func ExampleMachineLearning_Predict() {
svc := machinelearning.New(nil)
params := &machinelearning.PredictInput{
MLModelId: aws.String("EntityId"), // Required
PredictEndpoint: aws.String("VipURL"), // Required
Record: map[string]*string{ // Required
"Key": aws.String("VariableValue"), // Required
// More values...
},
}
resp, err := svc.Predict(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func ExampleMachineLearning_UpdateMLModel() {
svc := machinelearning.New(nil)
params := &machinelearning.UpdateMLModelInput{
MLModelId: aws.String("EntityId"), // Required
MLModelName: aws.String("EntityName"),
ScoreThreshold: aws.Float64(1.0),
}
resp, err := svc.UpdateMLModel(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func ExampleMachineLearning_CreateMLModel() {
svc := machinelearning.New(nil)
params := &machinelearning.CreateMLModelInput{
MLModelId: aws.String("EntityId"), // Required
MLModelType: aws.String("MLModelType"), // Required
TrainingDataSourceId: aws.String("EntityId"), // Required
MLModelName: aws.String("EntityName"),
Parameters: map[string]*string{
"Key": aws.String("StringType"), // Required
// More values...
},
Recipe: aws.String("Recipe"),
RecipeUri: aws.String("S3Url"),
}
resp, err := svc.CreateMLModel(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func ExampleMachineLearning_CreateDataSourceFromS3() {
svc := machinelearning.New(nil)
params := &machinelearning.CreateDataSourceFromS3Input{
DataSourceId: aws.String("EntityId"), // Required
DataSpec: &machinelearning.S3DataSpec{ // Required
DataLocationS3: aws.String("S3Url"), // Required
DataRearrangement: aws.String("DataRearrangement"),
DataSchema: aws.String("DataSchema"),
DataSchemaLocationS3: aws.String("S3Url"),
},
ComputeStatistics: aws.Bool(true),
DataSourceName: aws.String("EntityName"),
}
resp, err := svc.CreateDataSourceFromS3(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func ExampleMachineLearning_CreateDataSourceFromRDS() {
svc := machinelearning.New(nil)
params := &machinelearning.CreateDataSourceFromRDSInput{
DataSourceId: aws.String("EntityId"), // Required
RDSData: &machinelearning.RDSDataSpec{ // Required
DatabaseCredentials: &machinelearning.RDSDatabaseCredentials{ // Required
Password: aws.String("RDSDatabasePassword"), // Required
Username: aws.String("RDSDatabaseUsername"), // Required
},
DatabaseInformation: &machinelearning.RDSDatabase{ // Required
DatabaseName: aws.String("RDSDatabaseName"), // Required
InstanceIdentifier: aws.String("RDSInstanceIdentifier"), // Required
},
ResourceRole: aws.String("EDPResourceRole"), // Required
S3StagingLocation: aws.String("S3Url"), // Required
SecurityGroupIds: []*string{ // Required
aws.String("EDPSecurityGroupId"), // Required
// More values...
},
SelectSqlQuery: aws.String("RDSSelectSqlQuery"), // Required
ServiceRole: aws.String("EDPServiceRole"), // Required
SubnetId: aws.String("EDPSubnetId"), // Required
DataRearrangement: aws.String("DataRearrangement"),
DataSchema: aws.String("DataSchema"),
DataSchemaUri: aws.String("S3Url"),
},
RoleARN: aws.String("RoleARN"), // Required
ComputeStatistics: aws.Bool(true),
DataSourceName: aws.String("EntityName"),
}
resp, err := svc.CreateDataSourceFromRDS(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func TestPredictEndpoint(t *testing.T) {
ml := machinelearning.New(nil)
ml.Handlers.Send.Clear()
ml.Handlers.Send.PushBack(func(r *service.Request) {
r.HTTPResponse = &http.Response{
StatusCode: 200,
Header: http.Header{},
Body: ioutil.NopCloser(bytes.NewReader([]byte("{}"))),
}
})
req, _ := ml.PredictRequest(&machinelearning.PredictInput{
PredictEndpoint: aws.String("https://localhost/endpoint"),
MLModelId: aws.String("id"),
Record: map[string]*string{},
})
err := req.Send()
assert.Nil(t, err)
assert.Equal(t, "https://localhost/endpoint", req.HTTPRequest.URL.String())
}
func ExampleMachineLearning_DescribeMLModels() {
svc := machinelearning.New(nil)
params := &machinelearning.DescribeMLModelsInput{
EQ: aws.String("ComparatorValue"),
FilterVariable: aws.String("MLModelFilterVariable"),
GE: aws.String("ComparatorValue"),
GT: aws.String("ComparatorValue"),
LE: aws.String("ComparatorValue"),
LT: aws.String("ComparatorValue"),
Limit: aws.Int64(1),
NE: aws.String("ComparatorValue"),
NextToken: aws.String("StringType"),
Prefix: aws.String("ComparatorValue"),
SortOrder: aws.String("SortOrder"),
}
resp, err := svc.DescribeMLModels(params)
if err != nil {
if awsErr, ok := err.(awserr.Error); ok {
// Generic AWS error with Code, Message, and original error (if any)
fmt.Println(awsErr.Code(), awsErr.Message(), awsErr.OrigErr())
if reqErr, ok := err.(awserr.RequestFailure); ok {
// A service error occurred
fmt.Println(reqErr.Code(), reqErr.Message(), reqErr.StatusCode(), reqErr.RequestID())
}
} else {
// This case should never be hit, the SDK should always return an
// error which satisfies the awserr.Error interface.
fmt.Println(err.Error())
}
}
// Pretty-print the response data.
fmt.Println(awsutil.Prettify(resp))
}
func TestInterface(t *testing.T) {
assert.Implements(t, (*machinelearningiface.MachineLearningAPI)(nil), machinelearning.New(nil))
}
func init() {
Before("@machinelearning", func() {
World["client"] = machinelearning.New(nil)
})
}