the MLModel; that is, the location to send real-time prediction requests
// for the specified MLModel.
func (c *MachineLearning) CreateRealtimeEndpoint(input *CreateRealtimeEndpointInput) (*CreateRealtimeEndpointOutput, error) {
req, out := c.CreateRealtimeEndpointRequest(input)
err := req.Send()
return out, err
}
const opDeleteBatchPrediction = "DeleteBatchPrediction"
// DeleteBatchPredictionRequest generates a request for the DeleteBatchPrediction operation.
func (c *MachineLearning) DeleteBatchPredictionRequest(input *DeleteBatchPredictionInput) (req *request.Request, output *DeleteBatchPredictionOutput) {
op := &request.Operation{
Name: opDeleteBatchPrediction,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &DeleteBatchPredictionInput{}
}
req = c.newRequest(op, input, output)
output = &DeleteBatchPredictionOutput{}
req.Data = output
return
}
// Assigns the DELETED status to a BatchPrediction, rendering it unusable.
//
// After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction
// operation to verify that the status of the BatchPrediction changed to DELETED.
//
// Caution: The result of the DeleteBatchPrediction operation is irreversible.
func (c *MachineLearning) DeleteBatchPrediction(input *DeleteBatchPredictionInput) (*DeleteBatchPredictionOutput, error) {
req, out := c.DeleteBatchPredictionRequest(input)
err := req.Send()
return out, err
}
const opDeleteDataSource = "DeleteDataSource"
// DeleteDataSourceRequest generates a request for the DeleteDataSource operation.
func (c *MachineLearning) DeleteDataSourceRequest(input *DeleteDataSourceInput) (req *request.Request, output *DeleteDataSourceOutput) {
op := &request.Operation{
Name: opDeleteDataSource,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &DeleteDataSourceInput{}
}
req = c.newRequest(op, input, output)
output = &DeleteDataSourceOutput{}
req.Data = output
return
}
// Assigns the DELETED status to a DataSource, rendering it unusable.
//
// After using the DeleteDataSource operation, you can use the GetDataSource
// operation to verify that the status of the DataSource changed to DELETED.
//
// Caution: The results of the DeleteDataSource operation are irreversible.
func (c *MachineLearning) DeleteDataSource(input *DeleteDataSourceInput) (*DeleteDataSourceOutput, error) {
req, out := c.DeleteDataSourceRequest(input)
err := req.Send()
return out, err
}
const opDeleteEvaluation = "DeleteEvaluation"
// DeleteEvaluationRequest generates a request for the DeleteEvaluation operation.
func (c *MachineLearning) DeleteEvaluationRequest(input *DeleteEvaluationInput) (req *request.Request, output *DeleteEvaluationOutput) {
op := &request.Operation{
Name: opDeleteEvaluation,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &DeleteEvaluationInput{}
}
req = c.newRequest(op, input, output)
output = &DeleteEvaluationOutput{}
req.Data = output
return
}
// Assigns the DELETED status to an Evaluation, rendering it unusable.
//
// After invoking the DeleteEvaluation operation, you can use the GetEvaluation
// operation to verify that the status of the Evaluation changed to DELETED.
//
// Caution: The results of the DeleteEvaluation operation are irreversible.
func (c *MachineLearning) DeleteEvaluation(input *DeleteEvaluationInput) (*DeleteEvaluationOutput, error) {
req, out := c.DeleteEvaluationRequest(input)
err := req.Send()
return out, err
}
const opDeleteMLModel = "DeleteMLModel"
// DeleteMLModelRequest generates a request for the DeleteMLModel operation.
func (c *MachineLearning) DeleteMLModelRequest(input *DeleteMLModelInput) (req *request.Request, output *DeleteMLModelOutput) {
op := &request.Operation{
Name: opDeleteMLModel,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &DeleteMLModelInput{}
}
req = c.newRequest(op, input, output)
output = &DeleteMLModelOutput{}
req.Data = output
return
}
// Assigns the DELETED status to an MLModel, rendering it unusable.
//
// After using the DeleteMLModel operation, you can use the GetMLModel operation
// to verify that the status of the MLModel changed to DELETED.
//
// Caution: The result of the DeleteMLModel operation is irreversible.
func (c *MachineLearning) DeleteMLModel(input *DeleteMLModelInput) (*DeleteMLModelOutput, error) {
req, out := c.DeleteMLModelRequest(input)
err := req.Send()
return out, err
}
const opDeleteRealtimeEndpoint = "DeleteRealtimeEndpoint"
// DeleteRealtimeEndpointRequest generates a request for the DeleteRealtimeEndpoint operation.
func (c *MachineLearning) DeleteRealtimeEndpointRequest(input *DeleteRealtimeEndpointInput) (req *request.Request, output *DeleteRealtimeEndpointOutput) {
op := &request.Operation{
Name: opDeleteRealtimeEndpoint,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &DeleteRealtimeEndpointInput{}
}
req = c.newRequest(op, input, output)
output = &DeleteRealtimeEndpointOutput{}
req.Data = output
return
}
// Deletes a real time endpoint of an MLModel.
func (c *MachineLearning) DeleteRealtimeEndpoint(input *DeleteRealtimeEndpointInput) (*DeleteRealtimeEndpointOutput, error) {
req, out := c.DeleteRealtimeEndpointRequest(input)
err := req.Send()
return out, err
}
const opDescribeBatchPredictions = "DescribeBatchPredictions"
// DescribeBatchPredictionsRequest generates a request for the DescribeBatchPredictions operation.
func (c *MachineLearning) DescribeBatchPredictionsRequest(input *DescribeBatchPredictionsInput) (req *request.Request, output *DescribeBatchPredictionsOutput) {
op := &request.Operation{
Name: opDescribeBatchPredictions,
HTTPMethod: "POST",
HTTPPath: "/",
Paginator: &request.Paginator{
InputTokens: []string{"NextToken"},
OutputTokens: []string{"NextToken"},
LimitToken: "Limit",
TruncationToken: "",
},
}
if input == nil {
input = &DescribeBatchPredictionsInput{}
}
req = c.newRequest(op, input, output)
output = &DescribeBatchPredictionsOutput{}
req.Data = output
return
}
// Returns a list of BatchPrediction operations that match the search criteria
// in the request.
func (c *MachineLearning) DescribeBatchPredictions(input *DescribeBatchPredictionsInput) (*DescribeBatchPredictionsOutput, error) {
req, out := c.DescribeBatchPredictionsRequest(input)
err := req.Send()
return out, err
}
func (c *MachineLearning) DescribeBatchPredictionsPages(input *DescribeBatchPredictionsInput, fn func(p *DescribeBatchPredictionsOutput, lastPage bool) (shouldContinue bool)) error {
page, _ := c.DescribeBatchPredictionsRequest(input)
page.Handlers.Build.PushBack(request.MakeAddToUserAgentFreeFormHandler("Paginator"))
return page.EachPage(func(p interface{}, lastPage bool) bool {
return fn(p.(*DescribeBatchPredictionsOutput), lastPage)
})
}
const opDescribeDataSources = "DescribeDataSources"
// DescribeDataSourcesRequest generates a request for the DescribeDataSources operation.
func (c *MachineLearning) DescribeDataSourcesRequest(input *DescribeDataSourcesInput) (req *request.Request, output *DescribeDataSourcesOutput) {
op := &request.Operation{
Name: opDescribeDataSources,
HTTPMethod: "POST",
HTTPPath: "/",
Paginator: &request.Paginator{
InputTokens: []string{"NextToken"},
OutputTokens: []string{"NextToken"},
LimitToken: "Limit",
TruncationToken: "",
},
}
if input == nil {
input = &DescribeDataSourcesInput{}
}
req = c.newRequest(op, input, output)
output = &DescribeDataSourcesOutput{}
req.Data = output
return
}
// Returns a list of DataSource that match the search criteria in the request.
func (c *MachineLearning) DescribeDataSources(input *DescribeDataSourcesInput) (*DescribeDataSourcesOutput, error) {
req, out := c.DescribeDataSourcesRequest(input)
err := req.Send()
return out, err
}
func (c *MachineLearning) DescribeDataSourcesPages(input *DescribeDataSourcesInput, fn func(p *DescribeDataSourcesOutput, lastPage bool) (shouldContinue bool)) error {
page, _ := c.DescribeDataSourcesRequest(input)
page.Handlers.Build.PushBack(request.MakeAddToUserAgentFreeFormHandler("Paginator"))
return page.EachPage(func(p interface{}, lastPage bool) bool {
return fn(p.(*DescribeDataSourcesOutput), lastPage)
})
}
const opDescribeEvaluations = "DescribeEvaluations"
// DescribeEvaluationsRequest generates a request for the DescribeEvaluations operation.
func (c *MachineLearning) DescribeEvaluationsRequest(input *DescribeEvaluationsInput) (req *request.Request, output *DescribeEvaluationsOutput) {
op := &request.Operation{
Name: opDescribeEvaluations,
HTTPMethod: "POST",
HTTPPath: "/",
Paginator: &request.Paginator{
InputTokens: []string{"NextToken"},
OutputTokens: []string{"NextToken"},
LimitToken: "Limit",
TruncationToken: "",
},
}
if input == nil {
input = &DescribeEvaluationsInput{}
}
req = c.newRequest(op, input, output)
output = &DescribeEvaluationsOutput{}
req.Data = output
return
}
// Returns a list of DescribeEvaluations that match the search criteria in the
// request.
func (c *MachineLearning) DescribeEvaluations(input *DescribeEvaluationsInput) (*DescribeEvaluationsOutput, error) {
req, out := c.DescribeEvaluationsRequest(input)
err := req.Send()
return out, err
}
func (c *MachineLearning) DescribeEvaluationsPages(input *DescribeEvaluationsInput, fn func(p *DescribeEvaluationsOutput, lastPage bool) (shouldContinue bool)) error {
page, _ := c.DescribeEvaluationsRequest(input)
page.Handlers.Build.PushBack(request.MakeAddToUserAgentFreeFormHandler("Paginator"))
return page.EachPage(func(p interface{}, lastPage bool) bool {
return fn(p.(*DescribeEvaluationsOutput), lastPage)
})
}
const opDescribeMLModels = "DescribeMLModels"
// DescribeMLModelsRequest generates a request for the DescribeMLModels operation.
func (c *MachineLearning) DescribeMLModelsRequest(input *DescribeMLModelsInput) (req *request.Request, output *DescribeMLModelsOutput) {
op := &request.Operation{
Name: opDescribeMLModels,
HTTPMethod: "POST",
HTTPPath: "/",
Paginator: &request.Paginator{
InputTokens: []string{"NextToken"},
OutputTokens: []string{"NextToken"},
LimitToken: "Limit",
TruncationToken: "",
},
}
if input == nil {
input = &DescribeMLModelsInput{}
}
req = c.newRequest(op, input, output)
output = &DescribeMLModelsOutput{}
req.Data = output
return
}
// Returns a list of MLModel that match the search criteria in the request.
func (c *MachineLearning) DescribeMLModels(input *DescribeMLModelsInput) (*DescribeMLModelsOutput, error) {
req, out := c.DescribeMLModelsRequest(input)
err := req.Send()
return out, err
}
func (c *MachineLearning) DescribeMLModelsPages(input *DescribeMLModelsInput, fn func(p *DescribeMLModelsOutput, lastPage bool) (shouldContinue bool)) error {
page, _ := c.DescribeMLModelsRequest(input)
page.Handlers.Build.PushBack(request.MakeAddToUserAgentFreeFormHandler("Paginator"))
return page.EachPage(func(p interface{}, lastPage bool) bool {
return fn(p.(*DescribeMLModelsOutput), lastPage)
})
}
const opGetBatchPrediction = "GetBatchPrediction"
// GetBatchPredictionRequest generates a request for the GetBatchPrediction operation.
func (c *MachineLearning) GetBatchPredictionRequest(input *GetBatchPredictionInput) (req *request.Request, output *GetBatchPredictionOutput) {
op := &request.Operation{
Name: opGetBatchPrediction,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &GetBatchPredictionInput{}
}
req = c.newRequest(op, input, output)
output = &GetBatchPredictionOutput{}
req.Data = output
return
}
// Returns a BatchPrediction that includes detailed metadata, status, and data
// file information for a Batch Prediction request.
func (c *MachineLearning) GetBatchPrediction(input *GetBatchPredictionInput) (*GetBatchPredictionOutput, error) {
req, out := c.GetBatchPredictionRequest(input)
err := req.Send()
return out, err
}
const opGetDataSource = "GetDataSource"
// GetDataSourceRequest generates a request for the GetDataSource operation.
func (c *MachineLearning) GetDataSourceRequest(input *GetDataSourceInput) (req *request.Request, output *GetDataSourceOutput) {
op := &request.Operation{
Name: opGetDataSource,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &GetDataSourceInput{}
}
req = c.newRequest(op, input, output)
output = &GetDataSourceOutput{}
req.Data = output
return
}
// Returns a DataSource that includes metadata and data file information, as
// well as the current status of the DataSource.
//
// GetDataSource provides results in normal or verbose format. The verbose
// format adds the schema description and the list of files pointed to by the
// DataSource to the normal format.
func (c *MachineLearning) GetDataSource(input *GetDataSourceInput) (*GetDataSourceOutput, error) {
req, out := c.GetDataSourceRequest(input)
err := req.Send()
return out, err
}
const opGetEvaluation = "GetEvaluation"
// GetEvaluationRequest generates a request for the GetEvaluation operation.
func (c *MachineLearning) GetEvaluationRequest(input *GetEvaluationInput) (req *request.Request, output *GetEvaluationOutput) {
op := &request.Operation{
Name: opGetEvaluation,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &GetEvaluationInput{}
}
req = c.newRequest(op, input, output)
output = &GetEvaluationOutput{}
req.Data = output
return
}
// Returns an Evaluation that includes metadata as well as the current status
// of the Evaluation.
func (c *MachineLearning) GetEvaluation(input *GetEvaluationInput) (*GetEvaluationOutput, error) {
req, out := c.GetEvaluationRequest(input)
err := req.Send()
return out, err
}
const opGetMLModel = "GetMLModel"
// GetMLModelRequest generates a request for the GetMLModel operation.
func (c *MachineLearning) GetMLModelRequest(input *GetMLModelInput) (req *request.Request, output *GetMLModelOutput) {
op := &request.Operation{
Name: opGetMLModel,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &GetMLModelInput{}
}
req = c.newRequest(op, input, output)
output = &GetMLModelOutput{}
req.Data = output
return
}
// Returns an MLModel that includes detailed metadata, and data source information
// as well as the current status of the MLModel.
//
// GetMLModel provides results in normal or verbose format.
func (c *MachineLearning) GetMLModel(input *GetMLModelInput) (*GetMLModelOutput, error) {
req, out := c.GetMLModelRequest(input)
err := req.Send()
return out, err
}
const opPredict = "Predict"
// PredictRequest generates a request for the Predict operation.
func (c *MachineLearning) PredictRequest(input *PredictInput) (req *request.Request, output *PredictOutput) {
op := &request.Operation{
Name: opPredict,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &PredictInput{}
}
req = c.newRequest(op, input, output)
output = &PredictOutput{}
req.Data = output
return
}
// Generates a prediction for the observation using the specified ML Model.
//
// Note Not all response parameters will be populated. Whether a response parameter
// is populated depends on the type of model requested.
func (c *MachineLearning) Predict(input *PredictInput) (*PredictOutput, error) {
req, out := c.PredictRequest(input)
err := req.Send()
return out, err
}
const opUpdateBatchPrediction = "UpdateBatchPrediction"
// UpdateBatchPredictionRequest generates a request for the UpdateBatchPrediction operation.
func (c *MachineLearning) UpdateBatchPredictionRequest(input *UpdateBatchPredictionInput) (req *request.Request, output *UpdateBatchPredictionOutput) {
op := &request.Operation{
Name: opUpdateBatchPrediction,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &UpdateBatchPredictionInput{}
}
req = c.newRequest(op, input, output)
output = &UpdateBatchPredictionOutput{}
req.Data = output
return
}
// Updates the BatchPredictionName of a BatchPrediction.
//
// You can use the GetBatchPrediction operation to view the contents of the
// updated data element.
func (c *MachineLearning) UpdateBatchPrediction(input *UpdateBatchPredictionInput) (*UpdateBatchPredictionOutput, error) {
req, out := c.UpdateBatchPredictionRequest(input)
err := req.Send()
return out, err
}
const opUpdateDataSource = "UpdateDataSource"
// UpdateDataSourceRequest generates a request for the UpdateDataSource operation.
func (c *MachineLearning) UpdateDataSourceRequest(input *UpdateDataSourceInput) (req *request.Request, output *UpdateDataSourceOutput) {
op := &request.Operation{
Name: opUpdateDataSource,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &UpdateDataSourceInput{}
}
req = c.newRequest(op, input, output)
output = &UpdateDataSourceOutput{}
req.Data = output
return
}
// Updates the DataSourceName of a DataSource.
//
// You can use the GetDataSource operation to view the contents of the updated
// data element.
func (c *MachineLearning) UpdateDataSource(input *UpdateDataSourceInput) (*UpdateDataSourceOutput, error) {
req, out := c.UpdateDataSourceRequest(input)
err := req.Send()
return out, err
}
const opUpdateEvaluation = "UpdateEvaluation"
// UpdateEvaluationRequest generates a request for the UpdateEvaluation operation.
func (c *MachineLearning) UpdateEvaluationRequest(input *UpdateEvaluationInput) (req *request.Request, output *UpdateEvaluationOutput) {
op := &request.Operation{
Name: opUpdateEvaluation,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &UpdateEvaluationInput{}
}
req = c.newRequest(op, input, output)
output = &UpdateEvaluationOutput{}
req.Data = output
return
}
// Updates the EvaluationName of an Evaluation.
//
// You can use the GetEvaluation operation to view the contents of the updated
// data element.
func (c *MachineLearning) UpdateEvaluation(input *UpdateEvaluationInput) (*UpdateEvaluationOutput, error) {
req, out := c.UpdateEvaluationRequest(input)
err := req.Send()
return out, err
}
const opUpdateMLModel = "UpdateMLModel"
// UpdateMLModelRequest generates a request for the UpdateMLModel operation.
func (c *MachineLearning) UpdateMLModelRequest(input *UpdateMLModelInput) (req *request.Request, output *UpdateMLModelOutput) {
op := &request.Operation{
Name: opUpdateMLModel,
HTTPMethod: "POST",
HTTPPath: "/",
}
if input == nil {
input = &UpdateMLModelInput{}
}
req = c.newRequest(op, input, output)
output = &UpdateMLModelOutput{}
req.Data = output
return
}
// Updates the MLModelName and the ScoreThreshold of an MLModel.
//
// You can use the GetMLModel operation to view the contents of the updated
// data element.
func (c *MachineLearning) UpdateMLModel(input *UpdateMLModelInput) (*UpdateMLModelOutput, error) {
req, out := c.UpdateMLModelRequest(input)
err := req.Send()
return out, err
}
// Represents the output of GetBatchPrediction operation.
//
// The content consists of the detailed metadata, the status, and the data
// file information of a Batch Prediction.
type BatchPrediction struct {
// The ID of the DataSource that points to the group of observations to predict.
BatchPredictionDataSourceId *string `min:"1" type:"string"`
// The ID assigned to the BatchPrediction at creation. This value should be
// identical to the value of the BatchPredictionID in the request.
BatchPredictionId *string `min:"1" type:"string"`
// The time that the BatchPrediction was created. The time is expressed in epoch
// time.
CreatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`
// The AWS user account that invoked the BatchPrediction. The account type can
// be either an AWS root account or an AWS Identity and Access Management (IAM)
// user account.
CreatedByIamUser *string `type:"string"`
// The location of the data file or directory in Amazon Simple Storage Service
// (Amazon S3).
InputDataLocationS3 *string `type:"string"`
// The time of the most recent edit to the BatchPrediction. The time is expressed
// in epoch time.
LastUpdatedAt *time.Time `type:"timestamp" timestampFormat:"unix"`
// The ID of the MLModel that generated predictions for the BatchPrediction
// request.
MLModelId *string `min:"1" type:"string"`
// A description of the most recent details about processing the batch prediction
// request.
Message *string `type:"string"`
// A user-supplied name or description of the BatchPrediction.
Name *string `type:"string"`
// The location of an Amazon S3 bucket or directory to receive the operation
// results. The following substrings are not allowed in the s3 key portion of
// the "outputURI" field: ':', '//', '/./', '/../'.
OutputUri *string `type:"string"`
// The status of the BatchPrediction. This element can have one of the following
// values:
//
// PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate
// predictions for a batch of observations. INPROGRESS - The process is underway.
// FAILED - The request to peform a batch prediction did not run to completion.
// It is not usable. COMPLETED - The batch prediction process completed successfully.
// DELETED - The BatchPrediction is marked as deleted. It is not usable.
Status *string `type:"string" enum:"EntityStatus"`
metadataBatchPrediction `json:"-" xml:"-"`
}
type metadataBatchPrediction struct {
SDKShapeTraits bool `type:"structure"`
}
// String returns the string representation
func (s BatchPrediction) String() string {
return awsutil.Prettify(s)
}
// GoString returns the string representation
func (s BatchPrediction) GoString() string {
return s.String()
}
type CreateBatchPredictionInput struct {
// The ID of the DataSource that points to the group of observations to predict.
BatchPredictionDataSourceId *string `min:"1" type:"string" required:"true"`
// A user-supplied ID that uniquely identifies the BatchPrediction.
BatchPredictionId *string `min:"1" type:"string" required:"true"`
// A user-supplied name or description of the BatchPrediction. BatchPredictionName
// can only use the UTF-8 character set.
BatchPredictionName *string `type:"string"`
// The ID of the MLModel that will generate predictions for the group of observations.
MLModelId *string `min:"1" type:"string" required:"true"`
// The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory
// to store the batch prediction results. The following substrings are not allowed
// in the s3 key portion of the "outputURI" field: ':', '//', '/./', '/../'.
//
// Amazon ML needs permissions to store and retrieve the logs on your behalf.
// For information about how to set permissions, see the Amazon Machine Learning
// Developer Guide (http://docs.aws.amazon.com/machine-learning/latest/dg).