machinelearningiface

Documentation

Overview

Package machinelearningiface provides an interface to enable mocking the Amazon Machine Learning service client for testing your code.

It is important to note that this interface will have breaking changes when the service model is updated and adds new API operations, paginators, and waiters.

Index

• type ClientAPI

Types

type ClientAPI

type ClientAPI interface {
	AddTagsRequest(*machinelearning.AddTagsInput) machinelearning.AddTagsRequest

	CreateBatchPredictionRequest(*machinelearning.CreateBatchPredictionInput) machinelearning.CreateBatchPredictionRequest

	CreateDataSourceFromRDSRequest(*machinelearning.CreateDataSourceFromRDSInput) machinelearning.CreateDataSourceFromRDSRequest

	CreateDataSourceFromRedshiftRequest(*machinelearning.CreateDataSourceFromRedshiftInput) machinelearning.CreateDataSourceFromRedshiftRequest

	CreateDataSourceFromS3Request(*machinelearning.CreateDataSourceFromS3Input) machinelearning.CreateDataSourceFromS3Request

	CreateEvaluationRequest(*machinelearning.CreateEvaluationInput) machinelearning.CreateEvaluationRequest

	CreateMLModelRequest(*machinelearning.CreateMLModelInput) machinelearning.CreateMLModelRequest

	CreateRealtimeEndpointRequest(*machinelearning.CreateRealtimeEndpointInput) machinelearning.CreateRealtimeEndpointRequest

	DeleteBatchPredictionRequest(*machinelearning.DeleteBatchPredictionInput) machinelearning.DeleteBatchPredictionRequest

	DeleteDataSourceRequest(*machinelearning.DeleteDataSourceInput) machinelearning.DeleteDataSourceRequest

	DeleteEvaluationRequest(*machinelearning.DeleteEvaluationInput) machinelearning.DeleteEvaluationRequest

	DeleteMLModelRequest(*machinelearning.DeleteMLModelInput) machinelearning.DeleteMLModelRequest

	DeleteRealtimeEndpointRequest(*machinelearning.DeleteRealtimeEndpointInput) machinelearning.DeleteRealtimeEndpointRequest

	DeleteTagsRequest(*machinelearning.DeleteTagsInput) machinelearning.DeleteTagsRequest

	DescribeBatchPredictionsRequest(*machinelearning.DescribeBatchPredictionsInput) machinelearning.DescribeBatchPredictionsRequest

	DescribeDataSourcesRequest(*machinelearning.DescribeDataSourcesInput) machinelearning.DescribeDataSourcesRequest

	DescribeEvaluationsRequest(*machinelearning.DescribeEvaluationsInput) machinelearning.DescribeEvaluationsRequest

	DescribeMLModelsRequest(*machinelearning.DescribeMLModelsInput) machinelearning.DescribeMLModelsRequest

	DescribeTagsRequest(*machinelearning.DescribeTagsInput) machinelearning.DescribeTagsRequest

	GetBatchPredictionRequest(*machinelearning.GetBatchPredictionInput) machinelearning.GetBatchPredictionRequest

	GetDataSourceRequest(*machinelearning.GetDataSourceInput) machinelearning.GetDataSourceRequest

	GetEvaluationRequest(*machinelearning.GetEvaluationInput) machinelearning.GetEvaluationRequest

	GetMLModelRequest(*machinelearning.GetMLModelInput) machinelearning.GetMLModelRequest

	PredictRequest(*machinelearning.PredictInput) machinelearning.PredictRequest

	UpdateBatchPredictionRequest(*machinelearning.UpdateBatchPredictionInput) machinelearning.UpdateBatchPredictionRequest

	UpdateDataSourceRequest(*machinelearning.UpdateDataSourceInput) machinelearning.UpdateDataSourceRequest

	UpdateEvaluationRequest(*machinelearning.UpdateEvaluationInput) machinelearning.UpdateEvaluationRequest

	UpdateMLModelRequest(*machinelearning.UpdateMLModelInput) machinelearning.UpdateMLModelRequest

	WaitUntilBatchPredictionAvailable(context.Context, *machinelearning.DescribeBatchPredictionsInput, ...aws.WaiterOption) error

	WaitUntilDataSourceAvailable(context.Context, *machinelearning.DescribeDataSourcesInput, ...aws.WaiterOption) error

	WaitUntilEvaluationAvailable(context.Context, *machinelearning.DescribeEvaluationsInput, ...aws.WaiterOption) error

	WaitUntilMLModelAvailable(context.Context, *machinelearning.DescribeMLModelsInput, ...aws.WaiterOption) error
}

ClientAPI provides an interface to enable mocking the machinelearning.Client methods. This make unit testing your code that calls out to the SDK's service client's calls easier.

The best way to use this interface is so the SDK's service client's calls can be stubbed out for unit testing your code with the SDK without needing to inject custom request handlers into the SDK's request pipeline.

// myFunc uses an SDK service client to make a request to
// Amazon Machine Learning.
func myFunc(svc machinelearningiface.ClientAPI) bool {
    // Make svc.AddTags request
}

func main() {
    cfg, err := external.LoadDefaultAWSConfig()
    if err != nil {
        panic("failed to load config, " + err.Error())
    }

    svc := machinelearning.New(cfg)

    myFunc(svc)
}

In your _test.go file:

// Define a mock struct to be used in your unit tests of myFunc.
type mockClientClient struct {
    machinelearningiface.ClientPI
}
func (m *mockClientClient) AddTags(input *machinelearning.AddTagsInput) (*machinelearning.AddTagsOutput, error) {
    // mock response/functionality
}

func TestMyFunc(t *testing.T) {
    // Setup Test
    mockSvc := &mockClientClient{}

    myfunc(mockSvc)

    // Verify myFunc's functionality
}

It is important to note that this interface will have breaking changes when the service model is updated and adds new API operations, paginators, and waiters. Its suggested to use the pattern above for testing, or using tooling to generate mocks to satisfy the interfaces.