Documentation
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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 ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type MachineLearningAPI ¶
type MachineLearningAPI 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
DescribeBatchPredictionsPages(*machinelearning.DescribeBatchPredictionsInput, func(*machinelearning.DescribeBatchPredictionsOutput, bool) bool) error
DescribeBatchPredictionsPagesWithContext(aws.Context, *machinelearning.DescribeBatchPredictionsInput, func(*machinelearning.DescribeBatchPredictionsOutput, bool) bool, ...aws.Option) error
DescribeDataSourcesRequest(*machinelearning.DescribeDataSourcesInput) machinelearning.DescribeDataSourcesRequest
DescribeDataSourcesPages(*machinelearning.DescribeDataSourcesInput, func(*machinelearning.DescribeDataSourcesOutput, bool) bool) error
DescribeDataSourcesPagesWithContext(aws.Context, *machinelearning.DescribeDataSourcesInput, func(*machinelearning.DescribeDataSourcesOutput, bool) bool, ...aws.Option) error
DescribeEvaluationsRequest(*machinelearning.DescribeEvaluationsInput) machinelearning.DescribeEvaluationsRequest
DescribeEvaluationsPages(*machinelearning.DescribeEvaluationsInput, func(*machinelearning.DescribeEvaluationsOutput, bool) bool) error
DescribeEvaluationsPagesWithContext(aws.Context, *machinelearning.DescribeEvaluationsInput, func(*machinelearning.DescribeEvaluationsOutput, bool) bool, ...aws.Option) error
DescribeMLModelsRequest(*machinelearning.DescribeMLModelsInput) machinelearning.DescribeMLModelsRequest
DescribeMLModelsPages(*machinelearning.DescribeMLModelsInput, func(*machinelearning.DescribeMLModelsOutput, bool) bool) error
DescribeMLModelsPagesWithContext(aws.Context, *machinelearning.DescribeMLModelsInput, func(*machinelearning.DescribeMLModelsOutput, bool) bool, ...aws.Option) error
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(*machinelearning.DescribeBatchPredictionsInput) error
WaitUntilBatchPredictionAvailableWithContext(aws.Context, *machinelearning.DescribeBatchPredictionsInput, ...aws.WaiterOption) error
WaitUntilDataSourceAvailable(*machinelearning.DescribeDataSourcesInput) error
WaitUntilDataSourceAvailableWithContext(aws.Context, *machinelearning.DescribeDataSourcesInput, ...aws.WaiterOption) error
WaitUntilEvaluationAvailable(*machinelearning.DescribeEvaluationsInput) error
WaitUntilEvaluationAvailableWithContext(aws.Context, *machinelearning.DescribeEvaluationsInput, ...aws.WaiterOption) error
WaitUntilMLModelAvailable(*machinelearning.DescribeMLModelsInput) error
WaitUntilMLModelAvailableWithContext(aws.Context, *machinelearning.DescribeMLModelsInput, ...aws.WaiterOption) error
}
MachineLearningAPI provides an interface to enable mocking the machinelearning.MachineLearning service client's API operation, paginators, and waiters. 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.MachineLearningAPI) 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 mockMachineLearningClient struct {
machinelearningiface.MachineLearningAPI
}
func (m *mockMachineLearningClient) AddTags(input *machinelearning.AddTagsInput) (*machinelearning.AddTagsOutput, error) {
// mock response/functionality
}
func TestMyFunc(t *testing.T) {
// Setup Test
mockSvc := &mockMachineLearningClient{}
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.
Source Files
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